AN ABSTRACT OF THE DISSERTATION OF
Brenda O. Hoppe for the degree of Doctor of Philosophy in Public Health presented
on May 1, 2012.
Title: Analysis of Oregon’s Domestic Well Testing Act Data for Use in a Sentinel
Surveillance System for Private Well Contaminants
Abstract approved:
Anna K. Harding
The Safe Drinking Water Act ensures that public systems provide water that meets
health standards. However, no such protection exists for millions of Americans who
obtain water from private wells. Concern for safety is warranted as most wells draw
from underground aquifers, and studies demonstrate that groundwater is affected by a
range of contaminants, most often nitrate.
Oregon’s Domestic Well Testing Act (DWTA) links well testing to property sales,
enabling continuous data collection by the State. This research addresses a need for
identifying datasets for characterizing exposure to private well contaminants by
evaluating DWTA data for use in a sentinel surveillance system. Validation of DWTA
data was accomplished by developing a land use regression (LUR) model based on
agricultural nitrogen inputs and soil leachability to predict nitrate concentrations in
well water. Geographic information systems (GIS) were used to advance methods for
high resolution spatial modeling of fertilizer and manure nitrogen with statewide
coverage. Hazard mapping with these datasets suggests that nearly half of recently
drilled wells are susceptible to nitrate contamination. Spearman’s rank correlation
demonstrated a significant correlation between LUR-predicted nitrate levels and levels
reported in the DWTA dataset. These results suggest that DWTA data is valid for use
in a sentinel surveillance system, such that evidence of nitrate contamination in a
single well may indicate an area-wide health hazard. However, a low fraction of
variance explained by the LUR model highlighted the need for specific improvements
to datasets crucial for understanding nitrate contamination in well water, including the
DWTA.
©Copyright by Brenda O. Hoppe
May 1, 2012
All Rights Reserved
Analysis of Oregon’s Domestic Well Testing Act Data for Use in a Sentinel
Surveillance System for Private Well Contaminants
by
Brenda O. Hoppe
A DISSERTATION
Submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Doctor of Philosophy
Presented May 1, 2012
Commencement June 2012
Doctor of Philosophy dissertation of Brenda O. Hoppe presented on May 1, 2012.
APPROVED:
Major Professor, representing Public Health
Co-Director of the School of Biological and Population Health Sciences
Dean of the Graduate School
I understand that my dissertation will become part of the permanent collection of
Oregon State University libraries. My signature below authorizes release of my
dissertation to any reader upon request.
Brenda O. Hoppe, Author
ACKNOWLEDGEMENTS
Thank you, Dr. Anna Harding, for being by my side tirelessly through this journey
that was so much more than a doctoral degree. You were and always will be my
mentor in more ways than as an accomplished public health scientist. I look up to you
as a woman, a researcher, a teacher, a mother and a person I so want to emulate. There
is no possible way I can repay you for all you’ve done for me, except to do my best to
pass some of the wisdom, grace, and generosity you’ve always shared with me on to
others. I will always count you as one of my cherished blessings in life.
Thank you, Dr. Bruce Hope, for also hanging in there with me through thick and
thin, always generous with your time, brilliance, and humor, the last of which I
cherished as just about dark and dry as my own. I often felt like you and Anna
together were my Oz behind the curtain, pulling levers and shifting gears to keep me
functioning as a burgeoning PhD, when I could see nothing in front of me but
frustration and failure. You kept me grounded, moving forward, and encouraged me
with every step in a manner that was more stylish than Gregory House could ever
dream of. I promise to take this PhD and work like hell to make you both proud.
Thank you, Mr. Denis White, for eschewing the easy joy of retirement after a
highly accomplished career to help a fellow Cheesehead with her endless obstacles
and methodical wrong-turns trying to peel back the mysteries of GIS, modeling, and
statistics. I cannot believe my good fortune to have found someone with your
expertise, kindness, and that classic midwestern sensibility of always staying busy. I
often worried that I was taking advantage of these traits but can’t thank you enough
for all I learned from you in the process. On Wisconsin!
Thank you, Dr. George Mueller-Warrant, for so much of your time and effort
spent developing the python script that enabled my analyses. It was a messy business,
working with such imperfect data, a classic environmental public health challenge, and
I can’t thank you enough for bravely taking on this ―soft science‖ student. Without
your help, this dissertation would not have been possible, and I really enjoyed troubleshooting by your side.
Finally, thank you to my family. My dear husband and best friend (and spiritual
advisor), Thomas, with whom all things are possible. You carried me here, to this
accomplishment, and one of my greatest hopes is that I can someday return the favor,
to see great dreams of your own come true. Eva, Ben, Bruce, Jean, Beth, Jon, Levi,
Anna, Sami, Will, and George. My precious fan club, every one, believing in me and
encouraging me every step of the way.
And most importantly, Mom and Pops.
Pops, you planted this wild idea in my head that I could accomplish this task, and
Mom, you made sure without a doubt that I would make it. This PhD is hardly the
most difficult challenge that you two have carried me through (and there have been
many), but like all of my struggles you have always been there right by my side—―we
three musketeers!‖. With Thomas I dedicate all this dissertation represents to you, as I
dedicate everything that I do that is meaningful or kind.
To everyone mentioned here and many others who are not but whom I still hold in
my heart for their help and encouragement:
Thank you, and I will never forget what you have done for me.
CONTRIBUTION OF AUTHORS
Dr. Anna Harding, Dr. Bruce Hope, and Mr. Denis White provided substantial
contributions to conception, design and interpretation of the data, as well as critical
review and editing of manuscripts. Mr. Denis White, Dr. George Mueller-Warrant,
and Mr. Eric Main provided crucial assistance with development of methods and
modeling using geographic information system (GIS) software. Mr. Denis White and
Dr. Bruce Hope also contributed to statistical analyses. Dr. Jennifer Staab and Ms.
Marina Counter provided assistance with data analysis and editing.
TABLE OF CONTENTS
Page
CHAPTER 1 – INTRODUCTION……………………………….................
1
Private Wells and Nitrate Contamination in Oregon…………………
8
Objectives………………………………………………….................
11
Specific Aims………………………………………………………… 11
Significance and Justification………………………………………...
12
Format………………………………………………………………..
14
CHAPTER 2 – LITERATURE REVIEW…………………………………...
Drinking Water Nitrate and Associated Health Risks………………..
EPH Surveillance and the Need for Private Well Monitoring………..
GIS, Exposure Assessment and Drinking Water Contaminants……...
17
17
33
49
CHAPTER 3 – PRIVATE WELL TESTING IN OREGON FROM REAL
ESTATE TRANSACTIONS: AN INNOVATIVE
APPROACH TOWARD A STATE-BASED
SURVEILLANCE SYSTEM……………………………...
Abstract……………………………………………………………....
Introduction…………………………………………………………..
Methods……………………………………………………………....
Results………………………………………………………………...
Discussion………………………………………………………….....
Conclusions……………………………………………………….......
55
56
57
61
62
67
71
CHAPTER 4 – MODELING NITRATE CONTAMINATION IN
PRIVATE WELLS FOR UNDERSTANDING HUMAN
HEALTH RISK – PART I: REFINING SPATIAL
RESOLUTION OF AGRICULTURAL NITROGEN……..
Abstract……………………………………………………………….
Introduction…………………………………………………………...
Methods………………………………………………………………
Results………………………………………………………………...
Conclusion……………………………………………………………
73
74
76
79
93
99
CHAPTER 5 – MODELING NITRATE CONTAMINATION IN
PRIVATE WELLS FOR UNDERSTANDING HUMAN
HEALTH RISK – PART II: APPLICATION OF A LAND
USE REGRESSION MODEL TO WELL TEST DATA…..
101
TABLE OF CONTENTS (continued)
Page
Abstract……………………………………………………………..... 102
Introduction…………………………………………………………... 104
Model Development………………………………………………….. 107
Application to DWTA Data………………………………………….. 114
Results………………………………………………………………... 115
Discussion……………………………………………………………. 118
Conclusion…………………………………………………………… 121
CHAPTER 6 – SUMMARY AND CONCLUSIONS………………………
123
BIBLIOGRAPHY…………………………………………………………...
132
APPENDICES………………………………………………………………
APPENDIX A: DWTA flowchart……………………………………
APPENDIX B: Published manuscript 1……………………………...
APPENDIX C: Conceptual diagram………………………………….
APPENDIX D: Research questions…………………………………..
158
159
161
171
173
LIST OF FIGURES
Figure
1.1
1.2
2.1
3.1
3.2
3.3
4.1
4.2
4.3
4.4
4.5
4.6
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
Page
Comparison of fertilizer and manure nitrogen sources for 1999 by
world region………………………………………………………....
Location of Oregon GWMAs…………………………………….…
Conceptual model of environmental public health surveillance
systems……………………………………………………………....
Total number of real estate transactions per year, 1989-2008,
Oregon……………………………………………………….……....
Oregon private wells with Category 2 and Category 3 nitrate levels
and positive detections of coliform, 1989-2008……….…………….
Location of wells with Category 2 and 3 nitrate concentrations in
comparison with locations of Oregon’s three groundwater
management areas………………………………………………..….
Oregon study area with 9 ecoregions and 3 groundwater
management areas………………………………..………………….
State-permitted CAFOs, 2000-2007, and distribution of manure-N
estimates……………………..………………………………………
4
10
42
63
65
66
80
85
Map of ―very high‖ and ―high‖ soil sensitivity to nitrate leaching..... 91
New wells drilled between 2000-2007 and locations of groundwater
management areas………………………………………………...… 92
Total annual nitrogen from manure and fertilizer land applications
across Oregon……………………………..………………………… 96
Selected wells coded by quartile of total nitrogen loading within
well buffer and view of Willamette Valley GWMA………..………. 99
Geographical distribution of manure-N across Oregon…………...... 108
Geographical distribution of fertilizer-N across Oregon…………… 109
Geographical representation of soil sensitivity to nitrate leaching
across Oregon……………………………………………………….. 110
DEQ wells sampled for nitrate as nitrogen between 2000-2007…....
Geographical distribution of wells in the DWTA dataset, 20002007………………………………………………………………….
Predicted vs. observed nitrate concentrations in training dataset
with fitted regression line…………………………………………....
Predicted vs. observed nitrate concentrations in validation dataset
with fitted regression line…………………………………..………..
Nitrate concentrations predicted by LUR model vs. observed nitrate
concentrations in DWTA dataset……………………………….…...
111
115
116
117
118
LIST OF TABLES
Table
3.1
4.1
4.2
4.3
4.4
4.5
5.1
Page
Population growth estimates and forecasts for Oregon counties with
large numbers of wells with elevated levels of nitrates……………..
Datasets used in this study………………………………………..…
Nitrogen content of manure from livestock groups………………....
Manure-N estimates (kg-year) for Oregon counties using two
approaches…………………………………………………………...
Substitutions used for OR-CDL crops for which an EBS was not
available…………………………………………………………..…
Fertilizer-N estimates (kg-year) for Oregon counties using two
approaches…………………………………………………………...
Summary of final LUR model……………………………………....
67
81
83
87
89
90
113
Analysis of Oregon’s Domestic Well Testing Act Data for Use in a Sentinel
Surveillance System for Private Well Contaminants
CHAPTER 1 – INTRODUCTION
Ensuring access to clean drinking water is a critical component of public health. In
the United States, various federal regulations assist with the protection of water from
public drinking water systems, chiefly the Safe Drinking Water Act (SDWA) passed
in 1974 (42 U.S.C. § 300f). Under the SDWA, the Environmental Protection Agency
(EPA) sets national health-based standards (maximum contaminant levels or MCLs)
for drinking water supplied through public systems and oversees the states, localities
and water suppliers that implement these standards (EPA, 2010). However, many U.S.
households are not served by water from a public water system but from a single well
located on a private property. Approximately 43 million people, or 15 percent of the
U.S. population, obtain their drinking water from a private well (Hutson et al., 2004).
According to the 2009 American Housing Survey, 15,846,000 homes in the U.S. are
served by a private well (U.S. Census Bureau, 2009). The large majority of these
households are located in rural areas (Simpson, 2004). Reliance on private water
supplies is trending upwards; estimated withdrawals from private wells increased by
60% between 1965 and 2000 (Hutson et al., 2004). Yet, the SDWA does not regulate
wells serving fewer than 25 individuals or 15 service connections (EPA, 2010). In
fact, at this time there is no federal law or program in place to monitor the quality of
private wells to ensure that drinking water is free from contaminants that may pose a
risk to the health of individuals consuming this water on a daily, long-term basis.
Although some states and localities have tried to fill this gap with limited regulations
or programs, owners of private water systems are largely themselves responsible for
ensuring that their water is safe to drink (Backer & Tosta, 2011; Rogan et al., 2009).
Almost 99% of private wells in the U.S. draw water from underground aquifers
(Solley et al., 1998). Groundwater quality may be compromised by various sources of
contamination, such as landfill seepage, failed septic tanks, underground fuel tanks,
2
runoff from urban areas, leaching from natural geologic formations, animal waste
from confined animal feeding operations, and widespread field applications of
fertilizers, pesticides, manure, wastewater or sewage sludge (Burkart & Stoner, 2007;
DeSimone et al., 2009; Erickson & Barnes, 2005; WHO, 2006). Sampling studies
across the U.S. and Canada demonstrate that private well water can be affected by a
wide range of contaminants, including metals (Barringer et al., 2006; Meliker et al.,
2008; Shaw et al., 2005; Walker et al., 2005; Walker & Fosbury, 2009), volatile
organic compounds (Aelion & Conte, 2004; Ayotte et al., 2008; Hoffman et al., 2010;
Rowe et al., 2007), uranium and other radionuclides (Hughes et al., 2005), antibiotics
(Batt et al., 2006), pesticides (Troiano et al., 2001), microorganisms (Borchardt et al.,
2003; Corkal et al., 2004; Gonzalez, 2008; Swistock & Sharpe, 2005), and nitrate
(Aelion & Conte, 2004; Batt et al., 2006; Corkal et al., 2004; Kross et al., 1993; Liu et
al., 2005; Mitchell et al., 2003; Rogan et al., 2009; Townsend & Young, 2000; Weyer
et al., 2006).
In 2009, the United States Geological Survey (USGS) published the results of a
comprehensive sampling effort of private wells in 48 states by the National WaterQuality Assessment program (NAWQA) (DeSimone et al., 2009). From 1991-2004,
2,167 private wells were sampled for 219 contaminants. Nitrate was the most common
contaminant from anthropogenic sources found at concentrations over human-health
benchmarks. Over 4% of all sampled wells had nitrate concentrations exceeding the
MCL of 10 mg/L nitrate-nitrogen. A subset of 400 wells was sampled for microbial
contaminants and revealed that more than a third of sampled wells tested positive for
total coliform bacteria while 8% of wells tested positive for Escherichia coli. A
separate analysis of 436 additional private wells in areas with intensive agricultural
activity resulted in nitrate concentrations greater than the MCL in nearly 25% of
sampled wells. Of great concern is the finding that more than 20% of all sampled
wells had one or more contaminants at a concentration that exceeded the MCL for that
contaminant.
3
Without properly selected, installed and maintained treatment systems, private
well users are directly exposed to groundwater contaminants. Regrettably, many well
owners are not aware of the importance of maintaining their well and treating or
testing the water before consumption (Simpson, 2004). Walker and others (2005)
surveyed approximately 13,500 residents on private wells in a Nevada county and
found that only 38% of respondents applied treatment to water before consumption.
Similarly, Mitchell and Harding (1996) surveyed rural Oregon households on private
wells and found that 30% reported use of water treatment devices. Jones and others
(2006) surveyed private well owners in Ontario and found that approximately 56%
and 61% of respondents used in-home treatment devices and bottled water within their
homes, respectively, while only 8% reported testing their water according to
provincial guidelines.
Nitrate is considered a major contaminant of concern in private well water, given
that it is the most ubiquitous in groundwater (Spalding & Exner, 1993); most
frequently detected in well sampling efforts (DeSimone et al., 2009; Dubrovsky et al.,
2010; Rogan et al., 2009); and, its presence in high concentrations is a potential health
risk (Greene et al., 2005). Agricultural activities, such as field applications of nitrogen
fertilizer and manure, are primary sources of nitrate to groundwater (Bradford et al.,
2008; Burkart & Stoner, 2007; Tilman, 1999). Citing work from the Food and
Agriculture Organization of the United Nations along with work from Lander and
others (1998), Brukart and Stoner (2007) demonstrated that for many world regions,
including North America, inorganic nitrogen fertilizer accounts for approximately
twice the amount of environmentally available nitrogen compared to animal manure
(Figure 1.1). However, the researchers note that the trend of increasing concentration
of livestock and manure production in the United States is driving a proportionate
increase in nitrogen available for leaching to groundwater.
According to Tilman (1999), the doubling of world food production from 1961 to
1996 to support an expanding population could be achieved only with a 7-fold
increase in the rate of nitrogen fertilization. In particular, between 1950 and the early
4
1980s, the USGS estimates that the use of nitrogen fertilizers increased 10-fold
(Dubrovsky et al., 2010). Galloway and others (2008) report that approximately 40%
of the world’s dietary protein depends on nitrogen fertilizers and suggest that at least 2
billion people would not be alive today without this relatively modern invention.
Tilman and others (2001), incorporating estimates of population and economic
growth, forecast that global nitrogen fertilization will continue to grow 1.6 and 2.7
times current amounts by 2020 and 2050, respectively.
Figure 1.1. Comparison of fertilizer and manure nitrogen sources for 1999 by
world region (Reproduced from Brukart & Stoner, 2007).
The heavy use of nitrogen fertilizer is problematic due to the fact that of all the
fertilizer applied to land, only half will remain in the field or be harvested with a crop
(Liu et al., 2010; Tilman, 1999). In fact, there is a direct, quantitative link between the
amount of nitrogen in the major rivers of the world and the magnitude of agricultural
nitrogen inputs to their watersheds (Dubrovsky et al., 2010; Howarth et al., 1996).
When released to arable land, nitrogen fertilizers are rapidly converted to a highly
soluble form of nitrate. When the quantity of nitrate exceeds the ability of the plants
and soil to denitrify it, the excess percolates through the soil and can accumulate in
underlying groundwater aquifers (Kundu & Mandal, 2009).
5
Background nitrate concentrations in U.S. groundwater aquifers are estimated to
be less than 1 mg/L nitrate-nitrogen (Dubrovsky et al., 2010; Mueller & Helsel, 1996).
Nitrate concentrations above this level are considered to be driven by anthropogenic
inputs (Dubrovsky et al., 2010; Greene et al., 2005; Warner & Arnold, 2010; WHO,
2011). Nitrate in well-oxygenated groundwater is remarkably stable and resists
degradation, accumulating into a long-term water resource problem that is expensive
and difficult to remediate (Dubrovsky et al., 2010; WHO, 2006).
The amount of nitrate that leaches to the groundwater depends on many factors,
such as aquifer depth, soil drainage characteristics, precipitation or irrigation, and the
extent of nitrogen loading at the land surface (Burkart & Stoner, 2007; Dubrovsky et
al., 2010; Nolan & Stoner, 2000). A number of studies investigating nutrient loading
relative to groundwater quality have included some measure of fertilizer or manure
inputs, many relying on the use of geographic information systems (GIS) for
processing and distributing data spatially (Greene et al., 2005; Nolan & Hitt, 2006;
Kundu et al., 2009; Liu et al., 2010; Olson et al., 2009; Rekha et al., 2011; Showers et
al., 2008; Swartz et al., 2003). However, these measures are often based on coarsely
aggregated data applied to a limited study area. Given that fertilizer and manure
applications can vary substantially over short distances depending on the location and
type of farm facilities and crops grown (Luo & Zhang, 2009), low resolution datasets
may not adequately capture the spatial fluctuations in nutrient loading necessary for
predicting groundwater contamination at point locations where wells may exist (Elliot
& Savitz, 2008; Slaton et al., 2004). Furthermore, it is often difficult to find detailed
spatial information with full statewide coverage because of the effort, expense, and
often legislation that is required to compile data for such a large area.
Under the SDWA, the MCL for nitrate is 10 mg/L (nitrate measured as nitrogen).
This level was set mainly to prevent methemoglobinemia, or ―blue baby syndrome‖, in
infants (EPA, 2010). This condition can lead to shortness of breath, cyanosis, anoxia,
and in some extreme cases, death (WHO, 2006). Other adverse health outcomes linked
to consumption of high levels of drinking water nitrate include: thyroid hormone
6
disruptions (Gatseva & Argirova, 2008a; Gatseva & Argirova, 2008b; Tajtakova et al.,
2006), diabetes (Kostraba et al., 1992; Parslow et al., 1997), adverse reproductive
events (Brender et al., 2004), acute respiratory infections (Gupta et al., 2000), nonHodgkin’s lymphoma (Cocco et al., 2003; Gulis et al., 2002; Ward et al., 1996), and
bladder (Chiu et al., 2007; Weyer et al., 2001), colon (De Roos et al., 2003; Gulis et
al., 2002; McElroy et al., 2008), stomach (Sandor et al., 2001), and ovarian (Weyer et
al., 2001) cancers. The EPA recently completed its second 6-year review of existing
drinking water regulations required under the SDWA. Citing studies on thyroid
hormone disruption and a recent report by the International Agency for Research on
Cancer that labels ingested nitrate as ―probably carcinogenic to humans‖, the EPA is
considering initiation of a new health assessment for nitrate that may lead to lowering
the MCL (EPA, 2009).
There is some debate whether or not the concern over nitrate contamination of
drinking water is fully warranted (Powlson et al., 2008). Avery (1999) suggests that
nitrate contamination should be a public health concern only as it is an indicator of
bacterial contamination, given that bacterial and nitrate contamination may stem from
the same cause (a shallow, damaged well) and the same source (a barnyard, cesspool,
leaky septic tank or manure facility). Powlson and others (2008) present a thorough
review of this debate and the evidence for bacterial involvement in the etiology of
methemoglobinemia cases rather than nitrate per se. Exposure to bacteria in drinking
water is primarily associated with gastrointestinal illness, but can also lead to febrile
illnesses, systemic or pulmonary disease, or potentially fatal meningitis (Rogan et al.,
2009). Numerous studies have demonstrated bacterial contamination in private wells
(DEPNJ, 2008; DeSimone et al., 2009; Gonzalez, 2008; Kross et al., 1993; Strauss et
al., 2001; Swistock & Sharpe, 2005; Zimmerman et al., 2001). Authors of the 2009
USGS NAWQA private well study note that fecal indicator bacteria were among the
contaminants found most frequently in sampled wells at concentrations greater than
benchmarks and thus, ―are of potential concern for human health‖ (DeSimone et al.,
2009).
7
Given the health risks associated with exposure to nitrate, bacteria and other
contaminants, the documented presence of these contaminants in groundwater, and the
large number of U.S. households relying on private wells for drinking water, experts
have called for developing comprehensive monitoring or testing efforts of private
wells (CEH et al., 2009; Levin et al., 2002; Manassaram et al., 2006). Summarizing an
extensive review of the literature on nitrate in drinking water and reproductive
outcomes, Manassaram and others (2006) state,
―The lack of data on unregulated systems…is an important issue. The available
data on the occurrences of nitrate in drinking water indicate that users of private
water systems are most at risk for exposure to nitrate levels above the MCL.
However, a lack of studies focusing on users of private water systems means that
the extent of the problem is unknown….States with large numbers of private wells
where groundwater is vulnerable to contamination should be encouraged to increase
monitoring or surveillance of such systems. Future research could include long-term
monitoring or surveillance of water systems vulnerable to contamination. This
could provide valuable exposure assessment information to conduct studies on
drinking water contaminants such as nitrates.‖
In 2009, the American Academy of Pediatrics released a policy statement
regarding drinking water from private wells and risks to children, including a specific
recommendation to governments to require well testing for contaminants of local
concern when a dwelling is sold (CEH et al., 2009). That same year the U.S.
Department of Health and Human Services released, The Surgeon General’s Call to
Action to Promote Healthy Homes, which highlights the link between well water
quality and health and recommends annual testing of private wells for bacteria and
chemical contamination (DHHS, 2009). Further recognizing the need to address the
lack of data on private wells, the federal government through the U.S. Centers for
Disease Control and Prevention (CDC) has convened a work group of drinking water
experts from various state governments to investigate existing state data on private
wells (Backer & Tosta, 2011) and the feasibility of linking these data to a national
environmental public health tracking (EPHT) network (L. Backer, personal
communication, August 31, 2010).
8
The purpose of the EPHT network is to function as a repository of validated
scientific information on environmental exposures and adverse health outcomes to
facilitate analysis of the possible spatial and temporal relations between them
(McGeehin et al., 2004). The ultimate goal is to assist environmental public health
practitioners with risk reduction efforts (Litt et al., 2004). The main building blocks of
the national EPHT network will be existing state level surveillance systems that
collect data pertaining to environmental exposures or diseases of interest (McGeehin
et al., 2004; Ritz et al., 2005). These systems, while feeding into the federal network,
will ultimately be focused on collecting data for addressing priority issues of the state
(McGeehin et al., 2004). State level water quality monitoring of public water systems,
required by the SDWA, is an example of a state surveillance system that could link to
the national EPHT network (Niskar, 2007). Drinking water and water quality were
identified by state and local public health practitioners as top priorities for the EPHT
network (Litt et al., 2004). A surveillance system for private wells would assist in
characterizing the risk to the large number of individuals relying on private wells for
drinking water across the nation by supplying exposure information to the national
EPHT network.
Private Wells and Nitrate Contamination in Oregon
As in many states, groundwater contamination is a recognized problem in Oregon.
Long term, continuous contributions from agricultural fertilizers, animal feedlot
operations and above ground application of wastewater are responsible for the
widespread nitrate and microbial contamination in groundwater throughout the state,
while naturally occurring geologic formations introduce elevated levels of arsenic in
some areas (DEQ, 1991, 1997; Hinkle & Polette, 1999; LCG, 2006). This
contamination is a significant public health concern given that approximately 70% of
all Oregon residents and over 90% of rural residents rely on groundwater for their
household drinking water (DEQ, 2009). Furthermore, given that population density in
Oregon is increasing, the need for access to clean water across the State will only
9
intensify (Zaitz, 2009). Estimates based on U.S. Census Bureau distributions predict
that Oregon’s population will increase 37% from 2000 to 2040 (OEA, 2004).
According to current estimates, there are over 350,000 private wells in Oregon (DEQ,
2009). Using 2010 U.S. Census statistics, this number of wells would suggest that
approximately 23% of the State is relying on private wells for household drinking
water (USCB, 2010). In addition, approximately 3,800 exempt-use wells, comprised
largely of small group or single domestic use wells, are drilled each year (OWRD,
2008). These limited estimates suggest that Oregon has a large and growing
population dependent on private, unregulated water systems.
Recognizing the need to protect the state’s groundwater aquifers, the Oregon
legislature passed two key pieces of legislation in 1989, enabling statewide
groundwater monitoring. The Groundwater Protection Act (ORS. 468B. 162(3), 1989)
requires that the Oregon Department of Environmental Quality (DEQ) declare a
Groundwater Management Area (GWMA) if area-wide groundwater contamination
exceeds trigger levels. For nitrate, the trigger level is 70% of the MCL, or 7 mg/L.
Currently, there are three GWMAs in Oregon, together comprising approximately
3,000 square kilometers (Figure 1.2).
All three GWMAs were declared for widespread nitrate contamination (DEQ,
2009). Once a GWMA has been designated, a local committee is convened to work
with state agencies to develop and implement a voluntary action plan to reduce
contamination. The action plans often include education outreach efforts to residents
living inside the GWMA who may have private wells or on-site septic systems, a
potential point source of contamination (DEQ, 1991, 1997; LCG, 2006).
The second piece of legislation, the Domestic Well Testing Act (DWTA) (ORS.
488. 271, 1989), requires that any seller of a property with a well that supplies
groundwater for domestic purposes must have the well water tested for nitrate and
total coliform bacteria by a state-accredited laboratory upon accepting a purchase
offer. Results must be sent to the state public health agency, the Oregon Health
Authority (OHA), and are stored in a database. In 2009, the law was amended to
10
include arsenic as a test parameter and to require that buyers receive notification of the
test results. As written, the purpose of this legislation was not to protect public health,
but to ―establish a program to provide water quality monitoring of underground
aquifers‖ (OAR. 333-061, 0305-0335, 2009). The data collection process prescribed
in the DWTA is provided in Appendix A.
Figure 1.2. Location of Oregon GWMAs (Reproduced from DEQ, n.d.).
Two other states, New Jersey and Rhode Island, passed legislation similar to
Oregon’s DWTA in 2001 and 2008, respectively (DEPNJ, 2009; DOHRI, 2008).
Linking private well testing to a real estate transaction is an innovative policy option
for state governments to maintain continuous monitoring of the quality of private well
water in the state. The benefits of linking private well monitoring to the sale of a
property include: 1) potential buyers are informed of water quality and can negotiate
any treatment costs with the seller; 2) the state can collect and analyze sampling data
in order to characterize groundwater quality; and, 3) state and local public health
agencies can identify individuals or communities exposed to high levels of drinking
11
water contaminants and provide information on health-protective measures (DEPNJ,
2008). In addition, depending on quality assurance, these data may be a valuable
addition to a larger repository of water quality information in the national EPHT
network currently under development.
Objectives
The main objectives of this research are to examine Oregon’s Domestic Well
Testing Act (DWTA) and reported well test data in order to characterize statewide,
population-based exposure patterns related to private well water affected by nitrate
and coliform bacteria and evaluate these data for supporting a public health
surveillance system for monitoring exposures to nitrate in private wells.
Specific Aims
The specific aims of this research include:
Specific Aim 1: Assess statewide, population-based exposure to nitrate and
coliform bacteria in Oregon private wells using DWTA test results and evaluate if
provisions of the DWTA are sufficient for generating data that can support public
health actions (Chapter 3).
Specific Aim 2: Investigate nitrate contamination in groundwater relative to
private wells located in Oregon by advancing methods for distributing agricultural
nitrogen estimates using state-specific data sources to improve spatial resolution
(Chapter 4).
Specific Aim 3: Evaluate DWTA test data for use in a public health surveillance
system by developing a land use regression (LUR) model that combines geographic
information systems (GIS), high resolution agricultural nitrogen datasets, and
statistical analysis to predict nitrate levels at any well location across Oregon and
compare to levels reported in the DWTA dataset (Chapter 5).
12
Significance and Justification
The following section briefly summarizes the significance and justification of the
proposed research.
First, based on a thorough review of the literature, this is the first study to
investigate the suitability of a private well testing law for supporting a public health
surveillance system for monitoring exposures to drinking water contaminants. While
15 percent of the U.S. population obtains their drinking water from private wells, there
are very little data on these unregulated systems (Hutson et al., 2004). Data that are
available suggest that users of private water systems may be regularly exposed to
contaminants above levels considered safe for human health (DeSimone et al, 2009).
Manassaram and others (2006) call for research that includes long-term surveillance of
water systems vulnerable to contamination. The proposed research responds to that
call by evaluating whether an existing state law and associated data can serve as a
public health surveillance system for exposures to nitrate. In the process of conducting
this evaluation, more than twenty years of data will be analyzed for the prevalence and
spatial distribution of both nitrate and coliform bacteria in Oregon private wells,
providing a basis for analyses of trends and spatial-temporal variations (Malecki et al.,
2008). This baseline characterization will be important to establish as the integrity of
private well water quality may be increasingly affected by future extreme weather
events associated with global climate change (Simpson, 2004). For example, flooding
from extreme precipitation or dramatic snow melt could inundate wells and aquifers
with contamination from surface land uses, and rising temperatures could lead to
increased survival and presence of water-borne pathogens (Simpson, 2004). While the
original purpose of Oregon’s DWTA was to collect groundwater quality data for use
by environmental scientists, the DWTA data may be particularly valuable to public
health practitioners for monitoring human exposure to groundwater contaminants,
identifying high-risk areas, and initiating education or risk prevention programs.
Therefore, the DWTA has the potential to form the backbone of a state level public
health policy on private wells, when currently there is no equivalent at the federal
13
level. Furthermore, given the recent initiative by CDC and partners to develop a
national EPHT network, this study may also demonstrate that Oregon’s DWTA data
can contribute valuable environmental exposure data to the national EPHT effort and
serve as a template for other state governments interested in establishing state policy
on private wells.
In addition, it is anticipated that this research will highlight issues that arise when
data that have been collected for characterizing environmental quality are used for
public health applications. As stated by Nuckols and others (2004), ―A basic principle
in environmental sciences is that measurement data should be used within the bounds
of the purpose for which the sample was collected‖. Given that environmental public
health practitioners are commonly faced with overstepping this principle in order to
use environmental data to characterize risk to human populations, it is hoped that this
research will encourage environmental scientists planning for data collection to
consider potential public health uses of the data and collaborate with public health
colleagues. Doing so will maximize the potential use of data that is so often difficult
and costly to collect. Furthermore, the proposed research will highlight the need for
the ―communication and collaboration‖ called for by Vine and others (1997) among
researchers from a variety of different fields, including epidemiology and
environmental sciences, ―to realize the full potential of GIS technology in
environmental health studies‖.
Finally, this research will result in data that will contribute to a greater
understanding of the costs associated with adverse impacts on an essential ―ecosystem
service‖ that has direct implications for human health, namely access to safe drinking
water. The World Health Organization (WHO) highlights the importance of safe
drinking water and the need for sustainable nutrient management in the health
synthesis report of the 2005 Millennium Ecosystem Assessment (MEA, 2005). In
order to establish priorities for addressing the health consequences of ecosystem
changes, the report highlights a need for a systematic inventory of current and likely
population health impacts of ecosystem changes and an investigation into the direct
14
and indirect drivers causing these changes, or what is referred to in the report as
―policy-relevant scientific assessments‖. The U.S. Department of Agriculture (USDA)
has also called for more comprehensive data on groundwater and associated drinking
water quality for ―valuing environmental services‖ and the ―overall cost of agriculturerelated water-quality impairments‖ in order to develop policies to protect groundwater
(Crutchfield, 1995). This call was repeated by the National Research Council in the
2010 report, Toward Sustainable Agricultural Systems in the 21st Century (NRC,
2010).
The proposed research will result in a statewide population-based exposure
characterization that will facilitate an understanding of the potential impact on public
health associated with quality reductions of a key ecosystem service, groundwater, and
will do so in context of an evaluation of an established state policy on private well
testing and its potential use as a public health surveillance system. Given that the
majority of literature on exposures and health impacts related to drinking water
contamination is based on data from public water supplies, the proposed research will
not only contribute data on private water systems but highlight the need for continued
surveillance of these systems for the benefit of protecting public health.
Format
This dissertation is presented in manuscript format. Chapters three, four, and five
are presented as individual manuscripts following journal submission guidelines.
Chapter two provides the supporting literature review, while chapters one and five
introduce and summarize the research conducted for this dissertation.
The first manuscript determines prevalence of nitrate and coliform bacteria
contamination in Oregon private wells using well test data collected under Oregon’s
DWTA. GIS was used to locate affected wells, compare these data with existing
groundwater management areas and population growth estimates, and statistically
assess clustering of wells with elevated nitrate concentrations. Application of DWTA
test data to this exposure characterization provided an opportunity to evaluate
15
provisions of the DWTA law and make comparisons to legislation in other states that
attach private well testing to real estate transactions. This manuscript has been
published in Public Health Reports, and the published version is provided in
Appendix B.
The second manuscript identifies private wells across the state that may be
affected by high levels of nitrate due to surface applications of manure and fertilizer
nitrogen, as well as soil sensitivity to nitrate leaching. Methods published by USGS
researchers for estimating nutrient loading at the county level were adopted and
incorporated with state-specific datasets in a novel approach for characterizing spatial
fluctuations in manure and fertilizer nitrogen loading at a fine spatial resolution. All
datasets were integrated into a GIS model with statewide coverage for determining the
potential for nitrate contamination in groundwater at point locations where private
wells exist or may be constructed in the future. A conceptual diagram of the data used
in this model is provided in Appendix C. The datasets described in the second
manuscript were utilized in analyses conducted in the third manuscript.
The third manuscript evaluates the validity of DWTA well test data for use as a
sentinel surveillance system for monitoring exposures to private well contaminants.
Datasets developed for the second manuscript on agricultural nitrogen and soil
sensitivity were entered into a land use regression (LUR) model as predictors. A
conceptual diagram of the data used in this model is provided in Appendix C. The
LUR model was calibrated with high quality groundwater nitrate samples collected by
the DEQ and used to predict nitrate concentrations for wells represented in the DWTA
dataset. Validity of DWTA data was assessed by comparing reported nitrate
concentrations to those predicted by the LUR model.
Additional research questions were investigated for this dissertation but were not
incorporated into the manuscripts. They include:
1. Are elevated nitrate levels related to positive coliform bacteria detections in
Oregon private wells?
16
2. Are elevated nitrate levels or positive coliform bacteria detections related to
depth of Oregon private wells?
3. Are socioeconomic and demographic characteristics significantly associated
with elevated nitrate levels in Oregon private wells?
These questions support a thorough exposure characterization related to the
presence of nitrate and coliform bacteria in Oregon private well water. Well test data
from the DWTA were used to address each question. In addition to providing useful
information to public health practitioners and colleagues in water quality management,
research for these questions further demonstrates the strengths and weaknesses of the
DWTA policy and associated well test data for public health applications. Appendix D
presents each research question with a brief description of methods, results and
conclusions.
17
CHAPTER 2 – LITERATURE REVIEW
Drinking Water Nitrate and Associated Health Risks
Nitrate is a naturally occurring anion that is highly soluble in water and is
present throughout the environment, in mineral deposits, soil, seawater, freshwater,
and the atmosphere. (Grosse et al., 2006; Mensinga et al., 2003; WHO, 2007).
Humans are exposed to nitrate primarily through consumption of vegetables and cured
meats (Grosse et al., 2006; WHO, 2007). However, when nitrate levels in drinking
water exceed health-based standards, drinking water will be the main source of total
nitrate intake, especially for bottle-fed infants (WHO, 2007). Standards for nitrate in
drinking water are similar around the world, although reporting units differ. In the
U.S. the maximum contaminant level (MCL) for nitrate in drinking water is 10 mg/L,
expressed in concentrations of nitrogen in the nitrate ion per liter (EPA, 2010).
Conversely, Europe, UK and the World Health Organization (WHO) define the
standard as 50 mg/L, expressed in concentrations of the nitrate ion per liter (ECE,
2010; DWI, 2009; WHO, 2007). Translating the U.S. MCL into the European (EU)
units gives 44.3 mg/L nitrate ion per liter, which closely approximates the EU standard
(Kozisek, 2007). However, because of this discrepancy, care must be taken when
interpreting data in the literature and attention paid to the reporting units used by the
author. Throughout this dissertation, nitrate levels will be reported according to the
nitrate ion per liter, unless noted otherwise.
The health hazards from consuming water with high levels of nitrate are
attributable to the reduction of nitrate in the body to nitrite (Grosse et al., 2006;
Mensinga et al., 2003; WHO, 2007). Ingested nitrate is readily and completely
absorbed from the small intestine and rapidly distributed throughout the tissues
(Mensinga et al., 2003; WHO, 2007). Approximately 60-70% of an oral nitrate dose is
excreted in urine in the first 24 hours, while 25% is secreted into saliva, where it is
partly (20%) reduced to nitrite by bacteria (Mensinga et al., 2003; WHO, 2007). Both
the nitrite and remaining nitrate are then swallowed and re-enter the stomach.
Bacterial reduction of nitrate to nitrite will also occur in other parts of the
18
gastrointestinal tract (WHO, 2007). The mechanism of nitrite toxicity associated with
acute health effects involves the oxidation of the ferrous iron (Fe2+) in
deoxyhemoglobin to the ferric iron (Fe3+) producing methemoglobin (MetHb), which
is unable to transport oxygen to the tissues (WHO, 2007). The remaining nitrite binds
firmly to the oxidized hemoglobin (Hb) protein causing denaturation and hemolysis of
the red blood cell (Kross et al., 1992; WHO, 2007). The result is a loss of oxygencarrying capacity of the blood, the extent of which is dependent on the amount of
nitrite available as well as the presence of the enzyme, NADH-cytochrome b5
reductase, which converts MetHb back to Hb (Mensinga et al., 2003). Normal MetHb
levels in humans are less than 2%. In infants under 3 months of age, it is less than 3%
(WHO, 2007). Methemoglobinemia, or ―blue baby syndrome‖ results when the
amount of MetHb in the blood becomes high enough to trigger clinical symptoms of
cyanosis, usually 10-15% of total circulating hemoglobin (Avery et al., 1999; Ward et
al., 2005; WHO, 2007). While symptoms will vary among patients, levels greater than
70% are usually fatal (Kross et al., 1992).
The mechanism of nitrite toxicity associated with long-term health effects involves
the endogenous formation of N-nitroso compounds (NOCs), a class of genotoxic
compounds most of which are potent carcinogens (Klaassen, 2001). NOCs have been
linked to tumors in the esophagus, stomach, colon, bladder, lymphatics, and
hematopoietic system (Bogovski & Bogovski, 1981). NOCs have also been shown to
be teratogenic and capable of causing congenital malformations in animal models
(Inouye & Murakami, 1978; Park et al., 1992; Stahlman et al., 1983). NOCs are
formed when nitrite reacts with amines and amides in several areas of the body,
including the stomach, where the pH is most favorable for NOC formation (Klaassen,
2001; Mirvish, 1995). Mirvish and others (1992) found that endogenous nitrosation
increased among individuals drinking water with nitrate levels above the MCL of 10
mg/L, while van Maanen and others (1996) found evidence of genetic risk associated
with nitrosation among individuals exposed to levels below EU standards.
19
Various factors can alter the rate of endogenous nitrosation and formation of
NOCs in the individual. Certain dietary compounds, such as vitamin C and E, may be
able to decrease the conversion of nitrate to nitrite and the formation of NOCs
(Bartsch et al., 1988; Grosse et al., 2006; WHO, 2007). The presence of these
antioxidants in vegetables containing a high amount of nitrate is thought to explain
why consumption of these vegetables is not associated with cancer and is actually
health-protective (Bartsch & Frank, 1996; Grosse et al., 2006; Mirvish, 1994).
Conversely, consumption of preserved meats and cigarette smoking have been shown
to increase nitrosation and the formation of NOCs (Tricker, 1997). Certain medical
conditions can also increase nitrosation rates, such as inflammatory bowel disease (de
Kok et al., 2005; Singer et al., 1996), infective gastroenteritis (Dykuizen et al., 1996;
Forte et al., 1999) and other immunostimulatory conditions (Mirvish et al., 1995;
Mostafa et al., 1994), as well as use of nitrosatable drugs (Martelli et al., 2007).
The range of nitrosation inhibitors and precursors potentially active in any single
individual may act to mask or augment an association between drinking water nitrate
and adverse health outcomes and thus may contribute substantially to inter-individual
variability in study populations (Powlson et al., 2008; van Grinsven et al., 2006; Ward
et al., 2007).
Limitations in the current literature
The association between exposure to elevated drinking water nitrate levels and
adverse health outcomes is considered by some researchers to be inconclusive based
largely on the limitations of existing epidemiological studies (Forman, 2004;
Mensinga et al., 2003; Powlson et al., 2008; van Grinsven et al., 2006; Ward et al.,
2005). The most significant limitations appear to be a lack of a biologically relevant
dose, non-differential misclassification of exposure, and effect modification.
Most studies investigating the association between drinking water nitrate and
adverse health outcomes have been ecologic in design, linking disease incidence or
mortality rates to drinking water nitrate levels at the town, municipality or even county
20
level (Abu Naser et al., 2007; Brody et al., 2006; Chang et al., 2010; Cocco et al.,
2003; Coss et al., 2004; Croen et al., 2001; De Roos et al., 2003; Freedman et al.,
2000; Gulis et al., 2002; Joyce et al., 2008; Kuo et al., 2007; Muntoni et al., 2006;
Sandor et al., 2001; Volkmer et al., 2005; Ward et al., 2003; Ward et al., 2005; Ward
et al., 2007; Weyer et al., 2001; Yang et al., 2009; Zeegers et al., 2006). This approach
is facilitated by the implementation of regulations requiring regular monitoring of
public water systems as well as the growing number of disease registries (Gulis et al.,
2002; Ward et al., 2005). Because of the relative ease by which water quality data can
be obtained, most studies linking drinking water nitrate to health outcomes focus on
exposures through public systems. Conversely, as no state or nation requires consistent
monitoring of private wells, and these data are not easily obtained at the population
level, there are few studies on this exposure route (Manassaram et al., 2006; Ward et
al., 2005). At most, some studies investigating exposures through public systems will
refrain from excluding private well users from the study and will run analyses on this
group as a subpopulation, albeit with reduced power (Mueller et al., 2001; Ward et al.,
2007; Ward et al., 2008). The problem with excluding private well users from
population studies is that water from private wells are known to have much higher
levels of nitrate than water from public systems (Coss et al., 2004; De Roos et al.,
2003; Gulis et al., 2002; Manassaram et al., 2006; Ward et al., 2005). Consequently,
the population most at risk for nitrate-mediated health outcomes is consistently absent
from the large majority of epidemiological studies that have been conducted. Indeed,
many authors of studies with null findings note that the levels of nitrate identified in
their studies are low, often well below levels considered biologically relevant (Brody
et al., 2006, Cocco et al., 2003; Coss et al., 2004; Ward et al., 2003; Zeegers et al.,
2006).
From a scientific perspective, the ecologic study design is a relatively weak form
of evidence, since an association at the ecologic level may not hold at the individual
level, making it difficult to interpret findings exclusively in terms of nitrate exposure
(Bukowski et al., 2001; Gulis et al., 2002). A study that does not accurately quantify
21
the individual’s exposure may suffer from non-differential misclassification, which
may attenuate the odds ratio and push findings toward the null hypothesis (Brody et
al., 2006; Cocco et al., 2003; Coss et al., 2004; Croen et al., 2001; Nuckols et al.,
2004; Sandor et al., 2001; Vineis & Kriebel, 2006). Most studies, particularly those
investigating health outcomes with long latency periods, use historical public water
supply measures averaged over years to decades as a surrogate for individual exposure
at the tap (Brody et al., 2006; Chang et al., 2010; Cocco et al., 2003; Coss et al., 2004;
De Roos et al., 2003; Freedman et al., 2000; Gulis et al., 2002; Joyce et al., 2008; Kuo
et al., 2007; Muntoni et al., 2006; Volkmer et al., 2005; Ward et al., 2003; Ward et al.,
2007; Weyer et al., 2001; Yang et al., 2009; Zeegers et al., 2006). However,
individuals may not remain at the same residence served by the same water supply for
the entire time period under study (Meliker et al., 2005). Some authors attempt to
collect individual level data on residential mobility patterns or adjust for migration in
the quantification of individual exposure (Chang et al., 2010; Coss et al., 2004; De
Roos et al., 2003; Freedman et al., 2000; Ward et al., 2007). This is a superior
approach to other studies that use a single measurement from a public water system or
average a few values over a short interval of time to represent long-term exposure at
the tap and do not collect individual level data on resident mobility (Cocco et al.,
2003; Gulis et al., 2002; Joyce et al., 2008; Kuo et al., 2007; Volkmer et al., 2005;
Weyer et al., 2001; Yang et al., 2009; Zeegers et al., 2006). Other factors that can
contribute to non-differential misclassification of exposure include differences in
individual water use patterns, such as how much water is actually consumed daily or
outside the home (Brender et al., 2004; Freedman et al., 2000; Gulis et al., 2002;
Manassaram et al., 2006; Weyer et al., 2001), and varying contributions of source
wells to the drinking water distributed through the public system (Croen et al., 2001;
Freedman et al., 2000).
Effect modification may also distort characterization of the dose-response
relationship, when an association differs for certain subpopulations (Fewtrell, 2004).
Specific factors recognized as modifying the relationship between drinking water
22
nitrate and adverse health outcomes include nitrosation moderators such as meat and
vegetable consumption (Bartsch et al., 1988; Coss et al., 2004; Grosse et al., 2006;
WHO, 2007), smoking (Mirvish, 1995), use of nitrosatable drugs (Brender et al.,
2004; Mirvish, 1995) and some preexisting health conditions (Mirvish, 1995). Power
is often minimal for studies that control for effect modification from nitrosation
inhibitors and precursors for any single cancer site or health endpoint, thus limiting the
ability to draw firm conclusions about risk of adverse health outcomes (Ward et al.,
2005; Ward et al., 2007; Ward et al., 2008; Weyer et al., 2001).
Another problem seen in the literature, for which there is no easy solution, is how
to characterize exposure for disease outcomes for which a clear induction period has
not fully been defined (Forman, 2004; Meliker et al., 2005; Ward et al., 2003). Most
studies appear to choose an arbitrary period of time, usually determined by availability
of data, prior to diagnosis to represent the period when the casual exposure occurred
(Brody et al., 2006; Cocco et al., 2003; Coss et al., 2004; Freedman et al., 2000; Joyce
et al., 2008; McElroy et al., 2008; Volkmer et al., 2005; Ward et al., 2007; Yang et al.,
2009). Nitrate measures from water systems are averaged or interpolated over this
time period in order to derive a surrogate ―dose‖ for the individual that is linked to a
health outcome. Given genetic variability and the range of confounding factors that
may affect any one individual’s risk of developing a long-latency disease, such as
cancer, results from studies using this approach should be interpreted with some
caution (Chang et al., 2010; Meliker et al., 2005; Ward et al., 2003). Meliker and
others (2005) introduce an interesting methodology in context of exposure to arsenic
in drinking water for developing ―exposure life-lines‖ that facilitate scrutiny of
continuous exposure estimates and permit examination of whether exposure at any
point in time is associated with subsequent disease development.
In general, the best-designed studies on drinking water nitrate and adverse health
outcomes attempt to address misclassification of exposure and effect modification by
soliciting information, either through interviews or questionnaires, from each
participant in the study on demographics, health status, smoking, diet, changes in
23
residence, and water use patterns and sources (Manassaram et al., 2006). Furthermore,
more studies are needed on populations that are exposed to nitrate levels in drinking
water that are recognized as biologically relevant, such as private well users.
Acute health outcome
The primary human health endpoint associated with exposure to drinking water
nitrate is methemoglobinemia, particularly in infants (Avery, 1999; Fewtrell, 2004;
WHO, 2007). The association was first described by Hunter Comly in 1945 who
reported the condition in infants who were bottle-fed formula made with nitratecontaminated well water (Comly, 1945). Infants are especially vulnerable to
developing methemoglobinemia for many reasons: 1) they have lower amounts of the
enzyme NADH-cytochrome b5 reductase, which converts MetHb back to Hb; 2) they
have a higher pH in the gastrointestinal tract and thus a greater abundance of nitratereducing bacteria; 3) they have a higher proportion of fetal Hb which may be more
rapidly oxidized to MetHb than adult Hb; 4) they consume more water relative to their
body weight than adults; 5) boiling water, as occurs in formula preparation, further
concentrates nitrate; and 6) gastroenteritis with vomiting and diarrhea, which is more
common in infants than adults, enhances conditions for the formation of MetHb
(Fewtrell, 2004; Kross et al., 1992; Sadeq et al., 2007; WHO, 2007). The MCL
established under the Safe Drinking Water Act (SDWA) for public drinking water
supplies was established following a survey by the American Public Health
Association on infant methemoglobinemia incidence and water quality (Walton,
1951). Because no cases were observed with concentrations less than 10 mg/L, the
EPA established this value as the MCL for nitrate in drinking water (Avery, 1999).
Comly proposed the nitrate-to-nitrite toxicity mechanism, but also noted that this
conversion might only occur in the presence of a bacterial infection (Comly, 1945).
Since this time, others also have suggested that methemoglobinemia linked to
contaminated drinking water is caused, at least in part, by gastrointestinal illness
triggered by water-borne microbes (Avery, 1999; Fewtrell, 2004; van Grinsven et al.,
24
2006; Powlson et al., 2008; WHO 2007). The proposed mechanism involves the
endogenous production of nitric oxide from tissues in response to infection and
inflammation which can than lead to nitrite production. This pathway needn’t involve
any exposure to nitrate (Avery, 1999). Avery (1999) suggests that nitrate
contamination should be a public health concern only as an indicator of bacterial
contamination, given that bacterial and nitrate contamination often stem from the same
cause (a shallow, damaged well) and the same source (a barnyard, cesspool, leaky
septic tank or manure facility).
Avery (1999) also notes the declining incidence of methemoglobinemia as
evidence in favor of an infectious etiology. The author contends that this drop in
infantile methemoglobinemia incidence cannot be attributed solely to decreased
exposure to nitrate-contaminated water supplies, since in 1990 the EPA estimated that
66,000 infants are exposed annually in the U.S. to drinking water that exceeds the
nitrate MCL (EPA, 1992). However, methemoglobinemia may be regularly underdiagnosed and under-reported (Sadeq et al., 2008; Washington et al., 2007). Two
factors make reliable estimates of the number of methemoglobinemia cases in the U.S.
hard to establish: Methemoglobinemia is not a notifiable disease, and definitions of the
condition vary widely in the literature (Fewtrell, 2004; Washington et al., 2007).
Clinical symptoms of methemoglobinemia can range from irritability and lethargy,
subtle enough to be dismissed, to advanced cyanosis, frequently misdiagnosed as heart
or pulmonary disease (Knobeloch et al., 2000; Kross et al., 1992; Wintrobe et al.,
1981).
There are a limited number of studies that investigate a casual link between
drinking water nitrate and methemoglobinemia. A 2000 case report by Knobeloch and
co-authors described two separate cases of methemoglobenima in Wisconsin infants
fed formula prepared with water from private wells. Nitrate levels in the wells were 30
mg/L and 27 mg/L, with the later also testing positive for E. coli bacteria. Outside the
U.S. Abu Nasar and others (2007) investigated MetHb levels in infants from
Palestinian localities with varying nitrate levels in public water wells in a cross-
25
sectional study. Measures of bacterial contamination in well water were not available.
The authors found that infants with the highest mean MetHb were located in the area
with the highest mean nitrate concentrations. Children with the highest MetHb levels
were infants 3-6 months old, the reported age when supplemental feeding began.
There was a strong positive correlation between infants with high MetHb and formula
feeding. A negative correlation was found between breastfeeding and MetHb levels.
Use of boiled water was positively associated with raised MetHb levels, with 95% of
infants with high MetHb levels from families who reported boiling water before use.
A notable finding from this study was a significant association between high MetHb
levels in infants and anemia and underweight status. This suggests that MetHb levels
have a negative impact on the nutrition status of infants, which may lead to serious
and persistent health effects for infants that otherwise display no obvious clinical
symptoms of raised MetHb levels.
A retrospective, nested case-control study on methemoglobinemia risk factors in
Romanian children found a strong association between methemoglobinemia and
nitrate exposure through the dietary route via formula feeding and tea made with water
containing high levels of nitrate (Zeman et al., 2002). Measures of bacterial
contamination in well water were not available. There was also some evidence of an
association with diarrheal disease, and protection with breast-feeding in infants
younger than 6 months. Results of this study should be interpreted cautiously as the
sample size was small, and individual exposure assessments were based on dietary
recall by the caregiver and well water sampling, both taken five years after diagnosis.
Two cross-sectional studies were conducted in Morocco to determine the
prevalence and risk factors of methemoglobinemia among infants and children
exposed to drinking water from wells with nitrate concentrations over the European
standard compared to a similar population served by a municipal water supply (Sadeq
et al., 2008). Blood samples were obtained in order to diagnose methemoglobinemia
(defined as MetHb levels of 0.24 g/dl, corresponding to 2% of total Hb). The
prevalence of methemoglobinemia was 36.2% in the exposed area, and 27.4% in the
26
non-exposed area. Children drinking well water with nitrate above the EU standard
were significantly more likely to have methemoglobinemia than those drinking well
water below the standard or those on the municipal supply.
Several authors, questioning the role of drinking water nitrate alone as a risk factor
for methemoglobinemia, have suggested that the current MCL might be safely raised
to 15-20 mg/L (Avery, 1999; L’hirondel & L’hirondel, 2002). Conversely, Ward and
others (2005) as well as van Maanen and others (2000) suggest that a better
understanding of the conditions under which nitrate in drinking water poses a risk for
methemoglobinemia are needed, and more importantly, the role of nitrate in longterm, irreversible health effects must be more thoroughly explored before regulatory
changes are considered. Van Grinsven and others (2006) also recommend collection of
additional drinking water nitrate exposure data and advancing understanding of
exposure-response relationships before adjusting the nitrate standard. Furthermore,
from the current review of the literature, there are no studies that attempt to determine
what the long term health outcomes may be from a sustained reduction in oxygenated
blood associated with elevated levels of MetHb in newborns as they develop.
Chronic health outcomes (cancer)
Numerous investigations into the association between cancer and exposure to
drinking water nitrate have been conducted in Iowa, a state with high levels of nitrate
in surface and groundwater (DeRoos et al., 2003). A cohort study design was used to
investigate the association between drinking water nitrate and overall cancer incidence
in older women (Weyer et al., 2001), while a case-control study design was used to
investigate cancer in seven anatomic sites in the general Iowa population: colon,
rectum, bladder, brain, pancreas, kidney, and thyroid (DeRoos et al., 2003; Ward et
al., 2003; Ward et al., 2005; Coss et al., 2004; Ward et al., 2007; Ward et al., 2010).
Cases for all studies were ascertained from the Iowa Cancer Registry, a statewide
cancer registry. Study protocols were strengthened by a comprehensive effort to
characterize individual level nitrate exposure through questionnaires soliciting
27
information on demographics, medical conditions, diet, smoking history, changes in
residence and water use patterns. Data on these potential effect modifiers were
combined with historical monitoring data for Iowa public water supplies to calculate
risk for the various cancers.
The Iowa cohort study found a 2.8-fold and 1.8-fold risk of bladder and ovarian
cancers, respectively, associated with the highest quartile of exposure to drinking
water nitrate (> 2.46 mg/L) (Weyer et al., 2001). They observed significant inverse
associations for uterine and rectal cancer and no significant associations for nonHodgkin’s lymphoma, leukemia, colon, rectum, pancreas, kidney, lung and melanoma.
Adjustment for common cancer risk factors, modulators of nitrosation, dietary nitrate,
and water source had little influence on these associations.
Case-control studies demonstrated negligible associations between average nitrate
levels above 5 mg/L consumed for more than 10 years and colon (OR = 1.2) or rectum
cancers (OR = 1.1). However, an increased incidence of colon cancer was observed
among subpopulations expected to have increased nitrosation, such as those with low
vitamin C intake (OR = 2.0) and high meat intake (OR = 2.2) (DeRoos et al., 2003).
An increased risk of thyroid cancer was also observed with longer consumption of
water exceeding 5 mg/L of nitrate (RR = 2.6) (Ward et al., 2010). Other analyses
observed no increased risk of bladder (Ward et al., 2003), brain (Ward et al., 2005),
pancreatic (Coss et al., 2004) or kidney (Ward et al., 2007) cancers with increasing
nitrate levels.
A major weakness of these Iowa studies, common to the majority of studies on
drinking water nitrate, is the lack of sufficient power to evaluate risk at nitrate levels
above the MCL. For the large majority of individuals (75%) in the case-control
studies, less than 10% of daily nitrate intake came from drinking water; the majority
came from vegetables (Coss et al., 2004). In the cohort study, the mean level of nitrate
in the highest quartile of nitrate exposure is 5.6 mg/L, far below the MCL of 10 mg/L
(Weyer et al., 2001). Measurements of nitrate levels from private wells were not
28
available for either study. Therefore, risk estimates for individuals with the highest
potential nitrate exposures were not evaluated.
A death certificate-based, case-control study in Taiwan investigated the
association between drinking water nitrate from public water supplies and risk of
death from non-Hodgkin’s lymphoma (Chang et al., 2010), rectal (Kuo et al., 2007),
bladder (Chiu et al., 2007) and pancreatic (Yang et al., 2009) cancers. Mean nitrate
exposure for all cases and controls were far below the MCL (<0.5 mg/L). No
individual level data beyond simple demographics were collected or incorporated into
the exposure assessment or risk analyses. No significant association was observed
between drinking water nitrate levels and non-Hodgkin’s lymphoma (Chang et al.,
2010), nor pancreatic cancer (Yang et al., 2009). A significant increased risk for rectal
cancer (OR = 1.36) and bladder cancer (OR = 1.96) were observed for individuals
residing in municipalities with the highest levels of drinking water nitrate (1.48-2.85
mg/L).
Case control studies on non-Hodgkin’s lymphoma in Minnesota (Freedman et al.,
2000) and Iowa (Ward et al., 2006) found no association with exposure to nitrate in
public water supplies, while a case-control study in Italy found limited evidence of an
association with men only (Cocco et al., 2003). Nitrate levels from all three studies
were well below health-based standards. The Italian study did not collect or
incorporate individual level data into the analyses.
An ecologic study in Slovakia found a positive association for non-Hodgkin’s
lymphoma in both men and women exposed to nitrate in public water supplies,
although the trend in women did not reach statistical significance (Gulis et al., 2002).
The authors acknowledge the small sample size for this cancer as a major limitation of
the study, as well as a lack of exposure to nitrate levels above the health-based
standard. No individual level data beyond simple demographics were collected or
incorporated into the analyses.
A case-control study in Wisconsin found no association between colorectal cancer
risk in rural women and drinking water nitrate in private wells (McElroy et al., 2008).
29
However, when data was stratified by affected site there was an increased risk
observed for proximal colon cancer for women exposed to nitrate at or above the MCL
compared to women with exposures less than 0.5 mg/L. Colorectal cancer was also
investigated in the Slovakian ecologic study, and a significant positive association was
observed for women only (Gulis et al., 2002).
A case-control study of stomach and esophageal cancer in Nebraska observed no
overall association in the general population with intake of nitrate from public water
supplies (Ward et al., 2008). Participants who reported use of a private well were
asked to submit a water sample. Of this subpopulation, an increased stomach cancer
risk was observed for those with elevated nitrate concentrations (>0.5 mg/L).
However, this risk was based on a small number of cases.
A Hungarian ecologic study investigated gastric cancer mortality in a population
of small villages served by community water supplies with high and widely variable
nitrate content (median value of 72 mg/L) (Sandor et al., 2001). Significant risk
elevation was observed in those villages where nitrate content of drinking water
exceeded 80 mg/L (OR=1.42). No individual level data beyond simple demographics
were collected or incorporated into the analyses.
A Dutch cohort study, enrolling more than 120,000 men and women, demonstrated
no association between drinking water nitrate in public water supplies and bladder
cancer (Zeegers et al., 2006). The large majority of cases were estimated to have been
exposed to levels far below the legal threshold for drinking water given that the
average level of nitrate in Dutch public drinking water is 1.68 mg/L.
A case-control study of Massachusetts women observed no association between
nitrate measurements in public water supplies and breast cancer, although less than 2%
of cases were exposed to nitrate above 1.2 mg/L (Brody et al., 2006). A substantial
number of participants (n=521) were excluded from the study who lived in homes
served by a private well during some or all of the exposure period selected for the
study.
30
As part of the U.S. West Coast Childhood Brain Tumors (CBT) study, a casecontrol design was used to investigate the association between source of residential
drinking water during pregnancy and the occurrence of CBT in offspring (Mueller et
al., 2001). An increased risk of CBT was observed among children in western
Washington whose mothers reported well water as the sole source of household water
supply during their pregnancies. However, the number of cases in this category was
small (n=18), representing 14% of total cases reporting exclusive use of well water
during pregnancy, and the majority of wells when tested had undetectable nitrate
levels. No increased risk was measured in the other study regions, although well water
use was relatively uncommon in these areas.
A German ecological study compared incidence rates of urological malignancies
in communities exposed to high or low nitrate levels in municipal water supplies
(Volkmer et al., 2005). The authors observed a higher incidence of urothelial cancer in
both genders living in the high exposure community compared to the low exposure
community. Population level exposure estimates for nitrate were below the national
legal threshold for drinking water. No individual exposure assessment data was
included in the overall assessment of exposure, aside from smoking habits, which was
taken off of patient medical charts.
Chronic health outcomes (noncancer)
An Australian ecologic study found an increased risk of prelabor rupture of
membranes among pregnant women exposed to increasing tertiles of nitrate in public
water supplies (Joyce et al., 2008). The highest risk (OR=1.47) was associated with
the high exposure tertile and a cutpoint of >0.35 mg/L. It is unclear if any women
were exposed to levels above the regulatory standard. Limited individual exposure
assessment data were included in the overall assessment of exposure, although no
attempt was made to adjust for residential mobility in the study.
In a case-control study of Mexican-American women, Brender and others (2004)
examined the role of dietary nitrate and nitrosatable drug use on the occurrence of
31
neural tube defects (NTD). Water samples for nitrate measurement were collected for
a subset of participants (n=110). Nitrate levels ranged from 0 to 28 mg/L (median of
5.4 mg/L for case women). Women whose drinking water nitrate measured 3.5 mg/L
or greater were 1.9 times more likely to have an NTD-affected pregnancy than women
with lower levels of nitrate in water. With adjustment for maternal education, women
who took nitrosatable drugs and were exposed to nitrate levels of 3.5 mg/L or higher
were 14 times more likely to have an NTD-affected pregnancy.
A case-control study in California observed an increased risk for NTDs among
babies born to mothers living in areas where the level of nitrate in drinking water was
above the MCL compared to those in areas with nitrate below the MCL (OR=2.7
mg/L) (Croen et al., 2001). Increased risk with increasing levels of nitrate was
observed. Risk was also examined separately for anencephaly and spina bifida. An
increased risk for anencephaly was associated with maternal exposure to nitrate above
the MCL (OR=4.0). A doubling in risk for anencephaly was observed for women
living in areas where the nitrate level in groundwater was ≥5 mg/L compared with <5
mg/L. There was no increased risk for spina bifida with any level of nitrate exposure.
A Swedish ecologic study found no excess risk for congenital cardiac defects in
infants born to women exposed to any level of nitrate in public water supplies
included in the study (Cedergren et al., 2002). Nitrate concentrations were all below
the regulatory standard and the difference between the lowest and highest estimates
was small. Very limited individual level data (maternal age, parity, smoking and
educational level) were included in the analyses. Groundwater as a source of drinking
water was reported as a potential risk factor for cardiac defects (OR=1.31).
A separate Swedish ecologic study investigated the relationship between the
incidence of sudden infant death syndrome (SIDS) in relation to the concentration of
nitrate in drinking water from private wells and public distribution systems (George et
al., 2001). A correlation was found between the maximum nitrate concentration in
drinking water and the incidence of SIDS. However, due to the small number of cases
32
statistical power is low, and no individual level data were collected or incorporated
into analyses.
Two cross-sectional studies in Bulgaria investigated the association between
thyroid dysfunction and village residence associated with high (exposed) or low (nonexposed) average nitrate levels in community water supplies (Gatseva et al., 2008a;
Gatseva et al., 2008b). High nitrate levels were well above the EU regulatory level. In
one study, comparison between mean iodine urinary concentration of exposed and
non-exposed children showed a statistically significant difference for boys (Gatseva et
al., 2008a). A statistically significant difference was also found for goiter prevalence
between exposed and non-exposed children (OR=3.01). In a similar study, relative risk
(expressed as odds ratio) of goiter rate for pregnant women in a high exposure village
was significant (OR=5.29) (Gatseva et al., 2008b). A comparison between mean
iodine urinary concentration and goiter rate of exposed and non-exposed children did
not show a significant association in this study.
A cross-sectional study in Eastern Slovakia examined urinary iodine, thyroid
volume and thyroid hormone levels in schoolchildren exposed to high drinking water
nitrate supplied from shallow wells (Tajtakova et al., 2006). Levels in wells with high
nitrate were all above the EU regulatory level. Long term exposure to high nitrate
levels in drinking water was significantly associated with increased thyroid volume
and increased frequency of signs of subclinical thyroid disorders. As part of their casecontrol study on thyroid cancer, Ward and others (2010) also examined prevalence of
self-reported hypothyroidism and hyperthyroidism but observed no association with
nitrate intake from public water supplies.
A Dutch ecologic study was unable to find a significant association between
incidence rates of type 1 diabetes (T1D) in children across the country and exposure to
different categories of drinking water nitrate in public water supplies (van Maanen et
al., 2000). No child in the study was exposed to drinking water nitrate levels above the
EU regulatory standard, and no individual data were included in the overall
assessment of exposure. Using a similar methodology, an ecologic study in south-west
33
England observed no association between incidence rates of T1D and drinking water
nitrate in public water supplies (Zhao et al., 2001). Levels in this study also ranged far
below regulatory standards (3.66-7.83 mg/L). An ecologic study was performed on the
Italian island of Sardinia where T1D incidence ranked highest worldwide at the time
of the study (Muntoni et al., 2006). No association was observed between T1D
incidence in children (ages 0-14) and nitrate levels in public drinking water. Levels of
nitrate in the study (≤2.5-28.9 mg/L) were well below the regulatory standard. Another
Sardinian ecologic study on T1D incidence that included exposure to nitrate through
bottled water also resulted in a null association (Casu et al., 2000).
A German prospective study investigated the association between the development
of T1D in infants and drinking water quality. Nitrate levels were aggregated into two
categories, <9.58 and ≥9.58 mg/L. The authors do not provide the total number of
infants within each category. Concentrations of nitrate in drinking water from public
water supplies were not associated with risk of T1D progression (Winkler et al.,
2008).
In a cross-sectional study in rural India, a strong correlation was observed between
cases of recurrent respiratory tract infections (RRTI) in children exposed to nitrate
drinking water levels (26-459 mg/L) and blood MetHb levels (r = 0.731). The authors
state that occurrence of methemoglobinemia caused by high nitrate concentrations is
the most likely cause for RRTI in this population, although correlation between RRTI
and nitrate in drinking water was poor (r = 0.565) (Gupta et al., 2000).
Environmental Public Health Surveillance and the Need for Private Well Water
Monitoring
Surveillance for tracking health outcomes and describing trends is a cornerstone of
public health practice (Malecki et al., 2008; Ritz et al., 2005). The U.S. Centers for
Disease Control and Prevention (CDC) defines public health surveillance as ―the
ongoing, systematic collection, analysis, interpretation, and dissemination of data
regarding a health-related event for use in public health action to reduce morbidity and
34
mortality and to improve health‖ (German et al., 2001). These data can be used for
immediate public health actions, program planning and evaluation, and formulating
research hypotheses (German et al., 2001).
Surveillance is inherently outcome-oriented, and as such, the majority of public
health surveillance systems are focused on measuring health outcomes, such as death,
disease or injury (Teutsch & Churchill, 2000). However, depending on the goal of the
investigation, it is often a more efficient use of resources to measure risk factors that
are known links to health outcomes, such as prior exposures, demographics, or
behaviors, because they may occur more frequently and are often easier to detect or
measure (LaMontagne et al., 2002; Teutsch & Churchill, 2000). Recently, the CDC
included exposure to a toxic substance as a health-related event that may be targeted
for surveillance (German et al., 2001).
One of the nation’s most widely developed public health surveillance systems, the
state-based Behavioral Risk Factor Surveillance System (BRFSS), is focused mainly
on monitoring risk factors. The BRFSS is conducted in all 50 states and collects data
on behavioral risk factors underlying certain health conditions, such as chronic
diseases, injuries, and infectious diseases (CDC, 2008; Mokdad, 2009). Many states
also include questions relating to environmental exposures. A 2009 CDC study
describes the use of the BRFSS to investigate exposure to environmental tobacco
smoke in the home and workplace (CDC, 2009). Laflamme and Vanderslice (2004)
describe Washington State’s use of the BRFSS to collect information on exposurerelated behaviors in topic areas such as drinking water and radon.
Another example of a public health surveillance system focused on risk or
exposure factors is the Wisconsin Childhood Lead Poisoning Prevention Program’s
(WCLPP) comprehensive blood lead surveillance system. Under state statute,
laboratories must provide the WCLPP with results on all blood lead tests conducted in
the state along with specified demographic information associated with the test
(DOHWI, 2008). These data are maintained in a database (STELLAR), which
contains more than one million records of blood lead tests. The major health outcome
35
of concern in children exposed to lead is brain damage, a condition that is not likely to
manifest immediately following exposure and may be confounded by other factors,
such as diet or genetics (ATSDR, 2007). Therefore, surveillance of associated risk
factors (represented by blood lead levels and demographic characteristics) is a more
efficient use of resources, given that it provides information in a timely manner that is
suitable for directing risk reduction activities in the absence of clearly delineated proof
of disease burden in the population. Zierold and others (2007) analyzed data from
STELLAR in order to determine the length of time needed to make homes lead-safe as
indicated by reductions in blood lead levels. Results from this analysis showed that the
median length of time required to make a home lead-safe was 465 days, prompting the
authors to call for enforcement of time limits in lead-abatement orders, even in the
absence of health outcome data.
Legislation, either state or federal, is often necessary to develop public health
surveillance systems due to the cost of developing and implementing the system and
the number and range of stakeholders whose cooperation is essential for collecting
high quality data (German et al., 2001; Teutsch & Churchill, 2000). Furthermore, as
information collected by the system is often personal and confidential, a legal basis is
required for effective mandatory notification to be implemented (Abreu-Garcia et al.,
2002). Many states have mandatory reporting laws relating to infectious diseases,
which require clinician reporting when a patient is seen with the disease. Authority to
require this notification resides in the respective state legislatures (Teutsch &
Churchill, 2000). In some states, authority is conveyed through statutory provisions,
while in other states, authority is given to the state health agency. Still other states
require reporting both under statutes and under health agency regulations (Chorba et
al., 1990). The WCLPP lead surveillance system was implemented through state
statute (DOHWI, 2008). Trepka and others (2009) tested a pilot asthma incidence
surveillance system but found that without mandatory reporting laws, clinician
reporting was not likely to be a feasible method for asthma surveillance. The number
and variety of public health surveillance systems will continue to increase with
36
advances in electronic data interchange and integration (German et al., 2001). This
will heighten the importance of protecting individual privacy, data confidentiality, and
system security, issues that will need to be addressed in the process of legislating
authority for surveillance (German et al., 2001; Lawlor & Stone, 2001).
The design of a surveillance system will reflect its intended purpose and
objectives, as well as the budget, personnel, and infrastructure available for
implementation (German et al., 2001. While a challenging undertaking, there are a
number of published resources available to assist in the design and implementation of
a public health surveillance system, such as the World Health Organization’s (WHO)
STEPwise approach to Surveillance (WHO, 2010) and the World Bank’s Public
Health Surveillance Toolkit (Abreu-Garcia et al., 2002). Teutsch & Churchill (2000)
also provide a description of the general steps involved in designing and implementing
a public health surveillance system.
Periodic evaluation of a surveillance system is essential for ensuring that the stated
purpose and objectives of the system are being met and is likely to reveal
opportunities for improving quality, efficiency and usefulness of the system. The
CDC’s Updated Guidelines for Evaluating Public Health Surveillance Systems
provides detailed measures and methods for evaluating public health surveillance
systems (German et al., 2001).
According to the Updated Guidelines, the key attributes of a surveillance system
include simplicity, flexibility, data quality, acceptability, sensitivity, predictive value
positive, representativeness, timeliness, and stability (German et al., 2001). The
system should emphasize those attributes that are most important to the objectives of
the system (German et al., 2001).
Surveillance systems are traditionally classified as passive or active. Passive
systems are commonly disease notification systems (Teutsch & Churchill, 2000).
Based on a specified case definition, health-care providers report notifiable diseases
on a case-by-case basis to a central institution or agency. Passive surveillance systems
attempt to capture all cases of a health-related event in a population. An example of
37
passive surveillance is the CDC’s ArboNET system, implemented to track arboviral
disease, such as West Nile virus, in the U.S. (Lindsey et al., 2010).
Active systems involve focused outreach to potential reporters to stimulate
reporting of the health-related event. Active systems require more effort by system
personnel and resources for identifying and collecting data, yet often lead to better
rates of reporting than passive systems (Teutsch & Churchill, 2000). Some active
surveillance systems collect data from targeted groups that cover a subset of the
population. The reliance of these systems on selected sites, groups, or networks is the
reason why they are referred to as sentinel surveillance systems (Losos, 1996; Teutsch
& Churchill, 2000). Carefully selected sentinels can provide adequate information on
the epidemiology of a disease or risk factor without the need for comprehensive case
ascertainment (Ortiz et al., 2009). One of the most common sentinel surveillance
systems in the United States is the Outpatient Influenza-like Illness Surveillance
Network (CDC, 2010). This system relies on selected health care providers to report
the number of cases of influenza-like illness they diagnose to their state health
department on a weekly basis. This surveillance allows states to monitor trends using a
relatively small amount of information (Ortiz et al., 2009).
Sentinel surveillance systems offer an effective alternative to complex, resourceintensive passive surveillance systems that are known to have limitations due to
incomplete or under-reporting (Hsieh et al., 2009; Wheeler et al., 1999). Jernigan and
others (2001) describe the development of a sentinel surveillance system for tracking
antibiotic resistance using a limited network of twenty-seven hospitals. They found
that findings from the sentinel system were similar to those collected through
statewide surveillance systems. The authors conclude that general trends may be
measured adequately by the scaled-down approach of a sentinel system. Touch and
others (2009) found that a sentinel surveillance system for identifying
meningoencephalitis cases in Cambodia, where budget constraints make
comprehensive lab testing difficult, were consistent with previous research studies that
estimated national prevalence. Gauci and others (2007) piloted a sentinel surveillance
38
system for tracking infectious intestinal disease and found that the sentinel system led
to more precise estimates of the disease, confirming the high under-reporting of the
disease to a national passive surveillance system. It is worth noting that sentinels
volunteered for participation in the study, and the authors acknowledge that sentinel
commitment to notify lead to identification of more cases than routine passive
surveillance.
Sentinel surveillance systems also provide an opportunity to gather information on
the sentinel individuals, groups, or networks themselves, which can shed considerable
light on risk factors or relevant demographics. Kim and others (2009) employed a
sentinel surveillance system to monitor trends in HIV among female inmates as a
means of understanding risk in poor communities of color. Aside from incidence and
prevalence estimates, the authors collected other noteworthy data pertaining to the
sentinel group itself, such as indicators of sexual risk in female inmates, which would
be useful for guiding intervention strategies for this group.
Sentinel surveillance systems are not without their weaknesses, and chief among
them are selection bias and problems with sensitivity (Abreu-Garcia et al., 2002;
Losos, 1996; Touch et al., 2009). Selection bias is a systematic tendency to exclude
certain members of the target population from data collection (German et al., 2001).
For example, if sentinel individuals are predominantly located in urban areas, rural
residents would be underrepresented in the surveillance system, leading to a bias in the
data towards urban area residents (Touch et al., 2009). This would call into question a
key surveillance system attribute from the CDC’s Updated Guidelines,
representativeness (German et al., 2001). Assuming that sentinel surveillance data that
has been compromised by selection bias represent trends or occurrences in the overall
population may result in over- or under-estimating true risk factors or
disease/exposure trends, or missing these factors and trends altogether. Therefore, care
must be taken when choosing sentinels to ensure that they are as typical as possible of
the whole population. Randrianasolo and others (2010) describe their efforts to choose
sentinels for a national surveillance system for monitoring fever syndromes. While
39
noting that representation would have been improved had more sentinels participated
(the estimated population covered by sentinel sites represented 3% of the population),
the authors note that reporting rates were facilitated by choosing sentinels that were
easily accessible to surveillance staff and located in urban areas where outbreak
impact would be dramatic. Sentinels were also selected to represent different climate
trends in the nation. The authors note that a major problem with establishing the
system was connecting clinical reporters to the sentinel system and coordinating their
work, but that future advances toward electronic communications would help resolve
this. Touch and others (2009) also report carefully choosing sentinel sites that were
located in geographically diverse areas and taking into account other factors that may
have impacted the ability of sentinels to adequately report data, such as capacity of the
sentinel hospital to collect and transport specimens. Once surveillance has begun,
demographic and other data characteristic of the sentinels should be collected along
with disease and exposure data to aid in the investigation of selection bias.
Sensitivity of a surveillance system refers to the proportion of cases of a disease or
exposure detected by the surveillance system (Teutsch & Churchill, 2000).
Measurement of sensitivity of a sentinel surveillance system requires data external to
the system to determine the true frequency, or at least reliably gauge the likelihood, of
the event in the population under surveillance (German et al., 2001). According to the
World Bank’s Public Health Surveillance Toolkit (2002), sentinel surveillance is most
effective where the goal of surveillance is to estimate the magnitude and trends of a
disease, rather than to detect the earliest of all cases, which may or may not be within
the domain of the sentinel reporter. Sentinel surveillance is said to be sufficiently
sensitive to detect common diseases and generalized epidemics, but is less effective
for detecting localized epidemics or serious diseases that must be identified as early as
possible, such as hemorrhagic fevers or cholera (Abreu-Garcia et al., 2002). Sentinel
surveillance linked to antenatal clinics continues to be the backbone of HIV
surveillance in Sub-Saharan Africa due to its success at describing generalized
epidemics in this region (UNAIDS/WHO, 2003). Schrag and others (2002) evaluated
40
the validity of a sentinel surveillance system for tracking antibiotic resistance and
found that sentinel data did not consistently match prevalence estimates as compared
to population-based surveillance data. The authors concluded that sentinel surveillance
is a viable alternative to population-based surveillance only in situations where a high
degree of accuracy is not required. Conversely, Gauci and others (2007) report that for
every case of intestinal disease captured by the national passive surveillance system,
seven cases were captured by their pilot sentinel surveillance system leading to more
precise estimates of disease. Hsieh and others (2009) report that a pilot sentinel
surveillance system in Taiwan captured forty-eight gonorrhea cases (out of three
hundred seventeen) that were not notified to the national passive system. The authors
report that these results are evidence of under-reporting in the national system.
Randrianasolo and others (2010) also report that their sentinel surveillance system
detected ten cases of fever clusters in Madagascar that were undetected by traditional
surveillance systems. The authors note that choice of sentinels can significantly affect
sensitivity. Selected sentinels must represent individuals with a similar probability as
the general population of experiencing (and reporting) the disease or exposure event of
concern (Teutsch & Churchill, 2000).
Environmental public health surveillance
Historically, the majority of public health surveillance systems in the U.S. have
focused on infectious diseases (Ritz et al., 2005; Teutsch & Churchill, 2000).
However, there is increasing awareness of the need to develop surveillance systems
that are relevant to addressing environmental health risks (Pew Commission, 2000). In
the last half century, there has been a dramatic shift in the U.S. health burden from
infectious diseases to diseases such as cancer, birth defects, and asthma, many of
which are associated with environmental exposures (McGeehin et al., 2004).
According to 2007 WHO estimates, 460,900 deaths are occurring each year in the U.S.
that are associated with environmental factors. An estimated 13% of the nation’s entire
disease burden may be preventable through a healthier environment (WHO, 2009).
41
In 2000, the Pew Environmental Health Commission called for a national
environmental public health (EPH) surveillance system to track data on environmental
hazards, exposures and outcomes in order to address the ―environmental health gap‖
defined as, ―a gap in critical knowledge that hinders our national efforts to reduce or
eliminate diseases that might be prevented by better managing environmental factors‖
(Pew Commission, 2000). In Healthy People 2010, a report detailing the nation’s
health objectives, the CDC together with the Agency for Toxics Substances and
Disease Registry (ATSDR) and National Institutes of Health (NIH), recommend
much-needed improvements in environmental public health surveillance to achieve the
ultimate goal of ―promoting health for all through a healthy environment‖. Included in
these recommendations is the call for ―monitoring the population and its environment
to detect hazards, exposure of the public and individuals to hazards, and diseases
potentially caused by these hazards‖ (DHHS, 2000).
Thacker and others (1996) identify three distinct surveillance systems useful for
environmental public health applications--hazard, exposure and outcome surveillance
systems. The conceptual model in Figure 2.1 (reproduced from Thacker et al., 1996)
shows the casual pathway leading from hazard to outcome. Collecting, analyzing and
disseminating data from any one of these points along the continuum comprises EPH
surveillance activities (McGeehin et al., 2004).
A hazard surveillance system monitors the presence of the agent itself in air, water,
soil, food or other environmental media. Often data from hazard surveillance systems
are collected for regulatory purposes, and therefore while optimal for enforcement
activities, these data may be less ideal for supporting public health surveillance or
activities (McGeehin et al., 2004).
42
Figure 2.1. Process by which an environmental agent produces an adverse effect and
corresponding types of surveillance (Reproduced from Thacker et al,
1996).
Exposure surveillance is the monitoring of individuals, communities or
populations for the presence of the environmental hazard (McGeehin et al., 2004).
Exposure data are the essential link between hazards and outcomes. Hazard data alone
usually are not measures of individual exposure, given that other factors (such as
personal behaviors) may either augment or reduce the dose received by the individual
(Mather et al., 2004). For example, house paint may be contaminated with dangerous
levels of lead, but if the paint is inaccessible to residents, then there is no exposure.
Exposure data may be estimates modeled off of hazard data or direct measurements of
individual exposure from personal monitors or biologic specimens (Mather et al.,
2004). The aforementioned WLCPP is an example of an exposure surveillance system
(DOHWI, 2008).
Finally, outcome surveillance represents the majority of current public health
surveillance systems. Disease registries or vital statistics records are sources for
outcome surveillance. Health outcomes recommended for surveillance in the 2000
Pew Environmental Health Commission study include: birth defects, development
disabilities, asthma and other chronic respiratory diseases, cancer, and neurologic
diseases (Pew Commission, 2000).
43
According to Thacker and others (1996), regardless of the focus of surveillance,
each EPH surveillance system must: 1) enable measurement of specific hazards,
exposures or outcomes, 2) produce an ongoing data record, and 3) produce timely and
representative data useful in planning, implementing, and evaluating public health
activities. These functions are reflected in the CDC’s guidelines for evaluating public
health surveillance systems (German et al., 2001). Thacker and others (1996) also
describe issues significant to the practice of EPH surveillance that are distinct from
other types of public health surveillance, such as the challenge of defining the hazarddisease relationship and the use of environmental data for public health applications.
Hazard-Disease relationship
A major challenge to the development and implementation of EPH surveillance
relates to the difficulty in establishing the specific environmental hazard associated
with an adverse outcome (Thacker et al., 1996). Acute effects from exposure to a
hazard, such as pesticide poisoning, are usually clear given that exposure to the hazard
and presentation of adverse effects are closely linked in time and space. However,
establishing the hazard-disease relationship for chronic, low-dose exposures is much
more difficult (Pekkanen & Pearce, 2001). Environmentally-related disease is often
characterized by a lag time between recognition of the outcome and the time when the
individual or population may have been exposed to an environmental hazard (Mather
et al., 2004; McGeehin et al., 2004; Thacker et al., 1996). This lag time could be
months, years, or even decades, making it very difficult to identify and account for
confounding or modifying influences that may appear during this interval (Jarup,
2004; Ritz et al., 2005). Over the course of months or years, individuals often migrate
from the source of exposure, become exposed to other hazard sources, or adopt and
change behaviors that moderate or mediate the hazard-disease relationship (Jarup,
2004; Pekkanen & Pearce, 2001; Ritz et al., 2005). Epidemiologic studies where the
outcome is a chronic disease such as cancer, are often inconclusive because it is rarely
possible to reconstruct personal exposures accurately (Pekkanen & Pearce, 2001).
44
However, the difficulties in proving a hazard-disease relationship need not take
precedence over implementation of risk interventions. According to Ritz and others
(2005) if the hazard-disease relationship has been appropriately established in the
scientific literature, exposure tracking of the environmental hazard can still spawn
effective population-based interventions.
Use of Environmental Data for Public Health Applications
EPH practitioners are often faced with the challenge of using data collected for
other purposes in order to inform and guide public health actions. Thacker and others
(1996) give the example of data from vital records or disability claims that rarely
contain sufficient information to substantiate a link to an environmental exposure. By
virtue of being at the nexus of two disciplines, environmental science and public
health, the EPH practitioner is often challenged with applying environmental data
(usually collected for the purpose of addressing ecological questions or for regulatory
or planning purposes) to public health problems (McGeehin et al., 2004; Nuckols et
al., 2004; Thacker et al., 1996). In a survey of local public health agencies across the
nation, Litt and others (2004) found that most hazard tracking was handled by
environmental agencies, while public health agencies most frequently handled health
outcome tracking. Exposure tracking in all instances was rare. In an analysis of the
Safe Drinking Water Information System (SDWIS), a system that would be classified
as an exposure tracking system for use in public health surveillance, Niskar (2007)
concluded that ―to conduct public health surveillance with environmental data, data
owners need to plan for mechanisms to relate environmental and health data in a
meaningful and responsible manner that includes thoughtful consideration of time and
space factors‖. There is surprisingly little discussion in the EPH literature on the
challenges and issues related to the use of environmental data for public health
applications, aside from general mention of the need to increase collaboration across
disciplines. The research presented in this dissertation is intended to promote more
45
discussion on this issue by detailing the challenges encountered in a specific
application of environmental data to support public health surveillance.
Environmental public health tracking network
In response to the Pew Environmental Health Commission’s recommendation, the
CDC and partners are building a national environmental public health tracking
(EPHT) network to gather and integrate data on environmentally related diseases and
exposures (CDC, 2006). The EPHT network will meld data from hazard, exposure and
disease surveillance systems into a single network of standardized electronic data
(CDC, 2006). EPH surveillance is multidisciplinary, and relevant data are derived
from diverse programs and agencies, including environment, health, agriculture,
transportation and planning (Malecki et al., 2008). The purpose of the EPHT is to
function as a repository of validated scientific information on environmental
exposures and adverse health outcomes, as well as relevant ancillary data, from across
these varied sources to facilitate analysis of the possible spatial and temporal
connections between them (McGeehin et al., 2004). The ultimate goal is to assist EPH
practitioners with risk reduction efforts (McGeehin et al., 2004).
The main building blocks of the national EPHT will be existing state level EPH
surveillance systems, active, passive or sentinel (McGeehin et al., 2004; CDC, 2006;
Ritz et al., 2005). These systems, while feeding into the federal network, will
ultimately be focused on collecting data for addressing priority issues of the state.
State level water quality monitoring of public water systems, required by the Safe
Drinking Water Act (SDWA), is an example of this kind of nested surveillance
network (McGeehin et al., 2004). Under the SDWA, states are federally funded to
monitor contaminants in public water systems and report exceedences to the EPA.
These data at the federal level are collected and maintained in the SDWIS database.
SDWIS functions as a passive surveillance system for monitoring public drinking
water systems in the U.S: States do the work of collecting data on drinking water
utilities’ noncompliance with federal drinking water standards and submit data to the
46
SDWIS database (EPA, 2010a). Specified uses of SDWIS data are to track
contaminant levels, respond to public inquiries, prepare reports, evaluate program and
regulation effectiveness, and determine if new regulations are needed to protect human
health (EPA, 2009b). SDWIS is valuable both for state agencies to identify and
address contamination in public systems as well as for the federal government to chart
nationwide trends, and thus is recognized as a potential data source for the EPHT
(McGeehin et al., 2004).
Using SDWIS data on total trihalomethanes (TTHM), Niskar (2007) formally
assessed the utility of environmental data for use in public health surveillance. The
author used the CDC’s attributes of a public health surveillance system as assessment
criteria. Niskar revealed specific areas of strength and weakness not only in the TTHM
data but also with the SDWIS data collection and management process itself that have
implications for public health surveillance. For example, a major limitation of SDWIS
TTHM data is the lack of information on actual levels of TTHM, which are only
reported to SDWIS when levels are out of compliance. However, a major strength of
SDWIS is that data collection and management across states are based on uniform
protocols determined by the EPA. SDWIS provides data necessary to monitor drinking
water. In a survey of state and local public health agencies to identify priorities for the
EPHT, drinking water was consistently at the top of the list (Litt et al., 2004).
However, SDWIS only contains data on public water systems; it does not contain data
on water provided by private wells.
Surveillance role for Oregon’s private well water data
Oregon’s Domestic Well Testing Act (DWTA) (ORS 448.271) may provide the
regulatory framework and necessary data to support a sentinel surveillance system for
monitoring exposures to private well water contaminants. Under the DWTA, owners
of properties with domestic wells must have the well water tested for nitrate, total
coliform bacteria, and arsenic upon accepting an offer to purchase the property and
submit these results to the state health agency where they are stored in an electronic
47
database. The original intent of the DWTA was to establish a program for monitoring
the quality of underground aquifers; it was not meant to support public health
investigations (OHA, 2010). However, the existing well test database represents the
most comprehensive source of private well water data in the state, even though it is
drawn from only a subset of wells. As a sentinel surveillance system, the DWTA data
would use reports of drinking water contaminants in ―sentinel‖ wells for
understanding prevalence trends of these contaminants and exposure factors across the
state. The DWTA data could be seen as supporting both a hazard and exposure
surveillance system (Thacker et al., 1996). As a hazard surveillance system the DWTA
data represent the presence of actual contaminants in water. These data can also
function in an exposure surveillance system because studies indicate that many private
well owners do not treat their water before consumption and would therefore be
directly exposed to well water contaminants (Jones et al., 2006; Mitchell & Harding,
1996).
The advantages of evaluating and using the DWTA data as a sentinel surveillance
system include:
(1) Surveillance of hazards and exposure factors are often a more efficient use of
resources as both are often more frequent and easier to detect than disease outcomes
(LaMontagne et al., 2002). For example, according to Van Grinsven and others
(2006), while it is not yet possible to determine health losses from consuming nitrate
contaminated water, it is possible to make estimates of potential exposure. Several
studies indicate that data from public health surveillance of exposure factors are useful
for informing public health actions in the absence of health outcome data (Laflamme
& VanDerslice, 2004; LaMontagne et al., 2002; Link et al., 2007; Zierold et al., 2007).
(2) While sentinel surveillance systems are generally less expensive, complex, and
resource-intensive than other forms of public health surveillance, they have been
shown to produce similar results, especially with regard to detecting common, nonlocalized events (Abreu-Garcia et al., 2002; Gauci et al., 2007; Jernigan et al., 2001;
Teutsch & Churchill, 2000, p. 270; Touch et al., 2009).
48
(3) Nitrate, coliform bacteria, and arsenic are known contaminants in Oregon
groundwater. Given that an estimated 23% of Oregonians rely on private well water,
there is a potential for many individuals to be exposed to these contaminants, thus
requiring some form of public health surveillance (OEA, 2004; DEQ, 2009).
(4) Depending on the quality of the DWTA data, these data may also be a
contributing dataset to the national EPHT network, filling a gap for information on
private wells. The CDC and NCEH recognize the need for and potential use of private
well information in the EPHT implementation plan (CDC, 2006).
(5) As a sentinel surveillance system, the DWTA data is backed by state
legislation, providing a framework and authority for the ongoing collection and
maintenance of these data. Technical assessment of these data as part of a surveillance
system evaluation offers opportunities to identify provisions of the DWTA that
facilitate or hamper collection of high quality data (Malecki et al., 2008). The lessons
learned from this evaluation may be useful to other states considering enacting similar
legislation.
(6) In evaluating the DWTA data for supporting a sentinel surveillance system,
issues are likely to emerge relating to roles and collaborations between environmental
and health agencies. The DWTA is an example of a hazard and exposure tracking
effort that is handled by the state public health agency with support from the
environmental agency. Analysis of the DWTA data provides an opportunity to discuss
avenues for state and local public health agencies to get more involved in the design,
collection and analysis of environmental data that may help characterize human
exposures to environmental contaminants. Improved collaboration between public
health and environmental agencies is essential to protecting human health. As
recognized by Ritz and others (2005), ―the primary operational goal of environmental
health tracking has to be the ―treatment‖ of the environment in such a manner as to
reduce population risk.‖ Public health practitioners are dependent on their colleagues
in environmental science for developing this restorative ―treatment‖ of the
contaminated environment.
49
GIS, Exposure Assessment and Drinking Water Contaminants
Geographic information systems (GIS) software is widely used in environmental
public health (EPH) research and surveillance to assess the spatial distribution of
adverse health outcomes and relevant exposure factors in order to enhance the
understanding of their association (Beale et al., 2008; Jarup, 2004; Meliker et al.,
2010; Nuckols et al., 2004; Vine et al., 1997). With GIS, spatial patterns can be
examined at isolated points in time, thus allowing for assessment of temporal
variations as well (Meliker et al., 2005).
In their discussion of the use of GIS in environmental epidemiology studies,
Nuckols and others (2004) describe three key data elements relevant to the use of GIS
for exposure assessments: data representation, scale and accuracy. Data representation
is the format of the unit of analysis used in the GIS, most commonly, raster or vector
data structures. In raster models, grid cells serve as the basic unit of analysis, while
vector models use points, lines or polygons (Nuckols et al., 2004; Cromley &
McLafferty, 2002). Both structures represent a set of locations on the earth’s surface,
and the observations made at these locations represent a sample of the phenomenon
being studied. Therefore, sampling error is an important issue to consider when using
GIS in EPH studies (Cromley & McLafferty, 2002).
Selection of scale is a very important factor in creating and analyzing GIS data
(Nuckols et al., 2004). Data at different scales and in different projections require
additional processing to bring the data layers into a common scale and projection for
analysis (Cromley & McLafferty, 2002). When data layers are at different scales, the
larger scale data should be reaggregated to match the scale of the smallest scale data
layer (Cromley & McLafferty, 2002). Even when data layers are matched on scale, the
map’s appearance and the message it conveys may vary depending on the size,
number and configuration of the units of analysis (Beale et al., 2008; Cromley &
McLafferty, 2002; Elliot & Wartenberg, 2004). This problem was introduced by
Openshaw and Taylor (1979) as the modifiable areal unit problem (MAUP) and is
discussed in detail by Gotway and Young (2002). Broadly conceptualized, MAUP
50
concerns the different inferences that can result when the same set of data is grouped
into increasingly areal units (Gotway & Young, 2002). For example, studies of disease
incidence at the county level require environmental data to be aggregated to the same
scale. Such aggregation may obscure intracounty variation in exposure and thus the
association between the hazard and disease (Nuckols et al., 2004). As a way to address
MAUP, Carlos and others (2010) recommend kernel density estimation, which
produces a continuous surface based on estimates of value density at any location in
the spatial frame, irrespective of arbitrary administrative boundaries.
Accuracy refers to the level of error present in the GIS database. Because spatial
data contain both locational and thematic data, GIS users must be concerned with both
positional and attribute accuracy (Cromley & McLafferty, 2002). Positional accuracy
is a measure of agreement between data representation in the GIS and the actual
location of the data, while attribute accuracy measures how well information is linked
to the data representation format (Nuckols et al., 2004). A major issue in the
assessment of attribute accuracy is spatial dependence, the degree to which errors are
spatially systematic (Cromley & McLafferty, 2002). According to Cromley and
McLafferty (2002), spatial dependence, measured as spatial autocorrelation, means
that there is a correlation between errors for features located near each other. Beale
and others (2008) recommend using Moran’s index to test for autocorrelation. Meliker
and others (2008) used Moran’s index to examine autocorrelation in the residuals of
predictive models of arsenic concentrations in Michigan private wells in order to
assess whether any spatial pattern remained in the error terms. Since Moran’s index
was not significantly different from zero the authors concluded that the remaining
variability in the data would not be captured by additional spatial modeling. The
authors also noted that the close proximity of wells with both positive and negative
residuals indicated substantial variation in arsenic concentrations over short distances.
Other issues EPH users must be aware of when using GIS software include the
ethics of mapping confidential or personal data, ascertaining relevant sites of
exposure, and using environmental data for public health applications. GIS users often
51
face the ethical challenge of protecting the confidentiality of individuals while
mapping or modeling exposures factors and health outcomes, especially at small scale
resolutions (Cromley & McLafferty, 2002; Vine et al., 1997). As stated by Vine and
others (1997), ―GIS technology allows researchers to display the precise location of
individual residences, which may violate confidentiality, especially if the study area is
small or if the number of events per population is low.‖ The authors offer a range of
solutions, such as releasing only aggregated data or displacing points to conceal their
true locations.
GIS users must also be critical of the data used to represent the location of
exposure. Often residence is used as the exposure site, but occupation or school
location (or a metric combining all three) may be more appropriate (Briggs & Elliot,
1995; Vine et al., 1997). There are typically two ways to estimate exposures within a
geographic region: 1) spatial interpolation of measured data points, or 2) through
modeling techniques (Vine et al., 1997). If measured data points do not exist then the
later approach must be taken. Geographic modeling is accomplished by identifying a
hazard source and modeling the fate and transport of contaminants to predict
concentration at the exposure site (Beyea & Hatch, 1999). The model may include
personal activity patterns or other ancillary data that could modify individual exposure
levels (Beyea & Hatch, 1999). For example, Gallagher and others (2010) modeled
exposure to contaminated drinking water using residential history, water use patterns,
and data on public water supplies and wastewater effluent to examine associations
with breast cancer. Models tend to have greater predictive value when 1) the data used
to create them are accurate, 2) the conditions under which they are used are relatively
simple, and 3) the geographic area to which they apply is close to the hazard source
(Vine et al., 1997).
Lastly, the use of environmental data for public health applications violates a basic
principle in environmental sciences, that measurement data should be used only within
the bounds of the purpose for which samples are collected (Nuckols et al., 2004). For
example, under the Safe Drinking Water Act, most public water utilities are required
52
to report levels of disinfection byproducts to the EPA to show compliance with
regulatory standards. Utilities generally fulfill this requirement by taking four samples
at different locations in the distribution system every three months. While this
sampling design may be adequate for complying with the law, it may not be sufficient
for characterizing the spatial and temporal variability in exposure necessary to classify
risk to individuals consuming the water (Miranda et al., 2007; Nuckols et al., 2004).
Because exposure data in epidemiology studies are often surrogates (i.e., data from
environmental media used in place of biomonitoring data) that have not been collected
for the purpose of the study, it is possible to assume spurious associations in the data
due to information bias (Beale et al., 2008). When environmental measures do not
accurately reflect actual exposure, both differential and nondifferential
misclassification can occur, leading to biased study results and/or a reduction in study
power (Beale et al., 2008).
A number of EPH studies have used GIS to assess exposure patterns in relation to
drinking water contaminants (Chai et al., 2010; Gallagher et al., 2010; Gopal et al.,
2009; Hassan & Atkins, 2007; Janulewicz et al., 2008; Miranda et al., 2007). For
example, Miranda and others (2007) used GIS to analyze the potential effects on blood
lead levels (BLL) in children associated with switching from chlorine to chloramines
for disinfection in water treatment systems. The authors linked data from a tax parcel
dataset (such as physical address and year house was built) with BLL surveillance data
from a statewide registry and coverage area for two water systems (one that switched
to chloramines and one that did not). The authors found that while the change to
chloramines disinfection may lead to an increase in BLL, this effect may be
progressively mitigated in newer housing and conclude by underscoring the policy
implications of the study. However, in a response to the study, Weintraub (2007) note
that the ecologic design of the study cannot be used to determine causation, noting that
drinking water exposure was based on residence location and census-level exposures
for housing age.
53
The majority of EPH studies using GIS to assess exposure to drinking water
contaminants are similarly ecologic and focused mainly on public water supplies. Few
studies have used GIS to estimate exposure to environmental levels of a contaminant
at the individual level (Nuckols et al., 2004), and only a few have done so in the
context of private wells (Aelion & Conte, 2004; Gatto et al., 2009; Meliker et al.,
2008). Meliker and others (2008) conducted a valuable study comparing the ability of
different models to predict arsenic concentrations in private well water of southeastern
Michigan. The authors compared spatial models commonly used to predict arsenic: a
geostatistical model, a nearest neighbor approach, and arithmetic averages in U.S.
Public Lands Survey-defined townships and sections. The predictive ability of these
models was compared using a separate validation dataset of directly measured arsenic
concentrations from individual private wells in the same region. All models resulted in
significant correlation between measured and predicted concentrations. However, the
two models that were GIS-based—the geostatistical model and the nearest neighbor
approach—outperformed models based on geographic averages in townships or
sections. The authors note that while individual exposure is best estimated through
direct measurement at each drinking water source, cost considerations and lack of
access necessitate the use of models for predicting contaminant levels in private well
water.
Private well water is usually drawn directly from groundwater aquifers, and there
are a number of studies published in the environmental sciences literature that have
used GIS to assist in characterizing groundwater contamination, particularly for nitrate
(Aelion & Conte, 2004; Green et al., 2005; Nolan & Hitt, 2006). Nuckols and others
(2004) discuss the need to demonstrate ―geophysical plausibility‖ when using GIS to
develop exposure estimates in environmental epidemiology studies. The authors
define this term as the plausibility that a geophysical route of transport exists between
a contaminant source and the receptor. Results from several studies show that a
substantial number of private well users do not treat their water prior to consumption
(Jones et al., 2006; Mitchell & Harding, 1996; Walker et al., 2005). Thus, from a
54
conservative, health-protective perspective, measures and patterns of groundwater
contamination can be seen as surrogates of exposure for private well water users.
Evidence that private well users do not consistently treat their water underscores the
geophysical plausibility of well water as a direct route of exposure to groundwater
contaminants. This substantiates the application of the GIS-based model developed in
this dissertation for assessing exposures to nitrate in private well water even though it
is focused on measures and patterns of groundwater contamination.
55
CHAPTER 3 – MANUSCRIPT
Private Well Testing in Oregon from Real Estate Transactions: An Innovative
Approach Toward a State-Based Surveillance System
Brenda O. Hoppe, MS1
Anna K. Harding, PhD1
Jennifer Staab, PhD2
Marina Counter, MS2
1
College of Public Health and Human Sciences, Oregon State University, Corvallis,
Oregon
2
Oregon Health Authority, Office of Environmental Public Health, Portland, Oregon
Journal:
Public Health Reports
Published January – February 2011
56
ABSTRACT
Objectives: Approximately 43 million Americans rely on private wells for drinking
water. Surveillance is difficult due to budget constraints and owner resistance to
mandatory testing. Since 1989 Oregon requires private well testing (PWT) when a
property changes ownership through a real estate transaction (RET). We analyzed data
collected under the Oregon PWT-RET law in order to make recommendations for
improving existing legislation and provide guidance for other states considering
similar legislation.
Methods: Geographic information system software was used to spatially analyze
PWT-RET data and to compare these data with groundwater management areas
(GWMAs) and population growth estimates. Oregon’s PWT-RET law was contrasted
to legislation in New Jersey to assess the relationship between legal requirements and
data quality.
Results: Maps were produced of wells with elevated nitrate concentrations and
detections of coliform bacteria. Spatial analysis of wells with elevated nitrate
concentrations revealed highly significant clustering (z = 43.18). Comparisons with
existing GWMAs and population growth estimates identified areas where current and
future residents consuming well water may be exposed to contaminant levels
exceeding health-based standards. Evaluation of the New Jersey law revealed
provisions of the Oregon law that, if modified, could increase compliance and improve
data quality.
Conclusions: PWT-RET legislation is an innovative approach for implementing a
state-based surveillance system for private well water, and for identifying at-risk areas.
Recommendations include making real estate sales contingent on testing, electronic
reporting of results by laboratories, broadening legal scope to include rental properties,
ensuring test result confidentiality and improving public health outreach activities.
57
INTRODUCTION
In 2009, the U.S. Department of Health and Human Services released, ―The
Surgeon General’s Call to Action to Promote Healthy Homes,‖ which highlights the
link between well-water quality and health, and recommends annual testing of private
wells for bacteria and chemical contamination (HHS, 2009). Also in 2009, the
American Academy of Pediatrics (AAP) released new guidance for private well-water
testing to protect children from illnesses associated with exposure to drinking-water
contaminants (CEH, 2009) Chief among the new guidelines was the recommendation
that water be tested at least annually for nitrate and coliform bacteria. The AAP also
recommended that state governments require well testing when a dwelling is sold.
California, Colorado, Georgia, Idaho, Indiana, Oregon, Pennsylvania, Washington,
and Wisconsin have been identified as having the highest nitrate concentrations in
shallow groundwater (Fuhrer et al., 1999). Of these states, only Oregon has enacted
legislation that requires private well testing (PWT) at the point of a real estate
transaction (RET). New Jersey, in 2001, and Rhode Island, in 2008, also passed PWTRET legislation. These laws represent an innovative policy option for state
governments to assess the quality of private well water. The benefits of linking private
well surveillance to the sale of a property include: (1) Potential buyers are informed of
water quality and can negotiate any treatment costs with the seller; (2) the state can
collect and analyze sampling data to characterize groundwater quality; and (3) state
and local public health agencies can identify individuals or communities exposed to
high levels of drinking-water contaminants and provide information on healthprotective measures (NJDEP, 2009).
Currently, there is no federal law or program to monitor the quality of private
wells, as there is for public water systems under the Safe Drinking Water Act
(SDWA). Under the SDWA, the U.S. Environmental Protection Agency sets
maximum contaminant levels (MCL) for drinking water supplied through public water
systems and oversees the states, localities, and water suppliers who implement these
standards. Yet, approximately 43.5 million people, or 15% of the U.S. population, rely
58
on private wells for household needs, and the quality of this water cannot be
guaranteed (Hutson et al., 2004). Oregon has approximately 350,000 private wells,
and an estimated 16% of the state population consumes drinking water from a private
well (ORDEQ, 2009).
Private wells are generally supplied by groundwater aquifers, which are
susceptible to contamination from both point and nonpoint sources associated with
human activities on land. Sources frequently cited as threats to groundwater include
leaking underground storage tanks, septic systems, landfills, industrial facilities, and
agricultural activities, such as widespread field application of fertilizers and manure
storage or spreading (EPA, 2002).
Nitrate is associated with these agricultural activities, as are septic systems, and is
the most widespread contaminant in groundwater aquifers (Rupert, 2008; Nolan et al.,
2002). The normal background concentration of nitrate in groundwater is estimated to
be 2 milligrams per liter (mg/L) (Mueller & Helsel, 1996). The MCL for nitrate (10
mg/L) was established due to concerns that ingestion of nitrate in drinking water by
infants may lead to methemoglobinemia, or ―blue baby‖ syndrome, a condition that
can cause cyanosis and, in advanced stages, asphyxia (Ward et al., 2005; WHO, 2007).
Recent studies also have linked nitrate in drinking water to thyroid disorders (Gatseva
& Argirova, 2008; Tajtáková et al., 2006), insulin-dependent diabetes (Kostraba et al.,
1992; Parslow et al., 1997) reproductive anomalies (Brender et al., 2004; Croen et al.,
2001), acute respiratory infections (Gupta et al., 2000), non-Hodgkins lymphoma
(Ward et al., 1996), colon cancer (De Roos et al., 2003; McElroy et al., 2008), and
cancers of the bladder and ovary (Weyer et al., 2001).
Other common contaminants in groundwater that pose a significant problem for
private well owners are microorganisms, including bacteria, viruses, fungi, and
parasites (Rogan et al., 2009). An estimated 51% of waterborne disease outbreaks in
the U.S. during 1999–2000 were caused by microorganisms. Of these, 64% were
associated with private wells (Lee et al., 2002). Gastrointestinal illness, in particular,
59
has been found to be associated with Escherichia coli (E. coli) contamination of
private well water (Strauss et al., 2001).
The most common method of detecting microorganisms that may be an indicator
of fecal contamination from animals or humans is by testing total coliforms (Rogan et
al., 2009). The MCL in public drinking-water systems for total coliforms is zero,
indicating that a water source used for drinking should have no detectable coliform
bacteria. Total coliforms and nitrate contamination of drinking water can stem from a
common source, and some researchers argue that the health effects associated with
drinking-water nitrates can be attributed, instead, to co-occurring microorganisms
(Avery, 1999; Powlson et al., 2008).
A U.S. Geological Survey (USGS) study of private wells in 48 states found that
more than 20% of sampled wells contained one or more contaminants at a
concentration greater than health-based standards (DeSimone et al., 2009). More than
4% of sampled wells had nitrate concentrations exceeding the MCL for nitrate, and
34% tested positive for total coliform bacteria. Nearly 25% of sampled wells exceeded
the MCL that were located in predominantly agricultural areas, defined as greater than
50% agricultural land within a 500-meter radius around the well. The USGS study
warned that many states are facing the challenge of ensuring the safety of drinkingwater quality for residents on private wells.
Nitrate contamination of groundwater is a problem in Oregon due to long-term
contributions from agricultural fertilizers, animal feedlot operations, leaking septic
systems, and aboveground application of wastewater (Hutson et al., 2004).
Recognizing the need to preserve groundwater quality in the state, Oregon passed two
pieces of legislation in 1989 enabling statewide groundwater monitoring. The
Groundwater Protection Act requires the Oregon Department of Environmental
Quality (DEQ) to declare a Groundwater Management Area (GWMA) if area-wide
groundwater contamination exceeds trigger levels (DEQ, 2009). For nitrate, the trigger
level is 70% of the MCL, or 7 mg/L. Currently, there are three GWMAs in Oregon,
together comprising approximately 3,000 square kilometers. The Oregon DEQ
60
declared all three GWMAs due to widespread nitrate contamination (DEQ, 2009).
Once a GWMA has been designated, a local committee is convened to work with state
agencies to develop and implement a voluntary action plan to reduce groundwater
contamination. The action plans often include educational outreach efforts to residents
living inside the GWMA who may have private wells or on-site septic systems (DEQ,
1991; DEQ, 1997; 2006).
The second piece of legislation, the Domestic Well Testing Act, requires that any
seller of a property with a well that supplies groundwater for domestic purposes must
have the well water tested for nitrate and total coliform bacteria by a state-certified
laboratory upon accepting a purchase offer (DHS, 2009). Results must be sent to the
state health agency, the Department of Human Services (DHS), and are stored in the
RET database. The following information is submitted along with the test results:
Public Land Survey System description of the property; name and address of the
owner, buyer, and individual who collected the sample; sampling point; well ID
number; and well depth. In 2009, this law was amended to include arsenic as a test
parameter and to require that buyers receive notification of the test results (DHS,
2009).
The purpose of this legislation was not to protect public health per se, but to
―establish a program to provide water quality monitoring‖ (DHS, 2003). Currently, no
follow-up actions occur when test results reveal high nitrate levels or positive coliform
detections. Furthermore, the law is not enforceable. There is no penalty for
noncompliance, and the sale of the property can be finalized without completion of
testing. Due to a lack of resources, data in the RET database are not routinely
analyzed. This database, however, is a valuable repository of contaminant information,
and routine analysis may help direct public health measures by state agencies to those
individuals living in areas where well contamination is shown to be prevalent.
The objectives of this study were to analyze the RET data to develop policy
recommendations for PWT-RET legislation. These recommendations were based on
the results of the analysis and a comparison to similar programs in other states. The
61
specific goals of the analysis were to (1) assess compliance with the law—that is, the
extent to which the required testing and reporting actually occurred; (2) determine the
prevalence of MCL or action level exceedances; (3) examine the spatial distribution of
these exceedances; and (4) compare the spatial distribution of exceedances with the
location of GWMAs and centers of population growth.
METHODS
Data collection
The primary dataset used in this analysis consisted of nitrate and total coliform
bacteria testing results from individual private well-water samples submitted to the
DHS for the years 1989–2008 (DEQ, 2010). All database entries had been copied
manually from the submitted paper forms containing the property address and test
results. The addresses were geocoded, and latitudes and longitudes added. Prior to
analysis, duplicate entries per sampling event, attributed to data-entry error, were
removed. Some homes had been sold more than once during the time period captured
in the data, so there was more than one measurement available for one location. In
these cases, the median value was selected to represent the nitrate concentrations in
the well at the address because this measure of central tendency is least affected by
extremes.
To examine reporting trends over time, we counted the number of reports
submitted to the RET database by year. Our goal was to assess the completeness of
reporting by comparing the number of test reports with the total number of homes
having a private well that were sold. Next, we determined the number and percentage
of MCL violations and compared them with prevalence estimates from other surveys
and states.
For analytical purposes, the nitrate data were categorized based on concentration
levels. Category 1 concentrations were 0.0–6.9 mg/L (less than the GWMA trigger
level of 7.0 mg/L). Category 2 included concentrations of 7.0–9.9 mg/L, and Category
3 included concentrations of ≥10.0 mg/L (the MCL).
62
Spatial and statistical analysis
Statewide maps of Category 2 and Category 3 nitrate concentrations as well as
positive coliform detections were produced using ArcGIS 9.3 (ESRI, 2009) to allow
for a visual assessment of the locations with elevated contaminant levels. The visual
examination was followed by statistical analysis of the distribution of nitrate levels
across Oregon. The Getis-Ord General G statistic was computed to determine whether
nitrate levels were clustered in specific locations and if the clustering occurred for
high or low concentrations, while controlling for the density of testing in different
regions (Getis & Ord, 1992).
Finally, we determined the Oregon counties that had the highest number of wells
with elevated nitrate levels. We compared the location of these counties with that of
GWMAs in Oregon to examine whether remediation efforts were already under way
in affected areas. In addition, we used census data (USCB, 2008) and population
forecasts (DAS, 2004) to assess the potential risk for exposures to high nitrate levels in
these counties.
RESULTS
A total of 20,173 distinct sampling events were available in the RET database at
the time of analysis. Figure 3.1 shows the total number of sampling events reported
each year from 1989 to 2008 and illustrates the large fluctuations in the number of
reports submitted per year. The annual number of reports submitted has declined since
1996. After removing duplicate entries per address and retaining the median nitrate
value for analysis, there were 18,688 addresses represented in the spatial analysis.
Information about the number of RETs in Oregon that involved properties with
private wells was unavailable. Therefore, we could not determine the percentage of
RETs that actually complied with the law by reporting the PWT results to the Oregon
DHS.
63
Figure 3.1. Total number of real estate transaction entries per year, 1989–2008,
Oregon
Nitrate concentrations
A total of 409 records (2.2%) contained Category 2 nitrate concentrations, while
Category 3 nitrate concentrations were found in 322 records (1.7%). The percentage of
Category 3 concentrations (corresponding to MCL violations) of 1.7% equals the
percent of MCL violations reported as a result of a preliminary 2004 analysis by the
Oregon DEQ (DEQ, 2009). New Jersey found similar rates, with 2.7% of wells
showing nitrate MCL exceedances (NJDEP, 2008). Surveys in Wisconsin (Vanden
Brook, 2002) and Iowa (Kross, 1993) resulted in significantly higher percentages, with
14% and 18% exceedances, respectively.
Total coliform results
A total of 2,415 records (12.0%) in the RET database contained positive detections
of total coliform bacteria. This rate was much higher than the 2.2% of wells testing
positive for fecal coliform or E. coli presented in the New Jersey report (NJDEP,
2008). At the same time, it was substantially lower than the 62% of sampled wells
64
with positive detections of total coliforms found in a study of 78 private wells in
south-central and southeastern Pennsylvania. This latter finding probably reflects a
sampling bias toward wells that were in ―close proximity to agricultural land-use
areas‖ (Zimmerman et al., 2001).
Figure 3.2 presents the locations of private wells with Category 2 and 3 nitrate
concentrations and wells with positive detections of total coliform bacteria. It shows
that contaminated well water is found across most of the state, although most of the
wells with high nitrate levels are found in more rural counties and in rather
circumscribed areas. In line with this observation, the Getis-Ord spatial analysis of
nitrate levels revealed highly significant clustering of elevated nitrate concentrations
(z=10.86). Thus, a well with a high level of nitrate is likely to have other wells with
elevated nitrate concentrations in its vicinity.
The Oregon counties with the highest number of private wells with elevated nitrate
concentrations are Crook, Deschutes, Jackson, Josephine, Lane, Linn, Malheur,
Marion, and Umatilla (Figure 3.3). Of all wells represented in the RET database, 74%
were located in one of these counties. Oregon’s three designated GWMAs encompass
only portions of four of these counties—Lane, Linn, Malheur, and Umatilla—although
each GWMA does cover substantial portions of the clusters located in these counties.
As shown in Figure 3.3, other clusters of contaminated wells are located outside of
any GWMA, and these wells are not receiving the benefits of state efforts associated
with the GWMA designation to decrease contamination in groundwater.
65
Category 2 nitrate levels
(7–9 mg/L)
Category 3 nitrate levels
(≥10 mg/L)
Positive detections for
coliform bacteria
Figure 3.2. Oregon private wells with Category 2 (top) and Category 3 (middle) nitrate
levels and positive detections of coliform (bottom), 1989 – 2008
66
Figure 3.3. Location of wells with Category 2 and 3 nitrate concentrations in
comparison with locations of Oregon’s three Groundwater Management
Areas (in dark gray). Oregon counties with the largest number of domestic
wells with Category 2 and 3 nitrate concentrations are shaded in light gray.
Several of the counties with nitrate contamination clusters have experienced rapid
population growth over the last year (Table 3.1). Crook, Deschutes, Jackson, and Linn
counties, in particular, have experienced above average population growth compared
with the overall state growth rate of 9.5% from 2000 to 2007 (USCB, 2008). This
population growth is expected to continue, with much of the additional growth likely
to occur in counties containing many wells with elevated nitrate concentrations (DAS,
2004).
67
County
Population growth
estimates, 2000–2007
(percent)
Population growth
forecasts, 2000–2040
(percent)
Crook
19.4
50
Deschutes
33.5
55
Jackson
9.9
39
Josephine
7.0
35
Lane
6.4
31
Linn
9.9
29
Malheur
–1.5
29
Marion
9.3
36
Umatilla
4.2
33
Table 3.1. Population growth estimates and forecasts for Oregon counties with large
numbers of wells with elevated levels of nitrates
DISCUSSION
The analysis revealed that the number of testing records in the PWT-RET database
varied widely from year to year, and, unlike real estate sales, declined over time.
Although we could not directly assess the percentage of properties with private wells
that were actually tested, these results suggest that voluntary compliance with the
Oregon PWT-RET law is limited and not uniformly implemented. Even though the
data were limited, we noticed a pattern in clustering indicating that wells with a high
level of nitrate were likely to have other wells with elevated nitrate concentrations in
close proximity. This suggests that a single well testing high for nitrates is not an
isolated anomaly, but instead is a valuable indicator of more widespread
contamination, underscoring the need for other wells in the area to be tested. It is also
important to note that although each well sample represents a single point in time,
nitrate levels in groundwater can remain stable for decades (Ruckart et al., 2008).
68
Therefore, a single exceedence likely represents persistent contamination and,
therefore, a persistent public health concern.
A comparison of the locations of contaminated wells and GWMAs showed that
not all clusters of wells with elevated nitrate levels were located within a GWMA.
Clusters outside of GWMAs are less likely to be subject to contamination mitigation
efforts, including education outreach to private well owners. Populations in these areas
may, therefore, be at particularly high risk of consuming contaminated well water.
Given the population increase in Oregon generally and in the counties with the highest
numbers of contaminated wells in particular, it is likely that the number of people
exposed to well water with elevated nitrate levels will increase over time. This is a
particular concern for families with infants, given that the major human health risk
associated with exposure to nitrate is infant methemoglobinemia. Of the nine counties
with the highest number of contaminated wells, four of them (Linn, Malheur, Marion,
and Umatilla) have a greater than average number of children younger than 5 years of
age when compared with the rest of the state (USCB, 2008). Furthermore, three
counties (Malheur, Marion, and Umatilla) exceed the state average for homes where
English is not the primary language, and seven counties (Jackson, Josephine, Lane,
Linn, Malheur, Marion, and Umatilla) exceed the state average for people living below
the poverty line (USCB, 2008). These factors may indicate substantial barriers to
educating well owners on the need for testing and treatment, since some owners may
have limited English proficiency and may also not have the resources to pay for longterm treatment technologies. The existence of these susceptible populations requires
comprehensive monitoring and targeted information campaigns to mitigate their
exposure to contaminated water. Strengthening the existing PWT-RET legislation to
improve the quality of data received by the state will contribute to both of these
efforts.
To evaluate the relationship between the provisions of the Oregon PWT-RET law
and data quality, we reviewed similar programs in other states. Only two states, New
Jersey and Rhode Island, have PWT-RET laws that closely resemble those in Oregon.
69
Because Rhode Island’s program was initiated in 2008, we were unable to draw
comparisons to this program. New Jersey’s Private Well Testing Act (PWTA) and the
associated program, which were implemented in 2001–2002, provided a rich store of
information that was of considerable value in assessing the effectiveness of the
Oregon law.
Comparison of New Jersey and Oregon laws
Testing contingencies and communication of results. During the first four and a half
years of the New Jersey program, 51,028 test results were submitted. These
represented 13% of the estimated 400,000 private wells in New Jersey (NJDEP, 2008).
By comparison, in the 20 years since enactment of the Oregon law, the total number of
records submitted to the Oregon RET program represents only about 5% of the
estimated 350,000 private wells in Oregon (DEQ, 2009). This difference is likely the
result of compliance problems with the Oregon PWT-RET law.
The New Jersey law makes the sale of a property contingent upon completion of
well testing and the submission of written confirmation that both the buyer and seller
have received the results. New Jersey requires the lab performing the analysis to
submit test results directly to the state as well as to the financially responsible party.
Results are submitted on a standardized form and sent electronically to the state
database (NJDEP, 2001).
In Oregon, the sale of the property is not contingent upon well testing or
submission of results to the state. The seller is assigned the responsibility for obtaining
the test and submitting results to DHS with no consequences for failure to do so.
Forms are not standardized and submission is in paper format (DHS, 2003). A 2009
amendment made it mandatory for the seller to provide the buyer with a copy of the
test results. However, there are no consequences for failing to do so (DHS, 2009).
Well testing on rental properties. The New Jersey law also requires that property
owners of rental properties with private wells have the well tested at least once every
70
five years (NJDEP, 2001). As with tests arising from RETs, labs must submit results
to the state electronically and provide copies of the results to both parties—in this
case, the owner and the renter. This provision increases data completeness and
protects tenants whose water supply is provided by a private well by ensuring that they
are aware of the quality of their water. In Oregon, there are no requirements to test
private wells on rental property except when such a property is sold.
Confidentiality of test results. In New Jersey, test results are confidential. Information
can only be released when aggregated by municipality, county, region or state
(NJDEP, 2001). This provision addresses concerns expressed by realtor associations
and property owners about the impact on sales and property values of homes located
adjacent to properties with contaminated wells. When a well-water test result indicates
that a contaminant exceeds the drinking-water standard, the state database
automatically alerts the local health jurisdiction. While keeping the exact location of
the well at issue confidential, the local jurisdiction is responsible for notifying
property owners in the vicinity, when warranted (Zimmerman et al., 2001).
The Oregon law makes no provisions for confidentiality of test results. Although
not readily available to an individual other than one requesting the test, there are no
legal grounds for denying interested parties access to the results.
Outreach and educational activities. The New Jersey law requires that the state
establish a public information and education program to inform the public and
professional stakeholders about provisions of the PTWA, health effects of consuming
water from a contaminated private well, treatment techniques, and potential funding
available for water treatment (NJDEP, 2001). Additional local outreach efforts are
designed by each health jurisdiction in response to their assessment of local needs and
available resources.
The Oregon PWT-RET law does not require the state to undertake any outreach or
educational activities. Even if the tested water fails to meet drinking-water standards,
71
the lab is not required to notify the state or the local health jurisdiction about the
exceedance. Oregon currently does not have a public health program for private wells,
but information on relevant health risks and options for well-water treatment are
provided on state agency websites (http://www.oregon.gov/DHS/ph/dwp/dwt.shtml).
Analytes required to be tested. Another key difference between Oregon’s PWT-RET
law and the law in New Jersey is the number of analytes tested. In New Jersey, testing
is required for total coliform, 26 volatile organic compounds (VOCs), nitrates, lead,
arsenic, mercury, alpha particle radioactivity, pH, iron, and manganese (NJDEP,
2001). While an expanded analyte list increases the cost to the party responsible for
testing, the resulting information contributes to a more comprehensive characterization
of water quality and greater health protection for individuals and communities.
Oregon’s PWT-RET law only requires testing for total coliform bacteria, nitrate,
and, with a 2009 amendment, arsenic (DHS, 2009). The recent amendment also allows
DHS to adopt rules requiring testing for other contaminants in specific areas that are
of public health concern due to local geology or inputs from industry or agriculture.
CONCLUSIONS
The following recommendations, although specifically addressing shortcomings in
the existing Oregon PWT-RET law, may also be helpful to other states that are
considering implementing a private well-water testing law based on RETs.
Sale of real estate with a well that is used for drinking water should be
contingent on the completion of testing and notification of all pertinent parties.
Knowledge of the PWT-RET law, health risks, and treatment methods relating
to nitrate in well water should be included in the licensing of real estate agents.
Test results should be sent directly from the lab, via electronic means, to the
state agency overseeing implementation of the law and by mail to the buyer
and seller of the property.
72
The scope of the law should be expanded to include periodic testing of wells
on rental properties, with result reporting requirements analogous to those for
real estate sales.
Confidentiality of test results should be protected by provisions in the law that
allow public health departments to release only aggregated information.
A notification process should be developed that protects confidentiality while
allowing local health jurisdictions to inform property owners of a well in their
vicinity that has contaminants exceeding drinking-water standards.
Information regarding contaminant levels, health effects, and water-treatment
options should be sent to individuals receiving test results.
Contaminants that are of concern in various areas of the state should be
catalogued for DHS use in flexibly adjusting testing requirements to meet local
public health needs.
The Oregon PWT-RET law represents an innovative policy for ensuring the
quality of private well water for state residents. With the dramatic reduction in state
government budgets, public health agencies have to do more with less. A PWT-RET
law represents a modest cost to the state to implement and maintain a private wellwater monitoring program, yet the data that results from the law is crucial to
identifying areas in the state where individuals and communities are being exposed to
potentially harmful drinking-water contaminants such as nitrate and coliform bacteria.
If compliance can be strengthened through modifications in the existing law, the
resulting data, on an individual level, will give prospective home buyers information
they need to keep their families healthy and, on a population level, will enable state
agencies to target public health investigations and education outreach to especially
vulnerable communities, such as those families with newborns, limited English
proficiency, or living below the poverty line.
73
CHAPTER 4—MANUSCRIPT
Modeling Nitrate Contamination in Private Wells for Understanding Human
Health Risk – Part I: Refining Spatial Resolution of Agricultural Nitrogen
Brenda O. Hoppe, MS1
Denis White, MS2
Anna K. Harding, PhD1
George W. Mueller-Warrant, PhD3
Eric C. Main, MS4
1
College of Public Health and Human Sciences, Oregon State University, Corvallis,
Oregon
2
Office of Research and Development, Environmental Protection Agency, Corvallis,
Oregon
3
Agricultural Research Service, United States Department of Agriculture, Corvallis,
Oregon
4
Oregon Health Authority, Office of Environmental Public Health, Portland, Oregon
Journal:
Environmental Science & Technology
Submission Pending
74
ABSTRACT
Background: Public health specialists and water quality managers are challenged by a
limited amount of data on environmental hazards that threaten groundwater used by
private wells. In particular, characterizing land-applied manure and fertilizer inputs,
generally recognized as the most significant contributors to nitrate contamination of
groundwater, would be especially useful for identifying areas where private well water
safety may be an issue.
Objectives: The objectives of this study are: (1) to advance methods for estimating
and distributing data on nitrogen from fertilizer and manure land applications by
incorporating state-specific data sources to improve spatial resolution; and (2) to
integrate these data with information on new well construction to provide thematic
maps that communicate potential risks related to consumption of private well water in
an easily interpretable manner.
Methods: Existing USGS methods for estimating nutrient loading at the county level
were incorporated with datasets derived from a regulatory program for permitting
confined animal feeding operations and agricultural enterprise budget worksheets
published by Oregon State University-Extension to produce an agricultural nitrogen
raster map with a spatial resolution of 56 meters. This map was combined in a GIS
with polygons representing categories of soil sensitivity to nitrate leaching and point
features representing well locations obtained from a well log database. A Python script
enabled calculation of total nitrogen loading and average leachability rank within 1000
meter buffer areas surrounding each well. Wells with a nonzero nitrogen total and an
average leachability rank of ―high‖ or ―very high‖ were selected and categorized in
maps communicating well vulnerability across the State.
Results: Risk maps developed from this research indicate that nearly 50% of recently
drilled wells in Oregon may be susceptible to nitrate contamination. In addition, areas
of the State identified for mitigation efforts are insufficient for capturing large
numbers of vulnerable wells. Predicted increases in population density across the state
and the steady addition of approximately 3,800 new wells each year may lead to a
75
large number of Oregon residents, especially those living in rural areas, sustaining
long-term exposures to nitrate contamination in drinking water.
Conclusions: Given the lack of data specific to understanding private well water
quality, public health specialists and environmental scientists need to explore the use
of alternative datasets for characterizing exposures and health risks. This is especially
important given the large number of individuals, both in Oregon and across the U.S.,
that are dependent on private wells for drinking water. Results of this modeling work
are useful for decision making regarding the permitting of residential development and
well construction, as well as the implementation of water protection strategies and
well stewardship programs.
76
INTRODUCTION
Nitrate is one of the most common, widespread contaminants in aquifer systems
(Burkart & Stoner, 2002; Dubrovsky et al., 2010; Nolan et al., 2010; Warner &
Arnold, 2010). While nitrate can occur naturally in groundwater, concentrations
greater than 1 milligram per liter (mg/L) are likely due to anthropogenic inputs,
particularly from agricultural activities (Dubrovsky et al., 2010; Warner & Arnold,
2010; WHO, 2011). Nitrogen from synthetic fertilizer and manure land application
drives the accumulation of nitrate in the soil through organic matter mineralization and
fixation processes. Any nitrate not taken up by plant material or soil microorganisms
can leach through permeable soil into the water table, facilitated by rainfall and
irrigation (Brukart & Stoner, 2002). A recent report by the U.S. Geological Survey
(USGS) on nutrients in groundwater shows that nitrate concentrations exceeded
background levels in 64% of aquifers studied. Median concentrations of nitrate were
highest in groundwater located beneath agricultural areas (Dubrovsky et al., 2010).
Nitrate contamination of groundwater diminishes the supply of safe drinking water
and poses a public health threat, especially for individuals relying on private
residential wells (Levin et al., 2002). A 2009 USGS report on private wells states that
more than 4% of wells located within 30 sampled aquifers used for drinking water had
nitrate concentrations greater than 10 mg/L, the maximum contaminant level (MCL)
allowed for public water administered under the federal Safe Drinking Water Act
(SDWA) (DeSimone et al., 2009). Unlike public supply wells, the safety of water from
private wells is not ensured by SDWA regulations. Rather, individual homeowners are
principally responsible for maintaining the integrity of their well, testing the water
regularly, and taking any necessary actions to prevent exposures to contaminants that
may pose a risk to health (Backer & Tosta, 2011). However, numerous studies on
stewardship behaviors indicate that the majority of well owners are not caring for their
wells or testing the water according to health-protective guidelines (Hexemer et al.,
2008, Jones et al., 2006; Kreutzwiser et al., 2011; Mitchell & Harding, 1996; Shaw et
al., 2005). This lack of stewardship is a concern given that nitrate levels appear to be
77
increasing. A decade long assessment by USGS from 1993 to 2003 showed that the
proportion of private wells with concentrations of nitrate greater than the MCL
increased from 16% to 21% (Dubrovsky et al., 2010). In addition, an increasing
amount of cultivated land is being rapidly developed for residential use (FIC, 2006),
which may lead to a larger number of individuals exposed to agriculturally-linked
contaminants in small water systems (Showers et al., 2008). While the MCL for nitrate
was established mainly to reduce the risk of methemoglobinemia in exposed infants,
studies also suggest that nitrate in drinking water is associated with adverse effects on
thyroid function and morphology, respiratory tract infections, and reproductive and
developmental outcomes in infants (Abu Naser et al., 2007; Gatseva & Argirova,
2008a, 2008b; Gupta et al., 2000; Fewtrell, 2004; Manassaram et al., 2006; Sadeq et
al., 2008; Tajtáková et al., 2006; Ward et al., 2010; WHO, 2011).
In the state of Oregon, over 90% of residents in rural areas are dependent on
groundwater, and in many areas it is the only source of drinking water (DEQ, 2011).
While an exact count of active private wells is unavailable, it is estimated that Oregon
has approximately 350,000 private wells (DEQ, 2011). Using 2010 U.S. Census
statistics, this number of wells would suggest that approximately 23% of the State is
relying on private wells for household drinking water (USCB, 2010). In addition,
approximately 3,800 exempt-use wells, comprised largely of small group or single
domestic use wells, are drilled each year (OWRD, 2008).
Nitrate contamination of groundwater within the State has been demonstrated both
by the USGS (DeSimone et al., 2009) and State agencies (DEQ, 2011; Hoppe et al.,
2011). Oregon has three Groundwater Management Areas (GWMA), together
comprising approximately 3,000 square kilometers (Figure 4.1), designated due to
widespread nonpoint nitrate contamination (DEQ, 2011). Aside from Oregon’s
Domestic Well Testing Act (DWTA), which requires one-time testing when a property
with a well is changing ownership (Hoppe et al., 2011), there is no state legislation or
state-level program in place that would ensure well water safety equivalent to what
occurs under the SDWA for public supply wells. In addition, surveys confirm that
78
many Oregon well owners are not testing their well water as recommended (Mitchell
& Harding, 1996).
Public health specialists and water quality managers in Oregon need an
understanding of the condition of groundwater and the location of contamination in
order to protect source water and human health, especially for residents currently on
private wells or moving into areas not serviced by public utilities (Masetti et al., 2008;
Rowe et al., 2007). Identifying factors that affect the probability of well water
contamination could lead to an effective program for addressing susceptible wells
(Aelion & Conte, 2004). In particular, characterizing the influence of land-applied
manure and fertilizer, generally recognized as the most significant contributors to
nitrate contamination of groundwater (Burkart & Stoner, 2002; Fields, 2004;
Galloway et al., 2008; Nolan & Stoner, 2000), would be especially useful for
identifying areas where private well water safety may be an issue.
A number of studies investigating nutrient loading relative to groundwater quality
have included some measure of fertilizer or manure inputs, many relying on the use of
geographic information systems (GIS) for processing and distributing data spatially
(Greene et al., 2005; Nolan & Hitt, 2006; Kundu et al., 2008; Liu et al., 2010; Olson et
al., 2009; Rekha et al., 2011; Showers et al., 2008; Swartz et al., 2003). However,
these measures are often based on coarsely aggregated data applied to a limited study
area. Given that fertilizer and manure applications can vary substantially over short
distances depending on the location and type of farm facilities and crops grown (Luo
& Zhang, 2009), low resolution datasets may not adequately capture the spatial
fluctuations in nutrient loading necessary for predicting groundwater contamination at
point locations where wells exist (Elliot & Savitz, 2008; Slaton et al., 2004).
Furthermore, it is often difficult to find detailed spatial information with full statewide
coverage because of the effort, expense, and often legislation that is required to
compile data for such a large area.
A 2006 USGS report by Ruddy and others describes methods for allocating data
on state fertilizer sales and for estimating manure generation from livestock
79
populations in order to characterize nitrogen inputs to land surface at the county level.
These methods were adapted for use in the current study and incorporated with data
sources unique to Oregon, including crop-specific fertilizer expenditure estimates,
information on livestock populations submitted under a state confined animal feeding
operation (CAFO) permit program, and a spatial index of soil sensitivity to nitrate
leaching. These datasets were combined in a GIS and compared with new well
construction across the State in order to identify susceptible wells. The objectives of
this study were to:
(a) advance methods for estimating and distributing data on nitrogen from fertilizer
and manure land applications by incorporating state-specific data sources to improve
spatial resolution; and
(b) integrate these data with information on new well construction to provide
thematic maps that communicate potential risks related to consumption of private well
water in an easily interpretable manner.
This exposure characterization is part of a wider effort to understand the risk to
individuals who are regularly consuming private well water in Oregon. A companion
study will relate the datasets developed in this study to sampled nitrate concentrations
in groundwater and investigate the feasibility of using DWTA data for public health
surveillance.
METHODS
Study area
The study area included all of the State of Oregon, in the Pacific Northwestern
corner of the conterminous United States, which covers approximately 254,608 square
kilometers (USCB, 2010). The State is divided into nine major ecoregions (Figure 4.1)
(Thorson et al., 2003). Average annual rainfall in the State varies widely and is
generally highest within the Coast Range and driest in the plateau regions of the Blue
Mountains, Northern Basin Range, and Snake River Plain. Oregon’s agricultural
economy is very closely tied to its climate. Counties comprising the Willamette Valley
80
and northern Cascades ecoregions account for nearly 50% of the State’s gross farm
and ranch sales followed by counties of the Columbia Plateau (NASS, 2011).
Figure 4.1. Oregon study area with 9 ecoregions and 3 groundwater management areas.
Model development
We first estimated the quantity of nitrogen (N) associated with land surface
applications of manure (manure-N) and fertilizer (fertilizer-N). Our methods are based
on those published by Ruddy and others (2006) for estimating manure-N and
fertilizer-N to the county level within the conterminous U.S. We adjusted these
methods to reflect the temporal extent of our project (2000-2007), as well as specific
farming practices within Oregon. We then incorporated information on livestock
populations available through a state CAFO permit program and expert guidance for
crop-specific fertilizer expenditures to increase spatial resolution of distributed
manure-N and fertilizer-N data.
81
Layer
Land use
(base layer)
Nitrogenous
fertilizer
Nitrogen
from manure
Source
National
Agriculture
Statistics Service
Association of
American Plant
Food Control
Officials
Census of
Agriculture
Oregon State
University
Extension
Oregon Department
of Agriculture
Census of
Agriculture
Soil
Oregon Department
characteristics of Environmental
Quality
New well
Oregon Water
construction
Resources
Department
Data
Crop specific data &
other land use
categories
Fertilizer sales
Time
2007
Scale
State
2007
State
Expenditures on
fertilizer
Crop-specific fertilizer
recommendations
2007
State &
county
Acre
State-defined CAFO
locations, livestock type
& number
Livestock type &
number
Classification of soil
sensitivity to nitrate
leaching
Number & location of
new wells drilled
20002007
Point
locations
2007
State &
county
State
19882010
2007
20002007
State
Table 4.1. Datasets used in this study
Using ArcInfo 10.0 (ESRI, 2010), we distributed estimates of manure-N and
fertilizer-N across the State based on land use, in particular crop type, provided by the
U.S. Department of Agriculture (USDA) National Agricultural Statistics Service
(NASS) 2007 Oregon Cropland Data Layer (CDL). The CDL program utilizes spring
and summer seasonal satellite imagery to accurately locate and identify field crops,
adjusting results by using a regression estimator, farmer reported data, Farm Service
Agency (FSA) Common Land Unit (CLU) data, agri-business data, and data from the
USDA Census of Agriculture (Mueller & Ozga, 2002). The strength and emphasis of
the CDL is crop-specific land cover categories. For other land use categories, the 2007
CDL samples the 2001 National Land Cover Database (NLCD) wherever FSA/CLU
data are unavailable. Spatial resolution for the 2007 CDL raster is 56 by 56 meters
(USDA, 2011).
82
Finally, we incorporated datasets representing soil sensitivity to nitrate leaching
and new private well development in order to identify wells that may be susceptible to
nitrate contamination.
Estimating and distributing manure-N
Manure-N estimates were based on livestock type and population data collected by
the 2007 Census of Agriculture. All major animal categories included in work by
Ruddy and others (2006) were included in our analysis, with the addition of goats and
minks. Census of Agriculture regulations require that data not be published that would
disclose operations of an individual farm (USDA, 2009). For counties with a livestock
population reported as ―nondisclosed‖ we derived a population for that county by
summing the number of animals reported for all other counties and subtracting that
value from the total number of animals reported for the State as a whole. This residual
value was allocated to the ―nondisclosed‖ county. If more than one county had a
―nondisclosed‖ quantity for a livestock category, the residual was allocated using a
weighted percentage based on the number of farms in each ―nondisclosed‖ county that
reported producing this livestock. For example, there were 30 residual animals in the
―Hogs and pigs‖ category that were allocated to two counties, Gilliam and Sherman.
Gilliam had 2 farms (40%) that reported raising hogs and pigs, while Sherman had 3
farms (60%). Using this information as weights, Gilliam was allocated 12 animals (30
x .40), and Sherman was allocated 18 animals (30 x .60).
Using the method and assumptions of Ruddy and others (2006) and Goolsby and
others (1999), we estimated nitrogen content of manure produced by various types of
livestock (Table 4.2). Goolsby and others (1999) did not provide estimates for goat or
mink, so nitrogen content for manure from these animals were obtained from NJAES
(2006) and Wright and others (1998), respectively.
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Livestock group
Nitrogen content of manure
(kg per day per animal)
Milk cows
0.2040
Beef and other
0.1500
Hogs and pigs
0.0270
Goats
0.0230
Mink
0.0027
Sheep and lambs
0.2300
Horses and ponies
0.1270
Poultry-Layers
0.0015
Poultry-Pullets
0.0010
Poultry-Broilers
0.0010
Poultry-Turkeys
0.0044
Table 4.2. Nitrogen content of manure from livestock groups
Livestock populations are reported to the Census of Agriculture as calendar yearend inventory numbers. For all livestock groups, this number was assumed to be
representative of the population throughout the year; we did not adjust manure
nitrogen estimates for livestock that may be slaughtered before year’s end (i.e. heifers,
turkeys, hens). The final equation used to calculate annual estimates of total nitrogen
input per livestock group (LSTOCK-N) in each county was:
LSTOCK-Nij = LSTOCKij x Ni x LCYCLE
where
LSTOCK-Nij Estimated nitrogen input from manure from the ith
livestock group in jth Oregon county (kg)
Equation 1.
LSTOCKij
Number of animals reported in the ith livestock group
in jth Oregon county
Ni
N content of manure for the ith livestock group
(kg/day/animal)
LCYCLE
Assumed annual lifespan (365 days).
Estimates for manure-N per county were calculated by summing all LSTOCKN for the county. In order to distribute county-level manure-N estimates to a finer
spatial resolution, we used information submitted to the Oregon Department of
84
Agriculture (ODA) under Oregon Revised Statutes (ORS) Chapter 468B, § 205 (ORS
468B, 2009), which requires that any person who owns or operates a CAFO must
obtain a permit from ODA and the Oregon Department of Environmental Quality
(DEQ). The definition of a CAFO under ORS 468B is much broader in scope than the
federal definition, such that many Oregon farms that would not be classified as a
CAFO under federal law do meet the state’s CAFO criteria. The dataset of CAFO
permits obtained from ODA included farm location, type of livestock, and number of
animals for any farm that received a permit from 2000-2007. These years were
selected in order to be relevant to the temporal extent of the other datasets used in this
analysis. If a CAFO lacked information on the number of animals, a value was
assigned based on the average number of animals within that animal category for all
CAFOs in the dataset. CAFOs that were permitted for the category ―Other‖ were
removed from the dataset since most of these facilities were dog kennels, animal
boarders or wildlife nature parks, and waste from these facilities is not likely to be
widely distributed to land surface. CAFOs permitted as livestock wholesalers were
also eliminated as these are generally auction houses and would not maintain animals
for an entire year, a key assumption of Equation 1.
Following the same approach used by Ruddy and others (2006) to estimate
nitrogen release per animal group, we calculated the annual nitrogen input attributable
to each CAFO based on the animal category for which it was permitted. Using
ArcInfo 10.0 (ESRI, 2010), a map of CAFO locations was combined with the ORCDL and the nitrogen totals for each CAFO were distributed to cells of cropland,
pasture, or herbaceous grassland within a 16 km buffer around the CAFO (Figure 4.2).
This buffer distance was selected based on information from Bradford and others
(2008) indicating that manure and wastewater from a CAFO is usually land-applied
within 16 km from a CAFO facility. Herbaceous grassland was included as a manure
―receiving‖ category as it may be used for grazing (USGS, 2010). Nitrogen totals for
each CAFO were distributed within each buffer to receiving cells using a weighted
85
distance technique, given that manure land application is likely to be heaviest on lands
proximal to the farm and attenuate with distance to the buffer border.
Figure 4.2. State-permitted CAFOs, 2000-2007, and distribution of manure-N
estimates.
The weighted distance technique (Equation 2) involved creating centroids from the
grid cells of the receiving areas. Nitrogen was distributed from the CAFO to each
centroid. Nitrogen quantities were weighted by the distance of the grid cell centroid
from the CAFO. Nitrogen quantities at grid centroids were aggregated for centroids
where the centroid was potentially exposed to nitrogen from more than one CAFO.
where
Equation 2.
B
N = 16 kilometer buffer distance around CAFO
D
Dx = Distance of point x from the CAFO
D
Di = Distance of point i from the CAFO
N
B = Estimated quantity of nitrogen distributed from the CAFO
86
We then summed all nitrogen distributed from individual CAFOs to receiving cells
within individual counties (CAFO-N). These county CAFO-N totals were subtracted
from the LSTOCK-N county estimates derived from Equation 1 (Table 4.3). For those
counties where the residual was positive, the residual value was uniformly distributed
to all receiving cells located within all CAFO buffers for the relevant county. For
those counties where the residual was negative, it was assumed that LSTOCK-N
estimates had been completely allocated using the CAFO permit information, and we
made no further adjustments.
We did not include loss to volatilization in our determination of LSTOCK-N due
to the wide variability of nitrogen volalitization. Manure nitrogen loss to the
atmosphere occurs mainly through volatilization of ammonia. Atmospheric ammonia
stemming from agriculture can significantly impact the environment and has been
implicated in widespread damage to natural ecosystems (Asman et al. 1998; Hacker &
Du, 1993). However, ammonia volatilization losses vary greatly depending on
environmental conditions, manure type, storage, handling, and field application
methods (Ruddy et al., 2006; Moreira & Satter, 2006; Meisinger & Jokela, 2000).
Losses can range from close to 100% for surface application with optimal
environmental conditions for volatilization, to only a few percent when manure is
injected or incorporated immediately into the soil (Meisinger & Jokela, 2000). We did
not have access to farm-specific details regarding storage, handing, and field
application methods of manure, so we could not estimate nitrogen loss through
ammonia volatilization for individual farms. We recognize that for some farms this
may impact the LSTOCK-N estimate for that facility.
Estimating and distributing fertilizer-N
Fertilizer-N estimates were based on reports of fertilizer expenditures from the
2007 Census of Agriculture. First, 2007 fertilizer sales data for Oregon were obtained
from the Association of American Plant Food Control Officials (AAPFCO) and
converted from tons of product to kilograms of nitrogen, based on the chemical
composition for each product containing nitrogen. When nitrogen composition was not
87
specified for a product, a default percentage based on the product’s fertilizer code was
used.
COUNTY LSTOCK-N CAFO-N totals Residual
Baker
4,633,167
996,400
3,636,767
Benton
594,304
456,989
137,315
Clackamas
2,151,082
2,320,895
-169,813
Clatsop
380,397
852,061
-471,664
Columbia
681,803
112,382
569,421
Coos
1,411,522
792,766
618,756
Crook
2,411,553
549,475
1,862,078
Curry
692,302
99,549
592,754
Deschutes
1,022,856
228,496
794,360
Douglas
3,443,589
28,887
3,414,702
Gilliam
624,979
1,041,652
-416,673
Grant
2,240,275
53,960
2,186,315
Harney
6,125,958
573,551
5,552,406
Hood River
106,555
7,628
98,928
Jackson
2,562,260
750,535
1,811,724
Jefferson
1,601,381
1,315,788
285,593
Josephine
427,662
343,634
84,027
Klamath
5,662,221
1,724,069
3,938,152
Lake
4,652,764
1,245,563
3,407,201
Lane
2,119,988
622,317
1,497,671
Lincoln
280,119
16,264
263,855
Linn
2,835,589
2,336,027
499,562
Malheur
12,040,367
8,314,976
3,725,391
Marion
3,654,932
4,488,737
-833,806
Morrow
7,195,086
8,725,529 -1,530,443
Multnomah
205,152
72,460
132,692
Polk
1,220,275
1,412,231
-191,956
Sherman
239,962
15,105
224,857
Tillamook
3,184,838
6,177,191 -2,992,352
Umatilla
4,031,549
4,629,017
-597,469
Union
2,141,313
10,667
2,130,646
Wallowa
2,440,328
354,233
2,086,095
Wasco
1,551,905
20,207
1,531,698
Washington
780,085
658,828
121,257
Wheeler
956,771
160,958
795,812
Yamhill
1,913,745
2,125,959
-212,213
Table 4.3. Manure-N estimates (kg-year) for Oregon counties using two approaches
88
Following the approach published by Ruddy and others (2006), nitrogen totals for
each fertilizer product were summed to give a fertilizer statewide sales total (FFS) for
2007, which was then used in the following equation to estimate county-level nitrogen
inputs from farm fertilizer (FERT-N) as proportional to fertilizer expenditures in each
county:
FERT-N i = FFS x (FCEi/ FSE)
where
FERT-Ni
Equation 3.
Estimated nitrogen input from farm fertilizer use in
county i (kg N)
FFS
Total farm fertilizer sales in Oregon (kg N)
FCEi
Fertilizer expenditures for county i ($)
FSE
Total fertilizer expenditures for Oregon ($).
In order to distribute county-level FERT-N to crop fields represented in the ORCDL, improving spatial resolution, we used crop-specific fertilizer expenditure
estimates derived from Enterprise Budget Sheets (EBS) published by the Oregon State
University (OSU) Extension Service. An agricultural enterprise budget is a detailed
accounting of revenues and expenses related to farming a specific crop. Along with
other relevant expenses, EBS from OSU-Extension provide recommendations for the
amount and type of fertilizer to purchase for growing specific crops in the State (OSU,
2009). While some EBS quantified the amount of nitrogen in fertilizer for use on a
crop annually, others only provided a general cost estimate. Furthermore, some crops
had more than one EBS available depending on the year of establishing or maintaining
the crop. To derive a crop-specific dataset for fertilizer nitrogen from the EBS (EBSN), we used the most recent EBS available for a crop and averaged recommended
nitrogen fertilizer use across establishment and maintenance years when relevant. For
crops with an EBS that only provided cost estimates, the recommended quantity of
nitrogen fertilizer for use on the crop was requested directly from an OSU extension
agent or obtained from EBS provided by Washington State Extension services. If the
recommended nitrogen fertilizer was given as a range, the average between lowest and
89
highest values was used in the EBS-N dataset. Substitutions that were used for crops
represented in the OR-CDL for which no EBS was available are provided in Table 4.4.
OR-CDL crop missing an EBS
Rapeseed, safflower, sunflower
Sorghum
Soybeans
Misc veg and fruits
Other crops
Other small grains
Other tree fruits
Other tree nuts
Substituted fertilizer-N value
Canola
average of all grain crops
dry beans
average of all fruit and vegetable crops
average of all crops
average of all grain crops
average of all tree fruit crops
average of all tree nut crops
Table 4.4. Substitutions used for OR-CDL crops for which an EBS was not available
Crop-specific EBS-N data (values converted from lb/acre-year to kg/cell-year)
were distributed to relevant cells according to crop type as represented in the ORCDL, and a sum was derived for the total quantity of nitrogen from fertilizer predicted
by the EBS-N data for each county (Table 4.5). These county EBS-N totals were then
subtracted from the county FERT-N totals derived from Equation 3, and any residual
was uniformly distributed across all cropland within the county. For all counties but
three (Gilliam, Morrow, and Sherman) residuals were positive, suggesting that more
fertilizer was purchased and used in most counties than amounts recommended in the
OSU EBS. For the three counties with negative residuals, we assumed that FERT-N
estimates had underrepresented the amount of nitrogen from fertilizer that is likely
being applied, given specific crops identified in the county by the OR-CDL and
nitrogen fertilizer use recommended by the relevant OSU EBS, and no further
adjustments were made.
Soil sensitivity to nitrate leaching
Soil sensitivity refers to a soil’s tendency to allow a chemical to be transported
through the soil to groundwater (Huddleston, 1998). The DEQ has developed a
geospatial dataset to predict soil sensitivity to nitrate leaching (SSNL) for large areas
of the state (Seeds, 2011) using methods of the Oregon Water Quality Decision Aid
90
(OWQDA; Huddleston, 1998) on data from the Natural Resources Conservation
Service’s Soil Survey Geographic Database (SSURGO).
COUNTY
Baker
Benton
Clackamas
Clatsop
Columbia
Coos
Crook
Curry
Deschutes
Douglas
Gilliam
Grant
Harney
Hood River
Jackson
Jefferson
Josephine
Klamath
Lake
Lane
Lincoln
Linn
Malheur
Marion
Morrow
Multnomah
Polk
Sherman
Tillamook
Umatilla
Union
Wallowa
Wasco
Washington
Wheeler
Yamhill
FERT-N
EBS-N totals Residual
3,125,261
1,054,100 2,071,161
6,040,476
4,632,350 1,408,126
6,898,833
2,997,740 3,901,093
162,804
3,328
159,476
735,527
129,396
606,131
952,841
96,638
856,203
1,845,358
231,641 1,613,717
509,490
32,300
477,190
1,686,188
93,530 1,592,658
1,619,322
887,728
731,594
2,556,900
4,778,730 -2,221,830
249,294
32,025
217,269
1,310,429
295,458 1,014,971
1,603,332
11,709 1,591,623
1,247,197
41,786 1,205,411
4,108,629
1,089,170 3,019,459
224,583
16,270
208,313
5,841,332
1,768,560 4,072,772
1,932,574
505,951 1,426,623
5,616,022
4,470,110 1,145,912
152,629
1,464
151,165
18,069,825
16,235,400 1,834,425
14,099,290
7,522,000 6,577,290
25,350,231
11,974,900 13,375,331
8,576,299
11,189,600 -2,613,301
1,767,590
398,873 1,368,717
7,983,226
7,119,850
863,376
2,143,348
4,755,420 -2,612,072
279,093
224
278,869
22,045,449
17,517,600 4,527,849
4,658,820
3,629,580 1,029,240
1,606,239
425,298 1,180,941
2,969,725
2,195,340
774,385
9,380,872
4,466,170 4,914,702
173,706
92,638
81,068
10,815,585
6,293,810 4,521,775
Table 4.5. Fertilizer-N estimates (kg-year) for Oregon counties using two approaches
91
The OWQDA was developed as a first-tier screening tool for making broad
determinations of the likelihood that a specific chemical, including nitrate, applied to
specific Oregon soil, would move through the soil and affect groundwater. There are
five classes of groundwater vulnerability ranging from very low to very high, and
these ratings are carried over into the SSNL dataset. The DEQ has used the SSNL
dataset to identify factors influencing nitrate risks at public water systems (Seeds,
2011). We used the SSNL dataset to identify private wells located in soils that have a
―very high‖ or ―high‖ sensitivity to nitrate leaching (Figure 4.3). Ratings of soil
sensitivity for irrigated soil were used in the model. While the SSNL data layer covers
the majority of the state there were gaps where State Soil Geographic (STASGO2)
data were used to determine a sensitivity ranking.
Figure 4.3. Map of ―very high‖ (red) and ―high‖ (orange) soil sensitivity to nitrate
leaching.
92
New well construction
The Oregon Water Resources Department (OWRD) permits new well construction
for the state and maintains this information in a well log database that is publicly
accessible (OWRD, 2007). Well logs for new wells drilled for domestic use between
2000-2007 were used to locate wells across the state and within GWMAs (Figure 4.4).
Figure 4.4. New wells drilled between 2000-2007 and locations of groundwater
management areas.
A number of studies have shown that the size of the buffer around a sampled well
is significant when evaluating groundwater quality in relation to land use or cover
(Cain et al., 1989; Hay & Battaglin, 1990; Barringer et al., 1990; Tesoriero & Voss,
1997). We chose a buffer radius of 1000 meters as appropriate for establishing an area
of influence following work by Greene and others (2005). This buffer radius was
applied to each well and the total sum of manure-N and fertilizer-N allocated to cells
within the buffer area (approximately 314 hectares) was derived using a Python script
developed specifically for this effort.
The datasets described in Table 4.1 were uploaded to a single GIS database in
order to generate summary information and produce maps associating nitrogen
93
distribution (contaminant source) with the locations of new wells (potential exposure
points) for understanding the health risk related to consumption of elevated nitrate
concentrations in drinking water.
RESULTS
Manure-N
Using the approach published by Ruddy and others (2006), we estimated that
approximately 88 million kilograms (kg) of manure-N were applied to Oregon’s land
surface in 2007. This is an increase of approximately 10 million kg from 1997, the
most recent year for which Ruddy and others (2006) calculated manure-N input to
Oregon land surface from both confined and unconfined farm facilities. Alternatively,
using data from state CAFO permits, we estimated that approximately 54 million kg of
manure-N would have been applied in any one year between 2000-2007. For 75% of
counties, manure-N estimates were larger using the Ruddy approach. The Ruddy
approach relies on reports of animal type and number from the 2007 Census of
Agriculture, while the CAFO permit approach relies on information submitted to the
State under a CAFO permit program. Given the vague definition in statute for the type
of facility considered a CAFO (ORS 468B, 2009) and that the CAFO approach does
not take into account ranches or farms with free-ranging animals, it is likely that the
CAFO approach underestimates the true impact of manure-N across the state. For
example, based on the CAFO dataset used in this study, approximately 750 farms were
used to estimate and distribute annual manure-N input. Conversely, nearly 20,000
Oregon farms reported livestock or poultry inventory in the 2007 Census of
Agriculture. However, more than 80% of these farms produced cattle, which are often
free-ranging and therefore not likely to be a significant localized source of manure-N.
Yet, the discrepancy suggests that there are farms missing from the CAFO dataset,
either because they do not meet the state definition for CAFO permitting, or because
their operators are out of compliance with statutory requirements.
94
After distributing manure-N estimates across the State, values ranged from
approximately 0 to 318 kg N/hectare-year with an average of 159 kg N/hectare-year
(SD = 227). Areas of the State with the highest values of manure-N were located in
Tillamook county, which contains 20% of the State’s dairy farms. Hectares with the
highest manure-N values were located within a 12 square kilometer area containing
more than 90 dairy or beef farms that were in operation between 2000-2007. The high
density of farms in this small area is the likely cause of the high manure-N loading.
Fertilizer-N
Using the approach published by Ruddy and others (2006), we estimated that
approximately 178 million kg of fertilizer-N were applied to Oregon’s land surface in
2007. This is an increase of approximately 70 million kg from 2001, the most recent
year for which Ruddy and others (2006) calculated fertilizer-N input to Oregon land
surface. Alternatively, using EBS data and crop type identified in the 2007 OR-CDL
we calculated that approximately 117 million kg of fertilizer-N would have been
applied during this year. For more than 80% of counties, fertilizer-N estimates were
larger using the Ruddy approach. The Ruddy approach relies on reports of fertilizer
sales and use provided by regulatory agencies and farmers, while the ESB approach
relies on expert guidance for crop-specific fertilizer use. The discrepancy between
these two estimates may be evidence of the widely recognized problem of fertilizer
application rates that exceed well-researched agronomic needs (Hansen et al., 2011;
Hatfield & Prueger, 2004; Zarabi & Jalali, 2012).
Alternatively, the difference may be a consequence of misidentification of
cropland within the OR-CDL. After distributing fertilizer-N estimates across the State,
values ranged from approximately 0 to 1,383 kg N/hectare-year with a median of 31
kg N/hectare-year. Areas of the State with extremely high fertilizer-N values are
located in a single county, Hood River, which accounts for approximately 2% of
Oregon’s gross farm and ranch sales (NASS, 2011). The Ruddy approach for
estimating fertilizer-N indicated that the county used less than 1% of the State’s
95
fertilizer use in 2007. While this is a small percentage, by our calculations the 2007
OR-CDL identifies approximately 2 square kilometers of cropland in the county with
the result that this relatively small amount of cropland received all of the fertilizer-N
estimated for the county. While we only saw these extreme fertilizer-N values in one
county, the situation underscores the importance of accurately identifying land use and
crop type for estimating and distributing nitrogen from agricultural sources.
The CDL and NLCD datasets are commonly used in efforts to model land use
impacts to drinking water sources (Greene et al., 2005; Kutz et al., 2012; Mehaffey et
al., 2011; Wilson & Weng, 2010). While the CDL borrows non-agriculture land use
identification data from the NLCD, it uses a separate, unique process to identify
agricultural land use, particularly for cropland (Mueller & Ozga, 2002). We were
interested in how estimates of cropland for Hood River compare between the NLCD
and CDL, and went further to compare cropland estimates for each Oregon county in
order to understand how these two datasets match with regard to estimation of
agricultural land use. We calculated total square kilometers of land identified as either
pasture or cropland between the 2007 OR-CDL and the 2006 NLCD and found that
for all but four counties (Gilliam, Linn, Morrow, Sherman), the 2006 NLCD identified
on average nearly 183 square kilometers of land as agricultural over the amount
estimated in the 2007 OR-CDL.
The average percent difference was greater between the two estimates of cropland
(20%) than between the two estimates of pasture land (0.05%). With regard to Hood
River county in particular, the 2006 NLCD identified approximately 8,574 more
hectares of cropland than the 2007 OR-CDL. Using the NLCD to distribute fertilizerN to cropland would have resulted in approximately 30 kg N/hectare-year, a much
more realistic estimation compared to an average of 1,700 kg N/hectare-year of
fertilizer-N using the 2007 OR-CDL, especially given that the majority of cropland in
Hood River county is identified in the 2007 OR-CDL as tree crops (fruit, nut, holiday
trees), which require relatively low levels of nitrogenous fertilizer. However, CDL
crop areas are based on sample observations of what has been planted and then
96
extrapolated to the remaining areas for which training data are missing. Overall
accuracy for the 2007 Oregon CDL is given as 83% with an error value of 17%
(USDA, 2011). NLCD bases crop estimates on satellite imagery and does not use field
level training data. Thus, in most cases the CDL would have more accurate data for a
given year as to the number of hectares actually in production and the specific type of
crop. However, an exception to this would be an area with relatively little data with
which to train for the CDL (J. Dewitz, NASS, personal communication, September 26,
2010), such as cropland in a mostly forested area, which may be the case in Hood
River county given that over 70% of the county is forested. At this time, it is difficult
to determine the reason for the extremely high quantities of fertilizer-N loading on
cropland in Hood River county, and it will be interesting to see how OR-CDL
estimates may be adjusted in the next iteration of the dataset.
Figure 4.5. Total annual nitrogen (kg) from manure and fertilizer land applications
across Oregon
We combined both the final fertilizer-N raster with the final manure-N raster in
order to derive a map representing total nitrogen loading from agricultural inputs
97
across the State (Figure 4.5). We capped the total amount of nitrogen from fertilizer
and manure sources that could be attributed to any single cell at approximately 263 kg
N/hectare-year with the assumption that nitrogen loading above this level would be
damaging to most crops and not likely to reflect actual practice.
Identification of wells susceptible to nitrate contamination
A total of 30,893 new private wells were drilled between 2000-2007, based on the
well log dataset with duplicates removed (Figure 4.4). This result is in accordance
with estimates by the OWRD based on well log data that approximately 3,800 exemptuse wells, comprised largely of small group or single domestic use wells, are drilled
each year (OWRD, 2008). Of this total, 1,307 (4%) were drilled within a GWMA
(Figure 4.4), areas proven through extensive sampling to have elevated levels of
nitrate in groundwater. After combining all datasets in a GIS database, only wells with
a buffer area including soil classified as having ―very high‖ or ―high‖ sensitivity to
nitrate leaching and a nonzero sum for combined manure-N and fertilizer-N
(―selected‖ wells) were retained (n = 20,488) (Figure 4.6). The average amount of
nitrogen calculated within selected well buffers was approximately 6 kg N/hectareyear. The highest amount, approximately 129 kg N/hectare-year, was associated with a
well located in Tillamook county in an area of intensive animal farming. Figure 4.6
shows the distribution of selected wells across the state color-coded according to
quartile of total nitrogen loading. The darkest colored wells are associated with soils
with very high or high sensitivity to nitrate leaching and the top quartile for nitrogen
loading from manure and fertilizer inputs. These wells, totaling more than 15,000 or
50% of all new wells drilled between 2000-2007, may be especially susceptible to
nitrate contamination. Notably, the boundaries of two GWMAs located in the center
and eastern part of the State appear to ―capture‖ a large number of susceptible wells in
the area. However, the boundaries of the GWMA located in the southern Willamette
Valley are far too restricted to include all the susceptible wells identified in this area
(Figure 4.6).
98
While we considered developing maps showing total manure-N and fertilizer-N
per census tract in comparison to new well construction, we were concerned that this
would provide a misleading view of contaminant risk to wells at this level.
Aggregating contaminant sources to various areal units is a common approach to
characterizing population level environmental exposures and exploring links to health
outcomes (Brochu et al., 2011; Elliott & Wartenberg, 2004; Nuckols et al., 2004; Lupo
et al., 2011; Parenteau & Sawada, 2011; Whitworth et al., 2011). In addition, this
approach facilitates linking exposures and health outcomes to socioeconomic and
demographic (SED) information available at various census levels (Balazs et al., 2011;
Brochu et al., 2011; Carozza et al., 2008; Teschke et al., 2010). Regarding the current
study, an understanding of the SED characteristics of Oregonians relying on
susceptible wells would assist public health practitioners in developing effective risk
messaging and directing scarce program resources to the most vulnerable populations.
However, aggregating contamination that can vary substantially over short distances
and assigning a sum to a large areal unit, like a census tract, may wrongly infer that all
individuals living in the unit are equally exposed to the same amount of
contamination. This is referred to as the Modifiable Area Unit Problem (MAUP) and
is a common challenge for studies on the association between health and environment
(Elliott & Wartenberg, 2004; Nuckols et al., 2004; Openshaw, 1977; Parenteau &
Sawada, 2011). Our efforts to use state-specific data sources to characterize fine-scale
variability in agriculturally sourced nitrogen allow for understanding contamination
and potential exposure at point locations across Oregon. Aggregating all manure-N
and fertilizer-N within a census tract and inferring an association with all wells located
within the census tract is a step backwards from the fine-scale modeling of our data
and may demonstrate a mishandling of MAUP.
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Figure 4.6. Selected wells coded by quartile of total nitrogen loading within well
buffer and view of Willamette Valley GWMA.
CONCLUSION
This study demonstrates an approach to increasing the spatial resolution of data
representing agricultural nitrogen inputs potentially affecting nitrate contamination in
private wells across Oregon. Geographic information systems (GIS) modeling is
important as a low-cost alternative to identifying public health areas of concern related
to drinking water exposures (Swartz et al., 2003) and can facilitate the ―risk ranking‖
promoted by WHO Guidelines for performing drinking water system safety
assessments (WHO, 2011). Results of this modeling work can assist land use planners,
water quality managers and public health specialists in decision making regarding well
permitting, water protections, and implementation of well stewardship programs and
education outreach (Rowe et al., 2007). For example, our analysis indicates that the
existing boundaries of the GWMA located in the southern Willamette Valley do not
include all of the susceptible wells in the area. Given that the designation of a GWMA
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triggers activities to educate affected residents and mitigate contamination, it may be
advantageous from a risk reduction perspective to consider expanding the boundaries
of this GWMA, specifically to include susceptible wells to the north.
The likelihood that private well owners and their families will be exposed to high
levels of agriculturally linked contaminants through their drinking water increases as
residential development encroaches on historical farmland, especially given that
nitrate in groundwater can persist and accumulate over years to decades, even
centuries (Dubrovsky et al., 2010; Schlesinger, 2009; Showers et al., 2008). Results
from this study suggest that 50% of recently drilled wells may be susceptible to nitrate
contamination. However, recent research shows that regulation and technical
improvements in intensive farming can succeed in decreasing nitrogen surplus in
groundwater over time while maintaining crop yields and increasing animal
production (Hansen et al., 2011; Kladivko et al., 2004). Most current construction
regulations do not address the problem of existing groundwater contamination when
agricultural lands are converted to residential developments. An immediate action that
can be taken to protect private well owners is to require water quality testing of all
newly constructed wells as part of the permitting process, a policy that currently does
not exist in Oregon (Showers et al., 2008). The decision to transition long-standing
arable land from agricultural to residential use should include an open assessment of
the short- and long-term costs inherent in treating affected groundwater tapped for
new homes.
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CHAPTER 5—MANUSCRIPT
Modeling Nitrate Contamination in Private Wells for Understanding Human
Health Risk – Part II: Application of a Land Use Regression Model to Well Test
Data
Brenda O. Hoppe, MS1
Denis White, MS2
Anna K. Harding, PhD1
George W. Mueller-Warrant, PhD3
Bruce K. Hope, PhD4
1
College of Public Health and Human Sciences, Oregon State University, Corvallis,
Oregon
2
Office of Research and Development, Environmental Protection Agency, Corvallis,
Oregon
3
Agricultural Research Service, United States Department of Agriculture, Corvallis,
Oregon
4
CH2M Hill, Portland, Oregon
Journal:
Environmental Science & Technology
Submission Pending
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ABSTRACT
Background: The National Center for Environmental Health at the CDC has
recognized the need to ensure private well safety through surveillance and utilization
of relevant datasets. Oregon’s Domestic Well Testing Act (DWTA) links well testing
to property sales enabling continuous collection of statewide data on private drinking
water sources. These data may be useful as a sentinel surveillance system, wherein a
report of contamination in a single well signals risk to area neighbors. However,
given that compliance with the DWTA is not enforced or audited and sampling is
often not performed by qualified professionals, these data require validation to ensure
quality and suitability for public health applications.
Objectives: The objective of this study was to evaluate DWTA test data for use in a
public health sentinel surveillance system for monitoring exposures to private well
contaminants with a specific focus on nitrate.
Methods: A land use regression (LUR) model was developed for predicting nitrate
concentrations in private well water based on manure (manure-N) and fertilizer
nitrogen (fertilizer-N) inputs to land surface, soil leachability, and high quality
groundwater sampling data. Agricultural nitrogen datasets with fine spatial resolution
were used enabling hazard characterization relevant to point locations. The resulting
LUR model was validated with set-aside data and assessed for model bias. The final
LUR model was used to assess the validity of the DWTA data by calculating predicted
nitrate concentrations for wells in the DWTA dataset and statistically testing the
correlation with reported test data.
Results: Univariate regression analyses showed significant relationships between
nitrate concentrations and both fertilizer-N and soil leachability but no discernible
relationship with manure-N or any interactions terms. Results from the final LUR
model regressing fertilizer-N and soil leachability on measures of nitrate in
groundwater were significant (F = 51.34, p < 0.001). Application of the model to the
validation dataset demonstrated a positive correlation between predicted and observed
nitrate concentrations (r = .530, p < 0.05). Measures of model bias using both training
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and validation datasets demonstrated strong model performance. However, use of
these two predictors in the final LUR model explained only 15% of the variance in
nitrate concentrations. Application of the final LUR model to the DWTA dataset
demonstrated a positive correlation between predicted and observed nitrate
concentrations (r = 0.278, p < 0.01). However, this correlation was small.
Conclusions: Based on the significant correlation between predicted and observed
nitrate concentrations, interpretation of DWTA data as representing area-wide well
water quality is weakly supported. The validity assessment of DWTA data for use in a
sentinel surveillance system is contingent on the ability of the LUR model to
adequately predict nitrate concentrations in groundwater used by private wells. Given
the small fraction of variance explained by the final LUR model, improvements are
needed to the model before the DWTA data can be considered truly valid as ―sentinel‖
indicators. One option for increasing the overall predictive ability of the model would
be to include variables representing proximity to septic systems, well age or depth as
these factors also influence well susceptibility to nitrate contamination.
In addition, datasets representing agricultural nitrogen inputs used for the LUR
model rely on voluntary or unenforced reporting, suggesting that these data may be
affected by gaps or inaccuracies. Given the numerous studies demonstrating that
increases in groundwater nitrate are a result of agricultural practices, effective and
enforced policies are needed to ensure the collection of reliable data tracking the
amounts of land-applied fertilizer and manure to small-scale units. From the public
health perspective, surveillance of agricultural nitrogen as a potential health hazard
will enable accurate and effective surveillance of associated exposures in private well
water and facilitate linking this hazard to adverse health outcomes.
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INTRODUCTION
The federal Safe Drinking Water Act (SDWA) ensures that public supply systems
in the U.S. provide water that meets public health standards. However, no such
protection exists for over 43 million Americans who obtain their household drinking
water from private wells and other small water systems (USCB, 2009). Concern for
water safety is warranted as the majority of both public and private wells draw from
groundwater aquifers, and numerous studies demonstrate that these water sources are
affected by a range of contaminants (DeSimone, 2009; Dubrovsky et al., 2010; Fram
& Belitz, 2011; Greene et al., 2005; Lee et al., 2002; Rowe et al., 2007). Nitrate is
often the most prevalent and widespread contaminant found in groundwater used for
household consumption (Burkart & Stoner, 2002; Dubrovsky et al., 2010; Nolan et al.,
2010; Warner & Arnold, 2010). While nitrate can occur naturally in groundwater,
concentrations greater than 1 milligram per liter (mg/L) are likely due to
anthropogenic inputs, particularly agricultural activities (Dubrovsky et al., 2010;
Warner & Arnold, 2010; WHO, 2011). A recent report by the United States
Geological Survey (USGS) on nutrients in groundwater found that nitrate
concentrations exceeded background levels in 64 percent of aquifers studied. Median
nitrate concentrations were highest in groundwater located beneath agricultural areas
(Dubrovsky et al., 2010), ostensibly as land applications of nitrogen from manure and
synthetic fertilizer are leading sources of nitrate contamination in groundwater
(Hansen et al., 2011; Puckett & Cowdery, 2002). A 2009 USGS report on private
wells states that more than 4% of wells located within 30 aquifers used for drinking
water had nitrate concentrations greater than 10 mg/L, the maximum contaminant
level (MCL) allowed for public water administered under the SDWA (DeSimone et
al., 2009).
The National Center for Environmental Health at the Centers for Disease Control
and Prevention (CDC) has recognized the need for comprehensive action to ensure
private well safety through surveillance and other methods and to this end has
convened a national workgroup to address existing challenges (Backer & Tosta, 2011).
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This workgroup has identified as a fundamental goal the need to develop, organize and
track data on private wells, given the current lack of relevant information sources.
Statewide private well datasets of verifiable quality may also be valuable additions to
the CDC’s National Environmental Public Health Tracking Program, which compiles
environmental data for tracking and linking exposures to adverse health outcomes
(CDC, 2006).
Oregon’s Domestic Well Testing Act (DWTA) represents a state-level policy
enabling collection of private well water data that may be useful for supporting public
health surveillance of exposures and adverse health outcomes related to private wells.
The DWTA requires that an owner of a property with a private well test the water for
nitrate, arsenic and coliform bacteria upon accepting a purchase offer on the property
(DHS, 2009). The owner must share the results of the test with the buyer as well as
with the Oregon Health Authority (OHA), thus providing this agency with valuable
information on the State’s private wells with little overall cost to taxpayers. In
particular, these data may function as part of a sentinel surveillance system, wherein
reports of contamination at one or a few well locations may signal more far-ranging
contamination and thus an increased risk to area neighbors for which well water
information may not be available. However, given that compliance is not enforced or
audited and sampling is often not performed by qualified third-party professionals, the
information collected may not meet data standards defined for public health
surveillance systems (Hoppe et al., 2011).
Standards for assessing these systems are described in the CDC’s Updated
Guidelines for Evaluating Public Health Surveillance Systems and are premised on
attributes such as sensitivity, validity, completeness and representativeness (German et
al., 2001). Niskar (2007) used the attributes described in the Updated Guidelines for
assessing the use of total trihalomethane (TTHM) data from the Safe Drinking Water
Information System (SDWIS) in public health surveillance. Results of this effort
highlighted various challenges inherent in using environmental data for public health
applications. For example, there is a lack of information on actual levels of TTHMs,
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which are reported to SDWIS only when the TTHM levels are out of compliance. To
be useful for public health surveillance TTHM levels would be recorded in SDWIS
whether in or out of compliance to serve as an early-warning system, facilitating an
understanding of the magnitude of risk to the exposed population and planning for
public health response (Niskar, 2007).
The aim of the current study is to evaluate private well water data collected under
Oregon’s DWTA for use in a public health surveillance system in part by assessing a
key attribute, validity. According to the Updated Guidelines, validity can be measured
by comparing recorded data in the surveillance system to ―true‖ values through a
review of sampled data or a special record linkage. For the current study, we assessed
the validity of DWTA data by comparing reported nitrate concentrations to levels
modeled for the well location through an exposure prediction technique known as land
use regression (LUR).
LUR can predict fine scale spatial variation in contaminants by combining
monitoring data at a number of locations with spatially refined explanatory variable
datasets, usually obtained through geographic information systems (GIS) (Hoek et al.,
2008; Mölter et al., 2010). The resulting statistical model can then be applied to a
large number of unsampled locations in the study area. Various forms of LUR
methodology have been used in a number of groundwater contamination studies
(Aelion & Conte, 2004; Greene et al., 2005; Liu et al., 2005; McLay et al., 2001;
Meliker et al., 2009; Skeppstrom & Olofsson, 2006). For example, Greene et al (2005)
developed a spatial statistical methodology for determining the probability of nitrate
concentrations above various thresholds in groundwater for a region of the MidAtlantic. Explanatory variables were represented by land use and other environmental
data including fertilizer and manure inputs, land cover and geology type.
The quality of LUR-based exposure estimates relies largely on coverage and the
quality of the geographic data that support the LUR statistical model. Information
extracted from land-use datasets is often limited by the resolution of these datasets.
While coverage for the LUR developed in this study is statewide, we attempt to
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improve exposure estimates by using finely resolved datasets specific to Oregon farms
and farming practices to quantify and allocate nitrogen inputs for the explanatory
variables. We hypothesized that as soil leachability and agricultural nitrogen inputs to
groundwater increase, nitrate concentrations in sampled wells will also increase.
The specific objectives of this study were: (1) to develop a LUR model for predicting
nitrate concentrations in private well water based on groundwater, agricultural, and
environmental data, and (2) to use the LUR model to evaluate the validity of Oregon’s
DWTA data for use in a sentinel surveillance system for monitoring exposures to
private well contaminants.
MODEL DEVELOPMENT
LUR predictor variables
We began by constructing a spatial database for Oregon with information to
support development of three predictor variables: 1) nitrogen from manure land
applications (manure-N) (Figure 5.1), 2) nitrogen from fertilizer land applications
(fertilizer-N) (Figure 5.2), and 3) a ranking of soil sensitivity to nitrate leaching
(SSNL) (Figure 5.3). These variables are widely recognized as the major predictors of
nitrate contamination of groundwater (Dubrovsky et al., 2010; Hansen et al., 2011;
Lake et al., 2003; Puckett & Cowdery, 2002; Slaton et al., 2004). A complete
description of the datasets used to represent these variables and the procedures for
modeling the information relative to well locations are provided in the accompanying
paper (Hoppe et al., to be submitted). In brief, ArcInfo 10.0 (ESRI, 2010) was used to
combine geospatial data on land use provided by the National Agricultural Statistics
Service 2007 Oregon Cropland Data Layer (USDA, 2011) with information
representing SSNL, manure-N and fertilizer-N. The later two datasets were derived
based on methods by Ruddy and others (2006), which were adapted and incorporated
with information specific to Oregon farms and farming practices to quantify and
allocate nitrogen inputs to land surface. By using these Oregon-specific datasets, we
108
were able to model manure-N and fertilizer-N quantity and distribution to the spatial
resolution of the CDL data (56 x 56 meters).
Figure 5.1. Geographical distribution of manure-N across Oregon. Darker areas
represent higher predicted manure-N loading on land surface.
The SSNL dataset is a geospatial dataset, developed by the Oregon Department of
Environmental Quality (DEQ), which uses methods of the Oregon Water Quality
Decision Aid (Huddleston, 1998) on data from the Natural Resources Conservation
Service’s Soil Survey Geographic Database (SSURGO) and the U.S. General Soil
Map (STATSGO2). The result is a five-tier ranking of predicted soil sensitivities to
leaching by nitrate (very high to very low) with a statewide coverage (Seeds, 2011).
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Figure 5.2. Geographical distribution of fertilizer-N across Oregon. Darker areas
represent higher predicted fertilizer-N loading on land surface.
LUR dependent variable
The dependent variable used to develop the LUR model included nitrate as
nitrogen concentrations sampled throughout Oregon by the DEQ between 2000-2007
(Figure 5.4). This time frame was selected as being in agreement with the temporal
extent of the predictor variables. The DEQ collects a large amount of groundwater
quality data in order to identify contamination threats, evaluate trends and model
proposed actions. Sites sampled include over 500 domestic wells. DEQ studies over
the past 20 years have found that over 75% of study areas show some impairment of
groundwater quality, most commonly due to nitrate contamination (DEQ, 2011).
When multiple samples were available for a single site over the ten year period
relevant to this study, sample results were averaged so each geographic location was
associated with a single nitrate concentration. As these data did not follow a normal
distribution, concentrations were logarithmic transformed prior to statistical analysis.
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Figure 5.3. Geographical representation of soil sensitivity to nitrate leaching across
Oregon. Darker areas represent higher leaching potential.
Geographic analysis
The geographic analysis of predictor variables was based on circular buffer zones
assigned to each DEQ sample location. Several studies have shown that there is a
significant relationship between groundwater quality and types of land use near a
sampled well that is mediated by the size of the land use buffer around the well
(Barringer et al., 1990; Cain et al.,1989; Greene et al., 2005; Hay & Battaglin, 1990;
Tesoriero & Voss, 1997). In developing the LUR model, all three predictor variables
were used to determine an optimal radius for establishing a buffer (i.e. an area of
influence) around each sample location, given that all three are linked to land use and
dependent on the spatial area around the well. The optimal buffer was determined by
examining how well predictor variables at different radii fit nitrate data obtained at
each location.
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Figure 5.4. DEQ wells sampled for nitrate as nitrogen between 2000-2007.
Multivariate linear regression analyses were conducted for 20 buffer zones created
with radii that ranged from 250 to 5,000 meters in 250-meter steps. Within each buffer
zone separate calculations were used (through a Python script written specifically for
this process) to derive a sum for fertilizer-N and manure-N and an average rank for
SSNL. These data were extracted and entered into the regression analyses and the
best-fit model was determined by finding the radius that resulted in the largest R2 and
a significant (defined as p < 0.05) F statistic. Results of these analyses indicate that a
radius of 1,000 meters contributed the most information to the model. This radius
matches or approximates radii used in similar studies investigating the impact of land
use on ground or well water quality (Barringer et al., 1990; Greene et al. 2005;
Eckhardt & Stackelberg, 1995; Hay & Battaglin, 1990).
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LUR Model Development
Prior to statistical analysis, the nitrate dataset was split at random into training and
validation (20% of total data) datasets. To adjust for very large values, the manure-N
and fertilizer-N datasets were standardized prior to weighting, by subtracting the mean
of the dataset from each individual value and then dividing the result by the standard
deviation of the mean. Assumptions of normality were not an issue with these datasets
given the large sample size (Lumley et al., 2002). Separate Spearman’s rank
correlation analyses showed significant (p < 0.01) correlations between nitrate
concentrations and fertilizer-N (r = .365), manure-N (r = .272), and SSNL (r = .105).
In preliminary analyses, we linearly regressed each predictor separately against
nitrate concentrations in the training dataset. Results of these univariate regressions
showed significant relationships between nitrate concentrations and both fertilizer-N
(b = .366, t(882) = 10.61, p < .001) and SSNL (b = .127, t(882) = 3.28, p < .001).
However, there was no discernible relationship between manure-N and nitrate
concentrations. We used a multivariate regression analysis to assess the relationship
between fertilizer-N, SSNL, and an interaction term against nitrate concentrations;
however, the interaction term was not significant.
Therefore, only fertilizer-N and SSNL were used to construct the final LUR model
and obtain the intercept and slope coefficients according to the following equation
(Tabachnick & Fidell, 2007):
where y is the observed log nitrate concentration in well i, x1 is the fertilizer-N value
associated with well i, x2 is the SSNL value associated with well i,
coefficient associated with fertilizer-N,
is the slope
is the slope coefficient associated with
SSNL, and α is the intercept coefficient.
Figure 5.4 demonstrates obvious spatial clustering of the DEQ data used for
calibrating the LUR model. Given that sample collection often occurs where there are
a priori concerns for groundwater contamination, this clustering is not surprising, but
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it required evaluation for spatial autocorrelation in the residuals of the model
(difference between observed and predicted nitrate concentrations). If present,
autocorrelation of residuals indicates that the estimates of effect are biased and
inferences based on the estimates might be misleading. To test for autocorrelation, the
Moran’s Index was applied, and the result was not significant, indicating that
neighboring points do not display greater similarity for nitrate levels than expected by
chance (Moran’s I=0.25, z = 0.87) (Moran, 1950).
Intercept and slope coefficients from the final LUR model (Table 5.1) were
applied to the validation dataset for purposes of model corroboration.
Variables
Coefficient
Std. error
Constant
Fertilizer-N
SSNL
-.813
.411
.222
.112
.035
.037
Standardized
coefficient
.378
.192
Table 5.1. Summary of final LUR model
In addition, model performance was assessed using a measure of model bias
(Gobas et al., 1998):
MB = 10^(∑ [log(Np/No)]/n),
where MB is the geometric mean of the ratio of predicted nitrate (Np) and
observed nitrate (No) concentrations. It is a measure of the systematic over- (MB > 1)
or under-prediction (MB < 1) by the model. For example, a MB of 5 indicates that the
observed values are overestimated by a factor of 5, while a MB of 0.2 indicates that
they are underestimated by a factor of 5.
One of the key characteristics of MB and its 95% confidence interval is that it
represents all sources of error, including errors in model parameterization and
structure, as well as spatial and temporal variability in data used for model
performance (Arnot & Gobas, 2004). Uncertainty in the bias estimator is expressed by
the 95% confidence interval (CI) of the distribution of log(Np/No). The larger the CI,
the greater the uncertainty of the model. Because (Np/No) is log-normally distributed,
the 95% CI can be presented as a constant factor of the mean. For example, if
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log(Np/No) has a mean of 0 (i.e. the geometric mean of Np/No is 1) and a CI of 0.3
and -0.3, then the model produces no systematic over- or under prediction of the
observed concentrations, i.e. MB = 1, and model predictions between 2 or 0.5 times
the geometric mean include 95% of the observed nitrate concentrations (Arnot &
Gobas, 2004). All statistical analyses were carried out in SPSS 19.0 (SPSS Inc., 2010).
Model Application to DWTA Data
The primary aim of this study was to evaluate whether data collected under
Oregon’s DWTA could be used for supporting a sentinel surveillance system for
monitoring exposures to nitrate in private wells. A detailed description of the data
collection process prescribed by the DWTA and concerns regarding quality and public
health applications for the data are provided by Hoppe and others (2011). Records of
nitrate concentrations from sampled private well water, including geographic
coordinates, were obtained from a database maintained by the OHA for the years
2000-2007 (Figure 5.5). This time period corresponds to the temporal extent of data
used in developing the LUR model. When a property was found to have more than one
water sample associated with an on-site well, the maximum recorded value was kept.
Only records with a quantified, nonzero nitrate concentration were used in the final
analysis.
Using ArcInfo 10.0 (ESRI, 2010), a geospatial dataset of the DWTA wells was
combined with the fertilizer-N and SSNL datasets. The same approach for geographic
analysis described above was followed, whereby a radius of 1000 meters was applied
to each well and from within each buffer a sum was derived for fertilizer-N and an
average rank of SSNL. Fertilizer-N data were standardized as described above.
Finally, the coefficients from the LUR model (Table 5.1) were applied to the fertilizerN and SSNL values of the DWTA data in order to predict nitrate concentrations.
Predicted nitrate concentrations were plotted against observed nitrate concentrations
and the Spearman’s rank correlation test applied to assess correlation.
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Figure 5.5. Geographical distribution of wells represented in the DWTA dataset,
2000-2007.
RESULTS
Results from the final LUR model regressing fertilizer-N and SSNL on nitrate
concentrations of the DEQ data were significant (Adj. R2 = 0.15, F = 76.66, p <
0.001). However, these two predictors explained only 15% of the variance in nitrate
concentrations. A key hypothesis of this study was that as agricultural nitrogen and
soil leachability increase, so do nitrate concentrations in private well water. Because
only a modest percentage of nitrate data variability was explained by the predictors,
there may be factors influencing nitrate in private wells that are not represented in the
LUR model. These factors are discussed in more detail below.
Nitrate concentrations predicted by the LUR model were plotted against observed
nitrate concentrations for both the training (Figure 5.6) and validation (Figure 5.7)
datasets.
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Figure 5.6. Predicted vs. observed nitrate concentrations in training dataset with
fitted regression line.
Results from the Spearman’s test demonstrate a similar moderate positive
correlation between predicted and observed nitrate concentrations in both the training
(r = .423, p < 0.05) and validation (r = .530, p < 0.05) datasets. The MB for the
training dataset was 1.002 with a 95% CI of 0.861-1.167, indicating only a slight overprediction of nitrate concentrations. The MB for the validation dataset was 0.999 with
a 95% CI of 0.749-1.331, indicating only a slight under-prediction of nitrate
concentrations. The assessment of bias in both the training and validation datasets
indicate that the extent of model bias is minimal in the final LUR model and shows
that the model can be used for predicting nitrate concentrations.
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Figure 5.7. Predicted vs. observed nitrate concentrations in validation dataset with
fitted regression line.
Assessment of DWTA Data
Results from Spearman’s tests demonstrate small significant correlations between
nitrate concentrations and fertilizer-N (r=.170, p < 0.01) and SSNL (r=.174, p < 0.01)
values in the DWTA dataset. Figure 5.8 shows the plot of observed nitrate
concentrations from DWTA wells against nitrate concentrations predicted with the
LUR model. Based on Spearman’s test there is a small significant correlation between
observed and predicted nitrate concentrations in the DWTA dataset (r = 0.278, p <
0.01).
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Figure 5.8. Nitrate concentrations predicted by LUR model vs. observed nitrate
concentrations in DWTA dataset, with fitted regression line.
DISCUSSION
In their review of the top U.S. drinking water challenges in the 21st century, Levin
and others (2002) recognize that due to the absence of any systematic monitoring,
millions of U.S. residents on private wells are particularly vulnerable to long-term,
undetected exposures to water contamination. Despite growing awareness for the
potential health risks related to private well water consumption (Borchardt et al., 2003:
Charrois, 2010; Denno et al., 2009; Gatto et al., 2009; Rogan & Brady, 2005; Ward,
2009), there is a significant lack of data available to public health and environmental
professionals who are tasked with identifying relevant contaminant sources and
mitigating exposures (Backer & Tosta, 2011; Rowe et al., 2007; Van Grinsven et al.,
2006).
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Oregon’s DWTA is a policy mechanism for collecting continuous statewide data
on private well water quality that could be used to meet the widely recognized need
for identifying trends and ―hot spots‖ of contamination (Hansen et al., 2011; Rowe et
al., 2007). We developed a LUR model based on fertilizer nitrogen inputs and soil
leachability to assess the validity of DWTA data for representing ―true‖ nitrate
concentrations in private wells at any location across Oregon. Results of our LUR
model are supported by previous research demonstrating a significant relationship
between agricultural fertilizer, soil characteristics, and nitrate concentrations in
groundwater (Dubrovsky et al., 2010; Hansen et al., 2011; Masetti et al., 2008; Puckett
& Cowdry, 2002). Applying the LUR model to the DWTA data demonstrates a small
but significant correlation between predicted nitrate concentrations and those observed
in the DWTA dataset. The significance of the correlation suggests that DWTA data
may be valid for use in a sentinel surveillance system, such that evidence of
contamination in a single well may indicate contamination in wells located within
1000 meters. However, the correlation between predicted and observed nitrate
concentrations in the DWTA dataset was small. In addition, the validity assessment of
DWTA data is contingent on the ability of the LUR model to adequately predict
nitrate concentrations in groundwater used by private wells. Given the small fraction
of variance (15%) explained by the final LUR model, improvements are needed to the
model before the DWTA data can be considered valid as ―sentinel‖ indicators without
corroboration by higher quality datasets.
One option for increasing the overall predictive ability of the model would be to
include a variable representing septic systems. Leaking septic systems have been
recognized as a major point source contributor of nitrate and other contaminants to
groundwater and private wells (Aelion & Conte, 2004; Borchardt et al., 2003; Denno
et al., 2009; Gold et al., 1990). In a study assessing land use and groundwater impacts
on Cape Cod, MA, Swartz and others (2003) found poor correlations with fertilizer
use in areas of influence around study wells. They suggest that this poor correlation
may indicate that nitrate input from residential sources such as septic systems and
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lawn fertilizer overwhelms that from agricultural practices. We explored a variety of
datasets and modeling approaches for representing nitrogen inputs from private septic
systems, such as using U.S. Census data, city light data from the National Aeronautics
and Space Administration (NASA), and urban growth boundaries to model rural
residency given that septic systems are more prevalent in rural areas (Aelion & Conte,
2004). However, none of these datasets were sufficient to meet our criteria for
statewide coverage or spatial resolution and results from exploratory analyses were
equivocal.
Other factors relating directly to well construction, such as depth and age, have
also been recognized as influencing well water quality (Aelion & Conte, 2004; Fram
& Belitz, 2011; Liu et al., 2005; Rowe et al., 2007; Tesoriero & Voss, 1997). Using a
form of LUR to investigate perchlorate contamination in public supply wells, Fram
and Belitz (2011) reported that well construction characteristics were more a
determinant of contamination in their models than land use. It is possible that
inclusion of variables representing these well specific characteristics would
significantly improve the predictive ability of our model. While Oregon does maintain
a well log database which contains information on depth, age and other details of well
construction (OWRD, 2007), presently it is difficult to link this information to
corresponding wells in the DWTA dataset in a manner that is consistent and reliable.
The difficulty in obtaining data that are accurate and reliable for ―small-area‖
environmental public health studies is discussed in depth in an extensive review of
design issues attending high resolution spatial studies by Elliot and Savitz (2008).
While such studies offer more analytic control over geographically based confounders,
and are thus are more effective in isolating environmental exposures, modeling to
small area units often requires multiple data inputs and intensive data preparation
similar to those we experienced. Each additional data layer and any accompanying
refinement add incremental uncertainty, increasing the potential for error, missing
values, and bias. For example, inadequate data used for modeling proxy measures of
contamination and exposure can lead to exposure misclassification. Non-differential
121
exposure misclassification will generally lead to a statistical bias toward the null
hypothesis, which could lead to false-negative findings and false reassurance about
exposures to contaminants and potential health effects (Elliot & Savitz, 2008).
The predictive ability of the LUR model may also be affected by inadequate or
inaccurate information in the predictors. The major datasets used to characterize both
fertilizer and manure nitrogen inputs were based on information submitted voluntarily
or via policy mechanisms that due to a lack of enforcement, clarity or compliance have
substantial gaps, weakening their utility for research (Hoppe et al., 2011; Hoppe et al.,
submitted). Given the numerous studies demonstrating that increases in groundwater
nitrate are a result of agricultural practices (Liu et al., 2005; McLay et al., 2001;
Puckett & Cowdery, 2002; Shomar et al., 2008; White et al., 2012), effective and
enforced policies are needed to ensure the collection of reliable data tracking the
amounts of land-applied fertilizer and manure to small-scale units. From the public
health perspective, surveillance of agricultural nitrogen as a potential health hazard
will enable accurate and effective surveillance of associated exposures in private well
water and facilitate linking this hazard to adverse health outcomes. (Thacker et al.,
1996).
CONCLUSION
The aim of this study was to validate DWTA test data for public health
surveillance given that there are issues potentially affecting the quality of this dataset,
including 1) compliance with the DWTA is not enforced or audited; 2) sampling is
often not performed by qualified professionals; 3) evidence of contamination may
adversely impact sale negotiations, providing incentive for submitting false samples;
and, 4) data entry procedures have only recently been standardized (Hoppe et al.,
2011). Use of the LUR model developed in this study weakly substantiated DWTA
data for use in a sentinel surveillance system. However, supplemental data are needed
to fully corroborate an assessment of area-wide water quality before resources are
allocated to public health actions. Some of the data quality issues affecting the
122
DWTA data may also be affecting the quality of data used to develop the LUR model,
in particular compliance with laws requiring data submission or auditing of data
collected voluntarily.
Our efforts to model nitrate contamination of private well water in Oregon
demonstrate some of the difficulties obtaining, preparing, and applying environmental
data to public health investigations. The task of ensuring safe private well water for
the millions of individuals consuming private well water on a long-term, daily basis
requires an in-depth, engaged collaboration between public health practitioners and
colleagues with expertise in environmental science, agriculture, well construction,
geographic modeling, and statistical analysis. Given the complexity in designing
sampling plans, analyzing data, and enacting effective regulatory or policy
mechanisms to support these efforts, cross-disciplinary and cross-agency cooperation
is crucial. Ultimately, legal and programmatic structures are needed to ensure
collection of high quality data through reporting systems with an auditing function to
ensure compliance and submission of true information.
123
CHAPTER 6 – SUMMARY AND CONCLUSIONS
This research addresses a recognized need for the identification and evaluation of
datasets that can be used to characterize health risks associated with contaminants in
private well water (Backer & Tosta, 2011; Manassaram et al., 2006). Oregon’s
Domestic Well Testing Act links testing to real estate transactions enabling continuous
collection of well data by the State. This research represents the first in-depth effort to
explore the use of these data for characterizing exposures to contamination in private
wells across Oregon, involving both an assessment of data quality as well as the
DWTA policy itself. In the process of validating DWTA data as ―sentinel‖ indicators
of area-wide contamination, methods were advanced for fine resolution spatial
modeling of agricultural nitrogen inputs to groundwater. While results of this research
statistically validate DWTA data for use in a sentinel surveillance system, evidence of
gaps and inaccuracies in the datasets used for the assessment and within the DWTA
dataset itself, highlight the need for improving or implementing reporting systems that
collect reliable information relevant to hazards, exposures or health risks associated
with private wells.
124
CONCLUSIONS – MANUSCRIPT 1
Private Well Testing in Oregon from Real Estate Transactions: An Innovative
Approach Toward a State-Based Surveillance System
This research determined the prevalence of nitrate and coliform bacteria
contamination in Oregon private wells using well test data collected under Oregon’s
DWTA in order to understand potential contaminant exposures to well users. In
addition, application of DWTA test data to this exposure characterization provided an
opportunity to evaluate provisions of the DWTA law and make comparisons to similar
legislation in other states. The major findings of this research are:
(1) Statistically significant clustering of high nitrate concentrations suggests that
when interpreting DWTA well test data evidence of contamination in a single well
may be indicative of area wide contamination. This finding was the impetus for further
research conducted and described in chapter 4 and 5.
(2) Forecasts of population growth in counties with significant clustering of
contaminated wells suggest that the number of individuals exposed to private well
contaminants will increase over time. In addition, socioeconomic and demographic
factors, such as age, income, and English proficiency, may increase the risk of adverse
health outcomes for many of these individuals. This finding was explored further in
research conducted and described in Appendix D.3.
(3) Many contaminated wells represented in the DWTA are not captured within
the boundaries of a GWMA demonstrating that current efforts to mitigate exposure to
aquifer contaminants are not reaching all affected populations.
(4) While the DWTA generates continuous statewide private well water data for
State agencies at a modest overall cost, the quality of the data for use in public health
efforts may be compromised by weaknesses inherent in the language of the law. A
comparison of the DWTA with legislation in other states suggests that data quality
would be strengthened by making real estate sales contingent on testing, implementing
electronic reporting of results by laboratories, broadening the legal scope to include
125
rental properties, ensuring test result confidentiality and improving education outreach
activities.
MANUSCRIPT 2
Modeling Nitrate Contamination in Private Wells for Understanding Human
Health Risk. – Part I: Refining Spatial Resolution of Agricultural Nitrogen
Public health specialists and water quality managers in Oregon need an
understanding of the condition of groundwater and the location of contamination in
order to protect source water and human health, especially for residents relying on
private wells. In particular, characterizing the influence of land-applied manure and
fertilizer, generally recognized as the most significant contributors to nitrate
contamination of groundwater, would be especially useful for identifying areas where
private well water safety may be an issue.
This research developed high resolution datasets with full statewide coverage in
order to model spatial fluctuations in nitrogen loading from agricultural sources and
characterize groundwater contamination at point locations where private wells exist or
may be constructed in the future. Methods published by USGS researchers for
estimating nutrient loading at the county level were adopted and incorporated with
state-specific datasets in a novel approach for estimating and distributing nitrogen
from manure and fertilizer sources.
The major findings of this research are:
(1) The amount of nitrogen applied to Oregon agricultural land from fertilizer
sources has increased statewide 65% over previous estimates by the USGS for 2001.
In addition, the amount of nitrogen from manure sources has increased 13% over
previous estimates by the USGS for 1997. Applications of fertilizer to cropland
contribute a greater amount of nitrogen than applications of manure to both cropland
and pastureland across Oregon.
126
(2) Using published USGS methods, state estimates of manure nitrogen were 63%
greater than estimates using data collected through Oregon’s CAFO permitting
program. With regard to fertilizer nitrogen, state estimates using USGS methods were
52% greater than estimates derived using expert guidance from EBS published by
OSU-Extension. Data are needed to accurately quantify and locate actual manure and
fertilizer inputs to land surface at a small-scale. However, the large differences
between nitrogen estimates using USGS methods compared to estimates derived from
the CAFO or EBS datasets may indicate that these state-specific information sources
are inadequate for modeling hazards relevant to private well point locations. This
finding was explored further in research conducted and described in chapter 5.
(3) The distribution of manure and fertilizer nitrogen was based on crop
identification in the OR-CDL. The 2007 OR-CDL consistently underestimated the
total amount of cropland across Oregon counties by an average of 20 percent
compared to cropland estimates in the 2006 NLCD. For some counties where cropland
was dramatically underestimated, this led to extreme loading values modeled on a
small area of land. While the accuracy of future CDL iterations will continue to
improve, it is recommended that researchers using CDL data consider verifying
cropland identification with external datasets, such as the NLCD or Google Earth.
(4) Risk maps developed from this research indicate that nearly 50% of recently
drilled wells in Oregon may be susceptible to nitrate contamination stemming from
agricultural nitrogen inputs and soil leachability. Predicted increases in population
density across the state (described in chapter 3) and the steady addition of
approximately 3,800 new wells each year suggest that arable land affected by longterm nitrogen inputs will be increasingly transitioned to residential use. Residents who
have built on agricultural land not serviced by public utilities may face long-term,
costly treatment and testing of private well water to ensure that the water meets healthbased standards, particularly for nitrate. Planners, developers, and home buyers should
be aware of these costs when considering the location of homes or residential
developments without access to treated and monitored public water supplies.
127
Recommended policies to ensure safe water for home owners include required testing
of all newly constructed wells and proof of a potable water supply as part of permit
approval for development.
(5) Delineation of a GWMA is a major mitigation effort in Oregon addressing
groundwater contamination. However, our analysis indicates that the existing
boundaries of the GWMA located in the southern Willamette Valley do not include all
of the susceptible wells in the area. Given that the designation of a GWMA triggers
activities to educate affected residents and mitigate contamination, it may be
advantageous from a risk reduction perspective to consider expanding the boundaries
of this GWMA, specifically to include susceptible wells to the north.
Overall, results of this modeling work can assist public health practitioners, land
use planners, and water quality managers in decision making regarding well
permitting, water protections, and implementation of well stewardship programs and
education outreach.
MANUSCRIPT 3
Modeling Nitrate Contamination in Private Wells for Understanding Human
Health Risk. – Part II: Application of a Land Use Regression Model to Well Test
Data
Oregon’s DWTA represents a state-level policy enabling collection of private well
water data that may be useful for public health applications. This research evaluated
the validity of DWTA well test data for supporting a sentinel surveillance system for
monitoring exposure to nitrate in Oregon private wells. This evaluation was conducted
by assessing the correlation of observed nitrate concentrations in the DWTA dataset
with predicted concentrations derived from a land use regression (LUR) model that
incorporated datasets presented in chapter 4.
The major findings of this research are:
128
(1) DWTA well test data are significantly correlated with nitrate concentrations
predicted by a LUR model based on fertilizer nitrogen inputs and soil leachability.
This suggests that a report of nitrate contamination in a single well from this dataset
may be indicative of contamination in neighboring wells located within 1000 meters.
However, as the correlation was modest and the fraction of variance explained by the
final LUR model was small, the conclusion from this study is that DWTA data were
shown to be significant but weak indicators of area-wide private well water quality.
These findings indicate that the DWTA dataset can provide useful information on
localized well contamination, but supplemental data are needed to fully corroborate
assessments of area-wide water quality before resources are allocated to public health
interventions.
(2) The small fraction of variance associated with the final LUR model indicates
that there are other factors associated with nitrate concentrations in well water that
were missing from the model, such as septic systems. This study recommends the
development of a reliable comprehensive statewide dataset on private septic systems
for inclusion in future modeling efforts on private well contamination.
(3) Inclusion of well depth and age, additional factors associated with well water
quality, may also increase the fraction of variance explained by the LUR model and
contribute to understanding threats to well water. This information is available on well
logs submitted and maintained by the OWRD, but linking this information to wells in
the DWTA dataset is difficult given that there are few common fields between the
datasets that are reliably populated. Use of a single unique identifier for every well in
Oregon would certainly enable data linkage. However, the process of labeling
individual wells has been altered over time, introducing errors and gaps, and currently
the process is still underdevelopment. In addition, while the DWTA reporting form
requests a well identification number, many well owners are not aware of how to
obtain this number and leave the request blank or provide an inaccurate number. This
highlights another source of uncertainty in this study associated with the DWTA
reporting system. All the information on the DWTA reporting form used to identify
129
and locate wells is provided voluntarily by well owners, and currently there is no staff
or budget to follow-up on missing data or ensure the accuracy of submitted
information. Recommended improvements to the DWTA reporting system presented
in chapter 3, such as amending the law so sales are contingent on testing,
implementing electronic reporting, and improving education outreach to realtors and
well owners, are likely to improve completeness and accuracy of the forms and
consequently expand use of these data in future public health efforts. However, even
with these improvements, accurately linking DWTA data to other datasets with
relevant well information, such as the OWRD, will not be possible until a system is in
place that is successful in assigning unique well labels to each well in the State.
(4) There was statistically significant evidence for the hypothesis that nitrate
concentrations in private well water increase as agricultural nitrogen and soil
leachability increase. However, this relationship was true only for fertilizer nitrogen
inputs; manure nitrogen inputs were not significantly associated with nitrate in well
water in univariate regression analyses. These findings have implications for where
emphasis should be placed in managing and monitoring farming practices to protect
groundwater.
(5) The small fraction of variance explained by the LUR model may also be a
result of gaps or inaccuracies in the datasets used to distribute agricultural nitrogen to
a finer spatial resolution relevant to private wells across Oregon than has been seen in
the scientific literature. Data are needed to accurately quantify and locate actual
manure and fertilizer inputs to land surface at a small-scale. Information from
Oregon’s CAFO permit was useful for locating permitted farms and estimating
manure nitrogen input from the type and number of reported animals. However,
although the definition for a state CAFO is broad, many farms are missing from this
dataset, either because they were not covered by the definition in statute or were noncompliant with the permitting regulation. Information from OSU-Extension EBS was
valuable for calculating the amount of nitrogen from fertilizer recommended for crops
that were identified in the OR-CDL. Given the widely recognized problem of applying
130
fertilizer at rates that exceed agronomic need, use of EBS to distribute nitrogen is
likely to underestimate the true amounts applied in the field. The weaknesses of using
these two datasets to guide the characterization of agricultural nitrogen use highlights
the need for implementing or improving reporting systems for collecting data on
fertilizer and manure land applications at a small-scale, given the impact these sources
have on drinking water sources.
FINAL CONCLUSION
The research presented in this dissertation demonstrates the strengths of state-level
well testing legislation for addressing low data availability on private wells and
ensuring monitoring and testing efforts. DWTA data were shown useful for
identifying ―hot spots‖ of nitrate and coliform bacteria contamination in well water
and calculating prevalence estimates; investigating the relationship between these two
hazards as well as their connection to well depth, to further understanding of
multicontaminant exposures in well water and factors linked to well vulnerability, and
describing the socioeconomic and demographic status of individuals and communities
relying on vulnerable wells in order to tailor education and outreach efforts to those
most at-risk for adverse health impacts.
However, the technical applications of DWTA data highlight the need for quality
improvements before these data can be considered truly reliable for supporting either a
state-level surveillance system or the national EPHT network. Predictive modeling
demonstrated the need for trustworthy data that can assist with characterizing the
major threats to private well water quality, in particular land-applied manure and
fertilizer and on-site septic systems. The research underscores the need for
implementing reporting systems that that are regularly audited for compliance and
quality, especially given the forecasted increase in fertilizer and manure use alongside
the growing demand for clean water sources.
Understanding health risks to private well users requires an approach integrating
public health with environmental science, agricultural management, statistics, and GIS
131
modeling. This dissertation was based on input from a broad range of experts,
demonstrating the need for environmental public health scientists to be
interdisciplinary in their approach to problem-solving and at least familiar with, if not
skilled, in applying the data and methods of other science areas to mitigating health
risks from environmental hazards.
132
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158
APPENDICES
159
APPENDIX A
Flowchart of DWTA data collection process
160
161
APPENDIX B
Published version of manuscript 1 in Public Health Reports. 2011 Jan-Feb; 126(1):
107-115.
162
163
164
165
166
167
168
169
170
171
APPENDIX C
Conceptual diagram of data modeling for manuscripts 2 and 3
172
173
APPENDIX D
Additional research questions
174
D.1. Research Question: Are elevated nitrate levels related to positive coliform
bacteria detections in Oregon private wells?
Introduction
Oregon residents who use private wells for drinking water are particularly
vulnerable to multicontaminant exposures given that private wells are exempt from the
systematic monitoring required for public wells under the Safe Drinking Water Act
(SDWA) (Levin et al, 2002), and contaminant mixtures in groundwater are prevalent
(DeSimone et al., 2009; Dubrovsky et al., 2010; Squillace et al, 2002). Characterizing
the mixture of contaminants commonly found in private well water is essential for
identifying potential health effects and addressing contaminant sources (Squillace et
al, 2002). Nitrate and bacteria are the contaminants most often found to exceed health
criteria in both private and public water supplies (DeSimone, 2009; Dubrovsky et al.,
2010; Squillace et al, 2002). While exposure to these contaminants in groundwater
may not be the most toxic, the complex interaction of factors controlling the fate of
these contaminants in groundwater is poorly understood and at present difficult to
predict in some environments (WHO, 2006). Both contaminants may result from
sewage or animal waste. However, nitrate in groundwater may also stem from
agricultural use of synthetic fertilizers. Rogan and others (2009) suggest that high
levels of nitrate alone in groundwater indicate fertilizer as the source of contamination,
while nitrate co-occurring with detectable bacteria in groundwater indicate sewage or
manure as the source. Simpson (2004) supports this hypothesis stating that the
presence of indicator bacteria, such as total coliform bacteria, in a water sample is
considered an indication of sewage contamination.
An investigation into the presence of nitrate and bacteria in drinking water may
also assist in understanding the etiology of waterborne disease. Avery (1999) and
Powlson and others (2008) discuss the hypothesis that methemoglobinemia and other
diseases associated with drinking water nitrate in actuality have an infectious etiology.
Gastroenteritis caused by waterborne bacteria elicits the production of nitric oxide by
175
infected tissues, which triggers the same putative mechanisms attributed to ingested
nitrate. Avery (1999) suggests that nitrate contamination is notable merely as an
indicator of bacterial contamination given that both nitrate and bacteria can stem from
the same sources. However, Cam and others (2008) found no general correlation
between nitrate concentrations and coliform numbers in private wells, leading the
researchers to conclude that nitrate was not a good indicator of bacterial contamination
in well water. The authors do note that in most cases where nitrate levels were highest,
bacterial counts were also very high.
The objective of this analysis is to examine the relationship between nitrate
concentrations and positive detections of bacteria in Oregon private well water as part
of a comprehensive exposure characterization. Results from this analysis may also be
useful in future research investigating sources of contamination of private wells and
the causes of waterborne disease.
Methods
Data used for this analysis were obtained from the Oregon Health Authority’s
(OHA) Domestic Well Testing Real Estate Transaction (RET) database. The RET
contains information submitted to the OHA under the Domestic Well Testing Act
(DWTA), which requires that an owner of a property with a private well used for
household drinking water must test for nitrate, total coliform bacteria, and arsenic
upon accepting an offer to purchase the property (Hoppe et al., 2011). Results of this
testing must then be shared with the buyer and submitted to the OHA. The DWTA
requires that well water samples are sent to state-accredited laboratories that adhere to
protocols ensuring quality analyses and data.
Sample results used for this analysis included measures of nitrate and total
coliform bacteria submitted to the OHA in 1989-2009 and obtained from the RET
database. Data entry protocols for the RET database have only recently been
standardized. Previously, nitrate measures were entered as a specific concentration,
less than a detection limit or threshold, or as nondetect. The large majority of total
176
coliform bacteria results were entered as either ―positive/negative‖ or
―present/absent‖. For this analysis, nitrate measures were converted to two separate
datasets and used in separate analyses with the total coliform bacteria dataset (Table
D.3a.).
Data
Number
of
records
% at
or
above
SMT
% at
or
above
MCL
%
positive
for
bacteria
%
bacteria
with no
nitrate
N1
N2
21,701
15,536
4
6
2
3
11
11
10
10
%
bacteria
with
nitrate
at SMT
12
12
%
bacteria
with
nitrate
at MCL
13
13
Table D.3a. Characteristics of Datasets N1 and N2 and the probabilities of nitrate and
coliform bacteria co-occurrence.
For one dataset (N1), all nondetects were changed to one-half the stated limit of
detection (LOD). Using one-half of the LOD for a contaminant en lieu of an
unquantified or nondetect result is a common approach used in water chemistry
(Helsel, 2006). As the LOD for nitrate in groundwater can range from 0.005 to 1.0
mg/L (R. Haynes, personal communication), the average was taken between these two
values and divided by two, and the resulting value of 0.5 mg/L was substituted for all
nondetects or unquantified nitrate values in N1 when an LOD was not stated. A
second nitrate dataset (N2) was produced by eliminating all nitrate measures that were
not explicitly quantified. Total coliform bacteria measures were converted to a binary
variable with 0 = negative and 1 = positive for the presence of contamination. Any
values within each dataset that were not easily nor obviously interpreted were
removed. Approximately 18% of homes had more than one sample listed for the home
address. A sample was considered a duplicate and eliminated only if it was taken on
the same date at the same well with the same result.
Two concentration levels for nitrate were of interest. In Oregon, the Groundwater
Quality Protection Act includes provisions leading to the designation of a groundwater
management area when nitrate levels in an aquifer are 7 mg/L or above (DEQ, 2011).
177
For the purpose of this analysis, this level is referred to as a state management
threshold (SMT). Under the SDWA, the maximum contaminant level (MCL) for
nitrate in drinking water in public supply wells is 10 mg/L (EPA, 2010). The SMT and
MCL standards were evaluated in analyses of N1 and N2. Summary characteristics of
the datasets are shown in (Table D.3a.).
Given that the nitrate data are negatively skewed and the coliform bacteria data are
dichotomous, logistic regression was the method used for both analyses (SPSS Inc.,
2010). This statistical method is not dependent on assumptions about the underlying
distributions of predictor variables, and predictors can be any mix of continuous,
discrete or dichotomous (Tabachnick & Fidell, 2007). The goal of analysis for logistic
regression is to predict the probability of a particular outcome given one or more
predictors (Tabachnick & Fidell, 2007). Applied to the current investigation, logistic
regression will predict the probability that given a particular nitrate concentration
(continuous independent variable), coliform bacteria test results would be either
positive or negative (dichotomous dependent variable).
Results
The total number of records used in the N1 analysis was 21,701. Approximately
4% of the nitrate samples contained nitrate at or above the SMT. Of these, 15% were
also positive for total coliform bacteria. Approximately 2% of the nitrate samples
contained nitrate at or above the MCL, and of these 17% were also positive for
coliform. Nitrate concentrations in the N1 dataset ranged from 118 to 0 mg/L with a
median value of 0.5 mg/L. Approximately 2,926 nitrate records entered as nondetect
and with an undisclosed LOD were converted to 0.5 mg/L. Logistic regression
analysis using the N1 dataset indicated that nitrate concentration is a statistically
significant, although weak, predictor for the presence of coliform bacteria in private
well water, (χ2(1) = 15.01, p = < 0.05; Nagelkerke R2 = 0.001; Hosmer and Lemeshow
Goodness of Fit Test, χHL2(8) = 91.25, p = < 0.05). Interestingly, there is a 10%
probability of a sample of well water that is free of any nitrate testing positive for
178
coliform bacteria. However, that probability only increases slightly when nitrate levels
are at the SMT of 7 mg/L (12%) or the MCL of 10 mg/L (13%). At the highest
concentration seen in the N1 dataset (118 mg/L), the probability of coliform bacteria
contamination would be nearly 70%.
The total number of records used in the N2 analysis was 15,536. Approximately
6% of the nitrate samples contained nitrate at or above the SMT. Of these, 15% were
also positive for total coliform bacteria. Approximately 3% of the nitrate samples
contained nitrate at or above the MCL, and of these 29% were also positive for
coliform. Nitrate concentrations ranged from 118 to 0 mg/L with a median value of
0.9 mg/L. Logistic regression analysis using the N2 dataset also indicated that nitrate
concentration is a statistically significant, although weak, predictor for the presence of
coliform bacteria in private well water, (χ2(1) = 16.02, p = < 0.05; Nagelkerke
R2 = 0.002; Hosmer and Lemeshow Goodness of Fit Test, χHL2(7) = 71.18, p = < 0.05).
The probabilities are nearly identical to those predicted using the N1 dataset (Table
D.3a). There is a 10% probability of a sample of well water that is free of any nitrate
testing positive for coliform bacteria, and the probability only increases slightly when
nitrate levels are at the SMT of 7 mg/L (12%) or the MCL of 10 mg/L (13%).
Discussion
While many private well sampling studies report the prevalence of either nitrate or
bacterial contamination in well water (Cam et al., 2008; DeSimone et al., 2009;
Dubrovsky et al., 2010; Hexemer et al., 2008; Kross et al., 1993), few report on the
relationship between these contaminants. Results of this analysis appear to statistically
support the hypothesis that given a sample of Oregon well water, as nitrate
concentration increases the probability of detecting bacteria in the same sample also
increases. However, results from analyses using N1 and N2 datasets suggest that even
when nitrate concentrations are below background levels (1 mg/L; Dubrovsky et al.,
2010), the probability of bacterial contamination is 10%, suggesting that bacterial
contamination is fairly prevalent in Oregon private well water based on the DWTA
179
dataset. Furthermore, since the probability of bacterial contamination only increased
2-3% when nitrate levels increased to the SMT and MCL, the presence of bacteria
does not appear to be a sensitive indicator of nitrate contamination in private well
water.
The potential data quality issues inherent in the RET dataset should be considered
when interpreting these results. These issues are discussed in detail in chapter 3 and
are mainly related to sample collection and data entry. Data entry protocols for the
RET database have only been recently standardized. Previously, nitrate measures were
entered as either a specific concentration, less than a detection limit or threshold, or as
nondetect. A dataset where specific values for some observations are not quantified is
often referred to as ―censored‖. Various techniques have been introduced for dealing
with censored datasets depending on the goal of the analyses. The two approaches
used in this analysis have limitations that could lead to the introduction of bias (Helsel,
2006). However, other approaches considered more desirable would not result in a
dataset that could be used to complete a logistical regression calculation with a binary
dataset, such as the coliform bacteria dataset (Helsel, 2006). Given that the predicted
probability of bacterial contamination is the same using either the N1 or N2 dataset
suggests that the results are robust.
180
D.2. Research Question: Are elevated nitrate levels or positive coliform bacteria
detections related to depth in Oregon private wells?
Introduction
In numerous national and statewide sampling surveys of private well water, nitrate
and coliform bacteria are found to be the most common contaminants found at levels
deemed unsafe for consumption (DeSimone et al., 2009, Dubrovsky et al., 2010).
According to a 2010 report by the USGS, levels of nitrate over 1 mg/L or the presence
of any number of bacterial microorganisms in groundwater are considered evidence of
contamination (Dubrovsky et al., 2010). Under the federal Safe Drinking Water Act
(SDWA), 10 mg/L is the maximum contaminant level (MCL) for nitrate allowed in
public water systems. While exposure to these contaminants in groundwater may not
be the most toxic, the complex interaction of factors controlling the fate of these
contaminants in groundwater is poorly understood and at present difficult to predict in
some environments (WHO, 2006). Research has shown that well depth may be a
significant factor influencing the level and/or presence of contamination (Cam et al.,
2008; Corkal et al., 2004; Goss et al., 1998; Liu et al., 2005; Mitchell et al., 2003;
Richards et al., 1996; Simpson, 2004; Squillace et al., 2002; Townsend & Young,
2000).
A large water sampling survey of rural private wells in Ontario found that shallow
wells (defined as sand point, dug or bored wells) had nitrate concentrations greater
than the MCL more often than drilled wells (Goss et al., 1998). Likewise, an analysis
of 747 water samples from domestic, irrigation, monitoring, and public water supply
wells in Kansas indicated that shallower wells generally have higher nitrate values
than deeper wells (Townsend & Young, 2000). Mitchell and others (2003) measured
nitrate levels above the MCL in both shallow and deep wells. Assessing nitrogen
isotope values, the authors concluded that the sources of contamination were different
for shallow and deep wells. Zimmerman and others (2001) tested 78 private wells in
close proximity to agricultural areas and found evidence of a positive association
181
between coliform bacteria detections and depth to water table. A sampling survey of
50 wells located proximal to septage sources found no association between incidence
of enteric viruses and well depth (Borchardt et al, 2003). In an extensive review of
Canadian rural water safety, Corkal and others (2004) report that wells that are 40
meters deep or less are generally considered equivalent to surface water supplies given
the equivalent risk to surface activities. Richardson (2009) found that domestic water
taken from surface water was much more likely to be contaminated than water from
groundwater. A study by Cam and others (2008) showed a significant difference in
nitrate levels and coliform numbers between drilled and dug wells. However, there
were exceptions in which drilled wells produced water with high nitrate concentrations
and high coliform numbers. Nolan and others (2002) also report contradictory results.
The authors modeled nitrate contamination in shallow groundwater and found a
positive correlation between depth to water table and the probability of nitrate
contamination. The explanation for these unexpected results was that shallow depth to
water may indicate water logged conditions conducive to denitrification, while
increasing depth to water lessens denitrification potential, increasing the likelihood of
higher nitrate levels.
Often obtaining reliable well depth information is difficult. Aelion and Conte
(2004) studied residential wells in South Carolina identified as susceptible to
contamination and noted as a limitation to their study a lack of information on well
parameters such as well depth. They observed that while these details are often
unknown or incorrectly reported, information on well construction is important in
characterizing sources of well water contamination. Authors of a microbiological
survey of private water supplies in England also listed missing information on well
construction as a shortcoming to their analysis (Richardson et al., 2009).
The objective of this study is to examine the relationship between the presence or
concentration of total coliform bacteria and nitrate, respectively, in private well water
and the depth of the sampled well in order to understand how this characteristic may
contribute to well water vulnerability.
182
Methods
Data used for this analysis were obtained from the Oregon Health Authority’s
(OHA) Domestic Well Testing Real Estate Transaction (RET) database. The RET
contains information submitted to the OHA under the Domestic Well Testing Act
(DWTA), which requires that an owner of a property with a private well used for
household drinking water must test for nitrate, total coliform bacteria, and arsenic
upon accepting an offer to purchase the property (Hoppe et al., 2011). Results of this
testing must then be shared with the buyer and submitted to the OHA. The DWTA
requires that well water samples are sent to state-accredited laboratories that adhere to
protocols ensuring quality analyses and data.
Sample results used for this analysis included measures of nitrate or total coliform
bacteria obtained from the RET database in 1989-1997, which included information
on depth of the well sampled. A limited number of records in the RET database
include information on well depth. This information is requested on the RET reporting
form, which well owners are required to submit with test results, but the information is
not required under statute. Data entry protocols for the RET database have only
recently been standardized. Previously, nitrate measures were entered as a specific
concentration, less than a detection limit or threshold, or as nondetect. Nitrate data
used in the current analysis had been entered as either a quantified measure or
nondetect. Using one-half of the limit of detection (LOD) for a contaminant en lieu of
a nondetect result is a common approach used in water chemistry (Helsel, 2006). As
the LOD for nitrate in groundwater can range from 0.005 to 1.0 mg/L (personal
communication, R. Haynes, March 1, 2010), the average was taken between these two
values and divided by two, and the resulting value of 0.5 mg/L was substituted for all
nondetect nitrate results. The large majority of total coliform bacteria results were
entered as either ―positive/negative‖ or ―present/absent‖, which allowed for a
straightforward conversion of this dataset into a binary variable where 0 = negative or
absent, and 1 = positive or present.
183
Simple linear regression analysis was used to explore the relationship between
nitrate levels as a continuous variable and well depth. In addition, logistic regression
was used to estimate the probability of nitrate levels at or above 7 mg/L and 10 mg/L
given well depth as a predictor. In Oregon, 7 mg/L is a threshold level triggering
regulatory action to address nitrate contamination of groundwater (DEQ, 2011) and is
therefore relevant to studies of well water contamination. Under the SDWA, 10 mg/L
is the national health-based standard for nitrate allowed in public water systems.
Logistic regression was also used to estimate the probability of bacterial
contamination given well depth as a predictor.
Results
There were 2,783 records in the RET database that included the depth of the well,
submitted between 1989-1997. Depths ranged between 10 – 1156 feet. The mean and
median depths were 153 and 110 feet, respectively.
Nitrate
There were 2,772 records that provided data on both nitrate concentrations and
depth of the sampled well. Of this total, 952 (34%) wells had nitrate concentrations
above 1 mg/L, and well depths ranging from 12-1156 feet. Mean and median depths
were 138 and 90 feet, respectively. There were 68 (7%) wells that were positive for
coliform bacteria, while 2 samples did not have bacteria results available. There were
88 (3%) wells with nitrate concentrations at or above 7 mg/L and 6 (7%) were also
positive for coliform bacteria. Depths of these wells ranged between 90-400 feet with
mean and median depths of 90 and 62 feet, respectively.
There were 1,820 records reporting nitrate below 1 mg/L with depths ranging from
10-1000 feet. Mean and median depths were 161 and 120 feet, respectively. There
were 126 (7%) records that reported positive detections for total coliform bacteria,
while 8 did not have bacteria results available.
184
There were 2,684 wells with nitrate levels at or under 7 mg/L with depths ranging
from 10-1156 feet. Mean and median depths were 155 and 110 feet, respectively. Of
these wells, 188 (7%) were also positive for total coliform bacteria, while 10 did not
have bacteria information available.
There were 32 records reporting nitrate at or above 10 mg/L with depths ranging
from 30-200 feet. Mean and median depths were 77 and 61 feet, respectively. There
were 2 (6%) records that reported positive detections for total coliform bacteria. All 32
records had bacteria information available.
Using simple linear regression, the association between nitrate and depth was
found to be negative and significant. However the correlation was slight (r=-0.139, p <
0.001). Well depth explained 19% of the variance in nitrate concentrations, F(1, 2307)
= 45.57. p < 0.001. The final regression equation predicts that for every foot increase
in well depth, the level of nitrate is reduced by 0.002 mg/L. Using logistic regression,
depth was significantly related to the log-odds of obtaining a nitrate concentration at
or above 7 mg/L, x2(1) = 32.19, p < 0.001, Cox-Snell R2 = .012. The final logistic
regression equation predicts that at 50 feet there is approximately a 5% probability that
nitrate measures will be at or above the 7 mg/L. At 250 feet this probability is reduced
to 1%.
Coliform
There were 2,773 records that provided data on both total coliform bacteria
detections and depth of the sampled well. Of this total, 197 (7%) were positive for
coliform bacteria and had well depths ranging from 12-640 feet. Mean and median
depths were 109 and 75 feet, respectively. Of the 2,576 samples that were negative for
bacteria, well depths ranged from 10-1156 feet, with mean and median depths of 156
and 113 feet, respectively.
The association between well depth and the presence of coliform bacteria was
analyzed using logistic regression. As a single predictor, depth was significantly
related to the log-odds of obtaining a positive coliform bacteria result, x2(1) = 29.56, p
185
< 0.001, Cox-Snell R2 = .011. The final logistic regression equation predicts that at 50
feet there is a nearly 10% probability of a positive coliform bacteria result. At 250 feet
that probability is cut in half (4.5%).
Discussion
Results of this analysis support the hypothesis that as depth increases in private
wells, the probability of nitrate or coliform bacteria contamination decreases.
However, the effect of depth on these contaminants appears to be slight. At a shallow
well depth of 50 feet, the probability of nitrate or bacteria contamination is
approximately 5% and 10%, respectively. In order to reduce those probabilities by at
least half, a 4-fold increase in well depth is required. Depth is a determining factor in
the cost of well drilling, since most well contractors charge by the foot. Cost is the
reason why most private wells are generally not drilled beyond the point where
groundwater is first encountered (DOE, 2010). Results from this study suggest that the
cost of deepening a well may not merit the small added protection against nitrate or
bacteria contamination.
186
D.3. Research Question: Is there a significant difference between socioeconomic and
demographic characteristics of communities served by wells with nitrate
concentrations above Oregon’s state management threshold compared to wells with
concentrations below threshold?
Introduction
In 1994, President Bill Clinton signed the Executive Order, Federal Actions to
Address Environmental Justice in Minority Population and Low-Income Populations.
The Executive Order requires that federal agencies administer and implement
programs, policies and activities that affect human health or the environment so as to
identify and avoid ―disproportionately high and adverse‖ effects on minority and lowincome populations (Executive Order No. 12898, 1994). Populations categorized by
age, gender, education, and residence are also recognized as candidates for potentially
adverse exposures to environmental contaminants (USEPA, 1999). The 1999 EPA
report, “Sociodemographic Data Used for Identifying Potentially Highly Exposed
Populations” provides guidance for incorporating information on sociodemographic
status in risk assessments, including data on age, gender, lifestyle, behavior and
individual health (USEPA, 1999). This information assists the public health
practitioner in identifying individuals that are particularly vulnerable to environmental
contamination and supports the development of relevant, responsive risk reduction
efforts premised on the attributes and circumstances of these individuals.
A number of studies have used socioeconomic and demographic (SED) data to
identify and characterize populations who may be affected by contaminants in
drinking water. In a study of small water systems in California using water quality
monitoring datasets and census block group data, Balazs and others (2011) found that
systems serving larger fractions of Latinos and renters have higher nitrate levels.
Teschke and others (2010) looked at the relationship between age, gender, household
income, duration of residence, and private well use with incidence of intestinal
infectious disease and environmental factors impacting drinking water quality in a
187
mixed urban and rural community near Vancouver, Canada. While most associations
with SED variables were anticipated (higher physician visit and hospitalization rates in
females, in the very young and very old, and in those in low income areas), private
well users were found to have enteric disease rates similar to municipal water users,
which was unexpected. Preston and others (2000) found a highly significant
correlation between the minority composition of counties in the Mississippi Delta
region and indicators of watershed quality, especially the number of violations
resulting from exceeding permissible contaminant levels in drinking water. Wing and
others (2000) used economic and demographic data for census block groups to
demonstrate that hog confined animal feeding operations, which use waste pits that
can contaminate groundwater, are located disproportionately in communities with high
poverty, high proportions of nonwhite persons, and high percentages of households
dependent on well water. Stone and others (2007) used demographic data to
characterize exposures to arsenic in Oregon public drinking water systems and found
that a disproportionate number of Hispanics were receiving water with arsenic
detections above the health based standards. Additional demographic findings
associated with exposure to drinking water arsenic included a lower median age,
higher proportion of infants and young children, a lower median household income,
higher poverty rates, and a higher percent of another language besides English spoken
in the home. The authors concluded that ―community characteristics have potential
implications for population-level exposures, generalized risk and focused outreach of
arsenic in drinking water‖, and ultimately that ―demography can be informative for
risk management and risk communication‖.
Many of these authors used geographic information systems (GIS) software for
their analyses. GIS has become a powerful, widely used tool in environmental public
health research for mapping the disproportionate distribution of exposures of certain
populations to environmental contaminants (Maantay, 2002). Nuckols and others
(2004) discuss the use of GIS software to evaluate environmental justice issues by
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linking information on contaminant sources to census information on sociodemographic characteristics of a population.
The objective of this analysis is to characterize SED factors associated with
populations served by private wells in Oregon with nitrate concentrations over a state
management threshold and compare to populations served by wells with nitrate below
this threshold. GIS software was used to locate and link wells with SED factors at the
census block group level. Relative measures of SED factors were calculated for the
location of each well, and significant differences and variability for each SED factor
between populations were tested using the independent student t-test.
Methods
Two datasets were used in this analysis: 1) SED data from the 2000 U.S. Census,
and 2) nitrate concentrations in private well water collected by the Oregon Health
Authority (OHA) from 1995-2005.
SED factors
Census SED information was obtained for the block group from the 2000
Summary File 3 (SF3) long-form questionnaire. The SF3 collects detailed information
on social, economic and housing characteristics from a sample of housing units,
approximately 1-in-6 households (USCB, 2002). SED factors used in this analysis
(Table D.3b) were selected for their relevance to an individual’s susceptibility to
adverse health impacts from consuming high levels of nitrate in drinking water and
other attributes that are relevant for developing effective risk reduction strategies.
Rural and Farm residence: The majority of households in the U.S. that depend on
private wells for drinking water are located in rural areas (Simpson, 2004). Numerous
studies have shown that wells located in rural areas with intensive agricultural activity
often have a greater prevalence of nitrate contamination (DeSimone et al., 2009;
Dubrovsky et al., 2010; Warner & Arnold, 2010; WHO, 2011).
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Name
Rural
Census
Category
ID
P5
Farm
residence
P5
Race
P6
Age/Sex
P8
Language
P19
Household P52
Income
Public
P64
assistance
Education P37
Renter
occupied
H7
Attribute
Measure denominator
Total persons inside rural
areas
Total persons inside rural area
living on a farm
Total persons living in
block group
Total persons living in
rural area in block
group
Total persons living in
block group
Total persons living in
block group
Above the state average for
any race other than white
Total number or persons
inside specified age categories
under 1 year for both sexes
Percentage people speaking
English ―not well‖ or ―not at
all‖
Below the state average for
household income
Total number households
receiving public assistance
Below state average for total
persons with bachelor,
graduate or professional
degree
Occupied units which are not
owner occupied
Total persons 5 years
and older living in
block group
Total homes in block
group
Total homes in block
group
Total persons 25 years
and older living in
block group
Total occupied housing
units in block group
Table D.3b. SED factors relevant to susceptibility to nitrate-contaminated well water
Race and language: Increasing evidence suggests that minority groups may bear a
disproportionate risk of exposure to environmental hazards (Morello-Frosch et al.,
2001; Northridge et al., 2003; Preston et al, 2000). One review of 30 studies of
environmental risks identified racial/ethnic or socioeconomic disparities in >80% of
cases (Allen et al., 1995). Balazs and others (2011) showed that while half of surveyed
residents on small water systems were unaware of the status of their well water
quality, Spanish-speaking households were even less likely to know of contamination.
Knowledge of race and language barriers helps guide the development of effective
education materials and intervention strategies for an at-risk community.
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Age: Infants are considered the major susceptible population for adverse health
effects associated with consumption of nitrate-contaminated well water. Under the
Safe Drinking Water Act, the Maximum Contaminant Level (MCL) of 10 mg/L for
nitrate in drinking water was established primarily to protect infants from developing
methemoglobinemia or ―blue baby syndrome‖ (EPA, 2010). Renewed concern for the
risk to infants from nitrate-contaminated well water was demonstrated by a recent
policy statement released by the American Academy of Pediatrics, urging families
with an infant to routinely test well water (CEH et al., 2009).
Household income and public assistance: Low income communities are often
overrepresented among populations exposed to environmental or public health risk
factors (Evans & Kantrowitz, 2002; Pamuk et al., 1994). The cost to install and
maintain water treatment systems or purchase alternative water supplies can be
substantial, as is addressing damage to the structural integrity of a well or re-drilling to
greater depths. A recent community survey effort in California’s San Joaquin Valley
where nitrate contamination of groundwater is prevalent indicated that 70% of
residents bore water costs above national affordability standards (Moore et al., 2011).
Individuals who are low income or receiving public assistance may not be able to take
the necessary measures to prevent exposures to contaminated well water.
Education: Education is an important factor governing individual behaviors and
access to resources and has been demonstrated to have significant impact on personal
health and quality of life (EPA, 1999). In particular, higher education (beyond high
school) has been shown to be commonly and positively associated with indicators of
heightened environmental awareness as well as increased familiarity of agencies with
responsibilities in health and environment (Preston et al., 2000). Education can also
impact an individual’s ability for understanding outreach materials with instructions
for avoiding adverse exposures and reducing health risks (EPA, 1999).
Renter occupied: Individuals who rent the property where they live may not be
aware that the water they are consuming or cooking with is coming from a private
well, nor are they likely to be privy to any existing water tests. Studies have shown
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that many property owners themselves are not aware of the importance of maintaining
their well and treating or testing the water before consumption (Jones et al., 2006;
Mitchell & Harding, 1996; Simpson, 2004; Walker et al., 2005). Thus, it is doubtful
that renters will be any more informed or likely to take the actions necessary to ensure
well water safety.
Nitrate data
Nitrate measures in well water submitted to the OHA under the Domestic Well
Testing Act (DWTA) were obtained from the Real Estate Transaction (RET) database
for the years 1995-2005. A detailed description of the DWTA and RET database is
available in chapter 3. These years were selected in order to produce a large sample of
data that is still relevant to census data from 2000, the most recent year for which SF3
data were available at the time of this analysis.
In Oregon, the Groundwater Quality Protection Act requires actions to remediate
contamination when nitrate levels in an aquifer reach or exceed 70% of the MCL, or 7
mg/L nitrate-nitrogen. For the purpose of this analysis, this level is referred to as a
state management threshold (SMT). From a public health perspective it is useful to
identify contaminant levels that are trending toward health-based thresholds in order to
plan for and implement remediation actions or public education campaigns before
levels exceed threshold and communities are at-risk. This is particularly important for
nitrate in groundwater given that contamination can be persistent, depending on
conditions of the aquifer, and remediation is difficult and expensive. Thus, once an
aquifer is contaminated with nitrate, it can pose a health risk for years, decades, or
more (Dubrovsky et al., 2010).
Well data were categorized into two groups using the SMT as a cut-point:
Category 1 (C1) included wells with nitrate measures below the SMT, and Category 2
(C2) included wells with nitrate measures at or above the SMT. GIS software (ESRI,
2010) was used to locate wells from each category and identify the corresponding
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block groups. Selected SED information (Table D.3b) for each well’s block group was
downloaded from the U.S Census website.
For each well and for each SED factor listed in Table D.3b, a relative measure was
calculated to reflect the number of individuals in the SED group as a percentage of the
appropriate denominator. The median of all of the relative SED measures was then
calculated separately for C1 and C2 wells (Figure D.3a). Median values were
compared between C1 and C2 datasets and both were compared to the State average
for that SED factor.
In order to test if differences between C1 and C2 groups were significant, the
independent student t-test was applied to the entire distribution of relative measures
for each SED factor.
Figure D.3a. Diagram of SED data processing and analysis
Results
A total of 9,883 private well water records with nitrate measures sampled from
1995-2005 were used in the analysis. After categorizing the data according to the
SMT, there were 9,466 wells represented in C1 (Figure D.3b) and 417 wells in C2
(Figure D.3c).
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Figure D.3b. Location of wells with nitrate below the SMT (C1) with corresponding
block groups.
Figure D.3c. Location of wells with nitrate at or above the SMT (C2) with
corresponding block groups.
194
Table D.3c shows the median of each relative SED measure for C1, C2 and the
State average. Four SED measures differed between C1 and C2 groups. There were
larger relative measures of nonwhite individuals, higher education, and renters
associated with wells from the C2 group, but a smaller measure of rurality. Both C1
and C2 datasets were below the state average for rurality and farm residence and at or
above the state average for race/ethnicity or renting individuals. There were no
differences between the C1 and C2 datasets and the State average for measures of
infants, low English proficiency, income, or use of public assistance.
The application of the independent student t-test showed that differences in
relative measures for race/ethnicity, education, and renting were significant (p < 0.05)
between C1 and C2 groups.
SED Measure STATE Below SMT Above SMT
(C1)
(C2)
t statistic
p value
% Rurality
21%
20%
17%
1.46
.143
% Farm
2%
1%
1%
1.12
.264
% Nonwhite
14%
16%
20%
-5.69
.000*
% Infant
3%
3%
3%
-0.21
.830
% LEP
3%
3%
3%
-0.56
.579
% Higher Ed
32%
31%
34%
-4.25
.000*
% Income
49%
49%
49%
0.39
.700
% Assist
4%
4%
4%
-0.40
.693
% Renting
36%
36%
38%
-2.06
.039*
*significant at p < 0.05
Table D.3c. Comparison of relative measures of SED factors between C1 and C2
datasets and State averages.
Discussion
Results of this analysis demonstrate that there are some significant differences
between SED characteristics of populations served by wells with nitrate
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concentrations above Oregon’s SMT compared to populations served by wells with
concentrations below the SMT. That there was a significantly greater relative measure
of nonwhite populations associated with wells with higher levels of nitrate suggests a
potential unequal environmental burden of drinking water contamination based on
race and ethnicity. However, given the issues that may compromise the
representativeness and reliability of the RET dataset (Hoppe et al., 2011), this
suggestion of environmental inequity needs to be validated by further study.
The significantly greater relative measure of renting populations associated with
wells with high levels of nitrate highlights a need to encourage landlords to regularly
test drinking water serving rental properties and install appropriate treatment systems
when necessary. In addition, renters need to be educated to request that testing occurs
and to review the results.
The significantly greater relative measure of higher education associated with
populations exposed to high levels of well water nitrate was unanticipated. This result
may be an artifact of where well testing in compliance with the DWTA is occurring,
which appears to be in the more urbanized areas of the state where college educated
residents are likely to be more prevalent (Preston et al, 2000). This testing bias
towards more populated areas may also explain why measures of rurality and farm
residence for both well groups were less than the state average. In addition, realtors
and property sellers who live in more urbanized areas have greater access to education
materials and communication networks that would increase awareness of the DWTA
and data reporting requirements, or perhaps realtors and property sellers who live in
rural areas face more obstacles for sample testing. Thus, the smaller measures of
rurality and farm residence of both private well groups compared to State averages
suggest that rural populations are underrepresented in the RET database, and efforts
should be developed to increase compliance with DWTA in these areas.