Final Draft
Sensitivity of
Ecological Risk
Assessment to Model
Parameters
Masters of Engineering Report
Jeffrey S. Paley
Department of Biological and Environmental Engineering
Cornell University, Ithaca, NY
5/12/2011
EXECUTIVE SUMMARY:
Ecological risk assessments employ mathematical fate and transport models to
estimate pesticide concentrations in the environment. Results are highly based upon
input data, yet the sensitivity of such models to specific parameters is unknown. The
purpose of this study is to investigate the sensitivity of one such model, TPQPond
(Haith, 2010), a model of pesticide runoff and subsequent accumulation in a receiving
pond from various types of grass surfaces. The analysis was based on one-in-10 year
maximum pond concentrations of 38 different pesticides from 100 year simulations.
The simulations were conducted for nine geographic locations and three golf course
surface types. These concentrations are then compared to toxicity endpoints of target
species (invertebrates, fish, plants, and algae). Seven input parameters were modified
in order to create a range of values over which calculated risk quotients could be
compared to the original values. It was determined that the model was most sensitive to
changes in pond depth and turf organic carbon content. It was moderately sensitive to
pond half life and turf organic carbon content values and mildly sensitive to changes in
the application dates. Changes in values for minimum pond volume did not affect the
calculated risk quotients, and, due to the high carbon content, both high and low
modification of total suspended solids led to decreases in risk quotients. Based on
these findings, it is important to choose values for input parameters carefully for a given
risk assessment as small changes in parameter values that the model is sensitive to
can have significant impacts on the results of the assessment.
CHAPTER 1: INTRODUCTION
Ecological risk assessments of pesticides are performed using mathematical fate
and transport models that estimate pesticide concentrations in receiving waters. By
comparing these concentrations to toxicity endpoints (LC50, EC50) of selected species,
the threat posed by the pesticide in the runoff can be analyzed (Haith, 2010). Although
the quantitative results may imply a high degree of precision, the results depend heavily
on the input data used in the simulations. The sensitivity of the model to these
parameters is largely unknown and the purpose of this study is to examine the extent to
which ecological risks are modified by altering the input parameters. This study had two
2
goals: one was to determine the likely range of values for parameters and data used in
modeling pesticide runoff from grassed surfaces and associated impacts to receiving
waters and the other was to identify model parameters and data which have the largest
impacts on ecological risk assessment of pesticide runoff from grassed surfaces.
CHAPTER 2: METHODS:
This work builds upon a previous paper (Haith and Rossi, 2003) in which the
TurfPQ model was used to determine runoff susceptibility of turf pesticides, assess the
severity of the pollution hazards to aquatic organisms, and to evaluate importance of
regional weather variations on runoff loads and concentrations. It was found that runoff
varied greatly depending on the geographic location as well as the surface due to
differences in winter precipitation and soil permeability. Concentrations in these runoffs
frequently exceeded the toxicity endpoints for water fleas and rainbow trout but without
a method to model the receiving water it is difficult to determine the true threat posed by
the pesticide in the runoff. A subsequent paper (Haith, 2010) extended the work through
use of TPQPond, a model which combined the pesticide runoff calculations of TurfPQ
with a simulation of pesticide and water mass balances in a receiving pond. Pond
pesticide concentrations were used in an ecological risk assessment of 38 turf
pesticides for 3 surfaces in 9 U.S. locations. This risk assessment formed the basis of
the current study, which determined the sensitivity of the risk assessment results to
variations in selected model input parameters.
Risks were evaluated for four types of aquatic target species: invertebrates, fish,
plants, and algae. An acute risk quotient, RQ, was calculated for each target species by
comparing toxicity endpoints with the pond pesticide concentrations estimated by
TPQPond, as indicated in Table 1. The toxicity endpoint values, listed in Table 2, were
taken from the Pesticide Properties Database developed by the University of
Hertfordshire (2009) and were the lowest literature values for the most sensitive species
(University of Hertfordshire, 2009). For fish the species were rainbow trout
(Oncorhynchus mykiss), bluegill sunfish (Lepomis macrochirus), or zebra fish
(Brachydanio rerio). For invertebrates, values from the water flea (Daphnia magna)
were used, and for algae Raphidocelis subcapitata or Pseudokirchneriella subcapitata
3
were chosen. For plants duckweed (Lemna gibba) was used. For pesticides which
toxicity endpoints were unavailable for certain species, those species were omitted from
those simulations.
Target Species
Risk Quotient Equation
Invertebrates
1 𝑖𝑛 10 𝑦𝑒𝑎𝑟 𝑚𝑎𝑥. 48ℎ𝑟 𝑝𝑜𝑛𝑑 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛
48ℎ𝑟 𝐸𝐶50
1 𝑖𝑛 10 𝑦𝑒𝑎𝑟 𝑚𝑎𝑥. 96ℎ𝑟 𝑝𝑜𝑛𝑑 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛
96ℎ𝑟 𝐿𝐶50
1 𝑖𝑛 10 𝑦𝑒𝑎𝑟 𝑚𝑎𝑥. 96ℎ𝑟 𝑝𝑜𝑛𝑑 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛
7 𝑑𝑎𝑦 𝐿𝐶50
Fish
Plants
Algae
1 𝑖𝑛 10 𝑦𝑒𝑎𝑟 𝑚𝑎𝑥. 48ℎ𝑟 𝑝𝑜𝑛𝑑 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛
72ℎ𝑟 𝐿𝐶50
Table 1: Risk Quotient Calculations for Target Species
4
Pesticide
Herbicides
24-D
Benefin
Bispyribac-sodium
Carfentrazone-ethyl
Clopyralid
Dithiopyr
Fluroxypyr
Isoxaben
MCPA
Mecoprop-p
oryzalin
Oxadiazon
Pendimethalin
Penoxsulam
Prodiamine
Rimsulfuron
Sulfentrazone
Sulfosulfuron
Triclopyr
Trifluralin
Fungicides
Chlorothalonil
Cyazofamid
Fluopicolide
Iprodione
Mancozeb
Metconazole
Myclobutanil
Propamocarb-hydCl
Thiophanate-methyl
Insecticides
Acephate
Bacillus thuringiensis
Bifenthrin
Chlorantraniliprole
Clothianidin
Halofenozide
Imidacloprid
Indoxacarb
Permethrin
Invertabrate EC50(48h)
(mg/L)
Fish LC50(96h)
(mg/L)
100
100
95
9.8
100
14
100
1.3
190
91
1.02
2.4
0.28
98.3
0.658
360
60.4
96
131
0.245
100
0.081
95
1.6
100
0.36
14.3
0.87
50
100
3.54
1.2
0.138
100
0.829
390
94
91
117
0.088
0.084
0.19
1.8
0.66
0.073
4.2
17
100
5.4
0.038
0.56
0.36
3.7
0.074
2.1
2
99
11
67.2
13
0.0016
0.0116
40
3.2
85.2
0.6
0.006
110
0.656
0.00015
13.8
104.2
8.6
211
0.65
0.0125
Plants LC50(7d)
(mg/L)
Algae LC50(72h)
(mg/L)
0.58
0.02
0.0127
0.0057
89
12.3
0.011
0.051
1.6
0.0154
0.057
0.012
0.003
0.009
0.29
0.00096
0.8
0.0435
0.29
0.033
3.2
1
105
18
4.7
24.2
100
3.2
0.012
30.5
0.02
49.8
1.4
79.8
16.2
18.1
0.004
0.006
0.49
0.003
0.029
32.8
0.221
75.8
0.0122
0.21
0.025
0.029
1.8
0.044
1.7
2.66
85
25.4
980
2
121
0.084
50
2
55
0.63
10
0.11
0.0125
Table 2: Toxicity Endpoints for Target Species (Source: Haith, 2010)
Simulation Scenarios
For this study, I repeated the 100-yr simulations carried out in Haith (2010) for
three different surface types, fairways, lawns, and greens, in nine different locations:
5
Albany, NY; Atlanta, GA; Bismarck, ND; Columbus, OH; Fresno, CA; Houston, TX;
Madison, WI; Olympia, WA; and Roswell, NM. These locations, as shown in Table 3,
were chosen in order to obtain a wide variation of U.S. climate conditions (Haith, 2010).
Mean Annual
Mean Annual Growing Season
Temperature Precipitation
Location
(°C)
(mm)
Growing Season
Albany, NY
9
441
May-Sept
Atlanta, GA
16
696
April-Oct
Bismarck, ND
5
273
May-Oct
Columbus, OH
11
554
May-Oct
Fresno, CA
17
135
Mar-Nov
Houston, TX
20
917
Mar-Nov
Madison, WI
7
443
May-Sept
Olympia, WA
10
344
May-Oct
Roswell, NM
16
264
April-Oct
Table 3: Weather Data for the Investigation Locations (Haith, 2010)
Properties of the 38 pesticides included in the study are listed in Table 4.
water/sediment values were not available for all pesticides and in those cases, soil
values were used for both watershed and pond calculations.
6
Half life (d)
Pesticide
Herbicides
24-D
Benefin
Bispyribac-sodium
Carfentrazone-ethyl
Clopyralid
Dithiopyr
Fluroxypyr
Isoxaben
MCPA
Mecoprop-p
oryzalin
Oxadiazon
Pendimethalin
Penoxsulam
Prodiamine
Rimsulfuron
Sulfentrazone
Sulfosulfuron
Triclopyr
Trifluralin
Fungicides
Chlorothalonil
Cyazofamid
Fluopicolide
Iprodione
Mancozeb
Metconazole
Myclobutanil
Propamocarb-hydCl
Thiophanate-methyl
Insecticides
Acephate
Bacillus thuringiensis
Bifenthrin
Chlorantraniliprole
Clothianidin
Halofenozide
Imidacloprid
Indoxacarb
Permethrin
Soil
Lawn Appications
Water/Sediment
10
40
13
0.5
34
39
3
105
15
8
20
135
90
32
120
24.3
541
24
39
181
22
10
271
84
0.1
84
306
39.3
0.6
3
3
26
210
545
219
191
17
13
29
1
35
0.4
25
17
17
50
33
113
16
Rate (g/ha)
Fairway Applications
#/Year
1650
1260
2
1
60
3
430
260
840
800
230
1400
3360
2250
40
1210
1
2
1
2
2
1
1
1
2
1
280
70
840
1260
3
2
3
1
0.1
14
777
30
76
465
626
17
2
860
240
2
2
18300
480
770
2370
2900
910
140
190
220
1130
450
150
730
251
56
129
6
40
#/Year
Rate (g/ha)
#/Year
1650
1260
110
60
140
430
260
840
800
230
1400
3360
2250
40
1210
30
280
70
840
1260
2
1
3
3
2
1
2
1
2
2
1
1
1
2
1
3
3
2
3
1
1650
2
60
3
260
2
1400
1
2250
1
30
3
2
2
2
2
2
11200
860
240
2170
18300
480
1080
2370
1450
3
3-4
2
5
5-13
5
3-7
2
4
11200
860
240
2170
18300
480
1080
2370
2900
5-9
3-4
2
5
5-13
5
3-7
2
5-10
1
1
1
1
1
1
1
1
3030
910
140
190
220
1130
450
150
730
4-6
4
2
3
2
2
1
4-6
3
3030
910
140
190
220
1130
450
150
730
4-6
4
2
3
2
2
1
4-6
3
6
26
29
6
Rate (g/ha)
Green Applications
Table 4: Pesticide Properties (Haith, 2010)
The parameters I investigated were pesticide application dates, soil and pond
degradation half lives of each pesticide, minimum pond volume, organic carbon content
of the runoff surface, maximum pond depth, and total suspended solids (TSS) within the
pond. In order to determine sensitivity, ranges of values for the parameters were
established. For each of these parameters, simulations were run using the high and low
7
values, while keeping all other input parameters constant. The high and low values are
listed in Table 5.
Parameter
Original Value (Haith, 2010)
Application Dates
Label Recommendations
Soil Half Life
Minimum Pond
Volume
Organic Carbon
Content (kg/ha):
Fairways
Greens
Lawns
Pesticide Properties Database 308% of Original
50% of Maximum Pond
75% of Maximum
Volume
Pond Volume
10200
6000
15000
Modification High Modification Low
Shifted Two Weeks Shifted Two
Ealier
Weeks Later
30000
13700
24500
Pond Half Life
Pesticide Properties Database 308% of Original
Maximum Pond Depth 2m
4m
TSS
30mg/L, OC 4%
380 mg/L, OC 3%
27% of Original
25% of Maximum
Pond Volume
8900
4500
9300
27% of Original
1m
15 mg/L, OC 85%
Table 5: Ranges for Chosen Parameters
For the application dates, I reasoned that golf course superintendents could
feasibly decide to delay or advance their application schedule based on predicted
weather patterns. Two weeks seemed like a reasonable shift to both keep it feasible
from a reality standpoint as well as providing enough of a difference to be reflected in
the simulations.
The half life (τ1/2) of a pesticide is reflective of its persistence in the environment
after application, i.e. how long it takes to degrade. The model assumes a first order, or
exponential, pesticide decay rate α, where α = -0.693/ln(τ1/2). In this case, soil half life
values are used for τ1/2. The original soil half lives are listed in Table 4. In their review of
turf pesticide properties, Magri and Haith (2009) determined minimum and maximum
field dissipation rates for five of the 38 pesticides I investigated. By calculating the
percentage differences between the high and low values and the original value used
and averaging them, respectively, I was able to create factors by which to multiply all of
8
the original pesticide half lives. I also used these factors when adjusting the pond half
lives of the pesticides.
Minimum pond volume and maximum pond depth were chosen arbitrarily.
Minimum pond volume refers to the lowest acceptable amount of water a pond can
contain in relation to its full volume. Water may be added if the volume drops below this
value. Maximum pond depth determines the total volume of water the pond can
contain. Reducing and increasing the original values for minimum pond volume by 50%
and pond depth by a factor of two seemed feasible to me as management plans and
pond sizes are far from uniform. By keeping minimum pond volume and pond depth
high, I reasoned that pesticide concentrations would be lower simply by the fact that
they would be more diluted in a larger volume of water.
Organic carbon content of the surfaces proved to be the most challenging
parameter to create upper and lower limits for. Organic matter, namely organic carbon
(OC) plays a significant role in the sorption of pesticides by acting as a medium into
which nonionic compounds adsorb and are removed from the runoff. This prevents the
pesticide from reaching the receiving water by reducing its mobility and exposing it to a
highly active microbial system (Magri and Haith, 2009). However, this high adsorption
also prevents the pesticides from infiltrating into the soil and, in effect, makes it
available for transport in the runoff. Although soil may have high organic matter
content, the majority of the OC on the turf surfaces is found within the foliage and
thatch.
For foliage, OC was estimated as described in Haith (2010) using a 38% OC
content measured by Lickfeldt and Branham (1995). Madison (1962) measured foliage
dry matter of five bentgrasses and three different clipping heights. The data are
summarized in Figure 1 and exhibit a typical inverse relationship of density with height
(Turgeon, 1991). I used linear interpolation of the grass heights and their corresponding
OC contents in order to obtain OC values for the heights of grasses I selected.
9
Figure 1: Linear Interpolation of Bentgrass Heights and OC
Thatch is defined by Beard (1973) as “a tightly intermingled layer of dead and
living stems and roots that develop(s) between the zone of green vegetation and the soil
surface.” Building upon Lickfeldt and Branham (1995), Haith (2001) determined that a
value of 1120 kg/ha per mm of thatch depth was a plausible default value.
The published values I found for maximum and minimum grass heights extended
beyond the data provided by Madison (1962) (Figure 1), I used his maximum and
minimum grass heights along with thatch depths that I found (Richards, et al., 2008;
Provey, 2002; Harivandi, et al., 2007; Harivandi, 1984; Madison, 1962; Wood and
Burke, 1961), to create upper and lower bounds for OC of fairways and lawns. The
resulting values are shown in Table 6. As I was unable to find a published value for a
minimum thatch depth of greens that was lower than the original, I used the average
percent differences in OC of both fairways and lawns as multiplication factors for
greens.
10
Grass Height Grass OC
(mm)
(kg/mm*ha)
Fairways
Minimum
Maximum
Lawns
Minimum
Maximum
Grass OC
(kg/ha)
Thatch Depth Thatch OC
(mm)
(kg/mm*ha)
Thatch OC
(kg/ha)
Total OC
(kg/ha)
12.7
34.9
170
90
2157
3128
6
24
1120
1120
6720
26880
8877
30008
12.7
34.9
170
90
2157
3128
6.35
19.05
1120
1120
7112
21336
9269
24464
Table 6: Calculation of OC Values
Total suspended solids (TSS) is the sum of all solids, organic and inorganic,
suspended in the pond. In the TPQPond, the pesticide is partitioned into adsorbed and
dissolved forms (Haith, 2010). For the purposes of the risk assessment, only the
dissolved pesticide concentration is considered meaning that any pesticide adsorbed to
solids is disregarded. As the concentration of suspended solids increases, more
surface becomes available to adsorb pesticide, thereby lowering the dissolved
concentration.
The range for TSS was determined using data from a case study performed by
Clearflow Enviro Systems Group Inc. in Alberta, Canada that provides pond clarification
services. High TSS values can pose problems to golf course superintendents as they
can clog irrigation systems as well as be aesthetically unappealing. The “before
clarification” and “after clarification” values of TSS and organic carbon content of the
suspended solids served as feasible limits on the range of parameter values. Higher
TSS and organic carbon values may cause the pesticides to settle to the bottom of the
pond by becoming adsorbed to suspended solids. While this may lower the
concentration in the water, the sediment at the bottom of the pond would become
increasingly more concentrated.
CHAPTER 3: RESULTS
Seventeen sets of simulation results were used to evaluate parameter sensitivity:
the original runs from Haith (2010) and the 14 sets described previously: high and low
values for each of the seven parameters or conditions listed in Table 5. Each simulation
set estimated 1 in 10 yr pond concentrations of the various pesticides for each location
and grass surface. These concentrations were used to determine RQ values for each
11
species as shown in Table 1. Parameter sensitivity was measured by determining
percent difference in the new RQ (based on a higher or lower parameter value) and the
original RQ. The percent differences were then averaged over all pesticides at a given
location and grass surfaces. In general, results were similar between all four species for
each surface type. Additional results for all species and surface types can be found in
the Appendix.
Table 7 shows the change in RQs for fairways averaged over all target species.
The increased modification of turf OC content seems to have a significantly greater
impact on the RQs than that of the decreased modification.
Invertebrates, Fish,
Application Turf Half
Plants, and Algae:
Date
Life
Fairways
Low
-16%
-44%
Albany High
-6%
59%
Low
-10%
-33%
Atlanta High
-5%
33%
Low
-8%
-54%
Bismarck High
4%
54%
Low
8%
-35%
Columbus High
-4%
29%
Low
12%
-67%
Fresno High
9%
169%
Low
-20%
-33%
Houston High
4%
32%
Low
-1%
-45%
Madison High
16%
41%
Low
-16%
-79%
Olympia High
26%
197%
Low
-14%
-49%
1%
49%
Roswell High
Mean (Low):
-7%
-49%
Mean (High):
5%
74%
Median Low):
-10%
-45%
Median (High):
4%
49%
Min
Pond
Size
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
Turf
Organic
Carbon
Pond
Content Half Life
8%
-17%
-44%
33%
8%
-15%
-50%
16%
7%
-15%
-48%
33%
11%
0%
-48%
21%
2%
-25%
-27%
85%
8%
10%
-49%
47%
8%
-18%
-48%
29%
1%
-21%
-24%
23%
8%
-22%
-43%
36%
7%
-14%
-42%
36%
8%
-17%
-48%
33%
Pond
Depth
81%
-46%
70%
-46%
88%
-48%
80%
-46%
89%
-48%
64%
-43%
73%
-46%
53%
-39%
90%
-49%
77%
-46%
80%
-46%
TSS
-1%
-1%
-2%
-2%
-1%
-1%
-1%
-1%
-1%
-1%
-2%
-2%
-3%
-3%
-2%
-2%
-1%
-1%
-2%
-2%
-1%
-1%
Table 7: Percent Changes for 1 in 10 year Risk Quotients for Fairways Averaged across All Species
12
Table 8 shows the change in RQs for greens averaged over all target species.
Of all the surfaces, modification of turf half life for greens creates the biggest impact on
RQ values, whereas the impact from turf OC content is lowest.
Application Turf Half
Invertebrates, Fish,
Date
Life
Plants, and Algae: Greens
Low
-28%
-79%
Albany High
33%
381%
Low
7%
-53%
Atlanta High
4%
127%
Low
-34%
-61%
Bismarck High
680%
691%
Low
15%
-40%
Columbus High
-3%
58%
Low
0%
-100%
Fresno High
0%
0%
Low
-22%
-49%
Houston High
8%
71%
Low
-8%
-65%
Madison High
-12%
115%
Low
-28%
-87%
Olympia High
46%
383%
Low
-1%
-27%
High
3%
58%
Roswell
Mean (Low):
-11%
-62%
Mean (High):
84%
209%
Median Low):
-8%
-61%
Median (High):
4%
115%
Turf
Min
Organic
Pond
Carbon
Pond
Size
Content Half Life
0%
2%
-15%
0%
6%
31%
0%
13%
-11%
0%
-33%
17%
0%
-30%
-93%
0%
-8%
797%
0%
10%
0%
0%
-30%
0%
0%
0%
-100%
0%
0%
800%
0%
14%
17%
0%
-39%
109%
0%
13%
-13%
0%
-34%
24%
0%
2%
-10%
0%
-8%
15%
0%
-8%
-73%
0%
-5%
388%
0%
2%
-33%
0%
-17%
242%
0%
2%
-13%
0%
-8%
31%
Pond
Depth
92%
-46%
82%
-47%
741%
-50%
84%
-47%
0%
0%
76%
-46%
90%
-47%
88%
-47%
82%
-46%
148%
-42%
84%
-47%
TSS
-3%
-3%
-2%
-2%
0%
0%
-1%
-1%
0%
0%
-2%
-2%
-3%
-2%
-5%
-3%
0%
0%
-2%
-1%
-2%
-2%
Table 8: Percent Changes for 1 in 10 year Risk Quotients for Greens Averaged across All Species
The results for lawns are shown below in Table 9. Aside from TSS and minimum
pond size, the magnitudes of the differences in RQs were quite similar for both the high
and low modifications of the input parameters.
13
Application Turf Half
Invertebrates, Fish,
Date
Life
Plants, and Algae: Lawns
Low
-8%
-51%
Albany High
7%
73%
Low
-14%
-31%
Atlanta High
-18%
32%
Low
-8%
-49%
Bismarck High
4%
69%
Low
1%
-38%
Columbus High
-2%
41%
Low
4%
-80%
Fresno High
7%
172%
Low
-26%
-37%
Houston High
-13%
45%
Low
-1%
-62%
Madison High
24%
54%
Low
-11%
-82%
Olympia High
13%
126%
Low
2%
-82%
22%
62%
Roswell High
Mean (Low):
-7%
-57%
Mean (High):
5%
75%
Median Low):
-8%
-51%
Median (High):
7%
62%
Min
Pond
Size
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
Turf
Organic
Carbon
Pond
Content Half Life
20%
-27%
-16%
32%
32%
-29%
-24%
33%
25%
-23%
-23%
55%
27%
0%
-25%
0%
-3%
-26%
-7%
81%
35%
-16%
-23%
65%
27%
-20%
-22%
35%
3%
-27%
-12%
26%
22%
-26%
-17%
50%
21%
-22%
-19%
42%
25%
-26%
-22%
35%
Pond
Depth
70%
-43%
65%
-43%
92%
-48%
72%
-45%
82%
-48%
59%
-40%
73%
-45%
49%
-39%
89%
-47%
72%
-44%
72%
-45%
TSS
-2%
-1%
-1%
-1%
-1%
-1%
-2%
-2%
-1%
-1%
-2%
-1%
-2%
-1%
-2%
-2%
-1%
-1%
-2%
-1%
-2%
-1%
Table 9: Percent Changes for 1 in 10 year Risk Quotients for Lawns Averaged across All Species
Based upon the median high and low values, the risk assessment appears to be
most sensitive to changes in the values chosen for turf half life and pond depth or size.
It was moderately sensitive to pond half life and turf organic carbon content values and
mildly sensitive to changes in the application dates.
There does not seem to be much correlation across location in terms of adjusting
application date; advancing or delaying the pesticide application schedule does not elicit
the same response from each location, i.e. in Albany delaying the schedule decreases
the risk quotient while in Fresno it increases. However, with more precise, locationspecific scheduling based on historic precipitation data it may be possible to reduce the
risk posed by pesticide runoff.
14
As the turf and pond half lives increased, the RQs increased and vice versa.
With longer half lives on the turf, pesticides are more likely to persist until they are
carried into the pond by sufficient runoff. Once in the pond, pesticides with longer half
lives would remain in the pond for a longer period of time, thus allowing pesticide
inflows from multiple runoff events to sum together and increase the pesticide
concentrations in the pond.
Interestingly enough, TPQPond did not show any sensitivity to minimum pond
size change across all of the simulations.
On the whole, RQs decreased with the increased surface OC modification.
However, the magnitude of the effect varied widely from location to location and from
surface to surface. This is most likely due to the dual effect of pesticide adsorption.
While it is true that high OC will adsorb pesticide more strongly and expose it to highly
active microbial systems (Magri and Haith, 2009), it also prevents infiltration of the
pesticide and makes it more available to runoff. From these results, it appears that the
contribution from each phenomenon varies in relative importance from location to
location as for some an increase in OC creates a decrease in RQs while for others RQs
increase.
For the modifications in maximum pond depth, it makes sense that for a larger
volume of water the same amount of pesticide would be more diluted, thus leading to
lower concentrations and corresponding RQs.
In regards to modifications to TSS and its associated organic carbon content,
looking at the modified RQ values would lead one to believe that any deviation from the
original value would generate lower RQs as both the high and low modifications caused
reductions in the calculated RQs. However, this could be attributed to the fact that, in
the case study (Clearflow), the organic carbon fraction of the suspended solids
increased dramatically after clarification. The total amount of carbon suspended
remained close to the same and higher than the value used in the original runs as
shown in Table 10. This would cause more pesticide to become adsorbed and settle
15
out in both cases, suggesting that the most important factor in TSS is the amount of
carbon.
TSS (mg/L)
Organic Carbon
Fraction
Total Organic
Carbon (mg/L)
Original
Low
High
(Haith, 2010) Modification Modification
30
15
380
0.04
0.85
0.03
1.2
12.75
11.4
Table 10: Total Pond Organic Carbon for Original and Modified Simulations
CHAPTER 4: CONCLUSIONS
In this study I conducted a sensitivity analysis of the program TPQPond to seven
different input parameters. This was accomplished by establishing a reasonable range
of parameter values, running the model for 100 years of weather and irrigation data, and
comparing the one-in-10 year RQs for four different target species for each parameter
modification. Based on the results, I conclude the following:

With increasing turf half life and pond half life, risk quotients had a tendency to
increase. The model was most sensitive to changes in these two parameters.

With increasing turf organic carbon and pond size, averaged risk quotients had a
tendency to decrease although this is not the case for all specific locations.

Although the model does appear to be sensitive to changes in application dates, the
effects of such changes on RQs are most likely location specific.

The model appeared completely insensitive to minimum pond volume based on the
range chosen for this study.

Risk quotients decreased for both high and low pond TSS modifications. This is
most likely attributed to an increase in the suspended carbon content within the
pond.
Based on these findings, when using risk assessment models it is important to
choose the input values carefully. While changes in some parameters, such as
16
minimum pond volume, may not affect the model significantly, small changes,
specifically for parameters that the model is especially sensitive to, can cause drastic
differences in the calculated risk quotients. Accurate input values are essential to
conducting an ecological risk assessment that correctly reflects the area of study.
TPQPond, while providing quantitative results, is not precise. As these simulations
have shown, the model is extremely dependent upon certain input parameters and the
results might reflect the sensitivity of the model to the parameters rather than a
correlation between the magnitudes of the parameter changes and those of the risk
quotients.
17
REFERENCES
Beard, J.B. 1973. Turfgrass: Science and culture. Prentice-Hall, Englewood Cliffs, NJ.
Clearflow Enviro Systems Group. 2010. "Golf Course - Pond Clarification."
<http://www.clearflowconsulting.com/content.asp?ID=23>.
Haith, D. A. 2010. Ecological risk assessment of pesticide runoff from grass surfaces.
Environmental Science and Technology 44:6496-6502.
Haith, Douglas A. 2010. USERS' MANUAL for TPQPond.exe A PC Program for
Estimating Pesticide Concentration in a Receiving Pond Due to Runoff from
Turfgrass. Biological and Environmental Engineering, Cornell University. Ithaca,
NY.
Haith, D. A. 2001. TurfPQ, A pesticide runoff model for turf. Journal of Environmental
Quality 30(3):1033-1039.
Haith, Douglas A., and Matthew W. Duffany. 2007. Pesticide runoff loads from lawns
and golf courses. Journal of Environmental Engineering 133(4):435-46.
Harivandi, M. A., and K. N. Morris. 2007. An on-site study of bentgrasses for quality,
speed, thatch depth and annual bluegrass invasion. IHC2006: International
Symposium on Horticultural Plants in Urban and Peri-Urban Life 27:133-38.
Harivandi, M. A. 1984. Thatch- the turf manager's hidden enemy. California Turfgrass
Culture 34(1):1-8.
Horst, G. L., P. J. Shea, D. R. Miller, C. Stuefer-Powell, and S. K. Starrett. 1996.
Pesticide dissipation under golf course fairway conditions. Crop Science 36:36270.
Lickfeldt, D. W., and B. E. Branham. 1995. Sorption of nonionic organic compounds by
Kentucky bluegrass leaves and thatch. Journal of Environmental Quality 24:98085.
Madison, John H. 1962. Turfgrass ecology. Effects of mowing, irrigation, and nitrogen
treatments of Agrostis palustris Huds., 'Seaside' and Agrostis tenuis Sibth.,
'Highland' on population, yield, rooting, and cover. Agronomy Journal 54:407-12.
18
Magri, A., Haith, D. A. 2009. Pesticide decay in turf: a review of processes and
experimental data. Journal of Environmental Quality 38(1):4-12.
Provey, Joe. 2002. The easy way to maintain your lawn. Flower and Garden July:34-37.
Richards, J., D. Karcher, T. Nikolai, M. Richardson, A. Patton and J. Landreth. 2008.
Mowing height, mowing frequency, and rolling frequency affect putting green
speed. Arkansas Turfgrass Report 2007, Ark. Ag. Exp. Stn. Res. Ser. 557:52-56.
Semlitsch, Raymond D., Michelle D. Boone, and J. R. Bodie. 2007. Golf courses could
bolster amphibian communities. Turfgrass and Environmental Research
Online 6(1):1-16.
Turgeon, A.J. 1991. Turfgrass management. 3rd ed. Prentice-Hall, Englewood Cliffs, NJ.
University of Hertfordshire. Pesticide properties database; 2009.
http://sitem.herts.ac.uk/aeru/footprint/en/.
Wood, G. M., and Jane A. Burke. 1961. Effect of cutting height on turf density of Merion,
Park, Delta, Newport, and common Kentucky bluegrass. Crop Science 101: 31718.
19
APPENDIX
Application Turf Half
Date
Life
Invertebrates: Fairways
Low
-19%
-36%
Albany High
-8%
37%
Low
-13%
-29%
Atlanta High
-1%
25%
Low
-10%
-48%
Bismarck High
5%
40%
Low
6%
-29%
Columbus High
-12%
18%
Low
26%
-62%
Fresno High
12%
77%
Low
-12%
-21%
Houston High
14%
22%
Low
1%
-41%
Madison High
6%
29%
Low
-17%
-58%
Olympia High
28%
86%
Low
-24%
-39%
-19%
34%
Roswell High
Mean (Low):
-7%
-40%
Mean (High):
3%
41%
Median Low):
-12%
-39%
Median (High):
5%
34%
Min
Pond
Size
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
Turf
Organic
Carbon Pond Pond
Content Half Life Depth TSS
7%
-20%
78% -1%
-43%
39% -45% -1%
8%
-14%
65% -1%
-48%
18% -44% -1%
7%
-19%
87% -1%
-48%
39% -48% -1%
9%
0%
72% -1%
-50%
23% -45% -1%
-3%
-16%
87%
0%
-11%
67% -48%
0%
10%
-8%
62% -1%
-52%
17% -43% -1%
8%
-20%
65% -1%
-47%
35% -43% -1%
-4%
-20%
50% -1%
1%
22% -37% -1%
8%
-24%
89% -1%
-52%
36% -49% -1%
5%
-16%
73% -1%
-39%
33% -45% -1%
8%
-19%
72% -1%
-48%
35% -45% -1%
Table A 1: Percent Changes in Invertebrate 1-in-10 year Risk Quotients (EC50, 48 hr) for Fairways
20
Application Turf Half
Date
Life
Invertebrates: Greens
Low
-32%
-62%
Albany High
14%
163%
Low
7%
-48%
Atlanta High
0%
82%
Low
-28%
-72%
Bismarck High
2641% 2666%
Low
-9%
-35%
Columbus High
7%
23%
Low
0%
-100%
Fresno High
0%
0%
Low
-20%
-46%
Houston High
5%
51%
Low
-1%
-59%
Madison High
-6%
83%
Low
-34%
-72%
Olympia High
45%
237%
Low
0%
-40%
High
35%
26%
Roswell
Mean (Low):
-13%
-59%
Mean (High):
305%
370%
Median Low):
-9%
-59%
Median (High):
7%
82%
Min
Pond
Size
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
Turf
Organic
Carbon
Pond
Pond
Content Half Life Depth TSS
-15%
-10%
98% 0%
15%
45% -50% 0%
8%
-9%
88% -1%
-22%
19% -48% -1%
-19%
-93% 2707% 0%
-13% 2793% -50% 0%
0%
0%
75% 0%
-11%
0% -45% 0%
0%
-100%
0% 0%
0%
800%
0% 0%
11%
-10%
73% -1%
-34%
32% -45% -1%
6%
-11%
89% -1%
-19%
33% -47% -1%
-14%
-19%
83% -2%
6%
14% -47% -2%
-13%
-73%
85% 0%
9%
248% -44% 0%
-4%
-36% 366% -1%
-8%
443% -42% -1%
0%
-11%
85% 0%
-11%
33% -47% 0%
Table A 2 Percent Changes in Invertebrate 1-in-10 year Risk Quotients (EC50, 48 hr) for Greens
21
Application
Date
Invertebrates: Lawns
Low
-10%
Albany High
16%
Low
-14%
Atlanta High
-14%
Low
1%
Bismarck High
-3%
Low
11%
Columbus High
-9%
Low
1%
Fresno High
11%
Low
-18%
Houston High
-18%
Low
1%
Madison High
10%
Low
-14%
Olympia High
14%
Low
20%
High
15%
Roswell
Mean (Low):
-2%
Mean (High):
2%
Median Low):
1%
Median (High):
10%
Turf
Min
Half Pond
Life
Size
-40%
0%
38%
0%
-21%
0%
17%
0%
-37%
0%
49%
0%
-28%
0%
30%
0%
-86%
0%
105%
0%
-23%
0%
19%
0%
-93%
0%
34%
0%
-69%
0%
47%
0%
-98%
0%
29%
0%
-55%
0%
41%
0%
-40%
0%
34%
0%
Turf
Organic Pond
Carbon Half Pond
Content Life Depth TSS
11% -31%
61% -1%
-13%
37% -43% -1%
33% -31%
57% -1%
-21%
48% -41% -1%
20% -35% 103%
0%
-27%
78% -49%
0%
24%
0%
71% -2%
-28%
0% -45% -2%
-40% -15%
76% -1%
19%
53% -50% -1%
24% -28%
45% -1%
-24%
41% -38% -1%
27% -21%
71% -1%
-22%
41% -44% -1%
-19% -28%
43%
0%
0%
24% -39%
0%
30% -36%
96%
0%
-19%
78% -47%
0%
12% -25%
69% -1%
-15%
45% -44% -1%
24% -28%
71% -1%
-21%
41% -44% -1%
Table A 3: Percent Changes in Invertebrate 1-in-10 year Risk Quotients (EC50, 48 hr) for Lawns
22
Application Turf Half
Date
Life
Fish: Fairways
Low
-27%
-38%
Albany High
-23%
58%
Low
-11%
-35%
Atlanta High
1%
40%
Low
-7%
-57%
Bismarck High
1%
46%
Low
16%
-32%
Columbus High
-13%
32%
Low
22%
-62%
Fresno High
4%
205%
Low
-18%
-29%
Houston High
15%
28%
Low
-3%
-54%
Madison High
2%
48%
Low
-21%
-78%
Olympia High
37%
386%
Low
-20%
-42%
-25%
37%
Roswell High
Mean (Low):
-8%
-47%
Mean (High):
0%
98%
Median Low):
-11%
-42%
Median (High):
1%
46%
Min
Pond
Size
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
Turf
Organic
Carbon Pond Half Pond
Content
Life
Depth
12%
-11%
86%
-60%
40% -47%
11%
-12%
76%
-61%
13% -49%
12%
-13%
92%
-60%
27% -49%
18%
0%
90%
-62%
22% -45%
3%
-37%
79%
-27%
66% -48%
11%
-6%
68%
-62%
11% -43%
10%
-18%
71%
-61%
25% -49%
2%
-18%
49%
-31%
16% -39%
11%
-25%
91%
-61%
35% -51%
10%
-15%
78%
-54%
28% -47%
11%
-13%
79%
-61%
25% -48%
TSS
-1%
-1%
-6%
-5%
-1%
-1%
-1%
-1%
-2%
-1%
-3%
-3%
-7%
-7%
-2%
-2%
-1%
-1%
-3%
-3%
-2%
-1%
Table A 4: Percent Changes in Fish 1-in-10 year Risk Quotients (LC50, 96hr) for Fairways
23
Application Turf Half
Date
Life
Fish: Greens
Low
-34%
-85%
Albany High
55%
585%
Low
10%
-51%
Atlanta High
-4%
101%
Low
-22%
-63%
Bismarck High
10%
25%
Low
24%
-41%
Columbus High
-42%
36%
Low
0% -100%
Fresno High
0%
0%
Low
-22%
-50%
Houston High
6%
64%
Low
-5%
-72%
Madison High
-26%
119%
Low
-37%
-93%
Olympia High
60%
478%
Low
-7%
-22%
-10%
13%
Roswell High
Mean (Low):
-10%
-64%
Mean (High):
5%
158%
Median Low):
-7%
-63%
Median (High):
0%
64%
Min
Pond
Size
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
Turf
Organic
Carbon
Pond Pond
Content Half Life Depth TSS
0%
-11%
89%
0%
-8%
22% -47%
0%
19%
-3%
89% -1%
-41%
13% -48% -1%
-15%
-87%
79%
0%
-13%
50% -50%
0%
19%
0%
87% -2%
-45%
0% -49% -1%
0%
-100%
0%
0%
0%
800%
0%
0%
14%
-9%
76% -1%
-43%
30% -46% -1%
18%
-4%
91% -1%
-44%
15% -48% -1%
5%
-3%
88% -1%
-26%
13% -48% -1%
-5%
-70%
76%
0%
-12%
265% -46%
0%
6%
-32%
75% -1%
-26%
134% -42% -1%
5%
-9%
87% -1%
-26%
22% -48% -1%
Table A 5: Percent Changes in Fish 1-in-10 year Risk Quotients (LC50, 96hr) for Greens
24
Application Turf Half
Date
Life
Fish:Lawns
Low
-13%
-48%
Albany High
-5%
74%
Low
-36%
-24%
Atlanta High
-38%
30%
Low
-7%
-51%
Bismarck High
10%
69%
Low
-20%
-33%
Columbus High
-16%
50%
Low
21%
-73%
Fresno High
0%
156%
Low
-40%
-29%
Houston High
-23%
68%
Low
-10%
-54%
Madison High
6%
73%
Low
-11%
-76%
Olympia High
11%
114%
Low
-3%
-89%
8%
54%
Roswell High
Mean (Low):
-13%
-53%
Mean (High):
-5%
77%
Median Low):
-11%
-51%
Median (High):
0%
69%
Min
Pond
Size
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
Turf
Organic
Carbon
Pond Pond
Content Half Life Depth TSS
26%
-38% 64% -2%
-20%
41% -40% -2%
42%
-51% 54% -2%
-30%
49% -40% -2%
27%
-32% 89% -2%
-24%
75% -48% -2%
38%
0% 61% -3%
-30%
0% -42% -3%
5%
-39% 78% -1%
-10%
80% -47% -1%
69%
-45% 68% -3%
-29%
103% -37% -2%
31%
-33% 61% -2%
-25%
48% -41% -2%
3%
-27% 42% -3%
-11%
22% -36% -3%
24%
-37% 80% -2%
-25%
55% -46% -2%
29%
-33% 66% -2%
-23%
53% -42% -2%
27%
-37% 64% -2%
-25%
49% -41% -2%
Table A 6: Percent Changes in Fish 1-in-10 year Risk Quotients (LC50, 96hr) for Lawns
25
Application Turf Half
Date
Life
Plants: Fairways
Low
-6%
-49%
Albany High
9%
49%
Low
-14%
-26%
Atlanta High
-18%
22%
Low
-7%
-52%
Bismarck High
11%
47%
Low
11%
-34%
Columbus High
7%
29%
Low
2%
-76%
Fresno High
10%
230%
Low
-37%
-37%
Houston High
-25%
29%
Low
1%
-36%
Madison High
44%
31%
Low
-16%
-90%
Olympia High
25%
174%
Low
-1%
-62%
High
49%
61%
Roswell
Mean (Low):
-7%
-51%
Mean (High):
12%
75%
Median Low):
-6%
-49%
Median (High):
10%
47%
Min
Pond
Size
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
Turf
Organic
Carbon
Pond
Pond
Content Half Life Depth
3%
-17%
87%
-23%
15%
-49%
5%
-16%
82%
-34%
11%
-47%
2%
-14%
89%
-29%
16%
-48%
5%
0%
84%
-24%
15%
-47%
2%
-22% 100%
-25%
124%
-48%
3%
71%
74%
-27%
141%
-45%
5%
-19%
85%
-28%
16%
-48%
1%
-25%
61%
-25%
26%
-42%
2%
-14%
93%
-5%
21%
-49%
3%
-6%
84%
-24%
43%
-47%
3%
-16%
85%
-25%
16%
-48%
TSS
-1%
-1%
-1%
-1%
-1%
-1%
-1%
-1%
-1%
-1%
-1%
-1%
-1%
-1%
-2%
-2%
-1%
-1%
-1%
-1%
-1%
-1%
Table A 7: Percent Changes in Plant 1-in-10 year Risk Quotients (LC50, 7d) for Fairways
26
Application Turf Half
Date
Life
Plants: Greens
Low
-15%
-93%
Albany High
38%
541%
Low
9%
-67%
Atlanta High
10%
201%
Low
-40%
-54%
Bismarck High
66%
69%
Low
15%
-57%
Columbus High
2%
131%
Low
0%
-100%
Fresno High
0%
0%
Low
-18%
-61%
Houston High
4%
101%
Low
-10%
-79%
Madison High
-6%
171%
Low
-20%
-97%
Olympia High
40%
481%
Low
6%
-27%
High
-5%
187%
Roswell
Mean (Low):
-8%
-71%
Mean (High):
17%
209%
Median Low):
-10%
-67%
Median (High):
4%
171%
Min
Pond
Size
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
Turf
Organic
Carbon
Pond Pond
Content Half Life Depth TSS
33%
-5% 115%
0%
-8%
41% -40%
0%
14%
-9% 84% -5%
-36%
16% -47% -5%
-39%
-95% 122%
0%
-4%
328% -50%
0%
18%
0% 101% -1%
-34%
0% -49% -1%
0% -100%
0%
0%
0%
800%
0%
0%
16%
110% 81% -3%
-40%
321% -47% -3%
21%
-13% 97% -4%
-42%
22% -47% -4%
14%
-12% 95% -9%
-19%
19% -48% -9%
-3%
-83% 81%
0%
-15%
838% -45%
0%
8%
-23% 86% -2%
-22%
265% -42% -2%
14%
-12% 95% -1%
-19%
41% -47% -1%
Table A 8: Percent Changes in Plant 1-in-10 year Risk Quotients (LC50, 7d) for Greens
27
Application Turf
Date
Half Life
Plants: Lawns
Low
-8%
-54%
Albany High
12%
57%
Low
-5%
-29%
Atlanta High
-13%
24%
Low
-16%
-49%
Bismarck High
23%
52%
Low
20%
-41%
Columbus High
11%
31%
Low
-3%
-81%
Fresno High
10%
221%
Low
-36%
-37%
Houston High
-19%
28%
Low
7%
-43%
Madison High
68%
41%
Low
-12%
-92%
Olympia High
16%
188%
Low
-2%
-69%
High
57%
74%
Roswell
Mean (Low):
-6%
-55%
Mean (High):
18%
80%
Median Low):
-5%
-49%
Median (High):
12%
52%
Min
Pond
Size
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
Turf
Organic
Carbon
Pond
Pond
Content Half Life Depth
8%
-18%
87%
-9%
13% -47%
15%
-14%
83%
-15%
10% -47%
13%
-12%
90%
-11%
20% -48%
7%
0%
83%
-8%
0% -47%
3%
-25%
88%
-13%
108% -48%
12%
27%
75%
-10%
92% -46%
10%
-14%
83%
-10%
12% -47%
10%
-27%
60%
-13%
27% -43%
-4%
-13% 100%
4%
15% -49%
8%
-11%
83%
-10%
33% -47%
10%
-14%
83%
-10%
15% -47%
TSS
-1%
-1%
-1%
-1%
-1%
-1%
-1%
-1%
-1%
-1%
-1%
-1%
-1%
-1%
-3%
-2%
-1%
-1%
-1%
-1%
-1%
-1%
Table A 9: Percent Changes in Plant 1-in-10 year Risk Quotients (LC50, 7d) for Lawns
28
Application Turf Half
Date
Life
Algae: Fairways
Low
-12%
-52%
Albany High
-1%
92%
Low
-2%
-41%
Atlanta High
-1%
45%
Low
-8%
-58%
Bismarck High
-1%
82%
Low
-3%
-44%
Columbus High
2%
38%
Low
-2%
-66%
Fresno High
9%
163%
Low
-14%
-45%
Houston High
11%
48%
Low
-3%
-51%
Madison High
11%
56%
Low
-10%
-91%
Olympia High
13%
142%
Low
-13%
-53%
High
0%
66%
Roswell
Mean (Low):
-8%
-56%
Mean (High):
5%
81%
Median Low):
-8%
-52%
Median (High):
2%
66%
Min
Pond
Size
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
Turf
Organic
Carbon
Pond Pond
Content Half Life Depth TSS
9%
-20%
73% -2%
-52%
40%
-46% -2%
9%
-17%
58% -2%
-56%
24%
-43% -2%
9%
-14%
85% -2%
-55%
51%
-48% -2%
11%
0%
75% -2%
-57%
23%
-45% -2%
7%
-22%
91% -2%
-45%
81%
-48% -2%
9%
-17%
53% -2%
-55%
18%
-41% -2%
9%
-16%
70% -2%
-55%
41%
-44% -2%
3%
-22%
50% -3%
-40%
30%
-38% -2%
10%
-25%
88% -2%
-55%
51%
-47% -2%
8%
-17%
72% -2%
-52%
40%
-44% -2%
9%
-17%
73% -2%
-55%
40%
-45% -2%
Table A 10: Percent Changes in Algae 1-in-10 year Risk Quotients (LC50, 72hr) for Fairways
29
Application Turf Half
Date
Life
Algae: Greens
Low
-29%
-76%
Albany High
23%
236%
Low
4%
-48%
Atlanta High
11%
127%
Low
-46%
-54%
Bismarck High
2%
5%
Low
31%
-26%
Columbus High
21%
41%
Low
0%
-100%
Fresno High
0%
0%
Low
-28%
-41%
Houston High
15%
67%
Low
-16%
-51%
Madison High
-11%
87%
Low
-21%
-86%
Olympia High
38%
338%
Low
-4%
-16%
High
-7%
6%
Roswell
Mean (Low):
-12%
-55%
Mean (High):
10%
101%
Median Low):
-16%
-51%
Median (High):
11%
67%
Min
Pond
Size
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
Turf
Organic
Carbon
Pond Pond
Content Half Life Depth
-8%
-33% 64%
27%
15% -48%
13%
-23% 64%
-32%
20% -44%
-44%
-98% 55%
-2%
15% -50%
4%
0% 74%
-30%
0% -45%
0% -100%
0%
0%
800%
0%
16%
-24% 73%
-40%
53% -44%
7%
-26% 85%
-32%
26% -46%
3%
-5% 87%
6%
14% -47%
-11%
-67% 87%
-1%
201% -47%
-2%
-42% 66%
-12%
127% -41%
3%
-26% 73%
-2%
20% -46%
TSS
-10%
-10%
-4%
-4%
0%
0%
-1%
-1%
0%
0%
-3%
-3%
-6%
-3%
-6%
0%
0%
0%
-3%
-2%
-3%
-1%
Table A 11: Percent Changes in Algae 1-in-10 year Risk Quotients (LC50, 72hr) for Greens
30
Application Turf
Date
Half Life
Algae: Lawns
Low
-2%
-61%
Albany High
7%
120%
Low
0%
-49%
Atlanta High
-6%
56%
Low
-8%
-60%
Bismarck High
-16%
104%
Low
-5%
-49%
Columbus High
4%
52%
Low
-4%
-78%
Fresno High
7%
206%
Low
-11%
-58%
Houston High
7%
66%
Low
0%
-58%
Madison High
11%
67%
Low
-9%
-91%
Olympia High
10%
154%
Low
-7%
-72%
High
9%
90%
Roswell
Mean (Low):
-5%
-64%
Mean (High):
4%
102%
Median Low):
-5%
-60%
Median (High):
7%
90%
Min
Pond
Size
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
Turf
Organic
Carbon
Pond
Pond
Content Half Life Depth
33%
-23%
69%
-23%
38% -42%
37%
-20%
64%
-32%
26% -44%
40%
-11%
87%
-31%
49% -48%
39%
0%
72%
-33%
0% -45%
21%
-24%
85%
-23%
82% -48%
33%
-19%
48%
-29%
22% -39%
41%
-12%
75%
-32%
39% -46%
17%
-26%
50%
-23%
29% -40%
35%
-20%
80%
-29%
52% -45%
33%
-17%
70%
-28%
38% -44%
35%
-20%
72%
-29%
38% -45%
TSS
-2%
-2%
-2%
-2%
-2%
-2%
-2%
-2%
-3%
-3%
-2%
-2%
-2%
-2%
-2%
-2%
-2%
-2%
-2%
-2%
-2%
-2%
Table A 12: Percent Changes in Algae 1-in-10 year Risk Quotients (LC50, 72hr) for Lawns
31