WATER QUALITY OF THE CACHE RIVER WATERSHED, ARKANSAS:
CONTRIBUTIONS OF AGRICULTURAL ACTIVITY IN SUB-WATERSHEDS TO
NUTRIENT, SEDIMENT AND LEAD (Pb) CONTAMINATION AND POTENTIAL
TOXICITY IMPLICATIONS TO AQUATIC ORGANISMS
Mary K. Kilmer
A Dissertation presented to the faculty of Arkansas State University in partial fulfillment
of the requirements for the degree of
DOCTOR OF PHILOSOPHY
Arkansas State University
May 2017
Approved by
Dr. Jennifer L. Bouldin
Dr. Travis D. Marsico
Dr. Jonathan Merten
Dr. Virginie Rolland
Dr. Hubert B. Stroud
ProQuest Number: 10256033
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ii
ABSTRACT
Mary K. Kilmer
WATER QUALITY OF THE CACHE RIVER WATERSHED, ARKANSAS: CONTRIBUTIONS OF
AGRICULTURAL ACTIVITY IN SUB-WATERSHEDS TO NUTRIENT, SEDIMENT AND LEAD (Pb)
CONTAMINATION AND POTENTIAL TOXICITY IMPLICATIONS TO AQUATIC ORGANISMS
As the world population grows and agricultural production expands, increased stress is placed
on many waterways. Water quality is often degraded and can serve as a source for areas of
downstream environmental impacts. Of particular concern is the Gulf of Mexico hypoxic zone, a
result of agricultural inputs from the Mississippi River Basin. One watershed identified as a
potential source for this hypoxic zone is the Cache River Watershed in northeastern Arkansas.
This watershed has approximately 67% of its land in row-crop production. Waterways within
this watershed have poor water quality because of excessive turbidity, nutrients, pathogens and
metals.
In this study, 23 (19 headwater sub-watersheds, four main channel) sites were sampled
monthly within the Cache River Watershed from August 2013 to July 2016. Contaminant
concentrations including nutrients, sediment and lead (Pb) were assessed spatially and in terms
of land alteration to identify source areas and to determine locations in which Best
Management Practices (BMPs) could be effectively implemented. Land alteration had a
significant relationship with overall contaminant concentrations with most- or moderatelyaltered sites having significantly greater sediment and nutrient loads than least-altered sites. Pb
in all forms was ubiquitous throughout the Cache River Watershed, though detection
frequencies and mean concentrations were generally greater in most-altered sites than in least-
iii
altered sites. Pb detections/concentrations were positively correlated with discharge but not
with precipitation, suggesting that other components of discharge, such as irrigation runoff
could be contributing to Pb contamination within this watershed. Although several sites did
have excessive levels of dissolved Pb, accompanying toxicity tests indicated that measured
environmental concentrations were much lower than those required to produce acute or
chronic toxicity within aquatic organisms, though behavior could potentially be affected.
This study shows the value in performing greater spatial sampling at the cost of temporal
sampling, particularly when identifying areas for BMP implementation within a watershed.
Although water quality issues were detected, concentrations of all contaminants decreased
along a downstream gradient of the Cache River, likely due to the unaltered state of the Lower
Cache River, which is unchannelized and has relatively large amounts of natural wetlands and
riparian buffers.
iv
DEDICATION
This dissertation is dedicated to my family. To my parents, who always gave me the
confidence to be whomever I wanted to be and helped me to become a self-reliant,
independent person. To my husband, who supported me throughout this process, even when it
meant changing his life as well and joining me in graduate school. To my son Jacob who has had
a mother distracted by completing a Ph.D. for his entire life! I could not have done this without
all of you!
v
ACKNOWLDEGEMENTS
This degree and dissertation could not have been completed without the help of numerous
people who need to be acknowledged. First and foremost, the students and staff of the
Arkansas State University Ecotoxicology Research Facility, who provided hours of assistance and
support, especially my undergraduate students Nicole Poe and Shelby Chappell, whose research
projects directly aided my project. Molly Kennon was particularly helpful in helping me carry
out fieldwork while pregnant and as a new mother!
A further thanks to my graduate committee, all of whom were more than willing to spend
time working with me, not only on this degree, but also in making me a better scientist and
introducing me to opportunities that made me a more well-rounded researched and educator.
A special thanks to my advisor, Dr. Jennifer Bouldin. Your efforts on my behalf made me
appreciate the difference between simply having a graduate advisor and having a good graduate
advisor. This project was funded primarily by the Middle Cache Watershed Monitoring Project
(FY13-500), administered by the Arkansas Natural Resources Commission. Additional salary and
research support was provided by the Experiential Learning Fellowship from Arkansas State
University (NSF DUE-1060209), an NSF-MRI grant (CBET-1040466), the Arkansas Audobon
Society, the Arkansas Game and Fish Scholarship and the University of Arkansas. I would also
like to thank the employees at the Arkansas Department of Environmental Quality, particularly,
Jim Wise, who were always willing to answer my questions. Finally, a special thank you to my
sister and future collaborator, Dr. Teresa Boman. Your support and encouragement throughout
this process has been invaluable and I look forward to years of working with you!
vi
TABLE OF CONTENTS
Page
LIST OF TABLES ………………………………………………………………………………………………….…………….…….... x
LIST OF FIGURES …………………………………………………..…………………………………………………..……….…… xiii
CHAPTER
I
PREFACE ……………………………………………………….……………………………..………………………….…… 1
INTRODUCTION …………………………………………………………………..………………..………… 1
Importance of Surface Freshwaters ………………………………..………………….. 1
Threats to Surface Freshwater ……………………………....................…………… 4
Possible Solutions …………………………………………………………………..………… 13
The Cache River, Arkansas …………………………………………..……………….…… 14
PROJECT OBJECTIVES AND OVERVIEW OF CHAPTERS………..……………..……….…… 22
LITERATURE CITED………………………………………………………………………..………..…...… 27
II
WATER QUALITY OF THE CACHE RIVER WATERSHED, ARKANSAS ………………….…………… 33
ABSTRACT ……………………………………………………………………………..……….…………….. 33
INTRODUCTION …………………………………………..………………………..……………..……..… 34
MATERIALS AND METHODS …………………………………………………………………….…..… 43
Sampling …………………………………………………………………………………………… 43
Analysis ………………………………………………………..……………………………..…… 47
Statistical Analyses ………………………..………………………..……………..………… 49
State-Set Assessment Criteria ……………………………………………………………. 58
RESULTS ……………………………….……………………………………………..………………………… 59
Modeling the Effects of Discharge and Alteration …………………………….. 59
Spatial and Temporal Assessments ……………………..…………..….……..……. 64
Comparison to Assessment Criteria ………………………………………..….…….. 85
Effectiveness of Existing BMPs in the Cache River Watershed ………..…. 91
DISCUSSION ……………………………………………………………………….………………..……….. 93
Effects of Land Alteration and Discharge on Water Quality …………..….. 93
Spatial and Temporal Assessment of Sites ………………………………………… 97
Potential Best Management Practices …………………………………………….. 107
Potential Non-BMP Solutions ………………….………………………………………. 109
Conclusion ……………………………………………….……………………………………… 111
LITERATURE CITED ……………………………………………………………………………...………. 114
vii
III
LOOKING FOR LEAD (PB) IN THE CACHE RIVER WATERSHED, ARKANSAS …….…………… 124
ABSTRACT ……………………………………………………………………………………………………. 124
INTRODUCTION ……………………………………………………………………..………………….… 125
MATERIALS AND METHODS …………………………………………………………………………. 137
Sample Collection ……………………………………………………………………………. 137
Sample Analysis ………………………………………………………………………………. 140
RESULTS ………………………………………………………………………………….…………..………. 149
Effects of Land Alteration and Discharge …………………………………………..149
Spatial Assessment and Risk Assessment ………………………………………… 151
Comparison to Assessment Criteria …………………………………………………. 161
Particle Size Analysis ………………………………………………………………….…... 172
DISCUSSION ………………………………………………………………………………….…………….. 178
Effects of Land Alteration and Discharge on Pb Concentrations ……... 178
Spatial Assessment of Dissolved, Total and Sediment-bound Pb ……… 179
Source of Pb within the Cache River Watershed ……………………………... 179
Comparison of Cache River Watershed to Other
Geographic Areas ……………………………………………………………………………. 186
Comparison of Pb in the Cache River Watershed to Current and
Proposed Assessment Criteria …………………………………………………………..189
Effect of Sediment Composition on Sediment-Bound
Pb Concentrations …………………………………………………………………………... 194
Conclusion ………………………………………………………………………………………. 197
LITERATURE CITED …………………………………………………………………………………….… 200
IV
TOXICITY OF LEAD (PB) TO FATHEAD MINNOWS (PIMEPHALES PROMELAS) AND
WATER FLEAS (CERIODAPHNIA DUBIA) USING LETHAL AND SUBLETHAL ENDPOINTS ..210
ABSTRACT …………………………………………………………………………………………………… 210
INTRODUCTION ………………………………………………………………………………………..… 211
MATERIALS AND METHODS ……………………………………….……………..……………….. 220
Acute Toxicity Tests ……………………………………………………………………….. 220
Chronic Toxicity Tests …………………………….………………………………….…… 221
Behavioral Toxicity Tests ……………………………………………………….……….. 222
Instrumental Analysis ………………………………………………………..…….…….. 228
Statistical Analyses ………………………………………………..…………………….…. 229
RESULTS …………………………………………………………………………………………..……..…… 230
Acute Toxicology ……………………………………………………………………..……… 230
Chronic Toxicology …………………………………………………………………...…….. 235
Fish Behavioral Results ……………………………………………………………………..239
DISCUSSION ………………………………………………………………………………..……..……….. 246
viii
Difficulties in Comparing Toxic Endpoints ……………………………………….. 246
Comparing Trends in Toxic Endpoints ……………………………………………… 247
Comparing Acute Endpoints to Current CMCs …………………………………. 250
Comparing Chronic Endpoints to Current CCCs ……………………………….. 251
BLM-Based Criterion vs. Current Hardness-Based Criterion …………..… 252
Behavioral Toxicology …………………………………………………………………..… 253
Potential for Development of a Behaviorally Based Toxicity
Test in Pimephales promelas …………………….………………………………….…. 254
Conclusions …………………………………………………………………………….………. 255
LITERATURE CITED …………………………………………………………………………..………….. 257
V
RESEARCH OVERVIEW ……………………………….……………………………………….………….………… 263
OVERALL CONDITION OF THE CACHE RIVER WATERSHED BASED ON
MEASURED WATER QUALITY PARAMETERS ………………..…………………………….. 263
SUMMARY OF CHAPTER 2 ………………………………………………….………………………. 271
SUMMARY OF CHAPTER 3 ………………………………………………..………………………… 277
SUMMARY OF CHAPTER 4 ……………………………………………………..…………………… 284
OVERALL SUMMARY …………………………………………………………………………………… 288
LITERATURE CITED …………………………………………………………………..…………………. 294
APPENDIX A
MEASUREMENTS OF TSS, TURBIDITY, PH, TEMPERATURE, CONDUCTIVITY
AND DISSOLVED OXYGEN FROM THE CACHE RIVER WATERSHED
(AUGUST 2013-JULY 2016) ……………………………………………………………….….……… 301
APPENDIX B
MEASUREMENTS OF DISSOLVED PB, TOTAL RECOVERABLE PB, SEDIMENTBOUND PB AND WATER HARDNESS FROM THE CACHE RIVER WATERSHED
(AUGUST 2013-JULY 2016) ………………………………………………….………………….…… 339
APPENDIX C
MEASUREMENTS OF DISCHARGE, DISSOLVED NITRITE, DISSOLVED
NITRATE, DISSOLVED ORTHOPHOSPHATE, TOTAL NITROGEN AND TOTAL
PHOSPHORUS FROM THE CACHE RIVER WATERSHED
(AUGUST 2013-JULY 2016) ……………………………………………………………….………… 377
ix
LIST OF TABLES
1.1 Leading causes of impairment to assessed rivers and streams of the United States
(USEPA, 2009). These values represent only the top ten leading causes of impairments
and will not necessarily add up to 100% …………………………………….………………………….…..…….. 6
1.2 Leading sources of impairments to assessed rivers and streams of the United States
(USEPA, 2009). These values represent only the top ten leading sources of impairments
and will not necessarily add up to 100% …………………………………………………….…….…..…………… 7
1.3 Impaired Reaches of the Cache River Watershed, Assessment Summary for Reporting
Year 2008 (ADEQ, 2008) ……………………………………………………………….……………….………………… 20
1.4 Impaired Reaches of the Cache River Watershed and Causes, Draft 303(d) list 2010-2016
(ADEQ, 2010; 2012; 2014; 2016a) ………………………………………….……………………………..………… 21
2.1 Site names, abbreviations, waterway types and sampling frequency for all sampled
sites in the Cache River Watershed, Arkansas. Samples were collected monthly from
August 2013 to July 2016 and weekly at a subset of sites from September 2014 to
October 2015 …………………………………………………………………………………………………………………… 45
2.2 Site names, abbreviations, alteration scores and alteration category for all watersheds
or current USGS monitoring stations sampled during the course of this study
(August 2013-July 2016) ……..………………………………………………..…….……………….………………….. 51
2.3 Numerical limits (m3.s-1) of discharge categories for each of the sampled sites within
the Cache River Watershed, based on all discharge data collected for each site
between August 2013 and July 2016 ……….……………………………………………………….……………… 55
3.1 Impaired reaches of the Cache River Watershed due to elevated dissolved Pb
concentrations according to finalized and draft 303(d) lists 2008-2016
(ADEQ, 2008; 2010; 2012; 2014; 2016a) …………………….……….….…………………………………….. 128
3.2 Site names, abbreviations and waterway types for all sampled sites in the Cache River
Watershed, Arkansas. All sites were sampled monthly for dissolved Pb from August
2013-July 2016, monthly for total recoverable Pb from August 2014-July 2016 and
quarterly for sediment-bound Pb from August 2014-July 2016 ……………………………………. 139
3.3 Results of dissolved Pb analysis for all samples measuring above the Practical
Quanitation Limit (PQL) in the Cache River Watershed from August 2013 to July 2016
(n = 72). Detection frequency for each site calculated as number of samples exceeding
PQL divided by total number of samples for each site (n = 36). Mean concentrations do
not include samples that had concentrations lower than the PQL for dissolved Pb
(0.858 ppb) ……………..………………………………………………………………………………….…….……….….. 152
x
3.4 Results of total recoverable Pb analysis for all sites for samples measuring above the
Practical Quantitation Limit (PQL) in the Cache River Watershed from August 2014 to July
2016 (n = 422). Mean concentrations do not include samples with concentrations
measured below the PQL for total recoverable Pb (1.415 ppb). Detection frequency was
calculated as the number of samples exceeding the PQL at each site divided by the total
number of samples collected (24). The risk score was calculated as the product of the
detection frequency for each site and the mean concentration of total recoverable Pb
for each site …………………………………………………………………………………………………………….…….. 155
3.5 Results of sites with dissolved Pb levels exceeding state-set impairment criteria based
on water hardness at the time of sampling for samples collected from August 2013 to
July 2016 …………………………………………………………………………………………………………………….…. 163
3.6 Results of sites with dissolved Pb levels in which hardness is unknown (collected
between August 2013 to July 2014) but that are likely to exceed criteria based on average
hardness data for the month of sampling for the site (from samples collected between
August 2014 and July 2016) …………………………………………………………………………………………… 166
3.7 Frequency of impairment for land alteration categories (impaired samples within
category/total impaired samples) compared to expected frequencies for land use
categories based on sampling effort alone. Risk scores for each category were calculated
as the product of corrected impairment frequency and mean ± SE concentrations of
dissolved Pb within each category. Corrected impairment frequency was calculated as
the category impairment frequency/number of sites within the category ……..………....….. 168
3.8 Results of sites with dissolved Pb levels exceeding state-set impairment criteria or
suspected of exceeding criteria for samples collected from the Cache River Watershed
between August 2013 and July 2016. Impairment frequency was calculated as the
number of samples exceeding hardness-based criterion at a site divided by the total
number of samples collected (36). Risk score was calculated as the product of the
impairment frequency for each site and the mean concentration of samples exceeding
assessment criteria ……………………………………………………..…..………………..………………….………. 170
3.9 Results of replicate digestions of sediment (mg/kg) from six selected sites within the
Cache River Watershed collected in April 2016. Mean concentrations (ppb) and %
relative standard deviation (RSD) were calculated with the inclusion of outlying data
points, indicated by an *. Outlying data points were removed for calculations of
adjusted mean and adjusted % RSD. Adjusted means were also corrected to reflect
appropriate significant figures based on % RSD .………………………………………………….………… 175
3.10 Percent of Pb recovered from spiked samples of sediment (SSS) collected in April
2016 from sampling site SUCR. Corrected value is the measured concentration of Pb
after subtracting the mean value of Pb from all unspiked samples of SUCR (19.76 ppb,
indicated by *). Percent recovery from undigested spiking solutions (USS) and digested
spiking solutions (DSS) shown for comparison. Neither USS nor DSS had any sediment
added to samples ……………………………………………………………………………………………………….. 177
xi
3.11 Mean ± SE concentrations (ppb) of dissolved and total recoverable Pb for water
samples collected within HUC8 watersheds (main channel or tributaries) in
northeastern Arkansas with at least 50% of land used for crop production. Sites
with no SE associated with mean concentration had only a single sample with
measureable Pb. Sites with no mean concentration had no samples with
measureable Pb. Data for the Cache River Watershed are based on this present
study, data for all other watersheds were obtained from the USEPA STORET
database (USEPA, 2015). Percent cropland values obtained from Arkansas
Watershed Information System (AWIS, 2016) ……………….………….…………………………….….. 187
4.1 Mean ± SE LC50 values (µg/L Pb) calculated for C. dubia and P. promelas in response to
Pb for two tested natural waters of the Cache River Watershed, Arkansas (n = 3 for
each test). Natural waters for toxicity testing were collected under low and high
hardness conditions between October 2014 and January 2016. The allowable limit is
the criterion maximum concentration (CMC) for dissolved Pb ………………………….…………… 232
4.2 Chronic endpoints (Mean ± SE) for dissolved Pb tested in ambient water from the Cache
River, Arkansas and laboratory prepared, moderately hard water for both C. dubia and
P. promelas (n = 3 for each) …………………………………………….…………………..………………………… 236
4.3 Chronic endpoints (Mean ± SE) for dissolved Pb compared to allowable limits of Pb
(µg/L) (t-test, α = 0.05) as established by assessment criteria used by the state of
Arkansas (ADEQ, 2016b). The allowable limit represents the criterion continuous
concentration (CCC) for dissolved Pb. Each test was repeated three times (n = 3) for
both C. dubia and P. promelas. The p-value represents the comparison between either
the mean EC50 and allowable limit for the hardness used or mean IC25 and allowable limit
for the hardness used ……………………………………………………………………………………...……………. 238
5.1 Impaired Reaches of the Cache River Watershed, Assessment Summary for Reporting
Year 2008 (ADEQ, 2008; 2010; 2012; 2014; 2016a). If an impairment no longer appears
in any year, the designated reach was no longer considered impaired for that
substance ………………………………………………………………………………………………………………………. 265
5.2 Sub-watershed sites and or main channel reaches that failed to meet established
assessment criteria (ADEQ, 2016b) during the course of this study (August 2013July 2016) ………………………………………………………………………………………………………………………. 268
xii
LIST OF FIGURES
1.1 Surface freshwater withdrawals in the United States (bn L/day) in 2005 and
2010 (Kenny et al., 2009; Maupin et al.,2014) ……………………………………….…………................. 3
1.2 Location of the Cache River Watershed (HUC 08020302) in the State of Arkansas, as
well as the Cache River and its main tributary, Bayou DeView …………………………..………..….. 15
1.3 Land use (ha) in Cache River Watershed in the A) alluvial plains and B) Crowley’s
Ridge. Figures prepared by Kilmer from 2006 land use data available from the
Arkansas Watershed Information System (AWIS, 2006). Category of other includes
land use including pasture and urban/industrial use. ……………………………………….……...……… 18
1.4 Sites sampled from the Cache River Watershed from August 2013-July 2016. Solid
colored sub-watersheds are headwater sub-watersheds whereas hatched
sub-watersheds are those containing a main channel site or the site just prior to the
confluence with the White River (southernmost site) ……………………………………..…………….. 23
2.1 Location of the Cache River Watershed, Arkansas and location of sampled
sub-watersheds and main channel sites within the Cache River Watershed. Letters
within each site represent the four-letter designation of each site (Table 2.1). Samples
were collected from August 2013 to July 2016 ……………………………………………….…….…………. 46
2.2 Sampling sites within the Cache River Watershed according to designated
alteration category (Table 2.2). Sites were sampled monthly between August
2013 and July 2016 ………………………………………………………………………….……………………….…….. 52
2.3 Mean ± SE measurements of six water quality parameters by land alteration
category. A) turbidity B) NO2- C) PO4 -3 D) NO3- E) total phosphorus F) total nitrogen.
p-values (α = 0.05) indicate a significant difference between land use categories.
Colored bars correspond to land alteration categories shown in Figure 2.2
(green = least-altered, blue = moderately-altered, red = most-altered,
gold = main channel) ……………………………………………………………………………………….………..…….. 61
2.4 Mean ± SE measurements of six water quality parameters by discharge category.
A) turbidity B) NO2- C) PO4 -3 D) NO3-E) total phosphorus F) total nitrogen. p-values
(α = 0.05) indicate a significant difference between flow categories ……………….….……...…… 63
2.5 Mean concentrations of A) turbidity and B) TSS for all sampled sites within the
Cache River Watershed for samples collected monthly between August 2013 and
July 2016 ………………………………………………..……………………………………………………………….………. 65
2.6 Temporal assessment of A) turbidity and B) TSS data from most impacted sites for the
sampling period August 2013 through July 2016 …………………..…….…………………………..……… 66
xiii
2.7 A) Comparison of mean ± SE suspended sediment loads (TSS x discharge) by land alteration
category. * indicates a category that is significantly different from the others (α = 0.05).
B) Comparison of mean ± SE suspended sediment load per hectare drained for each
sampled headwater sub-watershed. Dotted line represents mean load per hectare of
all sites (0.0011 tons/day/ha). Color of bar indicates land
alteration category to which each site belongs (green = least-altered, blue =
moderately-altered, red = most-altered) …………………………………………..…...………………………. 68
2.8 Mean concentrations of A) NO3- B) NO2- and C) total nitrogen for all sampled sites
within the Cache River Watershed from Aug 2013 to July 2016. Categories divide the
range of results evenly with the exception of total nitrogen in which the greatest
category (red) represents samples exceeding the state-set assessment criteria for
total nitrogen (0.992 ppm) …………………………………….………………………………………………...……… 70
2.9 Temporal assessment of A) total nitrogen B) NO2- and C) NO3- at sites with the
greatest mean concentrations measured in samples collected from August 2013 to
July 2016 ……………………………………………………………………………………………………………………….… 71
2.10 Temporal assessment of NO3- concentrations at sites exhibiting a different
temporal pattern than NO2- and total nitrogen in samples collected from August
2013 to July 2016 …………………………………………..………………….………………………………………….. 73
2.11 A) Comparison of mean ± SE NO2- load by land alteration category. * indicates a
category that is significantly different from the others (α = 0.05). B) Comparison
of mean ± SE NO2- load per hectare drained for each sampled headwater
sub-watershed. Dotted line represents mean load per hectare of all sites (0.0011
tons/day/ha). Color of bar indicates land alteration category to which each site
belongs (green = least-altered, blue = moderately-altered, red = most-altered) …………... 75
2.12 A) Comparison of mean ± SE total N load by land alteration category. * indicates
a category that is significantly different from the others (α = 0.05). B) Comparison
of mean ± SE total N load per hectare drained for each sampled headwater
sub-watershed. Dotted line represents mean load per hectare of all sites (0.0082
tons/day/ha). Color of bar indicates land alteration category to which each site
belongs (green = least-altered, blue = moderately-altered, red = most-altered) ..…………. 76
2.13 A) Comparison of mean ± SE NO3- load by land alteration category B) Comparison of
mean ± SE NO3- load per hectare drained for each sampled headwater sub-watershed.
Dotted line represents mean load per hectare of all sites (0.0076 tons/day/ha).
Color of bar indicates land alteration category to which each site belongs (green =
least-altered, blue = moderately-altered, red = most-altered) ……………………….……………… 77
2.14 Mean concentrations of A) dissolved orthophosphate (PO4-3) and B) total phosphorus
for all sampled sites within the Cache River Watershed from Aug 2013 to July 2016.
Categories divide the range of results evenly with the exception of total phosphorus
in which the top category represents samples exceeding the state-set assessment
criteria for total phosphorus (0.250 ppm) …………………………………………………………..………… 79
xiv
2.15 Temporal assessment of dissolved PO4-3 for sites LCDI and FTSL. Correspondingly
colored dotted lines and boxes represent the overall trend line and R2 value for each
site. Four-letter code represents the site-specific designation for each site …………….…… 80
2.16 Temporal assessment of total phosphorus for sites A) LCDI and FTSL showing little
or no temporal trend and B) WIDI and FSDI showing a trend of increasing total P
concentrations over time. Correspondingly colored dotted lines and boxes represent
the overall trend line and R2 value for each site. Four-letter code represents the
site-specific designation for each site ……………………………………………………….………………….. 81
2.17 A) Comparison of mean ± SE PO4-3 load by land alteration category, * indicates
category that was significantly different from one or more other categories
(α = 0.05). B) Comparison of mean ± SE PO4-3 load per hectare drained for each
sampled headwater sub-watershed. Dotted line represents mean load per hectare
of all sites (0.0043 tons/day/ha). Color of bar indicates land alteration category to
which each site belongs (green = least-altered, blue = moderately-altered,
red = most-altered)…………………………………………....…………………………………………………………. 83
2.18 A) Comparison of mean ± SE total P load by land alteration category, * indicates
category that was significantly different from one or more other categories (α = 0.05).
B) Comparison of mean ± SE total P load per hectare drained for each sampled headwater
sub-watershed. Dotted line represents mean load per hectare of all
sites (0.0029 tons/day/ha). Color of bar indicates land alteration category to which
each site belongs (green = least-altered, blue = moderately-altered,
red = most-altered) ……………………………………………………………………….……………………………… 84
2.19 Percent exceedance of assessment criteria for turbidity for all sampled sites under
A) base flow conditions and B) all flow conditions. The dotted line represents the
assessment criteria threshold for each set of flow conditions. Color of the bar
indicates the land alteration category for each site. (green = least-altered, blue
moderately-altered, red = most-altered, gold = main channel) …………………………………….. 86
2.20 Spatial assessment of sites impaired for dissolved oxygen (DO) during the primary
(water temperature < 22°C and/or critical season (water temperature >22°C) based
on water samples collected in the Cache River Watershed between August 2013 and
July 2016 …………………………………………………………………………………………………………………….... 88
2.21 Mean ± SE concentrations of A) total nitrogen and B) total phosphorus for all sites
sampled in the Cache River Watershed. The black dotted line represents the
assessment criteria for each parameter (75th percentile value of all available data for
the watershed (total N = 0.992 ppm, total P = 0.250 ppm) (ADEQ, 2016b)). Colors of
bars denote land alteration category (green = least-altered, blue = moderately-altered,
red= most-altered, gold = main channel) ………………………………………………………….…………… 90
xv
2.22 Comparison of mean ± SE sediment and nutrient loads (tons/day/ha) at most-altered
sites within the Cache River Watershed from samples collected between August 2013
and July 2016. BMP implementation began in sites CCDI, SKDI and WIDI in 2013 and at
site BGLA in 2016. No BMPs have been at implemented at any other sites shown here.
Dotted lines represent the average load for all sites. A) suspended sediment load
(average = 0.001) B) total nitrogen load (average = 0.010) C) total phosphorus load
(average = 0.004). Four-letter code represents the site-specific designation for each
site …………………………………..……………………………………………………………………..……………………. 92
2.23 Mean ± SE concentrations of A) total N and total P and B) turbidity and TSS for
main channel sites from all samples collected in the Cache River Watershed
between August 2013 and July 2016. Sites are arranged from upstream (left)
to downstream (right) with site CREG being the most upstream site and site REFO
being the most downstream site ………………………………………………….…………………..……….. 102
2.24 Remaining wetland areas within the Cache River Watershed as compared to areas
in which water samples were collected from August 2013 to July 2016. Spatial
wetland data obtained from US Fish and Wildlife Service (USFWS) National
Wetlands Inventory (2016) ………………………………………………………………………………….……… 105
3.1 Distribution of top five crops in sub-watersheds in northeastern Arkansas with greater
than 50% of land use devoted to agriculture for production year 2015. Areas shown
in white represent portions of the sub-watersheds not used for crop production
(cropland data obtained from United States Department of Agriculture National
Agricultural Statistics Service (USDA-NASS), 2016) …………………………………..…………….……… 130
3.2 Major rivers in Arkansas relative to areas of significant Pb deposits in southern
Missouri and Arkansas (spatial data obtained from USGS: Mineral Resources Data
System, 2016a) …………………………………………………………..…………………..…………………………….. 132
3.3 Sites sampled for dissolved, total recoverable, and sediment-bound Pb in the Cache River
Watershed from August 2013 to July 2016 (dissolved Pb) and August 2014 to July
2016 (total recoverable Pb, sediment-bound Pb) …………………………………………………………… 138
3.4 Categorization of sampled sites for non-parametric MANOVA analysis. Sites were
categorized as least-altered, moderately-altered, most-altered depending on amount
of land used for agriculture within the sub-watershed, amount of forested cover within
the sub-watershed and amount of artificially channelized surface waterways. Main
channel sites received cumulative inputs from all upstream sites and were assigned
to an independent category ……………………………………………..…………………………………………… 145
3.5 Results of individual Kruskal-Wallis tests for mean ± SE concentrations of
A) sediment-bound Pb and B) total recoverable Pb by land alteration category for
samples collected from the Cache River Watershed from August 2014 to July 2016 ….….. 150
xvi
3.6 Risk categorization for dissolved Pb for sampled sites within the Cache River
Watershed from samples collected from August 2013 to July 2016 with
concentrations measuring above the Practical Quantitation Limit (PQL, n = 72).
Risk was calculated as the product of the mean concentration of samples measuring
above the PQL and the detection frequency of those samples for each sampled site ……. 153
3.7 Mean concentration and risk categorization of all sites for total recoverable Pb for samples
collected within the Cache River Watershed from August 2014 to July 2016, for
samples with concentrations measuring above the Practical Quantation Limit (PQL,
n = 422). Risk was calculated as the product of the mean concentration of samples
measuring above the PQL and the detection frequency of those samples for each
sampled site .……………………………………………………………………………………………………….………… 156
3.8 Mean sediment-bound Pb concentrations by site within the Cache River Watershed
for samples collected (n = 184) between August 2014 and July 2016 ………………….………… 158
3.9 Mean ± SE concentration of sediment-bound Pb by land use category for samples
collected in the Cache River Watershed between August 2014 and July 2016
(n = 184) .………………………………………………………………………………………………………………………. 160
3.10 Impaired sub-watersheds of the Cache River Watershed determined by hardness-based
assessment criteria calculated using water hardness at the time of sample collection
for samples collected between August 2013 and July 2016 ……….………………………..…...… 162
3.11 Mean hardness for all non-agricultural (n = 4) vs. agriculturally impacted (n = 19)
sampled sites from September 2014-July 2016 …………………………….…….…….….…………….. 165
3.12 Risk of impairment for dissolved Pb for sites within the Cache River Watershed based
on samples collected between August 2013 and July 2016. Risk was calculated as the
product of the mean concentration of dissolved Pb and the detection frequency of
samples exceeding state-set assessment criteria ………………………………………………………… 171
3.13 Sediment-bound Pb concentrations plotted as a function of sediment score for
samples collected from the Cache River Watershed between October 2015 and
June 2016 (n = 92). Outlying data points are identified by site and month of
sampling ……………………………………………………………………………………………………………………… 173
3.14 Comparison of mean ± SE concentrations of dissolved Pb in samples with dissolved
Pb measured above the Practical Quantitation Limit (PQL) for samples collected from
the Cache River Watershed between August 2013 and July 2016 (gold bars) to mean
± SE precipitation (blue line) for all sites. Precipitation was determined as the sum of
precipitation for 72 hours prior to sampling. Precipitation data were obtained from
closest available monitoring station to sampled sub-watershed (U.S. Climate Data,
2016) …………………………………………………………………………………………………………………………… 182
xvii
3.15 Cropland use during 2015 in the Cache River Watershed. White areas represent
areas not in use for crop production, including areas left fallow due to crop rotation,
remaining wetland areas and forested areas. Sampled sub-watersheds are outlined in
black. Cropland data obtained from United States Department of Agriculture National
Agricultural Statistics Service (USDA-NASS, 2016) …………………………………….………..………. 184
4.1 Relative amounts of lethal and sub-lethal endpoints reported for A) P. promelas
(n = 93) B) C. dubia (n = 61) and C) All Daphnia (n = 156). Endpoint data obtained
from EPA Ecotox database (USEPA, 2016b) ……………………………………….…………………………… 214
4.2 Mean (± S.E.) hardness for low-flow (July-November) and high-flow (December-June)
seasons of the year for both non-agricultural sub-watersheds and
agriculturally dominated, sub-watersheds of the Cache River, Arkansas for samples
collected between May 2014 and July 2016 ……………………………………………………..…………… 219
4.3 Behavioral test setup for fish trials measuring the effect of exposure to dissolved Pb on
predator-avoidance behaviors in P. promelas, indicating relative position of camera,
tank division and emplaced stimulus delivery line ………………….……………………………………… 224
4.4 Example of shoaling index scores based on proximity of fish to each other with lowest
score (1) at top and greatest score (5) at bottom. A score of one indicated no fish were
within one body length of another, a two indicated that two fish were within one body
length of each other, etc. Panels on the right indicate the relative proximity of fish to
each other from the corresponding still video image on the left ……………….…………………… 227
4.5 Mean ± SE calculated 48-hr acute LC50 at three different hardness levels of tested
waters for A) C. dubia and B) P. promelas ……………..……………………….……………………………… 234
4.6 Changes in A) standardized shoaling index (shoaling score (1-5) divided by total fish
in trial) and B) total tank zones occupied by all fish (out of 18 total zones possible) in
response to a control stimulus (DI water) and a stimulus of alarm substance. The
same group of fish were tested in each trial (n groups = 12). Blue bars represent results
pre- and post-stimulation with the control substance while red bars represent results
pre- and post-stimulation with the alarm substance …………………………………….………………… 240
4.7 Comparison of mean ± SE absolute differences in responses pre- and post-stimulation
with either a control (DI water, blue bar) or alarm substance (red bar) for A) shoaling
index and B) total zones occupied. Both the control and alarm stimulus were tested on
the same group of fish (n groups = 12) ……………………..………….…………………….………………….. 242
4.8 Absolute difference between pre- and post-stimulus shoaling index scores for
control stimulus (blue bar) and the alarm stimulus (red bar) in A) fish prior to
exposure to Pb and B) fish after exposure to Pb. The same group of fish was tested with
both the control and the alarm substance both before and after exposure to Pb
(n groups = 4) ………………………………………………………………………………………………………………... 244
xviii
4.9 Absolute difference between pre- and post-stimulus total zones occupied for control
(blue bar) stimulus and the alarm (red bar) stimulus in A) fish prior to exposure to Pb
and B) fish after exposure to Pb. The same group of fish was tested with both the
control and the alarm substance both before and after exposure to Pb
(n groups = 4) ……………………………..………………………………………………………………………….……… 245
5.1 Total number of failed assessment criteria in samples collected from August 2013
to July 2016 in the Cache River Watershed based on current state-set assessment
criteria. Current criteria have been established for turbidity (base flow and all flow),
pH, dissolved oxygen (primary season and critical season), dissolved Pb, total nitrogen
and total phosphorus …………………………………………………………………………………………………….. 269
5.2 Remaining wetland areas within the Cache River Watershed as compared to areas in
which water samples were collected from August 2013 to July 2016. Spatial wetland
data obtained from US Fish and Wildlife Service (USFWS) National Wetlands Inventory
(2016) …………………………………………………………………………………………………………….…….……… 275
5.3 Distribution of top five crops in sub-watersheds in northeastern Arkansas with greater
than 50% of land use devoted to agriculture for production year 2015. Areas shown
in white represent portions of the sub-watersheds not used for crop production
(cropland data obtained from United States Department of Agriculture National
Agricultural Statistics Service, 2016) …………………………………………….…..…………….…………….. 282
xix
CHAPTER 1
PREFACE
The five chapters of this dissertation are organized with initial chapter providing a description
of the overall scope of the research, followed by three research chapters organized as individual
manuscripts that either have been submitted or will be submitted in part or in whole to
scientific journals. The dissertation is concluded by a final overview of the research project as a
whole with suggestions for future research. Included within each research chapter are
individual abstracts and literature cited.
INTRODUCTION
Importance of Surface Freshwaters
Although 70% of the Earth is covered in water, only a small portion of that water is usable
freshwater. Of this freshwater, nearly all is in ice caps/glaciers or stored in as groundwater in
underground aquifers. Only 0.3% of freshwater is in the form of surface waters, contained in
rivers, lakes, streams and the atmosphere (Gleick, 1993). This limited freshwater resources is
placed under increasing stress as the world population grows and the demand for water for
agriculture, industry and personal use (e.g., drinking, cooking, cleaning) continues to increase
(reviewed in Gleick and Palaniappan, 2010).
1
In the most recent report of water use in the United States (Maupin et al., 2014), it was
estimated that about 1158 billion liters (bn L) of freshwater were used per day with 870 bn L
being drawn from surface water. Although this is a decrease of roughly 15% from the previous
report (Kenny et al., 2009), it still represents a significant portion of the available surface
freshwater resources. The primary uses of surface freshwater in the United States are for the
generation of thermoelectric power (442.9 bn L, 38%), in which surface waters are withdrawn
and heated to power steam-driven turbine generators, and for agricultural activities (478.6 bn L,
41%) including irrigation (435.3 bn L), aquaculture (36.7 bn L), and livestock watering (7.6 bn L).
Smaller scale uses include public water supplies and domestic use (172.6 bn L, 15%) and
industrial and mining applications (65.3 bn L, 6%); (Maupin et al., 2014). This represents a
decrease in surface freshwater withdrawals since 2005 for most purposes, with the exception of
aquaculture (Fig. 1.1). In-stream uses such as hydroelectric power generation, navigation and
recreation also have the potential to impact surface freshwater, though water is not withdrawn
for these uses.
2
70
500
60
400
50
300
200
100
0
Thermoelectric
Irrigation
Public Supply
water withdrawn (bn L/day)
water withdrawn (bn L/day)
600
40
30
20
10
0
Domestic
Usage
2005
Aquaculture
Livestock
Mining
2010
Figure 1.1. Surface freshwater withdrawals in the United States (bn L/day) in 2005 and 2010 (Kenny et al., 2009; Maupin et al., 2014).
3
Industrial
The ecosystem services provided by surface freshwater in the United States are myriad but can be
divided into two general categories: provision of direct market goods or provision of non-market goods
(Wilson and Carpenter, 1999). The category of direct market goods includes services such as provision
of drinking water, energy production, transportation and irrigation of crops. Research indicates that
water used for agriculture, for example, results in the production of $197 billion worth of food and fiber
annually (USEPA, 2000). Non-market goods include indirect services such as provision of animal and
plant habitat, aesthetic and recreational uses and contributions to biodiversity. Activities such as
fishing, hunting and nature photography (all of which rely on freshwater ecosystems) add nearly $90
billion to the economy each year (USEPA, 2000). Although valuation for the former category (direct
market goods) can be straightforward, valuation of the latter tends to be highly subjective, based on the
worth assigned to the particular service. However, from an ecological standpoint, non-market goods
may be significantly more valuable. Surface freshwater is an important component of most ecosystems,
providing habitat or resources for both plant and animal species that rely on them for survival. Natural
wetlands provide flood protection, slow water flows, reduce erosion, naturally improve water quality by
removing sediment and filtering nutrients and provide valuable habitat for wildlife (Finlayson et al.,
1999; Russi et al., 2013).
Threats to Surface Freshwater
Of the assessed rivers and streams (~31.5%) in the United States, 55% are considered impaired,
meaning they are unable to support one or more of their designated uses (USEPA, 2009). Designated
uses vary by state and waterway. For example, in the State of Arkansas, designated uses for rivers and
streams include use as a domestic water supply, use as in industrial water supply, use as an agricultural
water supply, supporting fisheries, primary contact recreation (swimming), secondary contact recreation
4
(i.e., boating, fishing, wading), serving as a natural or scenic waterway, serving as an ecologically
sensitive waterbody or serving as an extraordinary resource waterway (APCEC, 2011).
The most common causes of impairment include pathogens (14%), sediment (11%) and nutrients (9%,
Table 1.1). These impairments result from many types of natural and anthropogenic sources (Table 1.2).
The effects of the most significant identified anthropogenic sources are described in following sections.
5
Table 1.1. Leading causes of impairment to assessed rivers and streams of the United States (USEPA,
2009). These values represent only the top ten leading causes of impairments and will not necessarily
add up to 100%.
Rank
Cause
km affected
% of total impairments
1
Pathogens
282,642
13.44
2
Sediment
227,183
10.81
3
Nutrients
182,047
8.66
4
Mercury
167,880
7.99
5
Oxygen depletion
157,675
7.50
6
Temperature
148,288
7.05
7
Metals (other than mercury)
142,375
6.77
8
Polychlorinated Biphenyls
129,732
6.17
9
Habitat Alterations
107,846
5.13
10
Turbidity
77,176
3.67
6
Table 1.2. Leading sources of impairments to assessed rivers and streams of the United States (USEPA,
2009). These values represent only the top ten leading sources of impairments and will not necessarily
add up to 100%.
Rank
Source
km affected
% of total impairments
1
Agriculture
253,544
16.66
2
Unknown
220,417
14.48
3
Atmospheric Deposition
161,488
10.61
4
Hydromodification
157,496
10.35
5
Habitat Alterations*
110,854
7.28
6
Urban Runoff/Stormwater
109,512
7.20
7
Municipal Discharge/Sewage
103,221
6.78
8
Unspecified Nonpoint Source
86,919
5.71
9
Natural/Wildlife
82,643
5.43
10
Silviculture/Forestry
65,474
4.30
* Habitat alterations not directly related to hydromodifications
7
Agriculture
Agriculture in the United States is the leading user of surface freshwater. Agricultural activities are
responsible for 41% of all surface freshwater withdrawals, with the majority used for irrigation of crops,
accounting for 91% (435.3 bn L) of all agricultural use (Maupin et al., 2014). Smaller amounts of water
are used for other agricultural activities including aquaculture (36.7 bn L) and livestock watering (7.6 bn
L). Agricultural practices have long been associated with reduced surface water quality. The United
States Environmental Protection Agency lists agricultural non-point source (NPS) pollution as the leading
source of water quality impacts in freshwater surface waters, such as rivers and lakes (USEPA, 2009).
Agricultural activity can be further subdivided into two categories: animal-based agriculture and cropbased agriculture. Animal-based agriculture can have many negative impacts on surface waters. Animal
waste can contain pathogens such as the protozoan parasites Cryptosporidium and Giardia, that cause
gastrointestinal illnesses, and the gram-negative bacteria, Vibrio (CDC, 2011; Dreelin et al., 2014). Due
to the prevalence of antibiotic use in confined animal feeding operations (CAFOs), waste can also
contain antibiotic-resistant Escherichia coli bacteria, which can enter surface waterways and result in
health impacts to humans (Li et al., 2013). Livestock grazing in riparian or shoreline zones can decrease
bank stability, causing increased erosion and reduced water quality (Tufekcioglu et al., 2012; Hughes and
Quinn, 2014).
Crop-based agriculture can also have numerous negative impacts on surface water. To maximize
available land for production, wetlands are often drained and cultivated. This drainage is accomplished
by creating artificial canals or by channelizing natural waterways. The range of negative effects
associated with these physical alterations of the landscape are discussed in detail in the following
section.
One problem gaining attention in recent years is the issue of nonpoint agricultural runoff. This runoff
from field surfaces often contains large amounts of topsoil and associated contaminants, particularly
8
following periods of peak precipitation (Hu and Huang, 2014). Contaminants can include
anthropogenically applied substances such as fertilizers and pesticides or substances resulting from
atmospheric deposition, including heavy metals or organic compounds (Osterkamp et al., 1998; Smith et
al., 2008; Baldwin et al., 2016).
Nutrient runoff is of particular concern as seasonal nutrient fluxes in the Mississippi and Atchafalaya
River Basins have been closely linked with hypoxia in the Gulf of Mexico (Rabalais et al., 2002). Of the
total nutrient load entering the Gulf of Mexico, it is estimated that agricultural sources contribute as
much as 70% of the total nitrogen and phosphorus, with crop production playing a large role in both
nitrogen (52%) and phosphorus (43%) delivery (Alexander et al., 2008). Of particular concern is that
recent modeling suggests that even a considerable reduction in nitrogen fertilizer use in the United
States will not achieve target levels of nitrogen export to the Gulf of Mexico by 2050 (van Grinsven et al.,
2015). Nutrient pollution can also have high economic costs (USEPA, 2015). For example, Dodds et al.
(2009) estimated that economic impacts from nutrient pollution in the United States ranged from $ 1.5
to 4.8 billion. This figure accounts for financial losses from fishing and boating (due to lake closures),
property value loss, cost to develop conservation plans for species affected by eutrophication and
expenditures on alternative drinking water sources.
Soil contained in surface runoff is the largest contaminant of surface water by volume and weight
(Koltun et al., 1997) and is considered a leading pollutant in rivers and streams of the United States
(USEPA, 2009). It has been estimated that soil is being lost from agricultural areas 10 to 40 times faster
than the rate of soil formation (Pimentel and Burgess, 2013). Once within surface waters, this soil (now
termed sediment) can fill streambeds, reservoirs, and wetlands, and increase the likelihood of flooding,
as well as cause degradation or destruction of aquatic wildlife habitat (Heimlich, 2003). Economic
impacts can also be severe. High levels of suspended sediment can increase the cost of water
treatment, with annual costs estimated as high as $661 million (Holmes, 1988). Expanding costs to
9
include other negative effects (e.g., environmental effects, biological damage, loss of recreation) results
in a total annual damage cost from sediment (in North America) of more than $16 billion (Osterkamp et
al., 1998).
Hydromodifications
Hydromodification is any process that alters the natural flow of water through a landscape and can
range from more indirect processes, such as changes in land cover, to direct processes, such as
construction of dams or levees. One of the most widely utilized hydromodifications, especially in
agricultural areas, is channelization. Channelization refers to the group of engineering practices used to
control flooding, drain wetlands, and improve navigation of river channels (Brooker, 1985).
Channelization generally involves a straightening and deepening of natural channels as well as a removal
of normal pool-riffle sequences. This typically results in increased flow velocities in channelized reaches
of a stream that in turn can lead to greater bank erosion (Czech et al., 2015). Erosion is magnified by
removal of bank side and in-stream vegetation that can lead to increased sedimentation (Karr and
Schlosser, 1978; Zaimes et al., 2004) and bank erosion. Increased flow velocities can also result in
increased downstream impacts as contaminants, both water- and sediment-borne, can travel greater
distances from the original source (Karr and Schlosser, 1978; Poff et al., 1997).
Channelization of upstream portions of a river can increase downstream flooding. Channelization
increases the capacity and flow of a river. If the upstream capacity (due to channelization) is greater
than the downstream capacity, rivers can overflow their banks with extremely negative consequences.
When upstream portions of the Blackwater River in Missouri were channelized, for example, the
capacities of the upstream portions (vs. downstream) were increased by 40-70%, resulting in extensive
flooding in downstream areas (Emerson, 1971).
10
Streams are often channelized to improve drainage, thus making more land available for
development. Unfortunately, this increased drainage may result in a severe loss of wetland and
floodplain habitats (Poff et al., 1997). In the Wild Rice Creek Watershed of North and South Dakota, for
example, partial channelization in 1952 resulted in a loss of 77% of natural wetlands whereas only 22%
were lost in un-channelized portions (Erickson et al., 1979). Similarly, the channelization of the
Kissimmee River in Florida resulted in a loss of 12,000 to 14,000 ha of wetland habitat and severely
affected biological communities (Koebel, 1995). Important ecological functions of wetlands include
water quality improvement via denitrification and detoxification, floodwater storage, fish and wildlife
habitat, and biological productivity (Finlayson et al., 1999; Russi et al., 2013). Removal of wetlands
tends to result in decreased water quality in nearby waterways and increases in severity of flooding
events.
Channelization has numerous ecological impacts, often dramatically altering natural in-stream
communities. Removal of vegetation along stream banks can result in increases in water temperature of
6° to 9°C (Gray and Edington, 1969), causing shifts in community structure towards more warm-water
tolerant species. The loss of habitat, due to bank clearing and removal of in-stream obstacles such as
snags, can also result in a loss of environmentally sensitive species, causing an overall reduction in
biodiversity. Lau et al. (2006) found that channelization of a stream caused a decrease in heterogeneous
habitat and a correlated decrease in fish assemblages.
Urban and Industrial Development
As the global population continues to grow rapidly, land is being developed for urban and industrial
uses at an alarming rate. Increased impervious surfaces in urban areas directly increase the amount of
urban runoff, especially stormwater during times of high precipitation. This runoff often travels directly
11
into surface waters, transporting urban contaminants such as oil and grease, suspended solids, heavy
metals, and nutrients.
Heavy metals in urban stormwater runoff often exceed freshwater chronic criteria established by the
USEPA (Athayde et al., 1983). Modeling of urban runoff data collected by the USEPA indicates that the
degree of impervious and effective impervious area within a catchment had a strong influence on heavy
metal concentrations in urban surface waters (May and Sivakumar, 2009). Contaminant loads have also
been found to be positively correlated with traffic intensity (Czemiel-Berndtsson, 2014), which is often
greater in areas with more impervious surfaces (May and Sivakumar, 2009).
With an increased population also comes increased industrial development, often tied to a need for
increased resource extraction (e.g., mining, fossil fuel extraction). Industrial applications can lead to
environmental degradation (Hadibarata, 2012), particularly in regions where technological and industrial
growth outpace environmental regulations. Tabari et al. (2010) measured abnormally high heavy metal
concentrations in water and sediment samples from the southern Caspian Sea. These excessive levels
may be the result of industrial activities in Iran (Tabari et al., 2010). Similarly, Yunus et al. (2003)
reported an increase in heavy metal accumulation in water resulting from increased development of
industry in the Pinang River Watershed in Malaysia.
In developed regions, with more stringent environmental regulations, emerging contaminants from
pharmaceutical and health industries are being detected in surface water, even after treatment. The
presence of endocrine disrupting compounds (EDCs) and pharmaceutical and personal care products
(PPCPs) indicate that current wastewater treatment methods must be adapted to meet this emerging
threat to surface freshwater (Snyder et al., 2003).
Resource extraction, such as mining, has major potential environmental impacts. The mining of
metals produces large amounts of waste tailings that, if not properly contained, can lead to leaching and
acidification of nearby soils (Dudka and Adriano, 1997). In turn, leaching reduces the diversity and
12
abundance of soil microorganisms (Maxwell, 1995). Acute effects of mine waste can be severe. When a
dam breached at the Gold King Mine in Colorado in August 2015, for example, over three million gallons
of acid mine waste were released into the nearby Animus River, resulting in a plume of contaminated
water with drastically increased levels of heavy metals, including lead and zinc (Landers, 2015).
Acute events tend to be limited in duration, but the long-lasting chronic effects of the acute events
can affect the ecology and community structure of an ecosystem for long periods of time. In streams
draining lead-mining regions in Missouri, suspended sediment samples (taken more than 25 years after
mining activities ceased in the region) had concentrations of lead, zinc and cadmium greater than USEPA
screening values (Gale et al., 2004), indicating that metal contamination can be very persistent in natural
systems. Metal mining also leads to increases in trace metals in soils. These soils can wash into nearby
waterways in runoff, thus contaminating surface waterways. Surface water in historical mining areas of
Arizona were contaminated by various heavy metals, including arsenic, cadmium, iron, lead, copper, and
zinc (Rösner, 1998).
Possible Solutions
Traditional management of surface water contamination relies on identifying point sources (e.g.,
wastewater treatment plants, industrial outflows) and requiring permits for these facilities, ensuring
that any additions to surface waters meet state or federal standards for water quality. However, in
recent years, non-point sources (NPS), such as runoff from agricultural fields or pastures, have begun to
garner more attention from scientists and policy makers. The Clean Water Act defines NPS as any
pollution that does not meet the definition of point source pollution. Point source pollution is defined
as a
“discernable, confined and discrete conveyance, including but not limited to any pipe, ditch,
channel, tunnel, conduit, well, discrete fissure, container, rolling stock, concentrated animal
feeding operation, or vessel or other floating craft, from which pollutants are or may be
13
discharged. This term does not include agricultural storm water discharges and return flows from
irrigated agriculture.”
- Clean Water Act (USEPA, 2002)
NPS can originate from many diffuse sources, making it impossible to regulate them. Instead, more
attention has been focused on non-regulatory programs such as implementation of Best Management
Practices (BMPs) in areas affected by NPS.
BMPs are designed to improve water quality before runoff enters receiving waterways. Examples of
BMPs for control of agricultural runoff include planting buffer zones of vegetation near waterways to
slow and filter runoff, installing fencing between livestock-containing pastures and waterways to
prevent physical degradation of waterways by livestock, and restoring natural waterway contours and
wetlands to slow and filter surface waters.
BMPs can be effective in managing water quality in receiving waterways (O’Geen et al., 2007; Gabel et
al., 2012). For example, Lee et al. (2003) found that installation of a vegetative buffer strip of just 7 m
reduced sediment entering a stream by 95% and nutrient export by 58-80%. To determine BMP
effectiveness, water quality must be monitored before, during, and after implementation. This
information, along with knowledge of watershed land use, can be used to more effectively tailor BMPs
to specific watersheds, to make them more effective in controlling NPS.
The Cache River, Arkansas
The Cache River is an excellent example of a waterway that has been greatly affected by human
activities. From its headwaters in Southeastern Missouri, the Cache River flows generally southsouthwesterly through the alluvial plains ecoregion of northeastern Arkansas, before entering the White
River near Clarendon, Arkansas (Fig. 1.2).
14
Figure 1.2. Location of the Cache River Watershed (HUC 08020302) in the State of Arkansas as well as
the Cache River and its main tributary, Bayou DeView.
15
The Cache River Watershed is bordered on the east by Crowley’s Ridge, an area of rolling hills,
higher elevation and highly erodible loess soils. This area is unsuited for row crop production
and has distinctly different land use than the remainder of the watershed.
Historically, the Cache River Watershed was composed of bottomland hardwood forests with
extensive wetlands. As the land was developed for agricultural use, most of the bottomland
hardwoods were removed, with only 15% of the original forest remaining in 1987 and only in
fragmented stands (Kress et al., 1996). Simultaneously with the deforestation of the region,
most waterways, including large portions of the upper Cache River and its tributary streams,
were channelized to increase drainage of agriculturally productive lands (Kress et al., 1996). This
channelization involved the straightening and dredging of natural waterways and resulted in the
drainage of most of the original wetlands. One notable exception is the wetland area in the
Cache River National Wildlife Refuge, established in 1984. In 1989, the Ramsar Convention on
Wetlands named the Big Woods wetlands in the Cache River Watershed as a “Wetland of
International Importance”, one of only 35 such wetlands in the United States (Ramsar
Convention on Wetlands, 2013). This designation cemented the ecological importance of the
Cache River Watershed and further spurred efforts to restore the Lower Cache River to original
conditions. The possible sighting of an ivory-billed woodpecker (presumed extinct) in the Cache
River National Wildlife Refuge in 2004 drew international attention to this area (Fitzpatrick et
al., 2005). Although the report of a population of ivory-billed woodpeckers has since been
disputed (Sibley et al., 2006; Collinson 2007), it did help to further habitat preservation and
restoration efforts of bottomland hardwood forests throughout the southeastern United States,
including the Cache River Watershed (USFWS, 2009).
In recent years, the Cache River Watershed has been identified as a focus area watershed by
the Natural Resources Conservation Service (NRCS) as part of the Mississippi River Basin Healthy
16
Watershed Initiative (MRBI; NRCS, 2012). This initiative is attempting to mitigate non-point
source sediment and nutrient pollution in the Gulf of Mexico by addressing the problems in
priority watersheds, such as the Cache River. Although significant restoration and preservation
has occurred in the lower reaches of the Cache River, the upper reaches remain largely
channelized and subject to contamination from non-point source pollution. The overall Cache
River Watershed is dominated by agriculture, primarily row cropping, but some exceptions do
exist in sub-watersheds of the Upper Cache River. In this portion of the watershed, the
topography of the western slopes of Crowley’s Ridge makes row cropping a less viable option
than in the alluvial plain. Consequently, these watersheds have more land in non-cropland use
(53%) as compared to those in the alluvial plain, where only 18% of the land is not used for crop
production (Fig. 1.3). Waterways along Crowley’s Ridge also tend to be unaltered by
channelization.
17
Figure 1.3. Land use (ha) in Cache River Watershed in the A) alluvial plain and B) Crowley’s Ridge. Figures
prepared by Kilmer from 2006 land use data available at the Arkansas Watershed Information System
(AWIS, 2006). Category of other includes land use including pasture and urban/industrial use.
18
Both the Cache River and its primary tributary Bayou DeView (including its headwater, Lost Creek
Ditch) are listed as 303(d) impaired waterways by the State of Arkansas. A waterway is considered
impaired if it fails to meet at least one designated use in one or more reaches (Table 1.3). Designated
uses not being met in the Cache River Watershed include being unable to support aquatic life (fish,
shellfish, wildlife protection and propagation), being unable to be safely used as an agricultural or
industrial water supply, and being unsafe for primary contact recreation, primarily swimming (ADEQ,
2008).
Impairments include excessive levels of lead (Pb), total dissolved solids (TDS), siltation/turbidity,
pathogens, chlorides (Cl-), aluminum (Al), sulfates (SO4-2) and copper (Cu) with sources including
agriculture, industrial point source and municipal point source. It is important to note that impairment
status is a constantly changing situation. Table 1.3 represents data from the most recent approved
303(d) list published by ADEQ in 2008. However, more recent draft versions of the 303(d) list (ADEQ,
2010; 2012; 2014; 2016a) indicate that several reaches of the Cache River and its tributaries have met
attainment guidelines and have been delisted, whereas other reaches have failed assessment criteria for
different parameters, including dissolved lead (Pb), chlorides (Cl-), sulfates (SO4-2), dissolved oxygen
(DO), total dissolved solids (TDS), dissolved copper (Cu) and turbidity (Table 1.4). These assessments are
performed primarily at the level of the main channel and do not address potential impairments in
smaller tributaries of the Cache River or Bayou DeView. Although not included in the 303(d) report,
pesticide levels above the recommended threshold of 2 ppb have also been reported in the Cache River
in recent years (Mattice et al., 2010).
19
Table 1.3. Impaired Reaches of the Cache River Watershed, Assessment Summary for Reporting Year 2008 (ADEQ, 2008)
Waterbody and Reach
Length (km)
Failed Designated Use(s)
Cause
Bayou DeView 004
34.12
Aquatic Life
Pb
Bayou DeView 005
13.84
Aquatic Life
Pb
Bayou DeView 006
16.42
Aquatic Life
Pb
Bayou DeView 007
29.29
Aquatic Life
Pb
Bayou DeView 009
32.67
Agriculture and Industry
TDS, Cl, Al
Lost Creek Ditch 909
12.71
Cl
Cache River 016
35.08
Aquatic Life, Drinking Water
Agriculture and Industry
Aquatic Life
Pb
Cache River 017
25.43
Aquatic Life
Pb
Cache River 018
40.23
Aquatic Life
Pb
Cache River 019
22.05
Aquatic Life
Pb
Cache River 020
36.37
Aquatic Life
Pb
Cache River 021
18.4
Aquatic Life
Pb
Cache River 027
6.28
Aquatic Life, Agriculture and Industry
Pb, TDS
Cache River 028
9.50
Primary Contact
Pathogens
Cache River 029
6.28
Aquatic Life, Agriculture and Industry
Pb, TDS
Cache River 031
5.47
Aquatic Life, Agriculture and Industry
Pb, TDS
Cache River 032
18.35
Aquatic Life, Agriculture and Industry
Pb, TDS
20
Table 1.4. Impaired Reaches of the Cache River Watershed and Causes, Draft 303(d) list 2010-2016 (ADEQ, 2010; 2012; 2014; 2016a)
Waterbody and Reach
2010
2012
2014
2016
Bayou DeView 002
DO*
DO, SO4 *
Bayou DeView 004
Pb
Pb
DO*, SO4*, Turbidity*
DO, SO4
Bayou DeView 005
Pb
Pb
SO4*, Turbidity*
DO, SO4
Bayou DeView 006
Pb
Pb
SO4*, Turbidity*
DO, SO4
Bayou DeView 007
Pb
Pb
SO4*, Turbidity*
DO, SO4
Bayou DeView 009
TDS, Cl
Cu*
Cu, Turbidity*
Bayou DeView 012
Lost Creek Ditch 909
DO*
Cl
Big Creek Ditch 910
Cu
DO*, Cl, Cu*
DO, Cl
Cu*
Cu
Cu
DO, Pb
Cache River 016
Pb
Pb
DO*, Turbidity*
Cache River 017
Pb
Pb
Pb, Turbidity*
Cache River 018
Pb
Pb
Pb, Turbidity*
Cache River 019
Pb
Pb
Pb, Turbidity*
Cache River 020
Pb
Pb
Pb, Turbidity*
Cache River 021
Pb
Pb
Pb, Turbidity*
Cache River 027
Pb, TDS
Pb, TDS
SO4*, Turbidity*
Cache River 028
Pb, TDS
Pb, TDS
SO4*, Turbidity*
Cache River 029
Pb, TDS
Pb, TDS
SO4, Turbidity*
Cache River 031
Pb, TDS
Pb, TDS
SO4*, Turbidity*
Cache River 032
Pb, TDS
Pb, TDS
SO4, Turbidity*
* New cause of impairment for current waterbody reach or new waterbody reach listing
21
PROJECT OBJECTIVES AND OVERVIEW OF CHAPTERS
This study examined impairments in the Cache River Watershed by sampling 19 headwater subwatersheds of the Cache River and Bayou DeView, as well as the Cache River prior to the confluence
with the White River (Fig. 1.4). Three additional sampling sites were located along the main channel of
the Cache River and provided a comparison to both sub-watershed results collected in this study and to
existing water quality data from previous studies (all collected from main channel sites). Water and
sediment samples were collected at sites as near to the outflows of the sub-watershed as was accessible
while main channel sites were sampled at existing USGS monitoring stations.
22
Cache River
Bayou DeView
Fig 1.4. Sites sampled within the Cache River Watershed from August 2013-July 2016. Solid colored subwatersheds are headwater sub-watersheds whereas hatched sub-watersheds are those containing a
main channel site or the site just prior to the confluence with the White River (southernmost site).
23
Samples were analyzed to determine if or to what extent these headwater sites contribute to
the overall impairments of the Cache River. The goal of this study was to determine the extent
of the contamination within the watershed, the degree of contamination in terms of
environmental toxicity to aquatic organisms and the locations within the watershed that would
benefit most from implementation of BMPs. Three general types of questions were posed as
described below. Specific objectives and hypotheses for each chapter, as well as results of data
collection and interpretation and discussion of these results, are described in detail in the
subsequent chapters.
The first general question concerns the role of headwater watersheds, specifically land use
within these watersheds, in affecting overall water quality of the Cache River. This question is
addressed in Chapter 2, which highlights the water quality parameters that were assessed
throughout the three-year sampling period, including water temperature, pH, dissolved oxygen
(DO), conductivity, turbidity, total suspended solids (TSS), dissolved nutrients (nitrate (NO2-),
nitrite (NO3-), orthophosphate (PO4-3), and total nutrients (nitrogen, phosphorus). Analyses of
these data focused on the effects of land use within the sampled sub-watershed as well as
environmental factors such as precipitation and discharge within the waterway. Conclusions
from this study can be used to identify sub-watersheds that exceed assessment criteria and in
which BMP implementation might be most effective. Furthermore, understanding the specific
relationship between discharge and land use and the measured water quality parameters could
help determine which specific types of BMPs would be most effective.
Chapter 2 also compares land use categories and individual sites spatially to determine if any
geographic pattern is evident for elevated levels of assessed water quality parameters or overall
loading of contaminants. Sites in which current assessment criteria (DO, turbidity, total
nutrients, ADEQ, 2016b) have not been met are identified and discussed.
24
The second area of questions addresses the issue of lead (Pb) contamination within the Cache
River Watershed. First, what is the extent of the contamination? Can a general pattern of
contamination be determined and does land use correlate with detection of, and/or mean
concentrations of, Pb within the watershed? Can any specific land use category or
environmental factors account for the overall pattern of detection and mean concentrations of
Pb? These questions are addressed in Chapter 3, which highlights sampling for Pb in three
environmental matrices: dissolved Pb, total recoverable Pb (dissolved + particulate), and
sediment-bound Pb. Analyses of these data focused both on the frequency of detection, as well
as on the mean concentrations, of Pb in each of these matrices and the effect that land use or
environmental parameters might have on detection of this heavy metal. Conclusions from this
study can be used to help identify the source of this contaminant and identify areas of the
watershed where levels are of greatest ecological concern.
The final question posed by this study was designed to determine how ecologically relevant
the measured levels of dissolved Pb are, in terms of toxicity to aquatic organisms. This final
aspect of the project, presented in Chapter 4, included toxicological testing using several
endpoints, including lethality (acute testing), growth and reproductive effects (chronic testing),
and alteration of behavioral responses, which could lead to indirect lethality by increasing the
susceptibility of organisms to predation. Both laboratory-prepared, moderately hard water and
ambient water from the Cache River were used for standard chronic and acute tests and were
spiked with lead nitrate to determine toxicological endpoints. These endpoints were then
compared to actual concentrations of Pb detected within the Cache River Watershed to better
understand the ecological relevance of Pb within this watershed.
Chapter 5 provides a summary of the research project and ranks and prioritizes sites for
management, particularly sites in which assessment criteria have not been met (ADEQ, 2016b).
25
Recommendations are made for specific management practices, including BMP implementation.
This chapter also describes further research that should be carried out within the watershed to
more fully understand impairments within it and the most likely causes of those impairments.
26
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32
CHAPTER 2: WATER QUALITY OF THE CACHE RIVER, ARKANSAS
A portion of data within this chapter (samples from sites LCDI and CRPA collected between
February 2014 and May 2015) has been published as part of an independent M.S. thesis:
Kennon-Lacy, M. 2016. Water quality monitoring at impaired sites on the Cache River, AR, USA.
(M.S. thesis). Arkansas State University, State University, AR. 128 p.
ABSTRACT
The increase in agriculture in recent decades, in part to support a growing world population,
has placed increased stress on waterways in agricultural areas. Runoff containing nutrients and
sediments has many negative effects on aquatic environments and is the cause of hypoxic zones
such as the Gulf of Mexico ‘Dead Zone’. In this study, 23 sites were sampled throughout the
Cache River Watershed, a watershed dominated by agricultural activity. Samples were taken for
three years (August 2013 to July 2016) to determine the potential contributions to the Gulf of
Mexico hypoxic zone. Agricultural land alteration and discharge at the time of sampling were
compared to overall contaminant concentrations to determine if a relationship existed.
Furthermore, water quality data were compared spatially to determine areas within the
watershed where Best Management Practices (BMPs) might be most effectively implemented.
Both discharge and land alteration had a significant relationship with overall contaminant
concentrations and a significant interaction existed as well. Alteration had a significant positive
relationship with turbidity, nitrite (NO2-), total phosphorus, total nitrogen and orthophosphate
(PO4-3), with contaminant loads increasing as degree of land alteration increased. Discharge had
a less distinct relationship, only having a significant positive relationship with turbidity. Spatial
assessment indicated that most areas of concern had high amounts of agricultural land use and
were generally located along the western edge of the Upper Cache River Watershed. Waterways
33
in these areas would likely benefit from implementation of BMPs that reduce, slow or filter
surface runoff from agricultural fields. Although areas of excessive sediment and nutrient
loading were present in the watershed, a reduction in all contaminant loads was observed along
a downstream gradient of the main channel of the Cache River. This reduction could be
attributed to the relatively large areas of wetlands remaining in the Lower Cache River
Watershed as well the lack of artificially channelized stream reaches in this portion of the
watershed.
INTRODUCTION
As agricultural activity has increased in recent decades, in part to support a growing world
population, increased attention by scientists and policymakers has been focused on
environmental impacts of agricultural practices, particularly in aquatic ecosystems. Globally,
transport of nutrients to the ocean via riverine systems, has increased during the twentieth
century, largely from agriculture and urban development (Beusen et al., 2015). One result of
this increased nutrient transport, is the increase in algal blooms and resulting hypoxic zones due
to eutrophication in both freshwater (Jenny et al., 2016) and saltwater (Joyce, 2000; Rabalais et
al., 2002) environments that receive input from areas with high agricultural activity. One such
hypoxic zone is located just west of the mouth of the Mississippi River in the Gulf of Mexico and
is commonly referred to as the Gulf of Mexico ‘Dead Zone’. This hypoxic zone is a result of a
significant increase in nitrate flux from the Mississippi River Basin (Rabalais et al., 2002) and is
characterized by extremely low levels of dissolved oxygen (DO). This zone varies in size from
year to year, often shrinking during times of drought (when discharge and associated nutrients
are reduced) and increasing during years of elevated discharge (NOAA, 2015; USEPA, 2016).
The Mississippi River Basin covers approximately 41% of the landmass of the continental
United States and drains all or part of 31 U.S. states and two Canadian provinces. Although the
34
Mississippi River basin serves many ecological and economic purposes, its predominant use is
for agriculture (National Academy of Sciences, 2008). Agriculture is considered the primary
driver of nutrient fluxes within the Mississippi River Basin and the Gulf of Mexico. As part of the
Mississippi River Basin Healthy Watersheds Initiative (MRBI) several agriculturally dominated
watersheds of the Mississippi River, including the Cache River Watershed, in northeastern
Arkansas, have been identified as focus area watersheds, due to their likelihood to contribute to
eutrophication of aquatic ecosystems and resulting hypoxia (NRCS, 2016). Watersheds
identified as MRBI focus area watersheds are delineated by an 8-digit hydrologic unit code
(HUC). Within these HUC-8 watersheds, smaller-area catchments are delineated by 10 and 12digit HUCs, with 12-digit HUCs representing the smallest-area established catchment (Seaber et
al., 1994). These smaller HUC-10 and HUC-12 catchments (sub-watersheds) can be designated
as high-priority areas within the overall focus area watershed. Within the Cache River
Watershed, several HUC-12 sub-watersheds have been identified as high-priority sites by
member agencies of the MRBI, including the Natural Resources Conservation Service (NRCS,
2016).
The Cache River Watershed has historically been listed as impaired for excessive turbidity by
the State of Arkansas (ADEQ, 2014). Turbidity is defined as a measure of water clarity largely
determined by suspended or dissolved solids in the water (USEPA, 1997; USGS, 2016a).
Although sediments are the primary contributor to turbidity, other substances can indirectly
influence turbidity. For example, nutrients can lead to algal blooms that reduce water clarity,
causing an increase in turbidity (Fondriest Environmental, Inc., 2016). Thus, although nutrients
do not directly influence turbidity, their effects on aquatic ecosystems can have a direct effect
on turbidity. An increase in turbidity suggests that an increase in sediments and/or nutrients
has occurred within rivers and lakes (Fondriest Environmental, Inc., 2016). Turbidity is an
35
accurate predictor of nutrient concentrations, particularly in watersheds characterized by long
periods of base-flow and short, intense rainfall events (Lessels and Bishop, 2013).
Because of its potential to contribute to eutrophication and hypoxia, particularly the hypoxic
zone in the Gulf of Mexico, the Cache River Watershed was selected by the United States
Environmental Protection Agency (USEPA) for more extensive sampling to determine how the
water quality parameters are influenced by catchment land within the watershed. This
watershed is of enormous economic importance, due to its agricultural productivity. It is also
considered to be of great ecological importance, in part due to its remaining wetland areas. A
portion of these wetlands have been named a ‘Wetland of International Importance’ by the
United Nations Ramsar Convention, one of just 38 such sites in the United States (Ramsar
Convention on Wetlands, 2013).
Northeastern Arkansas, including the Cache River Watershed, is heavily used for row crop
production, with leading crops consisting of rice, cotton, corn (maize), soybeans, and sorghum
(USDA-NASS, 2016). In 2012, this area of Arkansas contained approximately 50% of all
harvested cropland within the state and was responsible for 57% of the market value of all crops
produced within the State of Arkansas (USDA-NASS, 2014). Many agricultural practices
associated with row crop production have been linked to elevated measurements of turbidity
and increased concentrations of total suspended solids (TSS) and nutrients in surface waters
(Ekholm and Mitikka, 2006; Hoorman et al., 2008). First, in preparation for planting, fields are
plowed, generally during late spring, a time of year associated with increased precipitation. This
loosening of the topsoil, combined with seasonal precipitation, increase the likelihood that soil
will be mobilized in surface runoff from fields. Harper et al. (2008) found that agroecosystem
plots cultivated with conventional tillage under simulated rainfall conditions produced runoff
with significantly greater turbidity and suspended solid concentrations than plots cultivated with
36
ridge-tillage or plots managed as native tallgrass prairie. In northeastern Arkansas plowing may
occur up to the edge of waterways, with little to no riparian buffer remaining (personal
observation). Thus, when the loosened topsoil in surface runoff reaches the waterways, there is
very little (if any) riparian buffer to slow/filter the runoff.
Secondly, to maximize crop yields, fields are typically treated with agrochemicals, including
pesticides and fertilizers. Fertilizer application is of particular concern as nutrients from
agricultural areas are considered the primary cause of the hypoxic zone in the Gulf of Mexico
(Rabalais et al., 2002). Much progress has been made in the area of precision farming and
custom fertilizer application (Koch et al., 2004), particularly in the United States where
investments in public sector research and extension education have resulted in increased
nutrient efficiency (Tilman et al., 2002). Globally, nutrient efficiency is slowly increasing, though
this is not consistent by area. For example, Lassaletta et al. (2014) found that many countries
showed an increase in nitrogen use efficiency over the past 50 years, whereas others,
particularly in developing regions of the world, showed a marked decrease in efficiency,
indicating that excess nitrogen was being applied in agricultural areas. This excess fertilizer can
enter adjacent waterways and alter normal nutrient cycling and assimilation processes (Lunau et
al., 2013).
Finally, waterways in many agricultural regions, including the Cache River Watershed, are
largely channelized to increase drainage of land suitable for crop production. The
channelization of waterways tends to result in a uniform channel with no impediments to flow,
which in turn produces increased flow rates. Furthermore, drained land was often natural
wetlands, that function to slow and filter water before it enters waterways. The loss of
wetlands combined with the increased flow rate within channels can greatly alter sediment and
nutrient transport in channelized systems (O’Donnell and Galat, 2007).
37
Together, agricultural practices and physical alterations of waterways can lead to increased
runoff of sediments and nutrients, altered in-stream nutrient cycling and assimilation and
increased downstream transport of contaminants, often resulting in problems which are
geographically separate from the source (Anderson et al., 2014; Sun et al., 2013). Physical
differences between watersheds and differences in flow regimes and land use can affect the
degree of these impacts. Therefore, understanding watershed-specific dynamics is crucial in
managing water quality while still allowing for efficient crop production.
The Cache River Watershed in Arkansas is heavily used for agriculture and significant portions
of the Cache River, particularly the Upper Cache River, have been artificially channelized (AWIS,
2016). A comparison of waterway stream miles that are natural vs. channelized indicates that
for the Upper Cache Watershed between 13 and 22% of all stream miles are channelized with
the remainder being unchannelized whereas in the Middle Cache Watershed, channelized
stream miles account for 11-16% of all stream miles (AWIS, 2016). In the Lower Cache
Watershed, 11.3 km of the main channel of the Cache River were initially channelized but are
currently in the process of being restored back to natural meanders (The Nature Conservancy,
2016). Channelized sections are often shorter in terms of stream miles (due to removing natural
meanders) than unchannelized sections. Although the percentage of stream miles gives an
indication of channelization within the watershed, it does not determine what percentage of the
watershed is drained by channelized streams. The main channel of the Cache River is almost
completely channelized in the Upper Cache Watershed but not channelized in the Middle and
Lower Cache Watershed.
Although a considerable portion of the Cache River Watershed is used for agriculture and/or is
channelized, some areas are relatively unaltered due to the presence of Crowley’s Ridge, an
area of increased elevation where the natural topography makes row crop production less
38
feasible. Although these areas are not pristine, they represent a relatively unaltered area, both
in terms of land use and artificial channelization and can be used as a contrast to more heavily
altered sites within the watershed. Northeastern Arkansas has relatively reliable precipitation,
although crop production often requires irrigation, particularly in the Cache River Watershed,
which has considerable amounts of land devoted to the cultivation of rice (USDA-NASS, 2016).
These irrigation practices can alter flow regimes and overall discharge independently of
precipitation. Therefore, it is necessary to understand how discharge within the waterway (not
necessarily precipitation) affects sediment and nutrient contamination.
The assumption that agricultural practices, including channelization, have a significant effect
on water quality in the Cache River was examined in this study. The following hypotheses and
predictions were tested to determine the effect of agriculture, physical alterations and
discharge on water quality in the Cache River and its sub-watersheds.
HA-1: Agriculturally related alterations (e.g., artificial channelization, agricultural land use,
reduced forest cover) have an effect on turbidity, TSS and nutrient levels in sampled subwatersheds due to an increased amount of contaminated surface runoff as a result of these
alterations
P-1: Turbidity, TSS and nutrient levels will be greater in watersheds with a greater percentage
of agriculturally related alterations than in those with lower levels of alteration.
HA-2: Discharge has an effect on turbidity, TSS and dissolved nutrient levels in sampled subwatersheds due to increased surface runoff and transport of contaminants.
P-2: Turbidity, TSS and nutrient levels will be greater during times of greater discharge.
In watersheds with high agricultural importance, such as the Cache River Watershed,
maintaining environmental health is often a balancing act between ensuring agricultural
39
productivity and reducing environmental impact due to agricultural practices. This has led to
the establishment of Best Management Practices (BMPs), which are practices that can be
implemented on a local scale to protect water quality and promote soil conservation (NCFS,
2016), particularly in regards to NPS pollution. In agriculturally dominated watersheds, the most
effective BMPs for controlling contaminants such as sediments and nutrients would be those
that either reduce overall surface runoff, such as improved irrigation techniques, those that
slow and filter surface runoff, such as riparian buffers, filter strips or the use of cover crops, or
those that slow water flow in surface waterways and provide areas for sedimentation and
assimilation, such as restoration of natural meanders or restoration and enhancement of natural
wetlands. The efficacy of these practices has been widely reported.
Surface runoff can originate from either precipitation or irrigation that flows across land
surfaces and into waterways. Practices that reduce overall surface runoff tend to be limited to
those that improve irrigation efficiency, as controlling precipitation is not possible. However,
several practices that improve irrigation efficiency, such as land leveling would be equally good
for reducing surface runoff from either irrigation or precipitation (Waskom, 1994).
BMPs that slow and filter surface runoff can also be an effective way to improve surface water
quality, by reducing the amount of sediment (and associated) contaminants that enter
waterways via runoff. Common examples of these practices include restoration or
enhancement of riparian buffers (both herbaceous and forested), installation of filter strips in
agricultural fields, practicing low or no-tillage land management and planting of cover crops.
Riparian buffers have been well reported as an effective means of slowing and filtering
surface runoff. Collins et al. (2013) found that restoration of riparian buffers could significantly
decrease turbidity and increase dissolved oxygen in receiving waterways, although in this study,
no significant decrease was seen in nutrient concentrations. Similarly, Miller et al. (2015) found
40
that restoration of riparian buffers in a headwater basin following cessation of logging
operations had positive effects on both suspended sediment concentrations and turbidity.
Although buffers are effective at removing both sediment and nutrients from runoff (Lee et
al., 2003), the width of the necessary buffer can prove to be an obstacle, particularly in
intensively cultivated areas. Lee et al. (2003) found that a buffer needed to be > 7 m to remove
sediment and sediment-bound nutrients but that an increase in width to 16.3 m increased
removal efficiency of soluble nutrients by 20%. In a review of studies investigating nitrogen
removal by riparian buffers, Mayer et al. (2006) found that vegetative composition of the buffer
was not significant but that to be effective at removing nitrogen, a vegetative buffer had to be a
> 25 m in width. Conversely, Wu et al. (2010) found that planting a narrow, 0.5-m grass hedge
of native grasses could reduce overland flow of surface water by as much as 72%, indicating that
even narrow buffers can be effective. Similarly, Rasouli et al. (2015) found that buffers of 2-4 m
are effective in reducing surface runoff, even on steep slopes, removing 68-78% of total
suspended solids and resulting in a 53-68% reduction in turbidity measurements. Thus, while
the width and composition of the buffer, as well as specific characteristics of the area in which
they are implemented, can affect overall effectiveness, buffer strips are effective in maintaining
or improving water quality in waterways affected by non-point surface runoff.
Planting cover crops and utilizing low tillage/no tillage techniques can also be an effective way
to slow or reduce surface runoff, thus improving the quality of runoff water. Slowing or
reducing runoff of surface water allows water to percolate into soils whereas the physical
structure of the cover crops helps to physically filter surface runoff. When comparing plots
planted with no cover crop or a cover crop of rye, Krutz et al. (2009) found that usage of a cover
crop delayed the time to runoff by 1.3 fold and volume of runoff by 1.4 fold, relative to no cover
crop. Locke et al. (2015) used rainfall simulations to determine that plots treated with no-tillage
41
or planted with cover crops had significantly reduced turbidity and phosphorus in runoff water,
when compared to plots with no cover crop. This was attributed in part to the significantly
increased time to runoff in plots treated with no-tillage or planted with cover crops.
An additional way to improve surface water quality and reduce transport of sediment and
nutrients is to slow the flow of water in surface waterways, either through channel restoration
or wetland restoration and enhancement as discussed above. Wetland restoration is an
effective means of reducing nitrogen export to surface waterways, by providing a natural sink
for nitrogen that does leave agriculturally productive areas. A meta-analysis performed by
Jordan et al. (2011) found that total removal of reactive nitrogen by wetlands in the contiguous
United States accounted for 20-21% of the total anthropogenic load. Hoffmann et al. (2011)
found that restored natural wetlands could remove up to 95% of NO3- and 71% of total N
whereas Borin and Tocchetto (2007) found that a constructed wetland had up to a 90% removal
efficiency of N from agricultural runoff. However, removal of nitrogen does tend to be
temperature-specific, as organisms necessary for denitrification and assimilation function best
under moderate to warm temperatures.
Although generally effective when applied alone, BMP effectiveness tends to improve when
used in conjunction with other BMPs. For example, Zhang et al. (2005) found that restored
natural wetlands were effective at reducing turbidity, particularly when used in conjunction with
riparian buffers. Similarly, Locke et al. (2015) found that plots treated with no tillage and
planted with cover crops were more effective at slowing surface runoff whereas Motsinger et al.
(2016) showed that combining no-tillage practices with a rye cover crop decreased NO3discharge by 13% over using the cover crop alone.
BMPs implemented thus far in portions of the Cache River Watershed have been effective at
improving water quality. Beginning in 2001, water conveyance and control structures were
42
implemented within Bayou DeView, the primary tributary to the Cache River, ultimately
resulting in an estimated soil savings of 219,660 tons per year, by reducing overall surface runoff
and reducing sediment entering waterways. This also caused a reduction in dissolved Pb (often
carried in surface runoff), resulting in several reaches of this stream being removed from the
303(d) list for the State of Arkansas (USEPA, 2014). Several HUC-12 sub-watersheds in the
Middle Cache Watershed began BMP implementation in 2013 (NRCS, 2013) and initial results
(presented in this paper) suggest that they have been effective in managing sediment and
nutrient loads in receiving waterways. Several other areas have been identified for BMP
implementation, particularly in the Upper Cache Watershed, with BMP implementation
beginning in 2016 (NRCS, 2015).
The objectives of this study were threefold. First, the effects of land alteration and discharge
on water quality were measured using water quality data collected throughout the Cache River
Watershed over a three-year period. Water quality results from sub-watersheds with existing
BMPs were also compared to the overall pool of sites to determine the relative effectiveness of
BMP implementation thus far. Finally, sampled sites within the Cache River were compared
both spatially and temporally to determine if any overall patterns of contamination were
evident that could be useful in prioritizing areas for further research or potential
implementation of Best Management Practices (BMPs). Water quality parameters analyzed
included pH, water temperature, conductivity, turbidity, total suspended solids (TSS), dissolved
nutrients (NO2-. NO3-, PO4-3), total nutrients (nitrogen, phosphorus), and dissolved oxygen (DO).
MATERIALS AND METHODS
Sampling
Water was collected from the outflow of each of the 20 12-digit HUC sub-watersheds, 19
headwater sub-watersheds as well as the sub-watershed just prior to the confluence of the
43
Cache River with the White River. Water samples were collected from the vertical centroid of
the flow, where the water was actively flowing and well mixed, using a 2-L sampling bucket
dropped from above. In the event water flow was completely absent from a site, sampling was
postponed until sufficient flow was present for sampling. Sampling took place monthly at the
12-digit HUC sub-watersheds for three years, with a total of 240 samples collected each year.
An additional 12 samples were taken at each of three existing USGS stage and discharge
monitoring stations on the Cache River (CRCP, CRPA, CREG; Table 2.1, Fig. 2.1) across a range of
discharge conditions each year, including storm events, for a total of 36 samples yearly. Thus, a
total of 276 samples were collected and analyzed each year. A FH950 portable velocity meter
(Hach Analytical) was used to assess flow velocity at each site. This meter was lowered into the
vertical centroid of the flow using a bridge suspension kit (Hach Analytical). A stream profile
(width and average depth) was completed at each site, allowing the velocity obtained from the
flow meter (m/sec) and the depth at time of sampling to be converted into discharge (m3/sec).
Three sites (MUCR, NTSD, BDDI), encompassing the range of land uses within the Cache River
Watershed, were selected for weekly sampling of all parameters, completed for a total of 14
months (September 2014 to October 2015). These three sub-watersheds are roughly consistent
in size and position in the Cache River Watershed (Fig. 2.1). Weekly sampling of this subset of
watersheds was performed to indicate any fine-scale seasonal/weather patterns that could
affect water quality and to determine if monthly sampling was sufficient to determine overall
water quality based on environmental factors.
44
Table 2.1. Site names, abbreviations, waterway types and sampling frequency for all sampled sites in the
Cache River Watershed, Arkansas. Samples were collected monthly from August 2013 to July 2016 and
weekly at a subset of sites from September 2014 to October 2015.
Sampling site
Site Code
Waterway type
Sampling Frequency
Fish Trap Slough
FTSL
Headwater
Monthly
Little Cache River Ditch
LCRD
Headwater
Monthly
South Fork-Big Creek
SFBC
Headwater
Monthly
East Slough
EASL
Headwater
Monthly
Big Gum Lateral
BGLA
Headwater
Monthly
Scatter Creek
SCCR
Headwater
Monthly
Sugar Creek
SUCR
Headwater
Monthly
Swan Pond Ditch
SPDI
Headwater
Monthly
Beaver Dam Ditch
BDDI
Headwater
Monthly/Weekly
Kellow Ditch
KEDI
Headwater
Monthly
Number Twenty-Six Ditch
NTSD
Headwater
Monthly/Weekly
Mud Creek
MUCR
Headwater
Monthly/Weekly
Lost Creek Ditch
LCDI
Headwater
Monthly
Three Mile Creek
TMCR
Headwater
Monthly
Flag Slough Ditch
FSDI
Headwater
Monthly
Cypress Creek Ditch
CCDI
Headwater
Monthly
Skillet Ditch
SKDI
Headwater
Monthly
Willow Ditch
WIDI
Headwater
Monthly
West Cache River Ditch
WCRD
Headwater
Monthly
Reeses Fork
REFO
Main
Monthly
Cache River at Cotton Plant
CRCP
Main
Monthly
Cache River at Patterson
CRPA
Main
Monthly
Cache River at Egypt
CREG
Main
Monthly
45
Fig 2.1. Location of the Cache River Watershed, Arkansas and location of sampled sub-watersheds and
main channel sites within the Cache River Watershed. Letters within each site represent the four-letter
designation of each site (Table 2.1). Samples were collected from August 2013 to July 2016.
46
Analysis
Field Measurements and Transport
Water temperature, conductivity, pH and DO were tested immediately upon sampling using a multiprobe field meter (Orion Star A329, ThermoScientific. Waltham, Massachusetts). Approximately 10 mL
of water was filtered using a 0.45-µm filter and transported back to the lab for analysis of dissolved
nutrients (NO3-, NO2-, PO4-3). Fifty mL of unfiltered water was transported back to the laboratory for
analysis of total nutrients (nitrogen, phosphorus). One liter of water was placed in an acid-washed
Nalgene container and transported back to the laboratory for analysis of turbidity, TSS, and water
hardness. All samples were stored on ice during transport to the Ecotoxicology Research Facility (ERF).
Upon arrival at the ERF, water for total and dissolved nutrients was frozen until analyzed. Water
samples for turbidity and TSS were refrigerated until analyzed, within 72 hours of arrival at the ERF.
Turbidity
Analysis followed APHA Method 2130-B (APHA, 2005). Prior to analysis of turbidity, water samples
were warmed to room temperature (25°C) and well mixed, ensuring a homogeneous sample. A sample
of collected water was poured into a glass cuvette. The cuvette was carefully cleaned and handled to
ensure that no contamination due to fingerprints or debris was present on the outer surface. Samples
were analyzed using a Hach 2100P Turbidimeter. Final turbidity values were recorded in Nephelometric
Turbidity Units (NTU). Millipore water was used as a control.
TSS
Analysis followed APHA Method 2540D (APHA, 2005). Prior to analysis of TSS, water samples were
warmed to room temperature (25°C) and well mixed, ensuring a homogeneous sample. A 100-mL
sample of water was measured in a graduated cylinder and immediately vacuum-filtered through a
47
weighed standard glass-fiber filter (ProWeigh Filters, supplier Environmental Express, Charleston, South
Carolina, Cat# F93447MM). Three replicates were filtered for each sampling location. A 100-mL sample
of Millipore water was filtered as well to serve as a control. After vacuum-filtration, filters were allowed
to dry in a 103-105°C oven for at least four hours. Upon removal from the oven, samples were placed
into a desiccator and allowed to cool for at least one hour. Filters were weighed using a calibrated
Shimadzu balance. Final TSS values were calculated using the following equation
TSS (mg/L) = [(final weight of filter + sample) – (filter weight)] x 1,000
volume (mL) of original sample
Dissolved Nutrients
Dissolved nutrients were analyzed using an OI Analytical DA 3500 Nutrient Analyzer (OI Analyticals,
College Station, Texas) or a Skalar San++ Flow-through Analyzer (Skalar, Inc, Buford, Georgia), according
to APHA methods 4500-NO3I, NO2B and 4500-PF (APHA, 2005). Analysis occurred within 24 hours after
samples were thawed. NO3-, NO2-, and PO4-3 standards were prepared and used to calculate calibration
curves. A regression equation was determined based on each calibration curve and used to measure
values of dissolved nutrients in water samples. NO3-, NO2-, and PO4-3 standards were also analyzed
periodically throughout the overall analysis to check the accuracy of the analysis. Any analysis in which
standards did not fall within + 10% of the presumed concentration were discarded and samples were reanalyzed. Concentrations measuring below the MDL were reported as 0.5x the MDL. This value
substitution helps to limit artificial skewing of mean values due to very low concentrations of
constituents of water samples (Kayhanian et al., 2002).
Total Nutrients
48
Analysis of total nutrients was performed according to APHA methods 4500-NO3F and 4500-PB (APHA,
2005). Samples were digested using a persulfate method, with digested samples being frozen until
analysis. Upon thawing, samples were analyzed within 24 hours using a DA-3500 Discrete Analyzer (OI
Analytical, College Station, Texas) or a Skalar San++ Flow-Through Analyzer (Skalar, Inc, Buford, Georgia).
NO3- and PO4-3 standards were prepared and used to check the accuracy of each analysis. Any analysis in
which standards did not fall within + 10% of the presumed concentration was considered invalid and
water samples were reanalyzed. Samples were analyzed for total nitrogen (nitrate-nitrite) and total
phosphorus. Concentrations measuring below the MDL were reported as 0.5x the MDL. This value
substitution helps to limit artificial skewing of mean values due to very low concentrations of
constituents of water samples (Kayhanian et al., 2002).
Statistical Analyses
All statistical analyses were performed using R (R Core Team, 2016) with significance set at α = 0.05
for all analyses. To determine if land alteration and/or flow rate had an effect on water quality, a subset
of data was analyzed. Samples collected between August 2014 and July 2016 were used for this
analysis, as flow rate at the time of sampling was measured for these samples. Sub-watersheds were
categorized according to degree of alteration, with a score calculated using values for percentage of the
watershed with altered land use (agricultural, urban, pasture), percentage of the watershed drained by
altered waterways, and percentage of remaining forest cover. Land use data were available from the
Arkansas Watershed Information System (AWIS, 2016). Because these data were from 2006, subwatersheds were analyzed using more recent satellite photography (2010-2011) and ArcGIS (ArcMap
10.3 ®(ESRI Inc., 2014)) to determine if land or waterway alteration values had changed significantly
since 2006. Because the resulting data were not normally distributed (and transformations failed to
achieve normality), a non-parametric Mann-Whitney U-test was used to determine if land use values
49
had altered significantly. This test indicated that 2006 data and more recent data were not significantly
different (W = 266, p = 0.980). Thus, the 2006 data were used in the following equation to calculate an
alteration score for each sub-watershed.
(% altered land use + % altered waterways) - (% forest cover)
Because altered land use and altered waterways typically would contribute negatively to water
quality, these values were summed. Percentage of forest cover should contribute positively to water
quality (more forest = more buffer); thus, this value was subtracted from the total. This equation would
therefore, result in a theoretical range of values from -100 (maximum forest cover, no alterations) to
200 (no forest cover, maximum alterations). Values between -100 and 0 represent sub-watersheds with
low alteration, values between 0 and 100 represent moderate alteration and values between 100 and
200 represent highly altered watersheds. Based on the above equation, alteration values for the
sampled sub-watersheds ranged from -47.46 (least impacted sites) to 188.05 (most impacted sites).
Three natural groupings were obvious from this score (Table 2.1), with values that are approximately the
same as the theoretical values described above. The sub-watersheds were divided into three categories
based on these natural groupings; least-altered (n = 4), moderately-altered (n = 4) and most-altered (n =
11). Main channel sites receive cumulative inputs from many sub-watersheds and thus water quality is
not necessarily a reflection of surrounding land use. Therefore, these sites were assigned to a separate
category, defined as main channel (n = 4) (Table 2.2, Fig. 2.2)
50
Table 2.2. Site names, abbreviations, alteration scores and alteration category for all watersheds or
current USGS monitoring stations sampled during the course of this study (August 2013-July 2016).
Sampling site
Site Code
Alteration Score
Alteration
Sugar Creek
SUCR
-47.46
Least-altered
Scatter Creek
SCCR
-45.6
Least-altered
Mud Creek
MUCR
-15.22
Least-altered
South Fork-Big Creek
SFBC
-6.48
Least-altered
Lost Creek Ditch
LCDI
88.73
Moderately-altered
Little Cache River Ditch
LCRD
93.02
Moderately-altered
Number Twenty-Six Ditch
NTSD
117.75
Moderately-altered
Swan Pond Ditch
SPDI
124.41
Moderately-altered
East Slough
EASL
164.09
Most-altered
Big Gum Lateral
BGLA
171.4
Most-altered
Willow Ditch
WIDI
175.67
Most-altered
West Cache River Ditch
WCRD
175.99
Most-altered
Beaver Dam Ditch
BDDI
177.75
Most-altered
Flag Slough Ditch
FSDI
178.57
Most-altered
Three Mile Creek
TMCR
178.77
Most-altered
Skillet Ditch
SKDI
179.65
Most-altered
Cypress Creek Ditch
CCDI
182.09
Most-altered
Fish Trap Slough
FTSL
185.37
Most-altered
Kellow Ditch
KEDI
188.05
Most-altered
Reeses Fork
REFO
NA
Main channel
Cache River at Cotton Plant
CRCP
NA
Main channel
Cache River at Patterson
CRPA
NA
Main channel
Cache River at Egypt
CREG
NA
Main channel
51
Figure 2.2 Sampling sites within the Cache River Watershed according to designated alteration category
(Table 2.2). Sites were sampled monthly between August 2013 and July 2016.
52
Data for discharge were not normally distributed and transformations failed to achieve normality,
thus discharge data were categorized in order to allow for non-parametric analysis. Discharge was
measured at the time of sample collection and categorized for each site into one of five categories;
base, low, moderate, high, and storm. Discharge was calculated by multiplying the volume of water in
the waterway with the velocity of the water, both measured at the time of sampling. This was easily
measured/calculated for most samples, although two exceptions existed. Measuring flow velocity
required a water depth ≥ 0.2 m, to submerge the sensor. In some instances, water was present and
flowing, but the depth was < 0.2 m. For these samples, a value of 0.5x the lowest measurable annual
flow for the site was used for categorization. The other exception occurred during times of very high
discharge, particularly in sites that discharged directly into the Cache River. During these times, high
discharge in the Cache River would effectively prevent water from exiting the sub-watershed. Flow
velocity would be measured as zero, due to no water movement, even though the volume of water at
the site was often much greater than during lower discharge regimes. Using a flow velocity of zero
would result in a discharge of zero that could be misleading as the same results would occur in a site
that was dry. To account for this, a flow velocity of 0.06 m.s-1 was used in calculations of discharge
when water was present but no flow could be measured. This value was chosen as a proxy because it
resulted in calculated discharges greater than those calculated using 0.5x the lowest annual flow,
ensuring that these two very different sets of environmental conditions were not treated the same in
terms of discharge measurements.
All discharge data collected for a site were analyzed (to account for seasonal variation) to determine
numerical limits for categorization. Quartiles were calculated for each site and categories assigned
based on quartile values with values from the minimum to the first quartile assigned as base discharge,
from the first to second quartile (median) assigned as low discharge, from second to third quartile
assigned as moderate discharge and the remaining quartile assigned as high discharge. Outlying data
53
points (determined visually with a boxplot) were categorized as storm discharge (Table 2.3). Because
not every site had outlying data points, the category of storm discharge did not necessarily occur at each
site.
54
Table 2.3. Numerical limits (m3.s-1) of discharge categories for each of the sampled sites within the Cache River Watershed, based on all
discharge data collected for each site between August 2013 and July 2016.
Site
n
Base
Low
Moderate
High
FTSL
24
<0.150
0.151 to 0.375
0.376 to 0.750
0.756 to 1.500
LCRD
24
<0.250
0.251 to 0.400
0.401 to 1.000
1.001 to 2.000
SFBC
23
<0.030
0.031 to 0.200
0.201 to 0.400
0.401 to 1.000
EASL
23
<0.050
0.051 to 0.200
0.201 to 0.600
0.601 to 2.000
BGLA
23
<0.250
0.251 to 0.750
0.751 to 3.000
3.001 to 7.000
SCCR
23
<0.020
0.021 to 0.100
0.101 to 0.150
0.151 to 0.300
SUCR
23
<0.025
0.026 to 0.050
0.051 to 0.100
0.100 to 0.500
SPDI
23
<0.200
0.201 to 0.350
0.351 to 1.375
1.376 to 3.000
BDDI
22
<0.080
0.081 to 0.400
0.401 to 2.500
2.501 to 6.000
KEDI
23
<0.010
0.010 to 0.050
0.051 to 0.150
0.151 to 0.500
NTSD
24
<0.040
0.041 to 0.140
0.141 to 0.340
0.341 to 0.750
MUCR
24
<0.060
0.061 to 0.120
0.121 to 0.350
0.351 to 0.750
LCDI
23
<0.040
0.041 to 0.080
0.081 to 0.320
0.321 to 0.500
TMCR
22
<0.130
0.131 to 0.250
0.251 to 0.600
0.601 to 1.000
FSDI
22
<0.020
0.021 to 0.040
0.041 to 0.120
0.121 to 0.400
REFO
15
<1.500
1.501 to 11.50
11.51 to 17.50
17.51 to 25.00
CRCP
36
<17.00
17.01 to 40.00
40.01 to 71.00
71.01 to 150.0
CRPA
23
<5.000
5.001 to 12.30
12.31 to 16.80
16.81 to 25.00
CCDI
23
<0.010
0.011 to 0.050
0.051 to 0.170
0.171 to 0.350
SKDI
23
<0.020
0.021 to 0.120
0.121 to 0.180
0.181 to 0.400
WIDI
22
<0.020
0.021 to 0.140
0.141 to 0.340
0.341 to 0.500
WCRD
23
<0.040
0.041 to 0.230
0.231 to 0.450
0.451 to 1.000
CREG
35
<3.600
3.601 to 7.500
7.501 to 60.00
60.01 to 135.0
55
Storm
>1.500
>2.000
>1.000
>2.000
>7.000
>0.300
>0.500
>3.000
>6.000
>0.500
>0.750
>0.750
>0.500
>1.000
>0.400
>25.00
>150.0
>25.00
>0.350
>0.400
>0.500
>1.000
>135.0
Two predictors (alteration and discharge) were considered, as well as seven response
variables (turbidity, TSS, NO3-, NO2-, PO4-3, total nitrogen, total phosphorus). Analyzing these
combinations separately would require 14 separate ANOVAs, which would greatly increase the
likelihood of a Type I statistical error (detecting a significant difference when one does not
exist). Furthermore, this type of analysis would not account for interactions between the two
predictor variables. To account for this, a multivariate statistical analysis was conducted with
the two predictor categories. This analysis accounted for any additive or interaction effects
between the two predictors on six response variables (turbidity, NO3-, NO2-, PO4-3, total nitrogen,
total phosphorus). Turbidity and TSS were highly correlated, thus only turbidity was used in the
multivariate analysis. Only samples that had data points for all predictors and response
categories were included, with a final total of 595 samples analyzed.
Because response variables were not normally distributed and transformations failed to
achieve normality, a non-parametric multivariate analysis was performed (R package: vegan v
2.4-0) with a post-hoc pairwise permutational analysis (R package: RVAideMemoire v 0.9-56)
using 999 permutations to look for statistically significant differences between categories.
Because this analysis incorporates all response parameters into a single variable, it is possible
for the grouped response parameter to be significantly affected, even if the individual response
variables were not necessarily significantly affected. Graphical analysis of the response data
indicated that some response parameters likely played a less significant role than others. To
account for this, individual Kruskal-Wallis tests were run for each response variable to
determine which parameter(s) did not play a significant role in the overall relationship.
A spatial assessment of water quality in sampled sub-watersheds was performed to identify
‘hot spots’ within the Cache River Watershed in which implementation of Best Management
Practices (BMPs) might be most effective in mitigating nutrient or sediment contamination
56
within the watershed, thus minimizing the contributions of the Cache River Watershed to the
Gulf of Mexico hypoxic zone. This assessment was not a statistical analysis of data but was
performed to identify regions of the overall watershed with consistently high concentrations of
turbidity, TSS, dissolved nutrients and total nutrients. Samples collected at all sites for the
three-year sampling period were compared spatially using ArcGIS 10.3 ® (ESRI Inc., 2014) and
sites with greatest mean concentrations, relative to other sampled sites, were analyzed
temporally to determine if inputs were steady throughout the year (representing continuous
contaminant inputs) or showed a sporadic temporal pattern (indicating isolated inputs due to a
seasonal or environmental effect).
To further understand the impact of discharge and land alteration on water quality, mean
load was compared for all sites for TSS and dissolved and total nutrients. Load incorporates
concentration and discharge at the time of sampling, thus providing a better comparison of
where problem areas are located. Because load requires a measurement of discharge, it could
only be calculated for sampling events where discharge was measured. Thus, for calculations of
load, only a subset of the data was used (August 2014-July 2016). To ensure that loading
comparisons were not influenced by the overall catchment area for each site, the load per
hectare was calculated as well. This allowed all sites to be compared to each other, regardless
of total area drained.
Measuring the mean concentration/load of a contaminant within a site is valuable for
identifying areas with greatest amounts of contaminants. However, the hazard posed by these
concentrations is best understood if these values can be compared to concentrations known to
have a negative effect on the overall health of the aquatic ecosystem. The USEPA has
established numerical criteria for several water quality parameters, including pH, DO, turbidity,
and total nitrogen and phosphorus. These criteria are used by the Arkansas Department of
57
Environmental Quality (ADEQ) to determine stream segments in which measured
concentrations of contaminants are likely to have an adverse effect on aquatic ecosystems.
When applicable, concentrations of these parameters measured within this study were
compared to appropriate assessment criteria used by the Arkansas Department of
Environmental Quality (ADEQ, 2016a). This additional comparison allowed for the
determination of the overall health of the Cache River Watershed, compared to other surface
freshwaters within the United States, rather than an among-site comparison within the
watershed. The assessment criteria analyzed in this study are described below.
State-Set Assessment Criteria
Assessment criterion for pH states that values should fall between 6.0 and 9.0 standard units
in at least 90% of the total samples collected for a particular stream segment. Assessment
criterion for turbidity states that a sampled area cannot exceed assessment criterion more than
20% of the time during base flow conditions (June to October) or 25% during all flow conditions.
Furthermore, within the Delta ecoregion, sites are classified as either least-altered or channelaltered. For least-altered sites, turbidity assessment criterion is 45 NTU during times of base
flow and 84 NTU for all flows whereas for channel-altered sites assessment criterion is 75 NTU
during base flow and 250 NTU for all flows. Most sites sampled in this study were categorized as
channel-altered (n = 19), though those with catchments located primarily on Crowley’s Ridge
were categorized as least-altered (n = 4). Main channel sites were categorized as channelaltered as the water within them was largely from inputs of channelized sub-watersheds in the
Upper and Middle Cache Watershed.
58
Assessment criteria for DO has two categories, primary season and critical season. Primary
season is defined as samples with water temperatures below 22°C whereas critical season is
defined as samples with water temperatures above 22°C. Within these seasons, area of the
watershed determines applicable criterion. Watersheds with an area between 10 and 100
square miles (16.09 to 160.93 km2) have a primary season threshold of 5 mg/L and a critical
season threshold of 3 mg/L. All sampled headwater sub-watersheds (n = 19) were between 10
and 100 square miles (16.09 to 160.93 km2) in area. Watersheds with an area of greater than
100 square miles (>160.93 km2; all main channel sites) have both primary season and critical
season thresholds of 5 mg/L. If more than 10% of the samples collected in either the primary or
critical season fail to meet the appropriate threshold, the site is considered to be nonsupporting for aquatic life for that particular season. Thus, a site could be considered nonsupporting during one or both seasons.
Although dissolved nutrients do not have established numerical assessment criteria, criteria
have been established for both total nitrogen and total phosphorus (ADEQ, 2016a). For both
total nitrogen and total phosphorus, the mean concentration of a sampled site must be greater
than the 75th percentile value of all available data for the state ecoregion in which the site is
located. The Cache River Watershed lies within the Mississippi Alluvial Plains Ecoregion. An
analysis of all total nutrient data from this ecoregion indicated that the 75th percentile value for
total nitrogen was 0.992 ppm and for total phosphorus was 0.250 ppm (ADEQ, 2016b).
RESULTS
A portion of data within this chapter (samples from sites LCDI and CRPA collected between
February 2014 and May 2015) has been published as part of an independent M.S. thesis:
Kennon-Lacy, M. 2016. Water quality monitoring at impaired sites on the Cache River, AR, USA.
(M.S. thesis). Arkansas State University, State University, AR. 128 p.
Modeling the Effects of Discharge and Alteration
59
The multivariate analysis performed indicated that both discharge category (F4,574 = 7.833, p =
0.001) and alteration category (F3,574 = 30.386, p = 0.001) had a significant effect on the
combined response variable and that a significant interaction between alteration and discharge
was also present (F12, 574 = 2.0422, p = 0.002). Although the post-hoc test for the multivariate
analysis does not analyze the interaction effect, a visual assessment indicates that increased
discharge most likely amplifies the effects of land alteration. A pairwise permutational
MANOVA indicated that for alteration, significant differences occurred between least- and
moderately-altered sites (p = 0.002), least- and most-altered sites (p = 0.002), least-altered and
main channel sites (p = 0.002), moderately and most-altered sites (p = 0.004), moderatelyaltered and main channel sites (p = 0.004), and most-altered and main channel sites (p = 0.002).
Individual Kruskal-Wallis tests were performed for each response variable which indicated
that for alteration, significantly affected response variables were turbidity (χ2 = 113.1172, df = 3,
p < 0.001), NO2- (χ2 = 42.4926, df = 3, p < 0.001), total nitrogen (χ2 = 66.8711, df = 3, p < 0.001),
total phosphorus (χ2 = 0.4406, df = 3, p < 0.001), and PO4-3 (χ2 = 10.7007, df = 3, p = 0.014).
Mean NO3- concentrations were not significantly different between land alteration categories (χ2
= 2.5878, df = 3, p = 0.460) (Fig. 2.3).
60
Figure 2.3. Mean ± SE measurements of six water quality parameters by land alteration
category. A) turbidity B) NO2- C) PO4 -3 D) NO3- E) total phosphorus F) total nitrogen. p-values (α
= 0.05) indicate a significant difference between land use categories. Colored bars correspond to
land alteration categories shown in Figure 2.2 (green = least-altered, blue = moderately-altered,
red = most-altered, gold = main channel).
61
A pairwise permutational MANOVA indicated that for discharge, significant differences
occurred between base and low discharge (p = 0.015), base and high discharge (p = 0.027), base
and storm discharge (p = 0.010), low and moderate discharge (p = 0.022), low and high
discharge (p = 0.005) low and storm discharge (p = 0.005), and high and storm discharge (p =
0.050). No significant difference existed between base and moderate discharge (p = 0.725) and
moderate and high discharge (p = 0.177).
For discharge categories, Kruskal-Wallis tests indicated that significantly affected response
variables included were turbidity (χ2 = 29.4568, df = 4, p < 0.001), NO3- (χ2 = 19.3922, df = 4, p <
0.001), total phosphorus (χ2 = 18.2572, df = 4, p = 0.001), and PO4-3 (χ2 = 20.2578, df = 4, p <
0.001). Neither total nitrogen (χ2 = 3.0829, df = 4, p = 0.544) nor NO2- mean concentrations
were significantly different between discharge categories (χ2 = 3.7042, df = 4, p = 0.448) (Fig.
2.4).
62
Figure 2.4. Mean ± SE measurements of six water quality parameters by discharge category. A)
turbidity B) NO2- C) PO4 -3 D) NO3-E) total phosphorus F) total nitrogen. p-values (α = 0.05)
indicate a significant difference between flow categories.
63
Spatial and Temporal Assessment
A subset of sites was sampled weekly to determine if the frequency of sampling (weekly or
monthly) had an effect on mean measurements/concentrations of water quality parameters.
No significant difference was detected between weekly and monthly sampling for any water
quality parameter at any selected site (Mann-Whitney U test, p >0.20 for all parameters at all
sites). Thus, for all spatial and temporal assessment mean concentrations were calculated
based on samples collected monthly.
Mean concentrations of measured water quality parameters were plotted geographically to
determine if any spatial patterns existed. Parameters examined were TSS, turbidity, total
nitrogen, total phosphorus, NO3-, NO2- and PO4-3. Spatial assessment indicated that for turbidity
and TSS, sites along the western edge of the Upper Cache Watershed had the greatest
measurements/concentrations. Sites BGLA and EASL consistently had the greatest mean values
for turbidity, whereas for TSS, sites BGLA, EASL, WCRD and CREG had greatest measured mean
concentrations (Fig. 2.5). A temporal comparison of these selected sites indicated that elevated
measurements/concentrations were most common in winter months, when precipitation is
generally greatest and vegetative ground cover the lowest. However, spikes also occurred in
other months, most notably in June of 2014 (Fig. 2.6).
64
Figure 2.5. Mean concentrations of A) turbidity and B) TSS for all sampled sites within the Cache River Watershed for samples collected
monthly between August 2013 and July 2016.
65
Figure 2.6. Temporal assessment of A) turbidity and B) TSS data from most impacted sites for
the sampling period August 2013 through July 2016.
66
When comparing suspended sediment load, calculated using total suspended sediment and
discharge at the time of sampling, a slightly different pattern emerges. When compared by
category of land alteration, alteration has a significant effect (χ2 = 44.287, df = 2, p < 0.001) with
least-altered sites having a significantly lower load than moderately-altered and most-altered
sites (p < 0.001 for both). When comparing loading at all sites, loading was divided by hectares
drained to determine the overall load per drained hectare. Loading per hectare indicated that
several most-altered sites were responsible for greater than average suspended sediment loads
with the greatest contributor being site WCRD, which contributed nearly 3x as much suspended
sediment as the next greatest contributing site (BGLA) (Fig. 2.7).
67
Figure 2.7. A) Comparison of mean ± SE suspended sediment loads (TSS x discharge) by land
alteration category. * indicates a category that is significantly different from the others (α =
0.05). B) Comparison of mean ± SE suspended sediment load per hectare drained for each
sampled headwater sub-watershed. Dotted line represents mean load per hectare of all sites
(0.0011 tons/day/hectare). Color of bar indicates land alteration category to which each site
belongs (green = least-altered, blue = moderately-altered, red = most-altered).
68
Spatial assessment of mean dissolved and total nitrogen concentrations showed some
geographical patterns. Sites with the greatest mean concentrations of dissolved NO2- and total
nitrogen were quite similar spatially, with greatest mean concentrations occurring in mostaltered sites along the western edge of the Cache River Watershed, specifically, at sites WIDI,
KEDI, BDDI and FTSL for total nitrogen and sites KEDI, BDDI and FTSL for NO2-. The spatial
distribution of sites with greatest measured mean concentrations of NO3- did not match NO2and total nitrogen and had no discernible spatial pattern (Fig. 2.8).
Because both FTSL and BDDI had elevated concentrations of all forms of nitrogen, these sites
were selected for further temporal assessment of these parameters at these sites. At each site,
peak concentrations of NO2- and total nitrogen generally occurred in summer months (May to
July). The same pattern is true for NO3- at sites FTSL and BDDI (Fig. 2.9).
69
Figure 2.8. Mean concentrations of A) NO3- B) NO2- and C) total nitrogen for all sampled sites within the Cache River Watershed from Aug
2013 to July 2016. Categories divide the range of results evenly with the exception of total nitrogen in which the greatest category (red)
represents samples exceeding the state set assessment criterion for total nitrogen (0.992 ppm).
70
Figure 2.9. Temporal assessment of A) total nitrogen B) NO2- and C) NO3- at sites with the
greatest mean concentrations measured in samples collected from August 2013 to July 2016.
71
A slightly different temporal pattern was noted in remaining sites with highest mean NO3concentrations (other than FTSL and BDDI). Two of these sites, SCCR (least-altered) and NTSD
(moderately-altered) tended to have peak NO3- levels throughout the year (Fig. 2.10) rather than
occurring primarily in summer months. A more fine-scale study of low agricultural use (leastaltered) sites in the Cache River Watershed found a similar pattern, with increased NO3concentrations in late summer/early fall months (Kilmer et al., 2015). This study only sampled
during the agricultural growing season (May-October) so it is unclear if elevated NO3concentrations occurred in winter months. The relatively great mean concentrations at site
CRPA are likely the effect of a single peak measurement in April 2015. Removing this outlying
data point reduces the mean NO3- at site CRPA from 1.55 ± 0.56 ppm to 1.03 ± 0.21 ppm.
72
Figure 2.10. Temporal assessment of NO3- concentrations at sites exhibiting a different temporal pattern than NO2- and total nitrogen in
samples collected from August 2013 to July 2016.
73
A comparison of nitrogen loading by category indicated that for dissolved NO2-, land alteration
categories had significantly different loads (χ2 = 19.116, df = 2, p < 0.001) with least-altered sites
having significantly lower loads than moderately-altered (p < 0.001) or most-altered sites (p <
0.001). When comparing NO2- loading per hectare by site, one moderately-altered site (LCRD)
and five most-altered sites (FTSL, BGLA, BDDI, FSDI, WCRD) were contributing greater than
average levels of NO2- per hectare, with sites BDDI, WCRD and FTSL contributing the most NO2per hectare drained (Fig. 2.11). For total N, land alteration categories had significantly different
loads (χ2 = 20.039, df = 2, p < 0.001) with least-altered sites having significantly lower loads than
moderately-altered (p < 0.001) or most-altered sites (p < 0.001). When comparing total N
loading per hectare by site, it was clear that one moderately-altered sites (LCRD) and five mostaltered sites (FTSL, BGLA, BDDI, FSDI, WCRD) were contributing greater than average levels of
total N per hectare, with sites WCRD and FTSL contributing the most total N per hectare drained
(Fig. 2.12). For NO3-, no significant difference existed (χ2 = 2.405, df = 2, p = 0.300) between land
use categories. When comparing NO3- loading per hectare by site, that two moderately-altered
sites (LCRD, SPDI) and six most-altered sites (FTSL, BGLA, BDDI, TMCR, FSDI, WCRD) were
contributing greater than average levels of NO3- per hectare, with sites WCRD and FTSL
contributing the most NO3- per hectare drained (Fig. 2.13).
74
Figure 2.11 A) Comparison of mean ± SE NO2- load by land alteration category. * indicates a
category that is significantly different from the others (α = 0.05). B) Comparison of mean ± SE
NO2- load per hectare drained for each sampled headwater sub-watershed. Dotted line
represents mean load per hectare of all sites (0.0011 tons/day/hectare). Color of bar indicates
land alteration category to which each site belongs (green = least-altered, blue = moderatelyaltered, red = most-altered).
75
Figure 2.12. A) Comparison of mean ± SE total N load by land alteration category. * indicates a
category that is significantly different from the others (α = 0.05). B) Comparison of mean ± SE
total N load per hectare drained for each sampled headwater sub-watershed. Dotted line
represents mean load per hectare of all sites (0.0082 tons/day/hectare). Color of bar indicates
land alteration category to which each site belongs (green = least-altered, blue = moderatelyaltered, red = most-altered).
76
Figure 2.13. A) Comparison of mean ± SE NO3- load by land alteration category B) Comparison of
mean ± SE NO3- load per hectare drained for each sampled headwater sub-watershed. Dotted
line represents mean load per hectare of all sites (0.0076 tons/day/hectare). Color of bar
indicates land alteration category to which each site belongs (green = least-altered, blue =
moderately-altered, red = most-altered).
77
No distinct spatial pattern was evident for greatest measured mean concentrations of PO4-3,
though lowest measured mean concentrations occurred in sites along the eastern side of the
Cache River Watershed with headwaters on Crowley’s Ridge (Fig. 2.14). A spatial pattern was
noted for both greatest and lowest mean concentrations of total phosphorus. Similar to PO4-3,
lowest measured mean concentrations of total phosphorus occurred in sites along the eastern
side of the Cache River Watershed with headwaters on Crowley’s Ridge. Greatest measured
mean concentrations of total phosphorus occurred most often in the center of the watershed
(from north to south).
FTSL and LCDI had the greatest measured concentrations of both PO4-3 and total phosphorus
(Fig. 2.14), but a temporal assessment of these sites indicates no specific temporal pattern with
peak concentrations occurring sporadically throughout the course of sampling (Fig. 2.15, 2.16).
For total phosphorus at sites FTSL, FSDI and WIDI, concentrations had an upward trend over the
course of this study with concentrations of total P being positively correlated with the month of
sampling (month 1 to month 36). A Spearmans correlation indicated that this increase was
significant for FSDI (S = 2930.7, p < 0.001, rho = 0.623), WIDI (S = 1692.6, p < 0.001, rho = 0.782)
and FTSL (S = 4790, p = 0.022, rho = 0.384). No increase was noted in precipitation, discharge or
TSS at these sites over the same time period.
78
Figure 2.14 Mean concentrations of A) dissolved orthophosphate (PO4-3) and B) total phosphorus for all sampled sites within the Cache
River Watershed from Aug 2013 to July 2016. Categories divide the range of results evenly with the exception of total phosphorus in
which the top category represents samples exceeding the state set assessment criterion for total phosphorus (0.250 ppm).
79
August 2013
September 2013
October 2013
November 2013
December 2013
January 2014
February 2014
March 2014
April 2014
May 2014
June 2014
July 2014
August 2014
September 2014
October 2014
November 2014
December 2014
January 2015
February 2015
March 2015
April 2015
May 2015
June 2015
July 2015
August 2015
September 2015
October 2015
November 2015
December 2015
January 2016
February 2016
March 2016
April 2016
May 2016
June 2016
July 2016
Dissolved PO4-3 (ppm)
1.80
1.60
R² = 0.0122
1.40
R² = 0.0235
1.20
1.00
0.80
0.60
0.40
0.20
0.00
FTSL
LCDI
Figure 2.15. Temporal assessment of dissolved PO4-3 for sites LCDI and FTSL. Correspondingly colored
dotted lines and boxes represent the overall trend line and R2 value for each site. Four-letter code
represents the site-specific designation for each site.
80
Figure 2.16. Temporal assessment of total phosphorus for sites A) LCDI and FTSL showing little or no
temporal trend and B) WIDI and FSDI showing a trend of increasing total P concentrations over time.
Correspondingly colored dotted lines and boxes represent the overall trend line and R2 value for each
site. Four-letter code represents the site-specific designation for each site.
81
A comparison of phosphorus loading by category indicated that for dissolved PO4-3, land alteration
categories had significantly different loads (χ2 = 12.737, df = 2, p = 0.002) with least-altered sites having
significantly lower loads than moderately-altered sites (p = 0.001). Least-altered and moderatelyaltered sites were not significantly different from most-altered sites (p = 0.064 for each). When
comparing PO4-3 loading per hectare by site, it was clear that two moderately-altered sites (LCRD, SPDI)
and five most-altered sites (FTSL, BGLA, BDDI, FSDI, WCRD) were contributing greater than average
levels of PO4-3- per hectare, with sites WCRD and FTSL contributing the most PO4-3 per hectare drained
(Fig. 2.17).
For total phosphorus, land alteration categories did have significantly different loads (χ2 = 10.968, df =
2, p = 0.004) with least-altered sites having significantly lower loads than moderately-altered sites (p =
0.002). Least-altered and moderately-altered sites were not significantly different from most-altered
sites (p = 0.060, p = 0.099, respectively). When comparing total phosphorus loading per hectare by site,
it was clear that one moderately-altered site (LCRD) and five most-altered sites (FSDI, BGLA, BDDI, FTSL,
WCRD) were contributing greater than average levels of total phosphorus per hectare, with sites WCRD
and FTSL contributing the most total phosphorus per hectare drained (Fig. 2.18).
82
Figure 2.17. A) Comparison of mean ± SE PO4-3 load by land alteration category, * indicates category that
was significantly different from one or more other categories (α = 0.05). B) Comparison of mean ± SE
PO4-3 load per hectare drained for each sampled headwater sub-watershed. Dotted line represents
mean load per hectare of all sites (0.0043 tons/day/hectare). Color of bar indicates land alteration
category to which each site belongs (green = least-altered, blue = moderately-altered, red = mostaltered).
83
Figure 2.18. A) Comparison of mean ± SE total P load by land alteration category, * indicates category
that was significantly different from one or more other categories (α = 0.05). B) Comparison of mean ±
SE total P load per hectare drained for each sampled headwater sub-watershed. Dotted line represents
mean load per hectare of all sites (0.0029 tons/day/hectare). Color of bar indicates land alteration
category to which each site belongs (green = least-altered, blue = moderately-altered, red = mostaltered).
84
Comparison to Assessment Criteria
Although pH values occasionally fell outside of the acceptable range, no site had values
exceeding the assessment criterion in more than 10% of the total samples. Turbidity
assessment criterion was never exceeded at least-altered sites, during either base flows or all
flows, although site SUCR was at the assessment criterion level for base flows. Turbidity
assessment criterion was exceeded at several sites in channel-altered sites under base flow
conditions. Ten channel altered sites did not exceed assessment criterion, another four (TMCR,
FSDI, SPDI, CREG) were at assessment criterion and the final six (REFO, BGLA, CRCP, CRPA,
WCRD, EASL) exceeded assessment criterion. Under all flow conditions, 15 channel-altered sites
did not exceed assessment criterion and four exceeded assessment criterion (BGLA, CREG,
WCRD, EASL) (Fig. 2.19).
85
Figure 2.19. Percent exceedance of assessment criterion for turbidity for all sampled sites under
A) base flow conditions and B) all flow conditions. The dotted line represents the assessment
criterion threshold for each set of flow conditions. Color of the bar indicates the land alteration
category for each site. (green = least-altered, blue = moderately-altered, red = most-altered,
gold = main channel).
86
During the primary season (water temperature < 22°C), two main channel sites (CRCP, REFO)
and three headwater sub-watersheds (FSDI, TMCR, WIDI) failed to meet the designated DO
threshold in at least 10% of samples. Of the sub-watersheds that failed to meet assessment
criteria, all were categorized as most-altered sub-watersheds. During the critical season (water
temperature > 22°C), two main channel sites (CRCP, REFO) and one headwater sub-watershed
(FSDI) failed to meet the designated DO threshold in at least 10% of collected samples. Spatial
assessment indicates that headwater sub-watersheds that exceeded assessment criteria were
located in close proximity to one another (Fig. 2.20).
87
Figure 2.20. Spatial assessment of sites impaired for dissolved oxygen (DO) during the primary
(water temperature < 22°C) and/or critical season (water temperature >22°C) based on water
samples collected in the Cache River Watershed between August 2013 and July 2016.
88
Based on the assessment criteria for total nutrients, seven of the sampled sites exceeded
criteria for total nitrogen and five for total phosphorus. Elevated total nitrogen and total
phosphorus concentrations occurred most frequently in most-altered sites whereas both leastaltered sites and main channel sites tended to have the lowest mean concentrations (Fig. 2.21).
89
Figure 2.21. Mean ± SE concentrations of A) total nitrogen and B) total phosphorus for all sites
sampled in the Cache River Watershed. The black dotted line represents the assessment
criterion for each parameter (75th percentile value of all available data for the watershed (total
N = 0.992 ppm, total P = 0.250 ppm) (ADEQ, 2016b)). Colors of bars denote land alteration
category (green = least-altered, blue = moderately-altered, red = most-altered, gold = main
channel).
90
Effectiveness of Existing BMPs in the Cache River Watershed
BMP implementation began at three sites in the Middle Cache River (CCDI, SKDI, WIDI) in
2013 (NRCS, 2013). These sites were all sampled monthly during the course of this present
study. Implemented BMPs were generally designed to improve irrigation efficiency, thus
decreasing surface runoff. Sediment and nutrient loads at these sites were greatly reduced
when compared to other most-altered sub-watershed sites and always fell well below the
average load (Fig. 2.22).
91
0.014
Suspended Sediment Load
(tons/day/hectare drained)
A
Total Nitrogen Load
(tons/day/hectare drained)
B
0.012
0.01
0.008
0.006
0.004
0.002
0
0.045
0.040
0.035
0.030
0.025
0.020
0.015
0.010
0.005
0.000
0.020
Total Phosphorus Load
(tons/day/hectare drained)
C
0.015
0.010
0.005
0.000
Figure 2.22. Comparison of mean ± SE sediment and nutrient loads (tons/day/hectare drained)
at most-altered sites within the Cache River Watershed from samples collected between August
2013 and July 2016. BMP implementation began in sites CCDI, SKDI and WIDI in 2013 and at site
BGLA in 2016. No BMPs have been implemented at any other sites shown here. Dotted lines
represent the average load for all sites. A) suspended sediment load (average = 0.001) B) total
nitrogen load (average = 0.010) C) total phosphorus load (average = 0.004).
92
DISCUSSION
The Cache River Watershed represents a critical agricultural resource within the State of
Arkansas. Counties contained partially or totally within this watershed accounted for
approximately 33% of the total market value of crops produced within the State of Arkansas in
2012 (USDA-NASS, 2014). Since approximately 67% of the land within the Cache River
Watershed is used for agricultural production (AWIS, 2016), understanding the effects of
agricultural practices on surface water quality can help to clarify the role that this watershed is
playing in contributing to sediment and nutrient loads within the Mississippi River Basin and the
Gulf of Mexico and the effect it has on hypoxia in the Gulf of Mexico.
Effects of Land Alteration and Discharge on Water Quality
The model used in this study indicates that both discharge and land alteration have a
significant effect on water quality parameters and that the interaction between the two is also
significant. However, the relationship differs depending on the predictor and the specific
response parameter. Land alteration had the most consistent pattern with mean
concentrations of contaminants tending to increase as land alteration increased (from leastaltered to most-altered). Measurements of contaminants from main channel sites tended to be
intermediate to least- and most-altered sites, unsurprisingly, as the main channel is composed
of inputs from all upstream sites, encompassing all categories of land alteration. Although the
difference between land alteration categories was not significant for every parameter
(e.g., NO3-), the same general pattern occurs for each measured parameter, indicating that
increased land alteration is associated with decreased water quality, as originally predicted.
Land use/cover (LULC) is well reported as a contributor to water quality in surface waterways,
with increased urbanization and agricultural activity generally correlated with reduced water
93
quality (Giri and Qiu, 2016). When examining the effects of LULC on water quality in a tropical
landscape, Uriarte et al. (2011) found that 30-58% of the observed variance in water quality
metrics could be explained by variance in LULC. Similarly, Hong et al. (2016) found that an
increase in cultivated land was positively correlated with an increase in concentrations of total
nitrogen and phosphorus within a freshwater river basin in China, indicating a likely reduction in
water quality.
Discharge had a less distinct relationship with water quality than land alteration. When
looking at the relationship between discharge and turbidity, a positive relationship was
observed, as predicted. A positive relationship between discharge/precipitation and turbidity is
widely reported in the literature for a variety of stream types across all categories of land use
(Mallin et al., 2009; Yongshan et al., 2015; Barry et al., 2016), including portions of the Cache
River Watershed (Kennon, 2016; Rosado-Berrios and Bouldin, 2016).
In contrast to the pattern seen for land alteration, where all response parameters had the
same pattern, the pattern observed in this study for discharge was not consistent. For all
dissolved and total nutrients, elevated concentrations were present during times of base
discharge, with concentrations reducing as discharge increased to low discharge, then generally
remaining the same or increasing as discharge increased, with the exception of NO2-. The
increase under base discharge conditions is unusual as increased loads of contaminants are
typically a result of increased precipitation or irrigation, particularly in agriculturally dominated
watersheds (Keener et al., 2010; Higashino and Stefan, 2014; Arvola et al., 2015; McIsaac et al.,
2016; Mirhosseini and Srivastava, 2016).
Base discharges typically occurred during times of year (June to November) when inputs of
water due to precipitation and/or irrigation were reduced. Greater contaminant concentrations
during times of base discharge could be due to a reduced volume of water in surface waterways
94
that would cause decreased dilution capabilities of waterways. Mosely (2015) found that during
times of drought, water quality showed deterioration, particularly for nutrients, due to reduced
dilution effects. During times of base discharge, evaporation could also contribute to decreased
volumes in waterways as surface inputs might not be great enough to replace water lost to
evaporation. This imbalance between evaporation and inflow could result in a hyperconcentration of solutes in waterways (Lewis et al., 2015). Base discharges were most common
during late summer and early fall, when inputs of water are limited (low precipitation, reduced
irrigation) and daily air temperatures are quite warm, increasing the likelihood of such an
imbalance. For samples collected in this study for which discharge was measured, 60% of base
discharge samples were observed between June and November with 40% observed between
September and November.
Visual observation of sub-watersheds at this time of the year (June-November) revealed many
upstream portions of the sub-watersheds to be completely dry (though not the sampled outflow
which was comprised of all inputs for the sub-watershed). Hyper-concentration of ions due to
evaporation is generally identified by reduced water levels at sites and increased conductivity in
samples with elevated concentrations of contaminants (Townsend, 2002). An analysis of
conductivity in water samples collected from the Cache River Watershed between August 2013
and July 2016 compared to discharge showed that conductivity tended to be at its greatest
during times of base discharge with a reduction under increased discharge conditions. This
supports the idea that hyper-concentration of dissolved ions is occurring under base discharge
conditions. An alternative explanation is that water in waterways during times of base
discharge is primarily pumped groundwater used for irrigation which tends to have much
greater conductivity than surface water (Justus et al., 2016).
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In keeping with this explanation of differences in nutrient concentrations between different
discharge categories, the decrease in concentrations as discharge increased from base to low
discharge would therefore represent a point where inputs of groundwater, precipitation or
surface runoff would be sufficient to prevent hyper-concentration of solutes. Beyond low
discharge, mean measured concentrations of nutrients generally stayed the same or increased
as discharge increased to storm discharge, with the exception of NO2-, which tended to decrease
as discharge increased. Overall mean concentrations of NO2- were much lower than NO3suggesting that either it is naturally less present in soils, less likely to be mobilized from soils or
more likely to be oxidized/reduced in the aquatic environment. NO2- concentrations are
typically lower than NO3- concentrations in the Cache Watershed (Justus et al., 2016) and in
other surface waterways, both in the United States (Aurand and Daiber, 1973; Flint and
McDowell, 2015) and globally (Georgieva et al., 2013).
When inorganic nitrogen is applied to fields as fertilizer, it is typically in the form of NH4+. In
soils, NH4+ is oxidized into NO2- by nitrifying archaea or bacteria, then oxidized to NO3- by more
oxidizing bacteria (Dodson, 2005). This oxidation is strongly temperature dependent, with
greatest rates occurring in spring and summer months (Casson et al., 2014). Both NO2- and NO3are water soluble, meaning they can be easily transported into waterways via surface runoff.
Most NO2- is converted into NO3- relatively quickly, particularly in warm months when conditions
are optimal for nitrifying organisms. Thus, large NO2- inputs in waterways would only be
observed if increased surface runoff due to precipitation/irrigation occurred soon after fertilizer
application, before NO2- was oxidized to NO3-. Because oxidation to NO3- occurs rapidly, most
soluble nitrogen in the soil throughout the year would likely be in the form of NO3-, meaning
under most discharge conditions, more NO3- would be mobilized in surface runoff than NO2-,
96
thus resulting in greater NO3- concentrations (relative to NO2-) in waterways receiving surface
runoff from agricultural areas (Zhang et al., 2013).
Total nitrogen was relatively unaffected by any change in discharge with no significant
difference between discharge categories and no discernable trend. A comparison of the total N
values from digested samples to the dissolved NO3- and NO2- concentrations (from the same
water sample) indicates that most of the nitrogen comes from insoluble nitrogen or ammonia,
with N from NO3-+ NO2- comprising only 22-32% of the total N, depending on discharge category.
Similar to total nitrogen, no significant difference existed for N from NO3-+ NO2- between
discharge categories.
A comparison of PO4-3 and total phosphorus under elevated discharge conditions indicated
that no difference occurred between moderate and high discharge for either PO4-3 or total
phosphorus and that both showed increased concentrations under storm discharge conditions.
PO4-3 constitutes a large portion of the total P load with P from PO4-3 contributing 44-54% of the
total P load depending on discharge category. A comparison of P from PO4-3 to total P reported
in other areas of the Cache River Watershed indicated that P from PO4-3 contributed 6-17% of
total P in wetlands of the Cache River Watershed (Justus et al., 2016) and 14-30% in main
channel reaches of the Cache River Watershed (ADEQ, 2016b).
Spatial and Temporal Assessment of Sites
Spatial and temporal assessment of land alteration categories within the Cache River
Watershed indicated that alteration was a strong contributor to sediment loads. As degree of
land alteration increased, mean suspended sediment loads also increased. A breakdown of
suspended sediment load per hectare drained (by site) indicated that one site (WCRD) was the
primary contributor to increased mean loads at most-altered sites. Site WCRD had a suspended
97
sediment load nearly 3x as great as the next greatest contributor. Although this watershed is
quite small in area, it is located next to the main channel of the Cache River and serves as a
relief channel during times of high discharge. Thus, the increased load from this site might not
represent land use within the sub-watershed, but rather is reflective of the suspended sediment
load in the main channel of the Cache River.
The spatial and temporal assessment of turbidity and suspended sediment
measurements/concentrations by site showed a close parallel between the water quality
parameters. This is unsurprising as suspended sediment is the primary contributor to turbidity
(Fondriest Environmental Inc., 2016). Furthermore, greatest concentrations/measurements of
turbidity and TSS tended to occur in months with elevated precipitation. Although several
sampled sites exceeded state-set assessment criterion for turbidity during the sampling period,
the general trend was a decrease in both turbidity and suspended sediment along a
downstream gradient of the main channel of the Cache River. This trend has been noted in the
past in both the main channel of the Cache River (Kleiss, 1996) and in the main channel of its
primary tributary, Bayou DeView (Rosado-Berrios and Bouldin, 2016). In both instances, the
reduction in turbidity and TSS was attributed to the increasing amounts of wetlands along the
main channel of each waterbody.
Elevated nutrient concentrations and loads were also closely tied to land alterations, though
the only significant difference in loading occurred between least-altered sites and moderately or
most-altered sites. The lack of significance between moderately-altered and most-altered sites
suggests that from a land use perspective, even a moderate amount of agricultural activity and
channelization is enough to have a noticeable impact on nutrient concentrations in surface
waters. Spatially, nutrients fell into one of two categories. Total and dissolved nitrogen tended
to have greatest mean concentrations in most-altered sub-watersheds of the western Cache
98
River Watershed. Conversely, total and dissolved phosphorus tended to have greatest mean
concentrations in the Middle Cache Watershed (from north to south). The exception to this
pattern was site FTSL, which had greatest measured concentrations of all dissolved and total
nutrients.
Site FTSL is located in the very upper portion of the Cache River. In fact, in this watershed, the
Cache is only referred to as the Cache Ditch and is not actually designated as a river. Because
the ‘river’ at this point consists only of a few small agricultural ditches, the overall volume of
water is quite limited, with little surface water present to provide dilution to the surface runoff
from this area. This could also explain why all other nutrients (total nitrogen, dissolved NO3-,
NO2-, PO4-3) were also measured at high concentrations in this sub-watershed. A comparison of
this site to several other sub-watershed sites of similar land type and with similar alteration
scores (indicating similar amounts of channelization, agricultural land use and forest cover)
show that for all nutrients measured, FTSL generally had greater concentrations than the
comparable sites.
Total and dissolved phosphorus concentrations were greatest in sub-watersheds of the
Middle Cache River Watershed, as well as in site LCDI. The elevated total P concentrations at
site LCDI are unsurprising as this site represents the outflow from the Jonesboro, Arkansas
municipal area. Jonesboro is the largest city in northeastern Arkansas and the fifth most
populous city in the state (USDC, 2016). This site also had the greatest measured mean
concentrations of dissolved PO4-3. P from PO4-3 accounted for 5-100% (mean = 57%) of the total
P load at this site. Runoff from urban areas has been long established as a source of dissolved
PO4-3. Mallin et al. (2009) found that when comparing an urban, suburban and rural stream, the
urban stream produced the greatest concentrations of PO4-3 and surfactants, with percent
development and percent impervious surfaces being strongly correlated with PO4-3
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concentrations. A similar result was found when comparing phosphorus exports to Chesapeake
Bay from areas with different types of land use/land cover, with total phosphorus exports
increasing as watershed impervious surface coverage increased (Duan et al., 2012).
The remaining sites with elevated phosphorus concentrations do not have an obvious source,
other than agricultural land use. However, it is interesting that increased concentrations do not
seem to be tied to agricultural activity in general within the Cache River Watershed but rather to
some physical feature or activity unique to the Middle Cache Watershed. Although non-point
sources such as agricultural runoff or point sources, such as wastewater treatment facilities tend
to be the leading sources of nutrients, some natural sources also exist. Phosphorus is the 11th
most abundant element in the earth’s crust and can be leached directly from bedrock into water
sources (Dodson, 2005; Minnesota Pollution Control Agency, 2007). Phosphorus can also attach
to soil particles and wash into waterways as surface runoff (USGS, 2016b). However, the inputs
of phosphorus from bedrock are generally very small compared to anthropogenic inputs. The
sub-watersheds affected by phosphorus in this present study showed no significant relationship
between either TSS and PO4-3 or TSS and total phosphorus. Thus, some other source seems to
be contributing to the observed concentrations of total phosphorus within the Middle Cache
River Watershed.
Several of the sites with elevated phosphorus concentrations failed to meet assessment
criterion for DO. Phosphorus is the limiting nutrient in freshwater systems and an excess of
phosphorus generally results in eutrophication, algal blooms and resulting hypoxia. The spatial
similarity between elevated phosphorus concentrations and low DO concentrations indicates
that phosphorus inputs from this portion of the watershed are potentially having an adverse
effect on the aquatic environment.
100
Several sites exceeded assessment criterion for either total nitrogen or total phosphorus
during the course of this study, typically in most-altered sub-watersheds. Although this
indicates that excessive amounts of nutrients are entering surface waterways from these areas,
mean concentrations either remained constant or decreased along a downstream gradient of
the Cache River, suggesting that nutrient concentrations are attenuated before the Cache River
confluences with the White River. A similar pattern was observed for turbidity and TSS (Fig.
2.23).
101
1.20
A
1.00
ppm
0.80
0.60
0.40
0.20
0.00
CREG
CRPA
Total N
B
CRCP
REFO
CRCP
REFO
Total P
250
200
150
100
50
0
CREG
CRPA
Turbidity (NTU)
TSS (mg/L)
Figure 2.23. Mean ± SE concentrations of A) total N and total P and B) turbidity and TSS for main
channel sites from all samples collected in the Cache River Watershed between August 2013 and
July 2016. Sites are arranged from upstream (left) to downstream (right) with site CREG being
the most upstream site and site REFO being the most downstream site.
102
Several natural processes could account for the observed decrease in nutrient and sediment
concentrations along the main channel of the Cache River. Processes that could increase overall
nitrogen retention include denitrification to N2 and subsequent volatilization, adsorption to
particulate matter followed by sedimentation and assimilation by aquatic organisms (Dodson,
2005). In freshwater systems, denitrification tends to be the primary mechanism of nitrogen
retention with both nitrogen sedimentation and assimilation by aquatic plants contributing to a
lesser degree (reviewed in Saunders and Kalff, 2001.) Jensen et al. (1990) found that
denitrification in shallow lakes accounted for 77% of all total N removed whereas Brinson et al.
(1984) found that denitrification in natural wetlands exceeded sedimentation by an order of
magnitude. River systems tend to be the least effective at nitrogen retention (compared to
lakes and wetlands), particularly because turbulence due to water velocity limits sedimentation
and denitrification (Ryder and Pesendorfer, 1989; Saunders and Kalff, 2001).
Overall phosphorus loads could be reduced by either assimilation or adsorption to particulate
matter, followed by sedimentation (Dodson, 2005). Because phosphorus is the limiting nutrient
in freshwater systems, a considerable portion of phosphorus retention is due to assimilation by
aquatic macrophytes, accounting for up to 20% of the total phosphorus load removed in rivers
(Shulz and Köhler, 2006).
Wetlands tend to be more efficient at phosphorus retention than rivers, due to slower water
velocities and greater abundance and diversity of macrophytes (Wang and Mitsch, 2000; Dodds,
2003). Though phosphorus retention can vary, depending on wetland-specific characteristics
(Hogan et al., 2004), retention rates for phosphorus tend to range from 50-90% (Wang and
Mitsch, 2000; Dodds, 2003). Although wetlands are generally more efficient than river systems,
sedimentation typically accounts for the majority of phosphorus retention (Mitsch et al., 1995),
rather than assimilation. Phosphorus can also precipitate out of wetland waters in the form of
103
calcium phosphate if the water has available calcium and pH is increased as a result of
photosynthesis or respiration (Reddy et al., 1999).
Removal of suspended sediment typically varies as a function of water velocity (Kozerski,
2002) with decreased water velocities resulting in increased sedimentation rates. Therefore, in
freshwater systems with reduced velocities, such as lakes and wetlands, sedimentation tends to
be greater than in riverine systems with greater velocities. Channelization of waterways causes
increased velocities as natural meanders and physical obstructions (gravel bars, vegetation) are
removed (Van Arsdale et al., 2003). In waterways with meandering channels, whether natural
or restored, mean water velocities are reduced (Gardeström et al., 2013) and velocities are
more uniform, with reduced temporal variation (Mason et al., 2012). This reduction in overall
velocity and velocity variability results in increased sedimentation in rivers with meanders
relative to those that have been channelized. Restoration of streams and rivers tends to lead to
overall increased in nutrient retention (Newcomer-Johnson et al., 2016). Overall, sedimentation
and nutrient retention tend to be greatest in waterways with natural meanders that slow water
velocity and/or associated wetlands, which can both slow water velocities, allowing for
sedimentation, and provide suitable habitat for aquatic vegetation that can assimilate nutrients.
Most of the lower Cache River has never been artificially channelized (with the exception of
the final 11.3 km, meaning that natural meanders and associated riparian buffers remain. These
would serve to slow flows of water and serve as depositional areas for suspended sediment and
any associated contaminants. Furthermore, relatively large portions of the watershed near the
river remain as bottomland hardwood forest/wetland, particularly when compared to the other
upper and middle portions of the Cache River Watershed (Fig. 2.24).
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Figure 2.24. Remaining wetland areas within the Cache River Watershed as compared to areas
in which water samples were collected from August 2013 to July 2016. Spatial wetland data
obtained from US Fish and Wildlife Service (USFWS) National Wetlands Inventory (2016).
105
These wetlands and natural meanders/riparian buffers are likely the primary reason that
nitrogen and sediment concentrations decrease along a downstream gradient in the Cache River
Watershed. Wetlands (both natural and constructed) are remarkably efficient at both sediment
and nutrient removal from water via the processes described above. A meta-analysis performed
by Jordan et al. (2011) found that total removal of reactive nitrogen by wetlands in the
contiguous United States accounted for 20-21% of the total anthropogenic load. Hoffmann et
al. (2011) found that restored natural wetlands were able to remove 8-95% of NO3- and 50-71%
of total N whereas Borin and Tocchetto (2007) found that a constructed wetland had up to a
90% removal efficiency of N from agricultural runoff. However, removal of nitrogen tends to be
temperature-specific, as organisms necessary for denitrification and assimilation function best
under moderate to warm temperatures. Temperatures below 5°C greatly inhibit the nitrogen
removal rate (Lu et al., 2009). Water temperatures within the Cache River Watershed rarely
drop below 5°C with just 13.5% samples collected in this study recorded a water temperature
below this point. Thus, removal of nitrogen by wetlands within the Cache River Watershed is
likely fairly efficient throughout the year. Although the lower Cache River Watershed has
considerable wetland areas, many of the agriculturally-dominant sub-watersheds of the upper
and middle Cache River Watershed have few remaining wetlands (see Fig 2.24). Restoration of
natural wetlands or construction of artificial wetlands in these sub-watersheds could be quite
beneficial in reducing exports of nitrogen to the main channel of the Cache River and Bayou
DeView.
Removal of sediments by natural wetlands, particularly those in the Cache River Watershed,
has been previously documented. In a 3-year study performed within the Cache River
Watershed, Kleiss (1996) found a decrease in sediment concentrations of 14% in water exiting a
wetland (vs. the water entering the wetland). Similarly, Rosado-Berrios and Bouldin (2016)
106
noted a decrease in turbidity and total suspended solids along a downstream gradient of Bayou
DeView, the primary tributary of the Cache River, an effect attributed to the remaining natural
wetlands occurring in the sampled stream reaches.
Both the natural meanders and relatively small elevational gradient in the main channel of the
Cache River would reduce water velocities under most discharge conditions, allowing
sedimentation to occur, particularly in lower reaches of the Cache River. Because the upper
reaches of the Cache River remain channelized, only relatively heavy particles such as sand
would be able to fall out of suspension in these reaches, while in the lower reaches of the
Cache, which largely remains unchannelized, lighter particles such as silt and clay would
precipitate. A comparison of sediment composition at main channel sites of the Cache River
using a one-way analysis of means with unequal variances indicates that a significant difference
(F 1,12.8 = 26.954, p< 0.001) exists in composition of bottom sediments between a channelized
main channel site (CREG) and unchannelized main channel sites (CRPA, CRCP, REFO), with site
CREG having significantly more sand (and consequently less silt/clay) than the unchannelized
sites.
Potential Best Management Practices
Overall, the Cache River seems to be relatively efficient at attenuating sediment and nutrient
concentrations prior to its confluence with the White River. However, several areas within the
watershed were identified as potential ‘hot spots’ that contribute a greater than average
amount of contaminants to the main channel of the Cache River or Bayou DeView. Sites BGLA
and EASL were ‘hot spots’ for turbidity/TSS, all most-altered sites along the western edge of the
watershed were ‘hot spots’ for total nitrogen and most-altered sites within the center of the
Cache River Watershed (TMCR, FSDI, WIDI) were ‘hot spots’ for total phosphorus. These areas
107
would be good candidates for implementation of BMPs, thus further reducing the contributions
of the Cache River to the Mississippi River and the Gulf of Mexico.
Site BGLA in the Upper Cache Watershed has been previously identified as a priority site for
management by the NRCS due to water quality degradation and inefficient use of irrigation.
Implementation of BMPs at site BGLA began in May of 2016 and is planned to continue until
2021 (NRCS, 2015). Because BMP implementation began just two months before the
completion of this project, the effectiveness of the BMPs cannot be observed in this data set.
However, based on the turbidity and TSS concentrations measured in this study, site EASL,
located next to BGLA, would also be an excellent candidate for implementation of BMPs. Both
BGLA and EASL had the greatest measured values for TSS and turbidity. Practices already
approved for sub-watershed BGLA, including land leveling, planting of cover crops, restoration
of riparian buffers and practices to improve the efficiency of irrigation (NRCS, 2015) would likely
be equally beneficial to mitigate water quality issues pertaining to agricultural surface runoff in
sub-watershed EASL.
Several sites within either the Upper Cache Watershed or Middle Cache Watershed were
noted for potential nutrient impacts, particularly those with a high degree of alteration.
Nitrogen concentrations tended to be greatest in agriculturally dominated watersheds along the
western edge of the Cache River Watershed whereas phosphorus concentrations were greatest
in small cluster of sites within the Middle Cache Watershed. Although further sampling is
recommended to determine if these elevated nutrient concentrations are having an adverse
effect on aquatic life, these areas might also serve as good candidates for BMP implementation.
Implementation of the practices described above would be beneficial in both reducing sediment
loads and nutrient loads, reducing the amount available for downstream transport, thus
reducing the contributions of the Cache River Watershed to the hypoxic zone in the Gulf of
108
Mexico. Practices established within the Cache River Watershed thus far have been shown to
be effective at reducing agricultural runoff and improving water quality health (USEPA, 2014).
This present study also indicates that several sub-watersheds with established BMPs had
reduced sediment and nutrient loads when compared to sub-watersheds with similar land use.
One obstacle when implementing BMPs in Arkansas is that implementation is voluntary and
the BMP chosen is at the discretion of the landowner. Further hindering the effectiveness of
BMP implementation is the fact that funding for projects tends to be limited. Although it is not
economically feasible to approve all areas for funding, this does emphasize the need for
monitoring at a sub-watershed level (such as what was performed in this study) to identify areas
that would benefit most from BMP implementation, allowing funds to be directed to the areas
where they can have the greatest environmental impact.
Potential Non-BMP Solutions
Although BMPs are largely considered the most cost-effective means of balancing
environmental health with agricultural productivity, several other solutions could help reduce
nutrient loads, thus reducing the contribution of the Cache River Watershed to the hypoxic zone
in the Gulf of Mexico, primarily caused by nitrogen inputs from the Mississippi River Basin. The
need for increased crop yields, as the world population continues to grow, means that
application of fertilizers is unlikely to be reduced significantly. However, many processes exist
to limit transport of water-soluble forms of nitrogen (NO3-, and NO2-) into surface waterways,
thus reducing the impact to aquatic ecosystems and reducing transport of these substances to
downstream waterways. These processes tend to fall into one of four categories: reducing
nitrogen sources via fertilizer inputs, increasing natural sinks for nitrogen by restoring wetlands,
reducing nitrogen transport by limiting subsurface drainage and surface runoff, and reducing
109
sources of nitrogen transport by converting to perennial cropping systems (McLellan et al.,
2015).
Many nitrogen fertilizers can be combined with the use of an inhibitor that can greatly slow
the oxidation of NH4+ to NO2- and NO3-, limiting the amount that can be transported away from
soils via surface or sub-surface flow before it can be utilized by plants. For example, Golden et
al. (2009) found that application of a urea fertilizer containing an inhibitor resulted in 49-65% of
nitrogen retained as NH4+ 20 days after fertilizer application, compared to a retention of 1-4% in
soils fertilized with no added inhibitor. This rate of inhibition can be greatly affected by soil
type, likely due to specific soil characteristics such as pH (Golden et al., 2009). Although much
research still needs to be done to study the effectiveness of various inhibitors, this is a promising
means of improving fertilizer efficiency, which would allow for reduced applications (Roberts et
al., 2016). Precision application of fertilizers using variable-rate technology (VRT) can also be
used to more accurately apply fertilizers to fields, resulting in both economic gains (Koch et al.,
2004) and environmental benefits (Ao et al., 2016).
The type of fertilizer used, irrigation techniques and method of field drainage can also greatly
impact losses of nitrogen to surface waters. Nitrogen loss due to percolation (sub-surface
transport) can be minimized by the use of organic nitrogen fertilizers, rather than inorganic, and
altered irrigation techniques (Aschonitis et al., 2013). In the Upper Mississippi River Basin, many
agricultural fields are drained via tile drainage systems, which allows subsurface water to flow
freely into receiving waterways. Although this reduces surface runoff of water and enhances
field drainage, tile drainage greatly increases the amount of nitrogen that leaves agricultural
fields (Ahiablame et al., 2010; Ikenberry et al., 2014). However, Sunohara et al. (2015) found
that using controlled tile drainage rather than conventional tile drainage was an effective means
of reducing nitrogen and phosphorus fluxes from an agricultural watershed during the growing
110
season. Similarly, a comparison of controlled tile drainage to conventional tile drainage using an
AnnAGNPS model, indicated that dissolved nitrogen could be reduced from a treatment field by
55% in a single season of corn production (Que et al., 2015). Tile drainage is not widely used in
Arkansas or the Lower Mississippi River Basin (Sugg, 2007) but is widely used in the Upper
Mississippi River Basin. Similar to the synergistic effect seen as a result of combining multiple
BMPs, these alternate methods of limiting nitrogen export can be combined to improve overall
efficacy. In areas that are tile-drained, application timing of fertilizers can significantly affect
nitrogen losses in field drainage. Jaynes (2015) found that applying anhydrous ammonia to
cornfields in the spring (before planting), rather than in the previous fall (after harvest), resulted
in a significant decrease in nitrogen losses in tile-drained fields.
Conclusion
Overall, it appears that while both land alteration and discharge affect surface water quality,
land alteration has the clearest effect on measured water quality parameters within the Cache
River Watershed. Increased mean concentrations of contaminants for the overall category of
most-altered sites was due to contributions from just a few sub-watersheds within the category.
Many sites within the most-altered category produced surprisingly low mean concentrations of
sediment and nutrients, given their degree of land alteration, particularly those in which BMPs
have been implemented (CCDI, SKDI, WIDI). Several sub-watershed and main channel sites
failed to meet the assessment criterion for turbidity, DO, and total nitrogen and phosphorus,
indicating that impacts from agricultural surface runoff are widespread throughout the Upper
and Middle Cache River Watershed. Water quality could likely be improved substantially in
these areas with the implementation of BMPs designed to reduce overall surface runoff via
improved irrigation practices and slow/filter the runoff that occurs.
111
Implementation of BMPs designed to slow and filter surface runoff such as land leveling,
riparian buffers and cover crops should help to ameliorate turbidity and total suspended solids
(Lee et al., 2003; Krutz et al., 2009; Wu et al., 2010; Collins et al., 2013; Locke et al., 2015; Miller
et al., 2015; Rasouli et al., 2015). Restoration and enhancement of existing natural wetlands or
construction of artificial wetlands would be useful in promoting removal of nutrients from
surface and subsurface flows (Zhang et al., 2005; Borin and Tochetto, 2007; Hoffman et al.,
2011; Jordan et al., 2011). Further sampling in impacted sub-watersheds is suggested to
determine where BMP implementation might be both environmentally beneficial and costeffective.
Despite the presence of identified ‘hot spots’, an examination of total sediment and nutrient
concentrations at main channel sites within the Cache River show an attenuation of impacts
along a downstream gradient, suggesting that the relatively unaltered river channel in the Lower
Cache Watershed is effective at mitigating contaminant levels before the river confluences with
the White River. This mitigation is likely due to dilution with relatively clean water from less
agricultural portions of the watershed along with natural processes promoted by intact
wetlands along the Lower Cache River. Furthermore, the majority of the lower Cache River
(south of Grubbs, Arkansas) remains un-channelized. The natural meanders of an unchannelized river and the riparian buffers associated with these meanders serve to further slow
flows of water and provide depositional areas for suspended sediments and associated
contaminants.
Agricultural land use in the Cache River Watershed negatively affects water quality in surface
waterways throughout the watershed. Given the economic importance of agriculturally
productive land, both in this watershed and in other agriculturally important watersheds
worldwide, conservation practices chosen will have to balance the ecological benefits of
112
restoration and water quality improvement against the potential economic losses of removing
land from production (Verhoeven and Setter, 2010), particularly when considering practices
such as wetland restoration that would remove relatively large portions of land from production
(when compared to less land-intensive practices such as irrigation management or planting
cover crops). Because BMP implementation is voluntary, engaging farmers and landowners in
the process is critical to ensuring successful implementation. Kalcic et al. (2015) found that
increasing communication with farmers and informing them of the benefits of adopting
agricultural conservation practices tended to build trust and increase the likelihood of farmers
being willing to adopt recommended practices. Financial incentives also tend to improve the
adoption of BMPs by farmers (Lamba et al., 2009).
Although BMP implementation comes with an economic price tag, the subsequent
improvements in overall water quality should yield considerable benefits, including improved
recreational opportunities, flood control and ecosystem health (Alvarez et al., 2016) while
simultaneously maintaining or improving agricultural yields (Liu et al., 2014). Promoting the
recreational benefits of restored areas and adjacent areas along with the environmental gains to
the entire watershed (and downstream watersheds) should help citizens see the benefits of
maintaining and restoring natural areas within this watershed that are of key importance in
ensuring its productivity and health for years to come.
113
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123
CHAPTER 3: LOOKING FOR LEAD (Pb) IN THE CACHE RIVER WATERSHED, ARKANSAS
A portion of this chapter has been published as: Kilmer, M.K and J.L. Bouldin 2016. Detection of
lead (Pb) in three environmental matrices of the Cache River Watershed, Arkansas. Bulletin of
Environmental Contamination and Toxicology 96(6):744-749.
A portion of data within this chapter (dissolved and total recoverable Pb samples from sites LCDI
and CRPA collected between February 2014 and May 2015) has been published as part of an
independent M.S. thesis: Kennon-Lacy, M. 2016. Water quality monitoring at impaired sites on
the Cache River, AR, USA. (M.S. thesis). Arkansas State University, State University, AR. 128 p.
ABSTRACT
Lead (Pb) is a naturally occurring element in the environment. Excessive dissolved Pb levels
have been reported within surface waters of the Cache River, Arkansas, for several years but the
source is unidentified. Several hypothesized sources have been proposed including surface
runoff of Pb from agricultural fields, leaching from natural Pb deposits, unidentified point
sources or resuspension of Pb-containing sediments during times of high discharge in
waterways. In this study Pb was measured in three abiotic aquatic matrices (dissolved Pb, total
recoverable Pb, sediment-bound Pb) in samples collected throughout the Cache River
Watershed. Spatial and temporal assessment of these data were used to identify the most likely
source(s) of Pb within this watershed. Pb in all matrices was ubiquitous throughout the Cache
River Watershed, though elevated concentrations were more likely to occur in agriculturally
dominated watersheds. A significant correlation was found between stream discharge (inputs
from precipitation, groundwater and runoff of irrigation water) and dissolved Pb
detections/concentrations, but no correlation was found between precipitation and dissolved
Pb detections. This suggests that surface runoff from agricultural fields is the most likely source
124
of Pb, though this is not necessarily a result of precipitation, but could instead be induced by
irrigation or draining of intentionally flooded fields. A site analysis based on current hardnessbased assessment criterion for dissolved Pb indicates that samples collected from most sites (16
of 23) exceeded assessment criterion, though mean concentration was low (2.57 ± 0.39 ppb Pb),
particularly when compared to the entire Lower Mississippi River Watershed (4.22 ± 0.74 ppb
Pb). Sediment composition was strongly correlated with sediment-bound Pb concentrations,
with the proportion of clay and silt being positively correlated with Pb concentrations and the
proportion of sand being negatively correlated with Pb concentrations. However, preliminary
results from a spike-recovery experiment suggest that the sand fraction within sediment could
be playing a larger role in Pb binding and sequestration than previously thought. Overall
detection frequencies and mean concentrations of dissolved, total recoverable, and sedimentbound Pb indicate that the environmental risk due to Pb within the Cache River Watershed is
low but that implementation of Best Management Practices (BMPs) that reduce surface runoff
could also reduce Pb concentrations within waterways.
INTRODUCTION
Lead (Pb) is a naturally occurring element in the environment, most commonly found in soils
(Ter Haar, 1975). Despite occurring naturally, most movement of Pb within the environment
results from human activity. Pb has one of the highest anthropogenic enrichment factors (AEF)
of any heavy metal, at 97% (Walker et al., 2012), indicating that of the total amount of Pb in the
environment, 97% results from human activity, rather than natural releases. Anthropogenic
sources of Pb include mining and smelting of metal ores (Dudka and Adriano, 1997), weathering
of Pb-based paint on older structures (Ter Haar, 1975), burning of leaded gasoline (Alexander
and Smith, 1988), degradation of Pb fishing sinkers or ammunition (Stansley and Roscoe, 1996;
125
Jacks et al., 2001), improper disposal of Pb-containing items including electronics (Spalvins et al.,
2008), and Pb-containing arsenate pesticides (Peryea and Creger, 1994).
Due to the numerous negative health effects and potential for bioaccumulation, there is no
level of Pb that is considered safe in terms of human exposure. The Agency for Toxic Substances
and Disease Registry (ATSDR) ranks Pb as the 2nd most prioritized hazardous substance, based on
its frequency, toxicity and potential for human exposure (www.atsdr.cdc.gov). Although Pb is
toxic to all ages, children and pregnant women are of special concern. Pb toxicity in children
affects the nervous system, leading to lowered IQ, behavior and learning problems, slowed
growth and anemia (Koller et al., 2004; Ahamed et al., 2011; Liu and Lewis, 2014); whereas in
pregnant women, Pb toxicity can result in reduced growth of the fetus and premature birth as
well as miscarriage (Al-Saleh et al., 2014; Edwards, 2014).
The effects of Pb are not limited to humans. Pb exposure results in skeletal deformities,
reduced fecundity and behavioral changes in fish (Newsome and Piron, 1982; Tulasi et al., 1989;
Weber et al., 1996; Weber et al., 1997), respiratory problems and slowed
growth/metamorphosis in amphibians (Stansley et al., 1996; Rice et al., 1999; Rice et al., 2002),
and increased aggressive behavior in birds and small mammals (Deville, 1999; Janssens et al.,
2003).
Due to the severity of toxic effects, many measures have been put in place to reduce Pb
emissions to the environment, such as the banning of Pb ammunition for certain types of
hunting in 1991 (U.S. Fish and Wildlife Service, 2013) and the phasing out of Pb additives in
gasoline, which began in the United States in the 1970s (Wu and Boyle, 1997). As a result of
these measures, Pb concentrations in the environment have declined in recent years. Anderson
et al. (2000) found that after a federal ban on the use of Pb ammunition for hunting waterfowl
was enacted in 1991, mortality in migrating ducks (due to Pb poisoning) was reduced by 64%.
126
Similarly, a decline in Pb blood concentrations in predatory and scavenging birds in California
was observed after a statewide ban on Pb ammunition for most hunting activities was enacted
in 2008 (Kelly et al., 2011).
A significant decline in both total and dissolved Pb occurred in many U.S. rivers (Alexander
and Smith, 1988) and in the waters of the North Atlantic Ocean (Wu and Boyle, 1997) after
1974, strongly correlating with the phasing out of Pb additives in gasoline. Current mean total
recoverable Pb concentrations in highway runoff have dropped to less than 45 µg/L from 1000
μg/L or greater in the 1970s (Kayhanian et al., 2012). Despite these reductions, Pb
concentrations are still above acceptable limits (generally based on water hardness) for surface
freshwaters in many waterways (Okweye and Golson-Garner, 2012), including the Cache River in
northeastern Arkansas. Reaches of the Cache River have been consistently impaired for Pb and
included in the Arkansas 303(d) list published every two years. However, the most recent
proposed versions of the list (draft 2016) indicate that several reaches are in the process of
being delisted (Table 3.1).
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Table 3.1. Impaired reaches of the Cache River Watershed due to excessive dissolved Pb concentrations
(based on water hardness at time of sampling) according to finalized and draft 303(d) lists 2008-2016
(ADEQ, 2008; 2010; 2012; 2014; 2016a).
Waterbody and Reach
2008
2010
2012
2014
2016
Bayou DeView 004
yes
yes
yes
no
no
Bayou DeView 005
yes
yes
yes
no
no
Bayou DeView 006
yes
yes
yes
no
no
Bayou DeView 007
yes
yes
yes
no
no
Cache River 016
yes
yes
yes
no
yes
Cache River 017
yes
yes
yes
yes
no
Cache River 018
yes
yes
yes
yes
no
Cache River 019
yes
yes
yes
yes
no
Cache River 020
yes
yes
yes
yes
no
Cache River 021
yes
yes
yes
yes
no
Cache River 027
yes
yes
yes
no
no
Cache River 028
no
yes
yes
no
no
Cache River 029
yes
yes
yes
no
no
Cache River 031
yes
yes
yes
no
no
Cache River 032
yes
yes
yes
no
no
128
Additionally, a total maximum daily load (TMDL) for the Cache River was proposed for Pb,
following the release of the 2008 303(d) list (FTN Associates, Ltd., 2012). Although this draft was
not finalized (due to the delisting of portions of the river), it included a survey of historical data
from several locations within the main channel of the Cache River, which indicated that
dissolved Pb levels tended to be highest in winter months, when precipitation within the
watershed was generally the greatest (FTN Associates, Ltd., 2012). The Cache River Watershed
is used heavily for row-cropping (AWIS, 2016). In winter months, ground cover on agricultural
fields tends to be lowest. Thus, the presumed source of contamination was hypothesized to be
due to removal of soil particles in runoff from agricultural fields during heavy precipitation
events in winter months (FTN Associates, Ltd., 2012). Because no natural sources of Pb exist
within the Cache River Watershed (USGS, 2016a), this Pb in soils was presumed to be
atmospherically deposited Pb. Due to its positive charge, this Pb becomes bound to negatively
charged soil particles such as silt and clay, which can easily be transported into surface
waterways by direct surface runoff from agricultural fields, due to their small size and weight. It
should be noted that the data set employed was relatively small and only included samples from
the main channel of the Cache River (FTN Associates, Ltd., 2012).
Although the explanation of agricultural surface runoff is widely accepted, it does not explain
why only the Cache River is impaired for Pb in northeastern Arkansas. Several other watersheds
are present in northeastern Arkansas and have similar land usage to the Cache, including the
White River, the L’Anguille River, the St. Francis River, and the Little River Ditches. Each of these
watersheds has greater than 50% agricultural land use, primarily used for row-cropping of rice,
corn, soybeans, cotton, and sorghum (Fig. 3.1), contain clay and silt soils (Web Soil Survey,
2016), and experience similar precipitation amounts (AWIS, 2016) yet none of these watersheds
have been listed as impaired for Pb.
129
Figure 3.1. Distribution of top five crops in sub-watersheds in northeastern Arkansas with
greater than 50% of land use devoted to agriculture for production year 2015. Areas shown in
white represent portions of the sub-watersheds not used for crop production (cropland data
obtained from United States Department of Agriculture National Agricultural Statistics Service
(USDA-NASS), 2016).
130
Several other hypotheses have been proposed to explain the presence of increased
concentrations of Pb in the Cache River. These include impacts from mining and processing of
ores, unidentified point sources from disposal of wastes, or leaching/release of Pb generally
buried within sediments. Each of these hypotheses has some support but does not seem to
definitively identify the source of the detected Pb.
As mentioned previously, the Cache River is head-watered in southeastern Missouri, a region
with some of the largest and most important Pb deposits in the world, accounting for 92% of the
total U.S. production of Pb (Missouri Department of Natural Resources, 2002). Rivers draining
mining and smelting regions have been long found to have excessive levels of many heavy
metals, including Pb, in water, sediments, and biological communities (Páez-Osuna et al., 2015;
El Azhari et al., 2016, Marasinghe-Wadige et al., 2016; McCauley and Bouldin, 2016). This is true
in the Pb mining regions of Missouri where excessive levels of Pb have been identified in water,
sediment, and animal tissues of organisms in several receiving waterways (Niethammer et al.,
1985; Gale et al., 2004; Roberts et al., 2009). These excessive levels can all be directly tied to
mining activities with waterways in question discharging through regions of historical or current
mining operations. The headwaters of the Cache River extend only slightly into Southeastern
Missouri and are not located near any areas currently or historically mined for Pb (Missouri
Department of Natural Resources, 2013) or any significant geologic deposits of lead-containing
bedrock (USGS, 2016a) (Fig. 3.2). Furthermore, the last remaining Pb smelter in the
Southeastern Missouri Mining District closed in 2013 (Doe Run Company, 2016), removing
smelting activities as a potential current source of Pb in the Cache River Watershed.
131
Figure 3.2. Major rivers in Arkansas relative to areas of significant Pb deposits in southern
Missouri and Arkansas (spatial data obtained from USGS: Mineral Resources Data System,
2016a).
132
Further evidence that fails to support this hypothesis can be found by examining other surface
waterways in the region. Both the Black and the St. Francis rivers, with watersheds in Northeast
Arkansas that are located on either side of the Cache River, are head-watered in the Pb mining
regions of Missouri (Fig. 3.2). Although portions of both of the St. Francis and Black rivers have
been listed as Pb impaired, the impairments are limited to the upper portions of the rivers,
located in Missouri; downstream reaches within Arkansas have never been listed as impaired for
Pb. Because there is no history of mining or natural Pb-containing deposits in the Cache River
Watershed and no obvious air or water routes between the nearest Pb mining area and the
Cache River Watershed, it seems unlikely that Pb mining, or other mining operations, are a
significant current contributor to the Pb impairment in the Cache River Watershed. Although it
is possible that historical inputs of Pb from mining and smelting activities might have
contaminated the Cache River Watershed, any activities that contaminated the Cache River, (for
example, atmospheric deposition of Pb from smelting activities) would also be expected to
affect nearby watersheds. Of the watersheds in northeastern Arkansas, only the Cache River
has been listed as impaired for Pb, indicating the source is specific to this watershed.
Another possibility for Pb contamination in the Cache River is via an unidentified point source,
such as an illegal dumping site for Pb-containing waste or an unidentified industrial source.
Elevated Pb levels have been historically reported throughout the length of the Cache River, as
well as its major tributary, Bayou DeView. In order to support the hypothesis of a localized
input, at least two unidentified point sources would have to occur, an unlikely scenario. If such
point sources are responsible for the contamination, more fine-scale spatial sampling is needed
to identify regions of the watershed in which the sources are likely to be located.
A final potential source of the Pb contamination of the Cache River is the river itself. Trace
metals (including Pb) dissolved in runoff water adsorb to particulate matter and settle quickly
133
into the sediments, where they can accumulate over time (Bryan and Langston, 1992;
Svobodová, 2002) and even enter the food chain (Čelechovská et al., 2008). Typically, sediment
is considered to be a sink for heavy metals, including Pb, but in aquatic systems, sediment can
also serve as a source (Chon et al., 2012). During times of heavy rainfall (and runoff), increased
discharge rates could re-suspend this sediment, potentially re-releasing Pb into the water
column. Huang et al. (2012) found that increases in discharge rate could increase suspended
particulate matter by as much as 25 times the background level and also increase heavy metals
in the dissolved phase. Thus, historical inputs of Pb from natural weathering of Pb-containing
rocks, mining activities, agricultural practices or disposal of Pb-containing wastes, rather than
current activities within the watershed, could be responsible for the current elevated levels.
During times of heavy discharge, sediment could be re-suspended, potentially releasing Pb back
into the dissolved phase. This hypothesis would also explain why detections of dissolved Pb
tend to occur during times of heavy precipitation, when discharge rates are elevated and
sediment resuspension (and release of Pb) is most likely (FTN Associates, Ltd, 2012).
Although each of these hypotheses has some support, the limited sampling for Pb thus far,
and the scattered and inconsistent pattern of Pb detection within the watershed make it
difficult to determine with any degree of certainty the most likely source, whether natural or
anthropogenic. More fine-scale spatial and temporal sampling, as well as sampling several
environmental matrices, should help to paint a clearer picture of the risk and hazard posed by
Pb contamination in the Cache River Watershed.
Sampling and analysis for dissolved Pb can be both expensive and time-consuming,
particularly if fine-scale sampling is necessary over a large geographic area. Development of a
relatively quick and easy screening tool for Pb contamination could help to narrow down areas
within a watershed in need of such intensive sampling. One potential method to screen samples
134
is through particle size analysis to determine the relative amounts of sand, silt and clay within
sediment samples. Sediments with greater concentrations of negatively charged soil particles,
such as silt or clay, have a greater capacity to bind positively charged particles, such as Pb2+, due
to their cation exchange capacity (CEC). Sipos et al. (2005) found that when comparing whole
soil samples to just the clay fraction of the sample, the clay fraction adsorbed 25 times more Pb
than the whole soil. Adsorption of Pb onto different soil particles can also be affected by
environmental conditions, primarily pH. Soils with increased pH tend to adsorb more Pb
whereas soils with lowered pH (acidic) adsorb less Pb (Nagy et al., 2003).
Although clay typically has the strongest CEC, other components of soil/sediment can also
contribute to the overall CEC, thus affecting the strength and capacity of Pb adsorption.
Malcolm and Kennedy (1970) showed that silt, sand, gravel in sediments also played a role in
the overall CEC and should be included in any consideration of the total exchange abilities of a
stream. Furthermore, organic matter also has an affinity with Pb in sediments. Liu et al. (2011)
found that organic matter within a sediment (measured as total organic carbon) had a stronger
positive correlation with Pb concentrations (r = 0.30) than either silt (r = 0.09) or clay (r = 0.07).
Similarly, Mitchell et al. (2016) found that sediment samples collected from a mining area had
very low water-leachable Pb, despite high concentrations of Pb within the sediment. This was
attributed to both the high CEC of the sediments and a high content of organic matter. Overall
in soils and sediments, the smaller the particle size (sand>silt>clay), the greater the
concentration of Pb associated with that particle (Momani, 2006), indicating that sediments
with a greater proportion of smaller particles could be associated with an increased likelihood to
adsorb and retain Pb.
In the water column, the affinity of suspended sediment to dissolved Pb tends to result in free
Pb ions becoming adsorbed to suspended sediment and eventually removed from the water
135
column via deposition (ATSDR, 2007). However, these ions can be released back into the
dissolved matrix under certain environmental conditions, most notably, when low water pH
occurs, such as in cases of acid mine drainage, resulting in a release of Pb back into the
dissolved/bioavailable form (Atkinson et al., 2007). Thus, both the composition of the sediment
and the environmental conditions present, could affect its role as both a sink and a source of Pb
in the aquatic environment.
If greater concentrations of total or sediment-bound Pb are indeed correlated with greater
amounts of clay particles in sediments of the Cache River Watershed, an analysis of the
composition of sediments could serve as a relatively easy and inexpensive way to screen sites
before investing in costlier and more time-intensive sampling. A correlation between sedimentbound Pb concentrations and dissolved Pb concentrations would also provide support for
sediment resuspension as a source of dissolved Pb within the Cache River Watershed.
The objective of this project was to determine if a spatial or temporal pattern was apparent
for either frequency of Pb detection or mean concentrations in the Cache River. Samples were
collected from three abiotic environmental matrices of Pb: dissolved Pb, total recoverable Pb
(particulate + dissolved), and sediment-bound Pb. Samples were collected from headwater subwatersheds of the Cache River and from the main channel of the Cache River for two (total
recoverable Pb and sediment-bound Pb) or three (dissolved Pb) years. Detection of a spatial or
temporal pattern could shed light onto potential sources of the Pb contamination within this
watershed and provide valuable information for the design and implementation of best
management practices (BMPs) to mitigate or control potentially harmful effects, both to
humans and aquatic organisms. Additionally, an analysis of sediment composition was
performed and compared to sediment-bound Pb concentrations to determine the viability of
using particle size analysis as a screening tool for areas of potential Pb contamination.
136
MATERIALS AND METHODS
Sample Collection
Samples were collected from 23 sites located throughout the Cache River Watershed, 19 of
which were headwater sub-watersheds and four of which were main channel sites of the Cache
River (Fig. 3.3). Samples were collected monthly for three years (August 2013 to July 2016) for
dissolved Pb. Based on initial results from dissolved Pb samples, it was decided to test
additional matrices for the presence of Pb. Consequently, samples were collected monthly for
analysis of total (dissolved+particulate) Pb and quarterly for sediment-bound Pb, beginning in
August 2014 and concluding in July 2016 (Table 3.2).
137
Figure 3.3. Sites sampled for dissolved, total recoverable, and sediment-bound Pb in the Cache
River Watershed from August 2013 to July 2016 (dissolved Pb) and August 2014 to July 2016
(total recoverable Pb, sediment-bound Pb).
138
Table 3.2. Site names, abbreviations and waterway types for all sampled sites in the Cache River
Watershed, Arkansas. All sites were sampled monthly for dissolved Pb from August 2013-July 2016,
monthly for total recoverable Pb from August 2014-July 2016 and quarterly for sediment-bound Pb from
August 2014-July 2016.
Sampling site
Site Code
HUC12/Reach
Waterway type
Sugar Creek
SUCR
080203020204
Headwater
Scatter Creek
SCCR
080203020201
Headwater
Mud Creek
MUCR
080203020501
Headwater
South Fork-Big Creek
SFBC
080203020104
Headwater
Lost Creek Ditch
LCDI
080203020502
Headwater
Little Cache River Ditch
LCRD
080203020102
Headwater
Number Twenty-Six Ditch
NTSD
080203020301
Headwater
Swan Pond Ditch
SPDI
080203020205
Headwater
East Slough
EASL
080203020105
Headwater
Big Gum Lateral
BGLA
080203020202
Headwater
Willow Ditch
WIDI
080203020305
Headwater
West Cache River Ditch
WCRD
080203020303
Headwater
Beaver Dam Ditch
BDDI
080203020207
Headwater
Flag Slough Ditch
FSDI
080203020601
Headwater
Three Mile Creek
TMCR
080203020602
Headwater
Skillet Ditch
SKDI
080203020401
Headwater
Cypress Creek Ditch
CCDI
080203020403
Headwater
Fish Trap Slough
FTSL
080203020101
Headwater
Kellow Ditch
KEDI
080203020208
Headwater
Reeses Fork
REFO
Reach 016
Main Channel
Cache River at Cotton Plant
CRCP
Reach 017
Main Channel
Cache River at Patterson
CRPA
Reach 018
Main Channel
Cache River at Egypt
CREG
Reach 021
Main Channel
139
Water samples were collected using a bucket lowered from overhead into the center of the
channel. Immediately following collection, samples collected for the analysis of dissolved Pb
were filtered using a 0.45-µm syringe filter (Environmental Express, Charleston, South Carolina)
and stored in 15-mL metal-free plastic tubes. Samples collected for the analysis of total
recoverable Pb were stored in 50-mL metal-free plastic tubes. All water samples were stored in
coolers with ice while being transported back to the Arkansas State University Ecotoxicology
Research Facility for analyses. Upon arrival at the laboratory, water samples for analyses of
dissolved Pb and total recoverable Pb were frozen until digestion or analysis took place. Water
samples were collected monthly from August 2013 to July 2016 for dissolved Pb and from
August 2014 to July 2016 for total recoverable Pb, with a total of 24 samples of total recoverable
Pb and 36 samples of dissolved Pb being collected from each site.
Sediment samples were collected from mid-channel, using either a scoop on an extendable
pole (for smaller waterways) or a Ponar ™petite sampler (Wildco, Yulee, Florida). The sediment
sampler was used at sites when the depth of the water precluded entering the waterway to
collect sediment directly. Each sample was comprised of the top 5 to 10 cm of sediment.
Sediment samples were stored in plastic bags (Ziploc ® or similar brand) in coolers with ice while
being transported back to the Arkansas State University Ecotoxicology Research Facility for
analyses. Upon arrival at the laboratory, bagged samples were refrigerated until digestion and
analysis took place. Sediment samples were collected quarterly from Oct 2015 to July 2016 with
a total of eight samples collected from each sampling site.
Sample Analysis
Preparation of samples
140
Once thawed for analysis/digestion, dissolved Pb samples were acidified using Optima grade
nitric acid (HNO3) (Fisher Scientific, Pittsburgh, Pennsylvania) to a final sample concentration of
1% HNO3. Total recoverable Pb samples were digested according to EPA method 200.8 (adapted
for Environmental Express HotBlock Digestion System), a procedure that resulted in an aqueous
sample with a final HNO3 concentration of 1%. All glassware used during analysis was washed
prior to use with 10% optima grade HNO3 and rinsed with deionized water to reduce the
likelihood of in-laboratory contamination of samples.
Sediment samples were sieved through a sediment sieve (No. 10, U.S.A. Standard Testing
Sieve, Fisher Scientific, Pittsburgh, Pennsylvania) to remove any large rocks or organic matter.
Sieves were constructed of a brass or stainless steel sleeve with stainless steel mesh. Although
it is possible for Pb to leach out of some composite metals under specific conditions, such as
when acid is applied, all sediments were rinsed through sieves using deionized water. Thus, the
sieves themselves were not expected to contribute any Pb to the final sediments. Sieved
sediments were dried in an oven at 150°C then placed into a porcelain mortar and pestle and
ground to achieve a relatively homogenous sample. Ground sediments used for analysis of
sediment-bound Pb were then sieved through polypropylene mesh (Product Number XN3234,
Industrial Netting, Minneapolis, Minnesota). Sediment samples were digested according to EPA
method 3050b (adapted for Environmental Express HotBlock Digestion System), a procedure
that resulted in a final sample concentration of 1% HNO3. After completion of digestion, both
total recoverable Pb and sediment Pb samples were filtered using a Filtermate ™PTFE-faced
polypropylene filter (Part Number SC0401, Environmental Express, Charleston, South Carolina)
to separate the aqueous portion of the sample from any remaining particulate matter.
An additional analysis of sediment samples was performed to determine particle size
composition based on the rapid particle size analysis method defined by Kettler et al. (2001).
141
This analysis determined the relative amount of sand, silt and clay in sediments collected at
each sampled site and was performed on sediments collected in October 2015, January 2016,
April 2016 and June 2016.
Instrumental analysis
All samples were analyzed using an Agilent 240 Zeeman graphite furnace atomic absorbance
spectrometer (GFAAS, Agilent Technologies, Santa Clara, California) according to EPA method
7010 (USEPA, 2007). A minimum detection limit (MDL) was determined for each method,
defined as the lowest quantity of a substance that can be distinguished from a blank within a
stated confidence limit. This level is determined by measuring the mean and standard deviation
of a blank signal and applying a confidence limit (EPA, 2006). This value was then used to set
the practical quantification limit (PQL), defined as the minimum level of analyte that can be
reliably detected under normal laboratory operating conditions (USEPA, 2006). This typically
involves multiplying the MDL by some pre-determined factor. For the purpose of this study, the
PQL was defined as 3x the MDL.
Based on the standard deviation of 30 blank samples, the MDL for dissolved Pb was
determined to be 0.286 ppb with a PQL of 0.858 ppb. Concentrations measuring below the MDL
were reported as 0.5x the MDL (0.143 ppb). This value substitution helps to limit artificial
skewing of mean values due to very low concentrations of metals or other constituents of water
samples (Kayhanian et al., 2002). Concentrations between the MDL and PQL were reported as
measured but denoted as falling within this range. Concentrations above the PQL were
reported as measured.
For total recoverable Pb, a known standard was digested and used to determine the MDL and
PQL. The MDL for total recoverable Pb was determined to be 0.472 ppb with a PQL of 1.415
142
ppb. Concentrations measuring below the MDL were reported as 0.5x the MDL (0.236 ppb).
Concentrations between the MDL and PQL were reported as measured but denoted as falling
within this range. Concentrations above the PQL were reported as measured.
Calibration standards were prepared immediately prior to analysis and were chosen to
provide a calibration curve most likely to encompass the measured concentrations within the
analyzed samples. For both dissolved and total recoverable Pb samples, the calibration
standards were 7.5 ppb, 15 ppb and 25 ppb. Preliminary results from sediment-bound Pb
analysis indicated much greater concentrations of Pb, thus a higher calibration curve consisting
of standards of 45 ppb, 105 ppb, and 150 ppb was used in analysis of sediment samples.
Additionally, a palladium matrix modifier was used to allow for greater analysis temperatures
and a more complete atomization of the samples (Varian, 1988).
Quality control
A quality control standard was prepared with a concentration within the range of each
calibration curve (20 ppb for dissolved and total recoverable Pb, 100 ppb for sediment-bound
Pb). This QC standard was run approximately every 10 samples and was required to measure ±
10% of the expected concentration. If the QC standard did not fall within acceptable ranges, the
calibration curve was rerun to ensure that results were accurate. For each digestion, a
laboratory blank was run along with samples. Furthermore, a field blank was collected at
multiple times throughout sampling and analyzed as well.
To determine the reliability of the sediment digestion, a subset of sites was selected (SUCR,
BDDI, WIDI, REFO, BGLA, WCRD) and 10 replicates of a single sediment sample from each site
were digested and analyzed. Furthermore, replicate sediment samples from a single site (SUCR)
were spiked with Pb at concentrations of 100, 200, 300, 400, and 500 ppb and digested
143
according to the standard method to determine the recovery rate of the digestion method
employed.
Statistical Analyses
All statistical analyses were performed using R (R Core Team, 2016). All data were tested for
normality and transformations were employed if necessary to achieve normality. If normality
could not be achieved via transformations, a non-parametric alternative was employed.
Three types of analyses were performed. Initially, to better understand the overall
relationship between land alteration, discharge, and concentrations of Pb within the overall
Cache River Watershed, a multivariate analysis was performed with land alteration and
discharge at the time of sampling as predictors and concentrations of Pb within different
matrices as response variables. Because dissolved Pb is technically a component of total
recoverable Pb (dissolved + particulate), it was not included in this analysis. Thus, the final
model included two predictors (land alteration and discharge) and two response variables (total
recoverable Pb and sediment-bound Pb). A MANOVA was performed to look for any
interactions between predictors. Sites were categorized as least-altered, moderately-altered,
most-altered or main channel, according to the alteration score described in the previous
chapter (Fig. 3.4).
144
Figure 3.4 Categorization of sampled sites for non-parametric MANOVA analysis. Sites were
categorized as least-altered, moderately-altered, most-altered depending on amount of land
used for agriculture within the sub-watershed, amount of forested cover within the subwatershed and amount of artificially channelized surface waterways. Main channel sites
received cumulative inputs from all upstream sites and were assigned to an independent
category.
145
Discharge data (either collected at time of sampling or obtained from USGS discharge gauge)
were categorized into one of five categories: base, low, moderate, high or storm, as described in
the previous chapter. Because response data were not normally distributed and
transformations failed to achieve normality, a non-parametric multivariate analysis was
performed (R package: vegan v 2.4-0) with a post-hoc pairwise permutational analysis (R
package: RVAideMemoire v 0.9-56) using 999 permutations to look for statistically significant
differences between categories. Because this analysis incorporates all response parameters into
a single variable, it is possible for the combined response variable to be significantly affected by
the predictors when individual response parameters might not be affected. Graphical analysis
of the response data indicated that some response parameters likely played a less significant
role than others. To account for this, individual Kruskal-Wallis analyses were performed for
each response variable to determine which parameters were not playing a significant role in the
overall relationship.
Secondly, a spatial assessment of concentrations of Pb at all sampled sites was performed, to
identify ‘hot spots’ within the watershed where Pb concentrations were greatest. This
assessment did not take into account properties of waterways that might mitigate the toxic
effects of Pb, such as hardness; rather, it resulted in a simple spatial assessment of areas that
might be contributing to the overall Pb levels within the Cache River Watershed. Only samples
that exceeded the PQL for Pb were used in this spatial assessment. A risk assessment was
performed for sampled sites within the Cache River Watershed. Risk is defined as the product of
hazard and exposure. For this risk assessment, hazard was represented by the mean
concentration of Pb at each site whereas exposure was the detection frequency of Pb
concentrations exceeding the PQL for each site. This spatial assessment and risk assessment
was performed for all sampled matrices of Pb (dissolved, total, sediment-bound).
146
Based on the range of risk scores and natural groupings of the scores sites were assigned to one
of three categories for both dissolved Pb and total recoverable Pb: low risk (0-15 for dissolved
Pb, 0-200 for total recoverable Pb), moderate risk (15 to 30 for dissolved Pb, 200-400 for total
recoverable Pb), or high risk (> 30 for dissolved Pb, >400 for total recoverable Pb).
The final analysis performed examined sites that exceeded assessment criterion most often.
Only dissolved Pb has state-set assessment criterion, which is based on the hardness of the
water at the time of sample collection (ADEQ, 2016b). The assessment criterion is determined
using the following equation
(
[ .
( (
.
∗ (1.46203 − (ln(ℎ"#$% && ∗ 0.145712
The appropriate criterion was calculated using water hardness collected at the time of sampling
and compared to measured concentrations of Pb to determine if samples exceeded assessment
criterion. Assessment criteria have not been set for total and sediment-bound Pb, though limits
have been proposed (Buchmann, 2008). Thus, only dissolved Pb was included in this analysis. A
minimum hardness value of 25 mg/L CaCO3 is used by the State of Arkansas when defining
assessment criterion for dissolved Pb. The criterion for samples with a hardness lower than this
value was calculated using 25 mg/L CaCO3 as the hardness. Because this criterion is hardnessbased, the assessment threshold increases as a function of increasing water hardness. Only
samples with concentrations exceeding the hardness-based criterion determined for that
particular sample were included in the analysis of impairment, because these are the only
samples that would be expected to have an adverse effect on aquatic organisms. Impaired sites
were compared in two ways. Firstly, sites were examined spatially to determine if a geographic
pattern was evident. Secondly, frequency of impairment was compared among four land use
categories to determine if impairments occurred more often in a particular type of site than
could be explained by sampling frequency alone.
147
A test for correlations was performed between sediment composition and sediment-bound Pb
concentrations, to determine if the relative amount of particular particle sizes were associated
with Pb concentrations. Due to the negative charges associated with silt and clay, these
particles were expected to have a positive relationship with positively charged Pb, meaning that
as relative amounts of these particles increased, sediment-bound Pb concentrations should also
increase. The relative amounts of these two particle sizes are not necessarily correlated with
each other. However, the sum of the two are correlated with relative amounts of sand. To only
account for uncorrelated factors, a sediment score was calculated that took into account the
contributions of both clay and silt.
(% +,"- + % &/,0 = & $/2 %0 &+3#
A greater sediment score would therefore indicate an increased amount of clay and/or silt at a
site. Particle size composition analysis was performed in duplicate for each sediment sample
from a single month (October 2015) of sampling to determine the reliability and consistency of
the method. Furthermore, particle size analysis was performed on samples collected from four
separate months (October 2015, January 2016, April 2016, June 2016) to measure the variability
in results over the course of a year. Sediment scores were calculated for each site for each
month and compared to sediment-bound Pb concentrations from the same sites/months using a
test for correlations. Because neither sediment score nor sediment-bound Pb concentrations
were normal and transformations failed to achieve normality, a non-parametric Spearman’s
rank correlation was performed.
A further quality control check was performed by spiking several samples of sediment from a
single site from a single collection month with known concentrations of Pb. Samples from the
April 2016 collection at SUCR were chosen for spiking. Sediment samples were spiked with 100,
200, 300, 400 and 500 ppb of Pb, then digested as normal to determine recovery rates of the
148
digestion method. To verify that the digestion process was not affecting the amount of Pb, the
spiking solutions were also digested independently (no sediment added) and analyzed.
Furthermore, spiking concentrations were analyzed without digestion to ensure that the
nominal concentrations were accurate.
RESULTS
A portion of this data set and accompanying analyses has been published as: Kilmer, M.K and J.L.
Bouldin 2016. Detection of lead (Pb) in three environmental matrices of the Cache River
Watershed, Arkansas. Bulletin of Environmental Contamination and Toxicology 96(6):744-749.
A portion of this data set (dissolved and total recoverable Pb samples from sites LCDI and CRPA
collected between February 2014 and May 2015) has been published as part of an independent
M.S. thesis: Kennon-Lacy, M. 2016. Water quality monitoring at impaired sites on the Cache
River, AR, USA. (M.S. thesis). Arkansas State University, State University, AR. 128 p.
Effects of Land Alteration and Discharge
The multivariate analysis (non-parametric MANOVA) indicated that both discharge category
(F4,128 = 2.2675, p = 0.019) and alteration category (F3,128 = 5.3572, p = 0.001) had a significant
effect on the combined response parameter but that no significant interaction between
alteration and discharge was present (F12,128 = 1.1222, p = 0.292). A pairwise permutational
MANOVA indicated that for alteration, significant differences occurred only between least- and
most-altered sites (p = 0.030). Although the non-parametric MANOVA indicated a significant
effect of discharge rate, the pairwise permutational MANOVA was unable to detect significant
differences between categories. Therefore, only alteration was considered when performing
individual Kruskal-Wallis tests. These tests indicated that for alteration, only sediment-bound
Pb was significantly affected (χ2 = 18.611, df = 3, p < 0.001) (Fig. 3.5).
149
Figure 3.5. Results of individual Kruskal-Wallis tests for mean ± SE concentrations of A)
sediment-bound Pb and B) total recoverable Pb by land alteration category for samples
collected from the Cache River Watershed from August 2014 to July 2016.
150
Spatial Assessment and Risk Assessment
Dissolved Pb
Detection frequency of dissolved Pb was relatively low within the Cache River Watershed.
Out of 604 samples analyzed, 72 had concentrations above the PQL, resulting in an overall
detection frequency of 11.9%. When comparing all sampled sites for both dissolved Pb
detections and mean dissolved Pb concentrations (in samples measuring above the PQL for
dissolved Pb), no geographic pattern was evident but several sites had either much greater
detection frequencies or much greater mean concentrations of dissolved Pb. All sites had at
least one sample that exceeded the PQL over the course of sampling and most sites had
between two and four detections. Site CRPA (main channel) had eight detections above PQL
(detection frequency of 22%) whereas site LCDI (moderately-altered) had only one detection
(2.8%). In terms of mean concentration, sites LCRD (moderately altered) and REFO (main
channel) had the greatest mean concentrations (8.51 and 9.61 ppb, respectively). BDDI (mostaltered) and CREG (main channel) also had elevated mean concentrations (6.24 ppb and 4.85
ppb, respectively). Based on the described risk assessment, five sites were identified as high
risk, an additional six sites were identified as moderate risk and the remaining 12 sites were
categorized as low risk (Table 3.3, Fig. 3.6) for dissolved Pb.
151
Table 3.3. Results of dissolved Pb analysis for all samples measuring above the Practical Quantitation Limit (PQL) in the Cache River
Watershed from August 2013 to July 2016 (n = 72). Detection frequency for each site calculated as number of samples exceeding PQL
divided by total number of samples for each site (n = 36). Mean concentrations do not include samples that had concentrations lower
than the PQL for dissolved Pb (0.858 ppb).
Site
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Samples>PQL
3
3
2
3
4
4
3
2
3
3
3
2
1
4
1
1
2
8
4
4
4
4
4
Detection Frequency (%)
8.3
8.3
5.5
8.3
11.1
11.1
8.3
5.5
8.3
8.3
8.3
5.5
2.8
2.8
11.1
2.8
5.5
22.2
11.1
11.1
11.1
11.1
11.1
Mean concentration (ppb)
2.168
8.508
1.497
2.302
1.200
1.279
1.402
1.648
6.238
1.241
1.011
2.068
2.354
1.102
0.862
9.61
2.89
2.81
2.751
1.84
1.328
1.86
4.854
152
Risk Score
17.99
70.62
8.23
19.11
13.32
14.20
11.64
9.06
51.78
10.30
8.39
11.37
6.59
3.09
9.57
26.91
15.90
62.38
30.54
20.42
14.74
20.65
53.88
Risk
Moderate
High
Low
Moderate
Low
Low
Low
Low
High
Low
Low
Low
Low
Low
Low
Moderate
Moderate
High
High
Moderate
Low
Moderate
High
A
B
Figure 3.6. Risk categorization for dissolved Pb for sampled sites within the Cache River Watershed from samples collected from August
2013 to July 2016 with concentrations measuring above the Practical Quantitation Limit (PQL, n = 72). Risk was calculated as the product
of the mean concentration of samples measuring above the PQL and the detection frequency of those samples for each sampled site.
153
Total Recoverable Pb
Detection frequency of total recoverable Pb was much greater than dissolved Pb within the
Cache River Watershed. Out of 551 samples analyzed, 422 had concentrations above the PQL,
resulting in an overall detection frequency of 76.6%. When comparing all sites for total
recoverable Pb detections and mean concentrations in samples measuring above the PQL, most
were fairly consistent with all sites having between 13 and 21 detections (36.1-58.3% detection
frequency). Sites with headwaters located on Crowley’s Ridge (SFBC, SCCR, SUCR) with minimal
agricultural or urban development tended to have the fewest detections.
Mean concentrations ranged from 3.293 ± 0.366 ppb (LCDI) to 10.160 ± 2.434 ppb (EASL) with
an average concentration overall of 5.912 ± 0.257 ppb. The risk assessment resulted in 17 of the
23 sampled sites being categorized as moderate risk for total recoverable Pb, the largest
category. Two sites were categorized as high risk (BGLA, EASL) and the remaining four as low
risk (SFBC, SCCR, LCDI, REFO) (Table 3.4, Fig. 3.7).
154
Table 3.4. Results of total recoverable Pb analysis for all sites for samples measuring above the Practical Quantitation Limit (PQL) in the
Cache River Watershed from August 2014 to July 2016 (n = 422). Mean concentrations do not include samples with concentrations
measured below the PQL for total recoverable Pb (1.415 ppb). Detection frequency was calculated as the number of samples exceeding
the PQL at each site divided by the total number of samples collected (24). The risk score was calculated as the product of the detection
frequency for each site and the mean concentration of total recoverable Pb for each site.
Site
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Samples>PQL
18
21
16
21
19
15
13
17
20
20
19
16
18
21
18
19
17
20
18
21
20
17
18
Detection Frequency (%)
50.0
58.3
44.4
58.3
52.8
41.7
36.1
47.2
55.6
55.6
52.8
44.4
50.0
58.3
50.0
52.8
47.2
55.6
50.0
58.3
55.6
47.2
50.0
Mean concentration (ppb)
4.983
5.180
4.172
10.160
7.766
4.684
7.695
7.897
6.274
4.688
4.259
4.927
3.239
6.716
5.756
3.521
5.282
6.675
6.738
6.574
5.673
7.656
4.953
155
Risk Score
249.15
301.99
185.24
592.33
410.04
195.32
277.79
372.74
348.83
260.65
224.88
218.76
161.95
391.54
287.80
185.91
249.31
371.13
336.90
383.26
315.42
361.36
247.65
Risk
Moderate
Moderate
Low
High
High
Low
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Low
Moderate
Moderate
Low
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
B
A
Figure 3.7. Mean concentration and risk categorization of all sites for total recoverable Pb for samples collected within the Cache River
Watershed from August 2014 to July 2016, for samples with concentrations measuring above the Practical Quantitation Limit (PQL, n =
422). Risk was calculated as the product of the mean concentration of samples measuring above the PQL and the detection frequency of
those samples for each sampled site.
156
Sediment-bound Pb
Due to the lack of a standard sediment, an MDL and PQL could not be determined. However,
based on the quantitation limits calculated for aqueous solutions (< 2 ppb) and the relatively
high concentrations (~10-1000 ppb) measured in sediment, it is likely that all sediment samples
contained great enough concentrations of Pb to be reliably measured. When performing a
spatial assessment for sediment-bound Pb concentrations, a geographic pattern is apparent,
particularly for sites with low mean concentrations. Sites located along the eastern side of the
watershed (along Crowley’s Ridge) consistently had the lowest mean concentrations of
sediment-bound Pb. Sites with elevated concentrations did not display a distinct geographic
pattern (Fig. 3.8).
157
Figure 3.8 Mean sediment-bound Pb concentrations by site within the Cache River Watershed
for samples collected (n = 184) between August 2014 and July 2016.
158
Because all samples had detectable levels of sediment-bound Pb, a comparison of detection
frequency in different land alteration categories is irrelevant. However, a comparison of mean
concentrations of sediment-bound Pb indicates that a significant difference exists between land
alteration categories (χ2 = 21.086, df = 3, p < 0.001 with least-altered sites having significantly
lower mean concentrations than most-altered sites (p < 0.001) and main channel sites (p =
0.004) (Fig. 3.9). Considerable variation in main channel sediment-bound Pb concentrations was
apparent.
159
25.00
Sediment-bound Pb (mg/kg)
20.00
15.00
10.00
5.00
0.00
Least
Moderate
Most
Main
Figure 3.9. Mean ± SE concentration of sediment-bound Pb by land use category for samples
collected in the Cache River Watershed between August 2014 and July 2016 (n = 184).
160
Comparison to Assessment Criterion
Using the hardness-based assessment criterion for dissolved Pb, 29 of the 604 (4.80%)
analyzed samples were found to contain excessive concentrations of dissolved Pb. Subwatersheds exceeding assessment criterion were evenly spread geographically throughout the
Cache River Watershed (Fig. 3.10) and included both headwater sub-watersheds and main
channel sites. Impaired samples most often had water hardness in the soft range (<60 mg/L
CaCO3) with only eight of the 29 samples that exceeded assessment criterion having moderate
to high water hardness at the time of sampling (Table 3.5). Of the eight samples, 50% came
from most-altered sites located along the western edge of the Upper Cache River Watershed.
161
Figure 3.10. Impaired sub-watersheds of the Cache River Watershed determined by hardnessbased assessment criteria calculated using water hardness at the time of sample collection for
samples collected between August 2013 and July 2016.
162
Table 3.5. Results of sites with dissolved Pb levels exceeding state-set impairment criteria based
on water hardness at the time of sampling for samples collected from August 2013 to July 2016.
Site
Alteration
Dissolved Pb (ppb)
Hardness (mg/L CaCO3) Criterion (ppb)
SFBC
least
2.7060
35
0.7892
SUCR
least
0.9195
30
0.6640
SUCR
least
1.8700
20*
0.5410
SUCR
least
0.9416
20*
0.5410
SUCR
least
0.7050
20*
0.5410
SUCR
least
1.4172
40
0.9162
SCCR
least
1.0720
40
0.9162
SCCR
least
1.0369
25
0.5410
SCCR
least
1.9294
45
1.0446
SCCR
least
1.0779
25
0.5410
MUCR
least
3.0772
80
1.9722
CREG
main
11.205
60
1.4370
CRCP
main
3.9368
90
2.2435
CRPA
main
6.3227
160
4.1815
CRPA
main
3.3700
40
0.9162
CRPA
main
0.9630
40
0.9162
LCRD
moderate
1.4271
45
1.0446
LCRD
moderate
23.169
210
5.5870
LCDI
moderate
2.3500
45
1.0446
FTSL
most
2.6540
60
1.4370
FTSL
most
2.6700
100
2.5166
EASL
most
3.1802
80
1.9722
EASL
most
2.1118
30
0.6440
BGLA
most
1.2100
50
1.1744
BDDI
most
4.3667
86
2.1347
BDDI
most
12.123
90
2.2435
TMCR
most
1.0000
30
0.6440
WCRD
most
2.0000
50
1.1744
WIDI
most
9.1238
40
0.9162
* Guidelines for calculating hardness-based criterion dictate using a hardness of 25 mg/L CaCO3
if the hardness of the sample is unknown or is less than 25 mg/L CaCO3.
163
An additional six samples are suspected of exceeding impairment criterion for dissolved Pb.
Although water hardness was not measured at the time of collection of these samples (sampling
period 2013-2014), hardness was measured for these sites in the same months for the following
two years (sampling period 2014-2015, 2015-2016). Hardness for all sites followed a fairly
predictable pattern over the course of the year with low water hardness measured for all sites in
December-May. For the remainder of the year (June-December), water hardness remained low
at non-agricultural sites but increased sharply at agriculturally impacted sites (Fig. 3.11). Based
on the mean hardness for these years for the sites in question, a probable impairment criterion
was calculated (Table 3.6). Inclusion of these additional samples would increase the frequency
of impairments from 4.80% to 5.80%. Although this does not greatly increase the degree of
impaired sites, it does increase the number of potentially impaired sites from 16 to 18, meaning
that almost all sampled sites were Pb-impaired at least once during the course of the study.
164
160
140
mg/L CaCO3
120
100
80
60
40
20
0
Non-Agricultural
Agricultural
Dec-May
Jun-Nov
Figure 3.11. Mean hardness for all non-agricultural (n = 4) vs. agriculturally impacted (n = 19) sampled
sites from September 2014-July 2016.
165
Table 3.6. Results of sites with dissolved Pb levels in which hardness is unknown (collected
between August 2013 to July 2014) but that are likely to exceed criterion based on average
hardness data for the month of sampling for the site (from samples collected between August
2014 and July 2016).
Criterion (ppb)
Site
Alteration
Pb (ppb)
Month
Hardness (mg/L CaCO3)
CREG main
4.2193
May
55.0
1.3052
CRPA main
5.4218
June
57.5
1.3710
REFO main
9.6102
June
55.0
1.3052
SKDI most
3.5700
March
57.5
1.3710
WCRD most
1.2743
May
47.5
1.1093
SFBC least
1.9151
May
30.0
0.6640
166
Comparison of impairment frequencies between land use categories to expected values based
on sampling intensity alone, indicated that impairments occurred significantly more often than
expected in least-altered watersheds (χ21 = 7.582, p = 0.006). Impairment frequency was not
significantly different than expected based on sampling intensity alone for main channel sites
(χ21 = 0, p = 1.000), most-altered sites (χ 21 = 1.616, p = 0.204) or moderately-altered sites (χ 21 =
0.500, p = 0.480) (Table 3.7). Although least-altered sites had significantly more impairment
events than expected, the mean concentration at this type of site was the lowest of the four
categories of land alteration. Including mean concentration and corrected impairment
frequency (category impairment frequency divided by number of sites within the category) into
a single risk score indicated that main channel sites actually have the greatest risk for
impairment, followed by least-altered sites, most-altered sites and moderately-altered sites
(Table 3.7).
167
Table 3.7. Frequency of impairment for land alteration categories (impaired samples within category/total impaired samples) compared
to expected frequencies for land use categories based on sampling effort alone. Risk scores for each category were calculated as the
product of corrected impairment frequency and mean ± SE concentrations of dissolved Pb within each category. Corrected impairment
frequency was calculated as the category impairment frequency/number of sites within the category.
Alteration
n
Imp. Freq.
Exp. Freq.
p-value
Corrected Imp. Freq. Mean Pb (ppb)
Risk Score
Least
11
37.9%
17.0%
0.006
9.475%
1.52 ± 0.24
14.40
Moderate
3
10.3%
17.0%
0.480
2.575%
1.89 ± 0.37
4.87
Most
10
34.5%
48.0%
0.204
3.136%
4.04 ± 1.1
12.67
Main
5
17.2%
17.0%
1.000
4.300%
5.16 ± 1.74
22.19
168
Because the risk score is a combination of all sites within a category, an additional site
assessment was performed to determine what percentage of sites within a category were
having the largest effect on final risk scores. Three of the four (75%) sampled main channel sites
(CREG, CRPA, REFO) were considered to have moderate to high risk of impairment. The risk in
moderately-altered and least-altered sites may be due to a single site (25%) in each category
(LCRD and SUCR, respectively) whereas the risk score for most-altered sites seemed to be due to
just two of the 11 (18%) sampled sites (BDDI, WIDI) (Table 3.8, Fig. 3.12).
169
Table 3.8. Results of sites with dissolved Pb levels exceeding state-set impairment criterion or suspected of exceeding criterion for
samples collected from the Cache River Watershed between August 2013 and July 2016. Impairment frequency was calculated as the
number of samples exceeding hardness-based criterion at a site divided by the total number of samples collected (36). Risk score was
calculated as the product of the impairment frequency for each site and the mean concentration of samples exceeding assessment
criterion.
Site
SFBC
SUCR
SCCR
MUCR
CREG
CRCP
CRPA
REFO
LCRD
LCDI
FTSL
EASL
BGLA
BDDI
TMCR
WCRD
WIDI
SKDI
Impaired Samples
2
5
4
1
2
1
4
1
2
1
2
2
1
2
1
2
1
1
Impairment Frequency
5.6
13.9
11.1
2.8
5.6
2.8
11.1
2.8
5.6
2.8
5.6
5.6
2.8
5.6
2.8
5.6
2.8
2.8
Mean Dissolved Pb (ppb)
2.311
1.171
1.279
3.077
7.712
3.937
4.019
9.610
12.298
2.3500
2.662
2.646
1.210
8.245
1.000
1.637
9.124
3.570
170
Risk Score
12.942
16.277
14.197
8.616
43.188
11.02
44.615
26.908
68.869
6.580
14.907
14.8176
3.388
46.171
2.800
9.1672
25.547
9.996
Risk
Low
Moderate
Low
Low
High
Low
High
Moderate
High
Low
Low
Low
Low
High
Low
Low
Moderate
Low
Figure 3.12. Risk of impairment for dissolved Pb for sites within the Cache River Watershed
based on samples collected between August 2013 and July 2016. Risk was calculated as the
product of the mean concentration of dissolved Pb and the detection frequency of samples
exceeding state-set assessment criterion.
171
Particle Size Analysis
Particle size analysis was performed on sediments collected from all sites quarterly over the
course of a year (October 2015, January 2016, April 2016, June 2016). Percentage of sand, silt
and clay at each site was determined for each collection. Sediment score was significantly
correlated with sediment-bound Pb concentrations (S = 57838, rho = 0.5543, p < 0.001). A plot
of the data points indicates at least four outlying data points in which sediment-bound Pb
concentrations were much greater than expected, based on the sediment score (Fig. 3.13).
172
90
LCRD (June 2016)
80
70
Sediment-bound Pb (mg/kg)
REFO (January 2016)
60
EASL (January 2016)
50
EASL (April 2016)
40
30
2
R =0.0707
20
10
0
0
10
20
30
40
Sediment Score
50
60
70
80
Figure 3.13. Sediment-bound Pb concentrations plotted as a function of sediment score for samples collected from the Cache River
Watershed between October 2015 and June 2016 (n = 92). Outlying data points are identified by site and month of sampling.
173
A subset of sediment samples was analyzed to determine the reliability of the digestion
method for sediment. After sample preparation was complete (see methods), 10 replicates of
sediment from each of six sites were digested and analyzed. All samples were from the April
2016 collection of sediment. The sites chosen represented a range of overall sediment values
measured in the Cache River Watershed. Overall digestion results were fairly consistent though
at least one outlier was present for most sites analyzed. Removal of this outlier resulted in %
relative standard deviation (%RSD) values of less than 15% for most sites (Table 3.9).
174
Table 3.9 Results of replicate digestions of sediment (mg/kg) from six selected sites within the
Cache River Watershed collected in April 2016. Mean concentrations (ppb) and % relative
standard deviation (RSD) were calculated with the inclusion of outlying data points, indicated by
an *. Outlying data points were removed for calculations of adjusted mean and adjusted % RSD.
Adjusted means were also corrected to reflect appropriate significant figures based on % RSD.
Site
Replicate
SUCR
EASL
BGLA
BDDI
REFO
WIDI
1
1.916
52.723
5.674
8204
17.504
8.978
2
3.186
56.587
5.049
12.355
16.135
8.281
3
1.194
57.623
5.240
9.992
16.029
9.039
4
6.706*
102.150*
5.262
11.397
17.680
9.426
5
1.305
60.795
5.345
9.530
15.939
9.849
6
1.048
53.858
4.756
109.622*
15.550
8.588
7
3.733
56.849
5.023
11.277
14.097
9.967
8
2.100
53.554
5.650
9.949
13.905
9.972
9
1.723
55.903
4.632
10.581
15.632
9.711
10
3.174
46.301
4.076
12.098
15.542
12.430
11
1.478
12
0.876
mean
2.369
59.634
5.071
20.501
15.801
9.624
% RSD
69.639
25.857
9.582
152.870
7.381
11.921
Adj. mean
2
55
5
10
16
10
Adj. % RSD
49.203
7.381
9.582
12.526
7.381
11.921
175
The base rate of Pb in SUCR (unspiked) was calculated as the mean concentration in all
analyzed unspiked samples from SUCR from the April 2016 collection (Table 3.10), with the
exception of one sample (replicate 4) that was determined to be an outlier (Table 3.9). Overall
recovery was low, particularly when accounting for the Pb contributed by the unspiked
substrate. Recovery rates for spiked sediment samples (SSS) ranged from 7.70% to 16.99% for
samples from SUCR. Analysis of undigested spiking solutions (USS) showed measured
concentrations ranged from 89-98% of nominal concentrations. Analysis of digested spiking
solutions (DSS) showed recoveries of 84-102%, indicating that the digestion process was not
significantly affecting recoveries of Pb (Table 3.10).
176
Table 3.10. Percent of Pb recovered from spiked samples of sediment (SSS) collected in April 2016 from sampling site SUCR. Corrected
value is the measured concentration of Pb after subtracting the mean value of Pb from all unspiked samples of SUCR (19.76 ppb,
indicated by *). Percent recovery from undigested spiking solutions (USS) and digested spiking solutions (DSS) shown for comparison.
Neither USS nor DSS had any sediment added to samples.
Spike (ppb)
NA
100
200
300
400
500
Measured amount (ppb)
19.76*
36.75
35.16
60.35
76.14
81.83
Corrected(ppb)
0.00
16.99
15.40
40.59
56.38
62.07
SSS (%)
NA
16.99
7.7
13.53
14.10
12.41
177
USS (%)
NA
95.25
88.97
95.63
96.15
98.47
DSS (%)
NA
102.44
90.40
85.57
83.54
94.59
DISCUSSION
Effects of Land Alteration and Discharge on Pb Concentrations
The current, most widely accepted hypothesis states that Pb contamination in the Cache River
is due to agricultural runoff during times of the year when precipitation, and thus discharge, is
greatest (FTN Associates Ltd, 2012). This hypothesis would be supported by increases in mean
concentrations of Pb during times of increased discharge or in areas with increased agricultural
use. However, the MANOVA performed on data collected from August 2014 to July 2016
indicated no significant differences between discharge categories for total or sediment-bound
Pb and no significant difference between land alteration categories on total recoverable Pb.
Land alteration had a significant effect on sediment-bound Pb concentrations with a general
increase in sediment-bound Pb concentrations as alteration increases. Main channel sites
tended to be intermediate in concentration, reflecting that they receive cumulative inputs from
all upstream site types.
These results must be interpreted carefully as no sites sampled were devoid of agricultural
activity (and agricultural runoff). Furthermore, this model only examined mean concentrations
of Pb, not the frequency of detection and only included total recoverable and sediment-bound
Pb, not dissolved Pb (for reasons described in the methods section). Thus, the results of this
model alone cannot be used to support or refute the current leading hypothesis concerning the
source of dissolved Pb. However, this model is useful when discussing mean concentrations of
total recoverable Pb and sediment-bound Pb. Further discussion of the differences between
land alteration categories, discharge categories and specific sites can be found in the following
sections, which discuss matrix-specific results that either support or refute each of the proposed
hypotheses concerning the source of elevated dissolved Pb concentrations within the Cache
178
River Watershed and compare the overall concentrations of Pb found within this watershed to
those of nearby watersheds as well as global concentrations.
Spatial Assessment of Dissolved, Total Recoverable, and Sediment-bound Pb
All forms of Pb were relatively ubiquitous within the Cache River Watershed, though
frequency of detection varied by analyzed matrix. Dissolved, total recoverable, and sedimentbound Pb were detected above the PQL (when applicable) at every sampled site within the
Cache River Watershed. However, dissolved Pb was detected above the PQL in only 11.9% of all
collected samples (n = 604). Detection frequencies of total recoverable Pb (76.6%, n = 551) and
sediment-bound Pb (100%, n = 184) were much greater. This increased rate of detection of Pb
in a bound form indicates that Pb does not remain in the dissolved phase under typical
environmental conditions within the Cache River Watershed but instead is adsorbed to
suspended or bottom sediments, thus greatly reducing its potential to have adverse
toxicological effects on aquatic organisms (Meyer et al., 2007).
Although all sites had Pb values above the PQL at least once in the course of this study, mean
concentrations tended to be greatest in either most-altered sites (total recoverable Pb) or main
channel sites (sediment-bound Pb). Mean concentrations of dissolved Pb were similar across all
land alteration categories.
Source of Pb in Cache River Watershed Based on Spatial Assessment of Data
Of the hypotheses presented concerning the source of Pb within the Cache River Watershed,
at least two can be discounted. Given the lack of contact between Pb mining/smelting regions,
the lack of current smelting activities and the overall lack of a spatial pattern indicating greater
concentrations of dissolved Pb in areas of the Cache River Watershed closest to the nearest Pb
179
mining regions, it is unlikely that current or previous mining/smelting operations are the primary
source of Pb within this watershed. However, historical inputs of Pb could have potentially
contributed to current elevated concentrations through either surface runoff of contaminated
soils or resuspension of contaminated sediments. Metals contamination in soils and sediments
due to past anthropogenic activities can persist long after the activities have ceased
(Aleksander-Kwaterczak and Ciszewski, 2013; Sager and Kralik, 2012).
Given the ubiquitous distribution of elevated dissolved Pb concentrations in the Cache River
Watershed, an unidentified point source for Pb is not supported by these data. Because the
watersheds sampled were all headwater sub-watersheds, with no surface hydrologic
connectivity, multiple unidentified point sources would have to exist, with at least one in each
affected sub-watershed. This is extremely unlikely, thus, this hypothesis is not considered to be
a viable explanation of the source of Pb within the Cache River Watershed.
Another hypothesis for the source of the Pb contamination is that the Pb is historically found
within the sediments of the Cache River and is re-suspended during times of high discharge.
Although sediments are typically considered to be a sink for heavy metals, such as Pb, they can
also serve as a source. Chon et al. (2012) found that aquatic sediments in a river could serve as
a significant source of metals in overlying waters. This hypothesis would be supported by both a
correlation between sediment-bound Pb and dissolved Pb levels in sub-watersheds as well as an
increase in detections of dissolved Pb during times of high discharge. Although dissolved Pb
concentrations are significantly correlated with discharge rate (Spearmans rank correlation: S =
523560, p-value = 0.031, rho = 0.173), no significant correlation exists between dissolved and
sediment-bound Pb concentrations (Spearmans rank correlation, S = 595970, p-value = 0.472,
rho = 0.058). The positive correlation between discharge and concentrations alone does not
support sediment as the source of the Pb, as a positive correlation would also be expected if
180
increased concentrations are a result of surface runoff of soils containing Pb. Thus, although
resuspension of sediment could theoretically contribute to dissolved Pb concentrations, the
data collected in this study do not fully support this hypothesis, as no significant relationship
was seen between sediment-bound Pb concentrations and dissolved Pb concentrations.
The final hypothesis concerning the source of Pb within the watershed is that Pb
contamination is the result of runoff of Pb-containing soil from agricultural fields during months
when precipitation is greatest and ground cover is reduced. This hypothesis is supported by at
least two pieces of evidence. First, as shown above, dissolved Pb concentrations are positively
correlated with discharge rate at the time of sampling. Furthermore, the spatial assessment
shows that moderate to high risk sites tend to be located in the more agriculturally dominated
western side of the Cache River Watershed. Conversely, sub-watersheds located in the eastern,
less-agricultural portion of the Cache River Watershed tended to have low risk of dissolved Pb
contamination. An important consideration is that times of increased discharge are not
necessarily due to increased precipitation. When looking at samples collected at all sites for all
months of this study (August 2013 to July 2016) compared to the total precipitation measured
for 72 hours prior to sample collection, no relationship was noted. Increases in precipitation did
not correlate with mean dissolved Pb concentrations (Spearmans rank correlation, S = 8537, pvalue = 0.5668, rho = -0.0987) (Fig. 3.14).
181
12.00
9.00
8.00
10.00
8.00
6.00
5.00
6.00
4.00
4.00
3.00
72 hr precipitation (cm)
Mean dissolved Pb (ppb)
7.00
2.00
2.00
1.00
Jul-16
Jun-16
May-16
Apr-16
Mar-16
Feb-16
Jan-16
Dec-15
Oct-15
Nov-15
Sep-15
Aug-15
Jul-15
Jun-15
May-15
Apr-15
Mar-15
Jan-15
Feb-15
Dec-14
Oct-14
Nov-14
Sep-14
Aug-14
Jul-14
Jun-14
May-14
Apr-14
Mar-14
Jan-14
Feb-14
Dec-13
Oct-13
Nov-13
Sep-13
Aug-13
0.00
0.00
Figure 3.14. Comparison of mean ± SE concentrations of dissolved Pb in samples with dissolved Pb measured above the Practical
Quantitation Limit (PQL) for samples collected from the Cache River Watershed between August 2013 and July 2016 (gold bars) to mean
± SE precipitation (blue line) for all sites. Precipitation was determined as the sum of precipitation for 72 hours prior to sampling.
Precipitation data were obtained from closest available monitoring station to sampled sub-watershed (U.S. Climate Data, 2016).
182
Because discharge is a result of both precipitation and agricultural runoff, the significant
correlation measured between dissolved Pb and discharge could actually be due to irrigationinduced runoff, rather than precipitation-induced runoff. This presents an alternative
hypothesis to the source of dissolved Pb within the Cache River Watershed. Although the
overall source is still surface runoff from agricultural fields, the timing of the detection events
indicates that this runoff may be induced by either irrigation or drainage of fields, rather than by
precipitation. Typical rice cultivation requires draining of flooded fields prior to harvest (Shipp,
2002), and the Cache River Watershed is used heavily for rice cultivation (USDA-NASS, 2016),
particularly the sub-watersheds that had the greatest measured concentrations of dissolved Pb
(Fig. 3.15).
183
Figure 3.15. Cropland use during 2015 in the Cache River Watershed. White areas represent
areas not in use for crop production, including areas left fallow due to crop rotation, remaining
wetland areas and forested areas. Sampled sub-watersheds are outlined in black. Cropland data
obtained from United States Department of Agriculture National Agricultural Statistics Service
(USDA-NASS, 2016).
184
Drainage of rice fields could result in considerable sediment-containing runoff, even when
precipitation is low and surface runoff would be expected to be minimal. Vigiak et al. (2008)
found that sediment concentrations in outflows from rice fields in Laos were four times greater
than inflow concentrations. Smaller sediment particle sizes are more likely to be transported by
field drainage. Tagiri et al. (2009) reported that run-off from rice fields during summer months
supplied elevated amounts of clay to rivers whereas Slaets et al. (2016) found that outflows
from rice fields consist almost exclusively of silt and clay particles, sourced either from land
surfaces or from irrigation waters. Overall in soils and sediments, the smaller the particle size
(sand>silt>clay), the greater the concentration of Pb associated with that particle (Momani,
2006), indicating that surface runoff containing a greater proportion of smaller particles, such as
silt and clay, could be associated with increased concentrations of Pb.
Although sediment transport in field drainage is a likely contributor to dissolved Pb
concentrations in surface waters, water used for irrigation could also play a role. Irrigation
within the Cache River Watershed is primarily pumped groundwater from underlying aquifers.
Groundwater is generally considered clean, however, aquifers (particularly shallow aquifers) can
be contaminated with heavy metals, including Pb. Tahir-Nalbantcilar and Yavuz-Pinarkara
(2016) found that all groundwater samples collected near areas used for oil storage, agriculture
or livestock in Batman, Turkey had Pb concentrations exceeding maximum contaminant level
goals. Similarly, Wongsasuluk et al. (2014) found that shallow groundwater wells located in
agricultural areas of Thailand had mean Pb concentrations of 16.6 ppb, above the acceptable
groundwater limits.
Monitoring data available for groundwater from irrigation wells within the Cache River
Watershed indicates no detectable amounts of dissolved Pb (ADEQ, 2016c). However, only a
small portion of the Cache River Watershed has been monitored and the most recent
185
monitoring event was in 2013. Even if groundwater is clean, metals, including Pb, could be
leached from the pipes or pipe fittings used for pumping. Both the timing of the Pb detections
(during dry times of year when groundwater irrigation is prevalent) and the locations (in
agriculturally dominated areas of the watershed) suggest that irrigation activities should be
examined as a potential source of Pb, whether from the groundwater itself or from pipes used in
pumping/irrigation.
Based on the results of monitoring from this study and data available from the water quality
monitoring database maintained by ADEQ, the hypothesis that elevated Pb in waterways is a
result of surface runoff from agricultural sites is the best supported. Given the potential
influence of groundwater on surface runoff, both from direct runoff of irrigation or drainage of
flooded fields, further monitoring of each type of water is suggested to determine if they could
be contributing to the increased detections of dissolved Pb during times of the year with
relatively low precipitation.
Comparison of Cache River Watershed to Other Geographic Areas
A comparison of the mean concentrations of both dissolved and total recoverable Pb in the
Cache River Watershed and other waterways in northeastern Arkansas (USEPA, 2015) with
similar amounts of land devoted to row-crop activities (AWIS, 2016) shows that the Cache River
has generally greater mean concentrations of total recoverable Pb than comparable watersheds
but similar concentrations of dissolved Pb. Neither dissolved nor total recoverable Pb detection
mean concentrations increase/decrease as a function of agricultural land use (Table 3.11).
186
Table 3.11. Mean ± SE concentrations (ppb) of dissolved and total recoverable Pb for water samples collected within HUC8 watersheds
(main channel or tributaries) in northeastern Arkansas with at least 50% of land used for crop production. Sites with no SE associated
with mean concentration had only a single sample with measureable Pb. Sites with no mean concentration had no samples with
measureable Pb. Data for the Cache River Watershed are based on this present study, data for all other watersheds were obtained from
the USEPA STORET database (USEPA, 2015). Percent cropland values obtained from Arkansas Watershed Information System (AWIS,
2016).
Watershed
HUC8
% Cropland
Dissolved Pb (µg/L)
Total Recoverable Pb (µg/L)
Lower Arkansas River
08020401
51.1
1.23
2.32 ± 0.33
Lower White River
08020303
53.4
3.62 ± 1.06
2.25 ± 0.24
Upper Black River
11010007
54.1
2.32 ± 0.33
3.43 ± 0.65
Upper White River-Village
11010013
54.9
1.27 ± 0.13
3.41 ± 0.36
Cache River
08020302
66.6
2.57 ± 0.39
5.91 ± 0.26
L’Anguille River
08020205
71.0
1.65 ± 0.18
2.83 ± 0.17
Lower St. Francis River
08020203
72.2
2.81 ± 0.52
3.80 ± 0.31
Big River
08020304
76.1
1.70 ± 0.25
2.79 ± 0.41
Little River
08020204
80.1
NA
6.39 ± 1.16
187
A more broad-scale comparison between the Cache River Watershed and the remainder of
the Lower Mississippi River Watershed (LMRW, n =12424), composed of watersheds draining
portions of Arkansas, Missouri, Kentucky, Tennessee, Louisiana and Mississippi and Illinois,
indicates that mean concentrations of both dissolved and total recoverable Pb were lower in the
Cache River Watershed. Dissolved Pb within the Cache River had a mean concentration of 2.57
± 0.39 ppb Pb (LMRW = 4.22 ± 0.74 ppb Pb). Total recoverable Pb within the Cache River
Watershed had a mean concentration of 5.91 ± 0.26 ppb Pb (LMRW = 12.32 ± 0.63 ppb Pb).
Data for sediment-bound Pb concentrations in freshwater systems are limited. In Arkansas,
the only reported sediment-bound Pb concentrations occurred as a result of this present study.
Expanding the comparison to the LMRW returned just 32 samples, 17 of which were excluded
from analysis because they were collected immediately downstream from a known Pb smelting
site in the southeastern Missouri Pb mining district and had mean concentrations well above the
remainder of samples (1282.22 ± 618.43 mg/kg vs. 23.84 ± 7.46 mg/kg). Mean concentrations
within sediment in the Cache River Watershed were 10.61 ± 0.52, approximately half the mean
concentration reported for sediments in the LMRW. However, most sites reported within the
LMRW that were used for comparison (14 of 15) still fell within the Southeastern Missouri Pb
mining district, though not located directly downstream of smelting sites. These sites might
have been affected indirectly by Pb mining and smelting.
Sites located near smelting regions often have elevated concentrations of Pb due to
atmospheric deposition of metals released during the smelting process, even if located a
considerable distance away from any direct impacts. For example, Sturges and Barrie (1989)
traced Pb deposited in water and soils in central Ontario to a Pb smelter in northern Quebec,
nearly 500 km away. Historical activities can also explain current Pb concentrations in soils and
sediments. Ma et al. (2014) found that soils and sediments in a currently pristine watershed in
188
the northeastern United States had elevated Pb concentrations due to atmospheric deposition
of Pb produced during the early industrial revolution (1850s-1920s). Thus, the sediments used
for comparison might have greater levels of Pb due to mining/smelting activities, explaining the
difference between these sites and the Cache River Watershed.
Concentrations of Pb in surface waters and sediments can vary greatly, depending on specific
activities near sampled sites and season (Song et al., 2013). Typically, Pb in sediments is
greatest at sites impacted by industrial or urban activities (Raygoza-Viera et al., 2014; Coxon et
al., 2016; Tamim et al., 2016). Surface water concentrations of Pb are typically affected more
strongly by season than sediment concentrations. For example, Okweye and Golson-Garner
(2012) found that total recoverable Pb concentrations within the Tennessee River Basin varied
from undetectable to 123 ppb annually, with greatest metal concentrations occurring in winter
and spring months. Similarly, Chakraborty et al. (2009) found that concentrations of dissolved
Pb in coastal regions of India varied by season, with lowest concentrations measured in the premonsoon season and greatest concentrations measured during the monsoon season.
Globally, mean Pb concentrations tend to range from <1 to 207 ppb in surface waters and
from 6 to 76 mg/kg in sediments (reviewed by Hua et al., 2016). Comparatively, the Cache River
Watershed has relatively low mean concentrations of Pb in all matrices, indicating that the
overall environmental risk is quite low.
Comparison of Pb in the Cache River Watershed to Current and Proposed Assessment Criteria
Based on current assessment criteria, most sampled sites (16 of the 23 sampled sites) were in
exceedance during this study, indicating that dissolved Pb is ubiquitous in the Cache River
Watershed. This is in contrast to more recent versions of the state 303(d) list on which several
reaches of the Cache River and Bayou DeView (previously listed as impaired for dissolved Pb)
189
have been removed, indicating that they were now in compliance with assessment criteria
(ADEQ, 2106a). The data set in this study represents primarily sub-watersheds of the Cache
River whereas previous listing decisions have been made based on main channel stream
reaches, largely due to the time and effort required for more intensive sampling. Because these
main channel reaches receive cumulative inputs from numerous upstream sites, it may be more
difficult to detect discrete exceedance events as the greatly increased water volume of the
larger order stream might dilute dissolved Pb concentrations below the detection limit.
Furthermore, in this present study, only 5% of all samples were in exceedance, indicating how
difficult it is to detect discrete exceedance events, particularly if samples are not collected on a
regular basis, in all seasons or under all discharge conditions. Thus it is possible for smaller subwatersheds to exceed assessment criteria while main channel sites remain in compliance.
Exceedances of impairment criteria existed for one of two reasons. In 27.6% of samples,
exceedances were due primarily to elevated dissolved Pb levels (mean =7.36 ± 2.51 ppb) and
occurred when water hardness was elevated (> 60 mg/L CaCO3), considered to be moderately
hard or hard water (USGS, 2016b). In most samples (72.4%), concentrations of dissolved Pb
were much lower (mean = 2.43 ± 0.59 ppb) but samples were collected at either sites with low
annual hardness or at times of lower water hardness (≤ 60 mg/L CaCO3), considered to be soft
water (USGS, 2016b). Because current assessment criterion is hardness-dependent, sites that
might otherwise seem relatively un-impacted could exceed criterion much more often than sites
that seem to be more impacted. For example, when examining the 21 samples that had
impairments due to low water hardness, nearly half of the samples (47.6%) came from leastaltered sites and only 2.9% from most-altered sites. Conversely, in the sites that had
impairments due primarily to elevated Pb concentrations, 50% of the samples were from mostaltered sites and only 12.5% from least-altered sites.
190
Although no assessment criteria currently exist for total recoverable Pb or sediment-bound
Pb, criteria have been proposed for each (Buchmann, 2008). For total recoverable Pb, this
criterion is a hardness-based criterion and can be calculated using the following equation.
(
[ .
( (
.
In this study, 63.5% of all samples would exceed this proposed assessment criterion. Of these
potentially impaired samples, mean concentrations were significantly different among land
alteration categories (one-way ANOVA, F3, 121.74 = 8.536, p < 0.001) with most-altered sites
having significantly greater concentrations of total recoverable Pb than moderately-altered sites
(p = 0.001) and least-altered sites (p < 0.001) and marginally significantly greater concentrations
than main channel sites (p = 0.049).
This difference is easily explained by the different amounts of suspended sediment found at
these sites. Because total recoverable Pb is comprised of both dissolved Pb and particulate Pb
(attached to suspended sediments), a site with greater amounts of suspended sediment may
also have greater amounts of particulate Pb, thus increasing the amount of total recoverable Pb.
A Spearman’s rank correlation indicated that total recoverable Pb and total suspended sediment
(TSS) were strongly correlated (S = 13509000, p < 0.001, rho = 0.513) over the course of
sampling.
No assessment criterion is currently in effect for sediment-bound Pb, although screening
limits have been proposed (Buchmann, 2008) based on a threshold effects limit (TEL) and a
predicted effects limit (PEL). A threshold effects limit represents the concentration of Pb in
sediment expected to produce an adverse effect in aquatic organisms in 10% of the tested
population whereas the PEL is the concentration expected to produce an adverse effect in 50%
of the tested population. In freshwater systems, the TEL is proposed to be 35.0 mg/kg of
sediment-bound Pb whereas the PEL is 91.3 mg/kg. Only seven (3.8%) of the samples analyzed
191
had concentrations exceeding the TEL and only one (0.05%) of those analyzed exceeded the PEL.
These samples came from five different sites, one moderately (LCRD) altered, two most altered
(EASL, CCDI) and two main channel sites (CRPA, REFO). Spatially, the sites are quite disparate,
although two (EASL and LCRD) are located near each other in the upper portion of the Cache
River Watershed. The PEL was only exceeded in one sample from a single site, CRPA. Overall,
excessive concentrations of sediment-bound Pb were relatively low, particularly when
compared to the sediment samples analyzed throughout the LMRW (USEPA, 2015). Just 3.8% of
analyzed samples collected within the Cache River Watershed exceeded the TEL whereas 13.3%
of all samples collected within the Lower Mississippi River Watershed exceeded the TEL (USEPA,
2015). It should be noted that most of these sediment samples were collected from waterways
within the southeastern Missouri Pb mining district, indicating that they might be directly or
indirectly affected by Pb mining/smelting activities and not be comparable to the Cache River
Watershed.
When comparing mean concentrations of sediment-bound Pb, least-altered sites had
significantly lower concentrations than in most-altered or main-channel sites. Considerable
variation in main channel site data existed but this is to be expected as main channel sites serve
as depositional sites for sediments removed from upstream sub-watersheds. During times of
high discharge, sediments are often transported from smaller headwater sub-watersheds to
downstream stream reaches. As waters enter larger channels and accompanying floodplains,
velocity decreases, causing the sediments to fall out of the water column and be deposited in
bottom sediments of these higher order streams (Beach, 1994; Stout et al., 2014). Thus,
considerable variation can exist in these depositional sites, particularly if hydrologic conditions
such as discharge rate have varied greatly prior to sample collection. Flood events tend to be
192
responsible for considerable sediment transport within watersheds (Tesi et al., 2013) and also
transport of attached contaminants, including heavy metals such as Pb (Lintern et al., 2016).
The sole site that exceeded the PEL (CRPA) is both a main channel site, meaning it would
receive cumulative inputs from all upstream sites, and located near a very busy highway, with
considerable traffic discharge, including many heavy commercial vehicles. The depth of water at
site CRPA required sediment sampling to be performed with the Ponar ™ petite sampler, which
was lowered from a bridge above the waterway. By necessity, this means that sediment
samples were taken in close proximity to the bridge and could be influenced by trafficassociated runoff. Elevated Pb concentrations in both soils and sediments near roadways have
been largely attributed to traffic, with Pb concentrations positively correlated with proximity to
the road (Leem et al., 2005; Bakirdere and Yaman, 2008; Rosenfellner et al., 2009).
The strong correlation between TSS and total recoverable Pb indicates that Pb within the
Cache River Watershed could potentially be mitigated through implementation of BMPs that
help to reduce sediment loss from agricultural areas. This in turn could reduce inputs of Pb
associated with surface sediment to waterways. Implementation of BMPs that slow and filter
surface runoff should prove beneficial in mitigating inputs of sediment to waterways, and thus
also mitigating total recoverable Pb concentrations resulting from these inputs. BMPs such as
irrigation management, riparian buffers and filter strips have been shown to significantly reduce
suspended sediment loads in surface runoff (Lee et al., 2003; Miller et al., 2015). Installation of
such BMPs can be effective at reducing agricultural runoff and decreasing surface sediment loss
in Bayou DeView, the primary tributary of the Cache River. This reduction in sedimentcontaining runoff also resulted in a decreased in measured concentrations of dissolved Pb within
receiving waterways (USEPA, 2014). Restoration or enhancement of natural wetlands around
193
waterways would also be useful in reducing flow velocities in stream channels, thus allowing for
deposition of suspended sediment in riverbeds, thus reducing Pb loads within the water column.
Effect of Sediment Composition on Sediment-Bound Pb Concentrations
Many components of sediment can affect the amount of Pb that is bioavailable. Typically,
sediment is considered to be a sink for heavy metals, including Pb, but in aquatic systems,
sediment can also serve as a source (Chon et al., 2012). The particular composition of the
sediment could affect its role as both a source and a sink, as different soil particles have varying
affinities for Pb. Although the relationship between sediment score and sediment-bound Pb
concentrations were significant in this watershed, this is only an indication of the total amount
of Pb that could potentially become environmentally available, not necessarily the amount that
will become available. Sediment-bound Pb typically partitions into the dissolved (bioavailable)
form under either acidic water conditions or in instances of elevated discharge. There is little
evidence of acidic water conditions in the Cache River Watershed, thus it is much more likely
that disturbances of the bottom sediment associated with discharge would be the primary
contributor of any such partitioning in this watershed. Dissolved Pb concentrations were
significantly correlated with discharge in this present study, but no correlation was observed
between sediment-bound Pb and dissolved Pb. Neill et al. (2004) also found no correlation
between sediment-bound Pb and dissolved Pb in a surface waterway of an agricultural area in
Taney County, Missouri, suggesting that sediments may not be contributing to dissolved Pb.
An interesting outcome of plotting sediment-bound Pb concentrations relative to sediment
score is that samples that are outliers are easily visible. Four samples had sediment-bound Pb
concentrations that were much greater than predicted based on their sediment composition.
Two of the four outlying samples came from site EASL, suggesting either that more Pb is stored
194
in sediments at this site or that characteristics of the sediment at this site make it more likely
that Pb will be released from this sediment source and become environmentally available.
Another interesting outcome of this analysis was a sample from site LCRD with much greater
sediment-bound Pb concentrations in June of 2016 than in all other samples collected from this
site (79.733 mg/kg vs. 11.827 ± 3.575 mg/kg for all remaining samples). The following month
(July 2016) this site had very elevated dissolved and total recoverable Pb concentrations relative
to the average concentration from all other months analyzed (dissolved Pb: 23.169 ppb vs 0.249
± 0.051 ppb; total recoverable Pb: 17.165 ppb vs. 4.122 ± 0.914 ppb), suggesting that some
discrete event had occurred that introduced high amounts of Pb into this sub-watershed.
Although the sediment samples analyzed help in determining relative spatial patterns of Pb
concentrations, the sediment digestion method employed in this study (USEPA 3050b) is not
considered to be a complete digestion, meaning that not all elements (including Pb) would be
released into solution as a result of the digestion. Thus, the total amount of Pb within the
digested sediments would not necessarily be released into the dissolved phase and measured
during instrumental analysis. This method is considered suitable because it should release all Pb
that could become environmentally available under typical conditions (USEPA, 1996). A spiking
experiment using a sand-dominated sediment (>99% sand) showed surprisingly low recovery
rates (7-17% recovery of Pb), despite very high spiking concentrations (100-500 ppb Pb). This
was unexpected as sand is considered the sediment particle least likely to adsorb Pb, thus
relatively high recovery rates were predicted for the spiked sediment. This indicates that either
the low amounts of silt and clay (< 1%) present in this sediment were able to adsorb and retain
large amounts of Pb or that sand is able to sequester Pb within its crystalline structure and thus
prevent it from being released during the digestion process.
195
Although the role of sand in sediments of the Cache River Watershed is unclear based on
these data, it suggests that sand plays a larger role than predicted in determining the
capabilities of sediment as both a sink and a potential source of dissolved Pb within this
watershed. Based on the results of this study, it seems that sediment composition within the
Cache River Watershed could affect the potential of sediment as a sink for Pb, with all particle
sizes contributing to overall sequestration of Pb within sediments, but not necessarily as a
source of dissolved Pb, as concentrations of sediment-bound Pb did not correlate with
concentrations of dissolved Pb. Under measured environmental conditions within the Cache
River Watershed, it is unlikely that sediment-bound Pb will partition into the dissolved phase,
becoming bioavailable and potentially having adverse effects on aquatic organisms, thus
sediment is probably not a source of dissolved Pb within the Cache River Watershed.
Future Experiments
Although the exact role of sediment as a sink in the Cache River Watershed is unclear, based
on these results, an interesting future experiment could include repeating the spike-recovery
experiments on pure sand samples. If no clay or silt are present and recoveries are still low, this
would suggest that the sand is capable of storing Pb in a form that cannot be digested and
would therefore be unlikely to become available under typical environmental conditions. A
complete digestion (Method 3052 (USEPA, 1996)) of the spiked sediments would indicate if the
Pb was being sequestered within the sand. Conversely, if the recovery rate were much greater,
this would indicate that even small amounts of silt/clay in sediment can effectively bind Pb in
such a way that it cannot be released via digestion. Regardless of which particle is contributing
the most to Pb storage within the sediments, the overall result is that sediments in the Cache
River seem likely to retain stored Pb, even under extremely harsh conditions.
196
A corollary of this line of thought would be to spike numerous sediments of the Cache River
with varying concentrations of sand, silt and clay to determine the relative recovery rate of
sediments with varied composition. If recoveries are generally low, this would indicate that
sediment re-suspension is most likely not a significant source of Pb within the Cache River
Watershed and would complement the lack of a correlation between sediment-bound Pb
concentrations and dissolved Pb concentrations.
The particle size analysis performed was originally intended to serve as a screening tool based
on relative concentrations of silt and clay in sediments, as these particles tend to be associated
with greater sediment-Pb concentrations. Because proportions of these two particle sizes are
relative to the proportion of sand, an even simpler screening process could potentially be used,
in which the proportion of sand is measured and the proportion of silt + clay determined based
on the relative amount of sand. In terms of ease of screening, this would actually be a simpler
process, as a high percentage of sand in sediments is easily detectable with the naked eye or
through simple filtration with a sediment sieve. A non-parametric Spearman’s rank correlation
test showed a strong correlation between percentage of sand and sediment-bound Pb
concentrations (S = 206440, rho = -0.591, p < 0.001).
Conclusion
Overall, Pb in all sampled matrices (dissolved, total, sediment) is ubiquitous within the Cache
River Watershed. Although several sub-watersheds had samples exceeding assessment
criterion, most were either quite low in concentration or had only sporadic exceedances.
Discrete spikes in concentrations of Pb did occur rarely, but mean concentrations over the
course of the study were low, particularly when compared to global concentrations.
197
The lack of spatial and temporal patterns in the detection of dissolved Pb fail to support many
hypotheses concerning the source of the Pb. The most strongly supported hypothesis indicates
that increased Pb concentrations are a result of runoff from agricultural activity, with mostaltered sites tending to have the highest concentrations of dissolved Pb. The correlation
between dissolved Pb concentrations and discharge, but not with precipitation, indicates that
water contributed by runoff of irrigation groundwater or field drainage might be influencing Pb
inputs in this watershed. Intermediate amounts of discharge, whether from precipitation or
runoff of irrigation waters, could be sufficient to mobilize Pb from the soils without providing an
increase in dilution capabilities of receiving waters, resulting in greater detected concentrations
or more frequent detections under these discharge conditions. Broder and Beister (2015) found
that although discharge explained much of the Pb transport from soils, fluctuations in the water
table also influenced the amount of Pb exported from soils. A similar effect has been noted with
organophosphate pesticides. Nasrabadi et al. (2011) found that in low-precipitation times of
year, greater relative concentrations of organophosphate pesticides were measured in a
receiving river, compared to high-precipitation times of year. This was attributed to both an
increased wash out of pesticides as a result of rainfall following a dry season in which pesticides
were applied and a decreased dilution capacity in receiving waterways, due to overall lower
precipitation.
A more thorough sampling of Pb concentrations in both irrigation and field drainage water, as
well as an analysis of particle sizes in suspended sediments in drainage water, would be useful in
clarifying the contribution of these potential sources of Pb to the overall concentrations within
the Cache River Watershed, particularly in high risk sub-watersheds identified in this study.
Sediments within the Cache River Watershed are likely serving a greater role as a sink of Pb than
198
as a source of Pb, with the sand fraction playing a greater role in adsorption and sequestration
of Pb than originally anticipated.
The generally low concentrations of Pb detected and the low detection frequencies indicate
that although Pb is present within the Cache River Watershed, it is not an issue of immediate
environmental concern. Based on the water hardness at the time of sampling, aquatic
organisms at most sites should be protected from Pb toxicity due to competition between Pb
and hardness-associated cations (i.e. Ca+2, Mg+) biotic ligands. However, hardness is likely not
the only factor affecting the toxicity of Pb to aquatic organisms. A further examination of the
relationship between environmentally relevant levels of Pb within the Cache River Watershed,
water hardness, and possible toxic effects to aquatic organisms is examined in Chapter 4 of this
dissertation.
199
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CHAPTER 4: TOXICITY OF LEAD (Pb) TO FATHEAD MINNOWS (PIMEPHALES PROMELAS) AND
WATER FLEAS (CERIODAPHNIA DUBIA) USING LETHALITY, GROWTH, REPRODUCTION AND
BEHAVIOR AS ENDPOINTS
ABSTRACT
The Cache River has consistently been listed as impaired for aquatic life due to lead (Pb)
contamination, with concentrations of dissolved Pb greater than the criterion chronic
concentration (CCC) regularly measured. This criterion is hardness-dependent, but does not
take into account other factors that could affect Pb toxicity in aquatic organisms. In this study,
acute (48-hour) and chronic (7-day) toxicity tests were performed using both C. dubia and P.
promelas in both laboratory-prepared waters (MH) and ambient waters (low and high hardness)
collected from the Cache River (CR-low, CR-high) to better understand the potential for Pb
toxicity within natural waters of this watershed. All tested waters were spiked with lead nitrate
(Pb(NO3)2). A behavioral test was also performed with P. promelas to determine if behavioral
responses could be altered as a result of exposure to sublethal Pb concentrations. Results of
chronic toxicity tests for P. promelas indicate that both lethal (EC50) and sublethal (IC25)
endpoints in all tested waters (CR-low, MH) were significantly greater than the established CCC
for dissolved Pb and relevant environmental concentrations of dissolved Pb measured within
the Cache River Watershed. The sub-lethal endpoint for reproduction in C. dubia under low
hardness conditions was greater than the established CCC but lower than environmentally
relevant concentrations of Pb. Both acute and chronic toxicity tests indicated that hardness had
210
a much greater protective effect against Pb toxicity in P. promelas than in C. dubia suggesting
that the different physiologies of these organisms affect their sensitivity to Pb. Behavioral tests
indicated that predator-avoidance behaviors in P. promelas were adversely affected by exposure
to dissolved Pb, even at concentrations well below the endpoints for other sub-lethal effects.
Overall, measured toxic endpoints for all waters tested and both test organisms were generally
greater than environmentally relevant concentrations (with the exception of reproduction in C.
dubia under low hardness conditions), indicating a low potential for Pb toxicity to aquatic
organisms in the Cache River Watershed.
INTRODUCTION
The Cache River has been consistently listed as impaired for aquatic life due to lead (Pb)
contamination by the Arkansas Department of Environmental Quality (ADEQ, 2008; 2010; 2012;
2014), though this listing is fluid and particular stream reaches have been delisted on more
recent drafts of the document (ADEQ, 2014, 2016a) of the 303(d) list. The assessment criterion
for Pb is based solely on water hardness and does not take into account other factors that can
mitigate toxic effects (e.g., pH, dissolved organic carbon (DOC)). Ambient water is likely to vary
greatly throughout the year or throughout the watershed, meaning that assessment criterion
might be under- or over-protective. Understanding the toxicological relevance of measured
concentrations of Pb should help to determine if environmentally relevant levels of dissolved Pb
within the Cache River Watershed are of immediate concern.
Detection frequencies and measured concentrations of Pb within the Cache River Watershed
for samples collected between August 2013 and July 2016 (see Chapter 3) tend to be relatively
low, indicating that the risk presented by dissolved Pb is quite low. When detected, dissolved
Pb is typically measured below 10 µg/L, a concentration not acutely toxic to the most commonly
used test organisms. For example, reported 48-hr acute LC50s (lethal concentration for 50% of
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the population) for Ceriodaphnia dubia range from 26.4 µg/L to >2700 µg/L (Schubauer-Berigan
et al., 1993; Diamond et al., 1997), whereas reported 48-hr acute LC50s for Pimephales promelas
range from 113 µg/L to 2913 µg/L (Diamond et al., 1997). Acutely toxic levels are generally
based on the occurrence of a discrete event, such as a chemical spill or a pulse event. For
example, when a dam breached at the Gold King Mine in Colorado in August 2015, a plume of
contaminated mine water was released into the nearby Animus River. Dissolved Pb levels
increased by 30x above typical levels in a three-hour period whereas total recoverable Pb
increased by as much as 3000x (USEPA, 2016a). Given the lack of mining activity in the Cache
River Watershed, a discrete event such as this is unlikely. However, even sub-lethal levels of
contaminants can still result in effects on growth, behavior and reproduction, which in turn
influences populations of resident aquatic organisms.
Results of Pb toxicity tests using non-lethal endpoints are somewhat underreported in
published literature, particularly in P. promelas. A search of the United States EPA Ecotox
database for Pb toxicity in P. promelas returned records for 93 reported endpoints, from 19
studies. Of these 93 reported endpoints, 75 represented a lethal endpoint (mortality) (USEPA,
2016b). Only eight of the reported endpoints were behavioral (changes in feeding or
locomotion), eight used overall growth as an endpoint and two endpoints were based on
reproduction (USEPA, 2016b). Sub-lethal endpoints are somewhat better reported for C. dubia.
A similar search of the Ecotox database for Pb toxicity in C. dubia returned records for 61
reported endpoints from 10 studies, 33 of which used mortality as an endpoint (USEPA, 2016b).
Reproduction was used as an endpoint in 27 of these studies whereas a behavioral endpoint was
reported only once (USEPA, 2016b). Expanding this search to all daphnids returned 156
endpoints from 40 studies, 108 of which used mortality as an endpoint, 46 of which used
reproduction as an endpoint and just two of which used a behavioral endpoint. Although this
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database is not necessarily a comprehensive list of all published endpoints, these results
indicate that sub-lethal endpoints are not commonly measured (Fig. 4.1).
213
A
C
B
Lethal
Sublethal
Figure 4.1. Relative amounts of lethal and sub-lethal endpoints reported for A) P. promelas (n = 93) B) C. dubia (n = 61) and C) All
Daphnia (n = 156). Endpoint data obtained from EPA Ecotox database (USEPA, 2016b).
214
Although reported less often as a toxic endpoint for Pb, from an ecological standpoint,
indirect lethality can have significant results. Organisms that exhibit reduced growth due to
contamination can suffer indirect effects that result in overall lower survival. For example,
Weiss et al. (2011) found that killifish living in a contaminated estuary in an industrialized area
of New Jersey were smaller than those in relatively uncontaminated environments. These
smaller killifish tended to be poor predators and have poor predator avoidance behavior,
resulting in an overall population that was smaller and less abundant than in areas with low
contamination. Smaller organisms can also be more susceptible to environmental stressors such
as temperature. Krams et al. (2011) found that smaller water striders (Aquarius najas) were less
likely to survive overwintering than their larger counterparts. From an ecological perspective, a
decrease in overall size can easily affect survival, both of an individual and of a population as
whole, as a result of reduced reproduction and increased mortality.
Not all indirect effects are due to easily identified physical or physiological alterations.
Alterations in locomotion or predator avoidance behaviors have also been reported in a variety
of aquatic organisms. Amphipods exposed to sublethal levels of cadmium, a non-essential
heavy metal, demonstrate alterations to many normal behaviors including feeding, locomotion,
and predator avoidance (Roast et al., 2001). Sornom et al. (2012), found that Dikerogammarus
villosus exposed to sublethal levels of cadmium for 24 hours exhibited a decrease in
antipredator behaviors, such as seeking refuge in the presence of predators. Similar reductions
in anti-predator behaviors have been reported in a variety of aquatic organisms, including fish,
amphibians, and aquatic snails in response to a variety of environmental stressors including
pesticides and heavy metals (Sullivan et al., 1978; Lefcort et al., 1998; Lefcort et al., 2000; Scott
et al., 2003).
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At least two heavy metals, cadmium and copper, can interfere with anti-predator behaviors in
P. promelas, most likely through inhibition of the olfactory system (Scott et al., 2003; McIntyre
et al., 2012). Fish exposed to sublethal levels of these metals did not produce anti-predator
responses when exposed to the Schreckstoff chemical (Scott et al., 2003; McIntyre et al., 2012),
a naturally produced substance known to cause anti-predator behavioral responses including
(but not limited to), increased sporadic movement, seeking shelter, increased shoaling behavior
and/or freezing/sinking to substrate (Lawrence and Smith, 1989; Krause, 1993; Mathis and
Smith, 1993). This chemical, commonly called the alarm substance, is released when a fish
incurs mechanical damage, for example when it is caught by a predator. The chemical is
released from specialized epidermal cells called ‘club cells’ and cannot be released voluntarily.
The released chemical can be detected by other fish in the area, warning them of the potential
danger and eliciting anti-predator behaviors, including a cessation of movement/sinking to
substrate or actively seeking shelter, both of which decrease their chance of predation. Predator
avoidance responses to the alarm substance are well reported in the superorder Ostariophysi,
which includes approximately 64% of all freshwater fishes (Nelson, 1994). Responses have also
recently been observed in other fish groups, including family Eleotridae in superorder
Acanthopterygii (Kristensen and Closs, 2004). This response has been observed in P. promelas,
which belongs to the family Cyprinidae in the superorder Ostariophysi (von Frisch, 1942).
In this study, standard EPA acute and chronic toxicity tests (USEPA 2002a; 2002b) were
performed using water spiked with lead nitrate (Pb(NO3)2). The objective was to determine the
lethal and sub-lethal endpoints to Pb in two types of water, synthetic moderately hard
laboratory water (in which hardness should be the only toxicity mitigating factor) and ambient
water collected from the Cache River. Tests were performed on two standard test organisms, C.
dubia and P. promelas, with C. dubia serving as the model invertebrate species and P. promelas
216
as the model vertebrate species. These organisms are well established as model organisms for
toxicity testing (USEPA, 2002a; 2002b) and are widespread throughout Arkansas and North
America (Nico et al., 2016; Robison, 2005).
Additionally, behavioral tests were performed to determine if sub-lethal concentrations of Pb
have a negative effect on predator-avoidance behaviors in P. promelas. Although it is unknown
what effect waterborne Pb could have on behavior in P. promelas, it seems probable (based on
other heavy metals) that it could interfere with anti-predator behaviors, thus potentially having
a sub-lethal effect on fish, that could result in indirect mortality. No standard toxicological test
using anti-predator behavior as an endpoint currently exists. Thus, the objectives of this part of
the study were twofold.
The first objective was to determine which (if any) behavioral responses could be reliably
elicited by the alarm substance in P. promelas. The range of anti-predator behavioral responses
observed in P. promelas is extensive (see above). A reliable toxicity test requires behaviors that
can be easily observed and measured, even by a relatively untrained individual, thus the need to
identify these behaviors. The results of this portion of the study would be important in
identifying whether or not a reliable behavioral test based on anti-predator responses could be
developed for P. promelas. Secondly, using the behaviors identified in part one, a pilot study
was carried out to identify the effect of Pb exposure on predator-avoidance in P. promelas to
determine if a sub-lethal behavioral effect could be observed/measured.
Hardness was measured on all water used for testing, as water hardness can provide a
protective effect against toxicity due to heavy metals (Erickson, 2013) because of competition
for essential binding sites between hardness-associated ions and metals (Ebrahimpour et al.,
2010). Hardness can vary greatly within the Cache River throughout the year, largely due to
environmental conditions and land use. During summer and autumn months, flow rates tend to
217
be quite low in the Cache River due to decreased precipitation. At this time of year, water
inputs to the Cache River are more likely to be the result of groundwater pumped from the
underlying alluvial aquifer for agricultural irrigation. This groundwater tends to be much harder
than surface water or rainwater (Morris, 1987). Consequently, surface waters in agriculturally
dominated sub-watersheds tend to have increased hardness levels during production season
(ranging from 180-220 mg/L CaCO3, see Chapter 2). During winter and spring months,
precipitation in the region increases greatly and the primary source of water for the watershed
is rainwater or snowmelt. Waterways tend to have much greater discharges and much lower
hardness (20-60 mg/L CaCO3). Non-agricultural sub-watersheds (no groundwater irrigation)
experience very little variation in hardness over the course of the year (Fig. 4.2). This variation
in water hardness indicates the importance of measuring toxicity using ambient water with
varying hardness, to more appropriately represent the environmental conditions under which
organisms would be exposed to Pb in the environment.
218
250
Hardness (mg/L CaCO3)
200
150
100
50
0
Non-Agriculture
Agriculture
Dec-Jun
Jul-Nov
Figure 4.2. Mean (±S.E.) hardness for low flow (July-November) and high flow (December-June) seasons of the year for both nonagricultural sub-watersheds and agriculturally dominated, sub-watersheds of the Cache River, Arkansas for samples collected between
May 2014 and July 2016.
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MATERIALS AND METHODS
Acute Toxicity Tests
Acute toxicity tests were performed on three types of water. Initial tests were performed
using synthetically prepared, moderately-hard water (MH). Moderately-hard water is defined as
having a hardness of 80-100 mg/L CaCO3 (USEPA, 2002a). This hardness is similar to the mean
annual hardness of waterways in the Cache River Watershed. Because this water has no other
constituents besides salts added to achieve the desired water hardness (USEPA, 2002a), it
represents water in which hardness should be the only mitigating factor. Using this water would
indicate if the LC50s obtained were solely a result of water hardness or if other natural
constituents were potentially affecting Pb toxicity. Additional acute toxicity tests were
performed using ambient water from the Cache River, collected under the two predominant
hardness conditions, low hardness (CR-low, hardness = 40 mg/L CaCO3) and high hardness (CRhigh, hardness = 217 mg/L CaCO3).
Acute toxicity tests were performed in triplicate for each type of water (MH, CR-low, CR-high),
following standard EPA testing guidelines (USEPA, 2002a). Samples were spiked with (Pb(NO3)2)
using a series of concentrations that allowed for at least 90% survival in control water and 0%
survival at the highest tested concentration. The lethal concentration of Pb for 50% of the
tested population (LC50) was calculated for both C. dubia and P. promelas. Furthermore, the
allowable limit of Pb based on water hardness was calculated for each test, based on the
criterion maximum concentration (CMC) established by the US EPA (USEPA, 2016c).
( [ .
( (
. 4 ]
) * (1.46203-(ln(hardness) * 0.145712))
The CMC represents the highest concentration of a chemical in water that aquatic organisms
can be exposed to acutely without causing an adverse effect. This formula accounts only for the
220
protective effect provided by water hardness and does not account for any other mitigating
water factors.
Chronic Toxicity Tests
Chronic toxicity tests were performed on two types of water, MH and CR-low. CR-low was
tested because water at this hardness would lack much of the protection from Pb provided by
competing cations responsible for hardness. Thus, sub-lethal effects would be most likely to be
observed under these conditions. The dilution range for chronic tests was chosen based on the
results from acute tests performed with water of a similar hardness and was designed to result
in at least 80% survival in control organisms and 0% survival in the highest tested dilution.
Chronic toxicity tests were performed in triplicate for each type of water, following standard
EPA testing guidelines (USEPA, 2002b). Two types of endpoints were analyzed for these tests,
lethal and sub-lethal. Lethal endpoints were calculated as the concentration of Pb required to
effectively kill or immobilize 50% of the tested population (EC50) whereas sublethal endpoints
were calculated as the concentration of Pb required to inhibit either reproduction (C. dubia) or
growth (P. promelas) in 25% of the tested population (IC25). All statistical endpoints were
calculated using ToxCalc™ (1996, v.5.0.20, McKineyville, CA). The allowable limit for each type
of water, based on hardness, was calculated according to the assessment criterion used by the
State of Arkansas (ADEQ, 2016b), that represents the criterion continuous concentration (CCC)
established by the US EPA. (USEPA, 2016c). The CCC is the highest concentration of a chemical
in water that aquatic organisms can be exposed to indefinitely without resulting in an adverse
effect.
[ .
( (
.
]
) * (1.46203-(ln(hardness) * 0.145712))
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Behavioral Toxicity Tests
Fish used for behavioral toxicity testing were obtained from laboratory-reared cultures
available at the Ecotoxicology Research Facility at Arkansas State University. A search of the
literature indicates that most behavioral tests with P. promelas to date have been carried out
with fish obtained from natural populations rather than laboratory-reared cultures (Mathis and
Smith, 1993; Chivers and Smith, 1994; Brown et al., 1995; Chivers and Smith 1995a; Chivers and
Smith, 1995b; Brown et al., 2001; Kusch et al., 2004; Ferrari et al., 2005 but for an exception, see
Carreau-Green et al., 2008). Fish from laboratory-reared cultures would be expected to be
relatively predator-naïve and might not react as strongly as fish that had been exposed to
predators during early life stages (Chivers and Smith, 1994). However, an anti-predatory
response has been consistently observed in predator-naïve fish from both natural and
laboratory cultured populations (Mathis and Smith, 1993; Brown et al., 1995; Chivers and Smith,
1995b; Carreau-Green et al., 2008).
Simple conditioning with a visual, chemical or tactile stimulus paired with the alarm substance
is typically enough to ensure fish respond consistently to the presence of a predator (indicated
by the visual, chemical or tactile stimulus), even when the alarm substance is no longer present
(Chivers and Smith, 1994; Ferrari et al., 2005). To ensure that the predator-naïve fish used in
this study associated the alarm substance with a threat, fish were first conditioned to the alarm
substance by exposing fish to the substance along with simultaneous visual/tactile stimulation
(dip net moved through water). All behavioral trials were conducted using only the alarm
substance as a ‘predator’ stimulus.
The alarm substance (AS) was obtained according to the method of Chivers and Smith (1994).
Adult female fish were sacrificed with a blow to the head. Females only were used to obtain skin
extract, since adult breeding males often do not produce this substance (Smith, 1973). Skin
222
fillets were removed from each side of the fish and rinsed with MilliQ water, then placed in 50
mL of chilled MilliQ water. The skin-water mixture was homogenized, and then vacuum-filtered
through a glass microfiber filter. The filtrate was diluted to a final volume of 400 mL with MilliQ,
pipetted into 5-mL containers and frozen until used.
Fish used for behavioral testing were kept in a 30-gal (113.5 L) tank with an airstone, and were
fed flake food (Tetramin™) ad libitum. To account for any differences in response due to sex,
only female fish were used for behavioral testing. Conditioned fish were held in tanks for at
least 48 hours post-conditioning before being moved to a testing tank. Fish were moved into
small transfer tanks using a dip net. This transfer tank was placed into the test tank and fish
were allowed to swim out independently, reducing any unnecessary handling.
Behavioral trials took place in a 10-gal (37.8 L) test tank with a single airstone. The test tank
was covered on three sides as well as the top and bottom to reduce the possibility of
unintended visual stimulation. A camera was placed facing the uncovered side of the test
aquarium. A pre-emplaced section of tubing was used to deliver either the alarm substance (AS)
or control (deionized water(DI)) (Fig. 4.3). This tubing resulted in the stimulus being released
below the surface of the water in the tank, further reducing visual or tactile stimulation
associated with stimulus delivery. To measure changes in area of tank occupied, the test tank
was divided into six zones horizontally and three zones from top to bottom, with a total of 18
possible zones.
223
~60 cm
~40 cm
~30 cm
~45 cm
Camera
Figure 4.3 Behavioral test setup for fish trials measuring the effect of exposure to dissolved Pb on predator-avoidance behaviors in P.
promelas, indicating relative position of camera, tank division and emplaced stimulus delivery line.
224
Groups of five fish were placed into test tanks filled with dechlorinated, Jonesboro, AR
municipal tap water and allowed to acclimatize for at least four hours prior to testing. Between
each test, water in the test aquarium was completely replaced to remove any previously added
alarm substance used in testing. The stimulus delivery line was also flushed with DI water
between each trial; to ensure that all previously used substances were completely removed.
Preliminary testing indicated that the most reliable responses to the alarm substance included
altered shoaling/schooling behavior and reduced movement, which are both typical
antipredator responses observed in P. promelas (Chivers and Smith, 1998; Ferrari et al., 2005).
Fish swimming behavior was recorded for 10 minutes before and after stimulus addition.
Recordings were analyzed post experiment to determine if fish behavior prior to stimulus
addition (AS or DI water) differed from behavior post-stimulus. Strongest behavioral responses
typically occurred immediately after stimulation and gradually tapered off, thus video analysis of
behavioral responses was limited to three minutes immediately preceding and following
stimulation with either DI or AS. The results of this behavioral trial (no Pb exposure) were used
to quantify the typical behavioral responses to DI water and AS.
Shoaling behavior was analyzed according to a shoaling index in which proximity of fish to
each other was used as quantification (Fig. 4.4). A higher shoaling index score indicated
increased schooling behavior. A shoaling index score was assigned based on the fish position at
the beginning of each 10-s interval of video analysis. Total zones of the tank occupied were
counted during each 10-s interval of video analysis. Thus for each three-minute period of
analysis, approximately 18 shoaling index scores and 18 total zone counts were performed. The
shoaling index score was converted into a standardized score based on the number of fish alive
in the tank. This was necessary as in a few instances individual fish within a group died during
225
the course of behavioral trials. The standardized shoaling score accounted for the difference in
the maximum possible shoaling score.
226
1
2
3
4
5
Figure 4.4. Example of shoaling index scores based on proximity of fish to each other with
lowest score (1) at top and greatest score (5) at bottom. A score of one indicated no fish were
within one body length of another, a two indicated that two fish were within one body length of
each other, etc. Panels on the right indicate the relative proximity of fish to each other from the
corresponding still video image on the left.
227
Once the typical behavioral responses to both DI and AS were identified and quantified, a
second experiment was carried out to determine the effects of Pb on predator avoidance
responses. Eight groups of fish were tested as described above to determine their non-exposure
responses to both DI and the AS. Based on the toxicity of Pb(NO3)2 determined in previous
toxicological testing and the reported values of dissolved Pb in the Cache River Watershed, four
of the groups of fish were exposed to a sub-lethal concentration of Pb (~50 ppb). Pb solutions
were prepared by spiking dechlorinated tap water with Pb(NO3)2.
The remaining four groups of fish were not exposed to Pb and served as controls. Thus, a
total of 40 fish were tested, 20 as controls and 20 as test fish. Because fish were tested as
groups, the entire group counted as a single sample, thus the final sample size was n = 4 for
both Pb-exposed and control fish. Fish were exposed to Pb for seven days. At the end of the
seven days, fish were removed and placed in 20-L containers with clean water and allowed to
remain for at least 48 hours, to allow for depuration of any remaining Pb. Groups of fish were
retested with both distilled water and the alarm substance and responses compared to their
pre-exposure responses.
Instrumental Analysis
Water was collected from each dilution of each toxicological test (acute, chronic, behavioral)
and filtered with a 0.45-um syringe filter to isolate Pb in the dissolved phase. These samples
were acidified and analyzed using an Agilent 240 Zeeman graphite furnace atomic absorption
spectroscopy (GFAAS) to determine the actual concentration of Pb to which organisms were
exposed during testing. Quality assurance checks were performed by analyzing standards of
known Pb concentrations along with samples. QA standards were required to measure within ±
10% of the presumed value. If a QA standard measured outside of this range, the instrument
228
was re-calibrated and samples were re-analyzed. A full description of quality assurance checks
for the GFAAS can be found in chapter 3 of this dissertation.
Statistical Analyses
All statistical testing was performed at α=0.05. Three types of endpoints were calculated
using acute and chronic toxicological tests, using statistical software ToxCalc v. 5.0. LC50 values
were calculated for acute tests, representing the concentration of Pb that was lethal to 50% of
the tested population. For chronic tests, both an EC50 (survival) and an IC25
(growth/reproduction) were calculated. The EC50 indicated the concentration of Pb that
resulted in an effective difference in at least 50% of the tested population, from the control.
The IC25 indicated the concentration that inhibited either growth or reproduction in at least 25%
of the tested population. For all ToxCalc calculations, the measured concentration of dissolved
Pb obtained from instrumental analysis with GFAAS was used.
The initial part of behavioral tests involved examining anti-predator behavioral responses to
determine if a reliable behavioral response or responses could be identified for development of
a behavioral toxicity test. First, responses to both DI and AS were compared to determine if
behavioral responses changed in response to the AS (but not DI). A change in behavior would
indicate that the behavior measured was indeed an ‘anti-predator’ response. Behavior both
before and after stimulation with DI and before and after stimulation with AS were compared.
Because the same groups of fish were tested in each category (pre-DI, post-DI, pre-AS, post-AS),
pseudoreplication was present. Thus, these categories were compared with a mixed model
ANOVA to account for both the stimulus used and any group effect (R package: lme4 v1.1-12,
nlme v3.1-128). In this model, the category (pre-DI, post-DI, pre-AS, post-AS) served as the fixed
effect while the test group number served as the random effect.
229
Although behavioral responses to the alarm stimulus were typically as predicted (increased
shoaling, decreased total movement), in at least one instance, a group of fish responded by
swimming much more erratically, demonstrated by decreased schooling and increased total
movement. Although unexpected, this still represented a clear difference from pre-stimulus
behavior. In order to account for the difference in behavioral responses, a further analysis was
performed to compare the strength of response to either DI water or the AS stimuli. The
absolute difference between pre and post-stimulation with either DI water or AS was calculated
and the mean absolute difference for each group was compared using a paired t-test. Using the
absolute difference allowed for a measurement of the strength of response, independent of the
actual direction of the response. It was predicted that fish would react more strongly to the
alarm stimulus than to the control stimulus, regardless of the nature of the actual response.
A similar analysis was used to determine the effect of Pb exposure to fish. The absolute
difference of pre- and post-stimulus scores was compared for both control fish (pre- and post-Pb
exposure) and for exposed fish. Strength of response to the alarm stimulus was predicted to
decrease in fish exposed to Pb, when compared to their pre-exposure strength of response.
Because control fish were not exposed to Pb, the strength of their pre- and post-exposure
responses should not be significantly different. This analysis was performed for both behavioral
responses, shoaling index, and total zones occupied.
RESULTS
Acute Toxicology
Results from acute toxicology tests performed using CR-low and CR-high indicated a decrease
in toxicity as water hardness increased. This pattern was apparent in both C. dubia and P.
promelas. A t-test comparing the LC50 for the two different natural waters tested indicated that
a significant difference was present in both C. dubia (t4 = 3.858, p = 0.018) and P. promelas (t2.053
230
= 10.781, p = 0.008). In C. dubia, this increase in hardness led to a seven-fold decrease in
toxicity while in P. promelas, the same increase in hardness led to a 30-fold decrease in toxicity.
A one-way, t-test was used to compare the mean calculated LC50 for each hardness to the
allowable limit (based on hardness alone) (Table 4.1). In C. dubia, the measured LC50 was
numerically (but not significantly) greater than the allowable limit for both natural waters (CRlow: t2 = 1.364, p = 0.153; CR-high: t2 = 2.10, p = 0.079). The measured LC50 for P. promelas was
significantly greater than the allowable limit for both natural waters tested in this study (CR-low:
t2 = 2.875, p = 0.051; CR-high: t2 = 11.289, p = 0.004).
231
Table 4.1. Mean ± SE LC50 values (µg/L Pb) calculated for C. dubia and P. promelas in response to Pb for two tested natural waters of the
Cache River Watershed, Arkansas (n=3 for each test). Natural waters for toxicity testing were collected under low and high hardness
conditions between October 2014 and January 2016. The allowable limit is the criterion maximum concentration (CMC) for dissolved Pb.
Species
Water
(mg/L CaCO3)
Mean ± SE LC50 (µg/L Pb)
Allowable Limit (µg/L Pb)
p-value
C. dubia
CR-low
40
38.66 ± 11.11
23.51
0.153
C. dubia
CR-high
217
290.75 ± 64.39
148.44
0.079
P. promelas
CR-low
40
210.81 ± 65.16
23.51
0.051
P. promelas
CR-high
217
6351.24 ± 565.84
148.44
0.004
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A comparison of toxic endpoints obtained using MH water indicated that hardness was a
strong contributor to Pb toxicity in P. promelas, with the LC50 for MH water calculated at 781.49
± 66.63 µg/L Pb, intermediate to the lower and greater tested hardness. However, in C. dubia,
the calculated LC50 was 40.29 ± 7.82 µg/L Pb, indicating that an increase in mean hardness from
40 to 87 µg/L CaCO3 had no effect on toxicity. An ANOVA with post-hoc Tukey performed on all
three types of water for C. dubia, indicated that although at least one significant difference
existed between categories (F2,6 = 16.917, p = 0.003) the MH water did not result in significantly
different LC50 values than the CR-low (p = 0.962). However, significantly different LC50 values
were measured in CR-low as compared to the CR-high water (p=0.007). For P. promelas, an
ANOVA indicated that at least one significant difference was present between all categories (F2,6
= 97.449, p < 0.001). A post-hoc Tukey test indicated that the MH water resulted in significantly
different LC50 values than both the CR-low water (p = 0.004) and the CR-high water (p < 0.001)
(Fig. 4.5).
233
400
A
350
LC50 (µg/L Pb)
300
CR-low vs. MH: p = 0.962
MH vs. CR-high: p = 0.007
250
200
150
100
50
0
40 (CR-low)
87 (MH)
Hardness (mg/L CaCO3)
217 (CR-high)
8000
B
7000
LC50 (µg/L Pb)
6000
CR-low vs. MH: p = 0.004
MH vs. CR-high: p < 0.001
5000
4000
3000
2000
1000
0
40 (CR-low)
87 (MH)
217 (CR-high)
Hardness (mg/L CaCO3)
Figure 4.5. Mean ± SE calculated 48-hr acute LC50 at three different hardness levels of tested
waters for A) C. dubia and B) P. promelas.
234
Chronic Toxicology
Results from chronic toxicology tests performed using MH water and CR-low had a measured
decrease in toxicity as water hardness increased for both C. dubia and P. promelas (Table 4.2)
for both 7-day survival and reproduction/growth. Results of 7-day survival mirrored those of
the acute (48-hr) tests. Increasing hardness from 33 mg/L CaCO3 (CR-low) to 97 mg/L CaCO3
(MH) had no effect on toxicity in C. dubia (t2 = 0.832, p = 0.488) but produced a significant
decrease in toxicity for P. promelas (t2 = -9.457, p = 0.011).
Results from 7-day sub-lethal endpoints (reproduction for C. dubia and growth for P.
promelas) indicated that a significant difference existed between CR-low and MH water. In C.
dubia, an increase in hardness from 33 mg/L CaCO3 to 93 mg/L CaCO3 resulted in a 12-fold
decrease in sub-lethal toxicity (t2.186 = -4.333, p = 0.042), with IC25 values increasing from 2.63 ±
1.43 ppb to 31.91 ± 6.61 ppb. In P. promelas, the effectiveness of increasing hardness was
somewhat reduced (when compared to survival results), but still resulted in a significant twofold decrease in sub-lethal toxicity (t3.224 = -15.654, p < 0.001) with IC25 values increasing from
137.5 ± 5.06 ppb to 294.39 ± 8.65 ppb (Table 4.2).
235
Table 4.2. Chronic endpoints (Mean ± SE) for dissolved Pb tested in ambient water from the Cache River, Arkansas and laboratory
prepared, moderately hard water for both C. dubia and P. promelas (n = 3 for each).
Species
Water
Hardness (mg/L CaCO3)
Mean EC50 (µg/L Pb)
Mean IC25 (µg/L Pb)
C. dubia
CR-low
33.0
69.86 ± 15.75
2.63 ± 1.43
C. dubia
MH
93.0
56.52 ± 3.02
31.91 ± 6.61
P. promelas
CR-low
33.0
142.66 ± 2.70
137.50 ± 5.06
P. promelas
MH
93.0
572.19 ± 45.33
294.39 ± 8.65
236
The allowable chronic limit for dissolved Pb based on water hardness was calculated as 0.74
µg/L for a hardness of 33 mg/L CaCO3 (CR-low) and 2.33 µg/L for a hardness of 93 mg/L CaCO3
(MH). EC50 values were significantly greater than these limits for C. dubia (CR-low: t2 = 4.390, p =
0.024; MH: t2 = 17.947, p = 0.002) and P. promelas survival (CR-low: t2 = 52.554, p < 0.001; MH:
t2 = 12.569, p = 0.003), indicating that current standards for dissolved Pb are sufficiently
protective. All IC25 values were significantly greater than allowable limits for P. promelas (CRlow: t2 = 27.035, p < 0.001; MH: t2 = 33.757, p < 0.001) but only MH water was significantly
greater than allowable limits for C. dubia (CR-low: t2 = 1.325, p = 0.158; MH: t2 = 4.477, p =
0.023) (Table 4.3).
237
Table 4.3. Chronic endpoints (Mean ± SE) for dissolved Pb compared to allowable limits of Pb (µg/L) (t-test, α = 0.05) as established by
assessment criterion used by the State of Arkansas (ADEQ, 2016b). The allowable limit represents the criterion continuous
concentration (CCC) for dissolved Pb. Each test was repeated three times (n = 3) for both C. dubia and P. promelas. The p-value
represents the comparison between either the mean EC50 and allowable limit for the hardness used or mean IC25 and allowable limit for
the hardness used.
Species
Water
CCC (µg/L Pb)
Mean EC50 (µg/L Pb)
p-value
Mean IC25 (µg/L Pb)
p-value
C. dubia
C. dubia
P. promelas
P. promelas
CR-low
MH
CR-low
MH
0.74
2.33
0.74
2.33
69.86 ± 15.75
56.52 ± 3.02
142.66 ± 2.70
572.19 ± 45.33
238
0.024
0.002
< 0.001
0.003
2.63 ± 1.43
31.91 ± 6.61
137.50 ± 5.06
294.39 ± 8.65
0.158
0.023
< 0.001
< 0.001
Fish Behavioral Results
Typical Behavior: No Exposure to Pb
Results from behavioral tests indicated that both shoaling behavior and total zones of the
tank used were effective indicators of anti-predator behaviors. Fish tended to shoal (increased
shoaling index) in response to an alarm stimulus as compared to a control stimulus and also
tended to use a smaller portion of the tank (reduced number of total zones used). A mixed
model ANOVA comparing four categories (pre-control, post-control, pre-alarm, and post-alarm)
indicated that shoaling behavior and total zones occupied were significantly different in the
post-alarm category vs. pre-control, post-control and pre-alarm (Fig. 4.6).
239
Figure 4.6. Changes in A) standardized shoaling index (shoaling score (1-5) divided by total fish in
trial) and B) total tank zones occupied by all fish (out of 18 total zones possible) in response to a
control stimulus (DI water) and a stimulus of alarm substance obtained from P. promelas. The
same group of fish was tested in each trial (n groups = 12). Blue bars represent results pre- and
post-stimulation with the control substance while red bars represent results pre- and poststimulation with the alarm substance.
240
To determine if overall responses to the alarm stimulus were greater than responses to the
control stimulus, the absolute difference of the pre- and post-stimulus data were compared for
both control and alarm stimuli. This analysis indicated that the strength of response (regardless
of direction) was significantly greater for alarm stimulus than the control stimulus for shoaling
index (V = 6, p = 0.003) and total zones occupied (t =-1.803, df = 11, p = 0.049) (Fig. 4.7).
241
Figure 4.7. Comparison of mean ± SE absolute differences in responses pre- and post-stimulation
with either a control (DI water, blue bar) or alarm substance (red bar) for A) shoaling index and
B) total zones occupied. Both the control and alarm stimulus were tested on the same group of
fish (n groups = 12).
242
Exposure to Pb
Eight groups of five fish were used for the Pb exposure study with four groups serving as
controls (no exposure to Pb) and four groups serving as experimental. Instrumental analysis
indicated that the actual concentration of Pb in experimental tanks was approximately 40 ppb
(average of all tanks = 36.92 ± 2.32 ppb). To account for differences in direction of response,
absolute differences of pre- and post-stimulus (DI or AS) were compared for fish prior to, and
after exposure to Pb. This analysis was used on both shoaling index and total zones occupied.
For shoaling index, prior to exposure to Pb, fish had a significantly stronger response to AS than
to DI (t =-3.633, df = 14, p = 0.003). After exposure to Pb, the strength of response was not
significantly different between AS and DI (t = 1.0716, df = 14, p = 0.302) (Fig. 4.8). A similar
result was measured for total zones occupied. Fish had a significantly stronger response to AS
than to DI prior to Pb exposure (t = -2.6322, df = 7.4882, p = 0.032) but no significant difference
was observed between the two groups after Pb exposure (t = 0.7918, df = 14, p = 0.442) (Fig.
4.9).
243
A
B 0.25
0.25
p=0.302
Absolute Difference of Shoaling Index
Absolute Difference of Shoaling Index
p=0.003
0.2
0.15
0.1
0.05
0.2
0.15
0.1
0.05
0
control
0
alarm
control
alarm
Figure 4.8. Absolute difference between pre- and post-stimulus shoaling index scores for control stimulus (blue bar) and the alarm
stimulus (red bar) in A) fish prior to exposure to Pb and B) fish after exposure to Pb. The same group of fish was tested with both the
control and the alarm substance both before and after exposure to Pb (n groups = 4).
244
A
B
p = 0.032
3
3.5
Absolute Difference of Total Zones
Absolute Difference of Total Zones
3.5
2.5
2
1.5
1
0.5
p = 0.442
3
2.5
2
1.5
1
0.5
0
0
control
control
alarm
alarm
Figure 4.9. Absolute difference between pre- and post-stimulus total zones occupied for control (blue bar) stimulus and the alarm (red
bar) stimulus in A) fish prior to exposure to Pb and B) fish after exposure to Pb. The same group of fish was tested with both the control
and the alarm substance both before and after exposure to Pb (n groups = 4).
245
DISCUSSION
Difficulties in Comparing Toxic Endpoints
When studying metal toxicity in aquatic organisms, a direct comparison of published toxic
endpoints is somewhat uninformative as endpoints can vary drastically depending on specific
water chemistry and test conditions (e.g., test medium, test duration, form of Pb analyzed). For
example, Erickson et al. (1996) found that LC50s for copper toxicity in P. promelas varied by 100fold based solely on water composition. Published endpoints are often affected by the test
medium used (ambient river water, laboratory-prepared water, effluent outflow) as different
media have different constituents that can either mitigate or exacerbate toxicity, depending on
the particular constituent. For example, an increase in cations associated with water hardness
(Mg+2, Ca+2) are well known to result in a decrease in heavy metal toxicity (Sprague, 1985;
Pascoe et al., 1986; Rathore and Khangarot, 2003; Yim et al., 2006; Ebrahimpour et al., 2010).
Heavy metals cause acute toxicity in aquatic organism by binding to specific receptors known as
biotic ligands (Paquin et al., 2002). In fish, such as P. promelas, this ligand is thought to be
sodium or calcium channel proteins in the surface of the gills (Di Toro et al., 2001). The
presence of other substances in water can provide a protective measure against metal toxicity
by either complexing with metals and altering their free ion activity, or by competitively binding
at biotic ligands, preventing metals from binding (Di Toro et al., 2001). Hardness ions tend to
competitively bind to biotic ligands, preventing heavy metals from binding and causing toxicity
whereas dissolved organic matter (DOM), including dissolved organic carbon (DOC) tend to form
complexes with metals, preventing them from binding at biotic ligands (Di Toro et al., 2001).
Duration of exposure can also affect endpoints, even within a test identified as acute or
chronic. An increase in test duration is typically associated with a decrease in LC50, as the longer
duration would increase exposure to the tested toxicant. When testing Pb toxicity in both a
246
copepod (Cyclop sp.) and a daphnid (Daphnia magna), Offem and Ayotunde (2008) found that
an increase in test duration from 24 to 48 hours resulted in a 25% decrease in LC50 for D. magna
(2.51 ppb vs. 1.88 ppb) and a 5% decrease in Cyclop sp. (3.11 ppb vs. 2.97 ppb). 96-hr LC50s
decreased by 35% for D. magna (relative to 24-hr LC50s) and by 17% in Cyclop sp.
Although the dissolved Pb phase is considered the most toxic, many published endpoints are
reported as a concentration of total recoverable Pb in the test medium, rather than as dissolved
Pb. Total recoverable Pb includes insoluble Pb that has precipitated out of the water column
whereas dissolved Pb represents the amount of bioavailable Pb and is a much more accurate
representation of Pb exposure (Diamond et al., 1997). Of the eight studies reporting acute Pb
endpoints in C. dubia, just two measured dissolved Pb (Spehar and Fiandt, 1986; Diamond et al.,
1997), rather than total recoverable Pb. A conversion factor can be calculated providing an
approximation of dissolved Pb based on total recoverable Pb if other parameters such as the
hardness of the water and the concentration of total suspended solids (TSS) is known (USEPA,
1996). When comparing endpoints, available water quality data can vary by study, making it
difficult to calculate an appropriate conversion factor. A generalized conversion factor can be
used but this only takes into account water hardness, which is just one of many parameters that
can affect metal toxicity.
Comparing Trends in Toxic Endpoints
Because of the variation in study parameters and the subsequent variation in endpoints, a
comparison of actual measured endpoints is thus less informative than a comparison of overall
trends in response to changing water conditions. For example, altering the pH of the test
medium tends to have the same relationship with toxicity, regardless of other water
constituents, with an increase in pH resulting in a decrease in toxicity of Pb across all tested
247
organisms. Wang et al. (2016) compared toxic endpoints to Pb for several crustaceans, fish,
worms, molluscs, insects, amphibians, and algae and found that an increase in pH from 6.3 to
8.3 resulted in a mean increase in HC10 (hazardous concentration to 10% of population) from 48
to 1610 ppb Pb. Conversely, a decrease in pH tends to cause Pb to be released from the bound
particulate matrix and partition into the more bioavailable dissolved matrix. Schubauer-Berigan
et al. (1993) found that even in very hard waters (hardness of 300-320 mg/L CaCO3), a reduction
in pH from 8.3 to 6.3 resulted in an increase in acute Pb toxicity.
An increase in hardness tends to result in a decrease in Pb toxicity; however, this effect is
organism-specific. Diamond et al. (1997) compared the acute toxicity of Pb for C. dubia and P.
promelas in soft laboratory-prepared water (20-30 mg/L CaCO3) and moderately hard, ambient
river waters (70-90 mg/L CaCO3). Furthermore, Diamond et al. (1997) measured total
recoverable Pb in some samples and dissolved Pb in others. Regardless of these differences in
test conditions, Diamond et al. (1997) found that an increase in hardness from soft to
moderately hard water (regardless of water source or fraction of Pb measured) resulted in a
significant reduction in toxicity for P. promelas (LC50-soft=284.3 ± 115.1 ppb Pb vs. LC50moderately hard=2729.3 ± 89.4 ppb Pb) but not in C. dubia (LC50-soft=72.2 ± 38.5 ppb Pb vs.
LC50-moderately hard=85.9 ± 21.2ppb Pb). A similar trend was found in this study with acute
LC50s in P. promelas increasing from 210.8 ± 65.2 ppb Pb to 781.5 ± 66.6 ppb Pb as hardness of
water increased whereas in C. dubia the increase was much less, with LC50s increasing from 38.7
± 11.1 ppb Pb to 40.3 ± 7.8 ppb Pb. Thus, although the discrete endpoints are not necessarily
comparable, because of differing test conditions, the same trend is evident in response to
altered hardness.
The difference in the effect of hardness on Pb toxicity between these two species was also
observed by Mager et al. (2011a, 2011b) who found that although hardness was protective
248
against Pb for P. promelas, it was not similarly protective for C. dubia. Instead, toxicity in C.
dubia was affected more by DOC and NaHCO3. This result was noted in both acute (Mager et al.,
2011b) and chronic tests with C. dubia (Mager et al., 2011a). This difference in protection due
to hardness suggests that the physiology of these two different species affects their overall
sensitivity to Pb. In fish, the biotic ligand for metals, including Pb, is located primarily on the
surface of the gills (Di Toro et al., 2001), where it could come into contact with other dissolved
metal ions. These other metal ions, particularly Ca2+ and Mg2+ (associated with hardness)
competitively bind at the biotic ligand, decreasing the availability of binding sites for Pb, thus
reducing Pb toxicity. Mager et al. (2011a) suggested that the difference in protection provided
by hardness in P. promelas and C. dubia indicates that a different biotic ligand exists in C. dubia
than the one identified for P. promelas. These authors (Mager et al., 2011a) suggested that Pb
enters C. dubia via a high affinity divalent metal transporter (DMT1) with a low affinity for Ca2+,
a receptor first identified by Gunshin et al. (1997). Thus, in C. dubia (and similar aquatic
organisms) an increase in hardness would not necessarily provide protection against Pb toxicity.
Instead substances that complex with dissolved Pb ions (DOM, DOC, NaHCO3) and alter free ion
activity, would provide more protection in these organisms. Conversely, Komjarova and Blust
(2009) found that an increase in hardness provided a protective effect against Pb uptake in D.
magna, with organisms in water with 2.5 mM Ca2+ having significantly inhibited uptake of Pb
when compared to organisms in water of 0.5 mM Ca2+. Although hardness had an influence on
Pb toxicity in C. dubia in this study (particularly when increasing from moderately hard (87 mg/L
CaCO3) to hard water (217 mg/L CaCO3)), the protective effect was much more prevalent in P.
promelas, supporting the idea that Pb toxicity in C. dubia is via a different biotic ligand than in P.
promelas.
249
Although the direct effect of hardness was less in C. dubia than in P. promelas, the overall
protective effect of Cache River water was similar in each species. The relative toxicity provided
by ambient water can be calculated using a water effects ratio (WER), which compares the toxic
endpoints of ambient water with those obtained using laboratory-prepared water, assumed to
be free of other substances that could affect toxicity, such as DOC (USEPA, 1994). Although true
paired tests were not performed in this study (ambient vs. laboratory water with the same
hardness), an idea of the WER can be obtained by interpolating the LC50 for ambient water at
the same hardness as the MH water used, based on the results obtained for both CR-low and
CR-high waters. This interpolation indicated an LC50 of 105.60 ppb Pb in C. dubia and 1841.30
ppb Pb in P. promelas, for a hardness of 87 mg/L CaCO3. A comparison of this value to the
measured LC50 for MH water (C. dubia: 40.22 ppb Pb, P. promelas: 781. 49 ppb Pb) indicates a
WER of 2.62 for C. dubia and 2.36 for P. promelas. Thus, regardless of the exact physiological
means of Pb toxicity and the degree of protection provided by different constituents of water,
water from the Cache River Watershed is roughly 2.5x as protective as laboratory water in which
hardness alone would provide protection from Pb toxicity. This WER is similar to what was
found for Pb by Diamond et al. (1997), who calculated a mean WER for C. dubia at 1.8 ppb Pb
(range 1.0-2.8 ppb Pb) and for P. promelas at 14.7 ppb Pb (range: 4.5-25.6 ppb Pb) when
comparing natural and laboratory waters with hardness values ranging from 68-82 mg/L CaCO3.
Comparing Acute Endpoints to Current CMCs
LC50 values for P. promelas and C. dubia tested in ambient waters were always greater than
the CMC, indicating that neither species would likely be affected by dissolved Pb within the
Cache River, even during times, or in areas, with low hardness. Depending on the hardness of
natural waters used, LC50 values for P. promelas were eight to 43 times greater than allowable
250
limits based on the CMC, indicating little to no risk to vertebrate species, while LC50 values for C.
dubia were 0.5 to two times greater. This also indicates that other mitigating factors besides
hardness are present in natural waters that protect both C. dubia and P. promelas from acute Pb
toxicity. Both alkalinity and DOC can also provide protection against Pb toxicity in both P.
promelas and C. dubia, regardless of water hardness (Mager et al., 2011a; Esbaugh et al., 2011).
As discussed above, pH can also strongly affect Pb toxicity with the toxicity of several metals,
including Pb, decreasing as pH increased (Wang et al., 2016). The difference between acutely
toxic endpoints measured in this study and established criterion (based solely on the effect of
hardness) indicates that water constituents other than hardness are providing protection
against toxicity to organisms in the Cache River Watershed.
Comparing Chronic Endpoints to Current CCCs
Similar results to acute toxicity tests were observed in 7-day chronic toxicity tests. For the
most part, EC50 values were significantly greater than the CCC for dissolved Pb for both lethal
(EC50) and sub-lethal (IC25) endpoints in C. dubia and P. promelas. This indicates that more Pb
was required to have a lethal effect in both species and a sub-lethal effect in P. promelas than
should be present in surface waters, based on current assessment criterion (ADEQ, 2016b). The
sub-lethal endpoint (IC25) for C. dubia, although measurably greater, was not significantly
greater than the CCC for dissolved Pb at the tested hardness (40 mg/L CaCO3), indicating that for
the given hardness, dissolved Pb levels at or exceeding impairment criterion (CCC) could have
adverse effects on reproduction in this species. Again, this is primarily a concern in portions of
the watershed with low annual hardness values. The majority of the Cache River Watershed and
the main channel tend to have low hardness values during winter months, when reproduction
by C. dubia would not be occurring (and thus would not be negatively affected). However, in
251
sub-watersheds with headwaters on Crowley’s Ridge, low hardness values occur regularly
throughout the year, indicating that organisms within these watersheds could be at risk of sublethal toxic effects. More concerning is the fact that the IC25 for reproduction in C. dubia (2.63
ppb) is well within the range of dissolved Pb concentrations measured within the Cache River
Watershed (0.86 to 23.17 ppb). This confirms the need for further toxicological testing with
ambient water from these areas of the watershed to more fully understand the risk to aquatic
life in these areas.
Chronic tests with P. promelas indicated that the EC50 for the typically used sub-lethal
endpoint (growth) ranged from 137 to 294 ppb, depending on the hardness of the water used,
well above any measured concentration of dissolved Pb within the Cache River Watershed (0-23
ppb Pb). This indicates that P. promelas faces very little, if any, adverse effect from
environmentally relevant concentrations of dissolved Pb.
BLM-Based Criterion vs. Current Hardness-Based Criterion
The difference in metals toxicity caused by the composition of water has led to an increasing
movement towards utilizing biotic ligand model (BLM) based criterion when determining metal
toxicity in natural waters for many metals (Hatano and Shoji, 2010; Smith et al., 2015), including
Pb (Di Toro et al., 2001; Macdonald et al., 2002; Nys et al., 2014). However, implementing BLMbased criteria remains difficult as the complex interactions between water quality parameters,
specific organism physiology and indirect toxicological effects are still not completely
understood (Paquin et al., 2002; Erickson, 2013). Furthermore, development of such criteria
within a regulatory framework would require considerably more sampling of ambient waters
under a wide range of conditions, which would greatly increase the expense of such endeavors.
Water hardness is relatively easy to measure when samples are collected for analysis of metals.
252
Thus, a hardness-based criterion, though likely over-conservative, should provide sufficient
protection against acute toxicity for Pb, assuming environmental concentrations do not exceed
the established hardness-based criterion. Nonetheless, the differences between protection
provided by hardness for P. promelas and C. dubia found in this study and others (Diamond et
al., 1997; Mager et al., 2011a) suggests that the very different physiologies of these organisms
should be taken into account when establishing CMCs and CCCs for aquatic organisms.
Behavioral Toxicology
Preliminary behavioral tests indicate that dissolved Pb concentrations as low as 35 ppb can
have a negative effect on predator avoidance behavior in P. promelas. A reduction in typical
predator avoidance behavior can easily result in lethal effects to affected organisms. Mathis
and Smith (1993) found that typical anti-predator responses, such as increased shoaling, can
have a strong effect on survival of prey when exposed to a predator. Consequently, decreases
in anti-predator responses would be expected to decrease survival.
Although the exact behavioral response can vary, fish exposed to Pb tended to demonstrate a
reduction in the strength of their responses to the natural alarm stimulus, with post-Pb exposed
fish reacting no differently to the alarm substance than to a control stimulus. This indicates that
fish are either unable to detect the stimulus or do not recognize it as an indicator of potential
danger. The exact physiological pathway affected by Pb is unclear but based on similar
responses to other heavy metals of toxic concern, it seems likely that the Pb is interfering with
the ability of the olfactory system to detect the alarm stimulus (Scott et al., 2003; McIntyre et
al., 2012).
253
Potential for Development of a Behaviorally Based Toxicity Test in Pimephales promelas
The behavioral effects in this study were measured in dechlorinated, municipal tap water with
an average hardness of 85 mg/L CaCO3. It is unclear if the effect of Pb would increase under
conditions of low hardness, but given the strong relationship between hardness and toxicity
noted in acute and chronic tests for P. promelas, it is likely that reduced water hardness could
affect behavioral responses to a greater degree. Conducting these tests in dechlorinated tap
water also means that any additional protection afforded by other components of ambient
water were most likely not present in these tests. It would be interesting to repeat these tests
under reduced hardness conditions and also in ambient waters, to more fully understand the
effects of dissolved Pb on predator avoidance behavior in P. promelas. Ideally these tests would
be carried out with a range of Pb concentrations to determine the lowest concentration that
would have a significant effect.
It should be mentioned that due to extenuating circumstances, behavioral trials were not
carried out in a strictly controlled environment, as originally proposed. The space in which fish
were maintained (and tested), experienced fluctuations in light and temperature and the
environment was typically noisy, with both mechanical and human noise. Thus, these fish could
have been exceptionally stressed and behaved abnormally. However, all fish tested were
maintained in this environment and were thus equally stressed. Therefore, if stress affected the
response, it would have been reflected in the control fish.
From an experimental standpoint, within-laboratory stress in this experiment might have
resulted in responses similar to those in the natural environment. Because of physical
alterations to most waterways, water volumes can very drastically by season, meaning fish are
also exposed to drastic fluctuations in water temperature, pH, dissolved oxygen and other
parameters. Thus in the natural environment, fish are likely experiencing stress at most times.
254
Testing fish under stressful conditions could help to mimic the natural environment, providing a
clearer picture of typical behavioral responses.
Although laboratory experiments are critical in identifying behavioral responses, actual
environmental responses can differ (Irving and Magurran, 1997), indicating that context can
have a direct effect on behavioral responses to a stimulus. However, although more realistic,
true field experiments are notoriously difficult to implement and interpret, due to lack of control
(Scherer, 1992; Krebs, 1999; Hardwick et al., 2015). When designing a reliable toxicological test
relying on behavioral response, an environment that is relatively easily maintained/controlled
but still mimics natural environmental conditions is needed. Performing these experiments in a
laboratory setting with laboratory cultured fish also allows for research to occur throughout the
year, regardless of outdoor conditions. This study indicates that behavioral responses can be
reliably detected in laboratory conditions, using laboratory-reared, predator-naïve fish and using
a relatively small amount of space and resources, making them a cost-effective alternative to
traditional in situ field experiments.
Conclusions
Overall, toxicity tests indicated that state-set assessment criterion for dissolved Pb, based on
the CCC are sufficient to protect aquatic life, except for some potential for reproductive effects
in C. dubia in areas or in times of the year with low water hardness. Furthermore, water
samples collected from the Cache River Watershed between August 2013 and July 2016
suggests that concentrations of dissolved Pb rarely occur in concentrations great enough to
affect aquatic organisms. However, given the potential risk to C. dubia (and other similar
invertebrates), further toxicity testing using ambient waters from sub-watersheds with
consistently low hardness should be conducted to determine if aquatic life is at risk in these
255
areas. Drawing conclusions about toxicity to an invertebrate or vertebrate aquatic species
based on the two model organisms used here should be done carefully, as cross-species
modeling may not always be accurate (Esbaugh et al., 2012). However, the two species used in
this study are well established as model organisms for toxicological testing by the EPA (USEPA
2002a, 2002b). The effect of hardness on toxicity varied between P. promelas and C. dubia,
suggesting that their unique physiologies determine their overall sensitivity to Pb and should be
considered when relying on hardness-based assessment criterion.
Pimephales promelas are well protected by water hardness and potentially other constituents
of ambient waters within the Cache River and measured endpoints were always well above
current established assessment criterion. However, behavioral testing indicates that antipredator responses are affected by relatively low concentrations of dissolved Pb, well below
values associated with more commonly measured sub-lethal endpoints such as growth. To
confirm this, additional behavioral experiments should be conducted with different
concentrations of dissolved Pb and water with different hardness values. To maximize the
realism of the experiment, tests using ambient waters would be best.
Results from acute and chronic tests using ambient waters show that they present a much
more realistic picture of actual effects of contaminants on aquatic organisms under typical
environmental conditions. Although slightly more difficult to carry out, due to the necessity of
obtaining and spiking ambient waters, the benefits gained by this type of testing are many. It is
recommended that if a surface waterway has elevated contaminant levels, toxicity testing using
ambient waters spiked with the contaminant in question be carried out to ensure that any
ensuing management decisions are both relevant and realistic.
256
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262
CHAPTER 5. RESEARCH OVERVIEW
OVERALL CONDITION OF THE CACHE RIVER WATERSHED BASED ON MEASURED WATER QUALITY
PARAMETERS
The Cache River Watershed, located in northeastern Arkansas, is used heavily for agriculture
with roughly 67% of the land within the watershed devoted to row-crop production (AWIS,
2016). Agricultural activities have long been known to have negative effects on water quality in
receiving waterways including increased sediment loads, increased turbidity and nutrient loads,
and elevated concentrations of inorganic and organic contaminants such as metals and
pesticides. Because of its potential contribution to the hypoxic zone in the Gulf of Mexico, the
Cache River Watershed was identified as a focus area watershed (NRCS, 2016) as part of the
Mississippi River Basin Healthy Watershed Initiative (MRBI). Furthermore, several smaller HUC12 sub-watersheds were identified as high-priority sites by member agencies of the MRBI,
including the Natural Resources Conservation Service (NRCS, 2016).
Both the Cache River and its primary tributary Bayou DeView (which includes Lost Creek Ditch
and Big Creek Ditch) are listed as 303(d) impaired waterways by the State of Arkansas, meaning
that they have failed to meet one or more designated uses in one or more reaches. Designated
uses that are not being met include the abiliity to support aquatic life (fish, shellfish, wildlife
protection and propagation), the ability for use as an agricultural or industrial water supply and
being safe for primary contact recreation (ADEQ, 2008). Listed impairments have included
excessive levels of dissolved lead (Pb), total dissolved solids (TDS), siltation/turbidity, pathogens,
chlorides (Cl), sulfates (SO4), copper (Cu) and aluminum (Al), and low levels of dissolved oxygen
263
(DO) with sources including agriculture, industrial point source and municipal point source
(Table 5.1).
264
Table 5.1. Impaired reaches of the Cache River Watershed, assessment summary for reporting year 2008
(ADEQ, 2008; 2010; 2012; 2014; 2016a). If an impairment no longer appears in any year, the designated
reach was no longer considered impaired for that substance.
Waterbody and Reach 2008
2010
2012
2014
2016
Bayou DeView 002
DO*
DO, SO4
Bayou DeView 004
Pb
Pb
Pb
DO*, SO4*, Turbidity
DO, SO4
Bayou DeView 005
Pb
Pb
Pb
SO4*, Turbidity*
DO, SO4
Bayou DeView 006
Pb
Pb
Pb
SO4*, Turbidity*
DO, SO4
Bayou DeView 007
Pb
Pb
Pb
SO4*, Turbidity*
DO, SO4
Bayou DeView 009
TDS, Cl, Al
TDS, Cl
Cu*
Cu, Turbidity
Bayou DeView 012
Lost Creek Ditch 909
DO*
Cl
Cl
Big Creek Ditch 910
Cu
DO*, Cl, Cu*
DO, Cl
Cu*
Cu
Cu
DO, Pb
Cache River 016
Pb
Pb
Pb
DO*, Turbidity*
Cache River 017
Pb
Pb
Pb
Pb, Turbidity*
Cache River 018
Pb
Pb
Pb
Pb, Turbidity*
Cache River 019
Pb
Pb
Pb
Pb, Turbidity*
Cache River 020
Pb
Pb
Pb
Pb, Turbidity*
Cache River 021
Pb
Pb
Pb
Pb, Turbidity*
Cache River 027
Pb, TDS
Pb, TDS
Pb, TDS
SO4*, Turbidity*
Cache River 028
Pathogens
Pb, TDS
Pb, TDS
SO4*, Turbidity*
Cache River 029
Pb, TDS
Pb, TDS
Pb, TDS
SO4, Turbidity*
Cache River 031
Pb, TDS
Pb, TDS
Pb, TDS
SO4*, Turbidity*
Cache River 032
Pb, TDS
Pb, TDS
Pb, TDS
SO4, Turbidity*
* New cause of impairment for current waterbody reach or new waterbody reach listing
265
Impairment listing status is fluid and the most recent draft version of the 303(d) list (draft 2016)
indicates that several reaches of the Cache River and its tributaries have met attainment guidelines and
will be delisted. It should be noted that this assessment was performed primarily at the level of the
main channel and does not address potential impairments in smaller tributaries of the Cache River or
Bayou DeView.
In this study, several headwater sub-watersheds of the Cache River were examined to determine how
they compared to main channel sites in terms of several water quality parameters. An important
objective was to determine if any state-set assessment criteria for water quality were currently not
being met and if a source of decreased water quality could be determined based on spatial and/or
temporal assessment of measured parameters. Water quality parameters examined for which current
state-set assessment criteria are available included pH, dissolved oxygen (DO), turbidity, total nitrogen,
total phosphorus and dissolved lead (Pb) (ADEQ, 2016b). Several other parameters measured did not
have established assessment criteria but were useful in determining overall patterns of contamination.
These parameters included total suspended solids (TSS), dissolved nutrients (NO3-, NO2-, PO4-3), total
recoverable Pb, and sediment-bound Pb. Other physical/chemical parameters measured that were
crucial in analysis of impairments but not directly addressed in the text included water temperature and
conductivity, discharge and hardness. All water quality measurements taken over the course of the
study are available in the following appendices.
Based on this study, all sampled reaches of the main channel and several sub-watersheds exceeded
state-set assessment criteria. A total of 20 of the 23 sites sampled exceeded at least one assessment
criterion and as many as four assessment criteria. Affected sites were scattered evenly throughout the
watershed. In order to rank and prioritize sites for management, the total number of potential
impairments was calculated for each site. Sites were assigned an impairment value of either 0 (not
impaired) or 1 (impaired). For parameters with different assessment criterion by season, both seasons
266
were included in overall scores. For example, turbidity has separate assessment criterion for base and
all flow conditions. Thus if a site exceeded assessment criterion in both seasons, it received a total value
of 2 (1 exceedance for each season) for turbidity (Table 5.2, Fig. 5.1).
267
Table 5.2. Sub-watershed sites and or main channel reaches that failed to meet established assessment criteria (ADEQ, 2016b) during the course
of this study (August 2013-July 2016).
Site
REFO
Site Type
Main
HUC 12/Reach
Reach 016
Failed Assessment Criteria
Turbidity (all flows), Dissolved oxygen (primary season, critical season)
BGLA
Headwater
080203020202
Turbidity (base flow, all flows), Total nitrogen, Dissolved Pb
CRPA
Main
Reach 018
Turbidity (all flows), Dissolved Pb
WCRD
Headwater
080203020303
Turbidity (base flow, all flows), Dissolved Pb
CRCP
Main
Reach 017
Turbidity (all flows), Dissolved oxygen (primary season, critical season), Dissolved Pb
EASL
Headwater
080203020105
Turbidity (base flow, all flows), Total nitrogen, Dissolved Pb
CREG
Main
Reach 021
Turbidity (base flow), Dissolved Pb
FSDI
Headwater
080203020601
Dissolved oxygen (primary season, critical season), Total phosphorus
TMCR
Headwater
080203020602
Dissolved oxygen (primary season), Dissolved Pb
WIDI
Headwater
080203020305
Dissolved oxygen (primary season), Total nitrogen, Total phosphorus, Dissolved Pb
SKDI
Headwater
080203020401
Total nitrogen
KEDI
Headwater
080203020208
Total nitrogen
FTSL
Headwater
080203020101
Total nitrogen, Total phosphorus, Dissolved Pb
BDDI
Headwater
080203020207
Total nitrogen, Dissolved Pb
LCDI
Headwater
080203020502
Total phosphorus
SFBC
Headwater
080203020104
Dissolved Pb
SUCR
Headwater
080203020204
Dissolved Pb
SCCR
Headwater
080203020201
Dissolved Pb
MUCR
Headwater
080203020501
Dissolved Pb
LCRD
Headwater
080203020102
Dissolved Pb
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Figure 5.1. Total number of failed assessment criteria in samples collected from August 2013 to July
2016 in the Cache River Watershed based on current state-set assessment criteria. Current criteria have
been established for turbidity (base flow and all flow), pH, dissolved oxygen (primary season and critical
season), dissolved Pb, total nitrogen, and total phosphorus.
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Several sampled sites have few or no failed assessment criteria whereas others failed several
assessment criteria. No particular spatial pattern is evident when combining all failed
assessment criteria with the exception that sub-watersheds along the eastern side of the Cache
River Watershed tend to have fewer failed assessment criteria. These sites have headwaters on
Crowley’s Ridge and tend to have less overall agricultural land usage than sites not located on
Crowley’s Ridge. Although this spatial assessment does indicate the overall condition of the
Cache River Watershed, an examination of which specific parameters are in exceedance and
how those values relate to land use and environmental conditions such as precipitation, is
critical for identifying the source of contaminants and the most effective locations to implement
Best Management Practices (BMPs). The following sections summarize the results of each
specific project carried out during this study.
Chapter 2 focused on the effects of land use and discharge on several water quality
parameters, including turbidity, TSS, DO, dissolved nutrients and total nutrients. Spatial and
temporal assessment of these parameters were performed to help identify potential sources of
poor water quality and to identify which types of BMPs would be most effective and the
locations at which they would likely have the greatest impact. Chapter 3 focused on the
potential sources of lead (Pb) within the Cache River Watershed, based on concentrations of
dissolved Pb, total recoverable Pb (dissolved + particulate), and sediment-bound Pb.
Suggestions for implementation of BMPs to control metals within the watershed were also
included in this chapter. Chapter 4 used a series of laboratory toxicological tests to determine
the potential toxic effects of dissolved Pb on aquatic organisms. Effects included both lethality
and sub-lethal effects (growth, reproduction, behavior). Toxic endpoints measured in these
laboratory studies were compared to actual concentrations of dissolved Pb measured within the
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Cache River Watershed to better understand the immediate environmental risk posed to
aquatic organisms within this watershed.
SUMMARY OF CHAPTER 2
In Chapter 2, water quality parameters that are often directly linked to agricultural land use
were examined. An overall analysis of land alteration (resulting from agricultural practices) and
sample-specific discharge indicated that land alteration was the most obvious contributor to
water quality impairments including turbidity, and dissolved and total nutrients, with
concentrations of all parameters increasing as the degree of land alteration increased. Land
use/land cover (LULC) is well reported as a contributor to decreased surface water quality
(Uriarte et al., 2011; Giri and Qiu, 2016; Hong et al., 2016). For discharge, a similar pattern was
noted for turbidity with measurements increasing as discharge increased. This positive
relationship between discharge and turbidity has been widely reported in the literature for a
wide variety of stream types (Mallin et al., 2009; Yongshan et al., 2015; Barry et al., 2016)
including portions of the Cache River Watershed (Kennon-Lacy, 2016; Rosado-Berrios and
Bouldin, 2016).
The relationship between discharge and nutrients was less obvious. Relatively high
concentrations of all measured nutrients were observed under base discharge conditions with
concentrations tending to decrease or remain fairly constant under increased discharge
conditions. This pattern suggests that under low discharge conditions, solutes within waterways
can hyper-concentrate due to reduced dilution caused by inputs and increased evaporation
(Lewis et al., 2015). Such hyper-concentration is typically indicated by increased conductivity
measurements under low discharge conditions (Townsend, 2002), a pattern that was noted in
this present study. A similar deterioration in water quality occurs under drought conditions,
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when the dilution capabilities of waterways are limited by greatly reduced inputs of water
(Mosely, 2015).
This relationship between discharge and nutrient concentrations also explains the decrease in
nutrients under increased discharge conditions. An increase in discharge would represent
increased inputs of water from groundwater, precipitation, or surface runoff. These increased
inputs would be sufficient to maintain or increase the dilution capabilities of the waterway, thus
preventing hyper-concentration of solutes. As predicted, both PO4-3 and total phosphorus
increased under storm discharge conditions. Nitrogen concentrations tended to stay relatively
constant under increased discharge conditions with the exception of NO2-, which decreased
under high and storm discharge conditions.
When inorganic nitrogen is applied to fields as fertilizer, it is typically in the form of NH4+
which is quickly oxidized into NO2- by nitrifying archaea or bacteria, then oxidized to NO3- by
more oxidizing bacteria (Dodson, 2005). Most NO2- is converted into NO3- relatively quickly, thus
elevated NO2- concentrations in waterways would only be observed if increased surface runoff
due to precipitation or irrigation occurred soon after fertilizer application, before NO2- was
oxidized to NO3-. Because oxidation to NO3- occurs relatively rapidly, most soluble nitrogen in
the soil throughout the year would likely be in the form of NO3-, meaning under most discharge
conditions, more NO3- would be mobilized in surface runoff than NO2-, thus resulting in greater
NO3- concentrations (relative to NO2-) in waterways receiving surface runoff from agricultural
areas (Zhang et al., 2013).
A spatial and temporal assessment of sampled sites within the Cache River Watershed
indicated that land alteration was a strong contributor to sediment loads with mean sediment
load increasing as a function of land alteration. Elevated nutrient concentrations and loads
were also closely tied to land alteration with least-altered sites having significantly reduced
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loads compared to moderately-altered and most-altered sites. The lack of significant difference
between moderately-altered and most-altered sites indicates that even a moderate amount of
agricultural land use and channelization is enough to have a significant impact on nutrient
concentrations in surface waters. Greatest mean concentrations of total and dissolved nitrogen
occurred in most-altered sub-watersheds along the western side of the Upper and Middle Cache
River Watershed (from north to south). Total and dissolved phosphorus tended to have greatest
concentrations in most-altered sites in the Middle Cache Watershed (from north to south). The
exception to this pattern was site FTSL, which had elevated mean concentrations of all dissolved
and total nutrients. This sub-watershed contains the uppermost portion of the Cache River,
which at this point, consists only of a few small agricultural ditches. The limited volume of water
within the ‘river’ at this point would result in a reduced dilution capability and an increased
likelihood of hyper-concentration of solutes, as described above.
Although several sub-watersheds had elevated concentrations or loads of sediment and
nutrients, the general trend was a decrease in sediment and nutrient concentrations along a
downstream gradient of the main channel of the Cache River. A similar trend was noted for
sediment concentrations within the Cache River by Kleiss (1996) and within Bayou DeView
(Rosado-Berrios and Bouldin, 2016). In both cases, this effect was attributed to remaining
natural wetlands within the Cache River Watershed. Relatively large portions of the Lower
Cache River Watershed remain as bottomland hardwood forest or wetland, particularly when
compared to the upper and middle portions of the Cache River Watershed (Fig. 5.2).
Furthermore, many portions of the Lower Cache River have never been artificially channelized,
meaning that natural meanders and associated riparian buffers remain. This area of the
watershed has been relatively unaltered, in part due to the designation of portions of the Lower
Cache Watershed, including the Big Woods wetlands, as a ‘Wetland of International Importance’
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by the United Nations Ramsar Convention, one of just 38 such sites in the United States (Ramsar
Convention on Wetlands, 2013). Preservation efforts have also been aided by the possible
sighting of an ivory-billed woodpecker (presumed extinct) in the Cache River National Wildlife
Refuge in 2004 (Fitzpatrick, et al., 2005).
274
Figure 5.2. Remaining wetland areas within the Cache River Watershed as compared to areas in
which water samples were collected from August 2013 to July 2016. Spatial wetland data
obtained from US Fish and Wildlife Service (USFWS) National Wetlands Inventory (2016).
275
Wetlands, both natural and constructed, are remarkably efficient at both sediment and
nutrient removal from water via processes such as denitrification and volatilization (nitrogen),
assimilation by organisms (nitrogen, phosphorus), and adsorption to particulate
matter/sedimentation (nitrogen, phosphorus, sediment). Wetlands in the United States account
for roughly 20% of the total removed anthropogenic load of reactive nitrogen (Jordan et al.,
2011). Restored wetlands were found to remove up to 95% of NO3- from water and up to 71%
of total nitrogen (Hoffman et al., 2011). Wetlands constructed specifically to treat agricultural
runoff were found to have up to a 90% removal efficiency of nitrogen (Borin and Tocchetto,
2007). Retention rates for phosphorus within wetlands tend to be consistent with nitrogen with
retention rates measured at 50-90% (Wang and Mitsch, 2000; Dodds, 2003). This retention is
typically due to sedimentation (Mitsch et al., 1995) or precipitation as calcium phosphate
(Reddy et al., 1999).
Removal of suspended sediment typically varies as a function of water velocity (Kozerski,
2002) with greater sedimentation occurring as water velocities decrease. Channelization of
waterways tends to cause increased velocities and reduced sedimentation. In waterways with
natural meandering channels, such as the majority of the Lower Cache River, mean water
velocities are reduced (Gardeström et al., 2013) and temporal fluctuations in water velocity are
more uniform (Mason et al., 2012). This reduction in velocity tends to result in increased rates
of sedimentation and an overall reduction in suspended sediment loads.
Given the strong relationship between land alteration, discharge and concentrations of
sediments and nutrients, controlling surface runoff would likely have the greatest effect on
improving surface water quality. Best management practices (BMPs) designed to slow and filter
surface runoff or provide natural depositional areas for suspended sediments would be most
beneficial in the Cache River Watershed. BMPs such as riparian barriers and filter strips were
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effective at reducing contaminant loads in surface runoff from agricultural areas (Lowrance et
al., 2002; Chiang et al., 2012). For example, Lee et al. (2003) found that installation of a
vegetative buffer strip of just seven meters reduced sediment losses to a stream by 95% and
nutrient losses by 58-80%. Although filter strips are relatively ineffective in controlling watersoluble nutrients in tile-drained agricultural areas (Motsinger et al., 2016), very little tile
drainage is used in Arkansas (Sugg, 2007) indicating that filter strips could be an effective BMP in
this watershed. Enhancement of existing wetlands or restoration of drained wetlands would
also prove beneficial as wetlands provide a natural sink for many contaminants, including
nutrients (Chavan et al., 2008; Jordan et al., 2011). However, Steinman and Ogdahl (2011)
found that agricultural fields converted into wetlands can serve as nutrient sources, rather than
sinks, so a careful analysis of sites proposed for conversion should be performed.
Based on the data collected in this study, several sub-watersheds were identified that are at
risk of impairment. These sub-watersheds would be ideal locations to implement BMPs though
further sampling within the sub-watersheds is recommended to identify specific locations in
which BMP implementation would be most cost-effective and ecologically beneficial to aquatic
ecosystems within the Cache River Watershed.
SUMMARY OF CHAPTER 3
In Chapter 3, the overall distribution and concentration of Pb levels within the Cache River
Watershed were examined to determine if a source for Pb could be identified. Previous
hypothesized sources include an unidentified point source within the watershed, leaching from
natural Pb deposits (either naturally or as a result of mining activities), resuspension of Pbcontaining sediments, or runoff of Pb-containing soils from agriculturally dominated
watersheds, particularly during winter months when average precipitation is elevated. Spatial
and temporal assessments were performed for Pb in three abiotic matrices; Pb dissolved within
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the water column, total recoverable Pb within the water column (particulate + dissolved Pb) and
sediment-bound Pb. Detection frequency and mean concentration of Pb in each matrix were
compared throughout the Cache River Watershed as a means to determine if any proposed
hypotheses were more strongly supported than other hypotheses.
The relationship between overall degree of land alteration, discharge and measured
concentrations of Pb were compared using a non-parametric MANOVA. Discharge was
composed of inputs from precipitation, groundwater and runoff of water used for irrigation. A
site analysis was also performed based on current or proposed hardness-based assessment
criteria (ADEQ, 2016b; Buchman, 2008) to determine if specific sites exceeded assessment
criterion or if a specific type of site had a greater risk of exceeding assessment criterion. These
criteria are based on concentrations of Pb within water or sediments that would be expected to
have either an acutely or chronically toxic effect on aquatic organisms.
All forms of Pb were relatively ubiquitous within the Cache River Watershed, though detection
frequencies did varied based on the sampled matrix. Dissolved, total recoverable and sedimentbound Pb were detected above the practical quantitation limit (PQL) at every sampled site
within the Cache River Watershed. Dissolved Pb was detected above the PQL in 11.9% of all
collected samples (n=604), in 76.6% of all collected samples analyzed for total recoverable Pb
(n=551) and in 100% of sediment samples (n=184). This indicates that Pb does not remain in the
dissolved phase, but is quickly adsorbed to suspended or bottom sediments within the Cache
River Watershed. Bound Pb has a greatly reduced potential for adverse toxicological effects in
aquatic organisms (Meyer et al., 2007). Mean concentrations of Pb tended to be greatest in
either most-altered sites (total recoverable Pb) or main channel sites (sediment-bound Pb).
Mean concentrations of dissolved Pb were similar across all land alteration categories.
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Of the proposed hypotheses for the source of Pb, two were considered unlikely based on
spatial assessment. The widespread detection of Pb indicated that a single unidentified point
source could not explain the spatial pattern observed. Elevated concentrations of dissolved Pb
were measured in 16 of the 23 sampled sites, indicating numerous unidentified point sources,
an unlikely scenario. In terms of geologic influence, no known deposits of Pb exist in the Cache
River Watershed and there is no history of mining. Furthermore, areas of the watershed in
closest proximity to Pb-mining regions in Missouri show no greater likelihood of having elevated
Pb concentrations than areas further away. Pb has also never been reported in other
watersheds of northeastern Arkansas, including those with surface waters whose headwaters
run directly through the Pb mining regions of Missouri. This indicates that the Pb within
waterways of the Cache River Watershed is attributable to some specific characteristic of this
watershed that either increases aerial deposition of Pb or increases the likelihood that Pb in
soils will wash into waterways. Although no current mining activity occurs within the Cache
River Watershed, historical inputs from mining or smelting activities could have contributed to
current excessive concentrations. Metals can persist in soils and sediments long after the
activities that produced them have ceased (Sager and Kralik, 2012; Aleksander-Kwaterczak and
Ciszewski, 2013).
The two most likely hypothesized sources of Pb within the Cache River Watershed would be
either surface runoff of contaminated soils or resuspension of contaminated sediments. Pb in
soils could enter waterways in surface runoff during times of high precipitation, particularly in
agriculturally dominated watersheds that often have little to no riparian buffer to slow or filter
surface runoff before it enters waterways. Similarly, under high flow conditions (typically a
result of heavy precipitation), sediment could be re-suspended in the water column, potentially
releasing Pb back into the dissolved matrix. Sediments in rivers often serve as both a sink for
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heavy metals and a potential source (Chon et al., 2012). Dissolved Pb concentrations were
correlated with discharge, indicating that either surface runoff or sediment resuspension could
be a contributing factor. However, a comparison of sediment-bound Pb concentrations to
dissolved Pb concentrations indicates no correlation between the two. If sediment
resuspension were a source of elevated dissolved Pb concentrations, watersheds with greater
sediment-bound Pb concentrations would also be expected to have greater dissolved Pb
concentrations.
Elevated Pb concentrations were detected most often in most-altered (agriculturally
dominated) sub-watersheds and under high discharge conditions, indicating that surface runoff
from these areas is most likely contributing to elevated Pb concentrations. Although a strong
correlation was seen between discharge and dissolved Pb, no such correlation was observed
between precipitation and dissolved Pb, contrary to the current hypothesis, which indicates that
detection of elevated concentrations should be greatest in times of year when precipitation is
greatest. This suggests that other sources of water that contribute to discharge (groundwater
or inputs from irrigation associated with agriculture) might be contributing to overall Pb
concentrations. In this study, quantifiable concentrations of dissolved Pb were detected twice
as often in summer and fall months, when precipitation is low but agricultural activity (and
accompanying irrigation) is high. Thus, although the overall most likely source is surface runoff
of soils, the timing of detection events indicates this runoff might not be precipitation-induced
but is instead the result of irrigation or drainage of flooded fields. Typical rice cultivation, which
dominates agricultural activity in the Cache River Watershed (USDA-NASS, 2016), requires
draining of flooded fields prior to harvest (Shipp, 2002). This drainage could result in
considerable sediment-containing runoff, even when precipitation is low. Furthermore, this
runoff would likely be composed of smaller sediment particles such as silt or clay (Tagiri et al.,
280
2009; Slaets et al., 2016), known to strongly associate with Pb due to their cation exchange
capacity (CEC) (Momani, 2006). Groundwater used for irrigation could also contribute to Pb
concentrations as aquifers in agricultural areas can be contaminated with heavy metals
(Wongsasuluk et al., 2014). The Cache River Watershed is used much more for rice production
than surrounding watersheds in northeastern Arkansas (Fig. 5.3), possibly explaining why
elevated Pb concentrations are detected more often in this watershed than in other
agriculturally-dominated areas.
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Figure 5.3. Distribution of top five crops in sub-watersheds in northeastern Arkansas with
greater than 50% of land use devoted to agriculture for production year 2015. Areas shown in
white represent portions of the sub-watersheds not used for crop production (cropland data
obtained from United States Department of Agriculture National Agricultural Statistics Service,
2016).
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Although several sites within the Cache River Watershed exceeded current or proposed
assessment criteria for forms of Pb, a comparison to the entire Lower Mississippi River
Watershed (LMRW) indicated that although frequency of detections exceeding the PQL were
greater in the Cache River Watershed, mean concentrations were lower. This was true for both
dissolved and total recoverable Pb. Because Pb was detected in all sediment samples, a
comparison of detection frequencies was not performed. However, mean sediment
concentrations within the Cache River Watershed were approximately half of mean
concentrations reported for other sediments within the LMRW. Sediment samples used for
comparison were largely collected from within the Southeastern Missouri Pb mining district and
were likely affected by historical mining and smelting activities. Pb deposited as a result of
smelting activities can remain in soils and sediments long after activities have ceased (Ma et al.,
2014). A comparison to global Pb concentrations indicated that measurements within the Cache
River Watershed were within (and often at the lower end) of the range of concentrations
reported worldwide for both dissolved Pb and sediment-bound Pb (reviewed by Hua et al.,
2016).
Particle size analysis indicated that sediment-bound Pb concentrations were correlated with
sediment composition. As the percentage of silt and clay in a sediment sample increased,
concentrations of recoverable sediment-bound Pb increased as well. This matches what was
predicted based on the cation exchange capacity of these two particle sizes. A surprising result
was obtained from a spike recovery experiment, originally designed to test the effectiveness of
the digestion technique employed. A sediment sample that was determined to be primarily
composed of sand (>99% sand) was spiked with Pb and then digested. This sediment was
chosen because of the extremely low proportion of silt and clay. Based on the cation exchange
capacity of the three particle sizes analyzed, sand is considered least likely to bind to Pb ions,
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thus it was expected that most or all of the added Pb would be recovered during the digestion
process. Surprisingly, recoverable sediment-bound Pb was quite low for the selected sediment,
ranging from 7-17%, despite the use of high spiking concentrations (100-500 ppb). This
indicates that sand may be playing a larger role in Pb binding and sequestration than previously
thought. Although this was only a preliminary result, based on spiking sediment from a single
site, it indicates the likelihood of sediment serving as a source of dissolved Pb within the Cache
River Watershed. The overall low recovery rates indicate that most Pb remains bound within
sediments, and is unlikely to be released under typical environmental conditions. This further
weakens the support for sediment resuspension as a major contributor to elevated dissolved Pb
concentrations within the Cache River Watershed. A more thorough spike-recovery experiment
using sediments with different compositions could help to clarify the role that each particle size
is playing in overall Pb sequestration within sediments of the Cache River Watershed.
Although the proportion of silt and clay in a sediment sample was proposed as a screening
tool for sediment-bound Pb concentrations, the proportion of sand might prove to be an even
simpler alternative. The proportion of sand is always inversely related to the sum of the
proportion of silt and clay. The proportion of sand in a sediment sample can be easily
determined visually or by filtering out the sand fraction from a dried sediment sample using an
appropriate sediment sieve. This would greatly simplify the particle size analysis process (for
screening purposes) as the amount of silt and clay, relative to each other, would be of less
importance than the sum of their proportions, which could be determined by the proportion of
sand in the sample.
SUMMARY OF CHAPTER 4
In chapter 4, the importance of using ambient waters for standard toxicological testing was
examined by testing two standard organisms (C. dubia, P. promelas) with both laboratory-
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prepared, moderately hard water and ambient water from the Cache River. In both cases, water
samples were spiked with Pb(NO3)2 to determine the concentration necessary to achieve a
standard toxicological endpoint. These tests included acute toxicity tests (endpoint: LC50) and
chronic toxicity tests (endpoint EC50, IC25). Additionally, the possibility of using behavioral
responses as an endpoint in P. promelas was examined and the effect of Pb exposure in adult
fish on behavioral responses was tested in a small pilot study.
Acute tests indicated that there is very little immediate toxicological concern to aquatic
organisms in the Cache River. Measured LC50s were always above limits established by current
hardness-based assessment criteria, which represent the criterion maximum concentration
(CMC) established by the US EPA (USEPA, 2016a). The CMC is considered to be the maximum
concentration of a chemical in water that aquatic organisms can be exposed to acutely, without
causing an adverse effect. More importantly, calculated LC50s were well above any actual
measured concentration of dissolved Pb in the Cache River Watershed, indicating that acute
effects on aquatic organisms are extremely unlikely.
Endpoints obtained from chronic tests generally followed the same pattern. Measured EC50s
and IC25s were above limits established by current hardness-based assessment criterion. This
limit represents the criterion continuous concentration (CCC) established by the US EPA (USEPA,
2016a), which is the greatest concentration of a chemical in water to which aquatic organisms
can be exposed indefinitely without resulting in an adverse effect. Measured chronic endpoints
were also well above measured concentrations of Pb within the Cache River Watershed, with
the exception of the endpoint for reproduction in C. dubia. Although the measured IC25 (2.63
ppb Pb) was above allowable limits (CCC) for dissolved Pb, it was well within the range of
concentrations of dissolved Pb measured in the Cache River Watershed during the duration of
this study (0.86 to 23.17 ppb Pb). Thus, although the CCC should be protective enough if met,
285
natural waters that exceed the CCC, which includes the Cache River, would have the potential to
adversely affect populations of C. dubia and potentially other invertebrate species. Drawing
general conclusions about toxicity based on a model organism should be approached carefully,
as cross-species modeling may not always be accurate (Esbaugh et al., 2012).
A clear effect of hardness on toxicity was apparent with increased hardness causing increased
toxic endpoints, indicating that more Pb was required to have the same toxic effect. This effect
was particularly strong in P. promelas but less so in C. dubia. This difference between species
can be explained by how Pb causes toxicity in these two organisms. Heavy metals, such as Pb,
cause acute toxicity in aquatic organism by binding to specific receptors known as biotic ligands
(Paquin et al., 2002). In fish, such as P. promelas, this ligand is thought to be sodium or calcium
channel proteins in the surface of the gills (Di Toro et al., 2001). The presence of other
substances in water can provide a protective measure against metal toxicity by either
complexing with metals and altering their free ion activity, or by competitively binding at biotic
ligands, preventing metals from binding (Di Toro et al., 2001). Hardness ions tend to
competitively bind to biotic ligands, preventing heavy metals from binding and causing toxicity.
The effect of hardness on Pb toxicity is well reported (Sprague, 1985; Pascoe et al., 1986;
Rathore and Khangarot, 2003; Yim et al., 2006; Ebrahimpour et al., 2010). Other constituents of
water, such as dissolved organic matter (DOM), including dissolved organic carbon (DOC) tend
to form complexes with metals, preventing them from binding at biotic ligands (Di Toro et al.,
2001).
The reduced protective effect provided by hardness in C. dubia suggests that a different biotic
ligand is used for Pb binding in this organism. Mager et al., (2011a) suggested that Pb enters C.
dubia via a metal transporter that has a high affinity for metals such as Pb but a low affinity for
Ca2+. Organisms possessing such a biotic ligand would not necessarily be protected by hardness
286
ions but would instead be more protected by substances that complex with dissolved Pb ions
(e.g. DOM, DOC, NaHCO3) and alter free ion activity. Several other studies have indicated that
Pb toxicity in C. dubia is more affected by water quality parameters such as pH and dissolved
organic carbon (DOC), than by hardness (Esbaugh et al., 2011; Mager et al., 2011a, 2011b). The
differences in protection provided by hardness for these two species, found both in this study
and others (Diamond et al., 1997; Mager et al., 2011a; 2011b) indicates that the different
physiologies of these organisms should be taken into account when establishing CMCs and CCCs
for aquatic organisms.
Preliminary behavioral tests indicated that relatively low concentrations of dissolved Pb (~35
ppb) can have a negative effect on predator avoidance behaviors in adult P. promelas, resulting
in a significant reduction in strength of predator avoidance behaviors. A reduction in typical
predator avoidance behavior can easily result in lethal effects to affected organisms. Mathis
and Smith (1993) found that typical anti-predator responses, such as increased shoaling, can
have a strong effect on survival of prey when exposed to a predator. Consequently, decreases
in anti-predator responses would be expected to decrease survival. Most ecotoxicological tests
are performed using larval organisms as these are deemed to be most sensitive, but this study
shows that even at a relatively insensitive portion of the life cycle, exposure to low
concentrations of dissolved Pb can have an adverse effect on ecologically important behaviors
such as predator avoidance.
Although the concentration of Pb used in this pilot study was greater than concentrations
typically measured within the Cache River Watershed, it was much lower than the sublethal
endpoints obtained using standard chronic toxicology tests in this study (IC25: 137.50 to 294.39
ppb Pb), indicating that even low levels of Pb can potentially cause indirect lethality in aquatic
organisms via alteration of normal behaviors. Only a single concentration of dissolved Pb was
287
used in this pilot study but behavioral effects might occur at even lower concentrations.
McIntyre et al. (2012) found that concentrations of copper as low as 1 ppb could reduce antipredator responses in test fish.
Behavioral toxicity testing indicated that at least two anti-predator behaviors (shoaling and
overall movement) were easily measured in response to the naturally produced alarm
substance. Stereotypical responses occurred in the majority of fish tested, even though these
were laboratory-raised, predator-naïve fish. This indicates that this type of testing could be
carried out in all times of the year using cultured organisms, rather than limiting studies to times
when fish could be obtained from the natural environment. Furthermore, these results were
obtained using a relatively simple experimental setup with 10-gal (37.8 L) tanks and a single
camera for video recording. Such testing would be relatively simple to implement in many
ecotoxicological testing and/or research facilities.
OVERALL SUMMARY
When the data from Chapters 3 and 4 are combined, the result is generally positive. Although
dissolved Pb was measured at concentrations exceeding assessment criterion (CCC) throughout
the Cache River Watershed, concentrations were generally quite low. Furthermore,
concentrations were well below any toxic endpoints found using ambient water spiked with Pb.
This indicates that if waterways remain at or below current assessment criterion, there should
be little to no adverse effect on aquatic organisms. Current assessment criterion is hardnessbased, which might actually be overprotective for aquatic organisms. The endpoints measured
in this study for different types of water and the overall recovery rates of dissolved Pb from
spike-recovery experiments indicate that ambient waters likely provide additional protection
beyond hardness.
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Although measured concentrations of Pb are probably safe for aquatic organisms, it is still
important to determine the source of the Pb to ensure that management measures are
implemented in an effective manner. Spatial and temporal assessment of data indicated that
dissolved Pb inputs are most likely a result of surface runoff from agriculturally dominated areas
of the Cache River Watershed, though it seems that runoff induced by agricultural practices
(irrigation, drainage of flooded fields) could be contributing more than precipitation-induced
runoff. A more intensive sampling of field drainage waters and/or groundwater used for
irrigation would be helpful to determine if either of these sources better explain the source of
Pb within the Cache River Watershed, particularly if this sampling took place in sub-watersheds
with the greatest measured concentrations of dissolved Pb.
A comparison of Pb concentrations in all sampled matrices to data collected for the entire
Lower Mississippi River indicates that concentrations within the Cache River Watershed tend to
be significantly lower than concentrations for the entire Lower Mississippi River (Kilmer and
Bouldin, 2016). The ubiquitous detection of Pb within the watershed indicates that a single
point source is unlikely and that management should be focused on areas with large amounts of
agricultural use, as surface runoff (whether precipitation-induced or irrigation-induced) from
agricultural watersheds remains the most strongly supported hypothesis.
Overall, land alterations associated with agriculture seem to be the primary factor behind
impaired water quality within the Cache River Watershed. Several sub-watersheds were
identified as potential sources of poor water quality where implementation of Best
Management Practices (BMPs) might be most effective, most notably, agricultural subwatersheds along the western edge of the Cache River Watershed (for nitrogen and turbidity)
and sub-watersheds in the Middle Cache Watershed (for phosphorus and dissolved oxygen).
The most effective BMPs for controlling nutrient inputs, turbidity and dissolved Pb would likely
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be those that slow and filter surface runoff, such as installation of riparian buffer strips between
agricultural fields and surface waterways and/or maintenance or restoration of natural
wetlands. Riparian buffer strips have been shown to reduce both sediment and nutrient loads in
surface runoff (Lee et al. 2003). Even very narrow buffers can be effective in reducing surface
runoff, even on steep slopes (Rasouli et al., 2015). Wu et al. (2010) found that planting a 0.5 m
grass hedge of native grasses reduced overland flow of surface water by as much as 72%.
Constructed wetlands were found to result in large decreases in suspended sediment (84-97%)
and nutrient loads (18-72%), particularly in more established wetlands with greater amounts of
emergent vegetation (O’Geen et al., 2007). BMPs would be best implemented in agriculturally
dominated sub-watersheds of the upper and middle Cache River Watershed though further
sampling within these watersheds is recommended to determine the most effective location for
these BMPs.
Restoration of the Cache River Watershed or the Cache River itself (or supporting tributaries)
is exceedingly unlikely, given the importance of the watershed as an agriculturally productive
site. Its agricultural importance is only likely to increase as the world population continues to
grow. Managing the resources within this waterway in such a manner that it continues to be
agriculturally productive while minimizing damaging environmental effects is crucial. The results
of this study indicate that although agricultural activities are affecting water quality, effects
seem to be limited to the watershed itself with concentrations of contaminants generally
decreasing along a downstream gradient. Although this does not mean that contaminants are
not transported downstream during peak flows, it indicates that for most of the year, the
watershed can effectively remove contaminants introduced from sub-watersheds before the
Cache River confluences with the White River, possibly because of the relatively large amount of
natural wetlands remaining in the lower portions of the Cache River Watershed. BMPs that
290
have been implemented thus far in the Cache River Watershed (Rosado-Berrios and Bouldin,
2016) seem to be effective, indicating that further implementation would continue to be
beneficial in improving and maintaining water quality within this watershed.
Implementation of BMPs in sub-watersheds of the Upper and Middle Cache River Watershed
would likely be most beneficial in terms of water quality. However, BMP implementation is
voluntary and the type of BMP chosen is at the discretion of the landowner. A comparison of
BMPs available for funding assistance and actual BMPs chosen in a sub-watershed of the Cache
River that has been approved for BMP implementation indicated that of the 35 approved BMPs,
landowners chose to implement only 13 and installation of riparian buffers or restoration of
wetlands was never chosen (NRCS, 2013; NRCS, 2015). Although wetland restoration is an
effective means of improving water quality in agricultural areas, the ecological gains provided by
the restoration of a wetland must be compared to the economic loss caused by the loss of
agriculturally productive land (Verhoeven and Setter, 2010). BMP implementation does
generally have an economic cost, however, the subsequent improvements in water quality
should yield considerable benefits, including improved recreational opportunities, flood control,
and ecosystem health (Alvarez et al., 2016).
Although BMP implementation remains the most viable way to improve water quality while
maintaining the economic contributions of a watershed, effectiveness of BMPs actually
implemented has been called into question. Meals et al. (2010) reviewed the results of
nonpoint source projects over the last four decades and found that it was common to find little
or no improvement in water quality as a result of BMP implementation. They suggest that many
problems exist in current BMP implementation, including low participation by landowners,
inappropriate selection of BMPs, insufficient numbers of BMPs or poor spatial distribution of
BMPs within a study area. Furthermore, pre-BMP sampling was also determined to be
291
insufficient in many cases, with a minimum of two to three years of samples necessary for
appropriate and effective management decisions (Spooner, 1991).
This present study reinforces the importance of collecting samples for an extended period of
time prior to BMP implementation and also sampling to a greater spatial degree. In this study,
samples were collected less frequently (monthly vs. weekly) but over a greater spatial area. A
comparison of mean monthly results to weekly sampling showed that there was no significant
difference due to sampling frequency. Thus, although weekly sampling could be useful when
trying to identify specific events that contribute to water quality, monthly sampling seems to be
adequate to understand overall temporal patterns of water quality variability within the
watershed.
The less frequent sampling was an important component of this project because it allowed
sampling resources to be directed toward more comprehensive spatial sampling of the
watershed. This is important when identifying sub-watersheds that could be sources of
contamination. By expanding the range of spatial sampling, a more complete idea of the overall
health of the watershed can be determined, which is crucial in identifying areas where BMP
implementation would be most effective, both ecologically and economically.
This study was also unique because all sub-watersheds sampled were considered to be
headwater sub-watersheds, meaning they had no upstream source. Any water quality problems
detected at the outflow of the sub-watershed could be presumed to have originated within that
sub-watershed. This type of targeted sampling also helps to effectively determine where to
implement BMPs in order to reduce downstream water quality impacts.
Water quality issues can vary greatly within a watershed and a one-size-fits-all approach to
BMP implementation is unlikely to be effective. For example, this study identified two
watersheds (BGLA, EASL) that would benefit from BMPs that reduce surface runoff and
292
sedimentation, such as riparian restoration, and several others that would benefit more from
nutrient mitigation practices, such as irrigation management and wetland restoration.
Furthermore, watersheds that were impacted by nitrogen were not necessarily impacted by
phosphorus and vice-versa, reinforcing the idea that BMP implementation must be tailored to
unique areas within a watershed to have the greatest impact. A similar conclusion was drawn
by Brueggen-Boman et al. (2015) who concluded that BMP implementation would be most
effective if chosen practices were tailored to both the parameter of concern and specific
locations of watersheds.
BMP implementation remains the most promising solution to the problem of improving
ecological health of agricultural watersheds without unduly compromising the economic value
of the same watershed. However, water quality can vary greatly within a single sub-watershed
and proper sampling prior to implementation is a key part of tailoring BMPs to ensure the most
effective outcome. This study shows the value in promoting greater spatial sampling at the cost
of temporal sampling as well as the importance of sampling in headwaters, where contaminant
sources are more easily identified.
Proper selection of BMPs and implementation locations remains just part of the solution to
the problem. As Meals et al. (2010) and Brueggen-Boman et al. (2015) point out, low landowner
participation is also a key problem with BMP implementation. Conveying the relative costs and
benefits to landowners remains a critical step in protecting agricultural watersheds such as the
Cache River Watershed and ensuring that they remain both ecologically sound and economically
productive in the future.
293
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300
APPENDIX A
MEASUREMENTS OF TSS, TURBIDITY, PH, TEMPERATURE, CONDUCTIVITY
AND DISSOLVED OXYGEN FROM THE CACHE RIVER WATERSHED
(AUGUST 2013-JULY 2016)
301
Appendix A. Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Aug 2013
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
24.0
21.9
23.1
23.2
24.6
22.0
24.2
24.0
24.7
25.2
26.0
22.8
27.0
22.8
23.8
25.6
23.3
23.1
22.8
23.8
23.4
24.3
24.7
pH
7.30
7.01
6.77
6.80
7.05
6.76
6.78
7.04
6.96
7.24
7.37
6.83
8.58
7.02
6.67
6.86
6.83
6.69
7.14
6.64
6.68
6.57
6.81
Conductivity (mS)
233.0
186.4
93.5
151.9
209.8
90.6
72.5
267.7
222.9
291.9
256.8
119.0
98.0
318.0
230.8
271.0
206.9
163.3
405.0
606.0
226.1
190.3
204.2
302
DO (mg/L)
7.71
7.15
7.98
5.91
7.00
8.37
7.77
6.33
5.86
6.42
7.04
7.43
10.8
5.08
5.01
5.02
5.06
6.13
4.44
4.18
4.10
5.59
5.97
TSS (mg/L)
18.33
40.57
7.03
107.37
50.90
9.73
5.47
46.37
35.70
40.37
19.60
6.40
19.10
65.73
24.70
16.57
17.93
26.23
24.33
27.17
29.20
42.37
52.27
Turbidity (NTU)
25.5
26.0
26.0
130.0
51.3
21.6
25.4
55.2
39.5
35.9
24.2
17.7
37.3
71.4
29.3
22.4
36.2
54.7
28.0
32.0
30.8
46.4
47.2
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Sep 2013
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
21.4
22.0
24.5
23.7
25.7
23.1
25.0
24.1
25.2
24.8
28.2
21.6
25.9
22.0
20.6
22.4
22.1
22.7
20.8
24.9
21.8
24.3
26.0
pH
7.75
7.86
8.10
8.16
8.14
7.59
7.42
7.99
8.01
7.97
8.51
7.27
7.20
7.60
7.61
7.36
7.65
7.54
7.54
8.27
7.84
8.41
8.22
Conductivity (mS)
425.0
459.0
358.0
453.0
517.0
237.5
151.0
676.0
854.0
604.0
526.0
243.1
243.8
608.0
589.0
371.0
371.0
524.0
484.0
410.0
606.0
614.0
668.0
303
DO (mg/L)
6.83
6.80
6.70
7.68
9.01
6.93
7.76
8.55
8.49
8.71
12.91
8.53
4.03
5.60
5.68
5.53
5.72
5.02
5.82
9.90
5.88
9.03
8.79
TSS (mg/L)
51.00
46.07
13.20
74.33
36.97
9.10
3.07
52.7
26.13
21.13
5.57
3.63
8.43
45.27
28.70
42.67
44.50
34.47
18.60
7.43
52.07
35.90
42.67
Turbidity (NTU)
27.1
25.0
8.45
50.2
26.5
8.36
9.26
32.2
14.5
15.7
4.86
4.02
12.2
34.8
13.2
47.2
45.3
34.8
18.4
10.5
44.2
36.2
32.7
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Oct 2013
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
25.0
24.9
25.0
24.9
26.3
24.3
25.3
27.0
26.5
25.4
25.4
22.8
25.2
18.3
17.5
23.9
23.1
22.4
19.3
20.7
20.2
19.3
19.8
pH
7.46
8.00
7.65
8.33
8.49
7.63
7.42
7.50
7.99
7.98
7.83
7.31
7.83
7.06
7.24
7.41
7.87
7.51
7.41
7.81
7.33
7.16
6.97
Conductivity (mS)
251.4
373.0
197.2
395.0
461.0
257.1
68.6
326.0
513.0
403.0
183.7
233.3
263.1
369.0
304.0
470.0
581.0
486.0
405.0
543.0
245.5
195.2
296.5
304
DO (mg/L)
TSS (mg/L)
6.90
6.60
6.51
6.12
9.36
6.98
7.72
4.62
7.51
8.11
6.07
6.82
8.51
6.40
6.15
6.80
6.22
5.92
5.69
6.73
4.84
5.08
4.58
18.50
24.47
10.60
94.67
20.30
7.00
4.77
316.87
17.07
17.20
425.13
11.07
12.90
111.07
121.73
63.13
58.80
73.00
56.13
47.87
353.53
92.00
217.6
Turbidity (NTU)
35.4
16.7
9.3
65.4
19.0
6.6
9.0
658.0
13.0
11.7
509.0
8.1
11.9
96.4
163.0
43.4
44.2
87.3
59.9
50.3
811.0
136.0
203.0
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Nov 2013
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
6.7
5.2
7.4
7.3
6.7
7.2
7.1
6.6
5.1
4.9
3.9
6.6
6.8
4.6
5.8
6.5
7.5
8.3
4.5
4.2
4.7
6.0
5.8
pH
6.91
6.91
6.64
6.83
7.16
6.50
6.53
7.20
7.34
7.55
7.73
6.57
6.85
7.58
7.21
7.22
7.22
7.21
7.66
7.61
7.58
7.54
7.56
Conductivity (mS)
198.3
195.4
167.6
161.5
310.0
108.0
85.0
345.0
444.0
377.0
211.8
174.1
172.7
370.0
208.7
400.0
471.0
274.4
429.0
391.0
317.0
483.0
434.0
NM = no value measured (site flooded, sample lost, equipment malfunction)
305
DO (mg/L)
TSS (mg/L)
NM
NM
NM
NM
NM
NM
NM
NM
NM
NM
NM
NM
NM
10.83
8.57
8.37
7.49
7.13
10.60
10.80
9.14
8.23
9.96
58.63
69.70
10.00
128.90
63.73
7.97
9.77
138.1
24.03
37.17
10.50
7.23
10.93
90.20
45.63
78.87
21.77
65.50
100.20
32.00
26.90
61.10
160.17
Turbidity (NTU)
165.0
201.0
37.6
392.0
129.0
24.7
59.6
121.0
39.8
81.6
24.0
51.1
43.2
254.0
134.0
168.0
48.2
178.0
318.0
91.8
66.5
86.3
335.0
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Dec 2013
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
3.1
3.1
3.1
3.0
5.3
5.4
6.5
5.7
5.4
6.5
6.7
6.9
6.0
3.7
3.4
5.2
4.6
4.2
4.4
5.3
4.3
3.2
2.6
pH
6.77
6.79
6.79
6.74
6.98
6.59
6.53
7.19
7.10
7.52
7.28
6.80
7.15
6.78
6.68
6.74
6.71
6.71
6.78
6.74
6.78
6.86
6.87
Conductivity (mS)
113.5
109.5
109.5
110.0
195.7
97.3
85.6
258.9
243.4
254.7
261.0
165.3
303.0
148.5
176.1
243.7
226.8
191.5
326.0
139.7
200.9
346.0
173.1
306
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
13.0
13.2
13.2
13.3
12.7
13.2
13.0
12.6
12.2
13.4
12.9
11.6
12.0
11.2
10.6
10.0
10.1
10.7
10.6
11.1
10.9
11.7
11.5
21.80
17.17
17.17
6.67
21.97
4.80
6.23
36.9
22.53
16.03
12.20
8.30
10.30
92.30
26.60
46.67
23.53
40.83
18.60
23.30
16.23
58.00
69.60
75.0
52.4
52.4
50.6
64.9
24.7
37.0
41.0
51.8
34.8
47.4
41.3
35.4
258.0
77.4
129.0
84.0
129.0
81.4
80.4
55.3
96.5
94.5
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Jan 2014
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
7.6
7.4
7.5
6.6
8.5
9.1
10.3
8.6
7.7
10.0
8.0
6.6
8.2
7.3
7.3
7.1
4.7
5.9
8.0
8.5
7.3
6.9
6.9
pH
6.70
6.24
6.09
6.23
6.52
6.09
6.14
6.59
6.46
7.07
6.47
6.31
6.37
6.04
6.09
6.18
6.21
6.35
6.61
6.39
6.34
6.34
6.35
Conductivity (mS)
137.4
101.5
67.3
78.0
112.1
63.2
58.5
109.4
127.5
203.4
122.1
54.8
145.0
112.9
114.0
131.5
107.1
115.0
156.7
108.1
265.1
102.6
101.3
307
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
10.74
10.38
11.15
10.65
9.92
10.87
10.42
10.00
9.59
10.18
10.21
11.67
11.24
9.05
8.67
9.05
9.87
9.94
7.98
8.88
7.99
9.55
9.58
66.13
121.23
22.07
98.47
384.0
8.77
8.10
193.00
210.60
74.77
165.30
41.63
36.07
148.77
55.70
22.37
14.97
21.60
64.03
53.43
165.80
148.97
170.23
168.0
261.0
57.2
236.0
886.0
31.4
35.7
460.0
583.0
128.0
559.0
66.0
75.9
491.0
165.0
124.0
138.0
145.0
231.0
153.0
598.0
353.0
398.0
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Feb 2014
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
0.7
0.6
0.2
3.2
1.4
0.9
2.2
1.6
1.0
1.9
1.4
3.5
4.3
3.5
2.7
2.4
3.0
2.7
3.2
2.6
1.7
3.1
1.9
pH
6.71
6.32
6.19
6.96
6.66
6.14
6.14
6.54
6.77
6.98
6.61
6.46
6.74
6.45
6.53
6.30
6.43
6.64
7.29
6.42
6.90
7.20
7.02
Conductivity (mS)
192.9
99.3
90.2
132.1
156.9
74.8
68.7
126.2
338.0
154.5
116.7
117.2
125.4
81.1
91.4
59.6
66.9
83.8
171.5
59.6
190.7
113.0
82.3
308
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
12.90
13.32
12.98
13.08
12.32
13.21
13.13
12.68
12.25
12.67
12.85
12.50
12.76
11.23
10.99
11.17
10.33
11.34
10.88
11.58
11.72
12.15
12.46
114.90
41.70
101.17
39.63
102.63
23.83
13.43
85.87
116.37
182.93
132.33
33.37
33.37
21.20
35.93
14.53
27.00
39.63
58.47
147.03
53.50
46.70
56.40
302.0
113.0
147.0
94.7
244.0
58.1
64.3
202.0
312.0
402.0
298.0
96.1
99.8
84.3
108.0
76.9
118.0
161.0
114.0
360.0
148.0
79.9
152.0
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Mar 2014
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
3.5
3.2
4.9
5.1
4.4
6.2
7.1
4.6
4.6
5.8
4.6
9.4
9.2
9.6
9.0
12.5
10.8
10.6
13.9
11.1
11.2
10.6
10.3
pH
6.35
6.30
5.59
6.13
6.02
5.54
5.53
6.38
6.53
6.73
6.41
6.01
6.21
6.12
6.13
6.06
5.81
6.24
6.51
6.40
6.46
6.23
6.20
Conductivity (mS)
111.7
94.7
53.9
75.1
72.0
55.2
52.4
103.4
144.9
177.6
110.1
62.6
84.3
59.3
63.5
60.0
104.9
58.8
132.7
119.4
148.3
105.8
103.7
309
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
11.46
11.65
11.49
11.02
11.14
11.50
11.16
11.39
11.09
11.73
11.54
10.77
10.80
9.55
8.93
8.39
7.62
8.88
9.72
9.60
9.05
9.38
9.49
168.97
321.40
62.20
266.13
269.87
43.23
23.67
121.73
194.00
294.67
114.00
132.17
95.43
120.60
68.77
12.73
18.63
61.63
200.00
99.50
157.20
171.50
191.17
412.0
732.0
88.2
813.0
697.0
43.1
45.4
255.0
527.0
552.0
226.0
70.4
73.8
430.0
250.0
74.9
110.0
240.0
359.0
613.0
379.0
446.0
421.0
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Apr 2014
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
10.5
10.1
11.0
11.6
12.2
11.2
12.2
10.5
12.4
12.8
15.5
15.6
15.8
15.0
15.3
17.6
15.7
15.6
21.9
18.6
16.0
17.6
17.5
pH
7.37
7.19
6.91
6.87
7.01
6.84
6.89
7.21
7.25
7.51
7.13
7.05
7.35
6.68
6.83
6.77
6.70
6.82
7.15
6.96
6.94
7.02
6.98
Conductivity (mS)
135.9
97.5
51.4
79.9
100.1
62.3
60.4
113.4
117.9
186.9
91.6
59.2
73.9
109.2
99.6
118.4
102.9
84.5
79.4
92.3
92.2
103.9
93.0
310
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
9.72
10.04
10.19
8.26
8.43
10.44
10.28
9.37
8.49
9.17
8.97
9.48
9.69
6.30
6.34
6.28
6.24
7.88
8.74
7.36
6.43
7.52
7.61
128.47
126.17
17.03
259.87
259.80
24.63
14.03
86.17
161.13
66.27
64.57
82.00
61.47
136.57
142.83
31.70
43.53
159.57
90.10
144.87
92.37
152.33
143.47
466.0
331.0
45.0
840.0
784.0
23.0
32.0
241.0
496.0
254.0
227.0
79.0
85.0
485.0
517.0
109.0
183.0
521.0
371.0
406.0
246.0
421.0
443.0
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
May 2014
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
24.8
22.4
22.9
23.3
28.0
22.5
24.3
22.7
27.3
27.3
28.9
23.7
27.4
23.5
23.5
26.3
20.9
22.8
23.9
28.6
27.1
29.6
28.4
pH
8.49
6.64
6.76
6.43
8.16
6.43
6.43
6.74
6.64
7.22
8.24
6.28
8.02
5.98
6.03
5.98
4.58
6.17
6.05
6.89
6.25
8.05
7.78
Conductivity (mS)
204.0
87.6
97.2
71.5
126.9
77.5
65.0
158.7
145.5
176.9
59.8
65.2
102.5
149.4
135.1
95.4
91.5
93.0
149.5
142.8
136.7
112.1
95.8
311
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
12.84
7.69
9.27
7.53
9.40
8.78
8.80
7.65
5.75
7.85
9.91
8.17
10.83
4.72
2.48
5.53
4.70
6.67
3.45
9.01
2.38
10.63
9.78
53.40
88.70
13.47
203.70
92.23
14.67
8.33
53.80
101.33
36.67
19.47
17.03
14.40
128.07
87.03
21.50
42.33
82.30
55.90
157.07
95.3
124.83
110.83
75.0
102.0
21.1
288.0
225.0
16.8
19.2
79.4
160.0
100.0
43.40
44.40
NA
235.0
128.0
81.0
159.0
205.0
178.0
274.0
149.0
215.0
196.0
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Jun 2014
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
26.1
25.3
25.2
24.8
24.9
25.5
26.4
29.7
29.1
32.2
27.5
26.4
32.4
26.0
25.9
25.8
25.9
27.3
26.9
30.7
28.6
29.5
28.1
pH
7.80
7.09
6.34
6.32
6.36
6.63
6.52
6.50
6.68
6.94
6.26
6.55
7.04
5.96
5.77
6.06
6.26
6.52
6.86
7.64
6.51
6.77
6.45
Conductivity (mS)
518.0
260.0
91.5
86.4
91.9
100.1
86.3
120.8
196.8
189.4
106.5
96.6
139.6
152.8
146.2
108.5
120.3
154.1
470.0
591.1
209.3
121.9
112.8
312
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
9.85
6.45
6.67
5.84
6.24
7.90
7.41
5.96
5.70
7.16
6.35
7.06
8.17
3.71
2.19
2.40
2.62
4.79
4.38
8.43
2.91
6.44
5.61
60.53
134.73
189.00
543.73
692.53
26.30
32.07
647.93
175.03
189.07
180.40
87.73
29.97
91.60
74.67
19.47
39.83
117.27
20.53
109.8
140.23
174.67
794.8
57.8
166.0
429.0
1388.0
1166.0
40.1
93.2
716.0
316.0
571.0
512.0
200.0
56.5
231.0
144.0
63.3
75.2
208.0
19.1
87.2
329.0
326.0
1324.0
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Jul 2014
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
26.0
25.7
27.2
27.1
28.7
26.3
27.8
32.4
29.9
29.9
33.1
26.8
34.1
25.5
26.3
26.5
25.3
25.5
24.3
26.3
24.7
26.2
27.4
pH
7.50
7.37
6.82
7.25
7.39
6.94
6.66
8.16
7.41
7.53
8.71
6.58
8.36
7.13
7.28
6.74
7.11
6.90
7.35
7.45
7.42
7.55
8.06
Conductivity (mS)
506.0
409.0
155.5
507.0
439.0
180.6
95.3
535.0
469.0
514.0
443.0
194.9
291.2
500.0
549.0
267.8
402.0
367.0
610.0
635.0
636.0
575.0
400.0
313
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
7.95
6.36
5.46
5.79
7.65
7.87
7.70
12.93
8.49
11.09
25.14
7.86
14.48
4.80
6.84
6.58
5.52
4.95
5.41
6.73
5.81
7.51
8.39
65.97
62.53
22.37
104.77
60.27
5.87
30.90
14.17
65.83
11.43
10.73
5.23
12.00
35.17
10.13
49.00
116.30
59.63
21.37
65.80
77.00
102.33
38.77
57.1
35.9
28.0
63.1
43.2
9.6
18.1
10.9
47.0
7.6
7.9
11.6
24.7
34.5
7.9
33.2
87.2
51.1
12.5
46.4
60.0
91.2
27.7
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Aug 2014
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
26.2
25.8
25.5
27.2
28.5
26.5
27.8
30.8
28.7
31.0
31.6
26.6
31.0
25.2
24.0
27.0
25.6
27.0
25.9
28.6
27.7
30.5
30.2
pH
7.13
6.84
6.37
6.95
7.27
6.79
6.53
7.59
7.32
7.74
7.74
6.52
7.48
6.93
7.24
7.03
7.09
6.85
7.30
7.33
7.38
7.66
7.85
Conductivity (mS)
387.0
285.0
111.7
386.0
452.0
195.5
85.3
746.0
568.0
625.0
669.0
227.6
342.0
526.0
624.0
458.0
470.0
337.0
612.0
708.0
588.0
622.0
465.0
314
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
7.20
6.56
6.86
5.35
7.44
7.71
8.19
10.56
7.13
13.74
11.93
7.55
9.65
4.80
5.84
6.27
6.01
5.87
6.55
7.36
6.79
10.29
9.38
51.10
61.37
16.63
118.20
51.50
6.83
11.60
33.00
60.97
11.87
8.83
6.50
10.77
68.57
23.70
92.67
88.73
67.60
41.73
85.20
74.57
42.37
28.27
44.8
51.3
31.6
110.0
53.0
10.0
15.0
24.0
41.0
9.0
7.0
9.0
9.0
64.8
22.8
85.5
86.2
68.0
34.1
65.1
58.4
41.8
20.8
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Sep 2014
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
19.3
19.4
19.5
20.8
19.6
19.0
19.8
19.3
19.7
19.8
20.5
18.8
20.1
21.3
21.2
22.8
21.8
21.8
21.9
22.6
21.5
23.5
23.3
pH
7.16
7.07
6.47
6.93
6.92
6.12
6.08
6.67
6.86
7.20
7.02
6.40
6.96
7.59
7.84
7.76
7.34
7.30
7.89
7.90
7.79
8.04
7.82
Conductivity (mS)
373.0
299.9
160.0
289.0
270.4
96.6
62.9
320.0
305.0
458.0
375.0
230.1
280.9
978.0
933.0
782.0
975.0
399.0
696.0
990.0
824.0
1638.0
512.0
NM = no value measured (site flooded, sample lost, equipment malfunction)
315
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
8.90
7.87
7.03
5.82
6.95
7.72
8.40
NM
NM
NM
NM
NM
NM
4.88
7.82
7.96
6.95
7.64
7.50
7.30
7.53
11.12
8.51
32.17
32.30
4.90
69.53
44.23
10.77
7.47
72.20
39.23
14.97
12.23
8.13
13.70
69.00
24.27
84.03
59.77
34.77
22.90
42.33
60.03
24.67
46.97
22.2
21.6
19.3
70.6
39.6
23.9
19.2
52.5
30.3
11.0
15.7
7.9
12.8
66.1
18.5
76.5
80.2
39.6
17.8
32.3
44.3
17.5
32.3
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Oct 2014
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
NM
15.8
15.0
15.5
17.8
15.7
13.0
13.2
15.0
13.9
15.2
15.3
19.4
14.2
13.3
16.2
15.9
16.8
16.6
21.8
18.4
19.0
18.0
pH
7.48
6.53
6.87
6.88
7.37
6.81
6.75
7.55
7.24
7.38
7.58
6.66
7.04
7.00
7.05
6.52
6.14
6.44
6.61
8.12
7.05
7.69
7.45
Conductivity (mS)
381.0
448.0
145.7
339.0
367.0
163.6
75.5
245.1
369.0
622.0
443.0
238.8
295.9
685.0
705.0
314.0
250.3
305.0
510.0
521.0
526.0
364.0
311.0
316
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
11.30
9.45
9.22
3.83
9.41
10.72
9.65
10.18
7.69
5.94
9.25
7.80
5.10
4.94
4.90
6.50
5.75
3.70
3.85
8.28
3.29
4.90
6.71
11.73
30.50
25.70
163.00
63.40
11.40
11.60
17.10
29.80
12.20
13.00
15.20
3.07
62.40
9.77
82.40
22.07
34.00
33.23
52.67
18.90
25.60
19.53
22.6
32.5
25.7
163.0
63.4
11.4
11.6
17.1
29.8
12.2
13.0
15.2
3.1
59.6
8.4
97.7
39.9
52.9
30.9
52.6
22.7
28.3
22.4
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Nov 2014
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
5.2
5.0
4.7
2.8
4.2
4.9
4.8
5.6
5.4
4.7
5.0
6.6
7.6
8.5
7.5
10.6
11.8
11.4
8.8
6.1
8.0
7.5
8.7
pH
8.18
8.12
7.70
8.28
8.60
7.40
7.48
7.90
8.35
8.22
8.74
7.38
6.71
6.84
6.78
7.10
6.88
7.18
6.58
7.42
7.07
7.63
8.10
Conductivity (mS)
395.0
272.2
282.7
278.2
333.0
135.3
71.7
124.2
408.0
732.0
427.0
222.5
277.1
421.0
424.0
331.0
338.0
360.0
423.0
555.0
483.0
399.0
334.0
317
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
13.26
11.62
12.09
12.75
14.28
11.11
12.69
13.52
13.11
13.36
16.15
10.44
13.35
6.87
7.84
9.37
8.57
5.22
3.93
10.88
6.18
9.12
10.84
5.07
8.93
4.33
50.40
15.17
2.27
1.93
3.00
6.27
18.40
8.27
5.17
8.57
16.27
16.20
106.40
21.20
29.17
7.13
19.63
40.77
21.60
23.00
13.0
11.6
5.3
118.0
33.1
7.8
5.7
11.1
13.2
25.6
15.4
9.2
16.2
29.7
52.1
233.0
29.0
42.5
16.4
25.7
36.1
28.2
23.0
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Dec 2014
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
7.6
6.1
5.9
6.6
8.0
7.2
7.4
7.0
6.9
7.4
7.5
8.0
6.9
6.8
6.6
9.1
8.6
8.6
6.7
6.7
7.1
7.2
6.8
pH
7.76
7.81
7.34
7.60
7.64
7.22
7.37
7.88
7.88
7.88
8.48
7.22
7.26
7.27
7.13
6.98
6.87
6.83
7.27
7.68
7.21
7.85
7.62
Conductivity (mS)
300.0
368.0
156.3
240.9
294.7
116.8
78.4
263.0
367.0
417.0
229.4
259.4
158.7
492.0
407.0
367.0
336.0
343.0
611.0
424.0
400.0
506.0
300.0
318
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
10.95
11.14
10.66
9.84
10.82
11.20
12.04
12.52
12.01
10.87
12.92
10.92
10.92
8.07
8.30
9.41
9.39
8.54
5.97
9.91
8.06
11.22
11.00
40.10
19.90
6.07
165.13
82.83
8.20
3.33
16.00
12.07
160.20
17.40
4.57
36.73
21.40
12.83
26.17
23.90
22.47
5.67
48.13
50.03
47.90
63.87
131.0
56.9
28.5
448.0
166.0
22.5
29.2
49.5
44.5
497.0
44.5
15.4
81.4
38.3
41.7
34.8
47.0
56.7
9.3
91.2
98.2
51.5
125.0
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Jan 2015
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
3.1
1.2
1.3
0.8
2.6
3.4
3.4
2.0
1.9
2.2
3.4
3.8
1.9
2.5
2.6
5.4
5.5
5.7
3.8
3.1
4.1
3.7
3.0
pH
7.41
7.43
7.00
7.12
7.45
7.00
6.97
7.45
7.32
7.65
7.23
6.95
7.41
7.26
6.97
7.35
7.33
7.32
7.22
7.31
7.14
7.19
7.15
Conductivity (mS)
100.3
142.2
70.0
90.7
128.4
75.4
77.5
195.8
183.4
295.0
207.0
166.0
201.0
267.9
249.2
322.0
279.4
227.0
247.0
224.0
214.0
135.0
131.8
319
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
11.70
12.40
13.20
11.50
10.90
13.20
12.90
11.90
12.10
12.90
11.00
11.70
12.90
11.50
11.00
11.00
11.30
10.90
11.80
11.70
10.20
11.40
11.60
153.40
321.87
17.77
125.57
135.93
7.10
10.03
82.03
111.90
102.90
118.67
23.23
18.90
89.30
77.40
41.60
31.17
74.20
65.63
80.43
95.37
164.27
181.70
440.0
813.0
74.6
390.0
345.0
30.8
44.6
204.0
244.0
246.0
337.0
85.2
62.5
217.0
168.0
71.0
65.7
170.0
161.0
192.0
250.0
393.0
423.0
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Feb 2015
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
1.5
0.0
0.0
-0.1
0.2
1.0
1.1
1.8
0.4
1.3
0.7
2.7
2.3
2.1
1.8
1.3
0.9
0.8
2.2
1.0
2.7
1.4
1.1
pH
7.49
7.52
7.48
7.33
7.28
7.08
7.10
7.09
7.04
7.30
7.29
7.23
7.17
7.15
6.81
7.34
7.36
7.13
7.11
6.85
7.00
7.08
7.00
Conductivity (mS)
124.6
125.3
79.7
116.7
122.2
67.4
78.5
147.0
169.2
213.7
142.6
70.4
94.6
167.7
144.6
167.5
146.8
99.4
214.9
146.1
146.4
124.7
111.8
320
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
12.63
12.50
13.70
12.18
12.66
13.70
13.69
12.87
11.47
12.34
12.90
13.36
13.37
11.98
11.17
12.38
12.45
13.05
11.27
11.43
11.42
11.82
12.31
29.10
20.07
41.33
15.97
55.23
16.77
10.90
68.83
27.50
22.97
36.03
27.17
32.63
42.57
49.17
28.10
33.20
38.80
29.00
34.37
16.13
45.37
62.23
42.9
34.4
55.6
29.8
59.3
31.7
44.7
66.0
43.6
45.8
67.7
68.3
53.4
94.8
118.0
91.1
114.0
82.2
75.7
88.0
44.9
63.9
75.4
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Mar 2015
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
pH
Conductivity (mS)
9.8
7.31
121.6
8.7
7.35
128.5
9.7
7.12
99.6
10.9
7.23
113.9
10.9
7.31
148.6
10.2
7.24
107.1
10.3
7.17
98.4
10.2
7.38
165.3
13.3
7.59
206.8
12.3
7.69
223.9
13.4
7.58
130.0
14.2
7.32
124.2
13.4
7.62
177.2
12.0
7.39
205.3
12.5
7.31
241.3
14.6
6.90
136.5
14.0
7.04
138.7
13.7
7.40
157.0
21.2
7.62
235.8
22.4
7.79
180.3
Access to site flooded, no sample collected
12.7
7.28
125.7
11.8
7.23
119.0
321
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
8.89
9.80
10.65
8.32
8.41
10.99
10.88
9.97
8.12
9.36
9.33
9.16
9.51
7.55
6.62
4.28
5.89
8.61
8.70
10.46
162.73
173.13
37.00
200.40
224.67
24.40
31.27
71.17
179.43
258.73
71.70
20.17
17.70
115.63
69.77
10.20
19.90
36.80
55.70
73.53
346.0
239.0
62.2
433.0
426.0
30.5
38.0
110.0
248.0
509.0
75.2
43.2
32.8
186.0
126.0
56.8
90.3
114.0
91.4
93.8
8.41
8.52
461.80
287.87
625.0
784.0
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Apr 2015
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
20.3
16.8
18.5
21.6
24.2
17.3
18.5
25.7
23.8
21.3
25.5
20.2
25.1
14.9
14.6
18.9
18.8
19.5
14.6
14.9
15.4
22.0
21.8
pH
7.93
6.86
7.31
7.31
8.20
6.70
6.67
7.54
7.21
8.61
8.33
7.08
7.79
7.06
6.71
6.50
6.84
6.90
7.15
7.85
6.82
8.28
7.27
Conductivity (mS)
228.6
85.8
102.4
111.9
102.2
74.7
67.5
75.4
152.1
186.1
86.2
107.8
188.5
144.0
131.3
95.4
91.7
101.5
189.9
169.2
173.9
107.3
157.8
322
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
10.39
9.13
10.37
8.88
10.42
10.27
10.18
9.46
7.26
10.72
10.97
9.27
11.74
5.50
4.60
4.58
4.56
6.88
4.55
10.01
4.02
10.80
6.97
50.53
20.80
5.97
86.33
110.70
4.23
11.60
15.97
77.37
12.70
17.83
15.90
25.07
70.00
75.77
7.90
26.07
91.20
30.30
53.80
94.70
209.20
32.20
72.3
26.1
13.8
173.0
181.0
11.6
38.6
41.6
109.0
25.6
26.8
51.0
14.4
171.0
118.0
40.2
87.8
189.0
78.7
95.3
133.0
219.0
43.9
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
May 2015
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
17.5
16.4
17.3
18.0
18.1
17.9
18.9
20.1
19.3
25.1
24.3
19.5
22.9
23.6
22.9
22.9
21.2
22.5
23.1
26.2
25.4
25.5
25.0
pH
6.71
6.52
6.27
6.10
5.96
6.50
5.49
6.59
6.70
6.79
6.50
6.37
6.86
6.83
6.75
6.75
6.62
6.95
6.80
7.19
7.15
6.17
7.08
Conductivity (mS)
140.2
84.5
54.4
50.8
62.9
49.4
50.7
122.5
91.9
149.9
101.5
62.3
60.4
150.3
83.1
69.6
66.7
115.2
95.6
90.3
106.1
81.5
78.7
323
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
8.68
9.17
9.33
7.34
7.58
9.55
9.28
7.94
7.25
8.00
7.87
8.58
9.43
5.51
5.46
4.47
4.58
6.80
3.83
6.74
4.94
5.40
6.25
39.37
37.30
14.80
92.73
100.50
8.53
8.20
33.40
43.13
18.90
26.27
22.13
27.27
235.10
196.37
17.13
33.33
67.23
23.53
45.77
39.87
205.83
223.87
99.8
67.4
42.7
181.0
192.0
21.9
26.10
67.1
121.0
54.5
62.0
50.7
42.5
370.0
48.0
70.1
132.0
144.0
70.1
76.1
66.0
309.0
348.0
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Jun 2015
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
31.8
29.6
31.7
31.0
31.5
29.6
31.0
34.6
32.0
35.9
32.5
28.9
35.3
28.9
27.5
30.2
31.0
29.8
32.4
33.3
32.2
36.0
34.3
pH
8.01
7.51
6.51
6.58
6.79
6.60
6.56
7.31
6.67
8.29
6.87
6.86
7.76
6.47
6.77
6.39
6.50
6.49
6.98
7.43
6.71
8.06
8.59
Conductivity (mS)
332.0
316.0
141.4
222.3
250.2
109.5
80.8
344.0
269.2
344.0
289.9
120.1
120.7
171.7
386.0
133.5
153.9
176.2
440.0
503.0
331.0
325.0
242.0
324
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
12.75
8.59
7.41
5.50
6.95
7.29
7.50
9.81
5.40
13.22
7.20
7.03
9.43
2.81
2.65
4.29
4.86
5.32
4.57
9.06
3.09
13.27
11.38
71.87
95.73
11.67
153.23
134.77
7.07
5.67
23.27
32.70
23.20
23.33
18.03
11.83
23.90
14.27
14.60
106.33
61.67
25.13
40.00
61.17
106.87
41.83
87.40
89.0
30.0
210.0
239.0
11.7
20.5
23.4
44.5
22.3
31.1
36.7
21.6
75.1
12.7
35.5
176.0
105.0
19.9
28.7
57.0
117.0
37.6
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Jul 2015
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
32.2
31.4
33.3
32.6
32.9
33.0
33.3
38.3
35.5
38.7
36.6
30.4
37.0
30.5
30.6
30.6
30.5
30.8
31.6
33.5
32.5
34.8
34.9
pH
7.88
7.56
6.98
6.65
6.67
6.67
6.76
7.73
7.35
8.88
7.97
6.28
8.02
6.29
6.18
6.09
6.36
6.18
3.52
7.02
6.84
7.27
8.03
Conductivity (mS)
308.0
313.0
177.9
218.4
186.7
115.0
71.7
337.0
362.0
253.0
251.3
114.9
116.1
315.0
426.0
216.0
214.7
250.1
322.0
595.0
276.0
323.0
298.9
NM = no value measured (site flooded, sample lost, equipment malfunction)
325
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
11.28
6.26
6.30
5.07
5.51
7.97
8.04
8.13
6.44
17.69
10.76
6.49
9.67
4.21
3.06
3.85
4.97
5.89
2.95
5.75
3.79
8.83
11.93
38.77
58.10
NM
129.43
101.80
9.60
8.97
42.33
71.50
9.93
11.77
72.03
6.60
48.43
68.33
10.00
30.80
37.10
33.60
52.47
69.17
82.67
29.60
35.8
48.4
NM
123.0
113.0
12.2
30.4
32.7
71.4
12.7
13.0
107.0
18.5
53.6
56.0
19.4
40.2
42.1
29.4
45.5
46.9
83.7
22.5
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Aug 2015
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Temperature (°C)
25.1
28.5
25.1
26.7
26.5
25.5
24.8
28.2
27.8
27.3
29.1
25.0
31.3
23.8
23.1
24.4
24.2
24.2
23.8
24.5
24.7
28.5
27.2
pH
7.34
7.07
6.34
6.97
7.00
6.50
6.31
7.35
7.05
7.22
7.64
6.25
7.33
6.24
7.00
6.72
6.52
6.92
7.10
7.06
6.93
7.18
7.54
Conductivity (mS)
446.6
297.8
176.2
355.5
314.2
137.3
148.5
525.4
845.0
475.4
442.7
123.7
263.5
334.0
515.7
274.8
227.9
347.9
567.2
599.1
385.9
410.6
448.0
NM = no value measured (site flooded, sample lost, equipment malfunction)
326
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
9.04
7.47
5.70
7.72
7.60
9.62
8.24
10.24
8.50
8.36
10.84
7.50
12.64
6.32
6.42
4.91
5.23
6.66
6.79
7.23
6.53
7.23
8.31
33.00
53.73
25.70
86.07
55.43
10.73
7.27
19.17
52.23
47.30
8.03
15.07
8.30
46.27
55.23
31.83
28.93
43.30
20.73
52.17
65.33
59.00
36.53
21.4
44.2
26.9
71.0
47.7
6.6
28.5
13.0
34.2
31.3
8.1
38.3
7.6
41.0
31.8
32.7
33.8
40.1
13.7
36.2
41.7
57.2
23.7
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Temperature (°C)
pH
Conductivity (mS)
Feb 2015
FTSL
23.8
7.27
513.20
LCRD
22.7
7.03
400.90
SFBC
23.6
5.87
175.60
EASL
24.7
7.54
503.30
BGLA
26.2
7.37
486.90
SCCR
23.5
6.45
214.50
SUCR
22.6
5.76
70.06
SPDI
29.8
7.33
160.80
BDDI
26.2
7.21
612.50
KEDI
No water at site
NTSD
27.9
7.69
480.40
MUCR
21.6
5.84
228.30
LCDI
27.3
7.77
433.10
TMCR
22.4
6.85
440.70
FSDI
21.7
7.67
526.40
REFO
23.5
6.75
407.60
CRCP
24.4
7.01
399.60
CRPA
23.9
6.78
427.20
CCDI
21.8
6.57
703.40
SKDI
24.5
7.74
678.30
WIDI
22.7
7.30
649.20
WCRD
23.9
7.18
729.10
CREG
23.6
7.81
544.70
NM = no value measured (site flooded, sample lost, equipment malfunction)
327
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
9.81
6.77
6.74
9.11
8.53
9.18
8.10
9.77
7.20
15.90
36.90
14.13
80.23
50.43
15.77
5.27
12.50
85.63
15.4
33.3
14.0
66.5
46.8
6.5
13.9
7.6
76.5
10.73
7.79
15.07
4.89
5.49
6.13
6.41
6.62
3.11
11.78
6.46
7.26
7.72
17.90
7.03
9.27
34.13
7.87
15.10
66.47
51.13
8.50
9.60
45.33
49.07
27.20
15.8
6.8
4.5
23.8
6.9
16.1
53.2
63.2
7.6
7.7
33.5
45.5
20.6
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Temperature (°C)
pH
Conductivity (mS)
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
Oct 2015
FTSL
16.6
6.78
415.1
LCRD
16.7
6.68
332.8
SFBC
18.0
6.40
126.1
EASL
17.6
6.68
267.9
BGLA
17.6
6.49
209.4
SCCR
18.2
6.34
101.3
SUCR
18.4
6.18
86.0
SPDI
18.7
6.40
354.3
BDDI
17.6
6.39
300.3
KEDI
17.7
6.39
323.7
NTSD
18.1
6.35
339.5
MUCR
13.6
6.13
262.7
LCDI
13.1
6.52
261.0
TMCR
16.2
6.68
388.2
FSDI
14.5
6.54
397.1
REFO
17.2
6.39
390.2
CRCP
17.1
6.60
343.6
CRPA
16.5
6.63
368.4
CCDI
14.8
7.11
889.5
SKDI
No water at site
WIDI
15.5
6.90
568.3
WCRD
15.2
6.52
578.3
CREG
16.2
7.02
521.7
NM = no value measured (site flooded, sample lost, equipment malfunction)
328
6.96
7.77
7.80
7.10
5.87
7.74
7.94
7.88
5.19
5.68
7.32
7.33
8.93
5.05
4.78
4.11
6.77
5.20
3.57
57.17
40.53
33.50
181.40
102.47
7.23
41.87
55.10
74.70
31.10
17.40
4.87
13.50
7.93
7.53
24.67
35.63
36.57
5.57
46.2
61.4
102.0
354.0
203.0
23.4
250.0
104.0
132.0
46.4
33.7
12.8
40.6
7.4
9.1
24.3
35.5
36.7
2.9
3.07
4.74
8.12
44.93
9.30
23.33
35.9
19.6
21.8
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Temperature (°C)
pH
Conductivity (mS)
Nov 2015
FTSL
11.3
6.08
117.5
LCRD
10.5
6.13
101.5
SFBC
11.1
6.05
69.5
EASL
12.2
6.01
76.7
BGLA
11.6
6.01
91.8
SCCR
12.0
6.02
62.1
SUCR
11.5
5.99
64.8
SPDI
12.7
6.46
99.6
BDDI
13.5
6.43
125.7
KEDI
12.9
6.39
127.3
NTSD
12.6
6.45
92.7
MUCR
14.5
6.42
56.4
LCDI
15.0
6.26
57.7
TMCR
7.4
6.12
121.4
FSDI
8.4
6.06
141.6
REFO
11.5
5.58
192.4
CRCP
10.4
5.58
127.1
CRPA
10.8
5.67
108.1
CCDI
7.1
5.94
194.7
SKDI
7.2
6.05
140.9
WIDI
8.3
5.74
110.2
WCRD
14.8
6.32
91.4
CREG
14.4
6.30
93.4
NM = no value measured (site flooded, sample lost, equipment malfunction)
329
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
6.70
8.57
9.81
7.71
7.13
10.29
10.11
6.74
5.83
6.78
6.67
7.81
7.64
9.33
7.89
6.13
7.52
8.43
8.99
9.22
8.01
5.87
5.71
65.93
62.53
22.13
69.40
56.43
28.83
24.10
53.03
43.13
31.73
39.37
65.63
90.17
67.03
12.83
11.00
10.53
17.77
22.97
93.00
49.67
142.43
159.67
213.0
169.0
73.0
182.0
127.0
89.4
119.0
105.0
101.0
75.9
85.4
76.6
100.0
164.0
34.5
26.5
44.3
88.0
57.3
147.0
101.0
264.0
258.0
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Temperature (°C)
pH
Conductivity (mS)
Dec 2015
FTSL
10.6
7.00
161.3
LCRD
10.0
7.07
131.5
SFBC
11.2
6.72
73.8
EASL
11.9
7.07
122.7
BGLA
12.3
6.88
122.5
SCCR
12.2
6.79
61.7
SUCR
12.9
6.59
61.2
SPDI
12.8
7.01
106.3
BDDI
12.4
6.92
141.1
KEDI
12.0
7.17
154.5
NTSD
9.4
7.50
130.9
MUCR
10.3
7.19
81.0
LCDI
9.1
7.29
102.9
TMCR
10.3
7.28
132.4
FSDI
9.7
7.02
139.9
REFO
11.5
6.75
104.0
CRCP
11.0
6.81
99.4
CRPA
9.5
6.94
115.2
CCDI
10.3
7.31
152.1
SKDI
10.7
7.12
223.3
WIDI
10.8
7.28
148.3
WCRD
9.9
7.30
147.7
CREG
10.0
7.41
139.8
NM = no value measured (site flooded, sample lost, equipment malfunction)
330
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
9.39
10.21
10.47
9.13
8.84
10.60
10.34
10.46
9.04
9.81
10.81
10.65
11.19
9.70
7.64
5.65
6.36
8.68
7.76
7.77
7.56
9.05
9.90
60.60
56.80
13.97
129.87
120.87
14.67
14.30
43.20
105.50
67.50
4.13
13.23
9.53
54.20
38.73
5.53
7.80
18.70
17.00
39.57
63.10
100.07
92.60
182.0
153.0
62.5
269.0
283.0
33.5
73.0
96.2
222.0
147.0
27.7
45.6
36.5
140.0
96.8
25.1
42.7
65.2
56.3
89.6
91.3
185.0
184.0
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Temperature (°C)
pH
Conductivity (mS)
Jan 2016
FTSL
7.0
7.51
257.3
LCRD
6.3
6.87
156.7
SFBC
7.4
6.62
117.6
EASL
8.0
6.76
99.7
BGLA
8.9
6.87
120.4
SCCR
8.7
6.46
89.9
SUCR
8.9
6.38
83.6
SPDI
10.1
6.72
90.6
BDDI
8.5
6.83
189.7
KEDI
9.9
7.95
144.4
NTSD
7.2
7.14
74.6
MUCR
8.3
7.28
103.7
LCDI
8.0
7.64
68.8
TMCR
5.5
7.13
79.3
FSDI
4.5
7.00
67.3
REFO
5.1
6.47
60.8
CRCP
4.1
6.34
55.4
CRPA
4.2
6.61
56.8
CCDI
5.6
6.75
74.9
SKDI
5.1
7.02
109.9
WIDI
6.6
6.81
62.9
WCRD
7.0
6.83
70.9
CREG
5.7
6.93
75.2
NM = no value measured (site flooded, sample lost, equipment malfunction)
331
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
13.40
12.19
12.83
12.60
12.24
12.28
11.77
11.74
11.59
14.22
12.25
12.35
13.17
12.04
12.28
10.57
11.21
12.35
11.89
12.53
12.27
12.30
13.17
28.33
30.40
9.07
45.13
96.47
5.13
13.10
33.67
41.60
13.93
13.93
14.50
31.80
24.77
58.10
8.97
16.03
27.70
20.17
65.80
201.80
30.97
46.93
56.8
62.1
29.7
100.0
248.0
18.4
97.3
95.2
87.8
44.8
46.0
66.3
43.2
88.1
135.0
50.5
85.2
106.0
68.3
197.0
359.0
72.6
112.0
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Temperature (°C)
pH
Conductivity (mS)
Feb 2016
FTSL
2.5
7.52
294.2
LCRD
1.5
7.14
114.2
SFBC
2.1
7.12
169.7
EASL
2.0
7.49
216.1
BGLA
2.8
6.82
139.1
SCCR
3.6
6.80
113.5
SUCR
4.7
6.69
96.0
SPDI
3.6
7.22
175.5
BDDI
3.5
7.39
231.2
KEDI
4.7
8.16
274.7
NTSD
4.2
7.76
191.7
MUCR
5.9
6.88
248.0
LCDI
5.9
7.13
400.5
TMCR
7.2
6.79
175.4
FSDI
6.8
6.67
151.0
REFO
8.4
6.51
129.6
CRCP
8.2
6.51
147.0
CRPA
7.4
6.79
267.2
CCDI
8.0
6.87
339.1
SKDI
8.3
7.43
109.9
WIDI
9.4
6.37
167.5
WCRD
7.7
6.14
165.3
CREG
6.6
6.62
166.4
NM = no value measured (site flooded, sample lost, equipment malfunction)
332
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
13.75
14.18
13.56
13.31
12.91
13.75
13.19
13.52
13.15
15.30
14.58
13.85
15.07
10.79
10.37
8.35
10.52
11.24
11.54
13.58
10.18
11.85
12.95
106.57
22.87
6.70
70.27
138.03
5.00
4.70
122.63
38.17
34.53
42.33
7.20
13.93
120.13
446.40
12.87
49.53
63.13
53.57
190.93
209.27
101.10
82.43
184.0
55.6
21.8
172.0
296.0
11.7
16.5
303.0
92.9
80.4
112.0
16.2
21.9
420.0
1038.0
50.4
138.0
192.0
102.0
426.0
739.0
222.0
175.0
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Temperature (°C)
pH
Conductivity (mS)
Mar 2016
FTSL
12.8
8.21
238.4
LCRD
9.9
7.78
94.9
SFBC
10.3
7.37
88.1
EASL
11.4
7.41
130.1
BGLA
13.4
6.87
113.6
SCCR
14.0
6.55
77.8
SUCR
14.8
6.82
73.0
SPDI
15.5
6.85
81.0
BDDI
13.3
6.61
131.1
KEDI
15.0
7.06
190.2
NTSD
16.2
6.79
66.4
MUCR
15.6
6.18
80.9
LCDI
17.4
6.37
129.0
TMCR
13.0
6.61
94.4
FSDI
13.3
6.56
93.8
REFO
14.4
6.68
78.7
CRCP
14.3
6.73
82.9
CRPA
14.2
6.89
87.3
CCDI
14.3
6.68
130.8
SKDI
14.7
6.99
118.7
WIDI
13.9
6.70
108.1
WCRD
14.8
6.80
105.0
CREG
13.9
7.01
95.60
NM = no value measured (site flooded, sample lost, equipment malfunction)
333
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
13.20
10.93
11.44
10.76
9.85
10.72
10.34
10.28
10.37
10.58
10.69
9.99
9.52
7.84
8.25
7.44
7.36
9.11
5.64
8.70
8.32
8.84
10.41
46.03
22.63
15.07
84.40
182.80
9.43
8.00
44.97
54.20
32.37
24.33
31.53
23.63
52.60
65.33
19.43
27.43
52.13
57.73
95.33
109.63
102.87
92.27
107.0
23.3
16.1
144.0
450.0
13.7
22.3
80.8
97.4
93.7
28.0
49.3
31.7
197.0
142.0
69.7
99.9
123.0
177.0
186.0
155.0
150.0
132.0
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Temperature (°C)
pH
Conductivity (mS)
Apr 2016
FTSL
24.9
9.16
208.8
LCRD
22.3
7.51
113.6
SFBC
22.7
7.61
88.5
EASL
23.4
7.24
91.6
BGLA
25.8
8.40
108.3
SCCR
23.0
7.13
89.2
SUCR
23.8
6.90
80.1
SPDI
27.5
7.93
77.9
BDDI
24.5
6.67
120.8
KEDI
24.4
7.18
181.3
NTSD
26.8
8.84
86.5
MUCR
22.8
6.59
105.4
LCDI
26.9
7.32
307.2
TMCR
18.7
6.73
102.4
FSDI
20.1
6.51
100.0
REFO
18.8
6.50
72.8
CRCP
18.8
6.49
70.3
CRPA
19.8
6.54
72.5
CCDI
20.6
6.66
126.5
SKDI
21.5
6.34
120.4
WIDI
22.2
6.27
107.9
WCRD
21.5
6.39
104.2
CREG
23.1
7.54
89.2
NM = no value measured (site flooded, sample lost, equipment malfunction)
334
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
12.93
7.53
7.40
7.37
9.27
9.16
8.36
8.43
6.17
7.76
11.57
8.04
9.92
3.49
4.39
5.57
5.67
7.03
8.02
5.22
4.41
7.70
9.62
26.97
85.93
38.63
235.93
72.53
19.07
34.67
12.90
76.57
37.23
32.00
19.80
16.40
67.13
82.67
18.53
40.37
105.60
63.80
43.70
69.23
176.63
47.13
52.2
99.8
57.5
564.0
170.0
16.2
222.0
30.0
129.0
140.0
52.4
35.8
13.0
223.0
166.0
78.6
133.0
209.0
126.0
91.2
112.0
252.0
63.7
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Temperature (°C)
pH
Conductivity (mS)
May 2016
FTSL
17.5
7.54
193.3
LCRD
15.0
7.20
100.4
SFBC
16.2
7.28
80.5
EASL
16.3
6.76
70.2
BGLA
18.4
7.39
94.7
SCCR
15.8
6.85
83.2
SUCR
16.3
6.79
77.3
SPDI
19.1
7.51
80.4
BDDI
18.6
6.82
119.9
KEDI
18.9
8.23
265.7
NTSD
19.5
8.31
188.5
MUCR
17.0
6.62
177.7
LCDI
20.1
7.17
311.8
TMCR
17.0
6.10
94.8
FSDI
16.6
6.15
99.3
REFO
19.3
5.99
85.1
CRCP
18.2
6.06
86.9
CRPA
18.7
6.08
73.9
CCDI
18.0
6.25
196.0
SKDI
21.1
6.41
122.9
WIDI
19.6
6.29
117.3
WCRD
22.8
6.28
104.6
CREG
20.3
6.35
87.9
NM = no value measured (site flooded, sample lost, equipment malfunction)
335
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
10.10
9.43
9.74
8.74
9.96
9.78
9.89
10.12
8.31
9.98
11.87
8.67
6.40
6.02
6.88
5.72
6.28
8.09
3.66
8.48
7.00
7.60
8.88
31.50
18.70
4.77
115.27
67.37
17.73
4.93
22.37
39.63
18.90
37.90
18.63
19.90
177.47
111.07
32.33
73.07
126.97
77.13
119.90
73.67
101.53
68.87
98.1
31.6
15.4
160.0
219.0
17.5
17.2
24.0
135.0
26.0
43.5
25.7
16.0
456.0
374.0
97.0
217.0
344.0
204.0
356.0
179.0
227.0
163.0
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Temperature (°C)
pH
Conductivity (mS)
Jun 2016
FTSL
26.2
7.46
409.8
LCRD
25.9
7.15
244.4
SFBC
28.5
6.60
219.3
EASL
29.4
6.94
177.6
BGLA
33.2
8.30
220.4
SCCR
26.5
6.64
97.6
SUCR
27.4
6.45
80.7
SPDI
34.6
8.11
143.8
BDDI
31.6
6.63
257.8
KEDI
35.1
8.07
302.7
NTSD
35.2
8.75
200.0
MUCR
26.6
6.36
168.4
LCDI
33.8
7.21
128.3
TMCR
28.7
6.73
265.1
FSDI
28.1
6.47
161.2
REFO
28.4
6.03
98.5
CRCP
27.5
6.09
192.4
CRPA
28.9
6.28
243.5
CCDI
30.4
7.02
589.1
SKDI
34.2
7.39
567.4
WIDI
30.3
6.65
406.3
WCRD
32.6
6.67
255.8
CREG
31.5
7.65
229.7
NM = no value measured (site flooded, sample lost, equipment malfunction)
336
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
8.09
7.10
7.21
7.28
12.38
8.97
8.17
10.81
5.45
13.27
17.78
7.07
9.39
4.64
3.70
3.02
4.00
5.74
5.26
9.68
3.43
7.80
8.63
72.60
68.20
29.57
125.50
37.70
28.40
6.87
14.60
34.47
21.07
27.40
19.37
13.73
27.17
73.93
22.03
28.83
90.93
25.87
72.23
81.50
216.03
126.77
57.3
67.1
40.1
174.0
43.5
15.4
13.7
17.7
38.1
18.8
14.7
41.7
23.9
26.7
110.0
76.6
90.4
135.0
19.4
54.1
85.3
289.0
165.0
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Temperature (°C)
pH
Conductivity (mS)
Jul 16
FTSL
26.8
8.52
442.9
LCRD
27.0
7.57
517.0
SFBC
29.7
7.69
606.5
EASL
29.6
7.14
408.3
BGLA
29.7
7.24
361.0
SCCR
28.4
6.51
273.5
SUCR
27.5
6.30
233.1
SPDI
35.4
7.87
578.5
BDDI
31.3
7.01
432.6
KEDI
33.3
7.74
613.1
NTSD
31.5
7.77
396.7
MUCR
27.6
6.37
195.4
LCDI
36.1
6.96
365.6
TMCR
26.9
7.01
434.8
FSDI
26.5
7.08
559.8
REFO
29.9
8.27
172.6
CRCP
29.4
8.39
351.9
CRPA
28.8
7.34
478.0
CCDI
28.6
7.07
615.4
SKDI
29.5
7.14
715.5
WIDI
28.2
7.18
653.1
WCRD
32.0
7.94
528.4
CREG
30.8
8.27
449.0
NM = no value measured (site flooded, sample lost, equipment malfunction)
337
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
6.59
7.15
8.59
5.93
6.70
7.60
6.96
13.66
5.44
14.42
11.38
7.83
11.10
3.62
3.71
4.35
6.12
8.73
4.84
6.02
4.51
13.48
13.96
67.97
59.77
15.33
110.87
52.30
33.67
6.83
14.47
60.87
16.50
25.23
3.80
9.10
48.00
41.20
31.47
74.03
59.70
45.57
65.47
101.57
81.23
49.00
54.4
37.0
8.4
99.2
40.6
13.7
12.0
7.3
52.4
9.1
23.4
4.8
8.6
38.6
32.7
38.9
76.4
48.5
33.1
51.6
67.8
74.2
26.7
Appendix A (Continued). Results of physical measurements for the Cache River Watershed from August 2013-July 2016. Samples
collected to make-up for months when sites were completely dry. All make-up samples collected on June 30, 2016.
Sample
Site
Jun 16
KEDI
SKDI
WIDI
Temperature (°C)
31.0
27.3
28.0
pH
7.77
8.78
8.00
Conductivity (mS)
639.3
717.9
592.6
338
DO (mg/L)
TSS (mg/L)
Turbidity (NTU)
12.71
4.47
3.51
14.47
57.60
70.97
9.0
44.3
59.5
APPENDIX B
MEASUREMENTS OF DISSOLVED PB, TOTAL RECOVERABLE PB, SEDIMENT-BOUND PB AND
WATER HARDNESS FROM THE CACHE RIVER WATERSHED (AUGUST 2013-JULY 2016)
339
Appendix B. Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River Watershed from
August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Aug 2013
FTSL
0.143*
NM
NM
NM
LCRD
0.143*
NM
NM
NM
SFBC
0.143*
NM
NM
NM
EASL
0.143*
NM
NM
NM
BGLA
0.143*
NM
NM
NM
SCCR
0.143*
NM
NM
NM
SUCR
0.143*
NM
NM
NM
SPDI
0.143*
NM
NM
NM
BDDI
0.143*
NM
NM
NM
KEDI
0.143*
NM
NM
NM
NTSD
0.143*
NM
NM
NM
MUCR
0.143*
NM
NM
NM
LCDI
0.143*
NM
NM
NM
TMCR
0.143*
NM
NM
NM
FSDI
0.143*
NM
NM
NM
REFO
0.143*
NM
NM
NM
CRCP
0.143*
NM
NM
NM
CRPA
0.143*
NM
NM
NM
CCDI
0.143*
NM
NM
NM
SKDI
0.143*
NM
NM
NM
WIDI
0.143*
NM
NM
NM
WCRD
0.143*
NM
NM
NM
CREG
0.143*
NM
NM
NM
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
340
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Sep 2013
FTSL
0.143*
NM
NM
NM
SFBC
0.143*
NM
NM
NM
EASL
0.143*
NM
NM
NM
BGLA
0.143*
NM
NM
NM
SCCR
0.143*
NM
NM
NM
SUCR
0.143*
NM
NM
NM
SPDI
0.143*
NM
NM
NM
BDDI
0.143*
NM
NM
NM
KEDI
0.143*
NM
NM
NM
NTSD
0.143*
NM
NM
NM
MUCR
0.143*
NM
NM
NM
LCDI
0.143*
NM
NM
NM
TMCR
0.143*
NM
NM
NM
FSDI
0.143*
NM
NM
NM
REFO
0.143*
NM
NM
NM
CRCP
0.143*
NM
NM
NM
CRPA
0.143*
NM
NM
NM
CCDI
0.143*
NM
NM
NM
SKDI
0.143*
NM
NM
NM
WIDI
0.143*
NM
NM
NM
WCRD
0.143*
NM
NM
NM
CREG
0.143*
NM
NM
NM
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
341
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Oct 2013
FTSL
0.143*
NM
NM
NM
LCRD
0.143*
NM
NM
NM
SFBC
0.143*
NM
NM
NM
EASL
0.143*
NM
NM
NM
BGLA
0.143*
NM
NM
NM
SCCR
0.143*
NM
NM
NM
SUCR
0.143*
NM
NM
NM
SPDI
0.143*
NM
NM
NM
BDDI
0.143*
NM
NM
NM
KEDI
0.143*
NM
NM
NM
NTSD
0.143*
NM
NM
NM
MUCR
0.143*
NM
NM
NM
LCDI
0.143*
NM
NM
NM
TMCR
0.143*
NM
NM
NM
FSDI
0.143*
NM
NM
NM
REFO
0.143*
NM
NM
NM
CRCP
0.143*
NM
NM
NM
CRPA
0.143*
NM
NM
NM
CCDI
0.143*
NM
NM
NM
SKDI
0.143*
NM
NM
NM
WIDI
0.143*
NM
NM
NM
WCRD
0.143*
NM
NM
NM
CREG
0.143*
NM
NM
NM
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
342
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Nov 2013
FTSL
0.143*
NM
NM
NM
LCRD
0.143*
NM
NM
NM
SFBC
0.143*
NM
NM
NM
EASL
0.143*
NM
NM
NM
BGLA
0.143*
NM
NM
NM
SCCR
0.143*
NM
NM
NM
SUCR
0.143*
NM
NM
NM
SPDI
0.143*
NM
NM
NM
BDDI
0.143*
NM
NM
NM
KEDI
0.143*
NM
NM
NM
NTSD
0.143*
NM
NM
NM
MUCR
0.143*
NM
NM
NM
LCDI
0.143*
NM
NM
NM
TMCR
0.143*
NM
NM
NM
FSDI
0.143*
NM
NM
NM
REFO
0.143*
NM
NM
NM
CRCP
0.143*
NM
NM
NM
CRPA
0.143*
NM
NM
NM
CCDI
0.143*
NM
NM
NM
SKDI
0.143*
NM
NM
NM
WIDI
0.143*
NM
NM
NM
WCRD
0.143*
NM
NM
NM
CREG
0.143*
NM
NM
NM
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
343
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Dec 2013
FTSL
0.143*
NM
NM
NM
LCRD
0.143*
NM
NM
NM
SFBC
0.143*
NM
NM
NM
EASL
0.143*
NM
NM
NM
BGLA
0.143*
NM
NM
NM
SCCR
0.143*
NM
NM
NM
SUCR
0.143*
NM
NM
NM
SPDI
0.143*
NM
NM
NM
BDDI
0.143*
NM
NM
NM
KEDI
0.143*
NM
NM
NM
NTSD
0.143*
NM
NM
NM
MUCR
0.143*
NM
NM
NM
LCDI
0.143*
NM
NM
NM
TMCR
0.143*
NM
NM
NM
FSDI
0.143*
NM
NM
NM
REFO
0.143*
NM
NM
NM
CRCP
0.143*
NM
NM
NM
CRPA
0.143*
NM
NM
NM
CCDI
0.143*
NM
NM
NM
SKDI
0.143*
NM
NM
NM
WIDI
0.143*
NM
NM
NM
WCRD
0.143*
NM
NM
NM
CREG
0.143*
NM
NM
NM
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
344
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Jan 2014
FTSL
0.143*
NM
NM
NM
LCRD
0.143*
NM
NM
NM
SFBC
0.143*
NM
NM
NM
EASL
0.143*
NM
NM
NM
BGLA
0.143*
NM
NM
NM
SCCR
0.143*
NM
NM
NM
SUCR
0.143*
NM
NM
NM
SPDI
0.143*
NM
NM
NM
BDDI
0.143*
NM
NM
NM
KEDI
0.143*
NM
NM
NM
NTSD
0.143*
NM
NM
NM
MUCR
0.143*
NM
NM
NM
LCDI
0.143*
NM
NM
NM
TMCR
0.143*
NM
NM
NM
FSDI
0.143*
NM
NM
NM
REFO
0.143*
NM
NM
NM
CRCP
0.143*
NM
NM
NM
CRPA
0.143*
NM
NM
NM
CCDI
0.143*
NM
NM
NM
SKDI
0.143*
NM
NM
NM
WIDI
0.143*
NM
NM
NM
WCRD
0.143*
NM
NM
NM
CREG
0.143*
NM
NM
NM
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
345
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Feb 2014
FTSL
1.3077
NM
NM
NM
LCRD
0.143*
NM
NM
NM
SFBC
0.143*
NM
NM
NM
EASL
0.143*
NM
NM
NM
BGLA
0.143*
NM
NM
NM
SCCR
0.143*
NM
NM
NM
SUCR
0.143*
NM
NM
NM
SPDI
0.143*
NM
NM
NM
BDDI
0.143*
NM
NM
NM
KEDI
0.143*
NM
NM
NM
NTSD
0.143*
NM
NM
NM
MUCR
0.143*
NM
NM
NM
LCDI
0.143*
NM
NM
NM
TMCR
0.143*
NM
NM
NM
FSDI
0.143*
NM
NM
NM
REFO
0.143*
NM
NM
NM
CRCP
0.143*
NM
NM
NM
CRPA
0.143*
NM
NM
NM
CCDI
0.143*
NM
NM
NM
SKDI
0.143*
NM
NM
NM
WIDI
0.143*
NM
NM
NM
WCRD
0.143*
NM
NM
NM
CREG
0.143*
NM
NM
NM
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
346
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Mar 2014
FTSL
0.143*
NM
NM
NM
LCRD
0.143*
NM
NM
NM
SFBC
0.143*
NM
NM
NM
EASL
0.143*
NM
NM
NM
BGLA
0.143*
NM
NM
NM
SCCR
0.143*
NM
NM
NM
SUCR
0.143*
NM
NM
NM
SPDI
0.143*
NM
NM
NM
BDDI
0.143*
NM
NM
NM
KEDI
0.143*
NM
NM
NM
NTSD
0.143*
NM
NM
NM
MUCR
0.143*
NM
NM
NM
LCDI
0.143*
NM
NM
NM
TMCR
0.143*
NM
NM
NM
FSDI
0.143*
NM
NM
NM
REFO
0.143*
NM
NM
NM
CRCP
0.143*
NM
NM
NM
CRPA
0.143*
NM
NM
NM
CCDI
0.143*
NM
NM
NM
SKDI
3.570
NM
NM
NM
WIDI
0.143*
NM
NM
NM
WCRD
0.143*
NM
NM
NM
CREG
0.143*
NM
NM
NM
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
347
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Apr 2014
FTSL
0.143*
NM
NM
NM
LCRD
0.143*
NM
NM
NM
SFBC
0.143*
NM
NM
NM
EASL
0.143*
NM
NM
NM
BGLA
0.143*
NM
NM
NM
SCCR
0.143*
NM
NM
NM
SUCR
0.143*
NM
NM
NM
SPDI
0.143*
NM
NM
NM
BDDI
0.143*
NM
NM
NM
KEDI
0.143*
NM
NM
NM
NTSD
0.143*
NM
NM
NM
MUCR
0.143*
NM
NM
NM
LCDI
0.143*
NM
NM
NM
TMCR
0.143*
NM
NM
NM
FSDI
0.143*
NM
NM
NM
REFO
0.143*
NM
NM
NM
CRCP
0.143*
NM
NM
NM
CRPA
0.143*
NM
NM
NM
CCDI
0.143*
NM
NM
NM
SKDI
0.143*
NM
NM
NM
WIDI
0.143*
NM
NM
NM
WCRD
0.143*
NM
NM
NM
CREG
0.143*
NM
NM
NM
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
348
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
May 2014
FTSL
0.143*
NM
NM
NM
LCRD
0.143*
NM
NM
NM
SFBC
1.915
NM
NM
NM
EASL
0.143*
NM
NM
NM
BGLA
0.143*
NM
NM
NM
SCCR
0.143*
NM
NM
NM
SUCR
0.143*
NM
NM
NM
SPDI
0.143*
NM
NM
NM
BDDI
0.143*
NM
NM
NM
KEDI
0.143*
NM
NM
NM
NTSD
0.143*
NM
NM
NM
MUCR
0.143*
NM
NM
NM
LCDI
0.143*
NM
NM
NM
TMCR
0.143*
NM
NM
NM
FSDI
0.143*
NM
NM
NM
REFO
0.143*
NM
NM
NM
CRCP
0.143*
NM
NM
NM
CRPA
0.143*
NM
NM
NM
CCDI
0.143*
NM
NM
NM
SKDI
0.143*
NM
NM
NM
WIDI
0.143*
NM
NM
NM
WCRD
1.274
NM
NM
NM
CREG
4.219
NM
NM
NM
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
349
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Jun 2014
FTSL
0.143*
NM
NM
NM
LCRD
0.143*
NM
NM
NM
SFBC
0.143*
NM
NM
NM
EASL
0.143*
NM
NM
NM
BGLA
0.143*
NM
NM
NM
SCCR
0.143*
NM
NM
NM
SUCR
0.143*
NM
NM
NM
SPDI
0.143*
NM
NM
NM
BDDI
0.143*
NM
NM
NM
KEDI
0.143*
NM
NM
NM
NTSD
0.143*
NM
NM
NM
MUCR
0.143*
NM
NM
NM
LCDI
0.143*
NM
NM
NM
TMCR
0.143*
NM
NM
NM
FSDI
0.143*
NM
NM
NM
REFO
9.610
NM
NM
NM
CRCP
0.143*
NM
NM
NM
CRPA
5.422
NM
NM
NM
CCDI
0.143*
NM
NM
NM
SKDI
0.143*
NM
NM
NM
WIDI
0.143*
NM
NM
NM
WCRD
0.143*
NM
NM
NM
CREG
0.143*
NM
NM
NM
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
350
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Jul 2014
FTSL
0.143*
NM
NM
NM
LCRD
0.143*
NM
NM
NM
SFBC
0.143*
NM
NM
NM
EASL
0.143*
NM
NM
NM
BGLA
0.143*
NM
NM
NM
SCCR
0.143*
NM
NM
NM
SUCR
0.143*
NM
NM
NM
SPDI
0.143*
NM
NM
NM
BDDI
0.143*
NM
NM
NM
KEDI
0.143*
NM
NM
NM
NTSD
0.143*
NM
NM
NM
MUCR
0.143*
NM
NM
NM
LCDI
0.143*
NM
NM
NM
TMCR
0.143*
NM
NM
NM
FSDI
0.143*
NM
NM
NM
REFO
0.143*
NM
NM
NM
CRCP
0.143*
NM
NM
NM
CRPA
0.143*
NM
NM
NM
CCDI
0.143*
NM
NM
NM
SKDI
0.143*
NM
NM
NM
WIDI
0.143*
NM
NM
NM
WCRD
0.143*
NM
NM
NM
CREG
0.143*
NM
NM
NM
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
351
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Aug 2014
FTSL
0.143*
0.236*
NM
NM
LCRD
0.143*
2.096
NM
NM
SFBC
0.143*
10.22
NM
NM
EASL
0.143*
2.070
NM
NM
BGLA
0.143*
6.436
NM
NM
SCCR
0.143*
2.053
NM
NM
SUCR
0.143*
0.236*
NM
NM
SPDI
0.143*
1.854
NM
NM
BDDI
0.143*
1.890
NM
NM
KEDI
0.143*
2.466
NM
NM
NTSD
0.793**
4.527
NM
NM
MUCR
0.143*
1.121**
NM
NM
LCDI
0.143*
1.774
NM
NM
TMCR
0.143*
5.855
NM
NM
FSDI
0.143*
6.588
NM
NM
REFO
0.143*
8.662
NM
NM
CRCP
0.143*
3.769
NM
NM
CRPA
0.143*
3.553
NM
NM
CCDI
0.143*
2.575
NM
NM
SKDI
0.143*
1.547
NM
NM
WIDI
0.143*
2.006
NM
NM
WCRD
0.143*
8.178
NM
NM
CREG
0.143*
3.100
NM
NM
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
352
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Sep 2014
FTSL
0.143*
1.669
NM
120.0
LCRD
0.143*
1.908
NM
80.0
SFBC
0.143*
0.9254**
NM
33.3
EASL
1.614
0.9571**
NM
106.6
BGLA
0.143*
1.0485**
NM
86.6
SCCR
0.143*
0.8882**
NM
13.3
SUCR
0.143*
2.577
NM
20.0
SPDI
0.143*
5.301
NM
104.0
BDDI
4.367
1.769
NM
85.7
KEDI
0.143*
1.536
NM
135.7
NTSD
0.143*
2.427
NM
150.0
MUCR
0.143*
11.717
NM
6.7
LCDI
0.7571**
2.248
NM
10.0
TMCR
0.143*
2.116
NM
250.0
FSDI
0.143*
1.527
NM
290.0
REFO
0.5662**
4.093
NM
220.0
CRCP
0.143*
1.166**
NM
110.0
CRPA
0.3993**
0.97**
NM
100.0
CCDI
0.5726**
0.4774**
NM
250.0
SKDI
0.143*
3.841
NM
320.0
WIDI
1.390
4.096
NM
210.0
WCRD
0.143*
NM
NM
240.0
CREG
0.143*
0.986**
NM
130.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
353
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Oct 2014
FTSL
0.143*
0.572**
16.601
140.0
LCRD
0.143*
0.893**
17.308
120.0
SFBC
0.143*
1.477
20.502
50.0
EASL
0.143*
3.447
14.485
100.0
BGLA
0.143*
1.268**
4.498
110.0
SCCR
0.143*
5.644
2.710
20.0
SUCR
0.143*
0.753**
0.683
25.0
SPDI
0.143*
1.385**
1.720
50.0
BDDI
0.143*
5.057
23.211
130.0
KEDI
0.143*
1.015**
9.706
190.0
NTSD
0.143*
5.559
1.235
130.0
MUCR
0.143*
0.664**
8.467
5.0
LCDI
0.143*
2.852
5.698
70.0
TMCR
0.143*
1.645
6.950
260.0
FSDI
0.143*
0.704**
9.463
230.0
REFO
0.143*
3.867
11.263
100.0
CRCP
0.143*
1.351**
18.354
80.0
CRPA
0.143*
3.828
10.559
200.0
CCDI
0.143*
0.700**
8.712
200.0
SKDI
0.143*
1.663
6.840
180.0
WIDI
0.143*
1.828
11.388
190.0
WCRD
0.414**
0.734**
6.381
140.0
CREG
0.143*
3.105
2.418
110.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
354
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Nov 2014
FTSL
0.143*
0.925**
NM
170.0
LCRD
0.294**
0.967**
NM
90.0
SFBC
0.289**
1.123*
NM
50.0
EASL
0.143*
4.908
NM
60.0
BGLA
0.143*
2.294
NM
90.0
SCCR
0.143*
0.622**
NM
30.0
SUCR
0.143*
0.742**
NM
15.0
SPDI
0.143*
1.729
NM
30.0
BDDI
0.143*
1.183**
NM
140.0
KEDI
0.143*
2.535
NM
275.0
NTSD
0.143*
2.214
NM
120.0
MUCR
0.143*
1.193**
NM
70.0
LCDI
0.143*
1.596
NM
80.0
TMCR
0.143*
0.913**
NM
150.0
FSDI
0.143*
1.582
NM
160.0
REFO
0.143*
5.437
NM
150.0
CRCP
0.143*
1.120**
NM
120.0
CRPA
0.464**
1.245**
NM
110.0
CCDI
0.954**
0.625**
NM
160.0
SKDI
0.143*
1.214**
NM
220.0
WIDI
1.205
4.219
NM
160.0
WCRD
0.143*
1.367**
NM
140.0
CREG
0.143*
2.568
NM
130.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
355
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Dec 2014
FTSL
0.143*
4.109
NM
110.0
LCRD
0.143*
1.824
NM
110.0
SFBC
0.143*
3.464
NM
40.0
EASL
0.143*
21.136
NM
70.0
BGLA
0.9108
4.646
NM
100.0
SCCR
0.143*
1.261**
NM
30.0
SUCR
0.143*
0.236*
NM
20.0
SPDI
0.143*
0.626**
NM
85.0
BDDI
0.143*
1.952
NM
120.0
KEDI
1.614
5.110
NM
150.0
NTSD
0.143*
1.471
NM
90.0
MUCR
0.143*
0.868**
NM
100.0
LCDI
0.143*
3.115
NM
90.0
TMCR
0.143*
1.628
NM
180.0
FSDI
0.143*
1.117**
NM
130.0
REFO
0.143*
1.498
NM
130.0
CRCP
0.143*
2.156
NM
100.0
CRPA
0.143*
2.903
NM
NM
CCDI
0.143*
1.103**
NM
230.0
SKDI
0.143*
4.277
NM
140.0
WIDI
0.143*
1.996
NM
120.0
WCRD
0.143*
9.364
NM
170.0
CREG
0.143*
4.036
NM
100.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
356
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Jan 2015
FTSL
0.143*
5.133
NM
40.0
LCRD
0.143*
4.503
NM
40.0
SFBC
2.706
1.836
NM
35.0
EASL
0.143*
1.099**
NM
20.0
BGLA
0.143*
1.306**
NM
30.0
SCCR
0.143*
1.079**
NM
20.0
SUCR
0.942
24.451
NM
20.0
SPDI
0.143*
0.236*
NM
50.0
BDDI
0.143*
0.236*
NM
50.0
KEDI
0.143*
1.080**
NM
60.0
NTSD
0.143*
0.236*
NM
50.0
MUCR
0.143*
1.037**
NM
20.0
LCDI
0.143*
1.840
NM
40.0
TMCR
0.143*
1.938
NM
50.0
FSDI
0.143*
2.504
NM
50.0
REFO
NM
3.370
NM
70.0
CRCP
0.3044**
2.653
NM
50.0
CRPA
0.554**
2.227
NM
60.0
CCDI
0.143*
2.083
NM
60.0
SKDI
0.487**
3.304
NM
50.0
WIDI
0.143*
0.236*
NM
50.0
WCRD
0.412**
0.541**
NM
50.0
CREG
0.143*
0.705**
NM
40.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
357
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Feb 2015
FTSL
0.143*
5.133
NM
40.0
LCRD
0.143*
4.503
NM
40.0
SFBC
2.706
1.836
NM
35.0
EASL
0.143*
1.099**
NM
20.0
BGLA
0.143*
1.306**
NM
30.0
SCCR
0.143*
1.079**
NM
20.0
SUCR
0.942
24.451
NM
20.0
SPDI
0.143*
0.236*
NM
50.0
BDDI
0.143*
0.236*
NM
50.0
KEDI
0.143*
1.080**
NM
60.0
NTSD
0.143*
0.236*
NM
50.0
MUCR
0.143*
1.037**
NM
20.0
LCDI
0.143*
1.840
NM
40.0
TMCR
0.143*
1.938
NM
50.0
FSDI
0.143*
2.504
NM
50.0
REFO
NM
3.370
NM
70.0
CRCP
0.3044**
2.653
NM
50.0
CRPA
0.554**
2.227
NM
60.0
CCDI
0.143*
2.083
NM
60.0
SKDI
0.487**
3.304
NM
50.0
WIDI
0.143*
0.236*
NM
50.0
WCRD
0.412**
0.541**
NM
50.0
CREG
0.143*
0.705**
NM
40.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
358
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Mar 2015
FTSL
0.143*
7.214
NM
90.0
LCRD
0.143*
3.049
NM
70.0
SFBC
0.143*
1.039**
NM
50.0
EASL
0.143*
13.123
NM
70.0
BGLA
1.653
9.9
NM
90.0
SCCR
1.072
0.236*
NM
40.0
SUCR
0.143*
0.236*
NM
40.0
SPDI
0.143*
1.022**
NM
70.0
BDDI
0.143*
3.897
NM
90.0
KEDI
0.143*
11.345
NM
80.0
NTSD
0.143*
0.601**
NM
50.0
MUCR
0.143*
0.236*
NM
60.0
LCDI
0.143*
0.236*
NM
NM
TMCR
0.143*
2.591
NM
80.0
FSDI
0.143*
2.314
NM
70.0
REFO
0.143*
0.236*
NM
50.0
CRCP
0.143*
0.970**
NM
40.0
CRPA
0.143*
0.703**
NM
NM
CCDI
0.143*
1.615
NM
70.0
SKDI
0.143*
2.011
NM
65.0
WIDI No water at site
WCRD
0.143*
12.516
NM
80.0
CREG
11.205
4.131
NM
60.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
359
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Apr 2015
FTSL
0.143*
2.056
14.211
120.0
LCRD
0.143*
2.836
22.503
80.0
SFBC
0.143*
1.190**
3.352
50.0
EASL
0.143*
7.637
9.277
70.0
BGLA
0.143*
9.296
17.308
50.0
SCCR
0.143*
5.898
4.367
40.0
SUCR
0.143*
18.306
0.992
40.0
SPDI
0.143*
4.164
16.373
50.0
BDDI
0.143*
6.605
13.987
70.0
KEDI
0.143*
1.346**
10.450
90.0
NTSD
0.143*
1.026**
5.541
40.0
MUCR
0.143*
4.705
11.486
60.0
LCDI
0.143*
2.681
11.348
80.0
TMCR
0.143*
11.317
22.056
85.0
FSDI
0.143*
3.791
13.690
60.0
REFO
0.143*
0.981**
83.668
60.0
CRCP
0.143*
2.198
22.137
60.0
CRPA
0.143*
4.777
15.026
60.0
CCDI
0.143*
4.158
9.833
85.0
SKDI
0.143*
3.742
5.651
70.0
WIDI
0.143*
4.486
15.208
NM
WCRD
0.143*
5.401
9.432
90.0
CREG
0.143*
3.212
10.095
60.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
360
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
May 2015
FTSL
1.181
2.663
NM
90.0
LCRD
1.427
2.530
NM
45.0
SFBC
0.576**
4.020
NM
40.0
EASL
0.772**
6.311
NM
40.0
BGLA
0.143*
7.489
NM
30.0
SCCR
1.078
3.114
NM
25.0
SUCR
0.143*
1.150**
NM
20.0
SPDI
0.551**
2.251
NM
65.0
BDDI
0.143*
3.005
NM
60.0
KEDI
0.405**
8.458
NM
70.0
NTSD
0.143*
3.362
NM
55.0
MUCR
0.611**
3.770
NM
50.0
LCDI
0.473**
0.236*
NM
30.0
TMCR
0.870
4.779
NM
70.0
FSDI
0.756**
5.014
NM
80.0
REFO
0.143*
1.942
NM
30.0
CRCP
0.143*
2.927
NM
40.0
CRPA
0.893
7.580
NM
45.0
CCDI
0.143*
2.376
NM
65.0
SKDI
0.143*
3.456
NM
60.0
WIDI
0.143*
2.012
NM
60.0
WCRD
0.394**
5.202
NM
55.0
CREG
0.693**
6.557
NM
80.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
361
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Jun 2015
FTSL
0.143*
4.226
NM
190.0
LCRD
0.143*
6.276
NM
120.0
SFBC
0.143*
1.498
NM
50.0
EASL
0.143*
5.113
NM
80.0
BGLA
0.143*
6.830
NM
90.0
SCCR
0.143*
3.273
NM
40.0
SUCR
0.143*
2.702
NM
25.0
SPDI
0.143*
18.339
NM
110.0
BDDI
0.143*
22.321
NM
85.0
KEDI
0.143*
2.680
NM
110.0
NTSD
0.143*
3.472
NM
110.0
MUCR
0.454**
3.365
NM
35.0
LCDI
0.143*
1.122**
NM
35.0
TMCR
0.143*
5.240
NM
80.0
FSDI
0.143*
1.062**
NM
15.0
REFO
0.143*
3.442
NM
60.0
CRCP
0.143*
5.609
NM
90.0
CRPA
1.229
4.545
NM
75.0
CCDI
0.143*
11.465
NM
205.0
SKDI
0.143*
11.900
NM
270.0
WIDI
0.143*
1.987
NM
130.0
WCRD
0.693**
8.124
NM
220.0
CREG
0.618**
5.889
NM
120.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
362
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Jul 2015
FTSL
0.143*
11.499
14.084
180.0
LCRD
0.143*
2.274
17.797
140.0
SFBC
0.534**
0.236*
14.817
NM
EASL
0.143*
2.718
3.038
140.0
BGLA
0.143*
1.310**
6.821
100.0
SCCR
0.446**
0.636**
2.643
50.0
SUCR
0.143*
4.336
1.066
40.0
SPDI
1.206
6.103
11.619
150.0
BDDI
0.744**
1.038**
25.276
180.0
KEDI
0.981
5.849
9.263
140.0
NTSD
1.058
0.236*
2.352
130.0
MUCR
1.059
9.977
7.041
65.0
LCDI
0.824**
0.236*
4.792
30.0
TMCR
1.129
6.591
14.883
160.0
FSDI
0.863
6.018
21.289
200.0
REFO
0.359**
0.236*
17.752
100.0
CRCP
0.492**
0.728**
19.178
100.0
CRPA
0.478**
23.741
15.875
120.0
CCDI
4.906
23.432
38.298
200.0
SKDI
0.382**
16.988
8.306
330.0
WIDI
0.401**
1.444
21.617
150.0
WCRD
0.832**
5.726
12.299
160.0
CREG
2.873
0.236*
12.288
150.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
363
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Aug 2015
FTSL
0.143*
0.236*
NM
250.0
LCRD
0.143*
9.344
NM
130.0
SFBC
0.143*
0.236*
NM
50.0
EASL
0.143*
0.236*
NM
180.0
BGLA
0.143*
0.236*
NM
170.0
SCCR
0.143*
1.920
NM
40.0
SUCR
0.300**
0.236*
NM
40.0
SPDI
0.143*
0.236*
NM
220.0
BDDI
0.143*
0.236*
NM
210.0
KEDI
0.143*
0.236*
NM
240.0
NTSD
0.143*
0.236*
NM
190.0
MUCR
0.143*
6.805
NM
90.0
LCDI
0.143*
0.236*
NM
80.0
TMCR
1.408
4.184
NM
150.0
FSDI
0.563**
0.236*
NM
240.0
REFO
0.143*
0.236*
NM
100.0
CRCP
3.937
0.236*
NM
90.0
CRPA
0.887
0.236*
NM
140.0
CCDI
0.910
0.236*
NM
260.0
SKDI
0.143*
0.236*
NM
290.0
WIDI
0.143*
0.236*
NM
150.0
WCRD
0.650**
0.962**
NM
180.0
CREG
0.775**
0.236*
NM
200.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
364
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Sep 2015
FTSL
0.143*
0.236*
NM
250.0
LCRD
0.687**
3.055
NM
170.0
SFBC
0.143*
1.982
NM
70.0
EASL
0.143*
2.865
NM
250.0
BGLA
0.143*
1.841
NM
220.0
SCCR
0.143*
0.236*
NM
50.0
SUCR
0.143*
1.084**
NM
40.0
SPDI
0.143*
0.969**
NM
60.0
BDDI
0.143*
1.467
NM
270.0
KEDI
No water at site
NTSD
0.376**
2.462
NM
200.0
MUCR
0.143*
0.813**
NM
100.0
LCDI
0.143*
2.040
NM
120.0
TMCR
0.143*
0.236*
NM
205.0
FSDI
0.143*
0.236*
NM
220.0
REFO
0.143*
1.489
NM
170.0
CRCP
0.143*
2.059
NM
180.0
CRPA
0.143*
5.957
NM
170.0
CCDI
0.143*
4.661
NM
310.0
SKDI
0.143*
0.236*
NM
330.0
WIDI
0.143*
0.830**
NM
280.0
WCRD
0.143*
0.236*
NM
350.0
CREG
0.143*
0.622**
NM
230.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
365
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Oct 2015
FTSL
0.143*
0.236*
11.763
170.0
LCRD
0.143*
2.878
18.886
120.0
SFBC
0.143*
3.894
16.569
40.0
EASL
3.180
11.368
26.337
80.0
BGLA
0.143*
8.404
19.040
80.0
SCCR
0.143*
1.670
3.504
30.0
SUCR
0.920
8.598
1.396
30.0
SPDI
0.143*
7.305
7.863
120.0
BDDI
12.123
9.379
22.494
90.0
KEDI
0.143*
3.703
18.620
100.0
NTSD
0.143*
5.634
7.691
85.0
MUCR
0.143*
4.318
4.268
80.0
LCDI
0.143*
3.225
31.510
80.0
TMCR
0.143*
0.236*
19.612
180.0
FSDI
0.143*
0.236*
23.743
160.0
REFO
0.143*
1.285**
13.332
160.0
CRCP
0.143*
0.236*
17.807
150.0
CRPA
6.323
2.505
5.903
160.0
CCDI
0.143*
0.236*
17.756
440.0
SKDI
No water at site
6.262
WIDI
0.143*
0.755**
16.365
260.0
WCRD
0.143*
0.236*
6.943
290.0
CREG
0.143*
0.236*
8.456
200.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
366
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Nov 2015
FTSL
0.143*
8.390
NM
40.0
LCRD
0.143*
4.483
NM
30.0
SFBC
0.143*
3.616
NM
15.0
EASL
2.112
5.867
NM
30.0
BGLA
0.143*
6.983
NM
40.0
SCCR
0.143*
10.325
NM
30.0
SUCR
0.143*
4.380
NM
20.0
SPDI
0.143*
5.324
NM
40.0
BDDI
0.322**
3.748
NM
60.0
KEDI
0.143*
5.328
NM
60.0
NTSD
0.143*
4.423
NM
40.0
MUCR
0.143*
4.843
NM
30.0
LCDI
0.143*
7.740
NM
30.0
TMCR
0.143*
4.523
NM
40.0
FSDI
0.143*
3.225
NM
50.0
REFO
0.143*
2.268
NM
60.0
CRCP
0.143*
5.590
NM
45.0
CRPA
0.143*
5.346
NM
50.0
CCDI
0.143*
3.552
NM
80.0
SKDI
0.143*
6.585
NM
60.0
WIDI
9.124
5.113
NM
40.0
WCRD
0.143*
7.017
NM
30.0
CREG
0.143*
6.523
NM
30.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
367
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Dec 2015
FTSL
2.654
7.454
NM
60.0
LCRD
0.929
7.932
NM
50.0
SFBC
0.143*
3.743
NM
20.0
EASL
0.672**
8.447
NM
50.0
BGLA
0.143*
8.020
NM
50.0
SCCR
0.143*
4.692
NM
20.0
SUCR
0.143*
3.592
NM
15.0
SPDI
0.143*
5.675
NM
30.0
BDDI
0.143*
8.078
NM
50.0
KEDI
0.143*
5.476
NM
60.0
NTSD
0.143*
4.364
NM
40.0
MUCR
0.143*
3.333
NM
40.0
LCDI
0.143*
4.013
NM
40.0
TMCR
0.143*
20.410
NM
50.0
FSDI
0.143*
5.123
NM
60.0
REFO
0.143*
1.663
NM
40.0
CRCP
0.143*
2.773
NM
30.0
CRPA
3.370
10.968
NM
40.0
CCDI
0.143*
3.631
NM
50.0
SKDI
0.143*
4.397
NM
50.0
WIDI
0.143*
4.804
NM
60.0
WCRD
0.143*
9.885
NM
60.0
CREG
0.343**
7.571
NM
60.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
368
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Jan 2016
FTSL
2.674
2.499
5.170
100.0
LCRD
0.143*
2.824
9.289
60.0
SFBC
0.143*
0.728**
8.845
30.0
EASL
0.143*
6.188
58.876
40.0
BGLA
0.143*
5.299
5.519
50.0
SCCR
0.143*
0.948**
2.280
20.0
SUCR
1.871
2.260
1.855
20.0
SPDI
0.143*
2.629
2.181
25.0
BDDI
0.143*
5.709
11.474
70.0
KEDI
0.143*
2.957
12.182
50.0
NTSD
0.143*
2.768
2.062
30.0
MUCR
0.143*
1.807
21.714
45.0
LCDI
0.143*
3.854
13.210
20.0
TMCR
0.143*
4.689
22.396
50.0
FSDI
0.143*
5.236
13.544
50.0
REFO
0.143*
4.055
66.465
30.0
CRCP
0.143*
3.265
12.138
35.0
CRPA
0.143*
3.532
9.035
40.0
CCDI
0.143*
2.898
14.083
40.0
SKDI
0.143*
6.687
5.471
60.0
WIDI
0.143*
9.824
16.060
35.0
WCRD
0.143*
3.036
10.917
45.0
CREG
0.143*
3.678
3.820
45.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
369
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Feb 2016
FTSL
0.143*
5.382
NM
145.0
LCRD
0.143*
2.593
NM
35.0
SFBC
0.143*
1.436
NM
30.0
EASL
0.143*
6.851
NM
70.0
BGLA
0.143*
12.727
NM
40.0
SCCR
0.143*
2.331
NM
30.0
SUCR
0.705**
0.916**
NM
20.0
SPDI
0.143*
10.658
NM
55.0
BDDI
0.143*
3.380
NM
70.0
KEDI
0.143*
5.095
NM
80.0
NTSD
0.143*
5.513
NM
60.0
MUCR
0.143*
0.722**
NM
85.0
LCDI
0.143*
2.287
NM
110.0
TMCR
0.143*
13.158
NM
80.0
FSDI
0.143*
23.009
NM
60.0
REFO
0.143*
2.147
NM
50.0
CRCP
0.143*
7.538
NM
60.0
CRPA
0.143*
6.702
NM
50.0
CCDI
0.143*
3.548
NM
140.0
SKDI
0.143*
9.617
NM
50.0
WIDI
0.143*
20.781
NM
60.0
WCRD
0.143*
6.052
NM
70.0
CREG
0.143*
5.278
NM
60.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
370
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Mar 2016
FTSL
0.143*
4.619
NM
100.0
LCRD
0.143*
1.320**
NM
30.0
SFBC
0.143*
2.135
NM
20.0
EASL
0.568**
3.624
NM
60.0
BGLA
1.210
12.219
NM
50.0
SCCR
0.143*
1.382**
NM
20.0
SUCR
0.143*
0.998**
NM
20.0
SPDI
0.620**
3.779
NM
30.0
BDDI
0.143*
3.721
NM
45.0
KEDI
0.143*
5.019
NM
70.0
NTSD
0.143*
1.713
NM
25.0
MUCR
0.552**
7.539
NM
35.0
LCDI
2.355
2.636
NM
45.0
TMCR
1.000
8.774
NM
30.0
FSDI
1.098
4.288
NM
50.0
REFO
0.143*
2.814
NM
40.0
CRCP
0.143*
3.190
NM
40.0
CRPA
0.143*
4.971
NM
50.0
CCDI
0.143*
7.560
NM
60.0
SKDI
1.001
7.348
NM
50.0
WIDI
0.347**
6.025
NM
50.0
WCRD
2.003
11.386
NM
50.0
CREG
0.143*
5.526
NM
30.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
371
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Apr 2016
FTSL
0.143*
4.293
3.977
80.0
LCRD
0.143*
4.595
7.308
40.0
SFBC
0.143*
4.828
3.061
40.0
EASL
0.143*
23.474
46.157
50.0
BGLA
0.143*
8.184
2.017
40.0
SCCR
0.143*
1.474
3.781
20.0
SUCR
0.143*
5.152
1.880
30.0
SPDI
0.143*
2.925
2.590
20.0
BDDI
0.143*
4.720
1.580
40.0
KEDI
0.143*
7.290
1.663
60.0
NTSD
0.143*
3.975
3.026
25.0
MUCR
0.143*
2.394
3.738
40.0
LCDI
0.143*
3.664
7.522
70.0
TMCR
0.143*
7.113
6.736
40.0
FSDI
0.390**
7.123
7.856
40.0
REFO
0.287**
3.474
3.455
30.0
CRCP
0.143*
5.481
2.622
30.0
CRPA
0.143*
6.943
2.028
30.0
CCDI
0.143*
6.573
0.488
50.0
SKDI
0.143*
3.766
4.005
55.0
WIDI
0.143*
5.155
2.670
40.0
WCRD
0.143*
7.534
11.249
50.0
CREG
0.143*
2.831
2.004
30.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
372
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
May 2016
FTSL
0.143*
7.184
NM
70.0
LCRD
0.143*
1.664
NM
40.0
SFBC
0.143*
0.630**
NM
20.0
EASL
0.143*
6.165
NM
45.0
BGLA
0.143*
6.112
NM
40.0
SCCR
1.037
2.515
NM
25.0
SUCR
0.143*
1.839
NM
20.0
SPDI
0.143*
1.499
NM
20.0
BDDI
0.143*
6.368
NM
40.0
KEDI
0.143*
1.516
NM
80.0
NTSD
0.143*
4.889
NM
60.0
MUCR
0.143*
3.207
NM
65.0
LCDI
0.143*
1.111**
NM
100.0
TMCR
0.143*
15.179
NM
50.0
FSDI
0.143*
13.916
NM
50.0
REFO
0.143*
6.346
NM
40.0
CRCP
0.143*
11.921
NM
40.0
CRPA
0.143*
13.558
NM
40.0
CCDI
0.143*
17.464
NM
70.0
SKDI
0.143*
13.012
NM
60.0
WIDI
0.533**
8.985
NM
50.0
WCRD
0.143*
11.629
NM
40.0
CREG
0.143*
8.981
NM
30.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
373
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Jun 2016
FTSL
0.143*
1.677
11.421
180.0
LCRD
0.143*
3.209
79.733
80.0
SFBC
0.143*
7.588
12.960
30.0
EASL
0.143*
7.999
12.768
70.0
BGLA
0.387**
3.458
4.909
75.0
SCCR
0.143*
1.797
4.206
30.0
SUCR
0.143*
1.902
1.158
30.0
SPDI
0.533**
1.239**
3.764
45.0
BDDI
0.379**
3.872
11.136
90.0
KEDI
0.143*
4.364
13.694
20.0
NTSD
0.143*
4.869
1.453
70.0
MUCR
0.566**
3.886
7.729
35.0
LCDI
0.143*
2.559
10.317
40.0
TMCR
0.143*
1.978
13.272
100.0
FSDI
0.143*
4.668
21.941
60.0
REFO
0.143*
2.815
14.730
50.0
CRCP
0.143*
3.336
12.186
50.0
CRPA
0.963
5.072
11.696
40.0
CCDI
0.810**
4.225
9.006
240.0
SKDI
2.007
6.365
3.512
230.0
WIDI
0.329**
2.065
7.969
180.0
WCRD
0.143*
4.602
5.169
110.0
CREG
0.143*
4.863
2.908
90.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
374
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Jul 2016
FTSL
0.572**
5.298
NM
200.0
LCRD
23.169
17.165
NM
210.0
SFBC
1.078
3.007
NM
220.0
EASL
0.143*
52.273
NM
170.0
BGLA
0.632**
17.458
NM
160.0
SCCR
1.929
16.728
NM
45.0
SUCR
1.417
19.943
NM
40.0
SPDI
2.090
41.993
NM
200.0
BDDI
2.223
22.875
NM
180.0
KEDI
1.129
6.270
NM
240.0
NTSD
0.981
9.598
NM
160.0
MUCR
3.077
4.491
NM
80.0
LCDI
0.143*
4.303
NM
100.0
TMCR
0.803**
2.741
NM
190.0
FSDI
0.517**
3.348
NM
210.0
REFO
0.143*
3.869
NM
70.0
CRCP
1.843
21.589
NM
140.0
CRPA
3.397
8.685
NM
190.0
CCDI
4.232
13.803
NM
260.0
SKDI
1.361
18.969
NM
320.0
WIDI
1.670
13.068
NM
280.0
WCRD
0.971
6.650
NM
230.0
CREG
1.119
3.723
NM
200.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
375
Appendix B (Continued). Results from analysis of dissolved Pb, total recoverable Pb and sediment-bound Pb for the Cache River
Watershed from August 2013-July 2016. Samples collected to make-up for months when sites were completely dry. All make-up
samples collected on June 30, 2016.
Date
Site
Dissolved Pb (ppb)
Total recoverable Pb (ppb)
Sediment-Pb (mg/kg) Hardness (mg/L CaCO3)
Jun 2016
KEDI
0.542**
2.148
NM
240.0
SKDI
2.993
3.093
NM
310.0
WIDI
1.045
7.273
NM
260.0
* denotes a value that measured below the MDL and was assigned a value of 1/2 x the MDL
** denotes a value that measured between the MDL and PQL
NM = parameter not measured
376
APPENDIX C
MEASUREMENTS OF DISCHARGE, DISSOLVED NITRITE, DISSOLVED
NITRATE, DISSOLVED ORTHOPHOSPHATE, TOTAL NITROGEN AND TOTAL PHOSPHORUS FROM THE
CACHE RIVER WATERSHED (AUGUST 2013-JULY 2016)
377
Appendix C. Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Aug 2013
NO2-(ppm)
Total P (ppm)
FTSL
NM
0.380
0.650
0.102
0.144
LCRD
NM
0.141
0.434
0.098
0.131
SFBC
NM
0.061
0.460
0.001*
0.051
EASL
NM
0.021
0.310
0.049
0.109
BGLA
NM
0.089
0.336
0.053
0.122
SCCR
NM
0.070
0.996
0.001*
0.080
SUCR
NM
0.010*
0.261
0.001*
0.059
SPDI
NM
0.098
0.252
0.043
0.110
BDDI
NM
0.138
0.456
0.039
0.029
KEDI
NM
0.070
0.239
0.046
0.218
NTSD
NM
0.221
0.332
0.030
0.119
MUCR
NM
0.058
0.447
0.007
0.042
LCDI
NM
0.043
0.020*
0.001*
0.101
TMCR
NM
0.043
0.323
0.049
0.037
FSDI
NM
0.159
0.239
0.023
0.112
REFO
NM
0.098
0.438
0.013
0.093
CRCP
61.447
0.141
0.699
0.013
0.098
CRPA
NM
0.172
0.757
0.001*
0.135
CCDI
NM
0.147
0.894
0.056
0.056
SKDI
NM
0.435
0.204
0.036
0.145
WIDI
NM
0.138
0.354
0.023
0.120
WCRD
NM
0.123
0.425
0.039
0.125
CREG
74.473
0.113
0.292
0.020
0.113
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
378
Total N (ppm)
0.406
0.353
0.377
0.340
0.469
0.439
0.295
0.363
0.350
0.344
0.362
0.309
0.420
0.515
0.417
0.316
0.384
0.318
0.328
0.433
0.432
0.517
0.343
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Sep 2013
NO2-(ppm)
Total P (ppm)
FTSL
NM
0.288
0.363
0.013
0.159
LCRD
NM
0.166
0.168
0.003
0.122
SFBC
NM
0.166
0.115
0.001*
0.032
EASL
NM
0.063
0.106
0.007
0.093
BGLA
NM
0.132
0.075
0.001*
0.096
SCCR
NM
0.061
4.018
0.039
0.066
SUCR
NM
0.010*
0.257
0.001*
0.044
SPDI
NM
0.218
0.155
0.007
0.123
BDDI
NM
0.166
0.305
0.013
0.082
KEDI
NM
0.239
0.146
0.001*
0.141
NTSD
NM
0.080
0.071
0.001*
0.067
MUCR
NM
0.010*
0.168
0.001*
0.035
LCDI
NM
1.618
0.925
0.079
0.588
TMCR
NM
0.166
0.248
0.013
0.144
FSDI
NM
0.248
0.341
0.016
0.136
REFO
NM
0.043
0.221
0.007
0.082
CRCP
16.679
0.166
0.243
0.001*
0.116
CRPA
NM
0.257
0.283
0.007
0.141
CCDI
NM
0.138
0.124
0.001*
0.102
SKDI
NM
0.273
0.190
0.007
0.110
WIDI
NM
0.184
0.381
0.013
0.120
WCRD
NM
0.202
0.040
0.003
0.091
CREG
5.493
0.138
0.020*
0.001*
0.111
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
379
Total N (ppm)
0.261
0.372
0.206
0.228
0.179
0.488
0.191
0.469
0.170
0.362
0.317
0.205
0.717
0.339
0.272
0.206
0.271
0.307
0.271
0.285
0.225
0.202
0.235
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Oct 2013
NO2-(ppm)
Total P (ppm)
FTSL
NM
0.500
0.668
0.066
0.259
LCRD
NM
0.101
0.243
0.010
0.094
SFBC
NM
0.028
0.217
0.001*
0.055
EASL
NM
0.031
0.195
0.010
0.086
BGLA
NM
0.077
0.173
0.001*
0.071
SCCR
NM
0.070
5.098
0.030
0.110
SUCR
NM
0.010*
0.270
0.003
0.041
SPDI
NM
0.010*
0.239
0.016
0.208
BDDI
NM
0.147
0.323
0.033
0.090
KEDI
NM
0.288
0.261
0.013
0.095
NTSD
NM
0.282
0.726
0.082
0.268
MUCR
NM
0.010*
0.257
0.007
0.046
LCDI
NM
0.904
0.124
0.033
0.391
TMCR
NM
0.242
0.885
0.026
0.189
FSDI
NM
0.512
2.053
0.095
0.311
REFO
NM
0.010*
0.066
0.001*
0.111
CRCP
4.559
0.169
0.420
0.007
0.126
CRPA
NM
0.147
0.451
0.016
0.075
CCDI
NM
0.294
0.527
0.039
0.188
SKDI
NM
0.230
0.195
0.020
0.145
WIDI
NM
0.113
2.053
0.138
0.195
WCRD
NM
0.264
0.230
0.072
0.228
CREG
51.537
0.233
0.650
0.033
0.256
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
380
Total N (ppm)
0.357
0.315
0.226
0.495
0.297
0.560
0.164
0.381
0.259
0.390
0.510
0.180
0.174
0.297
0.535
0.476
0.272
0.182
0.338
0.300
0.472
0.301
0.366
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Nov 2013
NO2-(ppm)
Total P (ppm)
FTSL
NM
0.677
0.611
0.056
0.205
LCRD
NM
0.297
0.827
0.066
0.180
SFBC
NM
0.175
0.164
0.016
0.095
EASL
NM
0.193
0.217
0.069
0.201
BGLA
NM
0.316
0.469
0.072
0.167
SCCR
NM
0.331
0.190
0.020
0.096
SUCR
NM
0.010*
0.080
0.026
0.052
SPDI
NM
0.113
1.093
0.059
0.093
BDDI
NM
0.549
1.027
0.043
0.130
KEDI
NM
0.313
0.655
0.053
0.095
NTSD
NM
0.175
0.142
0.033
0.057
MUCR
NM
0.031
0.146
0.030
0.069
LCDI
NM
0.687
0.478
0.030
0.223
TMCR
NM
0.172
0.327
0.026
0.133
FSDI
NM
0.463
0.513
0.020
0.215
REFO
NM
0.205
0.279
0.020
0.156
CRCP
15.348
0.230
0.020*
0.001*
0.123
CRPA
NM
0.386
0.119
0.007
0.189
CCDI
NM
0.294
0.367
0.023
0.208
SKDI
NM
0.466
0.509
0.023
0.173
WIDI
NM
0.429
1.181
0.026
0.211
WCRD
NM
0.276
0.699
0.030
0.168
CREG
23.050
0.481
1.128
0.033
0.217
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
381
Total N (ppm)
0.632
0.536
0.321
0.336
0.528
0.394
0.254
0.300
0.430
0.527
0.387
0.330
0.275
0.241
0.269
0.304
0.516
0.395
0.451
0.447
0.590
0.504
0.690
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Dec 2013
NO2-(ppm)
Total P (ppm)
FTSL
NM
0.297
0.566
0.001*
0.162
LCRD
NM
0.239
0.586
0.018
0.112
SFBC
NM
0.239
0.586
0.018
0.112
EASL
NM
0.107
0.743
0.001*
0.044
BGLA
NM
0.159
0.549
0.001*
0.065
SCCR
NM
0.092
0.451
0.001*
0.016
SUCR
NM
0.034
0.195
0.001*
0.010*
SPDI
NM
0.233
1.272
0.044
0.030
BDDI
NM
0.184
0.649
0.011
0.049
KEDI
NM
0.104
1.000
0.001*
0.010*
NTSD
NM
0.196
1.243
0.003
0.057
MUCR
NM
0.116
0.509
0.001*
0.067
LCDI
NM
0.218
1.631
0.011
0.067
TMCR
NM
0.031
0.429
0.017
0.068
FSDI
NM
0.156
0.450
0.001*
0.029
REFO
NM
0.098
0.117
0.015
0.056
CRCP
18.123
0.120
0.155
0.001*
0.071
CRPA
NM
0.291
0.541
0.006
0.093
CCDI
NM
0.175
0.486
0.027
0.056
SKDI
NM
0.218
2.410
0.004
0.087
WIDI
NM
0.181
3.387
0.005
0.033
WCRD
NM
0.285
1.499
0.007
0.076
CREG
108.170
0.300
1.374
0.005
0.062
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
382
Total N (ppm)
0.465
0.552
0.552
0.630
0.523
0.495
0.388
0.444
0.551
0.392
0.755
0.423
1.606
1.015
0.916
0.929
0.707
0.935
1.124
1.091
1.263
1.041
1.195
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Jan 2014
NO2-(ppm)
Total P (ppm)
FTSL
NM
0.156
0.217
0.082
0.126
LCRD
NM
0.061
0.773
0.017
0.102
SFBC
NM
0.010*
0.377
0.039
0.035
EASL
NM
0.055
0.473
0.033
0.079
BGLA
NM
0.058
0.452
0.052
0.095
SCCR
NM
0.010*
0.389
0.017
0.010*
SUCR
NM
0.010*
0.365
0.022
0.010*
SPDI
NM
0.052
0.534
0.044
0.109
BDDI
NM
0.095
0.645
0.057
0.229
KEDI
NM
0.095
0.239
0.010
0.124
NTSD
NM
0.110
0.723
0.071
0.187
MUCR
NM
0.010*
0.120
0.033
0.024
LCDI
NM
0.052
0.799
0.015
0.051
TMCR
NM
0.010*
0.717
0.059
0.120
FSDI
NM
0.120
0.556
0.031
0.135
REFO
NM
0.074
0.405
0.044
0.059
CRCP
109.303
0.034
0.038
0.041
0.051
CRPA
NM
0.061
0.360
0.032
0.044
CCDI
NM
0.010*
0.293
0.042
0.084
SKDI
NM
0.120
0.392
0.041
0.061
WIDI
NM
0.055
0.477
0.047
0.082
WCRD
NM
0.074
0.499
0.047
0.088
CREG
105.622
0.052
0.290
0.024
0.116
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
383
Total N (ppm)
1.579
1.675
1.425
1.655
2.006
1.226
1.131
1.786
1.925
1.553
2.028
1.349
1.526
1.755
1.701
1.273
1.281
1.488
1.192
1.633
1.619
1.474
1.448
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Feb 2014
NO2-(ppm)
Total P (ppm)
FTSL
NM
0.058
1.239
0.013
0.120
LCRD
NM
0.031
0.456
0.003
0.104
SFBC
NM
0.010*
0.611
0.001*
0.079
EASL
NM
0.010*
0.354
0.001*
0.037
BGLA
NM
0.028
1.620
0.043
0.071
SCCR
NM
0.052
0.589
0.001*
0.083
SUCR
NM
0.010*
0.443
0.010
0.029
SPDI
NM
0.010*
0.695
0.016
0.081
BDDI
NM
0.049
1.553
0.039
0.118
KEDI
NM
0.061
4.120
0.102
0.090
NTSD
NM
0.150
2.593
0.059
0.111
MUCR
NM
0.064
1.053
0.001*
0.102
LCDI
NM
0.110
2.611
0.030
0.068
TMCR
NM
0.021
1.633
0.007
0.063
FSDI
NM
0.058
2.359
0.003
0.061
REFO
NM
0.046
1.712
0.001*
0.062
CRCP
35.962
0.021
1.288
0.001*
0.079
CRPA
NM
0.046
1.708
0.036
0.070
CCDI
NM
0.034
1.053
0.023
0.050
SKDI
NM
0.010*
6.036
0.036
0.064
WIDI
NM
0.052
2.372
0.053
0.054
WCRD
NM
0.010*
1.982
0.056
0.036
CREG
7.419
0.034
1.611
0.053
0.070
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
384
Total N (ppm)
0.872
0.721
0.585
0.411
0.993
0.593
0.455
0.632
0.840
0.867
0.916
0.844
0.867
0.568
0.511
0.656
0.739
0.720
0.800
1.015
0.644
0.762
0.637
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Mar 2014
NO2-(ppm)
Total P (ppm)
FTSL
NM
0.322
2.061
0.037
0.099
LCRD
NM
0.178
1.872
0.098
0.153
SFBC
NM
0.126
1.052
0.066
0.041
EASL
NM
0.116
1.183
0.098
0.072
BGLA
NM
0.141
0.762
0.094
0.105
SCCR
NM
0.205
0.428
0.024
0.044
SUCR
NM
0.267
0.357
0.031
0.118
SPDI
NM
0.218
0.870
0.064
0.276
BDDI
NM
0.162
0.953
0.068
0.087
KEDI
NM
0.095
0.853
0.080
0.079
NTSD
NM
0.248
1.328
0.089
0.123
MUCR
NM
0.010*
0.177
0.049
0.051
LCDI
NM
0.227
1.410
0.021
0.102
TMCR
NM
0.092
0.948
0.029
0.106
FSDI
NM
0.144
0.575
0.036
0.107
REFO
NM
0.046
0.119
0.066
0.095
CRCP
69.659
0.089
0.065
0.086
0.085
CRPA
NM
0.052
1.072
0.055
0.088
CCDI
NM
0.126
0.811
0.062
0.121
SKDI
NM
0.061
0.812
0.093
0.080
WIDI
NM
0.101
0.378
0.041
0.152
WCRD
NM
0.055
0.811
0.078
0.082
CREG
105.055
0.052
0.864
0.114
0.129
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
385
Total N (ppm)
0.629
0.644
0.554
0.492
0.558
0.412
0.492
0.680
0.578
0.546
0.588
0.505
0.453
0.792
0.506
0.400
0.456
0.458
0.699
0.601
0.542
0.622
0.544
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Apr 2014
NO2-(ppm)
Total P (ppm)
FTSL
NM
0.331
1.690
0.112
0.171
LCRD
NM
0.086
1.527
0.076
0.125
SFBC
NM
0.003
0.553
0.020
0.066
EASL
NM
0.120
0.509
0.144
0.151
BGLA
NM
0.144
0.562
0.112
0.189
SCCR
NM
0.010*
0.243
0.020
0.028
SUCR
NM
0.010*
0.181
0.016
0.010*
SPDI
NM
0.061
0.482
0.066
0.096
BDDI
NM
0.129
0.496
0.089
0.095
KEDI
NM
0.107
0.292
0.062
0.077
NTSD
NM
0.772
1.323
0.095
0.217
MUCR
NM
0.010*
0.020*
0.026
0.066
LCDI
NM
0.010*
0.522
0.016
0.111
TMCR
NM
0.077
0.637
0.033
0.155
FSDI
NM
0.147
0.159
0.033
0.949
REFO
NM
0.028
0.020*
0.049
0.108
CRCP
53.519
0.126
0.690
0.049
0.091
CRPA
NM
0.162
1.319
0.062
0.193
CCDI
NM
0.064
0.212
0.125
0.147
SKDI
NM
0.077
0.575
0.062
0.140
WIDI
NM
0.080
0.420
0.082
0.077
WCRD
NM
0.104
0.673
0.108
0.164
CREG
92.313
0.138
0.743
0.098
0.176
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
386
Total N (ppm)
0.518
0.426
0.336
0.493
0.532
0.271
0.327
0.339
0.408
0.321
0.476
0.405
0.486
0.392
0.346
0.303
0.356
0.442
0.473
0.462
0.427
0.488
0.453
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
May 2014
NO2-(ppm)
Total P (ppm)
FTSL
0.023
0.120
0.292
0.062
0.278
LCRD
0.207
0.077
0.783
0.033
0.190
SFBC
0.239
0.010*
0.801
0.007
0.180
EASL
0.715
0.037
0.575
0.036
0.217
BGLA
0.031
0.141
0.270
0.053
0.190
SCCR
0.163
0.010*
0.991
0.023
0.180
SUCR
0.258
0.010*
0.460
0.016
0.176
SPDI
0.309
0.031
0.482
0.059
NM
BDDI
0.048
0.196
1.482
0.144
0.234
KEDI
0.002
0.120
0.438
0.023
NM
NTSD
0.115
0.098
0.168
0.013
0.244
MUCR
0.562
0.010*
0.451
0.016
0.180
LCDI
NM
0.031
0.257
0.026
0.180
TMCR
0.007
0.074
1.602
0.085
0.286
FSDI
0.027
0.251
0.969
0.059
0.268
REFO
NM
0.104
0.283
0.141
0.273
CRCP
110.719
0.187
0.987
0.056
0.283
CRPA
NM
0.184
1.000
0.043
0.224
CCDI
0.008
0.092
1.142
0.118
0.221
SKDI
0.021
0.067
0.327
0.072
0.257
WIDI
0.012
0.113
0.199
0.023
0.347
WCRD
0.024
0.043
0.075
0.033
0.290
CREG
3.256
0.092
0.137
0.102
0.308
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
387
Total N (ppm)
0.945
0.589
0.472
0.901
NM
0.611
0.438
0.649
1.362
1.165
0.538
0.876
0.812
1.384
1.379
0.678
0.877
0.949
0.706
1.282
1.320
0.792
0.749
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Jun 2014
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Discharge (m3/s)
0.143
1.113
5.203
1.127
6.611
0.258
0.257
0.474
0.424
0.048
0.897
0.147
0.533
0.126
0.070
13.291
41.343
4.812
0.027
0.021
0.045
0.481
20.048
PO4-3 (ppm)
NO3- (ppm)
NO2-(ppm)
Total P (ppm)
Total N (ppm)
0.010*
0.010*
0.010*
0.463
0.025
0.010*
0.010*
0.010*
0.294
0.010*
0.080
0.028
0.297
0.010*
0.064
0.104
0.089
0.010*
0.010*
0.010*
0.010*
0.010*
0.055
16.328
0.982
1.721
1.571
1.434
0.894
0.257
1.288
0.673
1.133
4.469
0.504
0.695
0.743
1.195
0.372
0.580
1.553
1.372
0.593
2.713
0.920
1.783
1.999
0.266
0.039
0.112
0.105
0.046
0.131
0.174
0.292
0.079
0.112
0.010
0.076
0.112
0.227
0.053
0.098
0.302
0.404
0.164
0.860
0.135
0.309
0.189
0.312
0.259
0.339
0.321
0.226
0.114
0.202
0.403
0.265
0.264
0.272
0.330
0.176
0.287
0.293
0.329
0.245
0.318
0.188
0.224
0.260
0.270
8.748
2.760
3.247
2.041
5.401
1.722
1.442
3.321
2.792
1.936
3.640
1.502
1.558
0.966
1.223
1.136
0.393
1.051
2.735
1.622
8.127
2.487
2.982
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
388
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Jul 2014
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Discharge (m3/s)
0.315
0.223
0.057
0.078
0.171
0.006
0.051
0.204
0.250
0.075
0.052
0.023
0.036
0.126
0.010
18.365
17.670
6.803
0.131
0.331
0.082
1.096
4.644
PO4-3 (ppm)
NO3- (ppm)
NO2-(ppm)
Total P (ppm)
Total N (ppm)
0.089
0.010*
0.010*
0.010*
0.010*
0.135
0.010*
0.010*
0.095
0.010*
0.010*
0.010*
0.202
0.010*
0.010*
0.010*
0.010*
0.010*
0.010*
0.010*
0.010*
0.010*
0.010*
0.221
0.181
0.093
0.252
0.345
2.584
0.181
0.020*
3.133
0.434
0.124
2.412
0.102
0.491
1.199
0.020*
0.066
2.690
0.420
0.305
0.991
0.058
0.020*
0.207
0.085
0.026
0.282
0.154
0.059
0.030
0.043
0.515
0.775
0.148
0.446
0.036
0.089
0.105
0.023
0.036
0.295
0.171
0.204
0.256
0.128
0.016
0.147
0.150
0.127
0.134
0.116
0.176
0.117
0.140
0.152
0.144
0.126
0.116
0.156
0.139
0.153
0.174
0.197
0.157
0.141
0.149
0.131
0.155
0.140
0.508
0.604
0.561
0.423
0.778
1.153
0.269
0.517
1.749
1.634
0.694
1.676
0.544
0.900
1.171
0.467
0.608
1.187
0.570
0.520
0.632
0.744
0.508
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
389
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Aug 2014
FTSL
LCRD
SFBC
EASL
BGLA
SCCR
SUCR
SPDI
BDDI
KEDI
NTSD
MUCR
LCDI
TMCR
FSDI
REFO
CRCP
CRPA
CCDI
SKDI
WIDI
WCRD
CREG
Discharge (m3/s)
0.368
0.634
0.043
0.109
0.329
0.006
0.031
0.432
0.211
0.032
0.086
0.005
0.008
0.251
0.027
11.447
18.717
5.629
0.166
0.196
0.318
0.024
5.890
PO4-3 (ppm)
NO3- (ppm)
NO2-(ppm)
Total P (ppm)
Total N (ppm)
0.310
0.138
0.123
0.107
0.113
0.239
0.113
0.166
0.227
0.138
0.184
0.113
0.359
0.153
0.285
0.218
0.233
0.178
0.159
0.110
0.202
0.199
0.156
0.239
0.522
0.292
0.199
0.053
1.974
0.571
0.159
1.389
0.774
0.128
0.987
0.266
0.527
0.987
0.283
1.053
1.217
0.372
0.062
0.650
0.071
0.062
0.039
0.095
0.003
0.016
0.001*
0.010
0.001*
0.001*
0.026
1.054
0.001*
0.007
0.001*
0.026
0.001*
0.001*
0.001*
0.043
0.001*
0.001*
0.001*
0.001*
0.001*
0.235
0.182
0.158
0.142
0.153
0.165
0.155
0.158
0.185
0.141
0.161
0.123
0.166
0.165
0.172
0.174
0.189
0.159
0.157
0.150
0.165
0.175
0.245
0.438
0.655
0.447
0.502
0.370
0.533
0.461
0.421
0.704
1.998
0.238
0.222
0.498
0.641
0.736
0.407
0.686
1.114
0.415
0.344
0.714
0.367
0.327
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
390
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Sep 2014
NO2-(ppm)
Total P (ppm)
FTSL
0.761
0.533
0.020*
0.033
0.169
LCRD
0.842
0.543
0.020*
0.001*
0.154
SFBC
0.019
0.533
0.177
0.001*
0.129
EASL
0.253
0.493
0.020*
0.033
0.159
BGLA
3.292
0.527
0.020*
0.001*
0.171
SCCR
0.019
0.518
1.416
0.033
0.170
SUCR
0.102
0.466
0.133
0.033
0.128
SPDI
1.924
0.497
0.020*
0.001*
0.165
BDDI
1.558
0.500
0.020*
0.001*
0.150
KEDI
0.161
0.466
0.020*
0.001*
0.123
NTSD
0.039
0.530
0.020*
0.033
0.162
MUCR
0.010
0.487
0.133
0.001*
0.101
LCDI
0.056
0.527
0.266
0.033
0.173
TMCR
0.126
0.509
0.354
0.033
0.174
FSDI
0.114
0.500
0.177
0.001*
0.167
REFO
12.098
0.524
0.089
0.001*
0.196
CRCP
26.788
0.515
0.177
0.033
0.181
CRPA
16.950
0.515
0.133
0.033
0.157
CCDI
0.063
0.490
0.089
0.001*
0.167
SKDI
0.146
0.484
0.020*
0.001*
0.163
WIDI
0.379
0.484
0.177
0.001*
0.141
WCRD
0.206
0.490
0.020*
0.001*
0.172
CREG
12.686
0.539
0.020*
0.001*
0.190
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
391
Total N (ppm)
0.527
0.517
0.592
0.510
0.594
1.184
0.299
0.396
0.447
0.356
0.280
0.249
NM
0.755
0.562
0.508
0.613
0.823
0.348
0.451
0.668
0.364
0.653
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Oct 2014
NO2-(ppm)
Total P (ppm)
FTSL
0.185
0.662
0.020*
0.001*
0.313
LCRD
0.095
0.613
0.020*
0.033
0.274
SFBC
0.081
0.524
0.020*
0.164
0.153
EASL
0.047
0.493
0.020*
0.230
0.244
BGLA
0.707
0.515
0.044
0.066
0.249
SCCR
0.037
0.469
1.416
0.066
0.142
SUCR
0.031
0.457
0.044
0.033
0.101
SPDI
0.016
0.478
0.020*
0.033
0.141
BDDI
0.042
0.509
0.531
0.328
0.153
KEDI
0.016
0.536
0.020*
0.033
0.211
NTSD
0.002
0.558
0.020*
0.033
NM
MUCR
0.034
0.478
0.133
0.033
0.179
LCDI
0.008
0.803
1.195
0.263
0.245
TMCR
0.126
0.539
0.044
0.131
0.151
FSDI
0.001
0.585
0.020*
0.001*
0.179
REFO
17.866
0.527
0.266
0.394
0.224
CRCP
36.812
0.521
0.020*
0.066
0.175
CRPA
6.254
0.527
0.221
0.197
0.194
CCDI
0.002
0.564
0.020*
0.098
0.222
SKDI
0.007
0.585
0.020*
0.033
0.247
WIDI
0.009
0.659
0.020*
0.066
0.282
WCRD
0.014
0.546
0.020*
0.164
0.223
CREG
0.623
0.530
0.020*
0.164
NM
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
392
Total N (ppm)
0.722
0.828
0.563
1.010
1.013
0.767
0.189
0.531
0.961
1.008
1.054
0.380
0.263
0.626
0.539
0.513
0.573
0.782
0.903
0.760
0.963
0.833
0.674
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Nov 2014
NO2-(ppm)
Total P (ppm)
FTSL
0.093
0.662
0.020*
0.033
0.206
LCRD
0.021
0.561
0.020*
0.001*
0.133
SFBC
0.032
0.490
0.020*
0.001*
0.189
EASL
0.005
0.490
0.089
0.066
0.190
BGLA
0.023
0.515
0.020*
0.033
0.197
SCCR
0.010
0.490
1.062
0.001*
0.129
SUCR
0.031
0.490
0.020*
0.001*
0.186
SPDI
0.059
0.527
0.020*
0.001*
0.146
BDDI
0.002
0.497
0.020*
0.001*
0.171
KEDI
0.092
0.613
0.020*
0.033
0.198
NTSD
0.003
0.604
0.020*
0.001*
0.281
MUCR
0.063
0.490
0.020*
0.001*
0.152
LCDI
0.016
0.723
2.832
0.492
0.266
TMCR
0.126
0.582
0.020*
0.033
0.177
FSDI
0.010
0.653
0.020*
0.033
0.221
REFO
0.864
0.579
0.089
0.098
0.318
CRCP
1.303
0.509
0.020*
0.066
0.206
CRPA
0.055
0.518
0.089
0.033
0.072
CCDI
0.004
0.705
0.398
0.033
0.201
SKDI
0.013
0.644
0.020*
0.033
0.204
WIDI
0.008
0.699
0.310
0.033
0.251
WCRD
0.048
0.573
0.020*
0.033
0.207
CREG
0.425
0.659
0.020*
0.033
0.257
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
393
Total N (ppm)
0.638
0.345
0.404
0.868
1.047
0.523
0.225
0.176
0.641
0.741
0.819
0.247
1.284
0.522
0.656
0.381
0.251
0.521
0.759
0.797
1.093
1.085
0.808
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Dec 2014
NO2-(ppm)
Total P (ppm)
FTSL
0.435
0.497
0.106
0.066
0.396
LCRD
0.296
0.374
0.035
0.030
0.220
SFBC
0.049
0.316
0.027
0.013
0.141
EASL
0.046
0.319
0.137
0.092
0.257
BGLA
0.183
0.340
0.443
0.089
0.249
SCCR
0.038
0.325
0.429
0.013
0.165
SUCR
0.031
0.303
0.020*
0.007
0.157
SPDI
0.082
0.337
0.020*
0.043
0.163
BDDI
0.109
0.420
0.190
0.016
0.185
KEDI
0.052
0.371
0.066
0.227
0.320
NTSD
0.022
0.359
0.020*
0.066
0.159
MUCR
0.063
0.307
0.020*
0.010
0.117
LCDI
0.303
0.497
0.571
0.085
0.314
TMCR
0.126
0.429
2.314
0.089
0.231
FSDI
0.030
0.484
0.155
0.020
0.250
REFO
2.963
0.371
0.020*
0.001*
0.187
CRCP
4.389
0.362
0.429
0.036
0.195
CRPA
0.494
0.365
0.606
0.001*
0.226
CCDI
0.012
0.383
0.089
0.023
0.172
SKDI
0.055
0.478
0.345
0.039
0.252
WIDI
0.177
0.481
0.535
0.039
0.204
WCRD
0.588
0.454
0.020*
0.013
0.229
CREG
7.079
0.420
0.274
0.118
0.232
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
394
Total N (ppm)
0.429
0.649
0.132
0.852
1.143
0.513
0.201
0.510
0.803
0.920
0.277
0.241
NM
0.597
0.488
0.161
0.622
0.946
0.536
0.733
0.545
0.745
0.736
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Jan 2015
NO2-(ppm)
Total P (ppm)
FTSL
4.920
0.595
1.040
0.056
0.378
LCRD
4.185
0.469
0.836
0.003
0.428
SFBC
0.437
0.389
0.934
0.001*
0.280
EASL
7.016
0.438
0.389
0.053
0.331
BGLA
5.108
0.454
0.673
0.056
0.409
SCCR
0.143
0.359
0.509
0.010
0.264
SUCR
0.211
0.325
0.323
0.010
0.252
SPDI
1.234
0.383
0.774
0.056
0.305
BDDI
NM
0.377
0.000
0.112
0.370
KEDI
0.258
0.420
0.558
0.007
0.315
NTSD
0.162
0.800
1.394
0.010
0.436
MUCR
0.133
0.429
0.319
0.030
0.311
LCDI
0.066
0.493
2.257
0.039
0.327
TMCR
0.290
0.429
2.500
0.013
0.337
FSDI
0.664
0.503
1.797
0.010
0.320
REFO
0.494
0.340
0.181
0.095
0.290
CRCP
16.650
0.331
0.358
0.089
0.260
CRPA
12.262
0.359
0.438
0.171
0.250
CCDI
0.099
0.460
1.637
0.010
0.303
SKDI
0.176
0.539
0.447
0.062
0.352
WIDI
0.098
0.475
0.314
0.128
0.361
WCRD
13.221
0.432
0.814
0.007
0.289
CREG
121.196
0.420
0.867
0.007
0.350
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
395
Total N (ppm)
0.657
0.813
0.320
0.465
0.591
0.322
0.309
0.469
0.668
0.870
0.419
0.272
NM
0.340
0.803
0.401
0.588
0.394
0.830
0.771
0.461
0.615
0.550
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Feb 2015
NO2-(ppm)
Total P (ppm)
FTSL
5.288
0.662
2.013
0.089
0.308
LCRD
8.394
0.549
1.496
0.144
0.321
SFBC
NM
0.411
0.142
0.233
0.330
EASL
NM
0.503
1.518
0.161
0.312
BGLA
NM
0.472
0.686
0.223
0.244
SCCR
NM
0.365
0.469
0.085
0.217
SUCR
NM
0.352
0.301
0.131
0.222
SPDI
NM
0.359
0.929
0.131
0.222
BDDI
NM
0.466
1.243
0.164
0.309
KEDI
NM
1.039
1.943
0.194
0.458
NTSD
NM
0.561
4.956
0.059
0.322
MUCR
NM
0.395
0.425
0.151
0.304
LCDI
NM
0.374
0.814
0.181
0.333
TMCR
NM
0.408
1.535
0.243
0.265
FSDI
NM
0.546
2.867
0.076
0.317
REFO
NM
0.402
1.398
0.053
0.294
CRCP
31.715
0.426
1.823
0.007
0.301
CRPA
NM
0.435
2.186
0.053
0.420
CCDI
NM
0.438
0.894
0.276
0.283
SKDI
NM
0.656
0.845
0.279
0.358
WIDI
NM
0.613
1.292
0.158
0.340
WCRD
NM
0.622
0.788
0.230
0.345
CREG
84.667
0.582
1.177
0.174
0.295
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
396
Total N (ppm)
1.223
1.127
0.666
1.288
1.134
0.343
0.405
0.923
1.408
1.442
1.611
0.626
0.983
0.987
1.050
0.656
0.837
1.020
1.052
1.200
1.144
1.241
1.031
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Mar 2015
NO2-(ppm)
Total P (ppm)
FTSL
5.110
0.748
0.066
0.066
0.294
LCRD
1.433
0.690
0.186
0.066
0.341
SFBC
2.691
0.638
0.509
0.033
0.255
EASL
5.003
0.533
0.040
0.053
0.284
BGLA
4.182
0.748
0.075
0.066
0.315
SCCR
0.504
0.641
0.597
0.020
0.326
SUCR
0.796
0.766
0.376
0.033
0.303
SPDI
1.605
0.567
0.248
0.036
0.266
BDDI
2.805
0.518
0.358
0.095
0.234
KEDI
1.119
0.564
0.239
0.053
0.280
NTSD
0.942
0.757
0.823
0.026
0.306
MUCR
0.176
0.567
0.155
0.010
0.253
LCDI
0.469
0.469
1.106
0.046
0.148
TMCR
0.126
0.723
0.606
0.092
0.302
FSDI
0.041
0.653
0.518
0.062
0.257
REFO
NM
0.619
0.089
0.020
0.247
CRCP
148.380
0.432
0.089
0.036
0.237
CRPA
16.706
0.588
0.301
0.036
0.134
CCDI
0.019
0.570
0.460
0.069
0.188
SKDI
0.041
0.512
0.053
0.033
0.235
WIDI
No water at site
WCRD
11.682
0.536
0.128
0.069
0.331
CREG
68.527
0.405
0.053
0.043
0.307
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
397
Total N (ppm)
0.649
1.398
0.743
0.610
0.667
0.429
0.338
0.545
0.830
0.783
0.573
0.367
0.762
0.789
0.664
0.414
0.430
0.554
0.733
0.537
0.880
0.855
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Apr 2015
NO2-(ppm)
Total P (ppm)
FTSL
0.054
0.319
0.111
0.079
0.242
LCRD
0.415
0.310
0.699
0.030
0.165
SFBC
0.195
0.285
1.332
0.023
0.165
EASL
0.009
0.279
0.301
0.118
0.209
BGLA
0.046
0.291
0.739
0.118
0.260
SCCR
0.127
0.273
0.854
0.056
0.174
SUCR
0.382
0.254
0.509
0.043
0.174
SPDI
0.395
0.264
0.332
0.059
0.180
BDDI
0.053
0.310
11.386
0.286
0.234
KEDI
0.003
0.294
0.102
0.023
0.162
NTSD
0.307
0.395
0.133
0.049
0.174
MUCR
0.076
0.313
0.827
0.072
0.229
LCDI
0.053
0.509
0.239
0.043
0.332
TMCR
0.123
0.313
0.823
0.171
0.250
FSDI
0.020
0.383
0.704
0.144
0.362
REFO
0.417
0.549
0.049
0.258
CRCP
69.376
0.386
0.597
0.072
0.274
CRPA
1.536
0.426
19.585
0.069
0.427
CCDI
0.012
0.346
0.681
0.174
0.224
SKDI
0.014
0.307
0.376
0.102
0.193
WIDI
0.015
0.310
0.319
0.108
0.245
WCRD
0.024
0.307
0.190
0.072
0.273
CREG
1.133
0.310
0.111
0.039
0.175
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
398
Total N (ppm)
0.509
0.597
0.559
0.782
0.830
0.472
0.316
0.421
1.266
0.835
0.776
0.754
0.820
0.978
0.993
0.696
0.710
1.023
1.004
0.725
1.006
0.717
0.354
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
May 2015
NO2-(ppm)
Total P (ppm)
FTSL
0.385
0.343
3.903
0.269
0.307
LCRD
0.647
0.285
1.049
0.069
0.181
SFBC
2.212
0.239
0.739
0.039
0.142
EASL
7.005
0.257
0.283
0.069
0.204
BGLA
5.170
0.273
0.102
0.046
0.209
SCCR
0.349
0.227
0.783
0.036
0.131
SUCR
0.507
0.215
0.580
0.036
0.163
SPDI
1.926
0.239
0.252
0.046
0.158
BDDI
NM
0.322
0.239
0.066
0.220
KEDI
0.019
0.248
0.350
0.076
0.198
NTSD
0.203
0.368
1.297
0.089
0.219
MUCR
0.960
0.264
0.496
0.053
0.162
LCDI
0.344
0.251
0.434
0.043
0.166
TMCR
0.708
0.270
0.690
0.148
0.196
FSDI
0.757
0.337
0.726
0.144
0.192
REFO
NM
0.408
0.836
0.059
0.244
CRCP
98.826
0.365
0.982
0.053
0.220
CRPA
19.998
0.346
0.912
0.046
0.212
CCDI
0.032
0.359
0.686
0.102
0.214
SKDI
0.094
0.334
0.513
0.056
0.295
WIDI
0.052
0.303
0.327
0.059
0.222
WCRD
0.226
0.313
0.531
0.125
0.282
CREG
21.492
0.322
0.708
0.141
0.313
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
399
Total N (ppm)
0.730
0.454
0.168
0.329
0.318
0.226
0.187
0.570
0.625
0.887
1.052
0.558
0.658
0.667
1.239
0.732
0.802
0.407
0.955
0.955
1.064
1.080
0.571
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Jun 2015
NO2-(ppm)
Total P (ppm)
FTSL
0.131
0.352
10.956
3.106
0.381
LCRD
0.067
0.297
5.589
1.687
0.359
SFBC
0.081
0.261
1.084
0.059
0.326
EASL
0.579
0.251
2.562
0.460
0.320
BGLA
0.165
0.261
2.695
0.965
0.358
SCCR
0.030
0.264
1.535
0.059
0.373
SUCR
0.051
0.230
0.504
0.046
0.354
SPDI
0.321
0.236
1.743
0.414
0.330
BDDI
1.317
0.307
5.040
2.206
0.405
KEDI
0.060
0.267
1.889
0.794
0.370
NTSD
0.369
0.359
1.482
0.535
0.475
MUCR
0.713
0.279
1.226
0.118
0.349
LCDI
0.336
0.374
0.128
0.039
0.442
TMCR
0.126
0.291
1.558
0.167
0.405
FSDI
0.003
0.303
0.540
0.194
0.366
REFO
3.292
0.356
1.226
0.043
0.452
CRCP
9.798
0.313
1.611
0.079
0.357
CRPA
16.295
0.291
6.722
1.211
0.338
CCDI
0.261
0.282
3.301
0.676
0.423
SKDI
0.178
0.328
1.482
0.236
0.413
WIDI
0.024
0.346
4.040
2.147
0.363
WCRD
0.332
0.267
1.938
1.054
0.411
CREG
2.803
0.267
3.496
0.627
0.383
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
400
Total N (ppm)
4.277
2.993
0.891
1.738
2.906
0.941
0.373
1.192
4.090
1.874
1.121
0.972
0.415
2.190
3.519
0.613
0.940
2.250
2.146
0.847
3.714
3.144
1.991
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Jul 2015
NO2-(ppm)
Total P (ppm)
FTSL
0.300
0.509
0.677
0.151
0.196
LCRD
0.282
0.319
0.407
0.148
0.166
SFBC
0.032
0.242
0.341
0.049
0.139
EASL
3.538
0.236
0.606
0.194
0.163
BGLA
3.214
0.264
0.863
0.128
0.181
SCCR
0.006
0.273
1.805
0.056
0.139
SUCR
0.063
0.300
0.416
0.046
0.121
SPDI
0.346
0.276
0.080
0.039
0.129
BDDI
4.279
0.365
1.535
0.105
0.147
KEDI
0.036
0.245
0.093
0.036
0.130
NTSD
0.127
0.276
0.066
0.030
0.156
MUCR
0.110
0.230
0.925
0.085
0.158
LCDI
0.212
0.310
0.894
0.135
0.148
TMCR
0.655
0.267
7.828
0.404
0.137
FSDI
0.126
0.288
1.456
0.095
0.185
REFO
5.596
0.325
1.049
0.082
0.185
CRCP
47.006
0.303
1.053
0.046
0.179
CRPA
22.516
0.325
1.748
0.072
0.139
CCDI
0.104
0.267
1.199
0.148
0.161
SKDI
0.169
0.245
0.239
0.082
0.145
WIDI
0.445
0.285
0.929
0.128
0.174
WCRD
0.374
0.261
0.381
0.167
0.160
CREG
10.902
0.257
0.186
0.092
0.140
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
401
Total N (ppm)
0.852
0.627
0.414
0.831
0.638
0.171
0.202
0.423
0.898
0.468
0.435
0.540
0.847
0.770
0.618
0.661
0.618
0.670
1.043
0.345
0.798
0.508
0.545
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Aug 2015
NO2-(ppm)
Total P (ppm)
FTSL
0.737
0.386
0.080
0.049
0.312
LCRD
0.587
0.349
0.062
0.043
0.294
SFBC
0.032
0.300
0.119
0.053
0.263
EASL
0.253
0.310
0.146
0.043
0.282
BGLA
2.760
0.310
0.168
0.049
0.278
SCCR
0.017
0.300
2.336
0.046
0.249
SUCR
0.031
0.282
0.336
0.043
0.282
SPDI
0.435
0.310
0.066
0.036
0.279
BDDI
1.717
0.395
0.248
0.049
0.285
KEDI
0.151
0.334
0.058
0.039
0.259
NTSD
0.346
0.331
0.040
0.036
0.281
MUCR
0.336
0.362
0.783
0.066
0.219
LCDI
0.008
0.463
0.119
0.039
0.319
TMCR
0.599
0.331
0.235
0.053
0.206
FSDI
0.904
0.334
0.279
0.046
0.206
REFO
18.879
0.322
0.288
0.043
0.217
CRCP
52.669
0.322
0.327
0.059
0.220
CRPA
15.691
0.340
0.602
0.039
0.215
CCDI
0.292
0.362
0.190
0.039
0.199
SKDI
0.325
0.337
0.159
0.043
0.218
WIDI
1.193
0.362
0.257
0.046
0.337
WCRD
0.211
0.316
0.062
0.039
0.330
CREG
11.440
0.319
0.020*
0.036
0.293
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
402
Total N (ppm)
0.329
0.337
0.389
0.334
0.258
0.926
0.126
0.383
0.417
0.256
0.288
0.538
0.441
0.447
0.542
0.356
0.496
0.429
0.287
0.283
0.434
0.429
0.315
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Sep 2015
NO2-(ppm)
Total P (ppm)
FTSL
0.315
0.631
0.283
0.030
0.324
LCRD
0.107
0.705
0.266
0.030
0.431
SFBC
0.021
0.582
0.159
0.023
0.342
EASL
0.084
0.567
0.164
0.036
0.353
BGLA
1.345
0.570
0.186
0.026
0.357
SCCR
0.004
0.588
8.270
0.318
0.359
SUCR
0.125
0.564
0.327
0.043
0.324
SPDI
0.008
0.549
0.177
0.023
0.208
BDDI
0.446
0.567
0.447
0.053
0.350
KEDI
No water at site
NTSD
0.238
0.582
0.159
0.023
0.455
MUCR
0.022
0.546
0.434
0.023
0.237
LCDI
0.008
0.656
0.164
0.023
0.385
TMCR
0.126
0.616
0.487
0.036
0.397
FSDI
0.012
0.613
0.389
0.030
0.459
REFO
1.317
0.570
0.168
0.023
0.358
CRCP
2.747
0.573
0.336
0.049
0.436
CRPA
0.658
0.723
0.319
0.049
0.489
CCDI
0.004
0.677
0.354
0.033
0.422
SKDI
0.007
0.619
0.177
0.023
0.350
WIDI
0.007
0.601
0.164
0.023
0.403
WCRD
0.428
0.592
0.177
0.043
0.404
CREG
0.878
0.588
0.150
0.023
0.389
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
403
Total N (ppm)
0.422
0.397
0.342
0.291
0.220
1.717
0.173
0.255
0.538
0.411
0.289
0.223
0.456
0.630
0.462
0.643
0.634
0.544
0.404
0.576
0.425
0.468
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Oct 2015
NO2-(ppm)
Total P (ppm)
FTSL
0.634
0.883
5.452
0.227
0.326
LCRD
1.118
1.241
6.992
0.171
0.529
SFBC
0.427
0.625
4.434
0.072
0.250
EASL
0.554
2.011
2.350
0.213
0.842
BGLA
1.242
1.392
1.443
0.187
0.575
SCCR
0.120
0.846
0.823
0.026
0.306
SUCR
0.360
0.647
0.204
0.066
0.306
SPDI
1.520
1.364
8.496
0.305
0.556
BDDI
6.530
1.447
4.323
0.141
0.637
KEDI
1.040
1.790
5.164
0.197
0.701
NTSD
0.922
1.508
5.788
0.213
0.653
MUCR
0.212
0.932
0.412
0.016
0.370
LCDI
0.097
0.680
1.190
0.085
0.290
TMCR
NM
0.567
0.513
0.023
0.283
FSDI
0.001
0.733
0.199
0.010
0.315
REFO
1.620
0.555
0.190
0.010
0.272
CRCP
0.680
0.512
0.274
0.010
0.248
CRPA
0.110
0.536
0.212
0.010
0.248
CCDI
0.008
0.010*
0.020*
0.001*
0.288
SKDI
No water at site
WIDI
0.006
0.558
0.177
0.023
0.250
WCRD
0.008
0.564
0.248
0.023
0.262
CREG
NM
0.650
0.195
0.013
0.227
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
404
Total N (ppm)
2.929
2.926
1.858
1.859
1.969
0.535
0.562
1.888
2.539
2.486
2.372
0.890
0.604
0.522
0.685
0.386
0.236
0.229
0.141
0.436
1.000
0.319
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Nov 2015
NO2-(ppm)
Total P (ppm)
FTSL
13.899
0.769
0.350
0.181
0.651
LCRD
18.119
0.582
0.518
0.167
0.470
SFBC
0.585
0.383
0.403
0.128
0.452
EASL
7.869
0.490
0.341
0.154
0.409
BGLA
6.688
0.515
0.332
0.138
0.402
SCCR
0.402
0.322
0.504
0.154
0.321
SUCR
0.204
0.282
0.434
0.144
0.320
SPDI
15.590
0.475
0.465
0.102
0.355
BDDI
7.598
0.423
0.478
0.092
0.323
KEDI
3.160
0.469
0.487
0.105
0.315
NTSD
15.956
0.506
0.469
0.102
0.322
MUCR
4.372
0.334
0.473
0.085
0.304
LCDI
3.522
0.371
0.562
0.098
0.406
TMCR
1.778
0.380
0.403
0.128
0.379
FSDI
0.905
0.460
0.358
0.121
0.343
REFO
5.185
0.417
0.358
0.112
0.343
CRCP
57.483
0.441
0.305
0.098
0.428
CRPA
21.480
0.512
0.496
0.144
0.322
CCDI
0.189
0.490
0.403
0.128
0.371
SKDI
0.171
0.506
0.420
0.135
0.444
WIDI
NM
0.466
0.482
0.102
0.349
WCRD
22.109
0.503
0.690
0.125
0.382
CREG
133.655
0.490
0.531
0.148
0.410
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
405
Total N (ppm)
0.706
0.779
0.778
0.550
0.564
0.570
0.427
0.542
0.509
0.496
0.360
0.645
0.579
0.720
0.548
0.655
0.480
0.422
0.400
0.472
0.538
0.691
0.716
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Dec 2015
NO2-(ppm)
Total P (ppm)
FTSL
1.093
0.454
0.540
0.049
0.417
LCRD
2.183
0.303
0.531
0.043
0.278
SFBC
0.802
0.230
0.535
0.043
0.222
EASL
0.522
0.208
0.540
0.053
0.249
BGLA
2.387
0.322
0.925
0.056
0.273
SCCR
0.612
0.218
0.566
0.046
0.201
SUCR
0.405
0.190
2.089
0.095
0.186
SPDI
1.629
0.196
1.651
0.079
0.200
BDDI
5.243
0.227
1.288
0.089
0.224
KEDI
1.211
0.224
1.580
0.115
0.228
NTSD
0.330
0.215
1.602
0.115
0.217
MUCR
0.720
0.178
0.606
0.056
0.196
LCDI
0.560
0.187
2.443
0.112
0.199
TMCR
0.380
0.187
1.491
0.076
0.251
FSDI
0.014
0.288
1.248
0.069
0.254
REFO
NM
0.282
2.102
0.115
0.244
CRCP
112.984
0.288
0.695
0.059
0.226
CRPA
19.233
0.276
0.553
0.049
0.237
CCDI
0.080
0.279
1.673
0.092
0.227
SKDI
0.110
0.261
1.513
0.089
0.229
WIDI
2.687
0.297
1.137
0.076
0.264
WCRD
0.392
0.273
0.938
0.076
0.267
CREG
21.549
0.303
0.633
0.062
0.265
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
406
Total N (ppm)
0.994
0.890
0.657
0.430
0.561
0.427
0.347
0.395
0.497
0.433
0.496
0.449
0.569
0.630
0.554
0.389
0.407
0.463
0.594
0.601
0.524
0.734
0.716
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Jan 2016
NO2-(ppm)
Total P (ppm)
FTSL
0.148
0.138
0.558
0.085
0.197
LCRD
1.019
0.120
1.354
0.089
0.189
SFBC
0.340
0.083
1.682
0.066
0.162
EASL
0.056
0.080
0.566
0.105
0.228
BGLA
0.401
0.083
0.257
0.135
0.200
SCCR
0.116
0.077
0.827
0.082
0.142
SUCR
0.362
0.070
0.478
0.095
0.149
SPDI
0.178
0.080
0.403
0.112
0.203
BDDI
0.152
0.095
0.673
0.108
0.187
KEDI
0.030
0.095
0.642
0.121
0.159
NTSD
0.978
0.089
1.527
0.079
0.167
MUCR
0.128
0.166
0.717
0.092
0.173
LCDI
0.165
0.132
1.416
0.089
0.180
TMCR
0.685
0.120
0.912
0.085
0.193
FSDI
0.126
0.116
0.491
0.105
0.194
REFO
NM
0.126
0.226
0.085
0.210
CRCP
83.818
0.138
0.177
0.098
0.213
CRPA
14.045
0.116
0.288
0.095
0.203
CCDI
0.172
0.110
0.827
0.102
0.193
SKDI
0.021
0.095
0.434
0.112
0.264
WIDI
0.398
0.095
0.239
0.108
0.304
WCRD
0.473
0.095
0.354
0.102
0.239
CREG
6.230
0.092
0.624
0.098
0.222
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
407
Total N (ppm)
0.568
0.730
0.593
0.644
0.552
0.646
0.430
0.516
0.924
0.700
0.719
0.686
0.887
0.896
0.874
0.573
0.470
0.501
0.707
0.540
0.299
0.628
0.661
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Feb 2016
NO2-(ppm)
Total P (ppm)
FTSL
0.221
0.346
0.531
0.338
0.364
LCRD
0.097
0.101
0.385
0.272
0.135
SFBC
0.016
0.070
0.305
0.177
0.113
EASL
0.121
0.132
0.606
0.358
0.165
BGLA
0.719
0.086
0.385
0.450
0.244
SCCR
0.135
0.064
1.044
0.158
0.117
SUCR
0.189
0.037
0.730
0.105
0.105
SPDI
0.625
0.052
0.385
0.341
0.155
BDDI
0.076
0.052
0.566
0.243
0.163
KEDI
0.002
0.101
0.314
0.276
0.207
NTSD
0.093
0.074
0.350
0.371
0.192
MUCR
0.111
0.031
0.473
0.194
0.140
LCDI
0.084
1.011
2.350
0.213
0.593
TMCR
0.701
0.432
0.491
0.446
0.524
FSDI
0.070
0.123
0.336
0.424
0.449
REFO
NM
0.175
0.376
0.200
0.199
CRCP
20.983
0.187
0.443
0.250
0.283
CRPA
4.362
0.190
0.443
0.158
0.242
CCDI
0.047
0.175
0.739
0.305
0.208
SKDI
0.007
0.193
0.407
0.463
0.572
WIDI
0.007
0.208
0.549
0.410
0.543
WCRD
0.058
0.202
0.443
0.213
0.232
CREG
4.049
0.224
0.504
0.128
0.234
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
408
Total N (ppm)
1.351
0.409
0.158
0.611
1.089
0.458
0.448
0.595
0.780
0.772
0.596
0.227
1.177
1.083
1.073
0.443
0.629
0.812
1.395
1.114
1.417
0.557
0.755
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Mar 2016
NO2-(ppm)
Total P (ppm)
FTSL
0.005
0.389
0.020*
0.023
0.359
LCRD
0.340
0.208
0.766
0.001*
0.204
SFBC
0.027
0.169
0.345
0.001*
0.185
EASL
0.059
0.233
0.310
0.003
0.266
BGLA
0.330
0.261
0.606
0.033
0.332
SCCR
0.134
0.190
0.699
0.003
0.205
SUCR
0.418
0.211
0.788
0.013
0.201
SPDI
0.247
0.236
0.611
0.001*
0.227
BDDI
0.132
0.392
0.996
0.023
0.313
KEDI
0.001
0.365
0.451
0.013
0.275
NTSD
0.087
0.322
0.615
0.001*
0.229
MUCR
0.927
0.267
0.243
0.003
0.237
LCDI
0.439
0.595
1.580
0.001*
0.323
TMCR
0.296
0.331
0.721
0.036
0.293
FSDI
NM
0.307
0.058
0.001*
0.272
REFO
NM
0.423
0.279
0.003
0.307
CRCP
125.443
0.371
0.540
0.013
0.281
CRPA
15.911
0.343
0.296
0.010
0.281
CCDI
0.046
0.352
0.695
0.049
0.303
SKDI
0.123
0.291
0.252
0.016
0.267
WIDI
0.269
0.359
0.323
0.007
0.307
WCRD
0.012
0.340
0.274
0.007
0.311
CREG
3.002
0.310
0.412
0.001*
0.281
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
409
Total N (ppm)
1.053
0.288
0.206
0.537
0.750
0.414
0.116
0.214
0.692
0.730
0.523
0.356
0.326
0.239
0.220
0.228
0.318
0.323
0.285
0.205
0.461
0.435
0.184
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Apr 2016
NO2-(ppm)
Total P (ppm)
FTSL
0.023
0.389
0.615
0.141
0.323
LCRD
0.088
0.236
0.097
0.013
0.286
SFBC
0.091
0.181
0.020*
0.007
0.265
EASL
0.009
0.187
1.398
0.112
0.338
BGLA
0.015
0.564
0.850
0.079
0.451
SCCR
0.125
0.184
0.956
0.020
0.237
SUCR
0.165
0.156
0.562
0.030
0.262
SPDI
0.217
0.199
0.159
0.010
0.252
BDDI
0.353
0.300
0.695
0.076
0.332
KEDI
0.002
0.696
0.190
0.033
0.485
NTSD
0.016
0.386
0.119
0.023
0.320
MUCR
0.319
0.218
0.363
0.023
0.251
LCDI
0.053
0.310
0.128
0.013
0.285
TMCR
0.123
0.395
1.323
0.095
0.448
FSDI
0.049
0.398
0.310
0.069
0.475
REFO
26.335
0.490
0.628
0.039
0.375
CRCP
73.907
0.426
0.611
0.036
0.350
CRPA
9.313
0.359
0.867
0.043
0.319
CCDI
0.010
0.316
0.416
0.039
0.356
SKDI
0.127
0.435
0.106
0.033
0.662
WIDI
0.015
0.362
0.358
0.053
0.347
WCRD
0.151
0.285
0.062
0.030
0.366
CREG
0.651
0.300
0.020*
0.007
0.290
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
410
Total N (ppm)
1.025
0.486
0.354
0.984
0.908
0.363
0.270
0.190
0.974
0.859
0.378
0.420
0.362
1.007
0.677
0.395
0.385
0.485
0.873
1.358
1.095
0.996
1.150
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
May 2016
NO2-(ppm)
Total P (ppm)
FTSL
0.065
0.644
4.602
0.535
0.410
LCRD
0.275
0.402
0.620
0.049
0.310
SFBC
0.032
0.362
0.243
0.033
0.286
EASL
0.181
0.383
0.934
0.118
0.343
BGLA
0.127
0.469
0.929
0.102
0.374
SCCR
0.291
0.426
1.354
0.046
0.350
SUCR
0.083
0.352
0.708
0.036
0.277
SPDI
0.127
0.371
0.522
0.039
0.281
BDDI
0.055
0.803
1.836
0.138
0.439
KEDI
0.003
0.377
7.363
2.272
0.305
NTSD
0.043
0.472
1.354
0.407
0.341
MUCR
0.062
0.371
0.858
0.056
0.299
LCDI
0.079
0.745
0.075
0.023
0.489
TMCR
0.123
0.402
2.482
0.125
0.396
FSDI
0.038
0.466
2.398
0.082
0.386
REFO
17.035
0.487
1.177
0.062
0.381
CRCP
65.695
0.493
2.186
0.066
0.390
CRPA
17.096
0.493
2.628
0.082
0.401
CCDI
0.015
0.647
2.841
0.492
0.443
SKDI
0.027
0.454
1.845
0.177
0.392
WIDI
0.562
0.634
1.443
0.161
0.473
WCRD
0.025
0.383
1.735
0.177
0.393
CREG
2.294
0.503
2.248
0.135
0.399
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
411
Total N (ppm)
2.335
0.642
0.378
1.016
0.929
0.643
0.777
1.170
5.985
4.298
1.067
0.783
1.011
1.155
1.281
0.821
0.761
1.048
2.757
1.267
1.353
1.290
2.887
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Jun 2016
NO2-(ppm)
Total P (ppm)
FTSL
0.588
0.690
2.921
0.424
0.371
LCRD
0.379
0.524
3.345
0.699
0.320
SFBC
0.027
0.469
0.020*
0.036
0.286
EASL
0.169
0.472
0.925
0.305
0.315
BGLA
0.476
0.539
2.527
0.811
0.382
SCCR
0.055
0.500
2.841
0.059
0.302
SUCR
0.142
0.472
0.947
0.108
0.287
SPDI
0.206
0.561
1.102
0.509
0.280
BDDI
0.022
0.840
5.987
2.406
0.407
KEDI
0.059
0.555
1.412
0.545
0.338
NTSD
0.037
0.497
1.305
0.640
0.370
MUCR
0.393
0.490
0.969
0.085
0.333
LCDI
0.202
0.512
0.606
0.135
0.303
TMCR
0.123
0.506
1.084
0.144
0.321
FSDI
0.044
0.631
2.951
0.276
0.371
REFO
NM
0.631
0.938
0.072
0.389
CRCP
101.940
0.650
2.199
0.121
0.383
CRPA
10.465
0.588
3.058
0.095
0.362
CCDI
0.240
0.552
1.243
0.125
0.352
SKDI
0.532
0.509
1.810
0.916
0.324
WIDI
0.037
0.530
1.177
0.417
0.356
WCRD
1.125
0.503
1.049
0.187
0.368
CREG
3.851
0.512
2.283
0.479
0.361
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
412
Total N (ppm)
2.385
2.496
0.382
5.383
2.099
0.798
0.424
1.139
4.497
2.106
1.930
0.714
0.600
1.118
1.700
0.713
0.989
1.239
0.886
5.214
2.932
2.310
3.080
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Jul 2016
NO2-(ppm)
Total P (ppm)
FTSL
0.751
0.929
2.412
1.300
0.367
LCRD
0.372
0.874
0.898
0.591
0.337
SFBC
0.027
0.775
0.690
0.883
0.289
EASL
0.241
0.803
0.575
0.867
0.339
BGLA
2.769
0.800
1.319
0.850
0.342
SCCR
0.027
0.852
3.027
0.108
0.309
SUCR
0.031
0.782
0.699
0.089
0.302
SPDI
0.184
0.769
0.633
0.575
0.306
BDDI
2.133
1.183
3.659
0.772
0.461
KEDI
0.123
0.806
1.890
2.104
0.319
NTSD
0.338
0.877
2.261
0.670
0.353
MUCR
0.073
0.779
2.898
0.171
0.327
LCDI
0.020
1.002
0.230
0.148
0.357
TMCR
0.444
0.824
0.571
0.151
0.336
FSDI
0.055
0.846
3.359
0.489
0.347
REFO
1.358
0.821
0.531
0.105
0.379
CRCP
11.270
0.895
0.938
0.082
0.430
CRPA
7.725
0.840
0.020*
0.082
0.428
CCDI
0.350
0.929
2.921
0.854
0.367
SKDI
0.797
0.852
1.168
0.588
0.327
WIDI
0.230
0.923
1.544
0.309
0.397
WCRD
0.374
0.782
1.894
0.995
0.410
CREG
7.023
0.763
2.283
0.768
0.329
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
413
Total N (ppm)
3.429
2.093
3.426
7.435
2.273
1.429
0.784
1.173
2.536
4.280
3.374
1.069
0.458
0.935
4.393
0.765
0.662
0.723
2.906
4.057
1.672
4.015
1.721
Appendix C (Continued). Results of discharge and nutrient measurements for the Cache River Watershed from August 2013-July 2016.
Extra samples collected to make up for months when no water was present at sampling site. All make-up samples were collected on
June 30, 2016.
Sample
Site
Discharge (m3/s)
PO4-3 (ppm)
NO3- (ppm)
Jun 2016
NO2-(ppm)
Total P (ppm)
KEDI
0.146
0.775
0.943
1.392
0.283
SKDI
0.215
0.840
1.345
0.712
0.321
WIDI
0.472
0.867
.226
0.253
0.333
* Concentration measured as below detectable limit (BDL). Value assigned represents 1/2x the BDL.
NM = no value measured (site flooded, sample lost, equipment malfunction)
414
Total N (ppm)
4.810
2.631
1.384