U.S. Department of the Interior

U.S. Geological Survey

Factors Associated with Sources, Transport,

and Fate of Volatile Organic Compounds in

Aquifers of the United States and Implications

for Ground-Water Management and Assessments

Scientific Investigations Report 2005–5269

NATIONAL WATER-QUALITY ASSESSMENT PROGRAM

NATIONAL SYNTHESIS ON VOLATILE ORGANIC COMPOUNDS
Factors Associated with Sources, Transport, 

and Fate of Volatile Organic Compounds 

in Aquifers of the United States and

Implications for Ground-Water

Management and Assessments

By Paul J. Squillace and Michael J. Moran

U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2005-5269
U.S. Department of the Interior

Gale A. Norton, Secretary

U.S. Geological Survey

P. Patrick Leahy, Acting Director

U.S. Geological Survey, Reston, Virginia: 2006

For sale by U.S. Geological Survey, Information Services

Box 25286, Denver Federal Center

Denver, CO 80225

For more information about the USGS and its products:

Telephone: 1-888-ASK-USGS

World Wide Web: http://www.usgs.gov/

Any use of trade, product, or firm names in this publication is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

Squillace, P.J., and Moran, M.J., 2006, Factors associated with sources, transport, and fate of volatile organic 

compounds in aquifers of the United States and implications for ground-water management and assessments: U.S. 

Geological Survey Scientific Investigations Report 2005-5269, 40 p.
 iii

Contents

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Purpose and Scope. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Background and Previous Studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Selected Aquifer Studies by NAWQA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Sampling and Analytical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Ancillary Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Statistical Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Quantile Plots. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Mixture Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Analysis of the Source, Transport, and Fate of VOCs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Logistic Regression to Determine Significant Associations . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Quantile Plots to Determine Significant Associations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Detection Frequency to Determine Significant Associations . . . . . . . . . . . . . . . . . . . . . . . . . 20

Network Analysis to Determine Significant Associations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Use of Mixtures to Determine Significant Associations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Synopsis of Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Fate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Implications for Ground-Water Management and Assessments. . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Sources. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Transport. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Fate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Well Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Mixtures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Figures

1. Map showing centroid of wells in 55 selected aquifer studies of the National

Water-Quality Assessment Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2. Boxplot showing statistical summary of population density in the conterminous

United States and within 500 meters of 1,631 wells sampled for selected 

aquifer studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3. Quantile/quantile plot of the concentrations of 10 volatile organic compounds

in oxic and anoxic conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
iv 

4. Quantile plots of concentrations of chloroform in anoxic and oxic conditions

by A, percent urban land use, and B, number of people using septic systems,

both within a 500-meter radius of the sampled well . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

5. Graph showing detection frequency of chloroform in oxic and anoxic conditions

for public and domestic water supplies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

6. Graph showing comparison of detection frequencies of 10 volatile organic

compounds in oxic water compared with anoxic water . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

7. Boxplot showing statistical summary of dissolved-oxygen concentrations 

in pre- and post-1953 recharge. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

8. Schematic showing chloroform in young oxic ground water biodegrades along

a ground-water flowpath to form methylene chloride and chloromethane in 

old anoxic ground water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

9. Pie charts showing comparison of the proportion of the total molar concentrations

of chloroform, methylene chloride, and chloromethane in oxic and anoxic ground

water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

10. Graph showing a significant correlation between population density and estimated

recharge for each selected aquifer study sampled by the National Water-Quality

Assessment Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

11. Graph showing the relation between the cumulative number of samples and the

type of mixture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

12. Boxplot showing statistical summary of the number of Resource Conservation 

and Recovery Act (RCRA) sites within 1,000 meters of sampled wells with 

random and nonrandom mixtures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Tables

1. Types and number of wells sampled in selected aquifer studies for the 

National Water-Quality Assessment Program. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2. Ten frequently detected VOCs (when calculated using no common assessment 

level) that were selected for statistical analysis in this report . . . . . . . . . . . . . . . . . . . . . . . 7

3. Variables tested in analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

4. Logistic regression analysis for 10 frequently detected VOCs in selected 

aquifer studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

5. Summary of significant factors in logistic regression models for 10 frequently 

detected VOCs in selected aquifer studies shown in table 4 . . . . . . . . . . . . . . . . . . . . . . . 17

6. Results of a linear regression model of detection frequency of chloroform in 

13 selected aquifer studies as the response variable and three explanatory 

variables partially representing the source, transport, and fate of chloroform. . . . . . . . 25

7. Statistical summary of 102 mixtures of volatile organic compounds (when 

calculated using no common assessment level) with a detection frequency 

of 1 percent or more . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

8. Estimated detection frequency of individual VOCs found in mixtures 

listed in table 7. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

9. Logistic regression analysis for samples with random/nonrandom mixtures . . . . . . . . . 32
 v

Conversion Factors and Abbreviations

Multiply By To obtain

cubic centimeter (cm3) 0.06102 cubic inch

meter (m) 3.281 foot

millimeter (mm) 0.03937 inch

Concentrations of chemical constituents in water are given either in milligrams per liter (mg/L) or 

micrograms per liter (μg/L).
Factors Associated with Sources, Transport, and 

Fate of Volatile Organic Compounds in Aquifers 

of the United States and Implications for

Ground-Water Management and Assessments

By Paul J. Squillace and Michael J. Moran

Abstract

Factors associated with sources, transport, and fate of 

volatile organic compounds (VOCs) in aquifer systems of the 

United States were evaluated using various statistical methods. 

VOC data from 1,631 wells sampled between 1996 and 2002 as 

part of the National Water-Quality Assessment (NAWQA) 

Program of the U.S. Geological Survey were used in the analyses. Sampled wells were randomly selected from aquifers used 

to supply drinking water in the regional study areas. Samples 

were analyzed for more than 50 VOCs from primarily domestic 

water supplies (1,184), followed by public-supply wells (216); 

the remaining wells (231) were from a variety of well types. The 

median well depth was 50 meters. Age-date information, available from 44 percent of the sampled wells, shows that about 

60 percent of the sampled water was recharged after 1953.

Ten VOCs frequently detected in aquifer samples were 

selected for statistical explanatory analyses and included the 

solvents chloromethane, methylene chloride, 1,1,1-trichloroethane, trichlorethene, and perchloroethene; the trihalomethanes bromodichloromethane and chloroform; and the gasoline compounds toluene, 1,2,4-trimethylbenzene and methyl 

tert-butyl ether. Concentrations of VOCs generally were less 

than 1 μg/L. Source factors, in order of decreasing importance, 

were general land-use activity (dispersed source), septic/sewer 

density (dispersed source), and sites where large concentrations 

of VOCs are potentially released (concentrated sources), such 

as leaking underground storage tanks. Mixture analysis showed 

that 11 percent of all samples had VOC mixtures that were associated with concentrated sources; 20 percent were associated 

with dispersed sources. Important transport factors included 

well depth, screen depth, precipitation/recharge, air temperature, various soil characteristics, and amount of water removed 

from storage during withdrawal from the aquifer. Dissolved 

oxygen was the explanatory factor that was strongly associated 

with the fate of VOCs; it proved crucial in explaining the detection and concentration of many VOCs. Chloroform, for 

example, is more stable in water that contains oxygen. This 

increased stability explained the larger detection frequencies 

and concentrations of chloroform in water containing oxygen 

compared with water having little or no oxygen. Well type 

(domestic or public supply) was also an important explanatory 

factor, but was classified as indeterminate because it was not 

clearly associated with the sources, transport, or fate of the 

VOC. 

Results of multiple analyses show the importance of (1) 

accounting for dispersed and concentrated sources of VOCs, 

(2) understanding the ground-water-flow system at different 

scales to help explain VOC detections, (3) measuring dissolved 

oxygen when sampling for VOCs, and (4) limiting the type of 

wells sampled in monitoring networks to avoid unnecessary 

variance in the data.

Introduction

 Ground water is used by about one-half of the population 

of the United States as a source of potable water, including 

nearly all of the 40 million or more people served by domestic 

water supplies (Alley and others, 1999). Concern about the 

quality of this heavily used resource has led to many small-scale 

investigations that define the risk and remediation of concentrated sources where contaminants are released at one location. 

A complementary interest in dispersed sources, those where 

contaminants are released over large areas, has led to waterquality investigations of aquifers. Dispersed sources result from 

routine activity prevalent in a given setting. Discharge from 

septic systems, runoff from paved surfaces, and volatile organic 

compounds (VOCs) in air are examples of dispersed sources. 

Large-scale studies can provide an indication of aquifer vulnerability and the quality of water in the aquifer as a whole, as well 

as identify contaminants that present the greatest risk to aquifers. As part of the National Water-Quality Assessment 

(NAWQA) Program of the U.S. Geological Survey (USGS), a 

plan was developed to define water-quality conditions of the 

most important aquifers in the Nation (Gilliom and others, 

1995). The results of these aquifer studies serve as a broad-scale 

assessment of water quality and important issues related to 

aquifers.
2 Factors Associated with Sources, Transport, and Fate of VOCs in Aquifers of the United States

Part of the design of the NAWQA selected aquifer studies 

was extensive sampling for VOCs; that is, organic chemical 

compounds that have a high vapor pressure relative to their 

solubility in water. VOCs include components of gasoline, fuel 

oils, and lubricants, as well as organic solvents, fumigants, 

some adjuvants in pesticides, and some by-products of chlorine 

disinfection. VOCs can be detected virtually everywhere in the 

environment. They are a concern in ground water because many 

are mobile, persistent, and toxic. Understanding the sources, 

transport, and fate of VOCs is crucial for the protection and 

management of the ground-water resource. Because VOCs 

are used in numerous domestic, commercial, and industrial 

products and applications, they can be released to ground water 

through sources such as septic systems, leaking water lines and 

sewer lines, landfills and dumps, leaking storage tanks, and 

stormwater runoff.

Purpose and Scope

The purpose of this report is to (1) summarize factors 

affecting the sources, transport, and fate of 10 frequently 

detected VOCs using various statistical techniques, and (2) 

describe implications of the preceding for ground-water 

management and future ground-water assessments. The VOC 

data were from samples collected between 1996 and 2002 

during NAWQA selected aquifer studies across the United 

States. The five approaches used to analyze the data were 

logistic regression, quantile plots, detection frequencies, 

network analysis, and mixture analysis.

Background and Previous Studies

Numerous household products contain VOCs (for 

example, toluene) and can be discharged to septic systems or 

disposed of improperly. In commerce and industry, VOCs are 

used in numerous applications, and this use results in considerable quantities of VOCs being released to the environment 

(U.S. Environmental Protection Agency, 1998). Considerable 

amounts of gasoline are, for example, released to ground water 

from leaking underground storage tanks. Once in the environment, VOCs can move between the atmosphere, soil, ground 

water, and surface water.

VOCs can be transported through the unsaturated zone in 

ground-water recharge, in soil vapor, or as non-aqueous-phase 

liquid. Any hydrologic condition that shortens residence time 

within the unsaturated zone can result in increased amounts of 

VOCs to the water table; for example, structures such as 

recharge basins and dry wells can accelerate transport of water 

and accompanying VOCs through the unsaturated zone. 

Furthermore, a shallow water table and abundant ground-water 

recharge would favor rapid transport through the unsaturated 

zone and increase the likelihood of VOCs reaching ground 

water. Some VOCs also can move slowly through the unsaturated zone in the gas phase and enter the top of the water table 

directly where concentrations partition between soil, air, and 

ground water; however, this type of transport also is enhanced 

by the movement of recharge water (Pankow and others, 1997). 

For example, methyl tert-butyl ether (MTBE) is likely one of 

the few VOCs measured in ground water where urban air could 

be a source of small concentrations in shallow ground water 

beneath urban areas (Baehr and others, 1999; Baehr and others, 

2001; Zogorski and others, in press). VOCs as non-aqueousphase liquids could migrate to the water table by gravity without 

the aid of recharge or any other transport mechanism.

The transport of VOCs dissolved in ground water may be 

affected by sorption, advection, and dispersion. Sorption of 

VOCs to organic carbon in the aquifer material may slow transport. The effect of sorption on VOC transport is dependent on 

the solubility of the VOC, the amount of organic carbon in the 

aquifer, and the density and porosity of the aquifer. Solubility 

tends to be inversely related to sorption. Some very soluble 

VOCs like MTBE have a small sorption tendency and thus 

move as quickly as water does, whereas other less soluble 

VOCs like carbon tetrachloride (Wiedemeier and others, 1999) 

have a larger sorption tendency and move very slowly relative 

to the rate of ground-water flow. The movement of solutes by 

the bulk motion of flowing ground water is known as advection. 

The rate of advective transport varies by many orders of magnitude in natural ground-water-flow systems (Reilly and Pollock, 

1995). The tendency of solutes to spread out from the path that 

would be expected from advective flow is known as dispersion. 

Dispersion contributes to the dilution of concentrations of 

VOCs in ground water. VOCs can eventually be discharged 

with ground water into pumped wells, streams, and rivers if 

traveltimes are short enough or aquifer conditions are such that 

complete attenuation of VOCs is prevented. 

The fate of any particular VOC in ground water depends 

largely on its persistence under the conditions present in the 

aquifer. VOCs that are persistent in water are more likely to be 

detected in ambient ground water because they can travel 

greater distances from their source before degradation and dilution occurs. In ground water, VOCs undergo selective abiotic 

and biotic transformation. Hydrolysis is an example of abiotic 

transformation in water; for example, 1,1,1-trichloroethane 

(TCA) is the only commonly used chlorinated solvent that can 

be transformed to 1,1-dichloroethene and acetic acid by hydrolysis (Wiedemeier and others, 1999). Biotic transformations 

generally are more important than abiotic transformations for 

most VOCs and are linked to redox conditions in ground water. 

In fact, certain microorganisms under specific redox conditions 

can transform only some organic compounds. In redox reactions, inorganic electron acceptors are sequentially used in 

biotic transformations of organic compounds in the following 

order: oxygen, nitrate, manganese, iron, sulfate, and carbon 

dioxide. This sequential use explains why some organic 

compounds are persistent in an aquifer until a particular redox 

condition occurs; other organic compounds can be transformed 

under many redox conditions, but the rates can vary with the 

redox condition (Wiedemeier and others, 1999). Some highly 

chlorinated ethenes such as perchloroethene (PCE) and trichloroethene (TCE) also can act as electron acceptors in microbial 
Methods 3

metabolism. In fact, PCE is a stronger oxidant than all of the 

naturally occurring inorganic electron acceptors with the exception of oxygen; consequently, PCE readily undergoes reductive 

dechlorination (hydrogen replaces chlorine) to TCE under 

anaerobic conditions (Chapelle and others, 2003). 

Bacteria may be unable to use VOCs as a sole source of 

carbon and energy when the compounds are at very small 

concentrations (nanograms per liter or a few micrograms per 

liter); this may slow the transformation of VOCs in ground 

water (Roch and Alexander, 1997). A decline in transformation 

rate with concentration may explain why some VOCs that 

degrade quickly for larger concentrations are commonly 

detected in NAWQA’s ground-water assessments at very low 

concentrations.

In typical technical reports and data sets, detection 

frequencies and concentrations are given for individual contaminants; however, contaminants may occur more frequently as 

mixtures in samples than by themselves (Stackelberg and 

others, 2001; Squillace and others, 2002). The health implication of contaminants (VOCs, pesticides, nitrate, and others) 

occurring together is an area of ongoing research. The challenge 

is to identify mixtures that present a health risk among the enormous number of mixtures humans are exposed to every day. 

The environmental importance of mixtures of contaminants is a 

new area of research that could help explain the sources, transport, and fate of VOCs.

Previous investigations have used statistical methods to 

show associations between land-use activity and ground-water 

quality for various anthropogenic contaminants. At a national 

scale, Kolpin and others (1998) showed an association between 

detection of pesticides and their use, mobility, and persistence. 

Nolan and others (2002) developed a logistic regression model 

to predict the probability of nitrate concentrations exceeding 

4 milligrams per liter (mg/L) in predominantly shallow, 

recently recharged ground waters of the United States. The 

model contains factors representing fertilizer loading, percent 

cropland-pasture, human population density, percent welldrained soil, depth to the seasonally high water table, and presence or absence of unconsolidated sand and gravel aquifers. 

Tesoriero and Voss (1997) used logistic regression to predict 

the likelihood that nitrate is present at concentrations of 3 mg/L 

or more in ground waters of the Puget Sound Basin in northwestern Washington. Variables in this model include well 

depth, ground-water recharge, soil hydrologic group, surficial 

geology type, land-use type, and population density. Eckhardt 

and Stackelberg (1995) used logistic regression to predict the 

probability of detecting several anthropogenic contaminants in 

ground water beneath agricultural, suburban, and undeveloped 

areas of Long Island, N.Y. Two important explanatory factors 

were population density and land-use activity near the sampled 

well. Squillace and others (1999) developed a national-scale 

logistic regression model showing an association between the 

detection of any VOC and population density. Squillace and 

others (2004) developed logistic regression models for 14 

frequently detected VOCs in the shallowest ground water 

beneath new residential/commercial areas in the United States. 

Dissolved-oxygen concentrations, estimated recharge, and land 

use were the most important explanatory factors for these 

models. This report is a national scale assessment of associations between the quality of the ground water used as a water 

supply and factors related to the sources, transport, and fate of 

VOCs.

Acknowledgments

The authors thank the many USGS personnel who 

collected and compiled the water-quality data used in this paper. 

We also thank Kerie Hitt, Barbara Ruddy, Curtis Price, and 

David Wolock for compiling and analyzing data using 

geographic information systems. Lastly, we thank Arthur 

Baehr, Paul Stackelberg, Gary Rowe, and John Zogorski for 

providing insightful reviews that substantially improved this 

report.

Methods

In this section, a number of methods used in this report are 

documented. These methods include design of NAWQA’s 

selected aquifer studies, sampling and analytical methods, 

ancillary data, statistical methods, quantile plots, and mixtures 

analysis.

Selected Aquifer Studies by NAWQA

Ground-water samples from 55 selected aquifers were 

collected between 1996 and 2002 as part of the USGS’s 

NAWQA Program (fig. 1 and table 1). The selected 55 aquifer 

studies are a subset of the 98 aquifer studies included in the 

NAWQA Program’s national assessment of VOCs (Zogorski 

and others, in press). The original design of the NAWQA 

Program was to study aquifers in 60 large hydrologic basins 

(Gilliom and others, 1995); however, the number of hydrologic 

basins has been reduced to 42 (Gilliom and others, 2001). Each 

selected aquifer study generally contained 30 sampled wells 

and was done by sampling existing wells. These studies have 

three unique characteristics. First, samples were collected 

before any treatment to define the quality of water in the 

aquifer. Second, sampled wells were spatially distributed and 

randomly selected among existing wells within the targeted 

aquifer. Third, most of the samples were collected from 

domestic wells; in fact, 73 percent of the samples (1,184) were 

collected from domestic wells, 13 percent (216) were from 

public water supplies, and the remaining 14 percent (231) were 

from monitoring wells and a variety of other well types 

(table 1). Domestic wells were sampled more frequently 

because they were most commonly available; in addition, 

public-supply wells can have high pumping rates and large 

capture zones, which can increase the number of potential 

contamination sources (Stackelberg and others, 2000) 

compared with other types of wells. Domestic wells were also 

the only type of well available for sampling in many parts of the 

investigated aquifers. Sampling these domestic wells saved 

installation costs associated with monitoring wells.
4 Factors Associated with Sources, Transport, and Fate of VOCs in Aquifers of the United States

Table 1. Types and number of wells sampled in selected aquifer studies for

the National Water-Quality Assessment Program.

Network Study

Unit

Well type

Total

Domestic Public Monitoring Other

acadsus1 acad 27 0 0 2 29

acadsus2 acad 25 0 1 2 28

almnsus1 almn 30 0 0 0 30

almnsus2 almn 30 0 0 0 30

cazbsus1a cazb 17 0 1 12 30

cazbsus2 cazb 23 1 2 1 27

cazbsus3 cazb 16 1 0 1 18

delrsus1 delr 26 1 2 1 30

delrsus2 delr 27 1 1 1 30

delrsus3 delr 12 0 1 3 16

eiwasus1 eiwa 32 0 0 1 33

eiwasus2 eiwa 32 0 0 0 32

grslsus1 grsl 33 7 1 11 52

hpgwsus1a hpgw 74 0 0 0 74

hpgwsus1b hpgw 45 0 0 2 47

hpgwsus2 hpgw 20 0 0 0 20

kanasus1 kana 21 0 0 9 30

kanasus2 kana 19 2 1 8 30

lerisus1 leri 27 0 0 1 28

linjsus1 linj 29 0 0 1 30

linjsus2 linj 30 0 0 0 30

linjsus3 linj 20 0 0 0 20

lirbsus1 lirb 26 1 0 3 30

lirbsus2 lirb 28 1 0 1 30

ltensus1 lten 8 3 12 9 32

ltensus2 lten 25 1 1 4 31

miamsus1 miam 30 0 0 0 30

misesus1 mise 2 10 0 17 29

misesus2 mise 0 29 0 1 30

misesus3 mise 3 1 0 21 25

moblsus1 mobl 23 0 1 6 30
Methods 5

necbsus1 necb 28 0 0 0 28

necbsus2 necb 30 0 0 0 30

necbsus3 necb 0 29 0 1 30

nroksus1 nrok 29 0 0 2 31

nroksus2 nrok 28 0 0 2 30

pugtsus1 pugt 29 0 1 0 30

sacrsus1 sacr 27 0 1 2 30

sanasus1 sana 4 23 0 0 27

sanasus2 sana 0 16 0 4 20

sanasus3 sana 0 16 0 6 22

santsus1 sant 2 18 2 5 27

santsus2 sant 29 1 0 0 30

santsus3 sant 29 0 0 1 30

sctxsus1 sctx 21 3 0 4 28

sctxsus2 sctx 25 3 1 2 31

sctxsus3 sctx 1 20 0 8 29

soflsus1 sofl 0 22 7 0 29

ucolsus1 ucol 24 4 1 0 29

uirbsus1 uirb 27 0 0 0 27

umissus3 umis 25 0 0 0 25

umissus4 umis 25 0 0 0 25

utensus1 uten 18 2 6 4 30

yellsus1 yell 1 0 23 0 24

yellsus2 yell 22 0 2 4 28

total 1,184 216 68 163 1,631

Table 1. Types and number of wells sampled in selected aquifer studies for

the National Water-Quality Assessment Program.—Continued

Network Study

Unit

Well type

Total

Domestic Public Monitoring Other
EXPLANATION

Study Unit boundary with

 4 letter identifier

Wells

PUGT

YELL

SACR

GRSL

SANA

SCTX

ACAD

CAZB MOBL

SOFL

SANT

ALMN

UMIS

EIWA

UIRB

LIRB MIAM

LERI

DELR

LINJ

NECB

UTEN

KANA

MISE

UCOL

NROK

LTEN

PUGT

YELL

SACR

GRSL

SANA

SCTX

ACAD

CAZB MOBL

SOFL

SANT

ALMN

UMIS

EIWA

UIRB

LIRB MIAM

LERI

DELR

LINJ

NECB

UTEN

KANA

MISE

UCOL

HPGW

NROK

LTEN

GRSL

0 200 400 MILES

0 200 400 KILOMETERS

Base modified from U.S. Geological Survey

digital data, 1:2,000,000, 1990

Albers Equal-Area Projection

North American Datum of 1983

6 Factors Associated with Sources, Transport, and Fate of VOCs in Aquifers of the United States

Figure 1. Centroid of wells in 55 selected aquifer studies of the National Water-Quality Assessment Program.

Selected aquifer studies were resource assessments of the 

targeted aquifer; consequently, sampled wells are spatially 

distributed and not stratified on the basis of water use. In terms 

of population, selected aquifer study wells are in areas where 

population density is higher on average than for the conterminous United States as a whole; in fact, the median population 

density near wells sampled for selected aquifer studies is about 

one order of a magnitude larger than the median density for the 

conterminous United States but most of the sampled wells were 

in rural areas (fig. 2). Selected aquifer studies, consequently, 

are ground-water resource assessments of rural areas where 

ground water is used intensively as a domestic water supply.

Sampling and Analytical Methods

VOCs were analyzed in samples from 1,631 wells—1,184 

domestic wells, 216 public water-supply wells, and 231 other 

well types, such as irrigation or monitoring wells (table 1). 

These wells were distributed across the United States in 55 

selected aquifer studies (fig. 1). Analyses included concentration data for 53 of the 55 VOCs that were identified and 

included as part of NAWQA’s national assessment of VOCs 

(Bender and others, 1999). Information on the major uses, 

chemical formulas, and other properties for each of these 53 

VOCs) is presented in Bender and others (1999). 

median

25th percentile

75th percentile

10th percentile

90th percentile

POPULATION DENSITY,

IN PEOPLE PER SQUARE KILOMETER

10

1

0.1

100

1,000

10,000

CONTERMINOUS

UNITED STATES 

AQUIFER

STUDIES

Rural

Urban

EXPLANATION

Figure 2. Statistical summary of population density in the 

conterminous United States and within 500 meters of 1,631 wells 

sampled for selected aquifer studies (U.S. Bureau of Census, 1992).
Methods 7

All 1,631 analyses were done at the USGS National Water 

Quality Laboratory in Denver, Colo., using the method that 

provided the lowest concentration information (method 

schedule 2020), currently (2005) in use at the USGS National 

Water Quality Laboratory (Connor and others, 1998). Although 

also analyzed by this low-level method, samples from Alaska 

and Hawaii were not included in analyses in this report because 

ancillary information needed for explanatory analyses was not 

available. Analyses were done by use of purge-and-trap, capillary column gas chromatography/mass spectrometry. Identification was confirmed by gas chromatographic retention time 

and by the resultant mass spectrum typically identified by three 

unique ions (Connor and others, 1998). Some of the smallest 

reported concentrations were estimated, indicating quantitative 

uncertainty. No common assessment level (for example, 0.02 or 

0.2 micrograms per liter (μg/L)) was applied to any of the VOC 

data for this analysis. A subset of 10 VOCs listed in table 2 were 

frequently detected VOCs (when calculated using no common 

assessment level) and were selected for more detailed statistical 

analysis in this report. All VOC data are discussed in more 

detail by Moran and others (in press).

Sampling protocols and quality-assurance/quality-control 

plans are described by Koterba and others (1995). One primary 

environmental sample per well was used for the analyses in this 

report. Source solution blanks, equipment blanks, field blanks, 

trip blanks, laboratory blanks, replicate samples, and spike 

samples are some of the types of quality-control samples that 

are routinely collected. All samples were reviewed to verify the 

quality of the data; in particular, field blank samples were 

reviewed to identify any systematic contamination. If contamination occurred, the environmental concentration data associated with contaminated quality-control samples were not used 

for this analysis. There were very few incidents of systematic 

contamination.

Ancillary Data

Information was collected to characterize the well 

construction, hydrogeology, geochemistry, land use, and age of 

the water (table 3 and Moran and others, in press). Well depths 

ranged from 4 to 825 m, with a median depth of 50 m. Domestic 

wells had a median well depth of 45 m, and public-supply wells 

had a median depth of 120 m. There were 922 wells finished in 

unconfined aquifers and 381 wells in confined aquifers; for the 

remaining 328 wells, aquifer types were mixed or not identified. 

This information was collected because unconfined aquifers 

generally are considered more prone to contamination than are 

confined aquifers. For example, recharge water can move relatively quickly through sand lying above an unconfined aquifer, 

but recharge water moves much slower through clay lying 

above a confined aquifer. Dissolved-oxygen concentrations in 

ground water were classified as oxic or anoxic. Water was classified as oxic if dissolved-oxygen concentrations were greater 

than 0.5 mg/L and anoxic if dissolved-oxygen concentrations 

were less than or equal to 0.5 mg/L. Direction of ground-water 

flow was not known at each sampled well. If accurate flowdirection information had been available, statistical associations 

between water-quality factors such as potential sources and 

land-use activities near the sampled well likely would have 

been stronger, because areas downgradient from sampled 

domestic wells could have been eliminated from consideration 

in statistical analysis.

Table 2. Ten frequently detected VOCs (when calculated using no common assessment level) 

that were selected for statistical analysis in this report.

[IUPAC, International Union of Pure and Applied Chemistry]

IUPAC name Selected alternative name Abbreviation VOC Group

Bromodichloromethane -- -- Trihalomethane

Trichloromethane Chloroform -- Trihalomethane

Chloromethane Methyl chloride -- Solvent

Dichloromethane Methylene chloride -- Solvent

1,1,1-Trichloroethane Methyl chloroform TCA Solvent

Trichloroethene -- TCE Solvent

Tetrachloroethene Perchloroethene PCE Solvent

Methyl-tert-butyl ether -- MTBE Gasoline oxygenate

1,2,4-Trimethylbenzene -- TMB Gasoline hydrocarbon

Methylbenzene Toluene -- Gasoline hydrocarbon
8 Factors Associated with Sources, Transport, and Fate of VOCs in Aquifers of the United States

Table 3. Variables tested in analyses.

[mm/yr, millimeter per year; mm/hr, millimeters per hour; g/cm3, gram per cubic centimeter; °C, degrees Celsius; mg/L, milligram per liter; μS/cm, microsiemens 

per centimeter; km2

, kilometer squared; cm, centimeters; m, meters; USGS, U.S. Geological Survey; NWIS, USGS National Water Information System; 

NAWQA, USGS National Water-Quality Assessment Program; USEPA, U.S. Environmental Protection Agency; CIESIN, Consortium for International Earth 

Science Information Network; CERCLA, Comprehensive Environmental Response, Compensation, and Liability Act; RCRA, Resource Conservation and 

Recovery Act; MTBE, methyl tert-butyl ether; DWS, domestic water supply; PWS, public water supply; LUST, leaking underground storage tank; UST, 

underground storage tank; NLCDE, national land-cover data enhanced]

Variable Units Description Coding Source

Source variables

Roads percent average percent road area 

around well

0 - 100 U.S. Bureau of the Census 

(1992)

CERCLA sites count number of sites within 1,000 m 

of well

continuous USEPA (2004)

RCRA sites count number of sites within 1,000 m 

of well

continuous USEPA (2004)

Dry-cleaning UST count number of sites within 1,000 m 

of well

continuous Vista Information Solutions 

(1999)

Dry-cleaning LUST count number of sites within 1,000 m

of well

continuous Vista Information Solutions 

(1999)

Gasoline UST count number of sites within 1,000 m

of well

continuous Vista Information Solutions 

(1999)

Gasoline LUST count number of sites within 1,000 m

of well

continuous Vista Information Solutions 

(1999)

MTBE use area categorical use of MTBE in gasoline 0 – low or no use

1 – high use

USEPA (2003a and 

2003b)/TRW Petroleum 

Technologies, written 

commun., 1999

Public sewerage percent block group percent of

households

0 - 100 U.S. Bureau of the Census 

(1992)

Septic sewerage percent block group percent of 

households

0 - 100 U.S. Bureau of the Census 

(1992)

People using septic 

systems

natural logarithm 

(people/km2)

block group population times

percent using septic sewerage

continuous U.S. Bureau of the Census 

(1992)

People using sewer 

systems

natural logarithm 

(people/km2

)

block group population times

percent using public sewerage

continuous U.S. Bureau of the Census 

(1992)

Population natural logarithm 

(people/km2)

block group population continuous U.S. Bureau of the Census 

(1992); CIESIN (1995); 

Dobson and others (2000)

Median year of construction years block group median age of homes 0 - 100 U.S. Bureau of the Census 

(1992)

Urban land use percent land use within 500 m of sampled 

well determined from NLCDE

0 - 100 Vogelmann and others 

(2001); C.V. Price, USGS, 

written commun., 2004
Methods 9

Source variables—Continued

Agricultural land use percent land use within 500 m of sampled 

well determined from NLCDE

0 - 100 Vogelmann and others 

(2001); C.V. Price, USGS, 

written commun., 2004

Undeveloped land use percent land use within 500 m of sampled 

well determined from NLCDE

0 - 100 Vogelmann and others 

(2001); C.V. Price, USGS, 

written commun., (2004)

Transport variables

Public-water-supply usage percent block group percent of

households

0 - 100 U.S. Bureau of the Census 

(1992)

Domestic-water-supply usage percent block group percent of

households

0 - 100 U.S. Bureau of the Census 

(1992)

Households with drilled wells percent block group percent of

households

0 - 100 U.S. Bureau of the Census 

(1992)

Households with dug wells percent block group percent of

households

0 - 100 U.S. Bureau of the Census 

(1992)

Well casing diameter cm average casing diameter by

well

continuous NWIS

Well depth m total depth of well continuous NWIS

Water level m average water level by well continuous NWIS

Depth to top of well screen m depth to the top of the screened 

interval in the well

continuous NWIS

Ground-water recharge mm/yr average recharge continuous Wolock (2003)

Soil permeability average mm/hr average soil permeability by

soil unit

continuous Wolock (1997)

Soil permeability minimum mm/hr soil permeability of

least permeable layer

continuous Wolock (1997)

Soil sand percent average soil sand content by

soil unit

0 - 100 Wolock (1997)

Soil silt percent average soil silt content by

soil unit

0 - 100 Wolock (1997)

Soil clay percent average soil clay content by

soil unit

0 - 100 Wolock (1997)

Soil organic matter percent average organic matter content

by soil unit

0 - 100 Wolock (1997)

Soil bulk density g/cm3 average bulk density of soil

by soil unit

continuous Wolock (1997)

Table 3. Variables tested in analyses.—Continued

[mm/yr, millimeter per year; mm/hr, millimeters per hour; g/cm3, gram per cubic centimeter; °C, degrees Celsius; mg/L, milligram per liter; μS/cm, microsiemens 

per centimeter; km2

, kilometer squared; cm, centimeters; m, meters; USGS, U.S. Geological Survey; NWIS, USGS National Water Information System; 

NAWQA, USGS National Water-Quality Assessment Program; USEPA, U.S. Environmental Protection Agency; CIESIN, Consortium for International Earth 

Science Information Network; CERCLA, Comprehensive Environmental Response, Compensation, and Liability Act; RCRA, Resource Conservation and 

Recovery Act; MTBE, methyl tert-butyl ether; DWS, domestic water supply; PWS, public water supply; LUST, leaking underground storage tank; UST, 

underground storage tank; NLCDE, national land-cover data enhanced]

Variable Units Description Coding Source
10 Factors Associated with Sources, Transport, and Fate of VOCs in Aquifers of the United States

Transport variables—Continued

Vertical soil permeability mm/hr average vertical soil permeability

by soil unit

continuous Wolock (1997)

Hydric soils percent average percent hydric soils

by soil unit

0 - 100 Wolock (1997)

Available water capacity fraction average water fraction by soil 

unit

0 - 1 Wolock (1997)

Slope of land surface percent average slope of land surface

by soil unit

0 - 100 Wolock (1997)

Annual precipitation cm/yr average estimated annual 

precipitation

continuous U.S. Dept. of Commerce 

(1995)

Annual air temperature °C average estimated annual air 

temperature

continuous U.S. Dept. of Commerce 

(1995)

Aquifer type unitless lithologic description of the 

aquifer

categorical based 

on major 

lithologic units

NWIS

Aquifer confinement unitless confining condition of the 

aquifer

confined

mixed 

unconfined

NWIS

Specific conductance μS/cm average specific conductance

of water in the well

continuous NWIS

Age of ground water years age of ground water at time of 

recharge determined by various 

methods

0 - older than or 

equal to 1953

1 - younger than 

1953

NWIS

Fate variables

Temperature of ground water °C average temperature of water

in the well

continuous NWIS

Dissolved oxygen (oxic/anoxic) categorical oxic water had average dissolved 

oxygen concentrations of water from 

the well greater than 0.5 mg/L; 

anoxic water had concentrations less 

than or equal to 0.5 mg/L

0 - oxic

1 - anoxic

NWIS

Indeterminate variables

DWS PWS categorical major use of water from 

the well

0 - DWS

1 - PWS

NWIS

Table 3. Variables tested in analyses.—Continued

[mm/yr, millimeter per year; mm/hr, millimeters per hour; g/cm3, gram per cubic centimeter; °C, degrees Celsius; mg/L, milligram per liter; μS/cm, microsiemens 

per centimeter; km2

, kilometer squared; cm, centimeters; m, meters; USGS, U.S. Geological Survey; NWIS, USGS National Water Information System; 

NAWQA, USGS National Water-Quality Assessment Program; USEPA, U.S. Environmental Protection Agency; CIESIN, Consortium for International Earth 

Science Information Network; CERCLA, Comprehensive Environmental Response, Compensation, and Liability Act; RCRA, Resource Conservation and 

Recovery Act; MTBE, methyl tert-butyl ether; DWS, domestic water supply; PWS, public water supply; LUST, leaking underground storage tank; UST, 

underground storage tank; NLCDE, national land-cover data enhanced]

Variable Units Description Coding Source
Methods 11

Ancillary data on land-use activity near the sampled well 

was available. The limitations and assumptions associated with 

ancillary data are summarized here but are discussed in more 

detail by Moran and others (in press). Data from the U.S. 

Bureau of Census (1992), Vista Information Systems (1999), 

U.S. Department of Commerce (1995), U.S. Environmental 

Protection Agency (2003a and 2003b), and Wolock (1997) also 

were used in the analysis (table 3). Census block data from the 

1990 U.S. Bureau of the Census (1992) were used to calculate 

the median age of homes and number of people using septic and 

sewer systems. Information from ancillary data was assumed to 

be relevant for an area within 500 m of the sampled well except 

for data from the U.S. Environmental Protection Agency (2004) 

and Vista Information Solutions (1999) where an area within 

1,000 m of the sampled well was used. The percentage of 

people using septic and sewer systems was multiplied by population density to obtain estimates of the number of people using 

septic and sewer systems. Vista Information Systems data 

(1999) were used to calculate the densities of underground 

storage tanks and leaking underground storage tanks (LUST) 

around the sampled wells. The gasoline tanks were distinguished from dry cleaners and other tanks by looking for a 

number of key words in the name of the business that would 

identify gasoline stations and dry cleaners. U.S. Environmental 

Protection Agency (2004) data were used to calculate the 

number of sites covered by the Comprehensive Environmental 

Response, Compensation, and Liability Act (CERCLA) and 

Resource Conservation and Recovery Act (RCRA). For each 

500-m grid cell, the number of underground tanks, leaking 

tanks, CERCLA sites, and RCRA sites was determined using a 

1,000-m circular buffer. A large circular buffer was used for 

these data because of the uncertainty associated with the locations. Data from U.S. Environmental Protection Agency (2003a 

and 2003b) and TRW Petroleum Technologies (Cheryl 

Dickson, Bartlesville, Okla., written commun., 1999) were used 

to define areas that used MTBE in gasoline at concentrations 

greater than 3 percent by volume. The enhanced national landcover data for 1992 (Vogelmann and others, 2001; C.V. Price, 

U.S. Geological Survey, written commun., 2004), which has a 

30-m resolution, was used to classify the general land use in a 

500-m buffer around the well. Urban land use included areas 

identified as low- to high-intensity residential, commercial, 

industrial, transportation, and urban/recreational grasses. 

Agricultural land use included areas identified as orchards/vineyards, row crops, or small grains. Data were not available to 

show how land use changed with time.

Ancillary information was evaluated before it was used in 

any statistical analysis. The quality of the ancillary data was 

evaluated by (1) comparing the ancillary data with known 

locations where more detailed information was available, 

(2) comparing similar ancillary data sets to determine which 

was the most accurate, and (3) critically evaluating the ancillary 

data. For example, there were only three dry-cleaning LUST 

sites near any of the sampled wells. Using this variable in 

statistical analysis may produce statistically significant results, 

but the small number of sites is an indication that these 

statistical relations are not environmentally important. A critical evaluation also included, for example, cumulative distribution plots that can show unusual plateaus in the data resulting 

from data manipulation or compilation. All these evaluations 

aided in understanding the strengths and weaknesses of the 

ancillary data.

Previous investigations provided information on estimated 

ground-water recharge. Ground-water recharge is an important 

explanatory variable because of the potential of recharge water 

to transport VOCs to the water table; areas with high recharge 

rates can have less residence time for degradation/transformation within the unsaturated zone. Wolock (2003) estimated 

ground-water recharge indices for the United States using data 

from 8,249 USGS stream gages with at least 10 years of record. 

Assumptions required for this method are (1) aquifer storage 

was steady state, implying that recharge was equal to groundwater discharge; (2) recharge was discharged to streams, and (3) 

ground-water discharge to streams could be estimated by multiplying a base-flow index (BFI) by long-term mean annual 

runoff. Stream gages on large river systems in the United States 

were not included in this analysis; consequently, the recharge 

estimates were representative of the local and possibly intermediate ground-water-flow system. Recharge to the deep, regional 

ground-water-flow system probably was not reflected in the 

recharge estimate. The BFI was calculated at each stream gage 

using the Wahl and Wahl (1995) hydrograph-separation technique. A grid was created from the BFI values using an inverse 

distance-weighting method. This grid was multiplied by a grid 

of average annual runoff (Wolock, 2003) to generate the 

average annual recharge value for the conterminous United 

States and to obtain point recharge estimates for the urban-landuse study wells. Irrigation may augment the transport of VOCs 

with ground-water recharge, but it is not known how much of 

this water applied to the land surface actually recharges the 

ground water. Evapotranspiration is largest during summer 

months when irrigation occurs; nevertheless, any irrigation 

return flow to the ground water should be reflected in the 

recharge index estimate. Although Wolock’s recharge estimate 

(Wolock, 2003) has limitations, this probably is the best 

recharge estimate currently (2005) available at a national scale 

for local and possibly intermediate ground-water-flow systems.

Thirteen selected aquifer studies were done in seven 

aquifers for which regional ground-water-flow models had been 

developed by the Regional Aquifer System Analysis (RASA) 

Program of the USGS (Johnston, 1999). These models did not 

include contaminant transport, but they can show how the 

hydrogeology of regional aquifers can help explain differences 

in water quality. RASA transient models were developed to 

show how factors such as withdrawal and irrigation return flow 

affected the transport of water within the aquifer (Johnston, 

1999); however, water budgets for these large-scale models 

cannot be used to describe the source of water to a particular 

well. Ground-water withdrawal can come from three sources 

(Johnston, 1999): (1) decrease in storage, (2) decrease in natural 

discharge, and (3) induced recharge and leakage from other 

aquifers and recharge from irrigation return flow. An overall 
12 Factors Associated with Sources, Transport, and Fate of VOCs in Aquifers of the United States

decline in water levels with time and ground-water withdrawal 

is an indication that water is being taken from storage. For 

confined aquifers, or for aquifers that have small amount of 

ground-water recharge, water removed from storage would 

likely be old water. In these situations, recharge likely occurred 

before anthropogenic activity began to affect ground-water 

quality. 

Ancillary information was classified as relating to sources, 

transport, or fate of VOCs, with the exception of well type, 

which was classified as an indeterminate factor because the 

reason why it affects VOC detection is uncertain. For example, 

public water supplies have larger pumping rates, are generally 

deeper, and are closer to urban areas than domestic water 

supplies. All these factors can affect the detection of VOCs. 

Sources of VOCs near domestic wells can be more difficult to 

determine from existing ancillary data. For example, VOCs that 

homeowners improperly dispose of on the land surface could be 

a source of ground-water contamination not identified in any 

ancillary information.

Statistical Methods

The concentration data used in this analysis were not 

censored to a common assessment level because (1) the same 

laboratory analysis was used for all samples, (2) censoring 

would reduce the ability to show associations between a particular VOC and explanatory factors, and (3) statistical methods 

like logistic regression are done for each VOC individually. 

Even though the lowest reported concentration for a particular 

VOC may change with instrument performance, the additional 

detections of VOCs at the smallest concentrations can be useful 

in establishing associations with explanatory factors. Censoring 

concentration data to a common assessment level is necessary 

when comparing, for example, detection frequencies among 

VOCs. 

Statistics can be used for many types of analyses with 

water-quality data, but all of these analyses only show associations and do not prove cause and effect. For example, the detection of a particular VOC may be associated with increased 

densities of septic systems near the sampled well, but the source 

of the VOC could be associated with some other type of anthropogenic activity or factor (for example road density) that can be 

correlated with septic-system density. 

An alpha level of 0.05 was used for all statistical analyses, 

which included contingency tables, Wilcoxon rank-sum test, 

Wilcoxon signed-rank test, Kolmogorov-Smirnov, Spearman’s 

rho, Pearson’s r, Wald’s t, Akaike’s Information Criteria, 

McFadden’s rho-squared, Hosmer/Lemeshow statistic, and log 

likelihood. These statistics are described in statistical books, for 

example, Menard (2002) and Helsel and Hirsch (1992). Contingency tables were used to measure the association between two 

nominal categorical variables if the populations of all cells were 

greater than or equal to five. Wilcoxon rank-sum test was used 

to test difference between two nominal categorical variables if 

a contingency table could not be used because one or more cells 

had a population of less than five. The Wilcoxon signed-rank 

test was used to test whether matched paired values in one 

group were larger than those in a second group. KolmogorovSmirnov and Wilcoxon rank-sum tests are nonparametric tests 

that can be used to determine whether the distributions of two 

data sets are different. Spearman’s rho was calculated to determine correlations between ranked concentrations in two data 

sets. Pearson’s r was calculated to measure the linear correlation 

between two data sets. Wald’s t, Akaike’s Information Criteria, 

McFadden’s rho-squared, Hosmer/Lemeshow statistic, and log 

likelihood are all statistics used to evaluate logistic regression 

models.

Logistic regression models were used to show associations 

of multiple explanatory variables (independent variables) with 

the presence/absence of a particular VOC (dependent variable). 

Models were developed for 10 frequently detected VOCs in the 

1,631 samples. The presence of a VOC in this analysis is based 

on all reported concentrations, many of which were below 

health benchmarks. Explanatory factors determined at these 

low levels could be different than those developed at higher 

assessment levels. The results of these models show associations only; they were not developed to be predictive models. 

Using logistic regression, explanatory variables are related 

to the probability of occurrence of the dependent variable in a 

manner similar to linear regression. For this report, the dependent variable is binary coded as 0 or 1, with 0 indicating a nonoccurrence and 1 indicating an occurrence of a particular VOC. 

The explanatory variables can be continuous or binary. The 

magnitude and sign of the estimated slope coefficient determines the strength and direction of the association of a single 

explanatory variable with the probability of detecting a VOC in 

water according to the following equation:

P e

β0 β1X1 ...βiXi ( ) + +

1 e β0 β1X1 ...βi

Xi ( ) + +

+

= -------------------------------------------------

where

P = probability of detecting a VOC;

β0 = the y-intercept;

βi = slope coefficient of Xi explanatory variables; and

Xi = 1 to i explanatory variables.

Estimated slope coefficients with positive signs indicate an 

increase in the probability of detecting a VOC with an increase 

in the explanatory variable, whereas estimated coefficients 

with negative signs indicate a decrease in the probability of 

detecting a VOC with an increase in the explanatory variable. 

For logistic regression models, unstandardized coefficients were recalculated in standard deviation units so that the 

magnitude of the standardized coefficients could be directly 

compared (Menard, 2002). A standardized coefficient indicated 

how many standard deviations of change in the dependent 
Methods 13

variable were associated with a 1-standard-deviation increase in 

the independent variable, as follows: 

b*YX bYX ( ) sX ( )( ) R /s

logit( ) Y = ˆ

where

b*YX = the standardized logistic regression coefficient;

bYX = the unstandardized logistic regression coefficient;

sX = the standard deviation of the independent variable X;

R = the correlation coefficient;

slogit(Y

ˆ ) = the standard deviation of the logit(Y

ˆ ); and

logit(Y

ˆ ) = the natural logarithm of the odds ratio. 

Explanatory variables were entered into the logistic regression manually in a step-wise manner, and the regression was 

analyzed for significance at each step. For the overall regression, if the likelihood ratio of the model produced a p-value of 

<0.05, all explanatory variables were considered significantly 

associated with the probability of occurrence of a VOC. The 

significance of nested logistic regression models was tested 

using the partial likelihood ratio test. For cases where one additional coefficient was added, the Wald statistic was used to 

determine significance of the coefficient. If the Wald statistic 

p-value of the slope coefficient was less than 0.05 and the upper 

and lower bounds of the odds ratio did not include 1, the additional variable was considered significantly associated with the 

probability of occurrence of a VOC. The significance of nonnested regression analyses was tested using Akaike’s Information Criteria (Helsel and Hirsch, 1992). The McFadden’s rhosquared can range between 0 and 1. A higher rho-squared corresponds to a more significant result. The Hosmer/Lemeshow 

statistic is a conservative test to help evaluate if the model fits 

the data and if the results are unduly influenced by a handful of 

unusual observations. The values of this statistic can range from 

0 to 1 with a higher value indicating a better model.

Logistic regression models were used to look for associations between ground-water quality at a particular location and 

current land-use activity within a circular buffer with a 500-m 

radius around the sampled well. No ground-water-flow analysis 

was done to document source of water to the sampled well. 

Consequently, the capture zone for the well may lie inside or 

outside of the 500-m radius. In these types of analyses, land use 

within the 500-m buffer is assumed to be representative of land 

use within the actual capture zone. Various conditions could 

adversely affect the results of this analysis, including land use 

within a 500-m radius that is not similar to land use within the 

actual capture zone of the sampled well, available land-use data 

that are not sufficiently accurate, prior land-use activity that has 

affected water quality, current land-use activity that has not had 

time to affect shallow ground water, only small areas within the 

500-m buffer that affect water quality, and concentrated sources 

that could be sufficiently spread out to give the appearance of 

dispersed sources. Spurious associations can result if VOCs 

were formed by the degradation of other VOCs, if the VOCs 

occur naturally (for example, chloroform (Laturnus and others, 

2002)), or if sources have not been included in the model. In 

these situations, however, model diagnostics usually indicate 

problems with the explanatory factors. In spite of these potential 

problems, several previous studies have shown a relation 

between land use and water quality (Eckhardt and Stackelberg, 

1995; Grady and Mullaney, 1998; Nolan and Stoner, 2000, 

Barbash and others, 2001; Robinson, 2002). 

For each VOC, various logistic regression models are 

possible, but information from a variety of statistical measures 

were used to select the final model (log likelihood, Wald’s t, 

Akaike’s Information Criteria, McFadden’s rho-squared, 

Hosmer/Lemeshow statistic) (Helsel and Hirsch, 1992; 

Menard, 2002). These models all had explanatory variables 

with a Wald’s t p-value of <0.05. The negative log likelihood 

was minimized, and nonnested models were compared by use of 

Akaike’s Information Criteria. Models with a large 

McFadden’s rho-squared were selected over those with lower 

values; and, when possible, models with a high Hosmer/Lemeshow statistic were selected over those with lower values to 

ensure that the model was not unduly affected by a small 

number of unusual observations. Although a variety of statistical measures were used to evaluate the many possible models, 

these models should not be viewed as definitive, unique, or suitable for predictive purposes. These models may change as more 

detailed ancillary or ground-water-flow information becomes 

available; nevertheless, some general conclusions can be drawn 

from the results of these initial models.

Quantile Plots

Quantile plots were used to show the distribution of the 

sample data. The quantile of a sample is the data point corresponding to a given fraction of the data at or less than a particular concentration (Helsel and Hirsch, 1992). A quantile plot 

looks like a cumulative sample distribution function. The 

spread, skewness, and other characteristics of all the data could 

be examined in these plots; furthermore, more of the data can be 

displayed in quantile plots than in boxplots. The quantile plots 

shown in this report start at the fraction of data with a measured 

concentration. Direct comparisons between concentration 

distributions in two data sets also were displayed by graphing 

the quantiles of one data set in relation to the second in quantile/

quantile plots (Helsel and Hirsch, 1992). 
14 Factors Associated with Sources, Transport, and Fate of VOCs in Aquifers of the United States

Mixture Analysis

A mixture is defined as a unique combination of two or 

more particular compounds, regardless of the presence of other 

compounds that may occur in the same sample (Squillace and 

others, 2002). Thus, a sample with three detected compounds 

(A, B, and C) contains four different mixtures (AB, AC, BC, 

and ABC). This approach is most useful if VOCs in a single 

sample originate from different sources or some are degradation 

products. The number of mixtures within a data set can be very 

large, and samples with the largest numbers of compounds yield 

most of the mixtures.

The theoretical probability of one VOC occurring with a 

second VOC is equal to the product of the individual detection 

frequencies of each VOC, if the detections are independent. For 

example, the theoretical probability of compound A occurring 

with compound B in a single sample is equal to the detection 

frequency of A multiplied by the detection frequency of B. 

Independence means that the detection of one VOC does not 

affect the probability of finding a second VOC; however, there 

are at least three reasons why independence is usually not 

achieved. First, areas of an aquifer that are most transmissive 

also are areas where VOCs are most likely to be detected, given 

a uniform contaminant load at the land surface. Second, VOCs 

can be transformed from one VOC to a second, resulting in a 

mixture of the parent compound and the transformation 

product. Third, VOCs that share a common source are more 

likely to be detected together in ground water.

The ratio of the actual detection frequency to the theoretical probability of detecting a mixture, called the detection 

frequency ratio (DFR), can be used to show associations with 

possible sources. If a mixture had a DFR close to 1, then the 

frequency of these VOCs occurring together can be explained 

by chance and, consequently, random co-occurrence of VOCs. 

The DFR can be somewhat larger than 1 for some random 

mixtures if there are variations in aquifer vulnerability given a 

uniform contaminant load. In vulnerable parts of the aquifer, 

many of the potential VOC contaminants reach the ground 

water because transport of all VOCs to the water table is quick, 

with little degradation. In less vulnerable parts of the aquifer, 

only the more mobile and persistent VOCs reach the ground 

water. If a mixture had a DFR much greater than 1, then the 

frequency of these VOCs occurring together cannot be 

explained by chance; consequently, nonrandom co-occurrence 

of VOCs is implied. These nonrandom mixtures could be 

derived from, for example, leaking underground storage tanks. 

The DFR also could be large for some mixtures if one VOC is 

transformed to a second and they both occur together in one 

sample. Possible transformation products, however, can sometimes be identified, especially if redox conditions favor the 

degradation of the parent compound.

Analysis of the Sources, Transport, and 

Fate of VOCs

In this section, the analysis of the sources, transport, and 

fate of VOCs is presented using various methods. These 

methods include logistic regression, quantile plots, detection 

frequency, network analysis, and the use of mixtures.

Logistic Regression to Determine Significant 

Associations

Logistic regression was used to determine associations 

between the occurrence of 10 frequently detected VOCs and 

explanatory factors related to their sources, transport, and fate 

(table 4). The advantage of this method is that it is multivariate 

and, if the coefficients are standardized, the relative importance 

of the factors in the model can be determined. A general discussion of factors associated with the 10 VOCs is followed by 

factors associated with groups of VOCs.

A summary of significant factors or groups of factors associated with sources, transport, and fate for all 10 VOCs is given 

in table 5. Important source factors include MTBE use areas, 

RCRA sites, LUST sites, underground storage tank (UST) sites, 

septic systems, sewer systems, and general land-use characteristics such as the amount of urban land. The relative importance 

of each of these factors varies by individual VOC, but RCRA, 

LUST, and UST sites were typically not as important as factors 

that describe general land-use characteristics. Important transport factors include well depth, screen depth, precipitation, 

ground-water recharge, air temperature, and various soil characteristics. The deeper wells had a smaller chance of VOC 

detection. Generally, more precipitation or ground-water 

recharge resulted in more VOC detections. Colder temperatures 

were associated with increased detection of some VOCs 

because volatility and biodegradation is reduced with cold 

temperatures, increasing the possibility of VOCs entering the 

ground water. Only one factor—dissolved-oxygen concentration—was associated with the fate of VOCs. This factor was 

significant for 8 of the 10 VOCs investigated and was the first 

or second most important factor for 6 VOCs. Only chloromethane was associated with anoxic conditions; detections of 

the other seven VOCs were associated with oxic conditions. 
Analysis of the Sources, Transport, and Fate of VOCs 15

Table 4. Logistic regression analysis for 10 frequently detected VOCs in selected aquifer studies.

[Type of variable: S, source; T, transport; F, fate; I, indeterminate. km2, square kilometers; mm/yr, millimeters per year; cm/yr, centimeters per year; m, meters; 

°C, degree Celsius. DWS, domestic water supply; PWS, public water supply; NLCDE, national land-cover data enhanced; USEPA, U.S. Environmental Protection 

Agency; RCRA, Resource Conservation and Recovery Act; TCA, 1,1,1-trichloroethane; TCE, trichloroethene; MTBE, methyl tert-butyl ether; LUST, leaking 

underground storage tank; UST, underground storage tank]

Compound Explanatory variable Type of 

variable

Unstandardized 

coefficient

Standardized 

coefficient

Hosmer/

Lemeshow 

statistic

Bromodichloromethane Dissolved-oxygen concentration (oxic or anoxic; 

oxic=0, anoxic=1)

F -1.42 -0.18 0.38

People using sewer systems/km2 (natural log) S .28 .17

Ground-water recharge (mm/yr) T -.01 -.10

Well type (DWS or PWS; DWS=0, PWS=1) I .94 .09

Chloroform Percent urban land use (NLCDE) S .69 .91 <.05

Dissolved-oxygen concentration (oxic or anoxic; 

oxic=0, anoxic=1)

F -1.73 -.31

Annual precipitation (cm/yr) T .01 .19

Well type (DWS or PWS; DWS=0, PWS=1) I 1.23 .17

Percent hydric soils T -.02 -.16

People using septic systems/km2 (natural log) S .21 .13

Count USEPA RCRA sites S .06 .08

Chloromethane Dissolved-oxygen concentration (oxic or anoxic; 

oxic=0, anoxic=1)

F .88 .12 .57

Percent silt in soil T .02 .08

Percent undeveloped land (NLCDE) S 1.04 .08

Methylene chloride Well type (DWS or PWS; DWS=0, PWS=1) I -2.06 -.10 .19

Well depth (m) T -.01 -.10

People using septic systems/km2 (natural log) S .36 .08

Percent sand in soil T -.03 -.07

TCA Dissolved-oxygen concentration (oxic or anoxic; 

oxic=0, anoxic=1)

F -2.47 -.24 .86

Depth to top of well screen (m) T -.02 -.23

Soil organic matter, percent by weight T -.33 -.16

Annual air temperature (°C) T -.16 -.15

People using septic systems/km2 (natural log) S .41 .14

Percent urban land use (NLCDE) S 2.02 .12

Count USEPA RCRA sites S .12 .09

Median year of construction S -.02 -.05

TCE Percent urban land use (NLCDE) S .69 .91 0.43

Dissolved-oxygen concentration (oxic or anoxic; 

oxic=0, anoxic=1)

F -1.73 -.31
16 Factors Associated with Sources, Transport, and Fate of VOCs in Aquifers of the United States

TCE—Continued Annual precipitation (cm/yr) T 0.01 0.19

Well type (DWS or PWS; DWS=0, PWS=1) I 1.23 .17

Percent hydric soils T -.02 -.16

People using septic systems/km2 (natural log) S .21 .13

Count USEPA RCRA sites S .06 .08

PCE Depth to top of well screen (m) T -0.01 -0.21 .34

Dissolved-oxygen concentration (oxic or anoxic; 

oxic=0, anoxic=1)

F -.99 -.18

Well type (DWS or PWS; DWS=0, PWS=1) I 1.04 .15

Percent urban land use (NLCDE) S 1.40 .15

People using septic systems/km2 (natural log) S .15 .09

Count USEPA RCRA sites S .04 .06

MTBE Annual precipitation (cm/yr) T .03 .27 .48

MTBE use area S 2.36 .26

Depth to top of well screen (m) T -.02 -.26

Well type (DWS or PWS; DWS=0, PWS=1) I 1.97 .20

Annual air temperature (°C) T -.12 -.15

Dissolved-oxygen concentration (oxic or anoxic; 

oxic=0, anoxic=1)

F -.83 -.11

Count gasoline LUST sites S .49 .10

1,2,4-Trimethylbenzene People using septic systems/km2 (natural log) S -.28 -.17 .07

Annual air temperature (°C) T -.06 -.11

Annual precipitation (cm/yr) T -.01 -.08

Count gasoline UST sites S .20 .07

Toluene Annual air temperature (°C) T -.08 -.13 .22

Median year of construction S -.03 -.10

Count gasoline LUST sites S .41 .10

Well type (DWS or PWS; DWS=0, PWS=1) I -.86 -.10

Dissolved-oxygen concentration (oxic or anoxic; 

oxic=0, anoxic=1)

F -.49 -.08

Percent hydric soils T .01 .08

Table 4. Logistic regression analysis for 10 frequently detected VOCs in selected aquifer studies.—Continued

[Type of variable: S, source; T, transport; F, fate; I, indeterminate. km2, square kilometers; mm/yr, millimeters per year; cm/yr, centimeters per year; m, meters; 

°C, degree Celsius. DWS, domestic water supply; PWS, public water supply; NLCDE, national land-cover data enhanced; USEPA, U.S. Environmental Protection 

Agency; RCRA, Resource Conservation and Recovery Act; TCA, 1,1,1-trichloroethane; TCE, trichloroethene; MTBE, methyl tert-butyl ether; LUST, leaking 

underground storage tank; UST, underground storage tank]

Compound Explanatory variable Type of 

variable

Unstandardized 

coefficient

Standardized 

coefficient

Hosmer/

Lemeshow 

statistic
Table 5. Summary of significant factors in logistic regression models for 10 frequently detected VOCs in selected aquifer studies shown 

in table 4.

[MTBE, methyl tert-butyl ether; LUST, leaking underground storage tank; UST, underground storage tank; RCRA, Resource Conservation and Recovery Act. 

Type of factor: S, source; T, transport; F, fate; I, indeterminate; --, not applicable]

Factor or group of similar factors Factors included in group Type of

factor

Times 

used

Rank 

average

Individual ranks

MTBE use MTBE use area S 1 2.0 2

Land use Year of construction

Percent urban land

Percent undeveloped land

S 7 3.3 1, 1, 2, 2, 3, 6, 8

Septic/sewer association People using septic systems

People using sewer systems

S 7 4.0 1, 2, 3, 5, 5, 6, 6

Concentrated-source association Gasoline LUST sites

Gasoline UST sites

RCRA sites

S 7 5.7 2, 4, 6, 7, 7, 7, 7

Depth Depth to top of well screen

Well depth

T 4 1.5 1, 1, 2, 2

Precipitation/recharge Annual precipitation

Ground-water recharge

T 5 2.6 1, 3, 3, 3, 3

Air temperature -- T 4 3.0 1, 2, 4, 5

Soil characteristics Percent hydric

Percent organic matter

Percent sand

Percent silt

T 6 4.0 2, 3, 4, 5, 5, 5

Dissolved oxygen (oxic/anoxic) -- F 8 2.5 1, 1, 1, 2, 2, 2, 5, 6

Well type -- I 7 3.1 1, 2, 3, 4, 4, 4, 4

Analysis of the Sources, Transport, and Fate of VOCs 17

Dissolved-oxygen concentration (oxic/anoxic) and aquifer 

type (unconfined/confined) are significantly associated with 

each other. Only 22 percent of samples collected from unconfined aquifers (n=443) were anoxic; in contrast, 50 percent of 

samples (n=951) collected from confined aquifers were anoxic. 

The Wilcoxon Signed Rank Test also showed that the matched 

values of the binary factor representing dissolved-oxygen 

concentration (oxic/anoxic) was not significantly different from 

the binary factor representing aquifer type (unconfined/

confined). These results are consistent with what one may 

expect; that is, more rapid transport through the unsaturated 

zone should result in greater concentrations of dissolved 

oxygen in unconfined aquifers than in confined aquifers. 

The detection frequency of any VOC and the age of water 

were not significantly different between unconfined and 

confined aquifers. The detection frequency of any VOC in 

unconfined aquifers (67 percent) was not significantly different 

than confined aquifers (62 percent). It appears that confining 

layers do not provide substantial protection against the infiltration of VOCs, but do cause a reduction in concentrations of 

dissolved oxygen. One may expect that water would be older in 

confined aquifers than in unconfined aquifers, because longer 

flowpaths from smaller recharge areas should be associated 

with confined aquifers; however, the age of ground-water 

samples collected from unconfined aquifers (n=386) was not 

significantly different than the age of samples collected from 

confined aquifers (n=256). It seems that leakage through the 

confining layers was sufficient to reduce the apparent age of 

ground water in confined aquifers. 

A comparison of two logistic regression models showed 

that dissolved oxygen was a much better explanatory factor than 

aquifer type. The significant association between dissolvedoxygen concentration and aquifer type means that these two 

variables can not be used in the same logistic regression model, 

so two different models were developed and compared to determine whether dissolved-oxygen concentration or aquifer type 
18 Factors Associated with Sources, Transport, and Fate of VOCs in Aquifers of the United States

was the best explanatory variable. Both models had chloroform 

(the most frequently detected VOC) as the dependent variable. 

The seven explanatory variables for chloroform shown in 

table 4 were used in one model, and the other model used 

aquifer type instead of oxic/anoxic. The model using dissolved 

oxygen was significantly better than the model using aquifer 

type as indicated by the Akaike’s Information Criteria. Furthermore, among the seven explanatory variables, dissolved oxygen 

was the second strongest in one model, and aquifer type was the 

seventh strongest factor in the other model. These results show 

that although dissolved-oxygen concentration is associated with 

aquifer type, dissolved oxygen also affects the fate of VOCs in 

ground water; consequently, dissolved oxygen is a better 

explanatory factor than aquifer type.

Well type (domestic/public water supplies) was a significant factor for seven VOCs. Five VOCs (bromodichloromethane, chloroform, MTBE, TCE, and PCE) were associated 

with public water supplies, and two VOCs (methylene chloride 

and toluene) were associated with domestic water supplies 

(table 4). Larger contributing areas associated with public water 

supplies (Stackelberg and others, 2001) could contribute to the 

associations of five VOCs with public water supplies. Public 

water supplies, however, also had significantly larger amounts 

of urban land use within 500 m of the sampled well compared 

with domestic water supplies; in fact, the median amount of 

urban land use for domestic water supplies was 0.2 percent 

compared to 55 percent for public water supplies. Three VOCs 

(chloroform, TCE, and PCE) were associated with publicwater-supply wells and septic systems. For these three VOCs, it 

is possible that public water supplies are more affected by septic 

systems than are domestic water supplies. Although chloroform 

was more strongly associated with public water supplies, it was 

still detected in 26 percent of the domestic wells. Chloroform 

can be formed in septic systems if, for example, household 

bleach is present. Triclosan, an antimicrobial agent used in 

household dishwashing soaps, also reacts with chlorinated 

water to produce chloroform (Rule and others, 2005). In anoxic 

conditions, at least some of the methylene chloride and chloromethane would be expected to come from degradation of 

chloroform. Methylene chloride also is used as paint stripper 

and could have been improperly disposed of in some septic 

systems. Chloromethane has been used in agricultural chemicals and as a refrigerant (in the past), and it can be naturally 

occurring (Gribble, 1992). Toluene is a solvent in home products and in gasoline. Improper disposal of either of these 

products could be associated with its detection in domestic 

water supplies. 

Water from public water supplies generally contain a 

blend of water with a continuum of ages, but the apparent age 

of water from 84 public water supplies was significantly 

younger than the apparent age of water from 528 domestic 

water supplies (Wilcoxon Rank Sum Test, p<0.01). Faster 

traveltimes associated with water from public water supplies 

would decrease the opportunity for degradation of VOCs and 

could partially explain why five VOCs were associated with 

public water supplies compared with two VOCs with domestic 

water supplies.

Important similarities and differences are apparent in 

factors associated with the occurrence of VOCs within three 

groups—trihalomethanes (THMs), solvents, and gasoline 

hydrocarbons (table 4). The occurrence of THMs (bromodichloromethane and chloroform) was associated with oxic 

conditions and public water supplies. Bromodichloromethane 

was most frequently detected in areas with low ground-water 

recharge, but chloroform was detected in wet climates. The 

reason for this difference is not known, but it may be related to 

the source and transport of these VOCs. The logistic regression 

analysis showed that bromodichloromethane was associated 

with low ground-water recharge and sewer systems, whereas 

chloroform was associated with increased ground-water 

recharge (based on increased annual precipitation), septic 

systems, and RCRA sites (table 4). Increased ground-water 

recharge could dilute a small amount of leakage of bromodichloromethane from sewer lines to concentrations less than the 

detection levels, but the larger volumes and concentrations of 

chloroform from septic systems and RCRA sites might not be 

as easily diluted. In this situation, ground-water recharge helps 

transport chloroform to public wells. 

Many similar factors were associated with the occurrence 

of solvents (chloromethane, methylene chloride, TCA, TCE, 

and PCE). Septic-system density, urban land use, and proximity 

to RCRA sites all were identified as possible sources of 

solvents. High silt, low sand, and low organic content, and 

shallow wells or screens all were transport factors. High silt and 

low sand content of soils (indicating low permeability) were 

associated with the occurrence of chloromethane and methylene 

chloride. In these situations, the slower transport of recharge 

through the unsaturated zone may enhance the degradation of 

chloroform to methylene chloride and chloromethane. The 

occurrence of methylene chloride, TCA, and PCE was associated with either shallow well depth or shallow well-screen 

depth. The occurrence of TCA, TCE, and PCE is strongly 

related to oxic water conditions, whereas chloromethane occurrence is strongly related to anoxic conditions. These results are 

consistent with previous investigations that have shown TCA, 

TCE, and PCE to be more stable under oxic conditions than 

anoxic conditions (Suarez and Rifai, 1999).

Proximity to LUST or UST sites was an important source 

factor associated with the gasoline hydrocarbons (1,2,4-trimethylbenzene (TMB) and toluene) and also with a gasoline 

oxygenate (MTBE). Subsurface leakage or surface runoff from 

these sites may be a source of these three gasoline-related 

VOCs. Cool climates tend to reduce volatilization of VOCs into 

the air and reduce biodegradation; consequently, cool climates 

were associated with the occurrence of TMB, toluene, and 

MTBE in ground water. Toluene and MTBE were associated 

with oxic conditions, indicating greater detection of these 

VOCs in oxic conditions. 
Analysis of the Sources, Transport, and Fate of VOCs 19

Quantile Plots to Determine Significant 

Associations

An analysis of quantile plots was used to compare quantile 

concentrations of a VOC detected in oxic water with those in 

anoxic water. This univariate analysis does not control for any 

other differences that may exist between the two groups of 

samples; for example, well depth, land use, temperature, and so 

forth. The advantage of this analysis is that all concentration 

data (even those with less-than values) are considered in the 

analysis. The number of samples in the two groups being 

compared also does not have to be the same. 

The quantile/quantile plot shown in figure 3 shows that 

concentrations of VOCs can be strongly affected by dissolvedoxygen concentrations. Points falling above the 1:1 line indicate 

greater concentrations in anoxic water, and points falling below 

the 1:1 line indicate greater concentrations in oxic water. All 

VOCs shown in figure 3 had significantly different concentrations in oxic conditions compared with anoxic conditions, 

except toluene and TMB. Another important observation is that 

most concentrations were small (less than 1 μg/L).

Bromodichloromethane, TCE, TCA, PCE, MTBE, and 

chloroform had significantly larger concentrations in oxic 

conditions than in anoxic conditions. These larger concentrations are expected on the basis of degradation studies that show 

slower degradation rates for many of these VOCs in oxic water 

when compared with anoxic water (Kotzias and Sparta, 1993; 

Suarez and Rifai, 1999). Dissolved oxygen seems to have the 

greatest effect on the concentrations of TCE; in fact, concentrations of TCE in oxic water are generally about two orders of a 

magnitude larger than those in anoxic water at an equal quantile 

(fig. 3). The plot in figure 3 also shows that the slope of PCE, 

chloroform, and bromodichloromethane increases as concentrations decrease. This change in slope could indicate greater 

stability of these VOCs in oxic water (with decreasing concentrations) than in anoxic water. Chloroform, for example, seems 

to become more stable in oxic conditions at concentrations less 

than 0.5 μg/L. Other VOCs (MTBE, bromodichloromethane, 

TCA, and toluene) show a similar pattern but inflect at different 

concentrations. 

Chloromethane

Methylene chloride Trichloroethene 

Perchloroethene 

Methyl tert-butyl ether

Chloroform 

1:1 

1,2,4-Trimethybenzene

Toluene

Bromodichloromethane

1,1,1-Trichloroethane

CONCENTRATIONS OF VOC IN ANOXIC CONDITIONS,

IN MICROGRAMS PER LITER

CONCENTRATIONS OF VOC IN OXIC CONDITIONS,

IN MICROGRAMS PER LITER

100

10

1

0.1

0.01

0.001

0.001 0.01 0.1 1 10 100

Figure 3. Quantile/quantile plot of the concentrations of 10 volatile organic compounds (VOCs) in oxic 

and anoxic conditions. For all VOCs, except toluene and 1,2,4-trimethylbenzene, the concentrations are 

significantly different in oxic compared with anoxic conditions.
20 Factors Associated with Sources, Transport, and Fate of VOCs in Aquifers of the United States

The concentrations of chloromethane, methylene chloride, 

toluene, and TMB were larger in anoxic conditions than in oxic 

conditions; however, only chloromethane and methylene chloride had significantly larger concentrations in anoxic conditions. If chloromethane and methylene chloride originate 

primarily from the degradation of chloroform in anoxic water 

(Norris and others, 1994), then finding larger concentrations of 

these two VOCs in anoxic water would be expected. Gasoline 

hydrocarbons such as toluene and TMB are known to be more 

stable in anoxic conditions than in oxic conditions (Suarez and 

Rifai, 1999); however, the concentrations detected in this study 

generally were less than 1 μg/L. At these concentrations, degradation rates may slow considerably (Roch and Alexander, 

1997) and may become similar to those in oxic conditions. 

The quantile plots in figure 4 show that concentrations of 

chloroform are more strongly affected by the amount of urban 

land use and people using septic systems in oxic conditions than 

in anoxic conditions. Data for figure 4A were divided into four 

groups on the basis of dissolved-oxygen concentrations and the 

amount of urban land use. Data for figure 4B were also divided 

into four groups on the basis of dissolved-oxygen concentrations and the number of people using septic systems near the 

sampled well—about 75 percent of the sampled wells had less 

than 40 people using septic systems within 500 m of the 

sampled well, and 25 percent had 40 or more people using 

septic systems. Figures 4A and 4B show that the concentrations 

of chloroform were less in anoxic conditions than oxic conditions. These results are consistent with data shown in figure 3; 

however, figures 4A and 4B also show that anthropogenic 

activity had a greater affect on the concentrations of chloroform 

in oxic conditions than in anoxic conditions. In anoxic conditions, the amount of urban land use had no effect on the concentrations of chloroform, and the number of people using septic 

systems had only minimal effect on the concentrations of 

chloroform. In oxic conditions, the amount of urban land use 

and the number of people using septic systems had a dramatic 

effect on the concentrations of chloroform (fig. 4A). These 

results generally are substantiated by Spearman’s correlation 

analysis. In oxic conditions, there was a significant correlation 

between chloroform concentrations and (1) urban land use and 

(2) people using septic systems. In anoxic conditions, chloroform concentrations were not correlated with urban land use but 

were correlated with the number of people using septic systems.

Detection Frequency to Determine Significant 

Associations

Detection frequencies can be used as a simple statistical 

analysis to help understand the sources, transport, and fate of 

VOCs. Detection frequencies can be calculated using all the 

data; alternatively, the data can be divided into two or more 

groups on the basis of a single variable. A contingency table 

using the Pearson’s chi square test can be used to determine if 

significant differences exist between the groups of data. This 

statistical method is easy to do, results are easy to understand, 

and all data (even those with values less than the reporting 

level) are included in the analysis; however, the disadvantage is 

that subdividing the data into groups can result in small 

numbers of samples in each group. 

Urban land use, in percent People using septic systems

CONCENTRATION OF CHLOROFORM,

IN MICROGRAMS PER LITER

0.001 0.01 0.1 1 10

FRACTION OF DATA

CONCENTRATION OF CHLOROFORM,

IN MICROGRAMS PER LITER

1.0

0.9

0.8

0.6

0.7

0.5

0.4

0.3

0.2

0.1

0

FRACTION OF DATA

1.0

0.9

0.8

0.6

0.7

0.5

0.4

0.3

0.2

0.1

0

0.001 0.01 0.1 1 10

Less than 40

Greater than or equal to 40

Less than 40

Greater than or equal to 40

Anoxic conditions

Oxic conditions

Less than 50

Greater than or equal to 50

Less than 50

Greater than or equal to 50

Anoxic conditions

Oxic conditions

A B

Figure 4. Quantile plots of concentrations of chloroform in anoxic and oxic conditions by A, percent urban land use, 

and B, number of people using septic systems, both within a 500-meter radius of the sampled well.
Analysis of the Sources, Transport, and Fate of VOCs 21

Confining layers are thought to provide some protection to 

ground water from anthropogenic contamination because of the 

slow movement of water through the confining layer. To test 

this hypothesis, paired detection frequencies of the 10 VOCs 

listed in table 2 were compared between aquifers that were 

classified as confined or unconfined. Using the Wilcoxon 

signed rank, there was no statistically significant difference in 

the detection frequencies of these 10 VOCs in the two types of 

aquifers. Results indicate that either the data or method used to 

classify aquifers into groups was not sufficiently accurate to 

identify aquifers less susceptible to contamination or that 

confining units in general do not protect ground water from 

VOC contamination. 

Domestic and public water supplies had larger detection 

frequency of chloroform in oxic water compared with anoxic 

water (fig. 5), when detection frequencies were calculated using 

no common assessment level. For public-supply wells, there 

was a significant difference in chloroform detection frequency 

in oxic conditions (81 percent) compared with anoxic conditions (13 percent). For domestic supply wells, there also was a 

significant difference in chloroform detection frequency in oxic 

conditions (31 percent) compared with anoxic conditions 

(12 percent). These differences demonstrate the importance of 

dissolved-oxygen concentrations but also illustrate the importance of well type. There was, however, no significant difference in detection frequencies of chloroform between domestic 

and public water supplies with anoxic water.

A comparison of the detection frequencies of 10 VOCs in 

oxic and anoxic ground water is shown in figure 6. Again, 

detection frequencies were calculated using no common assessment level. Detection frequencies for all VOCs are significantly 

different in these two oxygen conditions, except those for TMB 

and toluene. Those VOCs above the 1:1 line were more 

frequently detected in anoxic conditions than in oxic conditions, 

and VOCs below the 1:1 line were more frequently detected in 

oxic conditions than in anoxic conditions. Differences in detection frequencies are generally consistent with published rates of 

biodegradation (Suarez and Rifai, 1999). For example, chloroform, PCE, TCE, and TCA are more persistent in oxic ground 

water, which was confirmed by their higher detection frequency 

in oxic ground water than in anoxic ground water (fig. 6). 

Toluene is more stable in anoxic water (Suarez and Rifai, 1999) 

but was detected more frequently in oxic conditions; however, 

this was not a statistically significant difference. The remaining 

VOCs have very few published degradation rates; consequently, it is impossible to determine whether detection 

frequencies are consistent with degradation rates.

Two-thirds of the samples from aquifers were oxic; consequently, compounds that biodegrade more slowly in oxic 

ground water should be more persistent and have a greater 

detection frequency. In general, halogenated aliphatic VOCs 

such as TCE biodegrade more slowly than petroleum hydrocarbons in oxic conditions; consequently it is not surprising that 

halogenated VOCs were more frequently detected than petroleum hydrocarbons even though detection frequencies were not 

calculated using a common assessment level.

DETECTION FREQUENCY OF CHLOROFORM, IN PERCENT

PUBLIC WATER SUPPLY DOMESTIC WATER SUPPLY

80

90

50

60

70

10

20

30

40

0

Oxic conditions Anoxic conditions Oxic conditions Anoxic conditions

n=141

n=75

n=783

n=370

n=370 Number of samples

Figure 5. Detection frequency of chloroform in oxic and anoxic conditions for public and domestic water supplies 

(when calculated using no common assessment level).
DETECTION FREQUENCY OF VOLATILE

ORGANIC COMPOUND IN ANOXIC

CONDITIONS, IN PERCENT

DETECTION FREQUENCY OF VOLATILE ORGANIC COMPOUND IN OXIC CONDITIONS,

IN PERCENT

15

20

10

0

5

0 10 20 30 40

1:1

1,2,4-Trimethylbenzene

Perchloroethene Methylene

chloride

Toluene

Chloroform

Trichloroethene

Chloromethane

1,1,1-Trichloroethane

Methy tert-butyl ether

Bromodichloromethane

22 Factors Associated with Sources, Transport, and Fate of VOCs in Aquifers of the United States

Figure 6. Comparison of detection frequencies of 10 volatile organic compounds (VOCs) in oxic water compared with 

anoxic water (when calculated using no common assessment level). For all VOCs, except toluene and 1,2,4-trimethylbenzene, the detection frequencies are significantly different in oxic compared with anoxic conditions.

The strong influence of dissolved-oxygen concentrations 

on the detection of VOCs is evident from figure 6; however, 

dissolved-oxygen concentrations can vary by location in an 

aquifer and by the age of ground water. Young ground water in 

recharge areas of aquifers generally has larger dissolvedoxygen concentrations compared to old ground water in 

discharge areas because dissolved oxygen becomes depleted 

along an aquifer flowpath (Chapelle and others, 2003). Samples 

collected by NAWQA show that ground water dated 1953 or 

older had significantly smaller dissolved-oxygen concentrations than ground water dated younger than 1953 (fig. 7). On 

the basis of 720 samples with age-date information, about 

60 percent of sampled ground water was recharged after 1953. 

It is important to note that there were significantly fewer VOCs 

per sample in old water than young water.

The degradation of chloroform can partially explain the 

more frequent detection of methylene chloride and chloromethane in anoxic conditions than oxic conditions. By reductive dechlorination reactions, chloroform (trichloromethane—

3 chlorine atoms) can be transformed to methylene chloride 

(dichloromethane—2 chlorine atoms), which can be further 

transformed to chloromethane (1 chlorine atom) (Egli and 

others, 1988). In this reaction, the chlorinated methanes serve as 

electron acceptors; chlorine atoms are sequentially removed 

from a single carbon atom and are replaced by hydrogen atoms. 

The reaction rate for reduction of the chlorinated methanes 

generally decreases with decreasing chlorination (Egli and 

others, 1988). The transformation of at least some chloroform 

to methylene chloride and chloromethane in ground water is 

supported by three observations. First, among samples with 

young water (recharged after 1953), the detection frequency of 

chloroform was about 5 times higher in oxic water compared to 

anoxic water (fig. 8). If chloroform loading to ground water is 

similar for this oxic and anoxic water, then chloroform transformation has occurred in anoxic water. Second, among samples 

with oxic water, chloroform makes up more than 95 percent of 

the total molar concentrations of chloroform, methylene chloride, and chloromethane; however, in anoxic water, methylene 

chloride and chloromethane make up more than one-half of the 

total molar concentrations of these three compounds (fig. 9). 

Third, detection frequency of methylene chloride or chloromethane in young oxic water was significantly lower than 

detection frequencies in old (recharged on or before 1953) 

anoxic water, a finding that supports the possible formation of 

methylene chloride and chloromethane with time and redox 

condition (fig. 8).

Network Analysis to Determine Significant 

Associations

Selected aquifer study data were collected to define water 

quality in a particular aquifer within a particular area; consequently these data are appropriately analyzed as networks. Each 

selected aquifer study is defined as one network of wells. In the 

network-analysis approach, summary statistics of the water 

quality of one aquifer can be compared with those for another 

aquifer. This approach also provides a convenient summary of 

the water quality for a particular aquifer—something that is of 

interest to many—and can reduce the influence of a few anomalous samples. The disadvantage of this approach is that the 

number of networks is much smaller than the number of wells; 

consequently, the ability to discern significant relations 

between water quality and explanatory variables could be 

diminished. Also, differences within a particular aquifer are not 

distinguished. 
DISSOLVED OXYGEN CONCENTRATION, IN MILLIGRAMS PER LITER

15

1050

SAMPLED WATER

RECHARGED ON OR

BEFORE 1953

SAMPLED WATER

RECHARGED

AFTER 1953

n=267 n=453 267

Median (50th percentile)

Lower quartile (25th percentile)

Upper quartile (75th percentile)

Lower quartile minus 1.5 times the interquartile range or smallest value not less than

this computation

Upper quartile plus 1.5 times the interquartile

range or largest value not greater than this

computation

Number of samples

EXPLANATION

Analysis of the Sources, Transport, and Fate of VOCs 23

Figure 7. Statistical summary of dissolved-oxygen concentrations in pre- and post-1953 recharge.

In a linear regression model, the detection frequency of 

chloroform (in 13 selected aquifer studies with RASA groundwater-flow models) was selected as the dependent variable, and 

three independent variables were selected to account for the 

source, transport, and fate of chloroform. Chloroform was 

selected because it was the most frequently detected VOC, was 

widely distributed, and probably is most strongly associated 

with dispersed sources (table 4). The source of chloroform was 

represented by the median population density for the selected 

aquifer study. The transport of chloroform was represented by 

testing a number of factors from RASA water budgets (such as 

recharge, amount of withdrawal, and source of water to with

-

drawal) with source and fate factors. The fate of chloroform was 

represented by the median dissolved-oxygen concentration for 

each selected aquifer study. 

About 80 percent of the variability in the detection 

frequency of chloroform, in 13 selected aquifer studies, was 

explained by (in order of decreasing importance) dissolvedoxygen concentration, percentage of ground-water withdrawal 

coming from a decrease in storage, and population density. 

Increased detection frequencies of chloroform were associated 

with higher dissolved-oxygen concentrations, smaller 

percentage of water originating from storage, and areas with 

larger population densities (table 6). Water removed from 

storage is more likely to be old water and to not contain any 

VOCs, including chloroform. As new ground-water-flow 

models are developed for other aquifers, similar types of anal

-

ysis could help explain differences in water quality between 

aquifers.

Two example aquifer studies are presented to help demon-

strate how dissolved-oxygen concentration, percentage of with-

drawal originating from storage, and median population density 

for the network affect the detection frequency of chloroform. 

The detection frequency of chloroform was 76 percent (when 

calculated using no common assessment level) in a aquifer 

study done in the Edwards/Trinity aquifer (sctxsus3; table 1). 

The median concentration of dissolved oxygen for this network 

was 5.9 mg/L, no water is derived from a decrease in storage at 

the aquifer scale, and the median population density was 

13 people/km

2

. In contrast, when calculated similarly, the 

detection frequency of chloroform was 12 percent in the 

Cambrian-Ordovician aquifer (umissus4; table 1). The median 

concentration of dissolved oxygen for this network was 

2.1 mg/L, 18 percent of the water is derived from a decrease in 

storage at the aquifer scale, and the median population density 

was 20 people/km

2

.
Detection frequency, in percent

Chloroform

Methylene chloride or Chloromethane

Ground-water flow

Methylene chloride

Chloromethane

YOUNG OXIC

WATER

DETECTION FREQUENCY,

IN PERCENT

DETECTION FREQUENCY,

IN PERCENT

0

10

20

30

40

0

10

20

30

40

OLD ANOXIC

WATER

EXPLANATION

24 Factors Associated with Sources, Transport, and Fate of VOCs in Aquifers of the United States

Figure 8. Chloroform in young oxic ground water bioidegrades along a hypothetical ground-water flowpath to form methylene chloride 

and chloromethane in old anoxic ground water. (Detection frequencies were calculated using no common assessment level.)

OXIC CONDITIONS ANOXIC CONDITIONS

Chloroform

(96 percent)

Chloroform

(44 percent)

Methylene chloride

(35 percent)

Chloromethane

(21 percent)

Methylene chloride

(3 percent)

Chloromethane

(1 percent)

Figure 9. Comparison of the proportion of the total molar concentrations of chloroform, methylene chloride, and 

chloromethane in oxic and anoxic ground water.
Table 6. Results of a linear regression model of detection frequency of chloroform in 13 selected aquifer studies as the 

response variable and three explanatory variables partially representing the source, transport, and fate of chloroform.

Dependent

variable Independent variable Type of variable Unstandardizedcoefficient

Standardized

coefficient

Detection frequency of 

chloroform in network

Median dissolved-oxygen concentration in 

network

Fate 9.93 1.11

Percentage of withdrawal originating from 

storage in the aquifer

Transport -.90 -.55

Median population density for network Source 5.10 .37

Analysis of the Sources, Transport, and Fate of VOCs 25

As population density increases (surrogate for the number 

and strength of anthropogenic sources), the detection frequency 

of VOCs increases (Squillace and others, 1999); however, in 

selected aquifer studies, the population density also is correlated (Spearman’s rho=0.4, p=0.003) with ground-water 

recharge in rural and low-density residential areas (fig. 10), 

where most selected aquifer studies were done. The relation 

between population density and ground-water recharge has 

been documented in previous investigations in urban areas 

(Custodio, 1997; Coldewey and Messer, 1997; Lerner, 2002); 

however, the three selected aquifer studies done in urban areas 

by NAWQA had some of the smallest estimates of groundwater recharge (fig. 10). Impervious surfaces are associated 

with urbanization and result in increased surface runoff; 

however, in these same areas, irrigation with imported water, 

leakage from water lines and sewer lines, discharge from septic 

systems, decreased evapotranspiration, and ground-water 

recharge from engineered structures (for example, dry wells) 

could increase ground-water recharge. Correlations between 

detection frequency of VOCs, population density, and groundwater recharge indicate that areas with larger populations are 

more vulnerable to ground-water contamination, not only 

because of the greater number of sources of VOCs but also 

because greater recharge assists VOC transport to ground water. 

Even if the relation between population density and recharge is 

fortuitous—not related to anthropogenic activity but simply a 

result of where people have chosen to live—the increased 

recharge would still assist VOC transport through the unsaturated zone to ground water. 

Use of Mixtures to Determine Significant 

Associations

Analysis of mixtures of contaminants is a relatively new 

area of research, but it is particularly pertinent to the selected 

aquifer studies because many samples contained mixtures of 

VOCs. Although this mixture analysis is new, it has very important practical implications in that statistical analysis of the 

composition of mixtures can be used to help establish associations with possible sources. Such analysis also can yield additional information on the transport and fate of VOCs. The disadvantage of mixture analysis is that it can be difficult to 

understand, perform, and explain. Statistical packages are not 

designed to do this type of analysis, and even identifying the 

mixtures is a complicated process.

The detection of mixtures of VOCs is associated with well 

type. Samples from public-water-supply wells had the largest 

detection frequency of mixtures, followed by monitoring, irrigation, and other types of wells; domestic wells had the lowest 

detection frequency. The reason why samples from publicwater supply wells had greater incidence of mixtures than 

domestic water supplies is not known; however, it could be 

related to land use and pumping rates. The median amount of 

urban land use within 500 m of public water supplies was two 

orders of magnitude greater than that for domestic water 

supplies. The greater incidence of urban land use near public 

water supplies is a good indication of more potential sources of 

VOCs; furthermore, larger pumping rates associated with 

public water supplies have large capture zones that can integrate 

more of these sources. Stackelberg and others (2001) also have 

shown that public supply wells in the Atlantic Coastal Plain 

aquifer accelerate the vertical gradient and capture ground 

water with shorter traveltimes than domestic wells. Public 

supply wells also are more likely than domestic wells to capture 

ground water that has flowed along extended flowpaths with 

much longer traveltimes. Short flowpaths with young water 

could carry recently used VOCs and extended flowpaths with 

older ground water could carry previously used VOCs and their 

degradates. The combination of VOCs in short and extended 

flowpaths makes it more likely to detect multiple VOCs in a 

single water sample. 

Analysis of data indicates no difference in concentrations 

of VOCs if they occurred alone or in mixtures. Among 10 

frequently detected VOCs, 5 (TCA, MTBE, TCE, PCE, and 

bromodichloromethane) were rarely found by themselves (less 

than about 10 percent when detected). For these five VOCs, it 

is impossible to compare concentrations of VOCs occurring 
26 Factors Associated with Sources, Transport, and Fate of VOCs in Aquifers of the United States

alone with those in mixtures because of insufficient data. The 

remaining five VOCs (TMB, chloromethane, methylene 

chloride, toluene, and chloroform) were found alone about 

30 percent of the time when detected. Concentrations of chloromethane, methylene chloride, and toluene were not significantly different if these VOCs were found alone or in mixtures. 

The concentrations of TMB were significantly smaller in 

samples with mixtures than samples without mixtures, whereas 

the concentrations of chloroform were larger in samples with 

mixtures. Although these differences were statistically significant, the magnitudes of these differences were very small 

(medians changed by about 0.01μg/L).

Dissolved oxygen has a dramatic effect on the composition 

of mixtures. In oxic water, for example, the concentrations of 

TCE were significantly correlated with five other VOCs; 

however, in anoxic water, TCE was not correlated with any 

VOC. Identification of mixtures without regard to dissolvedoxygen concentration (approach taken in this analysis) gives the 

best indication of mixtures that are most likely to be detected in 

the ground-water resource, but the approach will result in a bias 

toward mixtures common within the dominant redox condition. 

Data used for this analysis are primarily oxic (two-thirds) with 

some anoxic samples (one-third). 

 A mixture that had an actual detection frequency that was 

similar to the theoretical probability of detection indicates the 

random co-occurrence of VOCs in the mixture. In contrast, a 

mixture that had a detection frequency that was much larger 

than the theoretical probability of detection indicates 

nonrandom co-occurrence of VOCs in the mixture. The 

mixtures were ranked according to their DFR, which ranged 

from 1 to about 3,000 (table 7). Almost all mixtures had a DFR 

greater than 1, indicating that mixtures normally had detection 

frequencies greater than what would be expected by chance. 

Table 8 lists the dectection frequencies of VOCs found in 

mixtures shown in table 7 and the long-term method detection 

levels. These estimated detection frequencies are not censored 

to a common assessment level so direct comparisons of the 

detection frequencies between individual VOCs may be 

misleading.

RECHARGE, IN MILLIMETERS PER YEAR

NATURAL LOGARITHM OF POPULATION DENSITY,

PEOPLE PER SQUARE KILOMETER

500

400

300

200

100

0

-1 0 1 2 3 4 5 6 7 8

Rural Low-density

Residential

Urban

Figure 10. Significant correlation between population density and estimated recharge for 

each selected aquifer study sampled by the National Water-Quality Assessment Program.
Analysis of the Sources, Transport, and Fate of VOCs 27

Table 7. Statistical summary of 102 mixtures of volatile organic compounds (when calculated using no common assessment level) with 

a detection frequency of 1 percent or more.

[PCE, perchloroethene; TCA, 1,1,1-trichloroethane; MTBE, methyl tert-butyl ether; TCE, trichloroethene; TMB, 1,2,4-trimethylbenzene]

Mixture

number

VOCs in the mixture

Number of 

detections

Actual 

detection

frequency1

(percent)

Theoretical 

probability of 

detection

(percent)

Detection

frequency ratio

(rounded to 

nearest integer)

1 Chloroform, TMB 54 3.3 4.7 1

2 PCE, TMB 25 1.5 2.1 1

3 Toluene, chloromethane 15 .92 1.3 1

4 TCA, TMB 17 1.0 1.3 1

5 Chloroform, toluene 71 4.4 4.7 1

6 Chloroform, chloromethane 35 2.1 2.4 1

7 Chloromethane, PCE 16 .98 1.1 1

8 Chloroform, benzene 20 1.2 1.3 1

9 Toluene, TCA 23 1.4 1.3 1

10 Toluene, TMB 45 2.8 2.5 1

11 Toluene, MTBE 22 1.3 1.1 1

12 Methylene chloride, TMB 17 1.0 .79 1

13 Chloroform, methylene chloride 32 2.0 1.5 1

14 Toluene, PCE 46 2.8 2.1 1

15 Chloroform, dichlorodifluoromethane 27 1.7 1.3 1

16 Toluene, methylene chloride 19 1.2 .79 1

17 Chloroform, cis-1,2-dichloroethene 17 1.0 .66 2

18 Chloromethane, TMB 32 2.0 1.3 2

19 Chloroform, 1,4-dichlorobenzene 21 1.3 .79 2

20 Chloroform, toluene, PCE 19 1.2 .63 2

21 Chloroform, TCE 54 3.3 1.5 2

22 Toluene, benzene 23 1.4 .66 2

23 Chloroform, PCE 138 8.5 4.0 2

24 Chloromethane, methylene chloride 15 .92 .41 2

25 Bromoform, chloroform 25 1.5 .66 2

26 Chloroform, TCA 94 5.8 2.5 2

27 Chloroform, 1,1 dichloroethane 34 2.1 .88 2
28 Factors Associated with Sources, Transport, and Fate of VOCs in Aquifers of the United States

28 Chloroform, MTBE 84 5.2 2.2 2

29 Styrene, TMB 15 .92 .37 2

30 Chloroform, trichlorofluoromethane 25 1.5 .59 3

31 Chloroform, toluene, TCA 16 .98 .39 3

32 Benzene, TMB 28 1.7 .67 3

33 Chloroform, 1,1-dichloroethene 31 1.9 .68 3

34 TMB, m,p-xylene 20 1.2 .39 3

35 Toluene m,p-xylene 20 1.2 .39 3

36 Chloroform, trichlorotrifluoromethane 15 .92 .31 3

37 Toluene, styrene 19 1.2 .37 3

38 PCE, 1,4-dichlorobenzene 18 1.1 .35 3

39 PCE, MTBE 49 3.0 .98 3

40 Chlorodibromomethane, chloroform 32 2.0 .59 3

41 PCE, TCA 61 3.7 1.1 3

42 PCE, cis-1,2-dichloroethene 18 1.1 .30 4

43 PCE, trichlorofluoromethane 19 1.2 .26 4

44 TCE, MTBE 24 1.5 .36 4

45 TCA, MTBE 38 2.3 .61 4

46 TCA, TCE 31 1.9 .41 5

47 PCE, 1,1-dichloroethane 29 1.8 .39 5

48 PCE, TCE 56 3.4 .66 5

49 1,1-Dichloroethane, TCA 21 1.3 .25 5

50 Trichlorofluoromethane, TCA 15 .92 .16 6

51 PCE, 1,1-dichloroethene 31 1.9 .30 6

52 1,1 Dichloroethane, MTBE 22 1.3 .21 6

53 1,1-Dichloroethene, TCA 23 1.4 .19 7

54 1,1-Dichloroethane, TCE 22 1.3 .14 9

Table 7. Statistical summary of 102 mixtures of volatile organic compounds (when calculated using no common assessment level) with 

a detection frequency of 1 percent or more.—Continued

[PCE, perchloroethene; TCA, 1,1,1-trichloroethane; MTBE, methyl tert-butyl ether; TCE, trichloroethene; TMB, 1,2,4-trimethylbenzene]

Mixture

number

VOCs in the mixture

Number of 

detections

Actual 

detection

frequency1

(percent)

Theoretical 

probability of 

detection

(percent)

Detection

frequency ratio

(rounded to 

nearest integer)
Analysis of the Sources, Transport, and Fate of VOCs 29

55 Chloroform, PCE, MTBE 45 2.8 0.29 9

56 Trichlorofluoromethane, TCE 16 .98 .096 10

57 Chloroform, PCE, TCA 54 3.3 .33 10

58 Chloroform, TCE, MTBE 20 1.2 .11 11

59 Chloroform, TCA, MTBE 36 2.2 .18 12

60 Chloroform, TCA, TCE 27 1.7 .12 13

61 Chloroform, PCE, trichlorofluoromethane 18 1.1 .079 14

62 1,1-Dichloroethene, TCE 25 1.5 .11 14

63 Chloroform, PCE, 1,1-dichloroethane 27 1.7 .12 14

64 TCE, cis-1,2-dichloroethene 26 1.6 .11 15

65 Chloroform, PCE, TCE 48 2.9 .20 15

66 1,1-Dichloroethane, cis-1,2-dichloroethene 16 .98 .065 15

67 Chloroform, 1,1-dichloroethane, MTBE 16 .98 .064 15

68 Chloroform, 1,1-dichloroethane, TCA 19 1.2 .073 16

69 1,1-Dichloroethane, 1,1-dichloroethene 20 1.2 .067 18

70 PCE, TCA, MTBE 26 1.6 .081 20

71 Chloroform, PCE, 1,1-dichloroethene 30 1.8 .091 20

72 Bromoform, chlorodibromomethane 17 1.0 .04 24

73 Chloroform, 1,1-dichloroethene, TCA 22 1.3 .057 24

74 PCE, TCE, MTBE 20 1.2 .048 26

75 Chloroform, TCE, cis-1,2-dichloroethene 16 .98 .032 30

76 Chloroform, 1,1-dichloroethane, TCE 21 1.3 .043 30

77 PCE, 1,1-dichloroethane, TCA 16 .98 .033 30

78 TCA, TCE, MTBE 15 .92 .030 31

79 PCE, TCA, TCE 29 1.8 .055 32

80 Chloroform, trichlorofluoromethane, TCE 16 .98 .029 34

81 Chloroform, 1,1-dichloroethene, TCE 23 1.4 .033 42

Table 7. Statistical summary of 102 mixtures of volatile organic compounds (when calculated using no common assessment level) with 

a detection frequency of 1 percent or more.—Continued

[PCE, perchloroethene; TCA, 1,1,1-trichloroethane; MTBE, methyl tert-butyl ether; TCE, trichloroethene; TMB, 1,2,4-trimethylbenzene]

Mixture

number

VOCs in the mixture

Number of 

detections

Actual 

detection

frequency1

(percent)

Theoretical 

probability of 

detection

(percent)

Detection

frequency ratio

(rounded to 

nearest integer)
30 Factors Associated with Sources, Transport, and Fate of VOCs in Aquifers of the United States

82 Chloroform, 1,1-dichloroethane, 1,1-dichloroethene 15 .92 0.020 46

83 PCE, 1,1-dichloroethene, TCA 23 1.4 .025 56

84 Chloroform, PCE, TCA, MTBE 25 1.5 .024 63

85 PCE, 1,1-dichloroethane, TCE 21 1.3 .019 67

86 PCE, trichlorofluoromethane, TCE 15 .92 .013 71

87 PCE, TCE, cis-1,2-dichloroethene 18 1.1 .014 76

88 Bromoform, chlorodibromomethane, chloroform 17 1.0 .013 80

89 Chloroform, PCE, TCE, MTBE 19 1.2 .014 81

90 PCE, 1,1-dichloroethene, TCE 23 1.4 .015 95

91 Chloroform, PCE, TCA, TCE 27 1.7 .016 101

92 PCE, 1,1-dichloroethane, 1,1-dichloroethene 16 .98 .0089 110

93 1,1-dichloroethene, TCA, TCE 18 1.1 .0093 119

94 Chloroform, PCE, 1,1-dichloroethene, TCA 22 1.3 .0076 178

95 Chloroform, PCE, 1,1-dichloroethane, TCE 21 1.3 .0058 222

96 PCE, TCA, TCE, MTBE 15 .92 .0040 230

97 Chloroform, PCE, trichlorofluoromethane, TCE 15 .92 .0039 238

98 Chloroform, PCE, 1,1-dichloroethene, TCE 23 1.4 .0045 315

99 Chloroform, PCE, 1,1-dichloroethane, 1,1-dichloroethene15 .92 .0027 343

100 Chloroform, 1,1-dichloroethene, TCA, TCE 18 1.1 .0028 396

101 PCE, 1,1-dichloroethene, TCA, TCE 18 1.1 .0012 888

102 Chloroform, PCE, 1,1-dichloroethene, TCA, TCE 18 1.1 .0004 2,961

1Values rounded to two significant figures. Unrounded values were used in calculating detection frequency ratio.

Table 7. Statistical summary of 102 mixtures of volatile organic compounds (when calculated using no common assessment level) with 

a detection frequency of 1 percent or more.—Continued

[PCE, perchloroethene; TCA, 1,1,1-trichloroethane; MTBE, methyl tert-butyl ether; TCE, trichloroethene; TMB, 1,2,4-trimethylbenzene]

Mixture

number

VOCs in the mixture

Number of 

detections

Actual 

detection

frequency1

(percent)

Theoretical 

probability of 

detection

(percent)

Detection

frequency ratio

(rounded to 

nearest integer)
Table 8. Estimated detection frequency of individual VOCs found 

in mixtures listed in table 7.

[MTBE, methyl tert-butyl either; PCE, perchloroethene; TCA, 1,1,1-

trichloroethane; TCE, trichloroethene; TMB, 1,2,4-trimethylbenzene]

VOCs in the mixture

Estimated

detection

frequency1

(percent)

Long-term method 

detection level2

(micrograms

per liter)

Benzene 4.2 0.0245

Bromoform 2.2 .05

Chlorodibromomethane 2.0 .024

Chloroform 30.0 .019

Chloromethane 8.1 .12

cis-1,2-Dichloroethene 2.2 .019

Dichlorodifluoromethane 4.3 .08

1,4-Dichlorobenzene 2.6 .025

1,1-Dichloroethane 2.9 .026

1,1-Dichloroethene 2.3 .021

m,p-Xylene 2.5 .031

Methylene chloride 5.0 .14

MTBE 7.3 .08

PCE 13.4 .038

Styrene 2.4 .021

TCA 8.3 .016

TCE 4.9 .019

Toluene 15.7 .03

Trichlorofluoromethane 2.0 .048

Trichlorotrifluoroethane 1.0 .0245

TMB 15.8 .028

1The listed values may be slightly different than those reported elsewhere 

(Zogorski and others, in press) because of the exclusion herein of samples for 

Alaska and Hawaii. Detection frequencies were calculated using no common 

assessment level.

2

Moran and others (in press).

Analysis of the Sources, Transport, and Fate of VOCs 31

The most frequently detected mixtures had some of the 

lowest DFRs, indicating a fairly random co-occurrence pattern; 

for example, the mixture of PCE and chloroform was the most 

frequently detected mixture and had a DFR of 2 (mixture 23, 

table 7). Some of the less frequently detected mixtures had very 

large DFRs; for example, the mixture of chloroform, PCE, 1,1-

dichloroethene, TCA, and TCE (mixture 102, table 7) was 

detected about 3,000 times more frequently than can be 

explained by chance. 

There were 102 mixtures that had detection frequencies of 

about 1 percent or more (table 7). Among the 37 mixtures with 

the lowest DFRs (less than about 3), chloroform is paired with 

just one other VOC in about one-half of these mixtures. Consequently, chloroform had a dominant effect on the composition 

of these 37 mixtures, and their occurrence can be explained 

primarily by the random co-occurrence of VOCs (because of 

the small DFRs). There were 320 samples (20 percent of the 

total) that contained only these 37 random mixtures. Among the 

remaining 65 mixtures with the largest DFRs, only one mixture 

paired chloroform with one other VOC. There were 174 

samples (11 percent of the total) that contained at least 1 of 

these 65 nonrandom mixtures. In conclusion, mixtures that had 

a DFR of less than about 3 were considered random, and 

mixtures that had a DFR greater than about 3 were considered 

nonrandom. This division was based on the magnitude of the 

DFR and chemical composition of the mixture; however, the 

number of VOCs in samples with nonrandom mixtures also is 

larger than samples with only random mixtures. 

Samples with nonrandom mixtures generally have more 

VOCs (median = 4.5) and have more than one nonrandom 

mixture (median = 3.0). Samples with only random mixtures 

have fewer VOCs (median = 2.0) and generally have only one 

random mixture (median = 1.0). The effect of these differences 

is illustrated in figure 11, which shows how the cumulative 

number of samples with mixtures increases with the inclusion 

of each mixture (starting with the most nonrandom mixture). 

The slopes of the two regression lines in figure 11 show that 

with the inclusion of each nonrandom mixture, only 1.3 new 

samples are identified; in contrast, with the inclusion of each 

random mixture, 9.3 new samples are identified. All these 

differences demonstrate how samples with random mixtures are 

fundamentally different than samples with nonrandom mixtures 

and supports a DFR of about 3 as separating random and 

nonrandom mixtures.

Samples with any of the 65 mixtures with the largest DFRs 

were associated with (in order of decreasing strength of association) more RCRA sites, public-water-supply wells (as opposed 

to domestic wells), and increased ground-water recharge 

(table 9). The mean number of RCRA sites within 1,000 m of a 

sampled well increased by a factor of almost 8 (0.5 to 3.9) if a 

nonrandom mixture was detected than if only random mixtures 

were detected. A statistical summary of the number of RCRA 

sites within 1,000 m of sites with random and nonrandom 

mixtures is shown in figure 12. This new approach of mixture 

analysis needs more research and validation before it should be 

used to define sources at any particular site location; however, 

the potential value of this new method to identify sources seems 

promising.
CUMULATIVE NUMBER OF SAMPLES WITH MIXTURES

MIXTURE NUMBER (see table 6)

600

500

400

300

200

100

0

110 80 100 70 60 50 40 30 20 10 0 90

Random mixtures

Nonrandom mixtures

32 Factors Associated with Sources, Transport, and Fate of VOCs in Aquifers of the United States

Figure 11. Relation between the cumulative number of samples and the type of mixture.

Table 9. Logistic regression analysis for samples with random/nonrandom mixtures.

Dependent

variable Independent variable Type ofvariable

Unstandardized

coefficient

Standardized

coefficient

Random/nonrandom mixtures 

(random mixtures=0, 

nonrandom mixtures=1)

Number of Resource Conservation and Recovery Act 

(RCRA) sites within 1 kilometer

Source 0.130 0.27

Domestic/public water supply well

(domestic water supply=0, public water supply=1)

Indeterminate 1.500 .25

Estimated ground-water recharge within 500-meter 

circular buffer around sampled well

Transport .003 .17
Value exceeding upper quartile plus 3.0 times

the interquartile range

Value exceeding upper quartile plus 1.5 times

the interquartile range

Median (50th percentile)

Lower quartile (25th percentile)

Upper quartile (75th percentile)

Lower quartile minus 1.5 times the interquartile range or smallest value not less than

this computation

Upper quartile plus 1.5 times the interquartile

range or largest value not greater than this

computation

EXPLANATION

NUMBER OF RCRA SITES WITHIN 1,000 METERS OF SAMPLED WELL

60

50

40

30

20

10

0

SAMPLED WELLS

WITH RANDOM

MIXTURES

SAMPLED WELLS

WITH NONRANDOM

MIXTURES

Analysis of the Sources, Transport, and Fate of VOCs 33

Figure 12. Statistical summary of number of Resource Conservation and Recovery Act (RCRA) sites within 1,000 meters 

of sampled wells with random and nonrandom mixtures.

Synopsis of Results

In the following paragraphs, the sources, transport, and 

fate of 10 frequently detected VOCs in NAWQA selected 

aquifer studies are summarized on the basis of the combined 

results of logistic regression, quantile plots, detection frequencies, network analysis, and mixture analysis. Where appropriate, the effect of the sources, transport, and fate of VOCs on 

their detections and concentrations is summarized. 

Sources

Dispersed sources were more strongly associated with the 

10 VOCs in ground water than were concentrated sources; in 

fact, all 10 VOCs were associated with dispersed sources such 

as land use, densities of septic/sewer systems, and MTBE use 

areas. Land-use factors (year of construction, percent urban 

land, and percent undeveloped land) indicated the effect of age 

and intensity of urban development. VOCs were all associated 

with more urban development, with the exception of chloromethane (which was weakly associated with undeveloped 

land). Septic systems were associated with the detection of five 

VOCs (chloroform, methylene chloride, TCA, TCE, and PCE); 

in contrast, two VOCs (bromodichloromethane and TMB) were 

specifically not associated with septic systems. Three VOCs 

(chloroform, TCE, and PCE) were associated with septic 

systems and public-supply wells. For these three VOCs, it is 

possible that public-supply wells sampled for this study are 

more affected than even domestic water supplies by septic 

systems. The intensity of MTBE use in gasoline was associated 

with its detection in ground water. The results of the mixture 

analysis also supported the importance of dispersed sources; 

about one-half as many samples containing VOC mixtures were 

associated with concentrated sources (11 percent of total 

number of samples) than with dispersed sources (20 percent of 

the total number of samples). 

Concentrations of VOCs generally were less than 1 μg/L in 

selected aquifer studies. These small concentrations can be 

derived from various dispersed sources not included in statistical models including natural formation in soils, urban runoff, 

and ground-water recharge from irrigation water that was 

treated with chlorine. Factors such as urban land use and population density could be surrogates for several dispersed sources. 

Associations with individual sources can be difficult to discern 
34 Factors Associated with Sources, Transport, and Fate of VOCs in Aquifers of the United States

with statistical methods because (1) the results depend on the 

accuracy of the ancillary information, and (2) VOCs can originate from a variety of dispersed and concentrated sources, have 

multiple sources (even at one location), and result from the 

degradation of other VOCs. 

Small concentrations of certain VOCs detected in selected 

aquifer studies also were associated with concentrated sources, 

for example, gasoline LUST sites and RCRA sites. Processes 

such as dispersion and degradation can reduce concentrations 

from these sources so that detected concentrations are similar to 

those from dispersed sources. Seven VOCs (all except bromodichloromethane, chloromethane, and methylene chloride) were 

associated with a concentrated source; however, these sources 

were not as strong as dispersed sources. Concentrated sources 

can supply more than one VOC; however, VOC concentrations 

in samples with mixtures compared with samples without 

mixtures showed that the difference in concentration was not 

statistically significant or was not environmentally important. 

These overall results show that the detection of small concentrations of VOCs in selected aquifer studies cannot be used 

exclusively to classify the type of source. 

Transport

A VOC detected in ground water would, in general, 

require first a release from a source and then subsequent transport to ground water. Factors that potentially affect the transport 

of VOCs to ground water are dependent on where the release 

occurred. If the release is at land surface (for example, a spill), 

climate and near-surface soil characteristics could affect transport to ground water. If the release is below land surface (for 

example, LUST sites or septic systems/sewer lines), climate 

would be less important; furthermore, soil characteristics at 

land surface also might not be relevant unless they are representative of material deeper in the unsaturated zone. Ground-water 

recharge could, however, be an important transport factor 

within the unsaturated zone. Once in the aquifer, VOCs are 

transported by advective ground-water flow and dispersion. 

Cool temperature was an important transport factor for the 

detection of four VOCs (TCA, MTBE, TMB, and toluene). In 

cool climates, evaporation of MTBE, TMB, and toluene from a 

gasoline spill would be reduced, increasing the potential of 

these VOCs to infiltrate to ground water with recharge water. 

Multiple VOCs in a single source could, however, become 

separated by in-situ processes such as sorption. Furthermore, 

some VOCs could evaporate from a gasoline spill while others 

could move to the top of shallow ground water with recharge 

water. Concentrations of MTBE in urban air appear sufficiently 

large in some areas to account for some concentrations of 

MTBE detected in shallow ground water (Zogorski and others, 

in press), assuming that equilibrium is achieved between urban 

air, soil gas, and the top of the water table. Ambient air concentrations in rural areas likely are not large enough to account for 

concentrations of MTBE detected in selected aquifer studies.

Several soil characteristics were important for the detection of six VOCs (chloroform, TCE, toluene, chloromethane, 

methylene chloride, and TCA). A soil is classified as hydric if 

it is sufficiently wet in the upper part to develop anoxic conditions during the growing season; consequently, this soil characteristic could be more related to the fate of VOCs than to transport. Nevertheless, chloroform and TCE were associated with 

non-hydric soils (oxic conditions), and toluene was associated 

with hydric soils (anoxic conditions). Percent sand and silt is 

related to soil permeability. Two VOCs (chloromethane and 

methylene chloride) were associated with less permeable soils. 

If these VOCs are degradation products of chloroform, then 

slow movement of water through the unsaturated zone may 

promote the degradation of chloroform and formation of its two 

degradation products. Sorption to organic carbon would slow 

the movement of VOCs through the unsaturated zone and 

within an aquifer; however, TCA was the only VOC associated 

with low organic carbon content in soils. Organic carbon in 

soils or aquifer material could also result in the separation of a 

mixture of VOCs (depending on their solubility in water) as 

they are transported through soil or an aquifer. 

High rates of ground-water recharge were associated with 

the detection of chloroform, TCE, and MTBE; in contrast, low 

recharge rates were associated with the detection of bromodichloromethane and TMB. VOCs are more frequently detected 

in more populated areas for two reasons: (1) the number of 

sources increase, and (2) recharge increases (fig. 10), hastening 

transport of VOCs through the unsaturated zone to ground 

water and reducing the opportunity for degradation. This means 

that one would expect high ground-water recharge rates to be 

associated with more frequent detections of VOCs in ground 

water. VOCs associated with low recharge rates are more difficult to explain, but they could be related to the source. If a 

source (for example, a leaky sewer line) releases a small volume 

and small concentrations of particular VOCs (for example, 

bromodichloromethane and TMB), then recharge water could 

dilute concentrations to below the reporting level. Concentrated 

sources probably are less affected by recharge than dispersed 

sources are.

Confining layers can prevent ground-water recharge (and 

VOCs) from reaching an aquifer; however, confined aquifers 

were not associated with fewer VOC detections compared with 

unconfined aquifers. This finding could mean that, in general, 

the presence of a confining unit does not adequately protect 

ground water from anthropogenic contamination.

The transport of VOCs within an aquifer was found to be 

affected by well depth and the percentage of ground-water withdrawal in the aquifer coming from a decrease in storage (water 

removed from the aquifer that probably is a one-time withdrawal). Shallow well depth (table 5) was an important factor in 

the detection of four VOCs (methylene chloride, TCA, PCE, 

and MTBE). Traveltimes to deeper wells generally is longer, 

allowing more time for degradation along the flowpath. In some 

situations, water is sufficiently old that no anthropogenic 

sources of VOCs existed when ground water was recharged. 

Source of water to a well, direction of ground-water flow, trav-
Implications for Ground-Water Management and Assessments 35

eltimes, and recharge rates can be helpful in explaining the 

detection of VOCs at a particular location; however, regional 

ground-water-flow models also can be used to help explain the 

detection of VOCs in selected aquifer studies. A comparison of 

regional ground-water-flow models can show major differences 

in how aquifers respond to withdrawal and the source of water 

to the aquifers. These differences can be used to explain differences in detection frequencies of some VOCs in selected 

aquifer studies.

Fate

Dissolved-oxygen concentration was the most frequently 

occurring explanatory factor in logistic regression models for 

the 10 VOCs. Seven VOCs were associated with oxic conditions (bromodichloromethane, chloroform, TCA, TCE, PCE, 

MTBE, and toluene), and only one VOC (chloromethane) was 

associated with anoxic conditions. These results are particularly 

important because this multivariate analysis controls for differences such as climate and hydrogeologic factors that also can 

affect the detection of VOCs in ground water. A comparison of 

detection frequencies of the 10 VOCs in oxic and anoxic conditions (fig. 6) generally supports the results of the logistic regression analysis; however, methylene chloride had significantly 

larger detection frequency in anoxic conditions than in oxic 

conditions, but logistic regression analysis did not identify oxic/

anoxic conditions as being a significant explanatory factor. For 

the network analysis, the median dissolved-oxygen concentration in a selected aquifer study was a significant explanatory 

factor for the detection frequency of chloroform.

Dissolved-oxygen concentrations in ground water not only 

affected the detection but also the concentrations of VOCs. 

Quantile/quantile plots showed that the amount of dissolved 

oxygen affected the concentrations of eight VOCs and are 

generally consistent with results from logistic regression and 

detection-frequency analysis. Methylene chloride, however, 

had significantly larger concentrations and detection frequencies in anoxic water, but logistic regression analysis did not 

identify this VOC as being associated with anoxic water. 

Toluene also did not have significantly different concentrations 

or detection frequencies in either oxygen condition, but logistic 

regression analysis showed an association with oxic conditions. 

These results indicate that univariate analyses such as quantile/

quantile plots and analysis of detection frequencies can produce 

conflicting results when compared with multivariate analysis. 

Multivariate analyses, however, probably are better because 

they control for a variety of factors that can affect the detection 

of VOCs. Quantile plots showed that the effect of anthropogenic activity on ground-water quality can depend on oxygen 

concentrations. Chloroform is more stable in oxic than anoxic 

conditions; therefore, the effect of urban land use and the use of 

septic systems (both surrogates for chloroform sources) on the 

concentrations of chloroform was greater for oxic conditions 

than anoxic conditions.

Implications for Ground-Water

Management and Assessments

Objectives of large-scale ground-water resource assessments often involve evaluating the occurrence, distribution, and 

status of contaminants in ground water; however, the value of 

these assessments could be enhanced if determining the 

sources, transport, and fate of contaminants also was part of the 

project objectives and analyses. For example, if associations 

can be established between VOCs in ground water and explanatory factors in a particular study area, then one could extrapolate the results into similar areas where water-quality information has not been collected. One might also be able to estimate 

the risk of ground-water contamination in similar hydrogeologic areas if certain types of development are allowed. Large 

aquifers can have many sources of anthropogenic contamination; consequently, the most important contaminant source for 

a particular aquifer can be difficult to identify. If, however, 

accurate ancillary information is combined with accurate waterquality data from resource assessments, hypotheses derived 

from small-scale studies also can be tested for their relevance at 

a larger scale; conversely, hypotheses derived from large-scale 

resource assessment can be used to prioritize and design smallscale research projects that address potentially important issues 

for large aquifers.

Accurate ancillary data related to sources, transport, and 

fate of VOCs is essential to establishing reliable associations 

with occurrence and concentrations of VOCs in ground-water 

resource assessments. Collecting ancillary information in a 

circular buffer around the sampled well is adequate for some 

analysis. The assumption behind this approach is that land use 

within the buffer is representative of land use in the true capture 

zone of the sampled well, even if it lies beyond the buffer. 

Ground-water-flow models can provide better estimates of the 

locations of the capture zones of public-supply wells; however, 

this approach probably is not feasible for domestic water 

supplies because of the small amount of withdrawal.

Sources

The best possible information on the location, nature, 

timing, and amount of any release of VOCs is needed along 

with general information on land-use activities. Land-use activities within circular buffers around wells can be associated with 

ground-water quality, but incremental improvements to this 

approach are possible if more is known about the direction of 

ground-water flow in the vicinity of the well. For completeness 

of ancillary data, both concentrated and dispersed sources of 

contamination should be delineated because both can result in 

small concentrations in ambient ground water. The known 

release of VOCs from a concentrated source at a particular location can be used to establish associations with VOCs in ground 

water. Small concentrations of VOCs that originate from 

concentrated sources could be a concern because they can 
36 Factors Associated with Sources, Transport, and Fate of VOCs in Aquifers of the United States

increase with time. Concentrations also would be expected to 

increase closer to the source. 

General land-use activities—for example, the amount of 

urban land use—also can be helpful in estimating input from 

dispersed sources; however, these types of associations are not 

as helpful as delineating a particular dispersed source, such as a 

subdivision served by septic systems. Potential sources that are 

found not to be associated with any contamination also are 

important because they help identify anthropogenic activity that 

does not pose a threat to ground-water quality. VOCs in ground 

water that are associated with septic systems may not present a 

substantial concern to human health; however, the presence of 

these VOCs could indicate the presence of other chemicals or 

bacteria associated with wastewater. Public-water-supply wells 

may be more affected by septic systems than domestic water 

supplies for some VOCs, probably because of the larger 

pumping rates and larger capture zones for public-supply wells 

compared with domestic wells. More research is needed to 

define the effect of septic systems on ground-water quality, in 

particular how the use of household products like bleach could 

contribute to the detection of chloroform in ground water or 

how improper disposable of solvents and other VOCs in septic 

systems also could contribute to the detection of other VOCs 

like toluene, PCE, and TCE. 

Transport

Well depth/screen depth, ground-water recharge/precipitation, air temperature, and soil characteristics were significant 

factors in helping explain the transport of VOCs to ground 

water. The availability of simple data such as well depth and 

depth to the top of the screened interval could be used in wellselection and sampling criteria because of their value in 

explaining the detection of VOCs in ground water. Estimates of 

ground-water recharge, precipitation, and air temperature are 

available for most of the Nation, although the quality of these 

estimates varies. Chemical and hydrologic characteristics of 

soils and aquifers can be more difficult to obtain, but these 

factors are important to adequately explain the transport of 

VOCs. 

The transport of VOCs can be best understood by defining 

the direction of ground-water flow and developing groundwater-flow models at various scales (small to regional). If the 

direction of ground-water flow is known, then land-use activities and hydrogeologic factors downgradient from the sampled 

well generally can be disregarded unless the sampling point is a 

public-supply well. For public-supply wells, a more sophisticated approach (but also more costly) would be to use smallscale flow models to estimate the location of the capture zone; 

defining capture zones for domestic-supply wells would be very 

difficult because of the small pumping rates associated with 

these wells. Ground-water-flow models may still not precisely 

define the capture zones to public-supply wells, but a simulated 

capture zone as an area for determining land-use activity probably is better than a circular buffer. Sources in the defined 

capture zone could then be related to water quality depending 

on traveltimes of ground water to the pumping well. 

Large-scale ground-water-flow-models can be used to 

understand the source of water to an aquifer and the way that 

pumping affects the overall ground-water-flow system. These 

models can show important differences between regional aquifers. Aquifers can differ in lithology, which is sometimes used 

as a surrogate for hydrogeologic characteristics; however, 

lithology has limitations because it is not quantitative, is subject 

to individual interpretation, and it represents only part of the 

flow system. Many hydrogeologic factors within regional aquifers also can vary tremendously; in fact, this variability can be 

larger than variability between regional aquifers making it difficult to distinguish aquifers on the basis of one factor. Flow 

models can include the effect of many hydrogeologic factors, 

are quantitative, can be developed at different scales, and can 

improve with time as additional data are collected for better 

model calibration. These models can, for example, help determine whether most of the ground water is coming from induced 

infiltration from a different aquifer, storage, or intercepted 

stream discharge. Differences in the quality of water derived 

from these different sources could be substantial. A comparison 

of water budgets between various regional aquifers may help 

explain differences in the water quality and may be helpful in 

managing the ground-water resource. 

VOCs typically are transported through the unsaturated 

zone to ground water with recharge water; nevertheless, for 

some VOCs, increased recharge could dilute concentrations in 

ground water. For example, if a particular VOC comes from 

small amount of leakage from sewer lines or occurs naturally 

within the unsaturated zone, then larger amounts of natural 

recharge could dilute concentrations of this VOC in ground 

water. 

Fate

The fate of VOCs in ground water was explained largely 

by dissolved-oxygen concentration in ground water. Individual 

VOCs generally are not equally stable in oxic and anoxic conditions; therefore, conditions that favor slower degradation result 

in increased concentrations and detection frequencies. 

Dissolved-oxygen data (or, even better, delineation of redox 

condition) can be used in the analysis of data from monitoring 

networks to (1) help explain variability in concentrations and 

detections of VOCs, (2) identify VOCs that are expected to be 

stable in the sampled ground water and therefore can be used as 

an early warning for more serious contamination, (3) define 

more accurately the effects of certain anthropogenic activities 

on ground-water quality by controlling for differences in 

dissolved-oxygen concentration, and (4) help determine 

whether certain VOCs are potential degradation products of 

other VOCs. If dissolved oxygen/redox changes along a flowpath, it may be more difficult to use in explanatory analysis of 

monitoring network data.
Summary 37

Well Type

Certain VOCs are more strongly associated with domestic 

wells, whereas other VOCs are more strongly associated with 

public-supply wells even after controlling for differences in 

factors such as land use, dissolved oxygen, and well depth. The 

effect of well type on the detection frequencies of some VOCs, 

for example chloroform (fig. 5), may only be apparent in 

oxygen conditions that favor the preservation of the VOCs. 

Differences in traveltimes and sources of VOCs near the wells 

could explain the association of a VOC with a particular type of 

well. An association of a VOC with a particular well type means 

higher detection frequencies in one well type compared to 

another; it does not mean an absence of detections in a particular type of well. These associations could be used to help 

identify which VOCs to include in the laboratory analysis. The 

variance in monitoring networks can be reduced by limiting the 

types of wells selected for sampling, or at least by controlling 

for well type in the analysis of the data.

Mixtures

VOCs commonly were found in mixtures. Analysis of 

mixtures of VOCs has the potential to provide greater insight 

into the interpretation of monitoring data. For example, small 

concentrations of individual VOCs in a mixture of VOCs in a 

ground-water sample may not exceed any individual drinkingwater standard; however, if the mixture is nonrandom, proximity to a concentrated source of VOCs could be indicated, and 

additional monitoring may be warranted. This preliminary 

method of mixture analysis used for this report needs to be verified at different reporting levels and with other environmental 

data.

The health consequence of mixtures of contaminants in 

potable water is not yet understood. Health risk associated with 

contaminants in drinking water typically is based on animal 

studies done at large concentrations for one contaminant over a 

short period of time. The relevance of these animal studies to 

human health often is debated because the effect of contaminants on animals may not be identical to that on humans, and 

extrapolating effects of exposure to large concentrations to 

smaller environmental concentrations in drinking water is 

uncertain. Tracking human health associated with the use of 

water with a known quality could take years to develop, but the 

human-health benefits could be substantial. These data could 

even be compared with risk estimates that are currently being 

used to establish drinking-water standards. 

Summary

Factors associated with sources, transport, and fate of 10 

frequently detected volatile organic compounds (VOCs) in 

selected aquifer systems of the United States were evaluated 

using various statistical methods. VOC data from 1,631 wells 

sampled between 1996 and 2002 as part of the National WaterQuality Assessment (NAWQA) Program of the U.S. Geological 

Survey were used in the analyses. These samples were a subset 

of samples included in the Program’s national VOC assessment. 

Sampled wells were randomly selected from aquifers used to 

supply drinking water in the regional study areas. Samples were 

analyzed for 53 VOCs from primarily domestic water supplies 

(1,184), followed by public-supply wells (216); the remaining 

wells (231) were from monitoring and other well types. The 

median well depth was 50 m. Age-date information, available 

from 44 percent of the sampled wells, shows that about 

60 percent of the sampled water was recharged after 1953. Ten 

VOCs that had a detection frequency of about 5 percent or 

greater when calculated using no assessment level were 

selected for more detailed statistical analysis. These compounds 

are bromodichloromethane, chloroform, chloromethane, methylene chloride, 1,1,1-trichloroethane (TCA), trichloroethene 

(TCE), perchloroethene (PCE), methyl-tert-butyl ether 

(MTBE), 1,2,4-trimethylbenzene (TMB), and toluene. Concentrations of VOCs generally were less than 1 microgram per liter. 

A VOC detected in ground water would, in general, 

require first a release from a source and then subsequent transport to ground water. Source factors, in order of decreasing 

importance, were general land-use activity (dispersed source), 

septic/sewer density (dispersed source), and sites where large 

concentrations of VOCs are potentially released (concentrated 

sources), such as leaking underground storage tanks (LUSTs). 

All 10 of the VOCs included in this analysis were associated 

with dispersed sources such as land use, densities of septic/

sewer systems, and methyl-tert-butyl-ether-use areas. Three 

VOCs (chloroform, TCE, and PCE) were associated with septic 

systems and public-supply wells. For these three VOCs, it is 

possible that public-supply wells sampled for this study are 

more affected by septic systems than even domestic water 

supplies. Factors such as urban land use and densities of septic/

sewer systems also could be surrogates for several dispersed 

sources. Small concentrations of certain VOCs detected in 

selected aquifer studies also were associated with concentrated 

sources, for example, gasoline leaking underground storage 

tank sites and Resource Conservation and Recovery Act sites. 

Processes such as advection, dispersion, and degradation can 

reduce concentrations from these sources so that detected 

concentrations are similar to those from dispersed sources. 

Seven VOCs (all except bromodichloromethane, chloromethane, and methylene chloride) were associated with a 

concentrated source; however, these sources were not as strong 

as dispersed sources. Mixture analysis showed that 11 percent 

of all samples had VOC mixtures that were associated with 

concentrated sources; 20 percent were associated with 

dispersed sources. These overall results show that the detection 

of small concentrations of VOCs in selected aquifer studies 

cannot be used exclusively to classify the type of source.

Important transport factors included well depth, screen 

depth, precipitation/recharge, air temperature, soil characteristics, and amount of water removed from storage during with-
38 Factors Associated with Sources, Transport, and Fate of VOCs in Aquifers of the United States

drawal from the aquifer. Factors that potentially affect the transport of VOCs to ground water are dependent on where the 

release occurred. If the release is at land surface (for example, a 

spill), climate and near-surface soil characteristics could affect 

transport to ground water. If the release is below land surface 

(for example, LUST sites or septic systems/sewer lines), 

climate would be less important; furthermore, soil characteristics at land surface also might not be relevant unless they are 

representative of material deeper in the unsaturated zone. 

Ground-water recharge could, however, be an important transport factor within the unsaturated zone. VOCs are more 

frequently detected in more populated areas for two reasons: 

(1) the number of sources increase, and (2) recharge increases, 

hastening transport of VOCs through the unsaturated zone to 

ground water and reducing the opportunity for degradation. 

Confining layers can prevent ground-water recharge (and 

VOCs) from reaching an aquifer; however, confined aquifers 

were not associated with fewer VOC detections compared with 

unconfined aquifers. It appears that confining layers do not 

provide a substantial protection against the infiltration of 

VOCs. Cool temperature was an important transport factor for 

the detection of four VOCs (TCA, MTBE, TMB, and toluene). 

In cool climates, evaporation of MTBE, TMB, and toluene from 

a gasoline spill would be reduced, increasing the potential of 

these VOCs to infiltrate to ground water with recharge water. A 

comparison of regional ground-water-flow models can show 

major differences in how aquifers respond to withdrawal and 

the source of water to the aquifers. These differences can be 

used to explain differences in detection frequencies of some 

VOCs in selected aquifer studies. 

 Dissolved oxygen was the explanatory factor that was 

strongly associated with the fate of VOCs; it proved crucial in 

explaining the detection and concentration of many VOCs. 

Chloroform, for example, is more stable in water that contains 

oxygen. This increased stability explained the larger detection 

frequencies and concentrations of chloroform in water 

containing oxygen compared with water having little or no 

oxygen. Dissolved-oxygen concentration was the most 

frequently occurring explanatory factor among logistic regression models for 10 VOCs. Seven VOCs (bromodichloromethane, chloroform, TCA, TCE, PCE, MTBE, and toluene) 

were associated with oxic conditions, and only one VOC (chloromethane) was associated with anoxic conditions. These 

results are particularly important because this multivariate analysis controls for differences such as climate and hydrogeologic 

factors that also can affect the detection of VOCs in ground 

water. A comparison of detection frequencies of the 10 VOCs 

in oxic and anoxic conditions generally supports the results of 

the logistic regression analysis. Dissolved-oxygen concentrations in ground water not only affected the detection but also the 

concentrations of VOCs. Dissolved-oxygen concentration 

(oxic/anoxic) and aquifer type (unconfined/confined) are 

significantly associated with each other; however, dissolvedoxygen concentration was determined to be a much better 

explanatory factor than aquifer type. 

Well type (domestic or public supply) also was an important explanatory factor, but was classified as indeterminate 

because it was not clearly associated with the sources, transport, 

or fate of the VOC. Well type (domestic/public water supplies) 

was a significant factor for seven VOCs. Five VOCs (bromodichloromethane, chloroform, MTBE, TCE, and PCE) were 

associated with public water supplies, and two VOCs (methylene chloride and toluene) were associated with domestic 

water supplies. Larger contributing areas associated with public 

water supplies could contribute to the stronger associations of 

five VOCs with public water supplies. Public water supplies, 

however, also had significantly larger amounts of urban land 

use within 500 m of the sampled well compared with domestic 

water supplies; in fact, the median amount of urban land use for 

domestic water supplies was 0.2 percent compared to 

55 percent for public water supplies. Three VOCs (chloroform, 

TCE, and PCE) were associated with public-water-supply wells 

and septic systems. For these three VOCs, it is possible that 

public water supplies are more affected by septic systems than 

domestic water supplies.

Results of multiple analyses show the importance of 

(1) accounting for dispersed and concentrated sources of VOCs, 

(2) understanding the ground-water-flow system at different 

scales to help explain VOC detections, (3) measuring dissolved 

oxygen when sampling for VOCs, and (4) limiting the type of 

wells sampled in monitoring networks to avoid unnecessary 

variance in the data.

References

Alley, W.M., Reilly, T.E., and Franke, O.L., 1999, Sustainability of ground-water resources: U.S. Geological Survey 

Circular 1186, 79 p.

Baehr, A.L., Charles, E.G., Baker, R.J., 2001, Methyl tert-butyl 

ether degradation in the unsaturated zone and the relation 

between MTBE in the atmosphere and shallow groundwater: 

Water Resources Research, v. 37, no. 2, p. 223–234.

Baehr, A.L., Stackelberg, P.E., and Baker, R.J., 1999, Evaluation of the atmosphere as a source of volatile organic 

compounds in shallow ground water: Water Resources 

Research, v. 35, no. 1, p. 127–136.

Barbash, J.E., Thelin, G.P., Kolpin, D.W., and Gilliom, R.J., 

2001, Major herbicides in ground water results from the 

national water-quality assessment: Journal of Environmental 

Quality, v. 30, no. 3, p. 831–845.

Bender, D.A., Zogorski, J.S., Halde, M.J., and Rowe, B.L., 

1999, Selection procedure and salient information for volatile organic compounds emphasized in the National WaterQuality Assessment Program: U.S. Geological Survey OpenFile Report 99–182, 32 p.

Chapelle, F.H., Widdowson, M.A., Brauner, J.S., Mendez, 

Eduardo, III, and Casey, C.C., 2003, Methodology for estimating times of remediation associated with monitored 

natural attenuation: U.S. Geological Survey WaterResources Investigations Report 03–4057, 51 p.
References 39

Coldewey, W.G., and Messer, Johannes, 1997, The effects of 

urbanization on groundwater recharge in the Ruhr region of 

Germany, in Chilton, John, and others, eds., Groundwater in 

the urban environment—Problems, processes and management: Rotterdam, Netherlands, A.A. Balkema, p. 115–119. 

Connor, B.F., Rose, D.L., Noriega, M.C., Murtagh, L.K., and 

Abney, S.R., 1998, Methods of analysis by the U.S. Geological Survey National Water Quality Laboratory—Determination of 86 volatile organic compounds in water by gas chromatography/mass spectrometry, including detections less 

than reporting limits: U.S. Geological Survey Open-File 

Report 97–829, 78 p.

Consortium for International Earth Science Information 

Network (CIESIN), 1995, Socioeconomic Data and Applications Center (SEDAC), Archive of Census Related Products: 

CIESIN/SEDAC, accessed July 21, 1998, at http://sedac.

ciesin.org/plue/cenguide.html

Custodio, Emilio, 1997, Groundwater quantity and quality 

changes related to land and water management around urban 

areas—Blessings and misfortunes, in Chilton, John, and 

others, eds., Groundwater in the urban environment—Problems, processes and management: Rotterdam, Netherlands, 

A.A. Balkema, p. 11–22. 

Dobson, J. E., Bright, E. A., Coleman, P. R., Durfee, R. C., and 

Worley, B.A., 2000, A global population database for estimating population at risk: Photogrammetric Engineering & 

Remote Sensing, v. 66, no. 7, p. 849-857.

Eckhardt, D.A.V., and Stackelberg, P.E., 1995, Relation of 

ground-water quality to land use on Long Island, New York: 

Ground Water, v. 33, no. 6, p. 1019–1033.

Egli, C., Tschan, T., Schlotz, R., Cook, A.M., and Leisinger, T., 

1988, Transformation of tetrachloromethane to dichloromethane and carbon dioxide by Acetobacterium woodii: 

Applied and Environmental Microbiology, v. 54, no. 11, 

p. 2819–2824.

Gilliom, R.J., Alley, W.M., and Gurtz, M.E., 1995, Design of 

the National Water-Quality Assessment Program—Occurrence and distribution of water-quality conditions: U.S. 

Geological Survey Circular 1112, 33 p.

Gilliom, R.J., Hamilton, P.A., and Miller, T.L., 2001, The 

National Water-Quality Assessment Program—Entering a 

new decade of investigations: U.S. Geological Survey Fact 

Sheet 071–01, 6 p.

Grady, S.J., and Mullaney, J.R., 1998, Natural and human 

factors affecting shallow water quality in surficial aquifers in 

the Connecticut, Housatonic, and Thames River Basins: U.S. 

Geological Survey Water-Resources Investigations Report 

98–4042, 81 p.

Gribble, B.W., 1992, Naturally occurring organohalogen 

compounds—A survey: Journal of Natural Products, v. 55, 

no. 10, p. 1353–1395.

Helsel, D.R., and Hirsch, R.M., 1992, Statistical methods in 

water resources: New York, Elsevier, 522 p.

Johnston, R.H., 1999, Hydrologic budgets of regional aquifer 

systems of the United States for predevelopment and development conditions: U.S. Geological Survey Professional 

Paper 1425, 34 p. 

Kolpin, D.W., Barbash, J.E., and Gilliom, R.J., 1998, Occurrence of pesticides in shallow groundwater of the United 

States—Initial results from the National Water-Quality 

Assessment Program: Environmental Science & Technology, v. 32, no. 5, p. 558–566.

Koterba, M.T., Wilde, F.D., and Lapham, W.W., 1995, Groundwater data-collection protocols and procedures for the 

National Water-Quality Assessment Program—Collection 

and documentation of water-quality samples and related data: 

U.S. Geological Survey Open-File Report 95–399, 113 p.

Kotzias, D., and Sparta, C., 1993, VOCs and water pollution, in

Bloemen H.J. Th., and Burn, J., eds., Chemistry and analysis 

of volatile organic compounds in the environment: London, 

Blackie Academic and Professional, p. 175–199.

Laturnus, Frank, Haselmann, K.F., Borch, Thomas, and Gron, 

Christian, 2002, Terrestrial natural source of trichloromethane—Chloroform, CHCL3—An overview: 

Biogeochemistry, v. 60, p. 121–139.

Lerner, D.N., 2002, Identifying and quantifying urban 

recharge—A review: Hydrogeology Journal, v. 10, no. 1, 

p. 143–152.

Menard, Scott, 2002, Applied logistic regression analysis (2d 

ed.): Thousand Oaks, Calif., Sage Publications, 110 p.

Moran, M.J., Zogorski, J.S., and Rowe, B.L., in press, 

Approach to an assessment of volatile organic compounds in 

the Nation’s ground water and drinking-water supply wells: 

U.S. Geological Survey Open-File Report 2005–1452.

Nolan, B.T., Hitt, K.J., and Ruddy, B.C., 2002, Probability of 

nitrate contamination of recently recharged ground waters in 

the conterminous United States: Environmental Science & 

Technology, v. 36, no. 10, p. 2138–2145.

Nolan, B.T., and Stoner, J.D., 2000, Nutrients in ground waters 

of the conterminous United States, 1992–1995: Environmental Science & Technology, v. 34, no. 7, p. 1156–1165. 

Norris, R.D., and others, 1994, Handbook of bioremediation: 

Boca Raton, Fla., Lewis Publishers, 257 p.

Pankow, J.F., Thomson, N.R., Johnson, R.L., Baehr, A.L., and 

Zogorski, J.S., 1997, The urban atmosphere as a non-point 

source for the transport of MTBE and other volatile organic 

compounds (VOCs) to shallow groundwater: Environmental 

Science & Technology, v. 31, no. 10, p. 2821–2828. 

Reilly, T.E., and Pollock, D.W., 1995, Effect of seasonal and 

long-term changes in stress on sources of water to wells: U.S. 

Geological Survey Water-Supply Paper 2445, 25 p.

Robinson, J.L., 2002, Ground-water quality beneath an urban 

residential and commercial area, Montgomery, Alabama, 

1999–2000: U.S. Geological Survey Water-Resources Investigations Report 02–4052, 37 p.

Roch, Francois, and Alexander, Martin, 1997, Inability of 

bacteria to degrade low concentrations of toluene in water: 

Environmental Toxicology and Chemistry, v. 16, no. 7, 

p. 1377–1383.

Rule, K.L., Ebbett, V.R., and Vikesland, P.J., 2005, Formation 

of chloroform and chlorinated organics by free-chlorinemediated oxidation of triclosan: Environmental Science & 

Technology, v. 39, no. 9, p. 3176–3185.
40 Factors Associated with Sources, Transport, and Fate of VOCs in Aquifers of the United States

Squillace, P.J., Moran, M.J., Lapham, W.W., Price, C.V., 

Clawges, R.M., and Zogorski, J.S., 1999, Volatile organic 

compounds in untreated ambient groundwater of the United 

States, 1985–1995: Environmental Science & Technology, 

v. 33, no. 23, p. 4176–4187.

Squillace, P.J., Moran, M.J., and Price, C.V., 2004, VOCs in 

shallow groundwater in new residential/commercial areas of 

the United States: Environmental Science & Technology, 

v. 38, no. 20, p. 5327–5338.

Squillace, P.J., Scott, J.C., Moran, M.J., Nolan, B.T., and 

Kolpin, D.W., 2002, VOCs, pesticides, nitrate, and their 

mixtures in groundwater used for drinking water in the 

United States: Environmental Science & Technology, v. 36, 

no. 9, p. 1923–1930.

Stackelberg, P.E., Kauffman, L.J., Ayers, M.A., and Baehr, 

A.L., 2001, Frequently co-occurring pesticides and volatile 

organic compounds in public-supply and monitoring wells, 

southern New Jersey, USA: Environmental Toxicology and 

Chemistry, v. 20, no. 4, p. 853–865.

Stackelberg, P.E., Kauffman, L.J., Baehr, A.L., and Ayers, 

M.A., 2000, Comparison of nitrate, pesticides, and volatile 

organic compounds in samples from monitoring and publicsupply wells, Kirkwood-Cohansey aquifer system, southern 

New Jersey: U.S. Geological Survey Water-Resources Investigations Report 00–4123, 78 p.

Suarez, M.P., and Rifai, H.S., 1999, Biodegradation rates for 

fuel hydrocarbons and chlorinated solvents in groundwater: 

Bioremediation Journal, v. 3, no. 4, p. 337–362.

Tesoriero, A.J., and Voss, F.D., 1997, Predicting the probability 

of elevated nitrate concentrations in the Puget Sound 

Basin—Implications for aquifer susceptibility and vulnerability: Ground Water, v. 35, no. 6, p. 1029–1039. 

U.S. Bureau of the Census, 1992, Statistical abstracts of the 

United States—1992 (112th ed.): Washington, D.C., U.S. 

Government Printing Office, 979 p.

U.S. Department of Commerce, 1995, Monthly station normals 

of temperature, precipitation, and heating and cooling degree 

days 1961-1990, Climatography of the United States No. 81: 

National Oceanic and Atmospheric Administration, National 

Climatic Data Center Asheville, North Carolina, accessed 

March 13, 2005, at ftp://ftp.ncdc.noaa.gov/pub/data/normals

U.S. Environmental Protection Agency, 1998, TRI data 

summary information—Top 20 chemicals with largest onsite and off-site release, 1998, original industries: accessed 

August 2, 2001, at http://www.epa.gov/tri/tri98/data/

rlmc98atold2.pdf

U.S. Environmental Protection Agency, 2003a, Map of current 

RFG areas: accessed January 7, 2004, at http://www.epa.gov/

otaq/rfg/whereyoulive.htm

U.S. Environmental Protection Agency, 2003b, List of reformulated gasoline program areas: accessed January 2003, at 

http://www.epa.gov/oms/rfgarea.htm

U.S. Environmental Protection Agency, 2004, Envirofacts Data 

Warehouse: accessed April 28, 2004, at http://www.epa.gov/

enviro/.

U.S. Geological Survey, 1999, National land cover data 1992 

[digital files]: available online at http://edc.usgs.gov//

products/landcover/nlcd.html

Vista Information Solutions, 1999, StarView Real Estate 2.6.1. 

[proprietary computer software with digital data].

Vogelmann, J.E., Howard, S.M., Yang, L., Larson, C.R., Wylie, 

B.K., and Van Driel, N., 2001, Completion of the 1990s 

National Land Cover Data Set for the conterminous United 

States from Landsat Thematic Mapper data and ancillary data 

sources: Photogrammetric Engineering & Remote Sensing, 

v. 67, p. 650-662.

Wahl, K.L., and Wahl, T.L., 1995, Determining the flow of 

Comal Springs at New Braunfels, Texas: Proceedings, Texas 

Water '95, American Society of Civil Engineers, San 

Antonio, Texas, p. 77–86 (available at http://www.usbr.gov/

wrrl/twahl/bfi/english).

Wiedemeier, T.H., Rifai, H.S., Newell, C.J., and Wilson, J.T., 

1999, Natural attenuation of fuels and chlorinated solvents in 

the subsurface: New York, John Wiley & Sons, 617 p.

Wolock, D.M., 1997, STATSGO soil characteristics for the 

conterminous United States: U.S. Geological Survey OpenFile Report 97-656, accessed June 27, 2003, at http://

water.usgs.gov/GIS/metadata/usgswrd/muid.html

Wolock, D.M., 2003, Estimated mean annual natural groundwater recharge in the conterminous United States: U.S. 

Geological Survey Open-File Report 03–311, digital data set, 

accessed February 23, 2005, at http://water.usgs.gov/lookup/

getspatial?rech48grd

Zogorski, J.S., Carter, J.M., Ivahnenko, Tamara, Lapham, 

W.W., Moran, M.J., Rowe, B.L., Squillace, P.J., and 

Toccalino, P.L., in press, The quality of our Nation’s 

waters—Volatile organic compounds in the Nation’s ground 

water and drinking-water supply wells: U.S. Geological 

Survey Circular 1292.
This publication is available online at

 http://pubs.water.usgs.gov/sir20055269

Prepared by the National Water-Quality Assessment Program,

 Rapid City, South Dakota.

Edited by Ella M. Decker.

Graphics by Connie J. Ross.

Information regarding the National Water-Quality Assessment Program

 and the National Synthesis on Volatile Organic Compounds is

 available at http://water.usgs.gov/nawqa/ and 

 http://sd.water.usgs.gov/nawqa/vocns/
Squillance and Moran—Factors Associated with Sources, Transport, and Fate of VOCs in Aquifers of the United States—SIR 2005–5269

Printed on recycled paper