Environmental Justice in Virginia’s Rural Drinking Water: Analysis of Nitrate Concentrations and Bacteria Prevalence in the Household Wells of Augusta and Louisa County Residents

David Frederick Arnold

Thesis submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of

Master of Science

In Geography

Dr. Laurence W. Carstensen Jr., Chair Dr. Korine N. Kolivras

Dr. Conrad D. Heatwole

June 12, 2007 Blacksburg, VA

Keywords: groundwater, contamination, nitrate, bacteria, socio-economic status (SES)

Copyright 2007, David Frederick Arnold

Environmental Justice in Virginia’s Rural Drinking Water: Analysis of Nitrate Concentrations and Bacteria Prevalence in the Household Wells of Augusta and Louisa County Residents

David F. Arnold

ABSTRACT

This research studied two predominantly rural counties in Virginia to understand

whether residents have equal access to uncontaminated drinking water by socio-economic

status. Statistical associations were developed with the total value of each residence

based on county tax assessment data as the independent variable to explain levels of

nitrate, the presence of bacteria (total coliform and Escherichia coli), and specific

household well characteristics (well age, well depth, and treatment). Nearest neighbor

analysis and chi-square tests based on land cover classifications were also conducted to

evaluate the spatial distribution of contaminated and uncontaminated wells.

Based on the results from the 336 samples analyzed in Louisa County, rural

residents with private wells may have variable access to household drinking water free of

bacteria; particularly if lower-value homes in the community tend to be older with more

dated, shallower wells. This study also suggested that, in Louisa County, the presence of

water treatment devices was also significantly related to total home value as an index of

socio-economic status. Analysis of the 124 samples taken from household wells in

Augusta County did not result in any significant associations among selected well

characteristics, total home value, and water quality. Lower community participation in

Augusta County as a result of a more expensive water quality testing fee may have

contributed to the lack of hypothesized relationships in that county’s case study.

iii

TABLE OF CONTENTS Abstract…………………………………………………………………………………....ii Table of Contents………………………………………………………………………....iii List of Tables and Figures………………………………………………………………iv Acknowledgements……………………………………………………………………….vi 1.0 INTRODUCTION…………………………………………………………………….1 2.0 LITERATURE REVIEW AND BACKGROUND…………………………………...2 2.1 Environmental Exposure-Socioeconomic Status Research…………………...2 2.2 Groundwater Contamination Research………………………………………..4 2.3 Household Well Protection in Virginia………………………………………7 2.4 Overview of Study Areas…………………………………………………….8 2.5 Significance of Study………………………………………………………...14 3.0 METHODS…………………………………………………………………………..16 3.1 Data Sources…………………………………………………………………16 3.2 Sampling Method…………………………………………………………….18 3.3 Data Management…………………………………………………………....19 3.4 Statistical Analysis…………………………………………………………...22 3.5 Spatial Analysis……………………………………………………………...27 4.0 RESULTS……………………………………………………………………………30 4.1 Analysis of Louisa County Samples………………………………………....30 4.2 Analysis of Augusta County Samples………………………………………..37 5.0 DISCUSSION………………………………………………………………………..41 5.1 County Comparison and Discussion of Key Findings……………………….41 5.2 Areas of Future Research…………………………………………………….46 6.0 CONCLUSION………………………………………………………………………50 WORKS CITED…………………………………………………………………………51 APPENDIX A: Supplementary Tables......………………………………………………56 VITA……………………………………………………………………………………117

iv

LIST OF TABLES AND FIGURES

Table 2.3.1: Overview of selected construction requirements for Class III wells in Virginia.….……………………………………………………………...………...8 Table 2.4.1: Comparison of Socio-economic Characteristics Based on 2000 U.S. Census Bureau Data – Augusta and Louisa Counties, VA………………………………12 Table 3.4.1: Summary of Kruskal-Wallis Tests: Louisa County Samples by Date……...24 Table 3.4.2: Summary of Water Quality By Region and Sample Date: Louisa County, VA………………………………………………………………26 Table 3.4.3: Groupings of Water Quality Indicators for Discriminant Analysis………...27 Table 3.5.1: Summary of Land Cover Reclassification from NLCD 1992……..……….29 Table 4.1.1: Correlation matrix: Louisa County Samples……………………………….32 Table 4.1.2: Summary of Discriminant Analysis Results: Louisa County, VA…………34 Table 4.1.3: Table 4.1.3: Summary of Nearest Neighbor Analysis Results: Louisa County, VA………………………………………………………………...…….35 Table 4.1.4: Agricultural Land Cover/Water Quality Chi-Square Test Results – Louisa County……………………………………………………………………36 Table 4.2.1: Correlation Matrix: Augusta County, VA………………………………….38 Table 4.2.2: Nearest Neighbor Analysis Results: Augusta County Samples……………39 Table 4.2.3: Agricultural Land Cover/Water Quality Chi-Square Test Results – Augusta County……………………………………………………………………………40 Table 5.1.1: Summary of Total Home Values versus Estimated County Median Values

of Owner Occupied Homes: Augusta and Louisa Counties, VA …….……...…..43 Table 5.2.1: Overview of Virginia Household Water Quality Education Programs: 1998- 2002………………………………..……………………………………………..49 Table A.1: Summary of Omitted Samples……………………………………………….56 Table A.2: Summary of Household Well to Parcel Matching Process: Louisa

County, VA………………………………………………………………………57 Table A.3: Summary of Household Well to Parcel Matching Process: Augusta

County, VA………………………………………………………………………77 Table A.4 Normality test results: Augusta County………………………………………85 Table A.5 Normality test results: Louisa County………………………………………..93 Table A.6: Summary statistics - Louisa County Samples………………………………115 Table A.7: Summary statistics - Augusta County Samples…………………………….116 Figure 2.4.1: Map of Augusta and Louisa Counties………………………………………9 Figure 2.4.2: Geographic Features and Socio-economic/Demographic Characteristics of Augusta County ……………………………………………...………………….10 Figure 2.4.3: Geographic Features and Socio-economic/Demographic Characteristics of Louisa County…………………………………………………………………………...13 Figure 3.3.1: Map of Successfully Located Household Well Sites: Augusta County…...21 Figure 3.3.2: Map of Successfully Located Household Well Sites: Louisa County …….22 Figure 3.4.1: Map of Louisa County Samples by Collection Date………………………25

v

Figure 5.1.1: Histogram of Z-scores for Total Home Values: Augusta and Louisa County……………………..……………………………………………..43 Figure 5.1.2: Comparison of Total Coliform and E.coli based on Total Home Value: Louisa County, VA Samples.…………………………………………………….44 Figure 5.1.3: Comparison of Total Coliform and E.coli based on Total Home Value: Augusta County, VA Samples……….…………………………………………..45 Figure 5.2.1: VA HHWQEP Participation Based on the Water Quality Testing Fee …..47

vi

Acknowledgements

I would like to express my gratitude for all the people who provided their

assistance and support as I completed my thesis. In particular, I would like to thank my

committee members, Dr. Bill Carstensen, Dr. Korine Kolivras, and Dr. Conrad Heatwole,

for their time and assistance.

I would also like to thank Julie Jordan, manager of the water quality laboratory in

the Department of Biological Systems Engineering at Virginia Tech, for providing the

data used in this research as well as for continually offering timely information related to

the procedures and practices of the Virginia Household Water Quality Education

Program. Additionally, I would like to thank Dr. Blake Ross and Dr. Brian Benham, for

sharing their expertise related to the Virginia Household Water Quality Education

Program.

Finally, I would like to thank my family and the Center for Geospatial

Information Technology at Virginia Tech for their financial support of my graduate

education. I am also very appreciative of all the support, encouragement, and advice

continually offered by my entire family and friends.

1.0 INTRODUCTION

In the United States, groundwater accounts for over half of the country’s drinking

water and roughly 90% of rural drinking water (Nolan et al, 1998). As a result of various

human activities, land uses, and hydrological and geological processes, groundwater can

be highly vulnerable to contamination, posing serious health and ecological risks. In

urban areas within the U.S., drinking water is subject to stringent monitoring and

treatment to ensure compliance with the standards that have been set forth by the U.S.

Environmental Protection Agency (EPA) and other state and federal regulatory agencies.

However, for rural areas in the U.S. relying on private household well systems, the EPA’s

maximum contaminant levels (MCLs) are only used as guidelines rather than legally

enforceable standards. Subsequently, in Virginia and other states, water quality

monitoring and treatment after initial well construction are left to the discretion of the

well owner or rural resident. Therefore, the burden of ensuring healthy drinking water

and staying informed about the status of one’s drinking water is the responsibility of the

inhabitant in most rural areas of the United States. Management of this burden requires

access to educational, financial, and community resources that are typically associated

with people of higher socio-economic status (SES). As a result, without access to these

resources, exposure to harmful contaminants in drinking water may be greater for rural

residents of low SES.

This research studied two predominantly rural counties in Virginia to understand

whether residents have equal access to uncontaminated drinking water by socio-economic

status. Statistical associations were developed with the total value of each residence

based on county tax assessment data as the independent variable to explain levels of

nitrate, the presence of bacteria (total coliform and Escherichia coli), and specific well

characteristics (well age, well depth, and treatment). Nearest neighbor analysis and chi-

square tests based on land cover classifications were also conducted to evaluate the

spatial distribution of contaminated and uncontaminated wells.

1

2.0 LITERATURE REVIEW AND BACKGROUND

2.1 Environmental Exposure-Socioeconomic Status Research

Several significant studies have been conducted on human-environment

interactions from the perspective of environmental injustice and inequity. The concepts

of environmental injustice and inequity revolve around the uneven spatial distribution of

harmful components found within the natural and man-made physical landscape based on

specific social factors such as race, gender, ethnicity, and SES (Most et al, 2004). Taken

a step further, environmental inequity can also imply that this uneven distribution is the

result of certain political policies and decisions aimed at protecting privileged social

classes (Harner et al, 2002). Therefore, environmental justice requires the equal

distribution of noxious, unhealthy, and undesirable environmental components regardless

of one’s place in society (Towers, 2000) and that “…all people and communities are

entitled to equal protection of environmental and public health laws and regulations

(Bullard, 1996, p. 493).”

Several factors that contribute to the disproportionate exposure of harmful

environmental conditions among minorities and people of low SES have been identified

in the scientific literature. One traditional school of thought formulated by historical

sociologists such as Thomas Malthus suggests that the occurrence of environmental

injustice is due to the greater disregard people of poverty have for their environment

since they only worry about meeting their immediate needs in order to survive (Gray and

Mosely, 2005). Today, however, researchers recognize that not only does the overuse

and misuse of environmental resources have the potential to take place among the poor,

but, within a politically-based ecological framework, marginalized environmental

conditions frequently produce marginalized inhabitants (Gray and Mosely, 2005).

Undesirable land uses such as hog farming, which may lead to groundwater

contamination from hog waste, have a strong spatial association with the rural poor of

Mississippi and Eastern North Carolina who rely heavily on well water (Wilson et al,

2002; Wing et al, 2000). Conversely, higher income areas with greater access to

economic, educational, and medical resources generally are more successful in

2

preventing polluting land uses from entering their communities (Mohai and Bryant,

1992).

To understand the so-called “ubiquitous SES-health gradient,” Evans and

Kantrowitz (2002) examined the influence of disproportionate exposure to specific

harmful conditions in the physical environment. They documented existing research

which suggests that sources of unhealthy environmental conditions are found not only in

the natural environment (i.e., air and water), but also in the housing, work, and

neighborhood conditions of people of lower income. One study cited by Evans and

Kantrowitz (2002) focused on bacterial concentrations in the groundwater of migrant

labor camps in eastern North Carolina – finding significantly high concentrations of total

coliform and E.coli at the sampled labor camps when compared with the water quality of

nearby rural residences and businesses (Ciesielski et al, 1991).

A large portion of environmental justice research focuses on urban rather than

rural areas within the United States. Urban locations commonly house facilities such as

power plants, toxic waste sites, landfills, and manufacturing and industrial plants that

have all been identified as having a strong, spatial association with poor, non-white

residents (Faber and Krieg, 2002). Margai (2001) used census data to analyze the

distribution of accidental release (spill) sites in urban areas. Harner et al (2002) applied

census block data and buffering around waste sites along with various environmental

hazard data sources and multiple regression tests in the development of exposure indices.

Mennis and Jordan (2005) utilized data on air pollution sites from the U.S.

Environmental Protection Agency’s (EPA) toxic release inventory along with census tract

socioeconomic data to determine that a geographically weighted regression analysis best

explains how the spatial relationship between air pollutant concentrations and

socioeconomic status can change over time.

Current environmental justice research also suggests the importance of

appropriate scale selection when attempting to establish a valid and potentially causal

relationship between social status and exposure to harmful conditions within the physical

environment. As Gray and Mosely (2005) point out, scale is also extremely important

when considering how different factors and environmental processes play out. The

selection of an appropriate geographic scale not only pertains to the researcher’s scope of

3

interest, but also to the role and effectiveness of regulations aimed at protecting

disadvantaged members of society from harmful environmental conditions (Towers,

2000). In an attempt to identify a reliable approach to measuring the existence of

environmental injustice within urban areas on the statewide level, researchers have

developed and compared a variety of indices to be used as predictors at the community

level (Harner et al, 2002). Research also recognizes the influence that inappropriate scale

selection has on the outcome of environmental justice analyses, suggesting that disregard

for spatial scale could question the scientific validity of environmental justice research

(Most et al, 2004). In spite of the fact that certain data (health, demographic, etc.) may

only be available at the state, municipal, or census tract level, it is generally understood

that neighborhood or community level analyses stemming from fine-scale geographic

units of analysis most accurately represent spatial patterns of environmental justice

(Maantay, 2002). In large part, geographic researchers of environmental justice issues

are at least partially aware of the shortcomings that can arise as a result of questionable

conceptualization, method selection, and data collection in GIS analyses involved in

assessing environmental injustice (Maantay, 2002). Scale and methodology selection are

both crucial to environmental justice research since findings in support or denial of

injustice can have enormous political implications (Bowen, 2001).

2.2 Groundwater Contamination Research

Groundwater contamination and assessments of vulnerability to contamination are

well documented. One of the most comprehensive documents was written by Focazio et

al (2002) with the U.S. Geological Survey (USGS). Their document provided the

framework for understanding key variables related to groundwater contamination

(groundwater flow system, geochemical systems, sources of contamination, etc.) in

addition to offering advice on research design, reliable data collection, statistical

methods, and mapping techniques. The USGS has a variety of other publications

critiquing and improving their methods, including the Depth to water, net Recharge,

Aquifer media, Soil media, Topography, Impact of vadose zone media, and hydraulic

Conductivity of the aquifer (DRASTIC) vulnerability mapping method (Rupert, 1999 and

2001). The USGS has played a key role in thorough research of land use and the

4

groundwater system as it relates to prevalent contaminants like nutrients and pesticides

(Nolan et al, 1998 and 2002; USGS, 1999).

Other studies on groundwater focused on the role of various human activities that

can contribute to contamination. Bourne (2001) focused her research on certain factors

such as population density, pollution sources, and well characteristics (age, depth, design,

etc.) that can contribute to nitrate and fecal coliform contamination in rural well water.

Liu et al (2005) and Armstrong et al (2004) conducted research using statistical and

cartographic analyses to evaluate agricultural and farming land use activities that

involved high levels of fertilizers, pesticides, and animal waste stores. Gardner and

Vogel (2005) combined the presence of septic tanks into their evaluation of land use and

nitrate concentrations and found that leakage from septic tanks strongly contributed to

elevated nitrate levels in groundwater in Massachusetts.

State and regional level studies that take into account specific land uses and

contaminants with respect to the area’s hydrological and geological composition are

reported as well (Seilig, 1994; Boyle, 2000). Groundwater research in specific regions,

defined by geological characteristics such as karst terrain, focuses on the interrelationship

between contamination sources and geological formations in assessing the risk of

contamination (Western Kentucky University, 2006). Other studies continue to focus on

a broad spatial scale with a specific contaminant in mind for the purpose of providing

political decision-makers with a base knowledge of what areas in the U.S. are potentially

at greatest risk (Nolan, 2005).

Previous studies were also conducted on specific land use, hydrogeologic, and

private well characteristics that are related to the bacteriological contamination of

groundwater. One study published by the U.S. Geological Survey sampled 146

household wells stratified by environmental factors such as physiography, land use and

well construction characteristics (i.e., well age and well depth) (Bickford et al, 1996).

Water from the sampled wells was analyzed for bacteria based on measured levels of

total coliform, E.coli, and fecal streptococcus. Kruskal-Wallis tests and Spearman’s rank

correlations were applied to evaluate the relationship between selected environmental and

well construction factors, and the measured levels of bacteria. This study demonstrated a

statistically significant correlation between well age and bacteria as well as a significant

5

relationship between areas of agricultural land use and bacteria. Bacteriological

contamination of household wells was also found to be more common in the Ridge and

Valley physiographic province than the Piedmont province. However, this study did not

find statistical associations between bacteria and well depth or bedrock type at the

sampled well sites (Bickford et al, 1996).

The U.S. EPA has also conducted and reviewed extensive research on

bacteriological groundwater contamination. The “National Primary Drinking Water

Regulations: Ground Water Rule and Proposed Rules,” published by the U.S. EPA in

2000, summarized potential risk factors for bacteriological groundwater contamination in

addition to proposing regulations and guidelines for monitoring and correcting

bacteriological contamination in public water systems. In this document, hydrogeologic

sensitivity assessments were discussed, which identify areas of karst terrain, shallow

aquifers, and aquifers where interconnected openings are present as being susceptible to

bacteriological groundwater contamination (U.S. EPA, 2000). The report also

recommends the importance of adequate setback distances from possible sources of

contamination such as septic systems and leaking sewage lines (U.S. EPA, 2000). The

U.S. EPA’s report also cites previous studies that analyzed which practices in the

management of public water systems played a significant role in controlling and

preventing bacteriological contamination. These studies, both of which were conducted

by the Association of State Drinking Water Administrators (ASDWA), suggest that

proper water treatment and disinfection, well construction according to state regulations,

and the hydrogeologic setting play important roles in reducing bacteriological

contamination (U.S. EPA, 2000). In the report’s discussion of well construction and age

as indicators of proper well function, it was advised that there was insufficient evidence

to suggest that well age was associated with contamination or deteriorating well

construction. The U.S. EPA based this recommendation in part on two studies conducted

in Missouri which showed that newer wells were more likely to be contaminated than

older wells (U.S. EPA, 2000).

Precautionary measures and health outcomes from exposure to contaminated

drinking water are also well documented. It is estimated that groundwater is the source

of over half the reported incidences of waterborne illness in the United States (Water

6

Quality and Health Council, 2007). The U.S. EPA has conducted research on acceptable

levels of exposure and human activities that can reduce vulnerability to contamination

(U.S. EPA, 2002). The health risks of ingesting harmful microorganisms and chemical

compounds while drinking contaminated well water from groundwater aquifers have

been shown to vary greatly in severity based upon the type of contaminant, exposure

time, and lifestyle (Strauss et al, 2001; Knobeloch, 2002; Ward et al, 1996). The Virginia

Groundwater Protection Steering Committee (2006), in its acknowledgement of the high

costs associated with preventing and treating groundwater contamination, accepts the fact

that many of the homes using wells are of modest means. As an example, in 1990, 36%

of the houses in Virginia using wells were valued at less than $50,000 compared to a

statewide median home value of $116,300 (U.S. Census Bureau, 2007; Virginia

Groundwater Protection Steering Committee, 2006).

2.3 Household Well Protection in Virginia

In Virginia, the Commonwealth of Virginia State Board of Health has established

standards and guidelines for well construction as stated in the Private Well Regulations.

The purpose of these regulations as stated explicitly in 12 VAC 5-630-30 is as follows:

1. “Ensure that all private wells are located, constructed and maintained in a

manner which does not adversely affect ground water resources, or the

public welfare, safety and health;

2. Guide the State Health Commissioner in his determination of whether a permit

for construction of a private well should be issued or denied;

3. Guide the owner or his agent in the requirements necessary to secure a permit

for construction of a private well; and

4. Guide the owner or his agent in the requirements necessary to secure an

inspection statement following construction” (VA Dept. of Health, 1992)

The Virginia Department of Health (VDH) has also established specific protocol

regarding the initial construction of a private household well. Table 2.3.1 provides an

overview of construction requirements of any private well used as a source of drinking

water (i.e., Class III wells).

7

Table 2.3.1: Overview of selected construction requirements for Class III wells in Virginia (Virginia Department of Health, 1992)

Overview of Situation Requiring Regulation

Summary of Regulation

Well construction in proximity to sewer line or septic system

Well construction no less than 50 ft.

Well construction in proximity to other sewage disposal systems (i.e., barnyard, hog lot, drainfield, etc.)

Well construction no less than 50 ft. for Class IIIA or B wells, no less than 100 ft. for Class IIIC or Class IV wells

Well sites in swampy areas or areas subject to flooding

Well terminus shall extend 18 inches above the annual flood level. Other requirements may be determined on a case by case basis.

Well sites down slope from potential contamination source

Greater minimum separation distances are required between the proposed well site and any sources of contamination within a 60 degree arc slope.

Post-construction inspection prior to operation as drinking water source

Sample is required to be collected and tested for bacteria prior to operation as a drinking water source. If positive for total coliform, further tests are required before operation is approved by VDH.

Application of private well regulations based on well construction date

Only wells constructed after Virginia Private Well Regulations were enacted on April 1, 1992 are required to comply with regulations

2.4 Overview of Study Areas

The counties of Augusta and Louisa were selected as the areas of study based on

data availability and the differences in the geologic and socio-economic conditions of the

two counties (figure 2.4.1). Augusta County is Virginia’s second largest county covering

approximately 974 square miles. Based on the 2000 Census, the county has a population

of 65,615 and is considered generally rural in character with agriculture and forest being

the dominant land uses (U.S. Bureau of the Census, 2005; Augusta County

Comprehensive Plan, 2005). The cities of Staunton and Waynesboro serve as the

political, cultural, and economic centers for the county and have an increase in

urbanization in the areas surrounding the two cities.

8

Augusta and Louisa Counties by Physiographic Province

±LEGEND

Augusta

Louisa

Physiographic ProvinceAPPALACHIAN PLATEAUS

BLUE RIDGE

COASTAL PLAIN

PIEDMONT

VALLEY AND RIDGE

Figure 2.4.1: Map of Augusta and Louisa Counties by Physiographic Province With a 20% population increase from 1990-2000, Augusta County is one of the

fastest growing counties in Virginia (U.S. Bureau of the Census, 2005). Ninety-five

percent of the county’s population is white and only 15% of the population has a

bachelor’s degree or higher (compared to 29.5% of Virginia’s population) (U.S. Bureau

of the Census, 2005). Augusta County has only 5.8% of its population living in poverty,

compared to 9.6% of Virginia’s population living in poverty. The median home value in

Augusta County is $110,900 with a median household income of just over $43,000 per

year (U.S. Bureau of the Census, 2005) (see Figure 2.4.2).

9

Figure 2.4.2: Geographic Features and Socio-economic/Demographic Characteristics of Augusta County (sources: U.S. Census Bureau, U.S. Census TIGER/Line, VA Dept. of Transportation)

Population Density per Block GroupAugusta County, VA

City of Staunton

City ofWaynesboro

²0 9 184.5 Miles

Legendpopulation density per square mile

5.04 - 50.00

50.01 - 100.00

100.01 - 150.00

150.01 - 200.00

200.01 - 250.00

250.01 - 300.00

300.01 - 350.00

350.01 - 400.00

Median Household Income per Block GroupAugusta County, VA

City of Staunton

City ofWaynesboro

²0 9 184.5 Miles

LegendMedian Household Income

18750.000 - 26000.000

26000.001 - 31000.000

31000.001 - 36000.000

36000.001 - 41000.000

41000.001 - 46000.000

46000.001 - 51000.000

51000.001 - 56000.000

Educational Attainment per Block GroupAugusta County, VA

City of Staunton

City ofWaynesboro

²0 9 184.5 Miles

LegendPercentage with Bachelor's Degree or Higher

0.00 - 5.00

5.01 - 10.00

10.01 - 15.00

15.01 - 20.00

20.01 - 25.00

25.01 - 30.00

SR 4

2

US 11

US 250

SR 2

52

US 340

SR 48

SR 254

SR 256

SR 262

SR 285

US 11

S

US 250W

SR 275

SR 48

SR 42

SR 254

SR 4

8

US

340

US 11

US

25 0

SR 48

SR 4

8

IS 8

1N

IS 81S

IS 64EIS 6

4W

Augusta County, VA

City of Staunton

City ofWaynesboro

²0 9 184.5 Miles

LegendRailroad lines

Hydrography

Primary roads

Interstate highways

10

The county lies primarily within the Valley and Ridge physiographic region with

the eastern portion of the county located in the Blue Ridge physiographic region. In the

Ridge and Valley portion of the county, the topography is characterized by gradual

mountain slopes and gently rolling hills. The area of the county located in the Blue

Ridge has steeper slopes and greater fluctuations in elevation.

The county’s geology is characterized by complex formations of limestone and

calcareous shale in addition to small amounts of sandstone and chert (Augusta County

Comprehensive Plan, 2005). In the eastern Blue Ridge portion of the county,

groundwater is of limited availability due to impermeable igneous rock formations.

Groundwater can only be extracted at well sites that intersect a fracture in the bedrock.

Water quality at these sites is typically high partly the result of low human development

and therefore few septic tanks in the Blue Ridge (Augusta County Comprehensive Plan,

2005). For the rest of the county, limestone, siltstone, and sandstone make up the

unsaturated zone between the land surface and the water table. In these areas,

groundwater quality is dependent upon well design and depth as well as proximity to

septic tanks and fertilized agricultural areas. Although the caverns associated with

limestone provide a good source of groundwater, they can also further the transmission of

pollutants. In the eastern portion of the county well depths can reach up to 1,000 feet.

However, in the rest of the county, well depths generally average around 300 feet

(Augusta County Comprehensive Plan, 2005).

Approximately 54% of homes in the county rely on individual water sources such

as private wells and springs (Ross et al, 1999). Due to the variations in well depths and

the potential for groundwater contamination through the county’s limestone formations,

administrators have recently adopted a groundwater protection plan to identify and

address well sites that may be vulnerable to groundwater contamination as a result of

runoff.

The second county chosen for analysis is Louisa County, VA. Louisa County

covers approximately 498 square miles and has a total population of 25,627 based on the

2000 Census (U.S. Bureau of the Census, 2005). According to the 2000 Census, Louisa

County residents have a median household income of $39,402 per year and median home

value of $96,400. The county also has a relatively high percent of the population living

11

below poverty which is estimated at 10.2%. In terms of educational attainment, 14% of

Louisa County residents have a bachelor’s degree or higher – and 76% of the county’s

population is white (see figure 2.4.3). For a comparison of select socio-economic

characteristics between Augusta and Louisa Counties, see Table 2.4.1.

Table 2.4.1: Comparison of Socio-economic Characteristics Based on 2000 U.S. Census Bureau Data – Augusta and Louisa Counties, VA (source: U.S. Census Bureau, 2007)

Augusta County Louisa County Virginia

County Population 65,615 25,627 7,078,515

% with Bachelor’s Degree 15.4 14 29.5

Median Household Income

(in USD)

43,045

39,402 46,677

Median Home Value (in

USD) (2000)

110,900 96,400 125,400

% of Individuals Below

Poverty

5.8 10.2 9.6

12

Town of Louisa

Town of Mineral

Town of Gordonsville

Educational Attainment per Block GroupLouisa County, VA

²0 5 102.5 Miles

LegendTowns

Percentage with Bachelor's Degree or Higher4.56 - 5.00

5.01 - 10.00

10.01 - 15.00

15.01 - 20.00

Town of Louisa

Town of Mineral

Town of Gordonsville

Median Household Income per Block GroupLouisa County, VA

²0 5 102.5 Miles

LegendTowns

Median Household Income24484 - 26000

26001 - 31000

31001 - 36000

36001 - 41000

41001 - 46000

46001 - 51000

Town of Louisa

Town of Mineral

Town of Gordonsville

Population Density per Block GroupLouisa County, VA

²0 5 102.5 Miles

LegendTowns

People Per Square Mile30.67 - 50.00

50.01 - 100.00

Lake Anna

IS 64E IS 64W

IS 64E

Town of LouisaTown of Mineral

Town of Gordonsville

Louisa County, VA

²0 5 102.5 Miles

LegendTowns

Interstate

Primary Roads

Hydrography

Lake Anna

Figure 2.4.3: Geographic Features and Socio-economic/Demographic Characteristics of Louisa County (sources: U.S. Census Bureau, U.S. Census TIGER/Line, VA Dept. of Transportation)

13

Louisa County, located entirely in the Outer Piedmont physiographic

subprovince, is primarily an agricultural area characterized by low to moderate slopes

(Louisa County Comprehensive Plan, 2001; William and Mary College Dept. of

Geology, 2007). Approximately 85% of Louisa County homes rely on individual water

systems such as private wells (Ross et al, 1999). Only the towns of Louisa and Mineral

offer public water systems, which are supported by public supply wells that yield

approximately 42 gpm (gallons per minute) at an average depth of 300 ft. (Louisa County

Comprehensive Plan, 2001). When considering all wells (public and private) in Louisa

County, the average yield is 14.2 gpm, with a large portion of private wells being

shallow, dug or bored wells (Louisa County Comprehensive Plan, 2001). The subsurface

rock types found in Louisa County are of limited permeability, requiring wells to extract

groundwater from water-filled fractures in the bedrock located at depths of 200 ft. or less

(Louisa County Comprehensive Plan, 2001).

Groundwater quality is generally considered good throughout the county with the

majority of reported problems being considered more nuisances than actual threats to

human health (i.e., iron, turbidity) (Louisa County Comprehensive Plan, 2001).

However, in addition to the majority of county residents relying on private wells as their

water source, citizens of Louisa Co. also depend on private waste systems such as septic

tanks.

As a result of the county’s strong ties to agriculture; its limited public water

supply; low population density; and reliance on private water and waste systems, Louisa

County has recently taken a more proactive approach to managing and protecting its

groundwater resources. According to the 2001 Louisa County Comprehensive Plan, one

goal of the county was to utilize GIS to map all well sites, points of septic system failure,

and detrimental land uses for the purpose of effectively managing areas of vulnerability

in terms of contaminated groundwater. A wellhead protection program was also

proposed in the county’s 2001 Comprehensive Plan.

2.5 Significance of Study

This study contributes to the existing body of relevant scientific literature in

several important ways. Current research concerning the relationship between socio-

economic status and environmental risk tends to focus more on urban areas where socio-

14

economic data is aggregated to a certain spatial scale as defined by a community,

municipality, or government-related agency (i.e., the U.S. Bureau of the Census).

However, this study takes place in predominantly rural areas and applies a site specific

indicator of socio-economic status, the monetary value of one’s residence, to each water

quality sample site; which avoids the ecological fallacy of generalizing about the

conditions of a specific place based upon the summary characteristics of a broader

geographic area. This study also does not focus on particular contamination sources such

as toxic waste sites or farming operations. Likewise, this research does not target a

potentially vulnerable subpopulation, but instead evaluates data collected from

participants of a community education/awareness program open to all residents of each

county. This study seeks to simply discover whether there is a statistically significant

association between people of lower SES, based on the county tax assessment of their

home, and the quality of their residential drinking water as determined by nitrate and

exposure to bacteria (total coliform and E.coli). As a result of this approach, the

connection between SES and unhealthy exposure is much clearer. This study uses the

empirical evidence of residential water quality tests to determine exposure and persons at

risk; whereas similar studies assume that based on geographic proximity, certain

environmental conditions equate to an unhealthy exposure.

15

3.0 METHODS

3.1 Data Sources

In 1999, as part of the Virginia Household Water Quality Education Program (VA

HHWQEP), 153 private household wells in Augusta County and 383 private household

wells in Louisa County were tested for health and nuisance contaminants (Ross et al,

2000). The health related contaminants included copper, sodium, fluoride, nitrate, total

coliform, and E.coli. I selected levels of nitrate measured in milligrams per liter (mg/l)

along with the presence/absence of total coliform and E.coli as the water quality

indicators for this analysis based on their potential impact on human health (EPA, 2000;

Ross et al, 1999; Vendrell et al, 2003; Ward et al, 1996). Water quality testing was

conducted at the Virginia Tech Department of Biological Systems Engineering water

quality laboratory.

Nitrate (NO3) is a common groundwater contaminant found in rural areas across

the United States (McCasland et al, 1985). Nitrate in groundwater is primarily the result

of contamination from animal waste, fertilizers, and effluent from septic systems

(McCasland et al, 1985). Nitrate levels greater than 3 mg/l are generally considered to be

the result of contamination from human activity. In terms of the impact on human health,

short-term nitrate consumption of 10 mg/l or greater has been directly attributed to a

condition known as methemoglobinemia, or blue-baby syndrome, found most often in

infants (Lamond et al, 1999). Therefore, the U.S. Environmental Protection Agency has

set a maximum contaminant level (MCL) of 10 mg/l. Aside from the potential health risk

of short term nitrate exposure in infants, other studies suggest that long term human

exposure to nitrate at levels much greater than the EPA’s MCL may contribute to cases of

cancer (McCasland et al, 1985; Ward et al, 1996).

A variety of forms of coliform bacteria found in soil, plant material, and the

intestines of warm-blooded animals have contaminated the water supply (National

Ground Water Association, 2007). Coliforms are commonly found naturally in the

environment and usually do not cause disease. However, coliforms are present in water

that is also contaminated by human or animal feces (EPA, 2000). Therefore, testing for

the presence of total coliforms is used as a way to screen for fecal contamination and to

assess the vulnerability of the groundwater distribution system and the effectiveness of

16

existing treatment methods (EPA, 2000). The VA HHWQEP evaluated total coliform in

Louisa County based on a presence/absence basis instead of retaining the raw value of

coliforms per 100 ml (Ross et al, 1999). For Augusta County, the VA HHWQEP

evaluated total coliform based on the original lab result of colonies per 100 ml.

Escherichia coli, or E.coli, was the third water quality indicator considered in the

quantitative analysis of residential and private well characteristics and human health-

related groundwater contaminants. The presence of E.coli indicates contamination from

bacteria that originate in the intestines of warm blooded animals (Vendrell et al, 2003).

Although human consumption of water contaminated with E.coli does not always lead to

illness, the presence of E.coli in drinking water greatly increases the risk of contracting

infectious diseases that are transmitted through human feces such as hepatitis, cholera,

and typhoid fever (Vendrell et al, 2003). The connection between E.coli and more

serious illnesses The VA HHWQEP evaluated E.coli in Louisa County based on a

presence vs. absence basis instead of retaining the raw value of colonies per 100 ml (Ross

et al, 1999). For Augusta County, the VA HHWQEP evaluated E.coli based on the

original lab result in colonies per 100 ml.

Characteristics of well age, well depth, and treatment devices used were gathered

from the participant survey forms, which were completed by each of the volunteer

participants of the HHWQEP. For 50 samples in Louisa County and 37 samples in

Augusta County, well age was not reported. Likewise, well depth was not reported for 80

samples in Louisa County and 20 samples in Augusta County. Aside from simply not

knowing the depth and/or age of his or her well, participants had no particular reason not

to report this information.

The well characteristics of age, depth, and treatment were chosen based on each

characteristic’s hypothesized association with well water quality. Older wells were

expected to be more susceptible to contamination for several reasons. Older wells were

more vulnerable to contamination as a result of possible deterioration of the well’s

structure over time (Ross et al, 1999). Limited construction standards in Virginia prior to

the 1970s and 1990s also contributed to the poor siting of wells relative to possible

contaminant sources (Ross et al, 1999). Water quality can also vary depending on a

well’s age due to the different methods of construction used over time. Older wells were

17

more likely to also be dug or bored, which are generally shallower than newer, drilled

wells (Bourne, 2001).

Well depth was also considered as an important factor in water quality since

shallower wells were expected to exhibit greater contamination. Frequently, shallow

wells are more likely to be contaminated by nitrate or bacteria due to having a close

proximity to specific human activities such as the application of fertilizers and the

disposal of human and animal waste (Mechenich and Shaw, 1996).

The presence of well treatment was also considered in this analysis, as treated

wells were hypothesized to be less likely to be contaminated by nitrate or bacteria.

Several of the forms of well water treatment used by the Augusta and Louisa County

participants include sediment filters, activated carbon filters, reverse osmosis systems,

and chlorination. Although each treatment type varies in it’s effectiveness to remove or

reduce nitrate and bacteria contamination, the presence of treatment in general was used

to evaluate the overall association between water treatment and SES and water quality.

Residential characteristics of total home value and size of the parcel were

gathered from the internet mapping sites of the Augusta and Louisa County governments

as described in greater detail in section 3.3 Data Management. Total home value was

based on the 2006 tax assessment and is calculated based on “fair market value” for the

parcel value and the improvement value determined by the housing structure.

Determining one’s socio-economic status should ideally include a number of factors, only

one of which is dwelling value (Manitoba Centre for Health Policy, 2003). Other factors

frequently include personal income, level of educational attainment, race, gender, etc.

However, in the absence of more specific information about each participant, and to

avoid the ecological fallacy of aggregating demographic/socio-economic data from

sources such as the U.S. Bureau of the Census, total home value and parcel size were

deemed to be the best available indicators available of socio-economic status.

3.2 Sampling Method

The VA HHWQEP was open to any county resident who relied upon a private,

individual water supply such as a well, spring, or cistern (Ross et al, 2000). Participation

was strictly voluntary and required a nominal fee of $40 for Augusta County residents

and $15 for Louisa County residents to have their water tested (Ross et al, 2000). The

18

residents of each county were made aware of the programs through local media,

extension agency newsletters, and community “kick-off” meetings (Ross et al, 1999 and

2000). In the case of the Louisa County program, water quality testing was also made

available for free to 100 low-income residents (Ross et al, 2000) through financial

assistance offered by the Louisa County Housing Foundation. When asked how the 100

low income participants were allowed to participate in the water quality education

program, Evergreen stated, “We simply found as many low income candidates as we

could that were willing to have their water tested (Evergreen, 2007).” In general, the

method of data collection used by the VA HHWQEP may have influenced the

distribution of participants based on socio-economic status since in most cases water

quality testing was not free of charge. However, the samples collected and analyzed for

this study do exhibit a relatively uniform geographic distribution across the inhabited

areas of each county (figures 3.3.1 and 3.3.2).

3.3 Data Management

Each sample, which included water quality, well age, well depth, and the

utilization of water treatment information, was assigned a location based on the address

of the participant reported on the survey form. Each address was then geocoded using an

address locator in a Geographic Information System (GIS). The “matched” locations of

the participants and water quality sample site were then manually reviewed to ensure the

correct address was properly located through the geocoding process. Additionally,

addresses that could not be located through geocoding as a result of not being a

recognizable physical address (i.e., post office box, rural route, etc.) were digitized based

on the locations of the samples recorded on paper maps at the time of data collection.

This review process resulted in some sample sites being relocated and others being

omitted due to an inability to properly match each sample with the correct address.

Additionally, only one sample was allowed per well. As a result of this process, the

Augusta County dataset was reduced from an original 153 household private wells to

124, and the Louisa County dataset was reduced from 383 household private wells to 336

samples. Figures 3.3.1 and 3.3.2 show the location of samples for Augusta and Louisa

counties. Table A.1 in Appendix A provides a summary of the samples that could not be

located and were subsequently not considered in this analysis.

19

Once each well site was mapped based on the physical address, paper map

location, and/or homeowner name match, total home value (in U.S. dollars) and the size

of each parcel (in acres) were gathered from each county’s online cadastral map/tax

parcel GIS database. In some instances, multiple residences shared the same address

and/or homeowner name. In these cases, the parcel with an “improvement value” listed

was chosen, indicating the value of a permanent structure on the property aside from the

value of just the residential lot. In other cases, aerial imagery from the Virginia Base

Mapping Program (VBMP) was used to determine which parcel likely contained the

housing structure. When “improvement value” or VBMP aerial imagery could not

decisively indicate which parcel contained the site of the sample well, sample locations

were assigned based on the logical association of the sampled well’s age to the age of the

residence. Although extra measures were taken to accurately site each water quality

sample, it should be noted that without using the Global Positioning System or some

other means of determining sample locations at the time the data was originally collected,

there was the possibility of incorrectly assigning samples to their corresponding location.

Each sample considered had at least a homeowner name or address match to its assigned

parcel. The results of the geocoding/manual location process are summarized for each

county in Appendices A.3 and A.4.

After collecting housing value and parcel size information for each private well

sample site, total coliform and E.coli categorical classifications of “present” and “absent”

were converted into two ordinal classes consisting of ones and zeros, respectively. Raw

data values for bacteria in the Augusta County dataset were also reduced to ones and

zeros to indicate presence and absence. The conversion of total coliform and E.coli

values into ordinal classes permitted all variables to be analyzed collectively and

consistently between the two counties.

20

SR 4

2

US 11

US 250

SR 2

52

US 340

SR 48

SR 254

SR 256

SR 262

SR 285

US 11

S

US 250W

SR 275

SR 48

SR 42

SR 254

SR 4

8

US

340

US 11

US

250

SR 48

SR 4

8

IS 8

1N

IS 81S

IS 64EIS 6

4W

124 Successfully Located Household WellsAugusta County, VA

City of Staunton

City ofWaynesboro

²0 9 184.5 Miles

LegendSampled household well sites

Interstate highways

Primary roads

Elevation in meters High : 1360.13

Low : 320.819

Figure 3.3.1: Map of Successfully Located Household Well Sites: Augusta County (sources: VA Dept. of Transportation, Radford University)

21

Lake Anna

IS 64E IS 64W

IS 64E

IS 64W

Town of Louisa

Town of Mineral

Town of Gordonsville

336 Successfully Located Household WellsLouisa County, VA

²0 5 102.5 Miles

LegendSampled household well sites

Towns

Interstate

Lake Anna

Extract_asci1Value

High : 173

Low : 48

Elevation in meters

Figure 3.3.2: Map of Successfully Located Household Well Sites: Louisa County

3.4 Statistical Analysis

To determine the most appropriate statistical methods for evaluating the

association of water quality and residence and well characteristics, a series of preliminary

22

analyses were applied to both the Augusta County and Louisa County datasets. Based on

the shape of the histograms, skewness, kurtosis, and Shapiro-Wilk tests for each

independent variable, it was determined that non-parametric statistical techniques were

necessary. Tables A.2 (see Augusta_normality_tests.pdf) and A.3 (see

Louisa_normality_tests.pdf) in Appendix A detail the distribution of each independent

variable for the two counties.

In addition to assessing the statistical distribution of the Augusta County and

Louisa County datasets, the nonparametric equivalent of an Analysis of Variance

(ANOVA) test, the Kruskal-Wallis test, was also used to determine whether the different

sample dates of June 1, 1999, June 15, 1999, and October 12, 1999 in the Louisa County

dataset had a statistically significant relationship to the measured levels of nitrate and the

presence of total coliform and E.coli. The Kruskal-Wallis test indicated statistically

significant differences among the Louisa County samples based on collection date (see

Table 3.4.1). However, the samples were not collected in the same portion of the county

at each date, so there is an excellent chance that the geographic distribution rather than

the temporal variation accounted for that variation. Kick-off meetings for the Louisa

County program were held in different parts of the county each time. According to the

original Louisa County HHWQEP summary report, kick-off meetings were held in Holly

Grove and the Town of Louisa for the samples collected in June. Based on the spatial

distribution of the samples collected on June 1, it appears the majority of the participants

attended the Holly Grove meeting (Figure 3.4.1). For the June 15 meeting, a large

portion of the participants resided around Lake Anna in the northern portion of the

county. The samples collected on October 12 were much more spatially distributed

across the county’s midsection. The differences in water quality by the predominantly

sampled areas for each collection date are provided in Table 3.4.2.

23

Table 3.4.1: Summary of Kruskal-Wallis Tests: Louisa County Samples by Date Summary of Kruskal-Wallis Test Results: Louisa County Samples by

Collection Date (α = .05)

Water Quality Indicator by Date

# of Samples Mean

Chi-Square Pr > Chi-Square

6/1/1999 56 0.78 6/15/1999 218 1.32

Nitrate

10/12/1999 62 1.13

1.28 0.527

6/1/1999 56 0.68 6/15/1999 218 0.44

Total Coliform

10/12/1999 62 0.71

20.68 <.0001

6/1/1999 56 0.05 6/15/1999 218 0.04

E.coli

10/12/1999 62 0.24

28.93 <.0001

24

[̀

[̀

Louisa County Household Wells by Sample Collection Date

² 4Miles

LegendSample Collection Date

6/1/1999

6/15/1999

10/12/1999

VA HHWQEP Community Meeting Sites

[̀ Holly Grove

[̀ Town of Louisa

Figure 3.4.1: Map of Louisa County Samples by Collection Date

25

Table 3.4.2: Summary of Water Quality By Region and Sample Date: Louisa County, VA

Summary of 92 Wells in SE Louisa County (Holly Grove/Locust Creek) Portion of Louisa County 6/1/1999 6/15/1999 10/12/1999 All # wells 51 29 12 92 Mean nitrate 0.84 1.36 1.08 1.04 Median nitrate 0.405 0.274 0.713 0.427 St. dev. Nitrate 1.27 2.27 1.24 1.67 # total coliform present 35 22 10 67 # E.coli present 3 3 3 9

Summary of 115 Wells Around Lake Anna Portion of Louisa County 6/1/1999 6/15/1999 10/12/1999 All # wells 5 100 10 115 Mean nitrate 0.25 1.33 1.17 1.27 Median nitrate 0.208 0.18 0.77 0.26 St. dev. Nitrate 0.13 2.95 1.21 2.78 # total coliform present 3 27 6 36 # E.coli present 0 1 0 1

Summary of 129 Wells in Remaining Portion (i.e., midsection) of Louisa County 6/1/1999 6/15/1999 10/12/1999 All # wells 0 89 40 129 Mean nitrate n/a 1.3 1.13 1.25 Median nitrate n/a 0.348 0.235 0.332 St. dev. Nitrate n/a 3.22 2.5 3.02 # total coliform present n/a 46 28 74 # E.coli present n/a 4 12 16

As a result of the Louisa County samples’ exhibiting a strong spatial pattern;

which could likely explain the water quality differences by collection date, the samples

from the three dates were lumped together. A total of 336 wells were considered for

analysis in Louisa County.

To determine the strength of the statistical association among residence

characteristics, well characteristics, and water quality, Spearman’s Rank correlation was

chosen. Spearman’s Rank uses a similar formula to the Pearson’s Product Moment

correlation, only with ordinal ranks rather than the raw data values for each variable. In

the case of treatment, total coliform, and E.coli, values of one and zero were substituted

for reports of “present” and “absent”, respectively.

26

Using an alpha (α) level of .05, the residence and well characteristics with a

statistically significant relationship to nitrate levels, total coliform, or E.coli were then

evaluated to determine their statistical influence on water quality using discriminant

analysis. Similar to an ANOVA, discriminant analysis determines which variables

discriminate between two or more groups (statsoft.com, 2003). Classes for discriminant

analysis were applied to each water quality indicator in the following manner shown

below in Table 3.4.3.

Table 3.4.3: Groupings of Water Quality Indicators for Discriminant Analysis GROUP 1 GROUP 2 Nitrate >= 3 mg/l Nitrate < 3 mg/l Total Coliform Presence Total Coliform Absence E.Coli Presence E.Coli Absence

The decision to use 3 mg/l as the cutoff value for nitrate classes was based on previous

research findings that 3 mg/l indicates contamination from human-related activities (Ross

et al, 1999). Results of the discriminant analysis can be interpreted based on the

magnitude of the F-value, the probability value, and the canonical correlation; which,

similar to a principal components analysis, weights each variable in terms of its influence

on the classification or discrimination of groups (Wuensch, 2006). Although

discriminant analysis is based on the assumption that the data are normally distributed,

the technique is considered to be relatively insensitive to skewness (Canadian Forest

Service, 2005). However, discriminant analysis can be greatly affected by outliers. As a

result, the predictor variables exhibiting outliers were logarithmically transformed prior

to conducting discriminant analysis.

3.5 Spatial Analysis

In addition to analyzing the statistical association and influence of the selected

residence and well characteristics on private well water quality, nearest neighbor analysis

was applied to assess the spatial randomness of each water quality indicator and the

likelihood of contaminated wells being in closer proximity to one another than

uncontaminated wells (Clark and Evans, 1954). Nearest neighbor analysis evaluates the

average distance of selected point features versus the expected average distance in the

form of a ratio known as the nearest neighbor index (NN index). The NN index falls

27

between the theoretical extremes of perfect concentration and perfect uniformity, or the

values of zero and 2.14, respectively. If the NN index falls closer to 0, clustering is

present. If the NN index falls closer to 1, spatial randomness occurs. The closer the NN

index is to 2.14, the more uniformly spaced the points (Ismail, 2001). The results of the

nearest neighbor analysis can also be evaluated in terms of statistical probability and the

value of Z. If Z is greater than +/-1.96, there is at least a confidence level of 95% or

higher that the spatial pattern is too unlikely to be the result of random chance.

The possible sources of nitrate and bacterial contamination were analyzed

spatially using chi-square tests between satellite imagery-based land cover and

contaminated vs. uncontaminated classes. Five generalized land cover classes were

originally derived from the 1992 National Land Cover Dataset (NLCD). NLCD was

produced by the U.S. Geologic Survey using a simplified version of the Anderson Land

Use and Land Cover Classification System (USGS, 2007). Table 3.5.1 provides a list of

the classification scheme used for this analysis based on the original Anderson Level 1

classification scheme. Due to the overwhelming majority of wells falling either into

agricultural or forested land cover classes, the wells were reassigned to either agricultural

or non-agricultural land cover. Although the NLCD data represented land cover

conditions of the early 1990s, its application to when the VA HHWQEP samples were

taken in 1999 was relevant. In Louisa County, the number of farms in the county actually

increased between the years of 1992 to 1997; with the average size of each farm only

decreasing by only 3% despite a 25% population growth from 1990 to 2000 (Louisa

County Comprehensive Plan, 2001). Similarly, Augusta County experienced an 18.6%

population increase from 1990 to 2000, but only saw a 2% reduction in the total acreage

occupied for farming purposes from 1992 to 1997 according to the U.S. Census of

Agriculture (Augusta County Comprehensive Plan, 2005; Cornell University Mann

Libraries, 2004).

28

Table 3.5.1: Summary of Land Cover Reclassification from NLCD 1992 NLCD Anderson class 1 values

New Generalized Class

11 (open water), 91 (woody wetlands), 92 (emergent herbaceous wetlands)

Water/wetlands

21 (low intensity residential) residential 41 (deciduous forest), 42 (evergreen

forest), 43 (mixed forest) forest

33 (transitional) transitional 81 (pasture/hay), 82 (row crops) agriculture

29

4.0 RESULTS

4.1 Analysis of Louisa County Samples

Analysis of the statistical association between the selected housing and well

characteristics and water quality parameters using Spearman’s rank correlation resulted in

a number of significant correlations (α = .05). Table 4.1.1 provides the Spearman’s rank

correlation coefficient (r) and probability (p) values for the selected housing and well

characteristics and water quality parameters. Of the five independent variables

considered, well age demonstrated the strongest correlation (r = .41) to nitrate, suggesting

that as wells become older, nitrate levels increase. A statistically significant negative

correlation between well depth and nitrate was also found (r = -.23), along with a

statistically significant positive correlation between parcel size (in acres) and nitrate (r =

.18). However, a statistically significant relationship between total home value and

nitrate was not observed based on the 336 samples analyzed from household wells in

Louisa County during the summer and fall of 1999.

Analysis of total coliform and total home value using Spearman’s rank correlation

did result in a statistically significant negative correlation, indicating that the presence of

total coliform was more common among lower value homes in the dataset. Of the well

characteristics considered, well depth had the strongest association with total coliform (r

= -.5), followed by well age (r = .29), and treatment (r = -.28). Parcel size also exhibited

a weak, yet statistically significant correlation to total coliform (r = .12). The results of

the Spearman’s rank correlation between total coliform and the five housing and well

characteristics considered suggested that shallower, older, and untreated wells serving

lower valued homes located on larger parcels may be most likely to test positive for

bacteria.

In terms of E.coli and the housing and well characteristics considered, well depth

(r = -.28), well age (r = .20), total home value (r = -.18), and treatment (r = -.12) each

exhibited statistically significant correlations. These observed statistical associations

insinuate that wells testing positive for E.coli tend to be shallower and/or older than wells

that tested negative for E.coli. Additionally, of the 336 wells analyzed in Louisa County,

30

E.coli was also more likely as homes decreased in value and where water treatment

devices were not used.

31

Table 4.1.1: Correlation matrix: Louisa County Samples (highlighted cell indicates statistically significant relationships [α = .05])

Louisa County Correlation Matrix

(Spearman's rank coefficients, p-values, and number of observations)

Total home value Well age

Well depth Acres Treatment Nitrate

Total coliform E.coli

1 -0.22211 0.39439 0.03818 0.38499 -0.05964 -0.28454 -0.17869 0.0002 <.0001 0.4854 <.0001 0.2757 <.0001 0.001

Total home value

336 286 256 336 309 336 336 336 -0.22211 1 -0.54158 0.18288 -0.05817 0.41305 0.29005 0.20243

0.0002 <.0001 0.0019 0.3419 <.0001 <.0001 0.0006

Well age

286 286 239 286 269 286 286 286 0.39439 -0.54158 1 -0.05862 0.27352 -0.23051 -0.50361 -0.27511 <.0001 <.0001 0.3502 <.0001 0.0002 <.0001 <.0001

Well depth

256 239 256 256 238 256 256 256 0.03818 0.18288 -0.05862 1 -0.0719 0.18219 0.11569 -0.00689

0.4854 0.0019 0.3502 0.2075 0.0008 0.034 0.8998

Acres

336 286 256 336 309 336 336 336 0.38499 -0.05817 0.27352 -0.0719 1 -0.06698 -0.27673 -0.11544 <.0001 0.3419 <.0001 0.2075 0.2404 <.0001 0.0426

Treatment

309 269 238 309 309 309 309 309 -0.05964 0.41305 -0.23051 0.18219 -0.06698 1 0.24402 0.15601

0.2757 <.0001 0.0002 0.0008 0.2404 <.0001 0.0041

Nitrate

336 286 256 336 309 336 336 336 -0.28454 0.29005 -0.50361 0.11569 -0.27673 0.24402 1 0.27448

<.0001 <.0001 <.0001 0.034 <.0001 <.0001 <.0001

Total coliform

336 286 256 336 309 336 336 336 -0.17869 0.20243 -0.27511 -0.00689 -0.11544 0.15601 0.27448 1

0.001 0.0006 <.0001 0.8998 0.0426 0.0041 <.0001

E.coli

336 286 256 336 309 336 336 336

Further review of the Spearman’s rank correlation matrix suggested that the

presence of nitrate and bacteria in private wells in Louisa County may be the result of

complex interactions among a variety of interdependent factors. Well age and well depth

were frequently found to be cross-correlated. This relationship was likely the result of

advancements in well drilling that have enabled wells to be constructed at greater depths.

In the case of the samples analyzed from Louisa county residents, total home value

exhibited a statistically significant negative correlation with well age and a statistically

significant positive correlation with well depth; which might explain the significant

negative correlation between total home value and total coliform. Total home value was

also found to have a significant positive statistical relationship with the usage of water

32

treatment devices. This was expected since the presence of water treatment devices and

higher valued homes are frequently dependent upon the financial means of the

well/homeowner. Well depth and the use of water treatment devices were also found to

have a positive association with one another. This finding may suggest that both well

depth and private well water treatment were the result of the well owner’s financial

means; however, further research to support this notion is still needed. Another

important observation was that older wells, which are potentially vulnerable to

contamination as a result of structural deterioration and poor well siting, were typically

not found to be treated.

To better determine which housing and well characteristics have the greatest

statistical influence on nitrate and bacteriological contamination of the household wells

considered in this analysis, discriminant analysis was applied to the Louisa County

samples. Using an alpha level of .05, three separate discriminant analyses were

conducted for each water quality indicator, with each analysis identifying statistically

significant independent variables capable of discriminating between contaminated and

uncontaminated wells. In the case of the groupings based on nitrate concentrations, log

of acreage (i.e., parcel size), log of well age, treatment, and log of well depth all had

loadings greater than .30, which typically indicates that a variable is an important

influential/discriminant factor (Wuensch, 2006). For total coliform, the log of well depth,

treatment, log of well age, and the log of total home value all exhibited loadings greater

than .30 in the total canonical structure. When evaluating which variables had the

greatest influence on the presence/absence of E.coli, the log of well depth, followed by

the logs of well age and total home value, and treatment were all considered to be

significant factors in discriminating whether a well was contaminated with fecal coliform.

A summary of the discriminant analyses conducted on the Louisa County samples can be

found in Table 4.1.2.

33

Table 4.1.2: Summary of Discriminant Analysis Results: Louisa County, VA Summary of Discriminant Analysis Results: Louisa County, VA (α = .05)*

Grouping P-Value

% of variance explained

by x Strongest predictor

variables (x)**

% misclassified using predictive

discriminant function

nitrate contaminated (≥3mg/l) vs. uncontaminated (<3mg/l) 0.05 5%

acres_log, well age_log, treatment,

well_depth_log 10%

total coliform present vs. total coliform absent <.0001 30%

well_depth_log, treatment,

well_age_log, total_valu_log 25%

E.coli present vs. E.coli absent 0.01 6%

well_depth_log, well_age_log, total_valu_log,

treatment 6% *Interpretation of discriminant analysis results is based on Wuensch, K. "Two Group Discriminant Function Analysis." 2006. **Strongest predictor variables are based on loadings > .30 (listed in descending order in terms of magnitude) in the total canonical structure.

In addition to analyzing the statistical association of housing and well

characteristics and private well water quality, nearest neighbor analysis was applied to

determine which contaminants were found to be in closer proximity to other wells

polluted by the same contaminant. As a result of this analysis, well sites testing positive

for total coliform were found to have a lower nearest neighbor index and higher z-score

value than well sites without total coliform. In the case of nitrate and E.coli, however,

well sites reporting below 3 mg/l of nitrate and testing negative for E.coli were found to

be in closer proximity to one another than wells contaminated by these two toxins. A

summary of the results of the nearest neighbor analysis for each contaminant and sample

date is provided in Table 4.1.3.

34

Table 4.1.3: Summary of Nearest Neighbor Analysis Results: Louisa County, VA Summary of Results: Nearest Neighbor Analysis - Louisa County Samples

Class # of

samples

observed avg. distance (in

meters)

expected avg. distance (in

meters) NN

index z-

score level of

significancenitrate ≥ 3 mg/l 34 2817 3783 0.74 -2.9 0.01 nitrate < 3 mg/l 302 708 1269 0.55 -14.8 0.01 total coli present 177 983 1658 0.59 -10.4 0.01 total coli absent 159 1103 1749 0.63 -9 0.01 E.coli present 26 2692 4326 0.62 -3.7 0.01 E.coli absent 310 707 1253 0.56 -14.7 0.01

These results of the nearest neighbor analysis for Louisa County suggest that

household wells testing positive for naturally abundant total coliforms can exhibit a

pattern of clustering. However, the average distance between wells testing positive for

total coliform is 983 meters (3224 feet); which greatly exceeds the distance of 50 feet that

is considered by the Virginia Department of Health to be the minimum separation

distance between a Class III well and a septic or sewerage system (VA Dept. of Health,

1992). As a result, wells with total coliform present may be located in closer proximity

to one another based on similar well characteristics (i.e., well age and depth) rather than

common exposure to the contaminant’s source. In the case of the less prevalent

contaminants of nitrate and E.coli, neighboring wells are not as likely to be contaminated.

In general, exposure to nitrate or E.coli appears to be more site-specific, based on the

complex interaction of numerous factors such as the location of the contaminant source;

the transport of the contaminant; and the structural quality and design of the household

well.

Previous studies suggest that one potential source of nitrate and bacteriological

groundwater contamination stems from livestock waste found in agricultural areas

(Bickford et al, 1996; Nolan et al, 1998). To determine whether agricultural areas were

particularly vulnerable in Louisa County based on the HHWQEP samples, land cover

classifications were assigned to each sample site based on the National Land Cover

Dataset (NLCD) of 1992 produced by the USGS. Land cover classifications for each

sample site were then compared to the health-related water quality indicators using Chi-

35

Square tests. When divided into two classes of greater than and less than 3 mg/l, nitrate,

along with wells testing positive for total coliform exhibited a statistically significant

association with agricultural land cover (α = .05). E.coli, however, was not found to be

significantly related to agricultural land cover (see table 4.1.4 for complete results).

Table 4.1.4: Agricultural Land Cover/Water Quality Chi-Square Test Results – Louisa County (α = .05)

Nitrate by Agricultural Land Cover: Louisa County Samples

Contaminated Uncontaminated Total Agricultural 21 88 109Non-agricultural 13 214 227Total 34 302 336 Degrees of freedom: 1 Chi-square = 14.8419401623997 p is less than or equal to 0.001. The distribution is significant.

Total Coliform by Agricultural Land Cover: Louisa

County Samples Present Absent Total

Agricultural 112 48 160Non-agricultural 65 111 176Total 177 159 336 Degrees of freedom: 1 Chi-square = 36.76 p is less than or equal to 0.001. The distribution is significant.

E.coli by Agricultural Land Cover: Louisa County

Samples Present absent Total

Agricultural 8 152 160Non-agricultural 18 209 227Total 26 361 387 Degrees of freedom: 1 Chi-square = 1.29 For significance at the .05 level, chi-square should be greater than or equal to 3.84. The distribution is not significant. p is less than or equal to 1.

36

The results of the land cover Chi-Square tests indicated that agricultural land

cover was primarily a factor only when considering contamination from nitrate and total

coliform, not fecal coliform (i.e., E.coli). This may suggest that the source of E.coli

found in the sampled household wells of Louisa County was not animal wastes, but

possibly from anthropogenic sources such as failing septic tanks or drainage fields.

However, further research is required to support this notion.

4.2 Analysis of Augusta County Samples

Analysis of the 124 samples analyzed from the Augusta County dataset did not

result in any statistically significant (α = .05) associations between total home value and

the considered well characteristics or total home value and the water quality indicators

used in this study. Parcel size and the presence of E.coli were found to be significantly

correlated using a confidence level of 95% (r = .21), which indicates that, based on the

samples analyzed in this study, private wells on larger parcels have a higher occurrence

of E.coli. However, it should be noted that only nine of the 124 samples analyzed from

Augusta County tested positive for E.coli. The presence of self-reported treatment

devices was also significantly correlated with E.coli (r = -.19), which suggests that the

use of any treatment device results in lower fecal coliform contamination. Besides

significant relationships between parcel size and E.coli and water treatment and E.coli,

well age exhibits a positive correlation with well depth (r = -.33, p = .0009), signifying

that older wells are shallower than newer wells. This finding was generally expected due

to recent technological advancements in well drilling. Results of Spearman’s Rank

correlation for the 124 wells analyzed in Augusta County are provided in Table 4.2.1.

37

Table 4.2.1: Correlation Matrix: Augusta County, VA (highlighted cell indicates statistically significant relationships [α = .05])

Augusta County Correlation Matrix

(Spearman's rank coefficients, p-values, and number of observations)

Total home value Well age

Well depth Acres Treatment Nitrate

Total coliform E.coli

1 0.10898 0.01468 0.64507 0.02316 0.07139 -0.14732 0.07729 0.315 0.8824 <.0001 0.8059 0.4308 0.1025 0.3936

Total home value

124 87 104 124 115 124 124 124 0.10898 1 -0.27119 0.18563 0.03979 0.04725 0.11564 0.07908

0.315 0.0137 0.0852 0.7244 0.6639 0.2861 0.4666

Well age

87 87 82 87 81 87 87 87 0.01468 -0.27119 1 -0.00125 0.17093 -0.07799 -0.092 -0.16496

0.8824 0.0137 0.99 0.0941 0.4313 0.353 0.0943

Well depth

104 82 104 104 97 104 104 104 0.64507 0.18563 -0.00125 1 -0.12026 -0.02187 -0.06938 0.21363 <.0001 0.0852 0.99 0.2005 0.8095 0.4438 0.0172

Acres

124 87 104 124 115 124 124 124 0.02316 0.03979 0.17093 -0.12026 1 0.03317 -0.02335 -0.19063

0.8059 0.7244 0.0941 0.2005 0.7249 0.8044 0.0413

Treatment

115 81 97 115 115 115 115 115 0.07139 0.04725 -0.07799 -0.02187 0.03317 1 0.08723 0.12901

0.4308 0.6639 0.4313 0.8095 0.7249 0.3354 0.1533

Nitrate

124 87 104 124 115 124 124 124 -0.14732 0.11564 -0.092 -0.06938 -0.02335 0.08723 1 0.2882

0.1025 0.2861 0.353 0.4438 0.8044 0.3354 0.0012

Total coliform

124 87 104 124 115 124 124 124 0.07729 0.07908 -0.16496 0.21363 -0.19063 0.12901 0.2882 1

0.3936 0.4666 0.0943 0.0172 0.0413 0.1533 0.0012

E.coli

124 87 104 124 115 124 124 124

Nearest neighbor analysis of the 124 wells analyzed in Augusta County indicated

that only wells with nitrate levels greater than 3 mg/l were found to be in closer proximity

than wells with nitrate less than 3 mg/l based on the nearest neighbor index.

Additionally, based on the samples analyzed for in this study, wells free of bacteria in

Augusta County are more likely to be found in closer proximity to one another than wells

testing positive for bacteria. Complete results of the nearest neighbor analysis of the

private wells analyzed in Augusta County can be found in Table 4.2.2.

38

Table 4.2.2: Nearest Neighbor Analysis Results: Augusta County Samples Summary of Results: Nearest Neighbor Analysis - Augusta County Samples

Class # of

samples

Observed avg. distance (in

meters)

Expected avg. distance (in

meters) NN

index z-

score Level of significance all 124 1582 2269 0.69 -6.5 0.01

Nitrate ≥ 3 mg/l 16 4239 6317 0.67 -2.6 0.05

Nitrate < 3 mg/l 108 1775 2432 0.73 -5.4 0.01

total coli present 48 3075 3647 0.84 -2.1 0.05 total coli absent 76 2074 2899 0.71 -4.8 0.01 E.coli

present 9 6959 8423 0.82 -1 random E.coli

absent 115 1772 2356 0.75 -5.1 0.01

Unlike the samples taken from Louisa County, analysis of land cover and water

quality at each sampled well site in Augusta County using chi-square tests did not result

in statistical significance at the .05 alpha level for any of the three contaminants

considered. The majority of the well sites tested are located in agricultural areas based on

the NLCD 92 dataset. However, as Table 4.2.3 shows, wells exceeding 3 mg/l of nitrate

or testing positive for bacteria (total coliform or E.coli) did not have a statistically

significant association to agricultural land cover.

39

Table 4.2.3: Agricultural Land Cover/Water Quality Chi-Square Test Results – Augusta County (α = .05)

Nitrate by Land Cover Classification Land Cover Class Nitrate > 3 Nitrate < 3 Total

agricultural 11 77 88 non-agricultural 5 31 36 Total 16 108 124 Degrees of freedom: 1 Chi-square = 0.04 For significance at the .05 level, chi-square should be greater than or equal to 3.84. The distribution is not significant. p is less than or equal to 1.

Total Coliform by Land Cover Classification

Land Cover Class Total Coli Present Total Coli

Absent Total agricultural 32 56 88 non-agricultural 16 20 36 Total 48 76 124 Degrees of freedom: 1 Chi-square = 0.703 For significance at the .05 level, chi-square should be greater than or equal to 3.84. The distribution is not significant. p is less than or equal to 1.

E.coli by Land Cover Classification Land Cover Class E.coli pres E.coli abs Total

agricultural 6 82 88 non-agricultural 3 33 36 Total 9 115 124 Degrees of freedom: 1 Chi-square = 0.087 For significance at the .05 level, chi-square should be greater than or equal to 3.84. The distribution is not significant. p is less than or equal to 1.

40

5.0 DISCUSSION

5.1 County Comparison and Discussion of Key Findings

Through a comparison of the results from Augusta and Louisa Counties, several

important observations emerge. Analysis of the statistical association between selected

housing and well characteristics and water quality did not result in statistically significant

relationships for the 124 wells evaluated in Augusta County. However, in the case of the

336 private wells analyzed in Louisa County, statistically significant correlations at the

.05 level were identified between total home value and well age, total home value and

well depth, total home value and treatment, and total home value and bacteria (total

coliform and E.coli). The differences in the results between the two counties were likely

due to several factors. Variations in the depth of the water table between the two

counties based upon water table depths generally decreasing as one moves closer to the

Coastal Plain, may help to explain why wells in Louisa County are generally shallower

than those in Augusta County. In her review of statewide VA HHWQEP data, Bourne

(2001) found shallower wells to generally be more susceptible to nitrate and

bacteriological contamination. This becomes evident when comparing the average well

depth of 145 ft. for the sampled wells in Louisa County, versus a mean depth of 292 ft.

for the samples collected in Augusta County. The Virginia Farmstead Assessment

System also suggests older wells are more vulnerable to contamination due to

deteriorating construction, shallower depths, and poor well-siting relative to possible

contaminant sources (Younos and Ross, 1996). This connection between well age, well

depth, and water quality was supported by the analysis of the samples from Louisa

County. However, in spite of the statistically significant (α = .05) correlation (r = -.27) in

the Augusta County samples between well age and depth, water quality was not related to

either well characteristic.

When evaluating the relationship between agricultural land cover and nitrate and

bacteria, only the samples collected in Louisa County showed a statistically significant

association between nitrate, total coliform, and agricultural land cover. This finding

suggests that further research is needed to determine possible causes of fecal

contamination in the wells of both counties. Additionally, further research is needed to

41

understand possible sources of nitrate and bacteriological contamination for the samples

collected in Augusta County.

Differences in the results can also be explained through variations in the

Household Water Quality Education Program between Augusta and Louisa counties.

Although both community education programs were conducted by the Virginia

Cooperative Extension, the programs differ from county to county based upon the

involvement and practices of the local county extension office. Supplementary funding

from the local extension office played a significant role in the cost of the program that

was incurred by each participant as well as the number of participants the program could

allow. For example, the Augusta County program cost $40 per sample, compared to only

$15 for Louisa County residents. Additional assistance was also provided by the Louisa

County Housing Foundation for 100 low income residents to participate in the program

free of charge. These monetary differences in the two programs likely impacted the

overall number of participants, and certainly affected which residents received water

quality testing. Figure 5.1.1 shows the distribution of samples based on total home value

z-scores for Augusta and Louisa Counties. Table 5.1.1 provides a summary of the total

home values represented by the samples from each county. As one can see from this

table, samples from both counties were greatly higher than the respective county’s

estimated median home values of 2006. However, the samples collected in Louisa

County are statistically closer to the county’s overall median household income,

indicating that the Louisa County samples are more representative of the county’s overall

population.

42

Comparison of Z-scores Based on Total Home Value: Augusta County vs. Louisa County Samples

0

5

10

15

20

25

30

35

-2 -1.5 -1 -0.

5 0 0.5 1 1.5 2 2.5 3 3.5 More

Z-scores

Perc

enta

ge o

f Sam

ples

per

Cou

nty

Louisa County SamplesAugusta County Samples

Figure 5.1.1: Histogram of Z-scores for Total Home Values: Augusta and Louisa County Table 5.1.1: Summary of Total Home Values versus Estimated County Median Values of Owner Occupied Homes: Augusta and Louisa Counties, VA Augusta County Samples Louisa County Samples Mean $303,080 $325,667 Median 272,900 244,400 Standard deviation 164,834 276,052 Estimated median home value(source: Reply! Inc., 2007)

138,485 132,370

Variations in the distribution of samples based on socio-economic status may

have also played a role in the overall number of contaminated wells from each county.

As Howard Evergreen of the Louisa County Housing Foundation pointed out, “Most of

these [low-income participants] were elderly, and therefore had elderly (old) wells. Most

were bored wells (Evergreen, 2007).” Figures 5.1.2 and 5.1.3 provide comparisons of the

number of samples testing positive for total coliform and E.coli based on total home

values. These figures suggest that when more wells of lower valued homes were

sampled, as in the case of Louisa County, bacteriological contamination was more

prevalent. The overall level of involvement of each county extension office in outreach

and community education may have also affected the results of this study and the

43

subsequent differences in the outcomes between the two counties. However, further

research is needed to determine the extent of differences between the two county

extension offices in terms of overall community involvement and familiarity.

Comparison of Total Coliform Test Results By Total Home Value: Louisa

County, VA Samples

0102030405060

5000

0

2000

00

3500

00

5000

00

6500

00

8000

00

9500

00

Total Home Value

# of

Sam

ples total coliform

absenttotal coliformpresent

Comparison of E.coli Test Results By Total Home Value: Louisa County, VA

Samples

0102030405060

5000

0

2000

00

3500

00

5000

00

6500

00

8000

00

9500

00

Total Home Value

# of

Sam

ples

E.coli absent

E.coli present

Figure 5.1.2: Comparison of Total Coliform and E.coli based on Total Home Value: Louisa County, VA Samples

44

Comparison of Total Coliform Test Results by Total Home Value: Augusta County, Virginia Samples

0

5

10

15

20

25

5000

0

1500

00

2500

00

3500

00

4500

00

5500

00

6500

00

7500

00

8500

00

9500

00

Total Home Value

# of

Sam

ples total coliform

absenttotal coliformpresent

Comparison of E.coli Test Results By Total Home Value: Augusta County, VA Samples

0

5

10

15

20

25

5000

0

1500

00

2500

00

3500

00

4500

00

5500

00

6500

00

7500

00

8500

00

9500

00

Total Home Value

# of

Sam

ples

E.coli absentE.coli present

Figure 5.1.3: Comparison of Total Coliform and E.coli based on Total Home Value: Augusta County, VA Samples Along with noteworthy differences in the results between the two counties,

several important findings should also be emphasized. In the case of the 336 samples

analyzed from Louisa County, lower home values were significantly related to shallower

45

and older wells, the lack of water treatment, and the presence of bacteria (both total

coliform and E.coli). This finding supports the notion that there was disproportionate

access to safe rural drinking water. This finding also demonstrates that well age and well

depth along with water treatment devices can play an important role in reducing well

water levels of nitrate and bacteria. As a result of the land cover/contaminant analysis in

Louisa County using Chi-Square tests, nitrate and total coliform did exhibit a statistically

significant relationship to agricultural land cover. This finding suggests that a source of

nitrate and bacteriological contamination may be found in agricultural areas; however,

the cases of E.coli contamination may be due to other sources such as failing septic tanks

that cannot be identified by land cover data.

5.2 Areas of Future Research

Upon consideration of this study’s findings and limitations, several

recommendations for future research emerge. These suggestions apply not only to rural

water quality education programs, but also to future public health studies related to

uniform access to safe rural drinking water.

Future studies analyzing the relationships among socio-economic conditions and

private well water quality could incorporate land use as an additional factor in the multi-

variable equation. This study analyzes the statistical association between agricultural

land cover and water quality independent of the analyses which considered SES, well

characteristics, and water quality. As a result, the associations and interrelationships

among land cover, SES, well characteristics, and water quality were not assessed.

Evaluating land cover/land use as it relates to SES and water quality may provide a

greater understanding of how exposure to contaminated private well water varies among

rural residents.

For nearly 20 years, the Virginia Household Water Quality Education Program

has provided thousands of Virginia residents with invaluable information on the quality

of their household water supply. In the case of Virginia, where private well water quality

testing is left solely to the discretion of the individual well-owner/operator, such a

program helps greatly to raise public awareness to issues that impact groundwater quality

and public health. When compared with the cost of water quality testing at private

laboratories, the VA HHWQEP is at the very least competitive, if not a bargain.

46

Additionally, the program’s “open to the public” approach generally lends itself to

participation regardless of social or geographic barriers. The VCE, through its

cooperation with local and state academic, administrative, and outreach organizations and

professionals, provides a wealth of resources to the general public through such

programs. Nevertheless, several opportunities come to light as a result of this study that

may increase overall participation across socio-economic lines. First, reducing or

eliminating the cost of the program incurred by each participant likely would extend

availability of the program to other members of the community who may otherwise opt

not to participate as a result of not being willing or able to pay for the water quality

testing. Figure 5.2.1 illustrates the relationship between program cost and the number of

participants based on information collected on VA HHWQEP participation levels in 32

counties between 1998 to 2002 (Ross et al, 1998-2002). Increasing awareness of the

program by extending advertising to other community centers such as schools, churches,

places of work, etc. may also increase overall participation.

VA HHWQEP Participation Based on Water Quality Testing Fee(Data collected from 32 counties; average cost = $24)

0

200

400

600

800

1000

1200

≤ $15 $16 - $24 $25 - $33 ≥ $34

Water Quality Testing Fee

# of

Par

ticip

ants

Figure 5.2.1: VA HHWQEP Participation Based on the Water Quality Testing Fee (Ross et al, 1998-2002)

The data collected by rural water quality education programs could also lend itself

for further analysis by implementing several minor changes in the collection and

reporting of information related to the site of each sampled well. First, spatial analysis of

47

household water quality using a GIS would greatly benefit by having access to the

physical location of each sample site. This could be accomplished by having each

participant enter not only his or her mailing address, but also the physical address of the

tested well and having the testing crew collect a GPS location when the sample is taken.

More detailed information about each well’s construction and hydrogeologic setting

could also be gathered from the original well log that was created at the time the well was

constructed. This information could be analyzed spatially to determine areas that may be

particularly vulnerable to contamination based on underlying hydrogeologic conditions

and specifics related to well construction. Statistical analyses to determine whether more

affordable homes are associated with poorer well water quality could also be conducted

by compiling information from publicly available tax assessments and well logs with

water quality data. Analysis of personal information from each participant such as age,

gender, race, etc. may also help to identify community subpopulations that may be

susceptible to contaminated groundwater and/or less aware of water quality issues.

Applying an appropriate sampling technique to analyze rural water quality would

also allow for stronger assertions to be made about the association between exposure to

contaminants and specific socio-economic variables and well characteristics. An

appropriate sampling method may involve stratifying the population based upon certain

factors of interest such as socio-economic, geographic location, hydrogeologic setting,

age of residence, etc. Random sampling of the population or each stratified

subpopulation would also ensure that each member of the population has an equal chance

of being selected; which likely would be more representative of the entire population

being sampled.

Aside from suggestions related to the methodology applied to future rural

household water quality studies, further analysis of counties who have already

participated in the VA HHWQEP may uncover additional findings related to the

association among home value, well construction, and water quality. Table 4.3.1

provides a summary of 16 counties that participated in the HHWQEP from 1998-2002.

Of these counties, several stand out as potentially worthwhile study areas. Most notably,

Rockingham County, where approximately 12% of the 300 treated and untreated samples

collected exceeded the EPA’s MCL for nitrate and 44% of the samples tested positive for

48

total coliform. A number of other sampled counties listed in Table 4.3.1 also have a high

prevalence of total coliform in household water supplies.

Table 5.2.1: Overview of Virginia Household Water Quality Education Programs: 1998-2002 (Ross et al, 1998-2002)

49

6.0 CONCLUSION

In closing, this study contributes to the larger body of scientific literature dealing

with public health and environmental justice by suggesting that in cases in which housing

values are related to household well age, depth, and treatment, a disparity in the

availability of safe drinking water may occur. Based on the results from the samples

analyzed in Louisa County, this study has identified several important factors affecting

household water quality; which have a statistically significant association (α = .05) to

total home value. Using total home value as an indicator of one’s socio-economic status,

this study demonstrates the potential for residents of rural Virginia to have variable

access to household drinking water free of bacteria; particularly when lower-value homes

in a community also tend to be older with more dated, shallower wells. This study also

suggests that in the case of the samples analyzed in Louisa County, the presence of water

treatment devices is significantly related to total home value. Also in Louisa County,

agricultural land cover is found to have a significant statistical association with nitrate

levels greater than 3 mg/l and the presence of total coliform, but not with wells testing

positive for E.coli. However, as the analysis of the Augusta County samples suggests,

numerous factors beyond the variables considered in this analysis should be taken into

consideration to account for rural household water quality in Virginia.

This study also provides the basis for further research to clarify the relationship

between SES and rural water quality in Virginia and the United States. Existing research

suggests that real estate and housing values are related to SES (Manitoba Centre for

Health Policy, 2007). However, other indicators such as income and educational

attainment should also be considered when available. Finally, as long as the monitoring

and treatment of rural household water quality remains the responsibility of the individual

well-owner/operator, community education programs such as the Virginia Household

Water Quality Education Program are an invaluable means of keeping citizens informed

about his or her water quality and play vital role in identifying and understanding water

quality-related issues.

50

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51

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55

APPENDIX A: SUPPLEMENTARY TABLES

Table A.1: Summary of Omitted Samples

56

Table A.2: Summary of Household Well to Parcel Matching Process: Louisa County, VA

57

Summary of Quality Check for Louisa County, VA SamplesSample

idTotal value acres

well age

well depth well yr NITRATE

TOTAL COLI E.COLI Quality Check Comments

L1 449900 10.00 4 365 1995 0.002 ABSENT ABSENT p.o. box and name match

L10 213700 15.00 0 165 1999 0.021 PRESENT ABSENT address and name match

L100 302000 0.92 4 425 1995 0.359 ABSENT ABSENT address and name match

L101 37400 1.00 0.055 PRESENT ABSENT address and name match

L102 226900 7.16 37 35 1962 0.287 PRESENT ABSENT address and name match

L103 193400 21.46 3 45 1996 1.165 PRESENT ABSENT address and name match: check w/ L163

L104 197500 4.42 1 120 1998 0.449 ABSENT ABSENT address and name match

L106 728900 5.00 18 300 1981 0.206 ABSENT ABSENT address match

L107 722800 1.00 11 44 1988 0.488 PRESENT ABSENT address match

L108 489800 2.76 16 140 1983 0.012 ABSENT ABSENT address match

L11 313500 1.05 0.008 ABSENT ABSENTaddress match-listed as a church, possible parsonage

on site per VBMP imagery

L112 126300 0.88 16 61 1983 0.299 ABSENT ABSENT name match

L113 413800 99.41 39 50 1960 1.041 PRESENT ABSENT address and name match

L114 721700 2.66 8 240 1991 0.017 ABSENT ABSENT address match

L115 202800 8.98 4 250 1995 0.544 ABSENT ABSENT address and name match

L116 67000 4.00 34 165 1965 0.731 ABSENT ABSENT address and name match

58

L117 218900 1.19 7 210 1992 1.168 ABSENT ABSENT address and name match

L118 796900 180.92 26 50 1973 0.450 PRESENT ABSENT address and name match

L12 112500 3.98 5 60 1994 0.005 PRESENT ABSENT address and name match

L120 127200 0.62 26 43 1973 0.491 ABSENT ABSENT p.o. box and last name match

L121 615600 1.49 4 160 1995 0.033 ABSENT ABSENT address and name match

L122 112900 17.58 3 225 1996 0.408 PRESENT ABSENT address match

L123 204200 20.00 11 100 1988 0.672 ABSENT ABSENT address match

L126 139800 9.44 20 1979 0.128 PRESENT ABSENT address match

L127 176200 20.00 4 375 1995 11.681 ABSENT ABSENT address and name match

L128 210400 4.99 18 325 1981 1.248 PRESENT ABSENT p.o. box and name match

L129 1007100 1.33 2 175 1997 0.118 ABSENT ABSENT address and name match

L13 174500 2.00 4 300 1995 0.002 ABSENT ABSENT address and name match

L130 688100 1.90 3 280 1996 0.141 ABSENT ABSENT address and name match

L133 29300 1.50 26 45 1973 0.830 ABSENT ABSENTverified based on Louisa ArcIMS address database

query

L134 296300 35.44 27 1972 0.166 PRESENT ABSENT address match

L135 480900 0.94 100 0.018 ABSENT ABSENT address and name match

L136 210300 17.56 27 175 1972 0.501 ABSENT ABSENT address and name match

L137 176400 3.49 21 128 1978 0.167 ABSENT ABSENT address and name match

59

L138 191300 1.87 10 1989 0.523 PRESENT ABSENT address and name match

L14 97300 1.00 24 45 1975 0.748 PRESENT PRESENT address and name match

L142 273400 42.00 54 36 1945 25.176 PRESENT ABSENT address and name match

L143 910000 0.92 1 1998 0.135 ABSENT ABSENT address and name match

L144 398800 3.00 21 90 1978 3.269 PRESENT ABSENT address and name match

L145 459900 0.96 3 140 1996 0.019 ABSENT ABSENT address match

L146 273400 42.00 33 144 1966 0.880 ABSENT ABSENT name match: different well depth and well yr from L142

L147 194500 3.00 25 225 1974 0.260 ABSENT ABSENT address and name match

L148 338600 8.21 4 350 1995 0.204 ABSENT ABSENT address and name match

L149 154200 4.16 25 45 1974 0.137 PRESENT ABSENT address and name match

L15 276700 15.49 17 42 1982 3.792 PRESENT ABSENT address match

L151 429400 83.90 33 144 1966 6.598 ABSENT ABSENT address and name match

L152 551200 0.81 14 180 1985 0.029 ABSENT ABSENT address and name match

L153 663700 1.67 8 180 1991 0.128 ABSENT ABSENT address and name match

L154 288000 0.93 11 140 1988 0.050 ABSENT ABSENT address and name match

L155 30200 1.00 2 1997 0.692 PRESENT PRESENT address and name match

L156 443900 1.04 10 120 1989 0.078 ABSENT ABSENT address and name match

L157 159300 4.40 4 1995 0.237 PRESENT ABSENT address match

60

L158 144900 3.75 0.070 ABSENT ABSENT address and name match

L159 86500 2.86 39 50 1960 3.438 PRESENT ABSENT address and name match

L160 563200 0.92 15 150 1984 2.068 ABSENT ABSENT address match

L161 104000 15.68 3.692 PRESENT ABSENT address and name match

L162 324800 2.15 1 1998 5.294 ABSENT ABSENT address and name match

L163 28100 1.79 1 120 1998 0.027 ABSENT ABSENT address match: check w/ L103

L164 205400 1.41 36 32 1963 0.110 PRESENT ABSENT address match

L165 140800 1.00 0.040 ABSENT ABSENT address and name match

L167 588300 173.30 8.432 PRESENT ABSENT address and name match

L168 131900 3.00 13 80 1986 0.348 PRESENT ABSENTverified based on Louisa ArcIMS address database

query

L169 155000 1.27 34 1965 0.148 PRESENT ABSENT address match

L17 187700 3.16 2 400 1997 0.360 ABSENT ABSENT address and name match

L170 206400 1.20 15 42 1984 0.100 PRESENT ABSENT address and name match

L171 101100 23.42 1.679 PRESENT ABSENT address match

L172 89800 0.50 24 1975 3.264 ABSENT ABSENT address and name match

L173 71400 3.85 6 50 1993 0.081 PRESENT ABSENTverified based on Louisa ArcIMS address database

query

L174 101100 23.42 38 18 1961 0.077 PRESENT ABSENT name match

L175 128900 9.00 34 60 1965 6.296 PRESENT ABSENT address and name match

61

L176 127100 1.00 0.628 ABSENT ABSENT address and name match

L177 161200 2.08 33 40 1966 0.448 PRESENT ABSENT address match

L178 164600 5.00 0.578 PRESENT ABSENT address and name match

L179 116400 0.87 0.079 PRESENT ABSENT address match

L18 282700 3.30 2 300 1997 0.007 ABSENT ABSENT address and name match

L180 517400 84.56 33 1966 0.426 ABSENT ABSENT address match

L181 253600 1.00 10 260 1989 0.040 ABSENT ABSENT address and name match

L182 700400 0.95 11 105 1988 2.095 PRESENT ABSENT address and name match

L183 326200 81.55 1.112 ABSENT ABSENT address and name match

L184 490900 125.00 0.433 PRESENT ABSENT address and name match

L185 243200 0.94 15 450 1984 0.014 ABSENT ABSENT address match

L186 306700 22.92 5 210 1994 1.858 ABSENT ABSENT address and name match

L188 245600 3.20 6 200 1993 0.017 ABSENT ABSENT name match - no house # 87 listed

L189 100 0.03 34 1965 1.593 PRESENT ABSENT address and name match

L19 112200 4.64 27 100 1972 0.695 PRESENT ABSENT address and name match

L190 181100 2.10 21 40 1978 0.568 PRESENT ABSENT address match

L193 185600 5.00 41 45 1958 1.154 PRESENT PRESENT address and name match

L194 553800 1.16 13 505 1986 1.690 ABSENT ABSENT address and name match

62

L195 1877700 459.24 31 137 1968 2.258 ABSENT ABSENT address and name match

L198 143200 5.20 27 48 1972 0.151 PRESENT ABSENT address and name match

L199 554900 1.18 13 43 1986 0.208 PRESENT ABSENT address and name match

L2 181600 1.00 31 36 1968 1.356 PRESENT PRESENT address and name match

L20 143600 5.00 1.032 PRESENT ABSENT address and name match

L202 216300 15.18 1 230 1998 0.153 ABSENT ABSENT address and name match

L203 257700 14.45 5 385 1994 0.064 ABSENT ABSENT p.o. box address and name match

L204 136200 0.67 34 100 1965 1.321 PRESENT ABSENT address match

L205 168700 6.95 30 40 1969 0.763 PRESENT ABSENT address match

L206 352100 77.63 14 465 1985 0.017 ABSENT ABSENT address and name match

L207 2125200 415.00 0.465 PRESENT PRESENTverified based on Louisa ArcIMS address database

query

L208 917300 0.97 13 175 1986 0.036 ABSENT ABSENT address match

L209 295600 1.50 2 205 1997 0.085 ABSENT ABSENT address and name match

L21 333500 11.73 5 145 1994 0.012 PRESENT ABSENT address match

L210 220000 7.42 2.694 PRESENT ABSENT address and name match

L211 160600 9.25 53 35 1946 0.182 PRESENT ABSENT address and name match

L212 164500 2.00 5 50 1994 1.184 PRESENT ABSENT address and name match

L213 206100 1.88 14 175 1985 0.762 ABSENT ABSENT address match

63

L214 349600 0.97 8 190 1991 0.268 ABSENT ABSENT address match

L215 214500 16.25 30 365 1969 0.721 ABSENT ABSENT address match

L216 677400 2.01 1 380 1998 0.142 ABSENT ABSENT address match

L217 617100 1.00 25 1974 22.343 PRESENT ABSENT address match

L218 679100 30.32 21 200 1978 1.587 ABSENT ABSENT address and name match

L219 113500 1.00 24 100 1975 0.120 PRESENT ABSENT address and name match

L22 40600 3.49 22 30 1977 0.405 PRESENT ABSENT address and name match

L220 430000 0.93 12 160 1987 3.262 PRESENT ABSENT address and name match

L222 279500 0.93 4 145 1995 2.116 ABSENT ABSENT address and name match

L223 291000 24.70 27 166 1972 0.015 PRESENT ABSENT address and name match

L224 563800 1.15 4 1995 0.003 ABSENT ABSENT address and name match

L225 401700 0.92 11 1988 0.026 PRESENT ABSENT address match

L227 700000 2.08 8 1991 0.171 ABSENT ABSENT address and name match

L228 232400 7.35 11 100 1988 0.020 PRESENT PRESENT address match

L229 269100 9.25 11 24 1988 0.809 ABSENT ABSENT address match

L23 136300 1.00 27 35 1972 1.078 PRESENT ABSENT address and name match

L230 84600 1.00 27 1972 0.154 PRESENT ABSENT address and name match

L232 168700 5.00 55 0.008 PRESENT ABSENT address and name match

64

L233 832600 113.60 47 80 1952 4.078 ABSENT ABSENT address and name match

L234 203000 5.81 22 1977 0.245 PRESENT ABSENT address and name match

L236 405200 12.00 10 325 1989 0.265 ABSENT ABSENT address and name match

L237 263200 0.91 10 179 1989 0.006 ABSENT ABSENT address and name match

L238 265000 2.00 7 240 1992 0.017 ABSENT ABSENT address and name match

L239 261300 1.84 1 200 1998 0.000 ABSENT ABSENT address and name match

L240 237100 44.65 28 315 1971 9.529 ABSENT ABSENT address and name match

L241 1560200 498.10 74 40 1925 1.746 PRESENT PRESENT address match

L242 282700 3.30 0 200 1999 7.421 PRESENT ABSENTverified based on Louisa ArcIMS address database

query

L247 89900 29.97 25 150 1974 11.709 ABSENT ABSENT address and name match

L248 287500 29.55 18 75 1981 5.027 PRESENT ABSENT address match

L249 235100 23.00 23 40 1976 1.268 PRESENT PRESENT address and name match

L25 238500 11.22 29 50 1970 0.517 PRESENT ABSENT address and name match

L251 311400 0.96 7 285 1992 0.000 ABSENT ABSENT address and name match

L252 337700 0.92 11 1988 1.074 ABSENT ABSENT address and name match

L253 88500 26.80 3 320 1996 0.062 ABSENT ABSENT address and name match

L254 409400 83.00 41 100 1958 7.317 ABSENT ABSENT address and name match

L255 426700 0.97 11 125 1988 1.273 ABSENT ABSENT address and name match

65

L256 270000 3.27 49 1950 2.578 PRESENT PRESENTaddress and name match: due to well age coinciding w/

age of res. (1844) better than other res. on prop.

L257 610400 1.17 300 0.944 PRESENT ABSENT address match

L258 439200 0.96 19 145 1980 2.137 ABSENT ABSENT address and name match

L259 508800 1.38 14 1985 0.830 PRESENT ABSENT address match

L26 140100 1.98 24 52 1975 2.877 PRESENT ABSENTname match and verified based on Louisa ArcIMS

database query

L261 575700 1.42 3 210 1996 0.666 ABSENT ABSENT address and name match

L263 386000 1.16 7 246 1992 0.116 ABSENT ABSENT address and name match

L266 500700 1.02 1 300 1998 0.088 ABSENT ABSENT address match

L269 665800 0.92 10 1989 0.024 ABSENT ABSENT address and name match

L27 42100 3.00 0.038 PRESENT ABSENT address and name match

L270 523000 1.11 105 2.074 PRESENT ABSENT address and name match

L271 715900 0.99 8 1991 0.150 ABSENT ABSENT address match

L273 750000 0.92 6 205 1993 0.400 ABSENT ABSENT address match

L274 762800 1.46 8 240 1991 0.000 ABSENT ABSENT address and name match

L275 699400 1.27 28 125 1971 0.754 ABSENT ABSENT address and name match

L276 313000 0.92 150 0.192 PRESENT ABSENT address match

L278 504200 1.52 15 210 1984 0.000 ABSENT ABSENT address and name match

L280 832600 113.60 35 35 1964 0.551 PRESENT ABSENT address and name match

66

L281 230400 4.59 10 1989 0.252 PRESENT ABSENT address and name match

L282 336900 23.01 0 50 1999 0.028 PRESENT ABSENT address and name match

L283 757200 0.98 1 280 1998 0.010 PRESENT ABSENT address and name match

L284 182400 4.76 7 66 1992 0.303 PRESENT ABSENT address match

L286 52900 11.37 43 42 1956 0.055 ABSENT ABSENT address and name match

L287 240500 0.92 0.020 ABSENT ABSENT address and name match

L288 178700 5.54 16 75 1983 0.021 ABSENT ABSENT address and name match

L29 311100 36.50 60 0.049 ABSENT ABSENT address and name match

L290 360500 11.59 200 0.135 ABSENT ABSENT address and name match

L293 95500 2.95 28 45 1971 4.284 PRESENT PRESENT address match

L297 236100 10.00 12 47 1987 5.954 ABSENT ABSENT address and name match

L299 251700 6.00 11 1988 0.030 ABSENT ABSENT address and name match

L3 153600 1.50 8 55 1991 0.587 ABSENT ABSENT address match

L30 444100 58.68 17 180 1982 1.088 PRESENT ABSENT address and name match

L300 274400 0.92 7 97 1992 0.036 ABSENT ABSENT address and name match

L301 176700 8.00 64 27 1935 2.739 PRESENT ABSENT address match

L302 49500 1.10 27 40 1972 0.370 PRESENT PRESENT address and name match

L304 80900 15.35 16 290 1983 0.012 ABSENT ABSENT address and name match

67

L305 115700 2.00 20 26 1979 0.425 PRESENT ABSENTname match-street correct-no other homeowner w/

same name on this st. beside L452

L306 701000 1.01 3 60 1996 6.306 PRESENT ABSENT address and name match

L307 175700 25.55 19 70 1980 1.248 PRESENT ABSENT address and name match

L308 663200 1.01 13 205 1986 0.413 PRESENT ABSENT address and name match

L31 291400 33.15 40 3.850 PRESENT ABSENT address and name match

L310 165700 0.90 26 30 1973 0.274 ABSENT ABSENT address and name match

L312 493100 1.09 1 1998 0.002 ABSENT ABSENT address and name match

L313 702400 3.21 44 45 1955 1.153 PRESENT ABSENT address and name match

L315 302300 11.61 7 308 1992 0.073 ABSENT ABSENT address match

L316 311600 0.92 1 200 1998 0.078 ABSENT ABSENT address and name match

L317 286800 1.24 14 150 1985 0.105 ABSENT ABSENT address match

L318 371000 1.07 1 285 1998 0.013 ABSENT ABSENT address match

L319 251400 6.09 2 325 1997 0.116 PRESENT ABSENT address and name match

L32 377600 10.59 1.687 PRESENT ABSENT address and name match

L321 218600 6.21 18 50 1981 0.063 ABSENT ABSENT address and name match

L323 186500 3.16 13 50 1986 0.023 PRESENT ABSENT address and name match

L324 207600 5.35 4 1995 0.067 ABSENT ABSENT address and name match

L325 183400 4.00 14 175 1985 0.058 PRESENT PRESENT address and name match

68

L326 480100 1.49 11 300 1988 0.100 PRESENT ABSENT address and name match

L327 973900 1.13 5 110 1994 0.001 ABSENT ABSENT address and name match

L329 269000 0.92 12 1987 0.007 ABSENT ABSENT address match

L33 193300 1.50 8 50 1991 1.797 PRESENT ABSENT address and name match

L330 130500 5.58 39 33 1960 0.598 PRESENT ABSENT address and name match

L331 401000 5.00 0.133 ABSENT ABSENTaddress match-2 dwellings on site-listed as apts. in

database

L332 253100 13.77 2 110 1997 1.110 PRESENT ABSENTname and address match-L360 also listed at same

address

L333 266700 0.91 14 1985 0.007 ABSENT ABSENT address and name match

L334 172600 3.10 24 45 1975 1.070 PRESENT ABSENT address match

L335 262700 20.00 25 35 1974 0.895 PRESENT ABSENT address and name match

L336 583600 1.87 10 130 1989 0.089 ABSENT ABSENT address and name match

L337 311000 47.70 3 180 1996 7.189 ABSENT ABSENT address and name match

L338 305800 26.25 26 45 1973 0.021 PRESENT ABSENT address and name match

L34 129500 1.00 38 36 1961 0.483 PRESENT ABSENT address and name match

L340 182400 3.25 30 1.050 PRESENT PRESENT address and name match

L341 639100 0.93 8 1991 0.028 ABSENT ABSENT address and name match

L342 162500 5.11 70 40 1929 1.110 PRESENT PRESENT address and name match

L344 225000 6.00 11 33 1988 0.012 PRESENT ABSENT address and name match

69

L345 632200 55.70 1 100 1998 0.014 ABSENT ABSENT address and name match

L346 138100 1.13 31 72 1968 0.813 ABSENT ABSENT address and name match

L347 497600 0.93 1 1998 0.015 ABSENT ABSENT address and name match

L348 254100 38.43 15 50 1984 4.619 PRESENT ABSENT address and name match

L349 396000 65.78 44 1955 0.501 ABSENT ABSENT address and name match

L35 114900 3.00 35 0.350 PRESENT ABSENT address and name match

L350 195600 3.43 7 65 1992 0.109 PRESENT ABSENT address and name match

L351 500500 0.98 21 1978 1.452 PRESENT ABSENT address and name match

L352 223900 4.15 20 300 1979 0.398 ABSENT ABSENT address and name match

L354 174600 1.50 23 1976 0.825 PRESENT ABSENT address and name match

L355 1208300 1.14 5 200 1994 0.335 ABSENT ABSENT address and name match

L356 65800 4.25 29 40 1970 0.194 ABSENT ABSENT address and name match

L357 249400 6.62 0.045 PRESENT ABSENT address and name match

L358 327800 99.00 7.885 ABSENT ABSENT address and name match-same address as L337

L359 108800 2.57 2 305 1997 0.032 ABSENT ABSENTname match and verified based on Louisa ArcIMS

database query

L36 327500 72.77 49 105 1950 1.995 ABSENT ABSENT address and name match

L360 253100 13.77 39 45 1960 0.531 PRESENT ABSENTname and address match-L332 also listed at same

address (kept sample due to diff. depth and well age)

L37 631000 142.18 2 325 1997 0.072 PRESENT ABSENTname match and verified based on Louisa ArcIMS

database query

70

L38 985900 0.92 10 250 1989 0.121 PRESENT ABSENT address and name match

L39 205100 10.00 1.027 ABSENT ABSENT address match

L4 291700 3.24 26 0.139 PRESENT ABSENT address and name match

L40 292500 13.91 7 130 1992 0.005 ABSENT ABSENT address and name match

L402 59200 9.16 0.277 ABSENT ABSENT address match

L404 268800 41.18 0.147 PRESENT ABSENT address match

L407 249400 3.65 51 40 1948 1.031 PRESENT ABSENT address match

L41 192700 5.00 13 50 1986 0.008 PRESENT ABSENT address and name match

L413 48800 1.55 34 1965 0.733 PRESENT PRESENT address and name match

L414 142400 30.00 24 200 1975 0.963 PRESENT ABSENT address match

L416 33700 5.00 18 66 1981 0.135 ABSENT ABSENT address and name match

L418 143900 2.15 0.121 PRESENT ABSENT address and name match

L419 123800 3.36 20 100 1979 1.340 ABSENT ABSENT address and name match

L42 141200 2.94 4 60 1995 0.008 PRESENT ABSENT address and name match

L420 111500 10.27 50 35 1949 4.788 PRESENT ABSENT address match

L421 242000 3.13 17 150 1982 0.934 ABSENT ABSENT address match

L424 62100 3.21 25 1974 2.957 PRESENT ABSENT address match

L425 44400 5.14 40 6.332 PRESENT PRESENT address and name match

71

L426 48800 7.25 9 1990 14.132 PRESENT ABSENT address match

L43 416100 68.73 33 50 1966 1.344 PRESENT ABSENT address and name match

L431 554400 0.92 38 1961 1.369 ABSENT ABSENT address and name match

L434 85800 1.00 2 125 1997 0.739 PRESENT ABSENT address and name match

L435 106400 1.00 26 52 1973 0.756 PRESENT PRESENT address and name match

L438 190900 4.18 20 105 1979 1.720 PRESENT ABSENT address and name match

L439 270000 3.27 125 2.672 PRESENT ABSENT address and name match

L44 190800 4.49 10 50 1989 0.391 PRESENT ABSENT address and name match

L440 400900 0.92 13 1986 0.801 PRESENT ABSENT address and name match

L441 650000 0.92 14 160 1985 0.397 PRESENT ABSENTaddress match: took avg. of 2 closest parcels w/

structure

L444 45300 5.00 20 65 1979 0.107 ABSENT ABSENT address and name match

L445 738500 249.10 24 42 1975 0.877 PRESENT ABSENT address and name match

L446 18100 1.35 0.483 PRESENT ABSENT address and name match

L448 65200 2.10 1.385 PRESENT PRESENT address match

L449 78400 2.30 0.166 PRESENT PRESENT address and name match

L45 386000 13.00 3 300 1996 0.092 PRESENT ABSENT address match

L450 80300 2.37 35 1964 0.711 ABSENT ABSENT address match

L452 22000 2.00 18 300 1981 0.032 PRESENT ABSENTname match-street correct-no other harris on this st.

beside L452

72

L453 115700 2.00 0 305 1999 0.068 PRESENT ABSENTname match-street correct-no other harris on this st.

beside L452-different well from L305

L454 118500 9.00 100 0.304 PRESENT ABSENT address match

L455 17400 0.47 40 1.830 PRESENT PRESENT address match

L456 341800 8.94 0.059 PRESENT ABSENTname match and address verified based on Louisa

ArcIMS database query

L458 224100 1.00 18 40 1981 0.502 PRESENT ABSENT address and name match

L459 504700 18.78 3 450 1996 0.106 ABSENT ABSENT address and name match

L46 278300 0.92 10 130 1989 0.273 ABSENT ABSENT address and name match

L462 466300 41.40 0.332 PRESENT PRESENT address and name match

L463 118100 8.07 27 160 1972 0.118 ABSENT ABSENT address and name match

L464 34200 1.88 20 1979 0.050 PRESENT ABSENT address match

L467 216100 3.00 15 90 1984 0.438 ABSENT ABSENT address and name match

L469 133700 2.00 25 43 1974 0.054 PRESENT ABSENT address match

L47 722700 0.92 13 150 1986 0.178 ABSENT ABSENT address and name match

L470 718700 199.07 12 275 1987 1.060 ABSENT ABSENT address match

L471 184400 7.70 16 90 1983 0.036 PRESENT ABSENT address and name match

L472 111700 1.00 90 0.064 PRESENT ABSENT address and name match

L474 718700 1.50 12 400 1987 9.445 ABSENT ABSENT address and name match

L475 103300 4.00 30 0.576 PRESENT PRESENT address and name match

73

L48 596900 3.36 12 125 1987 0.012 ABSENT ABSENT address and name match

L49 296700 15.78 7 1992 0.025 ABSENT ABSENT address and name match

L5 352000 29.13 1 125 1998 0.173 ABSENT ABSENT address and name match

L50 565100 148.68 54 35 1945 0.448 PRESENT ABSENT address and name match

L51 436500 83.83 19 52 1980 0.009 PRESENT ABSENT address and name match

L52 610400 27.03 39 160 1960 5.041 ABSENT ABSENT address and name match

L54 273900 49.97 11 155 1988 0.010 ABSENT ABSENT address match

L55 175200 3.00 14 89 1985 0.455 PRESENT ABSENT address and name match

L56 229000 1.06 11 50 1988 0.034 PRESENT ABSENT address and name match

L57 239200 36.01 7 230 1992 0.016 ABSENT ABSENT address and name match

L58 397100 91.84 12 260 1987 1.366 PRESENT ABSENT address and name match

L59 498400 120.00 33 65 1966 0.998 PRESENT ABSENT address match

L6 419200 10.82 3 165 1996 0.177 ABSENT ABSENT address and name match

L60 228600 41.00 62 50 1937 0.847 ABSENT ABSENT address match

L61 207600 0.98 14 30 1985 1.712 ABSENT ABSENT address and name match

L62 162500 5.11 25 55 1974 5.006 PRESENT PRESENT address and name match: different well from L342

L63 136000 0.66 1.990 PRESENT PRESENT address match

L64 302500 1.17 14 326 1985 0.018 ABSENT ABSENT address and name match

74

L65 112600 1.00 41 35 1958 0.065 PRESENT PRESENT address and name match

L68 629200 95.48 11 300 1988 0.193 ABSENT ABSENT address match

L69 628600 5.00 15 385 1984 0.418 PRESENT ABSENT address and name match

L7 204800 7.36 1 1998 0.024 ABSENT ABSENT address match

L70 278600 0.92 17 365 1982 2.655 ABSENT ABSENT address and name match

L71 213100 3.00 10 300 1989 1.093 PRESENT ABSENT address and name match

L72 371400 25.09 7 1992 0.058 ABSENT ABSENT address and name match

L73 122100 14.69 23 1976 3.700 PRESENT ABSENT address and name match

L74 411500 70.50 7 86 1992 0.059 PRESENT PRESENT address and name match

L76 971500 258.56 9 400 1990 0.187 ABSENT ABSENT address and name match

L77 727600 1.01 0 205 1999 0.041 ABSENT ABSENT address and name match

L78 87600 0.31 0.080 PRESENT ABSENT address match

L79 166600 3.05 7 175 1992 0.057 PRESENT ABSENT address and name match

L8 74800 8.63 14 50 1985 7.246 PRESENT ABSENT address and name match

L80 336700 1.51 6 220 1993 0.444 ABSENT ABSENT address match

L81 202300 5.49 21 60 1978 0.048 PRESENT ABSENT address and name match

L83 463800 27.54 3 275 1996 0.473 ABSENT ABSENT address and name match: different well from L92

L84 169300 3.86 2 40 1997 0.400 PRESENT ABSENT address match

75

L85 133700 1.55 9 1990 0.860 ABSENT ABSENT address match

L88 583300 6.00 1.623 PRESENT ABSENT address and name match

L89 138300 5.49 1 185 1998 0.034 PRESENT ABSENT address and name match

L9 227800 12.56 13 1986 0.354 PRESENT ABSENT address and name match

L90 89400 15.74 41 50 1958 0.014 PRESENT ABSENT address and name match

L91 248600 1.01 11 285 1988 1.682 ABSENT ABSENT address match

L92 463800 27.54 26 40 1973 0.375 PRESENT ABSENT address and name match

L93 177400 7.64 0 205 1999 0.667 PRESENT ABSENT address match

L94 552100 0.92 9 150 1990 0.056 ABSENT ABSENT address and name match

L95 380000 1.28 2 140 1997 0.386 PRESENT ABSENTaddress match: assigned prop value based on nearest

lakefront neighbor w/ similar lot size

L96 890200 0.92 9 200 1990 0.027 ABSENT ABSENT address match

L97 143400 1.50 2 45 1997 0.028 ABSENT ABSENT address match

L98 51400 5.01 13 40 1986 0.289 PRESENT ABSENT address and name match

L99 41300 7.08 3 1996 0.011 ABSENT ABSENT address match

76

Table A.3: Summary of Household Well to Parcel Matching Process: Augusta County, VA

77

Summary of Quality Check for Augusta County, VA SamplesSample

idTotal value acres

well age

well depth well yr NITRATE_N

TOTAL COLI E.COLI Quality Check Comments

A1 535300 71.69 74 100 1925 0.480 ABSENT ABSENT name match

A100 326400 6.54 8 600 1991 0.221 ABSENT ABSENT name and con/well yr match

A101 281600 6.86 4 230 1995 2.025 PRESENT ABSENT address match

A104 361900 5.55 10 325 1989 2.293 ABSENT ABSENT name match

A105 366900 105.60 19 450 1980 2.806 ABSENT ABSENT address match

A106 225300 8.78 6 265 1993 0.000 ABSENT ABSENT name match

A108 276800 1.94 14 333 1985 6.548 ABSENT ABSENT name match

A109 401100 5.12 6 485 1993 1.254 PRESENT ABSENT name and con/well yr match

A111 170600 6.56 18 240 1981 0.028 PRESENT ABSENT name match

A112 583000 155.50 22 300 1977 3.027 ABSENT ABSENT name match

A114 92400 1.23 400 2.473 ABSENT ABSENT name match

A115 202600 4.14 2.332 ABSENT ABSENT address match

A117 257300 89.76 54 40 1945 0.000 ABSENT ABSENT name and address match

A119 220300 14.16 18 720 1981 0.000 PRESENT ABSENT address match

A12 349400 20.88 15 110 1984 0.066 ABSENT ABSENT address match

A120 228100 27.81 14 210 1985 1.569 ABSENT ABSENT name and con/well yr match

78

A121 819700 308.98 29 318 1970 1.945 ABSENT ABSENT address match

A122 200400 34.15 15 100 1984 0.000 PRESENT ABSENT address match

A123 265900 5.05 2.790 PRESENT ABSENT address match

A124 203800 2.76 4 275 1995 0.231 PRESENT ABSENT name match

A125 135100 23.63 6 250 1993 0.403 PRESENT ABSENT name and address match

A128 306400 75.16 1.997 PRESENT PRESENT name match-no 99 on this st.

A129 168600 0.99 24 460 1975 8.761 PRESENT ABSENT name match

A13 164100 0.33 13 80 1986 5.498 PRESENT ABSENT address match

A131 203800 84.50 5 700 1994 2.158 ABSENT ABSENT name match

A132 342300 7.70 8 200 1991 4.803 PRESENT ABSENT name and con/well yr match

A133 53000 5.49 180 2.387 ABSENT ABSENT address match

A134 292700 25.30 50 40 1949 20.880 PRESENT ABSENT address match

A135 328200 16.52 0 455 1999 1.132 ABSENT ABSENT address match

A136 240200 5.00 0 390 1999 0.007 ABSENT ABSENT address match

A138 214600 5.03 23 275 1976 1.641 ABSENT ABSENT address match

A139 206800 2.61 199 25 1800 1.029 PRESENT ABSENT name match

A141 350800 34.86 14 235 1985 0.110 ABSENT ABSENT address match

A142 488300 63.80 7 455 1992 2.130 ABSENT ABSENT address match

79

A143 440900 34.50 28 325 1971 1.054 ABSENT ABSENT address match (diff. well from A44)

A144 440900 34.50 22 300 1977 5.396 ABSENT ABSENT address match

A146 272200 46.50 220 1.399 PRESENT ABSENT address match

A149 740500 242.38 20 350 1979 0.752 ABSENT ABSENT name and con/well yr match

A150 740500 242.38 42 230 1957 3.707 ABSENT ABSENT name and con/well yr match

A151 151800 2.16 34 256 1965 8.720 ABSENT ABSENT name match

A152 167700 62.87 19 360 1980 1.560 PRESENT ABSENT address match

A156 274100 28.26 2.076 PRESENT ABSENT name match

A157 669600 225.39 17 355 1982 0.000 ABSENT ABSENT name and address match

A16 177400 5.78 8.753 ABSENT ABSENT address match

A160 99300 3.86 25 70 1974 0.304 ABSENT ABSENT name match

A162 546100 115.85 6.932 ABSENT PRESENT name and address match

A163 362800 33.02 16 200 1983 3.696 ABSENT ABSENT name and address match

A164 138100 2.09 13 1986 0.000 ABSENT ABSENT name and address match

A166 257200 10.00 2.528 ABSENT ABSENT address match

A168 162100 14.93 35 1964 1.442 ABSENT ABSENT name match

A169 552300 5.00 0.646 ABSENT ABSENT name match

A17 199600 1.32 12 125 1987 5.558 PRESENT ABSENT name match

80

A170 354700 64.00 30 180 1969 0.061 PRESENT ABSENT address match

A18 338900 48.91 27 360 1972 1.918 PRESENT ABSENT name and con/well yr match

A19 358100 68.59 19 288 1980 0.033 PRESENT ABSENT address match

A2 614000 100.00 14 397 1985 0.937 ABSENT ABSENT address match

A20 512700 102.68 2.109 PRESENT PRESENT address and name match

A21 70100 6.90 25 300 1974 2.450 ABSENT ABSENT address match

A22 222400 5.12 9 480 1990 2.412 ABSENT ABSENT name match, well yr/con yr match

A23 664600 104.84 17 110 1982 5.223 PRESENT PRESENT address match

A24 35200 3.99 26 279 1973 0.000 ABSENT ABSENT address and name match

A25 407600 87.65 28 338 1971 0.079 ABSENT ABSENT address and name match

A26 339600 5.47 0.587 PRESENT ABSENT name match

A27 196700 1.00 27 470 1972 0.627 ABSENT ABSENT name match

A28 110900 2.00 2 475 1997 2.542 PRESENT ABSENT address match

A29 318900 71.96 10 300 1989 2.099 ABSENT ABSENT address match

A3 592900 179.12 28 110 1971 1.323 PRESENT PRESENT name match

A31 512700 102.68 48 180 1951 3.417 PRESENT ABSENT address match

A32 300100 2.38 9 250 1990 1.607 ABSENT ABSENT name match

A33 279400 4.85 10 150 1989 2.364 ABSENT ABSENT name and con/well yr match

81

A34 149300 23.19 0.193 PRESENT PRESENT name match (source=spring instead of well)

A35 325000 1.50 15 190 1984 1.098 PRESENT ABSENT name and con/well yr match

A36 331000 34.35 15 620 1984 0.142 ABSENT ABSENT address and name match

A38 193400 21.54 11 275 1988 0.085 PRESENT ABSENT address and name match

A39 301900 5.31 13 395 1986 0.155 ABSENT ABSENT address and name match

A4 183300 1.84 30 503 1969 2.102 PRESENT ABSENT name match

A40 306700 8.91 29 275 1970 2.007 PRESENT ABSENT address match

A44 61900 4.66 11 100 1988 1.969 ABSENT ABSENT address match

A45 273600 5.05 21 390 1978 0.084 ABSENT ABSENT address match

A46 97400 0.99 38 191 1961 0.865 ABSENT ABSENTname match and only house on street not vacant w/

same owner name

A47 218600 3.11 45 1954 1.430 ABSENT ABSENT name and con/well yr match

A48 316700 18.41 1.248 ABSENT ABSENT address match

A49 111600 3.06 6 160 1993 1.299 ABSENT ABSENT address match

A5 242100 15.40 2.669 PRESENT PRESENT name match

A53 224600 4.57 13 1986 0.190 ABSENT ABSENT name match

A55 152300 14.01 32 1967 0.000 ABSENT ABSENT name match

A57 249100 5.00 15 600 1984 0.247 PRESENT ABSENT name match

A58 158300 2.23 16 180 1983 0.065 PRESENT ABSENT name match

82

A59 43100 2.79 4 475 1995 0.076 ABSENT ABSENT address and name match

A6 554200 5.35 11 250 1988 2.171 ABSENT ABSENT name match

A60 324800 7.00 10 225 1989 0.219 ABSENT ABSENT name match

A62 218200 5.33 15 350 1984 1.063 PRESENT PRESENT name and con/well yr match

A63 433100 43.13 4 140 1995 2.606 ABSENT ABSENT address match

A64 442700 91.03 7 240 1992 1.261 ABSENT ABSENT name and con/well yr match

A66 50800 8.34 10 500 1989 0.000 ABSENT ABSENT address and name match

A67 250600 22.86 6 300 1993 2.545 ABSENT ABSENT address match

A68 327200 1.76 1 300 1998 0.608 ABSENT ABSENT address match

A69 532500 161.32 1 740 1998 0.075 ABSENT ABSENT name match

A7 628600 134.00 1.232 PRESENT ABSENT name match

A70 114300 0.37 0.461 PRESENT ABSENT name match and yr of well/well yr match

A72 171500 2.50 4 300 1995 0.506 ABSENT ABSENT name and con/well yr match

A73 220900 4.33 15 190 1984 0.063 ABSENT ABSENT address match

A74 822900 266.91 27 125 1972 0.059 ABSENT ABSENT address match

A75 660800 171.60 24 230 1975 2.822 PRESENT ABSENT name match

A76 263300 18.54 19 450 1980 2.343 PRESENT ABSENT name and con/well yr match

A77 147400 7.96 31 100 1968 1.022 PRESENT PRESENT name match

83

A78 407900 93.02 19 300 1980 0.000 ABSENT ABSENT name match

A8 312500 12.37 12 500 1987 0.237 PRESENT ABSENT address match

A80 201200 1.00 28 253 1971 2.590 PRESENT ABSENT address match

A81 286100 13.20 8 185 1991 1.200 ABSENT ABSENT name match

A82 251200 15.76 12 500 1987 0.000 PRESENT ABSENT name and con/well yr match

A83 278700 18.49 18 480 1981 0.231 ABSENT ABSENT name and con/well yr match

A84 390000 38.62 8 110 1991 2.027 ABSENT ABSENT name match and address match based on google map

A85 186200 27.44 18 150 1981 1.875 ABSENT ABSENT address match

A86 158300 47.00 190 1.759 PRESENT ABSENT address match

A87 201300 1.87 35 90 1964 4.367 ABSENT ABSENT address match based on google map

A88 236000 4.62 25 50 1974 0.000 PRESENT ABSENT address match

A89 202600 72.60 8 525 1991 1.607 ABSENT ABSENT name match

A9 169500 0.98 36 235 1963 2.302 PRESENT ABSENT name match

A91 536200 273.32 30 425 1969 1.599 PRESENT ABSENT name and con/well yr match

A94 323400 35.45 39 35 1960 0.023 ABSENT ABSENT address match

A96 564300 5.30 0.027 ABSENT ABSENT name and address match (p.o. box)

A98 479800 97.61 7 460 1992 1.333 ABSENT ABSENT name match

A99 479800 97.61 12 250 1987 0.144 ABSENT ABSENT name and address match-diff. well than A98

84

Table A.4 Normality test results: Augusta County

85

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Augusta County Analysis 1

The UNIVARIATE Procedure Variable: total_val (total_val)

Moments

N 124 Sum Weights 124 Mean 305676.613 Sum Observations 37903900 Std Deviation 163666.441 Variance 2.67867E10 Skewness 1.01472832 Kurtosis 0.82547804 Uncorrected SS 1.48811E13 Corrected SS 3.29476E12 Coeff Variation 53.5423495 Std Error Mean 14697.6802

Basic Statistical Measures

Location Variability

Mean 305676.6 Std Deviation 163666 Median 273850.0 Variance 2.67867E10 Mode 158300.0 Range 787700 Interquartile Range 169800

NOTE: The mode displayed is the smallest of 6 modes with a count of 2.

Tests for Location: Mu0=0

Test -Statistic- -----p Value------

Student's t t 20.79761 Pr > |t| <.0001 Sign M 62 Pr >= |M| <.0001 Signed Rank S 3875 Pr >= |S| <.0001

Tests for Normality

Test --Statistic--- -----p Value------

Shapiro-Wilk W 0.928772 Pr < W <0.0001 Kolmogorov-Smirnov D 0.113536 Pr > D <0.0100 Cramer-von Mises W-Sq 0.470681 Pr > W-Sq <0.0050 Anderson-Darling A-Sq 2.741336 Pr > A-Sq <0.0050

Quantiles (Definition 5)

Quantile Estimate

100% Max 822900 99% 819700 95% 628600 90% 552300 75% Q3 364850

86

50% Median 273850

----------------------------------------------------------------------------------------------------

Augusta County Analysis 2

The UNIVARIATE Procedure Variable: total_val (total_val)

Quantiles (Definition 5)

Quantile Estimate

25% Q1 195050 10% 138100 5% 99300 1% 43100 0% Min 35200

Extreme Observations

-----Lowest---- -----Highest----

Value Obs Value Obs

35200 61 664600 60 43100 89 669600 43 53000 27 740500 39 61900 78 819700 17 92400 11 822900 103

----------------------------------------------------------------------------------------------------

Augusta County Analysis 3

The UNIVARIATE Procedure Variable: well_age (well_age)

Moments

N 105 Sum Weights 105 Mean 359.438095 Sum Observations 37741 Std Deviation 749.648891 Variance 561973.46 Skewness 1.76587431 Kurtosis 1.14489336 Uncorrected SS 72010793 Corrected SS 58445239.8 Coeff Variation 208.561335 Std Error Mean 73.1582408

Basic Statistical Measures

Location Variability

Mean 359.438 Std Deviation 749.64889 Median 19.000 Variance 561973 Mode 1999.000 Range 1999

87

Interquartile Range 25.00000

Tests for Location: Mu0=0

Test -Statistic- -----p Value------

Student's t t 4.913159 Pr > |t| <.0001 Sign M 51.5 Pr >= |M| <.0001 Signed Rank S 2678 Pr >= |S| <.0001

Tests for Normality

Test --Statistic--- -----p Value------

Shapiro-Wilk W 0.472364 Pr < W <0.0001 Kolmogorov-Smirnov D 0.467682 Pr > D <0.0100 Cramer-von Mises W-Sq 5.501126 Pr > W-Sq <0.0050 Anderson-Darling A-Sq 28.27654 Pr > A-Sq <0.0050

Quantiles (Definition 5)

Quantile Estimate

100% Max 1999 99% 1999 95% 1999 90% 1999 75% Q3 35 50% Median 19 25% Q1 10 10% 5

----------------------------------------------------------------------------------------------------

Augusta County Analysis 4

The UNIVARIATE Procedure Variable: well_age (well_age)

Quantiles (Definition 5)

Quantile Estimate

5% 4 1% 0 0% Min 0

Extreme Observations

----Lowest---- ----Highest---

Value Obs Value Obs

0 30 1999 71

88

0 29 1999 82 1 98 1999 84 1 97 1999 99 2 65 1999 100

Missing Values

-----Percent Of----- Missing Missing Value Count All Obs Obs

. 19 15.32 100.00

----------------------------------------------------------------------------------------------------

Augusta County Analysis 5

The UNIVARIATE Procedure Variable: WELL_DEPTH (WELL_DEPTH)

Moments

N 104 Sum Weights 104 Mean 292.365385 Sum Observations 30406 Std Deviation 158.90475 Variance 25250.7196 Skewness 0.61229547 Kurtosis 0.06562015 Uncorrected SS 11490486 Corrected SS 2600824.12 Coeff Variation 54.3514241 Std Error Mean 15.5818927

Basic Statistical Measures

Location Variability

Mean 292.3654 Std Deviation 158.90475 Median 275.0000 Variance 25251 Mode 300.0000 Range 715.00000 Interquartile Range 216.00000

Tests for Location: Mu0=0

Test -Statistic- -----p Value------

Student's t t 18.76315 Pr > |t| <.0001 Sign M 52 Pr >= |M| <.0001 Signed Rank S 2730 Pr >= |S| <.0001

Tests for Normality

Test --Statistic--- -----p Value------

Shapiro-Wilk W 0.963827 Pr < W 0.0061 Kolmogorov-Smirnov D 0.10584 Pr > D <0.0100 Cramer-von Mises W-Sq 0.151615 Pr > W-Sq 0.0228 Anderson-Darling A-Sq 0.940167 Pr > A-Sq 0.0181

89

Quantiles (Definition 5)

Quantile Estimate

100% Max 740 99% 720 95% 600 90% 500 75% Q3 396 50% Median 275 25% Q1 180 10% 100

----------------------------------------------------------------------------------------------------

Augusta County Analysis 6

The UNIVARIATE Procedure Variable: WELL_DEPTH (WELL_DEPTH)

Quantiles (Definition 5)

Quantile Estimate

5% 70 1% 35 0% Min 25

Extreme Observations

----Lowest---- ----Highest---

Value Obs Value Obs

25 32 600 87 35 121 620 73 40 28 700 25 40 13 720 14 50 117 740 98

Missing Values

-----Percent Of----- Missing Missing Value Count All Obs Obs

. 20 16.13 100.00

----------------------------------------------------------------------------------------------------

Augusta County Analysis 7

90

The UNIVARIATE Procedure Variable: acres (acres)

Moments

N 124 Sum Weights 124 Mean 38.6456452 Sum Observations 4792.06 Std Deviation 57.9198479 Variance 3354.70878 Skewness 2.39948167 Kurtosis 6.38680487 Uncorrected SS 597821.43 Corrected SS 412629.18 Coeff Variation 149.874191 Std Error Mean 5.20135589

Basic Statistical Measures

Location Variability

Mean 38.64565 Std Deviation 57.91985 Median 12.78500 Variance 3355 Mode 5.00000 Range 308.65000 Interquartile Range 43.50500

Tests for Location: Mu0=0

Test -Statistic- -----p Value------

Student's t t 7.429918 Pr > |t| <.0001 Sign M 62 Pr >= |M| <.0001 Signed Rank S 3875 Pr >= |S| <.0001

Tests for Normality

Test --Statistic--- -----p Value------

Shapiro-Wilk W 0.675355 Pr < W <0.0001 Kolmogorov-Smirnov D 0.254137 Pr > D <0.0100 Cramer-von Mises W-Sq 2.568819 Pr > W-Sq <0.0050 Anderson-Darling A-Sq 13.60335 Pr > A-Sq <0.0050

Quantiles (Definition 5)

Quantile Estimate

100% Max 308.980 99% 266.910 95% 161.320 90% 102.680 75% Q3 47.955 50% Median 12.785 25% Q1 4.450 10% 1.840

----------------------------------------------------------------------------------------------------

Augusta County Analysis 8

91

The UNIVARIATE Procedure Variable: acres (acres)

Quantiles (Definition 5)

Quantile Estimate

5% 1.000 1% 0.370 0% Min 0.330

Extreme Observations

----Lowest---- -----Highest----

Value Obs Value Obs

0.33 24 179.12 67 0.37 100 225.39 43 0.98 119 242.38 39 0.99 80 266.91 103 0.99 23 308.98 17

92

Table A.5 Normality test results: Louisa County

93

----------------------------------------------------------------------------------------------------

37

6/01/1999 The UNIVARIATE Procedure Variable: Total_valu (Total_valu)

Moments

N 56 Sum Weights 56 Mean 283580.357 Sum Observations 15880500 Std Deviation 187439.35 Variance 3.51335E10 Skewness 1.49295928 Kurtosis 2.8946401 Uncorrected SS 6.43574E12 Corrected SS 1.93234E12 Coeff Variation 66.0974378 Std Error Mean 25047.6367

Basic Statistical Measures

Location Variability

Mean 283580.4 Std Deviation 187439 Median 233150.0 Variance 3.51335E10 Mode . Range 956600 Interquartile Range 216200

Tests for Location: Mu0=0

Test -Statistic- -----p Value------

Student's t t 11.32164 Pr > |t| <.0001 Sign M 28 Pr >= |M| <.0001 Signed Rank S 798 Pr >= |S| <.0001

Tests for Normality

Test --Statistic--- -----p Value------

Shapiro-Wilk W 0.885146 Pr < W <0.0001 Kolmogorov-Smirnov D 0.132813 Pr > D 0.0151 Cramer-von Mises W-Sq 0.283135 Pr > W-Sq <0.0050 Anderson-Darling A-Sq 1.722816 Pr > A-Sq <0.0050

Quantiles (Definition 5)

Quantile Estimate

100% Max 985900 99% 985900 95% 722700 90% 554900 75% Q3 364800 50% Median 233150 25% Q1 148600

94

10% 112200

----------------------------------------------------------------------------------------------------

38

The UNIVARIATE Procedure Variable: Total_valu (Total_valu)

Quantiles (Definition 5)

Quantile Estimate

5% 42100 1% 29300 0% Min 29300

Extreme Observations

-----Lowest---- -----Highest----

Value Obs Value Obs

29300 36 565100 47 40600 33 631000 6 42100 49 722700 27 74800 28 722800 21 97300 34 985900 20

----------------------------------------------------------------------------------------------------

39

The UNIVARIATE Procedure Variable: Total_valu (Total_valu)

Moments

N 56 Sum Weights 56 Mean 283580.357 Sum Observations 15880500 Std Deviation 187439.35 Variance 3.51335E10 Skewness 1.49295928 Kurtosis 2.8946401 Uncorrected SS 6.43574E12 Corrected SS 1.93234E12 Coeff Variation 66.0974378 Std Error Mean 25047.6367

Basic Statistical Measures

Location Variability

Mean 283580.4 Std Deviation 187439 Median 233150.0 Variance 3.51335E10 Mode . Range 956600 Interquartile Range 216200

95

Tests for Location: Mu0=0

Test -Statistic- -----p Value------

Student's t t 11.32164 Pr > |t| <.0001 Sign M 28 Pr >= |M| <.0001 Signed Rank S 798 Pr >= |S| <.0001

Tests for Normality

Test --Statistic--- -----p Value------

Shapiro-Wilk W 0.885146 Pr < W <0.0001 Kolmogorov-Smirnov D 0.132813 Pr > D 0.0151 Cramer-von Mises W-Sq 0.283135 Pr > W-Sq <0.0050 Anderson-Darling A-Sq 1.722816 Pr > A-Sq <0.0050

Quantiles (Definition 5)

Quantile Estimate

100% Max 985900 99% 985900 95% 722700 90% 554900 75% Q3 364800 50% Median 233150 25% Q1 148600 10% 112200

----------------------------------------------------------------------------------------------------

40

The UNIVARIATE Procedure Variable: Total_valu (Total_valu)

Quantiles (Definition 5)

Quantile Estimate

5% 42100 1% 29300 0% Min 29300

Extreme Observations

-----Lowest---- -----Highest----

Value Obs Value Obs

29300 36 565100 47 40600 33 631000 6 42100 49 722700 27

96

74800 28 722800 21 97300 34 985900 20

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41

The UNIVARIATE Procedure Variable: well_age (well_age)

Moments

N 47 Sum Weights 47 Mean 16.1297872 Sum Observations 758.1 Std Deviation 13.3172153 Variance 177.348224 Skewness 0.98349128 Kurtosis 0.44189345 Uncorrected SS 20386.01 Corrected SS 8158.0183 Coeff Variation 82.5628703 Std Error Mean 1.9425155

Basic Statistical Measures

Location Variability

Mean 16.12979 Std Deviation 13.31722 Median 13.00000 Variance 177.34822 Mode 13.00000 Range 53.90000 Interquartile Range 21.00000

Tests for Location: Mu0=0

Test -Statistic- -----p Value------

Student's t t 8.303557 Pr > |t| <.0001 Sign M 23.5 Pr >= |M| <.0001 Signed Rank S 564 Pr >= |S| <.0001

Tests for Normality

Test --Statistic--- -----p Value------

Shapiro-Wilk W 0.907325 Pr < W 0.0012 Kolmogorov-Smirnov D 0.167371 Pr > D <0.0100 Cramer-von Mises W-Sq 0.216454 Pr > W-Sq <0.0050 Anderson-Darling A-Sq 1.304353 Pr > A-Sq <0.0050

Quantiles (Definition 5)

Quantile Estimate

100% Max 54.0 99% 54.0 95% 41.0 90% 33.0 75% Q3 26.0

97

50% Median 13.0 25% Q1 5.0 10% 2.0

----------------------------------------------------------------------------------------------------

42

The UNIVARIATE Procedure Variable: well_age (well_age)

Quantiles (Definition 5)

Quantile Estimate

5% 1.0 1% 0.1 0% Min 0.1

Extreme Observations

----Lowest---- ----Highest---

Value Obs Value Obs

0.1 1 33 43 1.0 3 38 44 1.0 2 41 45 2.0 6 49 46 2.0 5 54 47

Missing Values

-----Percent Of----- Missing Missing Value Count All Obs Obs

. 9 16.07 100.00

----------------------------------------------------------------------------------------------------

43

The UNIVARIATE Procedure Variable: well_depth (well_depth)

Moments

N 47 Sum Weights 47 Mean 113.425532 Sum Observations 5331 Std Deviation 102.177753 Variance 10440.2932 Skewness 1.39725653 Kurtosis 0.86056625 Uncorrected SS 1084925 Corrected SS 480253.489 Coeff Variation 90.0835566 Std Error Mean 14.9041571

98

Basic Statistical Measures

Location Variability

Mean 113.4255 Std Deviation 102.17775 Median 55.0000 Variance 10440 Mode 50.0000 Range 374.00000 Interquartile Range 111.00000

Tests for Location: Mu0=0

Test -Statistic- -----p Value------

Student's t t 7.610329 Pr > |t| <.0001 Sign M 23.5 Pr >= |M| <.0001 Signed Rank S 564 Pr >= |S| <.0001

Tests for Normality

Test --Statistic--- -----p Value------

Shapiro-Wilk W 0.766445 Pr < W <0.0001 Kolmogorov-Smirnov D 0.277971 Pr > D <0.0100 Cramer-von Mises W-Sq 0.788261 Pr > W-Sq <0.0050 Anderson-Darling A-Sq 4.418701 Pr > A-Sq <0.0050

Quantiles (Definition 5)

Quantile Estimate

100% Max 400 99% 400 95% 325 90% 300 75% Q3 155 50% Median 55 25% Q1 44 10% 35

----------------------------------------------------------------------------------------------------

44

The UNIVARIATE Procedure Variable: well_depth (well_depth)

Quantiles (Definition 5)

Quantile Estimate

5% 35 1% 26 0% Min 26

99

Extreme Observations

----Lowest---- ----Highest---

Value Obs Value Obs

26 52 300 8 30 33 300 10 35 53 325 6 35 47 365 11 35 37 400 5

Missing Values

-----Percent Of----- Missing Missing Value Count All Obs Obs

. 9 16.07 100.00

----------------------------------------------------------------------------------------------------

45

The UNIVARIATE Procedure Variable: acres (acres)

Moments

N 56 Sum Weights 56 Mean 20.4601786 Sum Observations 1145.77 Std Deviation 35.3257999 Variance 1247.91214 Skewness 2.4119535 Kurtosis 5.25489489 Uncorrected SS 92077.8265 Corrected SS 68635.1677 Coeff Variation 172.656362 Std Error Mean 4.72060858

Basic Statistical Measures

Location Variability

Mean 20.46018 Std Deviation 35.32580 Median 5.00000 Variance 1248 Mode 1.00000 Range 147.76000 Interquartile Range 12.54000

Tests for Location: Mu0=0

Test -Statistic- -----p Value------

Student's t t 4.334225 Pr > |t| <.0001 Sign M 28 Pr >= |M| <.0001 Signed Rank S 798 Pr >= |S| <.0001

100

Tests for Normality

Test --Statistic--- -----p Value------

Shapiro-Wilk W 0.592524 Pr < W <0.0001 Kolmogorov-Smirnov D 0.356272 Pr > D <0.0100 Cramer-von Mises W-Sq 1.84808 Pr > W-Sq <0.0050 Anderson-Darling A-Sq 9.267605 Pr > A-Sq <0.0050

Quantiles (Definition 5)

Quantile Estimate

100% Max 148.680 99% 148.680 95% 120.000 90% 72.770 75% Q3 14.455 50% Median 5.000 25% Q1 1.915 10% 1.000

----------------------------------------------------------------------------------------------------

46

The UNIVARIATE Procedure Variable: acres (acres)

Quantiles (Definition 5)

Quantile Estimate

5% 0.920 1% 0.920 0% Min 0.920

Extreme Observations

----Lowest---- -----Highest----

Value Obs Value Obs

0.92 27 81.55 48 0.92 20 91.84 23 0.92 19 120.00 43 1.00 44 142.18 6 1.00 41 148.68 47

----------------------------------------------------------------------------------------------------

47

6/15/1999

101

The UNIVARIATE Procedure Variable: Total_valu (Total_valu)

Moments

N 218 Sum Weights 218 Mean 350648.165 Sum Observations 76441300 Std Deviation 260781.306 Variance 6.80069E10 Skewness 1.96343493 Kurtosis 6.68834696 Uncorrected SS 4.15615E13 Corrected SS 1.47575E13 Coeff Variation 74.3712166 Std Error Mean 17662.3387

Basic Statistical Measures

Location Variability

Mean 350648.2 Std Deviation 260781 Median 265850.0 Variance 6.80069E10 Mode 101100.0 Range 1877600 Interquartile Range 323000

NOTE: The mode displayed is the smallest of 6 modes with a count of 2.

Tests for Location: Mu0=0

Test -Statistic- -----p Value------

Student's t t 19.85287 Pr > |t| <.0001 Sign M 109 Pr >= |M| <.0001 Signed Rank S 11935.5 Pr >= |S| <.0001

Tests for Normality

Test --Statistic--- -----p Value------

Shapiro-Wilk W 0.841831 Pr < W <0.0001 Kolmogorov-Smirnov D 0.172073 Pr > D <0.0100 Cramer-von Mises W-Sq 1.409291 Pr > W-Sq <0.0050 Anderson-Darling A-Sq 7.843035 Pr > A-Sq <0.0050

Quantiles (Definition 5)

Quantile Estimate

100% Max 1877700 99% 1208300 95% 757200 90% 688100 75% Q3 497600 50% Median 265850

----------------------------------------------------------------------------------------------------

48

102

The UNIVARIATE Procedure Variable: Total_valu (Total_valu)

Quantiles (Definition 5)

Quantile Estimate

25% Q1 174600 10% 112900 5% 84600 1% 28100 0% Min 100

Extreme Observations

-----Lowest---- -----Highest-----

Value Obs Value Obs

100 173 973900 47 14500 207 1007100 22 28100 7 1208300 48 37400 197 1560200 195 41300 26 1877700 167

----------------------------------------------------------------------------------------------------

49

The UNIVARIATE Procedure Variable: well_age (well_age)

Moments

N 195 Sum Weights 195 Mean 15.96 Sum Observations 3112.2 Std Deviation 13.7835454 Variance 189.986124 Skewness 1.32836735 Kurtosis 1.96935397 Uncorrected SS 86528.02 Corrected SS 36857.308 Coeff Variation 86.3630664 Std Error Mean 0.98706019

Basic Statistical Measures

Location Variability

Mean 15.96000 Std Deviation 13.78355 Median 12.00000 Variance 189.98612 Mode 1.00000 Range 74.00000 Interquartile Range 19.00000

Tests for Location: Mu0=0

Test -Statistic- -----p Value------

103

Student's t t 16.16923 Pr > |t| <.0001 Sign M 97 Pr >= |M| <.0001 Signed Rank S 9457.5 Pr >= |S| <.0001

Tests for Normality

Test --Statistic--- -----p Value------

Shapiro-Wilk W 0.882945 Pr < W <0.0001 Kolmogorov-Smirnov D 0.158532 Pr > D <0.0100 Cramer-von Mises W-Sq 1.042036 Pr > W-Sq <0.0050 Anderson-Darling A-Sq 6.086345 Pr > A-Sq <0.0050

Quantiles (Definition 5)

Quantile Estimate

100% Max 74.0 99% 64.0 95% 41.0 90% 34.0 75% Q3 24.0 50% Median 12.0 25% Q1 5.0 10% 2.0

----------------------------------------------------------------------------------------------------

50

The UNIVARIATE Procedure Variable: well_age (well_age)

Quantiles (Definition 5)

Quantile Estimate

5% 1.0 1% 0.1 0% Min 0.0

Extreme Observations

----Lowest---- ----Highest---

Value Obs Value Obs

0.0 1 53 191 0.1 3 54 192 0.1 2 62 193 1.0 18 64 194 1.0 17 74 195

Missing Values

104

-----Percent Of----- Missing Missing Value Count All Obs Obs

. 23 10.55 100.00

----------------------------------------------------------------------------------------------------

51

The UNIVARIATE Procedure Variable: well_depth (well_depth)

Moments

N 166 Sum Weights 166 Mean 163.63253 Sum Observations 27163 Std Deviation 112.540379 Variance 12665.3369 Skewness 0.72176609 Kurtosis -0.1949559 Uncorrected SS 6534531 Corrected SS 2089780.58 Coeff Variation 68.7762872 Std Error Mean 8.73482742

Basic Statistical Measures

Location Variability

Mean 163.6325 Std Deviation 112.54038 Median 147.5000 Variance 12665 Mode 50.0000 Range 487.00000 Interquartile Range 180.00000

Tests for Location: Mu0=0

Test -Statistic- -----p Value------

Student's t t 18.73334 Pr > |t| <.0001 Sign M 83 Pr >= |M| <.0001 Signed Rank S 6930.5 Pr >= |S| <.0001

Tests for Normality

Test --Statistic--- -----p Value------

Shapiro-Wilk W 0.925742 Pr < W <0.0001 Kolmogorov-Smirnov D 0.102718 Pr > D <0.0100 Cramer-von Mises W-Sq 0.470327 Pr > W-Sq <0.0050 Anderson-Darling A-Sq 3.407442 Pr > A-Sq <0.0050

Quantiles (Definition 5)

Quantile Estimate

100% Max 505.0

105

99% 465.0 95% 380.0 90% 325.0 75% Q3 230.0 50% Median 147.5 25% Q1 50.0 10% 40.0

----------------------------------------------------------------------------------------------------

52

The UNIVARIATE Procedure Variable: well_depth (well_depth)

Quantiles (Definition 5)

Quantile Estimate

5% 33.0 1% 24.0 0% Min 18.0

Extreme Observations

----Lowest---- ----Highest---

Value Obs Value Obs

18 179 400 98 24 93 425 41 26 134 450 123 27 194 465 112 30 211 505 107

Missing Values

-----Percent Of----- Missing Missing Value Count All Obs Obs

. 52 23.85 100.00

----------------------------------------------------------------------------------------------------

53

The UNIVARIATE Procedure Variable: acres (acres)

Moments

N 218 Sum Weights 218 Mean 16.714633 Sum Observations 3643.79

106

Std Deviation 52.4719458 Variance 2753.30509 Skewness 7.06650956 Kurtosis 57.1949381 Uncorrected SS 658371.818 Corrected SS 597467.205 Coeff Variation 313.928195 Std Error Mean 3.5538486

Basic Statistical Measures

Location Variability

Mean 16.71463 Std Deviation 52.47195 Median 3.00000 Variance 2753 Mode 0.92000 Range 498.07000 Interquartile Range 8.99000

Tests for Location: Mu0=0

Test -Statistic- -----p Value------

Student's t t 4.703248 Pr > |t| <.0001 Sign M 109 Pr >= |M| <.0001 Signed Rank S 11935.5 Pr >= |S| <.0001

Tests for Normality

Test --Statistic--- -----p Value------

Shapiro-Wilk W 0.296515 Pr < W <0.0001 Kolmogorov-Smirnov D 0.375253 Pr > D <0.0100 Cramer-von Mises W-Sq 10.07717 Pr > W-Sq <0.0050 Anderson-Darling A-Sq 49.42777 Pr > A-Sq <0.0050

Quantiles (Definition 5)

Quantile Estimate

100% Max 498.10 99% 258.56 95% 77.63 90% 35.44 75% Q3 10.00 50% Median 3.00 25% Q1 1.01 10% 0.92

----------------------------------------------------------------------------------------------------

54

The UNIVARIATE Procedure Variable: acres (acres)

Quantiles (Definition 5)

Quantile Estimate

107

5% 0.91 1% 0.53 0% Min 0.03

Extreme Observations

----Lowest---- -----Highest----

Value Obs Value Obs

0.03 173 125.00 206 0.50 145 173.30 208 0.53 207 258.56 73 0.62 156 459.24 167 0.66 214 498.10 195

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55

10/12/1999 The UNIVARIATE Procedure Variable: Total_valu (Total_valu)

Moments

N 62 Sum Weights 62 Mean 275843.548 Sum Observations 17102300 Std Deviation 372179.861 Variance 1.38518E11 Skewness 3.39236863 Kurtosis 13.5182334 Uncorrected SS 1.31671E13 Corrected SS 8.44959E12 Coeff Variation 134.924258 Std Error Mean 47266.8896

Basic Statistical Measures

Location Variability

Mean 275843.5 Std Deviation 372180 Median 153200.0 Variance 1.38518E11 Mode 48800.0 Range 2107800 Interquartile Range 230200

NOTE: The mode displayed is the smallest of 2 modes with a count of 2.

Tests for Location: Mu0=0

Test -Statistic- -----p Value------

Student's t t 5.835873 Pr > |t| <.0001 Sign M 31 Pr >= |M| <.0001 Signed Rank S 976.5 Pr >= |S| <.0001

Tests for Normality

108

Test --Statistic--- -----p Value------

Shapiro-Wilk W 0.605778 Pr < W <0.0001 Kolmogorov-Smirnov D 0.243714 Pr > D <0.0100 Cramer-von Mises W-Sq 1.283841 Pr > W-Sq <0.0050 Anderson-Darling A-Sq 7.021288 Pr > A-Sq <0.0050

Quantiles (Definition 5)

Quantile Estimate

100% Max 2125200 99% 2125200 95% 796900 90% 650000 75% Q3 311100 50% Median 153200

----------------------------------------------------------------------------------------------------

56

The UNIVARIATE Procedure Variable: Total_valu (Total_valu)

Quantiles (Definition 5)

Quantile Estimate

25% Q1 80900 10% 45300 5% 33700 1% 17400 0% Min 17400

Extreme Observations

-----Lowest---- -----Highest-----

Value Obs Value Obs

17400 48 738500 27 18100 51 796900 31 30200 3 832600 38 33700 21 1817400 41 34200 23 2125200 55

----------------------------------------------------------------------------------------------------

57

The UNIVARIATE Procedure Variable: well_age (well_age)

109

Moments

N 44 Sum Weights 44 Mean 21.6818182 Sum Observations 954 Std Deviation 14.9117914 Variance 222.361522 Skewness 0.98046622 Kurtosis 1.49121829 Uncorrected SS 30246 Corrected SS 9561.54545 Coeff Variation 68.7755577 Std Error Mean 2.24803713

Basic Statistical Measures

Location Variability

Mean 21.68182 Std Deviation 14.91179 Median 20.00000 Variance 222.36152 Mode 20.00000 Range 70.00000 Interquartile Range 14.50000

Tests for Location: Mu0=0

Test -Statistic- -----p Value------

Student's t t 9.644778 Pr > |t| <.0001 Sign M 21 Pr >= |M| <.0001 Signed Rank S 451.5 Pr >= |S| <.0001

Tests for Normality

Test --Statistic--- -----p Value------

Shapiro-Wilk W 0.935969 Pr < W 0.0169 Kolmogorov-Smirnov D 0.133407 Pr > D 0.0476 Cramer-von Mises W-Sq 0.1135 Pr > W-Sq 0.0755 Anderson-Darling A-Sq 0.709915 Pr > A-Sq 0.0620

Quantiles (Definition 5)

Quantile Estimate

100% Max 70.0 99% 70.0 95% 50.0 90% 41.0 75% Q3 27.0 50% Median 20.0 25% Q1 12.5 10% 2.0

----------------------------------------------------------------------------------------------------

58

The UNIVARIATE Procedure Variable: well_age (well_age)

110

Quantiles (Definition 5)

Quantile Estimate

5% 2.0 1% 0.0 0% Min 0.0

Extreme Observations

----Lowest---- ----Highest---

Value Obs Value Obs

0 2 41 40 0 1 47 41 2 5 50 42 2 4 51 43 2 3 70 44

Missing Values

-----Percent Of----- Missing Missing Value Count All Obs Obs

. 18 29.03 100.00

----------------------------------------------------------------------------------------------------

59

The UNIVARIATE Procedure Variable: well_depth (well_depth)

Moments

N 43 Sum Weights 43 Mean 106.511628 Sum Observations 4580 Std Deviation 93.4875525 Variance 8739.92248 Skewness 1.93673102 Kurtosis 3.72027877 Uncorrected SS 854900 Corrected SS 367076.744 Coeff Variation 87.7721563 Std Error Mean 14.2567181

Basic Statistical Measures

Location Variability

Mean 106.5116 Std Deviation 93.48755 Median 66.0000 Variance 8740 Mode 40.0000 Range 420.00000 Interquartile Range 83.00000

111

Tests for Location: Mu0=0

Test -Statistic- -----p Value------

Student's t t 7.470978 Pr > |t| <.0001 Sign M 21.5 Pr >= |M| <.0001 Signed Rank S 473 Pr >= |S| <.0001

Tests for Normality

Test --Statistic--- -----p Value------

Shapiro-Wilk W 0.746465 Pr < W <0.0001 Kolmogorov-Smirnov D 0.227381 Pr > D <0.0100 Cramer-von Mises W-Sq 0.691869 Pr > W-Sq <0.0050 Anderson-Darling A-Sq 3.831346 Pr > A-Sq <0.0050

Quantiles (Definition 5)

Quantile Estimate

100% Max 450 99% 450 95% 300 90% 275 75% Q3 125 50% Median 66 25% Q1 42 10% 40

----------------------------------------------------------------------------------------------------

60

The UNIVARIATE Procedure Variable: well_depth (well_depth)

Quantiles (Definition 5)

Quantile Estimate

5% 35 1% 30 0% Min 30

Extreme Observations

----Lowest---- ----Highest---

Value Obs Value Obs

30 57 275 11 35 42 290 17 35 38 300 20 40 58 305 1

112

40 48 450 6

Missing Values

-----Percent Of----- Missing Missing Value Count All Obs Obs

. 19 30.65 100.00

----------------------------------------------------------------------------------------------------

61

The UNIVARIATE Procedure Variable: acres (acres)

Moments

N 62 Sum Weights 62 Mean 27.921129 Sum Observations 1731.11 Std Deviation 69.2737032 Variance 4798.84595 Skewness 3.95963869 Kurtosis 17.5344831 Uncorrected SS 341064.149 Corrected SS 292729.603 Coeff Variation 248.10495 Std Error Mean 8.7977691

Basic Statistical Measures

Location Variability

Mean 27.92113 Std Deviation 69.27370 Median 5.00000 Variance 4799 Mode 1.00000 Range 414.53000 Interquartile Range 9.59000

Tests for Location: Mu0=0

Test -Statistic- -----p Value------

Student's t t 3.17366 Pr > |t| 0.0024 Sign M 31 Pr >= |M| <.0001 Signed Rank S 976.5 Pr >= |S| <.0001

Tests for Normality

Test --Statistic--- -----p Value------

Shapiro-Wilk W 0.429398 Pr < W <0.0001 Kolmogorov-Smirnov D 0.360113 Pr > D <0.0100 Cramer-von Mises W-Sq 2.838272 Pr > W-Sq <0.0050 Anderson-Darling A-Sq 13.80696 Pr > A-Sq <0.0050

Quantiles (Definition 5)

113

Quantile Estimate

100% Max 415.00 99% 415.00 95% 180.92 90% 70.50 75% Q3 11.59 50% Median 5.00 25% Q1 2.00 10% 1.00

----------------------------------------------------------------------------------------------------

62

The UNIVARIATE Procedure Variable: acres (acres)

Quantiles (Definition 5)

Quantile Estimate

5% 0.92 1% 0.47 0% Min 0.47

Extreme Observations

----Lowest---- -----Highest----

Value Obs Value Obs

0.47 48 113.60 38 0.92 56 180.92 31 0.92 39 199.07 11 0.92 13 249.10 27 0.92 12 415.00 55

114

Table A.6: Summary statistics - Louisa County Samples Summary Statistics from Louisa County Household Well Samples:

Summer and Fall 1999 Variable N Mean Std Dev. Median Minimum Maximum

total home value 336 325667 276052 244400 100 2125200

well age 286 16.86713 13.9916 13 0 74

well depth (in ft.) 256 144.82031 110.3522 120 18 505

acres 336 19.40676 53.67032 3.36 0.03 498.1 Treatment present?

(0=no, 1=yes) 309 0.32039 0.46738 0 0 1

Nitrate 336 1.19594 2.63527 0.352 0 25.176 total coliform

present? 336 0.52679 0.50003 1 0 1 E.coli present? (0=no, 1=yes) 336 0.07738 0.26759 0 0 1

115

Table A.7: Summary statistics - Augusta County Samples Summary Statistics from Louisa County Household Well Samples:

Summer and Fall 1999

Variable N Mean Std Dev Median Minimum Maximum

total home value 124 305677 163666 273850 35200 822900

well age 87 20.21839 23.42997 15 0 199

well depth (in ft.) 104 292.3654 158.9048 275 25 740

acres 124 38.64565 57.91985 12.785 0.33 308.98 Treatment present?

(0=no, 1=yes) 115 0.54783 0.49989 1 0 1

Nitrate 124 1.80352 2.52971 1.28 0 20.88 total coliform

present? 124 0.3871 0.48906 0 0 1 E.coli present? (0=no, 1=yes) 124 0.07258 0.2605 0 0 1

116

VITA David Arnold was born in Roanoke, Virginia, on April 16, 1975 to the parents of

James and Mary Arnold. He grew up in Salem, VA, and graduated with a Bachelor of

Arts in Geography from Emory and Henry College in May 1998. He enrolled in the

Virginia Tech Department of Geography master’s program in the fall of 2005 with an

emphasis in GIS. During his graduate studies he was a graduate research assistant with

the Center for Geospatial Information Technology. He also was a public affairs assistant

for the College of Natural Resources. David is now beginning a career that focuses on

environmental analysis and GIS.

117