Prepared in cooperation with the

New Hampshire Department of Health and Human Services and the

New Hampshire Department of Environmental Services

Estimated Probability of Arsenic in Groundwater 

from Bedrock Aquifers in New Hampshire, 2011

Scientific Investigations Report 2012–5156

U.S. Department of the Interior

U.S. Geological Survey
This page has been left blank intentionally.
Estimated Probability of Arsenic in 

Groundwater from Bedrock Aquifers in 

New Hampshire, 2011

By Joseph D. Ayotte, Matthew Cahillane, Laura Hayes, and Keith W. Robinson

Prepared in cooperation with the 

New Hampshire Department of Health and Human Services and the 

New Hampshire Department of Environmental Services

Scientific Investigations Report 2012–5156

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2012

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Suggested citation:

Ayotte, J.D., Cahillane, Matthew, Hayes, Laura, and Robinson, K.W., 2012, Estimated probability of arsenic in 

groundwater from bedrock aquifers in New Hampshire, 2011: U.S. Geological Survey Scientific Investigations Report 

2012–5156, 25 p., at http://pubs.usgs.gov/sir/2012/5156/.
iii

Acknowledgments

The authors thank Laurie Cullerot of the New Hampshire Department of Environmental Services 

for providing data on arsenic in public wells and the National Institutes of Health, National 

Cancer Institute for providing data on arsenic in private wells used in this study. Special thanks 

to B. Thomas Nolan and to Eliza Gross, U.S. Geological Survey, for technical reviews of this 

report. This project was funded in part by the New Hampshire Department of Health and Human 

Services and the New Hamphire Department of Environmental Services.
This page has been left blank intentionally.
v

Contents

Acknowledgments........................................................................................................................................iii

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

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

Methods and Data .........................................................................................................................................2

Probability Modeling ............................................................................................................................2

Data Used in the Probability Models.................................................................................................5

Probability of Arsenic in Groundwater from Bedrock Aquifers.............................................................6

Probability of Arsenic Greater Than or Equal to 1 μg/L in Groundwater.....................................6

Probability of Arsenic Greater Than or Equal to 5 μg/L in Groundwater.....................................6

Probability of Arsenic Greater Than or Equal to 10 μg/L in Groundwater.................................10

Evaluation of Model Performance ...................................................................................................11

Calibration...................................................................................................................................11

Validation.....................................................................................................................................11

Limitations of Models................................................................................................................11

Summary and Conclusions.........................................................................................................................13

References Cited..........................................................................................................................................14

Figures

1. Map showing locations and concentrations of 1,715 samples of arsenic in

groundwater from bedrock aquifer wells in New Hampshire...............................................4

2. Maps showing the model-predicted probabilities of arsenic concentrations in

groundwater from bedrock aquifers in New Hampshire: A, greater than or equal to

1 microgram per liter (μg/L); B, greater than or equal to 5 μg/L; and C, greater than

or equal to 10 μg/L ........................................................................................................................7

3. Maps showing Pearson residuals for model-predicted probabilities of arsenic

concentrations in groundwater from bedrock aquifers in New Hampshire: 

A, greater than or equal to 1 microgram per liter (μg/L); B, greater than or equal to 

5 μg/L; and C, greater than or equal to 10 μg/L ......................................................................12

Tables

1. Summary statistics for arsenic concentrations in groundwater from bedrock wells

in New Hampshire .......................................................................................................................3

2. Summary of predictor variables tested in the development of the multivariate

arsenic models............................................................................................................................16

3. Summary of model coefficients, Wald p-values, exponentiated coefficients, and

standardized coefficients for the 1, 5, and 10 micrograms per liter (μg/L) -threshold

multivariate logistic regression models....................................................................................8

4. Classification tables for predicted probabilities of arsenic greater than or equal to

1, 5, and 10 micrograms per liter in groundwater from bedrock aquifers.........................10

5. Summary of evaluation statistics for the 1, 5, and 10 micrograms per liter (μg/L)

-threshold logistic regression models.....................................................................................11
vi

Conversion Factors, Datum, and Abbreviations

Multiply By To obtain

Length

meter (m) 3.281 foot (ft) 

volume

liter (L) 33.82 ounce, fluid (fl. oz)

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88).

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Altitude, as used in this report, refers to distance above the vertical datum.

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

or micrograms per liter (μg/L).

Abbreviations

AIC Akaike’s information criterion

CDC Centers for Disease Control and Prevention

HL Hosmer-Lemeshow

NHDES New Hampshire Department of Environmental Services

NHEPHT New Hampshire Environmental Public Health Tracking

NHDHHS New Hampshire Department of Health and Human Services

NWIS National Water Information System

USGS U.S. Geological Survey
Estimated Probability of Arsenic in Groundwater from 

Bedrock Aquifers in New Hampshire, 2011

By Joseph D. Ayotte1, Matthew Cahillane2, Laura Hayes1, and Keith W. Robinson1

1 U.S. Geological Survey. 

2

 New Hampshire Department of Health and Human Services.

Abstract

Probabilities of arsenic occurrence in groundwater from 

bedrock aquifers at concentrations of 1, 5, and 10 micrograms 

per liter (μg/L) were estimated during 2011 using multivariate 

logistic regression. These estimates were developed for use by 

the New Hampshire Environmental Public Health Tracking 

Program. About 39 percent of New Hampshire bedrock 

groundwater was identified as having at least a 50 percent 

chance of containing an arsenic concentration greater than 

or equal to 1 μg/L. This compares to about 7 percent of New 

Hampshire bedrock groundwater having at least a 50 percent 

chance of containing an arsenic concentration equaling or 

exceeding 5 μg/L and about 5 percent of the State having 

at least a 50 percent chance for its bedrock groundwater to 

contain concentrations at or above 10 μg/L. The southeastern 

counties of Merrimack, Strafford, Hillsborough, and 

Rockingham have the greatest potential for having arsenic 

concentrations above 5 and 10 μg/L in bedrock groundwater.

Significant predictors of arsenic in groundwater from 

bedrock aquifers for all three thresholds analyzed included 

geologic, geochemical, land use, hydrologic, topographic, and 

demographic factors. Among the three thresholds evaluated, 

there were some differences in explanatory variables, but 

many variables were the same. More than 250 individual 

predictor variables were assembled for this study and tested 

as potential predictor variables for the models. More than 

1,700 individual measurements of arsenic concentration from 

a combination of public and private water-supply wells served 

as the dependent (or predicted) variable in the models.

The statewide maps generated by the probability models 

are not designed to predict arsenic concentration in any single 

well, but they are expected to provide useful information in 

areas of the State that currently contain little to no data on 

arsenic concentration. They also may aid in resource decision 

making, in determining potential risk for private wells, and in 

ecological-level analysis of disease outcomes. The approach 

for modeling arsenic in groundwater could also be applied 

to other environmental contaminants that have potential 

implications for human health, such as uranium, radon, 

fluoride, manganese, volatile organic compounds, nitrate, 

and bacteria.

Introduction

Approximately 40 percent of New Hampshire’s 

population depends on domestic wells for water supply, 

and more than 75 percent of those wells are drilled bedrock 

wells (U.S. Census Bureau, 1999). Arsenic concentrations 

above the Federal and State limit for safe drinking water 

of 10 micrograms per liter (μg/L) for public water supplies 

affect 20 to 30 percent of all private bedrock wells in New 

Hampshire (Ayotte and others, 2003; Peters and Blum, 2003; 

Moore, 2004). In the three southeast New Hampshire counties 

of Rockingham, Strafford, and Hillsborough, private drinkingwater supplies for more than 40,000 people are estimated 

to have arsenic concentrations above the 10 μg/L limit 

(Montgomery and others, 2003). As the population of New 

Hampshire continues to grow, reliance on private bedrock 

wells for water supply is expected to increase, potentially 

exposing more residents to groundwater that has arsenic 

concentrations greater than 10 μg/L.

A recent study of arsenic in bedrock aquifer wells in the 

New England region used a model to identify areas having 

a probability of arsenic concentrations equal to or exceeding 

5 μg/L in drinking-water wells (Ayotte and others, 2006). 

About 5.3 percent of the New Hampshire portion of that 

area was classified as having concentrations of arsenic in 

bedrock aquifer wells equal to or above 5 μg/L. An increased 

probability of arsenic in groundwater was indicated by the 

presence of certain source rocks, arsenic concentrations in 

stream sediments, areas of Pleistocene marine inundation, 

proximity to intrusive granitic plutons, and hydrologic 

and landscape variables (related to increased groundwater 

residence time).
2 Estimated Probability of Arsenic in Groundwater from Bedrock Aquifers in New Hampshire, 2011

The New Hampshire Department of Health and Human 

Services (NHDHHS) and the New Hampshire Department 

of Environmental Services (NHDES) developed the New 

Hampshire Environmental Public Health Tracking (NHEPHT) 

Program (New Hampshire Department of Health and Human 

Services, 2011). The NHEPHT Program is part of the National 

Environmental Public Health Tracking Program funded by the 

Centers for Disease Control and Prevention (CDC) to improve 

public health by providing science-based information about 

the presence of and trends in environmentally related diseases 

(Centers for Disease Control and Prevention, 2011). A focus 

area for the NHEPHT Program is that of understanding the 

occurrence of arsenic in both public and private drinkingwater supplies throughout the State. To further understand 

arsenic in private drinking-water supplies, the NHDHHS, 

NHDES, and the U.S. Geological Survey (USGS) conducted 

a cooperative study to develop models for assessing the 

probability of arsenic in groundwater from wells in bedrock 

aquifers. These models are similar to one developed for New 

England (Ayotte and others, 2006), but they incorporate data 

specific to New Hampshire in order to improve the probability 

assessments of arsenic for the State. The ability to more 

accurately predict the probability of arsenic occurrence in 

water from the bedrock aquifers is designed to assist public 

health efforts by providing citizens, government agencies, 

and researchers with state-of-the-art information on arsenic 

contamination in bedrock groundwater.

The objectives of this study were (1) to assemble arsenic 

data from bedrock aquifer wells and possible descriptors of 

sources of arsenic and (2) to develop predictive probability 

models for arsenic occurring in bedrock aquifer wells in 

New Hampshire at or exceeding concentration thresholds 

of 1, 5, and 10 μg/L. The results from the study support the 

goals of the NHEPHT Program and are presented in this 

report. The geospatial data representing the probability models 

can be used as a tool for resource decision-making and risk 

assessment; they also may have value for ecological-level 

analysis of disease outcomes. In this light, these datasets are 

available on the Internet from the USGS at http://pubs.usgs.

gov/sir/2012/5156/ and are intended to be available through 

the online databases of the CDC and the NHEPHT programs.

Methods and Data

Probabilities of arsenic occurrence in bedrock 

groundwater were estimated using multivariate logistic 

regression models (“probability models”) similar to models 

described by Ayotte and others (2006). The probability models 

were developed using measurements of arsenic from public 

and private wells as the dependent (or predicted) variable, 

and using a variety of geologic, geochemical, hydrologic, and 

anthropogenic data as the independent (predictor) variables 

(Ayotte and others, 2006; Harte and others, 2008). Logistic 

regression models were used because they can make use of 

censored data—data reported as “less than” some laboratory 

reporting limit.

Probability Modeling

Probability models for predicting arsenic concentrations 

that were greater than or equal to 1, 5, and 10 μg/L in bedrock 

wells were developed in order to produce and compare 

individual threshold-level maps. The models also allow 

researchers to explore the possibility that the explanatory 

variables selected may differ among the various models. 

These three thresholds were chosen because they represent 

common arsenic reporting levels in water in the State and 

because 10 μg/L is the standard for safe drinking water 

with which public water supplies in the United States must 

comply. The multivariate logistic regression techniques used 

to generate the probability values are well suited for modeling 

censored dependent-variable data because data that are below 

reporting limits can be used directly without having to modify 

or substitute values (Helsel and Hirsch, 1992; Hosmer and 

Lemeshow, 2000; Helsel, 2005). The well-water arsenic 

concentration data (dependent data) include censored data that 

were reported as below laboratory reporting levels (LRLs). 

How censored data were handled is described in more detail in 

the “Data Used in the Probability Models” section. The model 

takes the form:

P[ | ]

( ... )

( ... ) y x

e

e

o k k

o k k

x x x

x x x = = +

+ + + +

+ + + + 1

1

1 1 2 2

1 1 2 2

β β β β

β β β β (1)

where

P is the probability of observing the event,

y is an indicator (threshold) variable (“y = 1” 

denoting an event or measurement greater 

than or equal to a specific value (such as 

1 , 5 , and 10 μg/L), and “y = 0” denoting 

a non-event or measurement less than a 

specific threshold),

x1, x2,...xk are explanatory or independent variables, and

	β0

, β1,..., βk are unknown parameters (coefficients) to 

be estimated.

The exponential of a parameter estimate (exp(βi)) specifies the 

proportional increase in the odds of an arsenic concentration 

being above the modeled threshold per unit increase in 

the explanatory variable. An exp( β) value greater than 1 

represents an increasing effect of the parameter, and values 

less than 1 represent a decreasing effect. Threshold values of 

1, 5, and 10 μg/L were modeled to identify areas of the State 

where the probabilities are high for finding low-level (greater 

than or equal to 1 μg/L) and high-level (greater than or equal 

to 10 μg/L) arsenic contamination in groundwater. Probability 

models developed with higher thresholds are typically more 
Methods and Data 3

uncertain since the probability of an “event” (a measurement 

of arsenic concentration greater than or equal to 10 μg/L, for 

example) is smaller and the corresponding binomial variance 

is greater.

The SAS System statistical software was used to model 

the probabilities using backwards selection followed by 

selective evaluation of variables (SAS Institute, Inc., 2008). 

Akaike’s information criterion (AIC) was used to indicate the 

overall goodness of fit for models tested at each threshold for 

the dependent variable. AIC is not limited to nested models, 

and it trades off improving a model by adding variables with 

imposing a penalty for adding too many variables (Helsel 

and Hirsch, 1992). Smaller values of AIC indicate a better 

model. The generalized r-squared is an overall metric for 

model performance that is related to AIC, and is based on 

the likelihood ratio for testing the null hypothesis that all 

model coefficients are equal to zero (Allison, 1999). More 

specifically, the generalized r-squared value utilizes the ratio 

of the log likelihood of the intercept-only model divided 

by that of the specified model. This quantity, however, 

achieves a maximum of less than one for discrete models; 

thus, a re-scaled quantity, the “max re-scaled r-squared,” 

is the original r-squared value divided by the upper limit 

of the r-squared value; it is generally somewhat larger than 

the original r-squared, and it can achieve a maximum value 

of one.

The Hosmer-Lemeshow goodness-of-fit test (HL) was 

used to compare observed to fitted values for the model, 

and the Wald probability was used to test individual model 

variables, using a 0.10 significance level (Hosmer and 

Lemeshow, 2000). Standardized coefficients for model 

variables also were computed, so that relative importance 

of model variables could be compared directly, utilizing 

indirect calculations of the standard deviation of the predicted 

logit (Menard, 2002). Modeled variable interactions were 

tested because the effect of an independent variable on 

the dependent variable can depend on the value of another 

independent variable. Model discrimination is the capability 

of the probability model to discriminate between wells having 

arsenic concentrations greater than the thresholds and wells 

having arsenic concentrations less than the threshold; this 

was quantified using the measure of concordance (c statistic), 

which is the area under the Receiver Operating Characteristics 

curve (Hosmer and Lemeshow, 2000). The closer the c statistic 

is to 1, the better the model is at discriminating the correct 

outcome. A model for which the c statistic is equal to 0.5 

suggests no discrimination (50 percent chance of getting 

the correct classification). Models for which the c statistic 

ranges from 0.7 and 0.8 are considered to have acceptable 

discrimination, whereas those for which the c statistic ranges 

from 0.8 to 0.9 are considered to have excellent discrimination 

(Hosmer and Lemeshow, 2000).

We computed the overall rate of correct classification, 

the sensitivity (the rate of predicting a true event), and the 

specificity (the rate of predicting a true non-event), based 

on a cut point of 0.5 for the predicted probability. Thus, the 

value 0 was assigned if the prediction was < 0.5, and the 

value 1 was assigned if P Yj

∧

( ) = 1 was ≥ 0.5. Sensitivity and 

specificity cannot be compared directly among the models 

because of differences in the number of events and non-events 

for each threshold level. The results of the classification favor 

the group with the larger number of samples. Thus, as the 

concentration threshold increases from 1 to 10 micrograms 

per liter, the number of events (observations with arsenic 

concentations greater than or equal to the threshold) decreases, 

the percent of correct event predictions decreases, and the 

percent of correct non-event predictions increases.

A calibration dataset of about 1,500 arsenic 

measurements (85 percent of the entire dataset) was used to 

develop the initial model, and a randomly selected validation 

dataset of about 250 (15 percent of the entire dataset) 

measurements was withheld to test (or validate) model 

performance. The calibration data and the validation data 

were combined for the final model. Multicollinearity was 

assessed using the tolerance statistic, and it was considered 

not problematic if values were greater than about 0.4. Finally, 

Pearson residuals were used to indicate where the models 

predicted well, and where overprediction and underprediction 

were occurring.

Table 1. Summary statistics for arsenic concentrations in groundwater from bedrock wells in New Hampshire. 

[NHDES, New Hampshire Department of Environmental Services; PSW, public-supply well; NIH, National Institutes of Health; NEBCS, New England 

Bladder Cancer Study; SENH, Southeast New Hampshire; PRW, private wells; USGS, U.S. Geological Survey; NWIS, National Water Information System; 

<, less than; --, no data available ]

Data source Type of data

Number 

of 

samples

Maximum Median Minimum

Percent of wells with arsenic 

greater than or equal to 

(micrograms per liter)

1 5 10

NHDES PSW Non-random 954 5,300 2.50 < 1 ,< 5 78 37 23

NIH NEBCS Population random 399 295.6 1.00 0.004 50 28 18

SENH PRW Geographic random 352 215.0 2.00 < 1 59 33 21

USGS NWIS Geographic random 10 6.0 < 1 < 1 20 20 0

All wells -- 1,715 5,300 2 <1, < 5 66 34 21
4 Estimated Probability of Arsenic in Groundwater from Bedrock Aquifers in New Hampshire, 2011

Ayotte_fig 01

EXPLANATION

Town boundaries

MASSACHUSETTS

0 20 40 60 MILES

0 20 40 60 KILOMETERS

< 5 (some data with higher reporting limit) 

< 1

1 to < 5

5 to < 10

10 or greater

Arsenic, in micrograms per liter

VERMONT

MAINE

NEW HAMPSHIRE ROCKINGHAM

COUNTY

HILLSBOROUGH

COUNTY

COOS

COUNTY

GRAFTON

COUNTY CARROLL

COUNTY

BELKNAP

COUNTY

STRAFFORD

MERRIMACK CO

SULLIVAN

COUNTY COUNTY CHESHIRE

COUNTY

Figure 1. Locations and concentrations of 1,715 samples of arsenic in 

groundwater from bedrock aquifer wells in New Hampshire.
Methods and Data 5

Data Used in the Probability Models

The dependent-variable data for the probability models 

consisted of concentrations of arsenic in water samples from 

public and private supply wells located in bedrock aquifers. 

In total, 1,715 arsenic concentration samples from four data 

sources were used in the study (table 1; fig. 1). By contrast, the 

New Hampshire portion of the earlier arsenic model covering 

the entire New England region used arsenic measurements 

from water samples from 937 wells (Ayotte and others, 

2003). Arsenic measurements from public water-supply wells 

comprise 56 percent of the data, with the remainder of the 

measurements being from private wells. The arsenic data were 

from multiple sources (table 1) and were stored in the USGS 

National Water Information System (NWIS) database, as 

appropriate. Data were censored at multiple reporting levels 

and were handled as described below. About 28 percent of the 

data were reported as < 1 μg/L, and 8 percent were reported as 

< 5 μg/L. For models in which the threshold was 5 or 10 μg/L, 

all data were used as reported—that is, data reported as < 5 

were assigned to the < 5 category or to the < 10 category. For 

the 1 μg/L threshold model, data reported as < 5 μg/L were 

deleted before developing the model, since it is not possible 

to determine whether these values were greater or less than 

1 μg/L. Selected summary statistics and the percentage of 

samples that equaled or exceeded 1, 5, and 10 μg/L are shown 

in table 1. Some of the data used in the current modeling were 

from studies that randomly selected wells to characterize 

arsenic occurrence in specific geographic areas, whereas 

other data, which are not random, were selected based on 

criteria specific to how representative they are of the generally 

accepted chemistry data for bedrock aquifer wells. All data 

are assumed to be independent and appropriate (they do not 

violate model assumptions) for use in this type of model.

Independent (predictor) variables used to develop 

the models included information on geologic, hydrologic, 

geochemical, land use, topographic, and demographic 

features (table 2 at back of report). More than 250 individual 

predictor variables were assembled for this study and tested 

as potential predictors for the model. Many of the variables 

were similar to or the same as variables used for the regional 

New England arsenic model (Ayotte and others, 2006). All 

predictor variables were limited to mapped features that 

could be represented using a Geographic Information System. 

These mapped features varied in scale ranging from 1:24,000 

to 1:500,000.

Many predictor variables were binary variables 

(indicating whether a sampled well was in or out of a mapped 

area) representing geologic information characterized 

either by bedrock geologic unit or by information related 

to the depositional history or lithogeochemistry of the rock 

units (Lyons and others, 1997; Robinson and Kapo, 2003; 

Robinson and Ayuso, 2004; Robinson and Ayotte, 2007). 

Other predictor variables were surrogates for factors or 

processes that can affect arsenic solubility and mobility. 

For example, one surrogate variable—areas of Pleistocene 

marine inundation—was intended to represent likely areas of 

geochemical ion-exchange processes, where the exchange of 

calcium for sodium can contribute to increased dissolution 

of calcite, resulting in increased groundwater pH, which 

is related to arsenic solubility (Ayotte and others, 2003). 

Similarly, soluble arsenic minerals may enrich areas near 

intrusive granitic plutonic rocks as a result of hydrothermal 

alteration during late-stage pegmatite formation, and may 

thereby contribute to higher arsenic conditions in groundwater 

(Peters and others, 1999).

Data for continuous variables were extracted for 

each well based on the location of that well. For example, 

generalized stream-water pH (Robinson and others, 2004), 

alkalinity (Omernik and Kinney, 1985), and information 

on soil characteristics (Wolock, 1997; U.S. Department of 

Agriculture Natural Resources Conservation Service, 2006), 

including permeability, percent organic matter, and texture, 

were evaluated in this way because these features are factors 

related to the presence of arsenic in water in other parts of the 

world (Smedley, 2003).

Hydrologic and topographic data assessed included 

precipitation, elevation, slope characteristics, recharge, and 

well-yield; these data can correlate with hydrologic factors 

such as groundwater residence time in the aquifer, and 

they also relate to the transmissive properties of the aquifer 

(Rogers, 1989; Medalie and Moore, 1995; Daly and others, 

2002; U.S. Geological Survey, 2003; Wolock, 2003; U.S. 

Geological Survey, 2004; U.S. Environmental Protection 

Agency, 2006). For some variables, the data were extracted 

based on a buffered area around the well location (such as a 

500-meter-radius circle), which is indicated in table 2 (at back 

of report). Some data from the explanatory variables were 

tested that were specific to New Hampshire and also may 

relate to groundwater residence time and arsenic occurrence. 

Such variables include the distance of wells to lineaments 

(potential bedrock fracture zones mapped from 1:1,000,000 to 

1:80,000 scale imagery) and predicted well-yield probabilities 

(Moore and others, 2002).

Proximity to surface-loaded contaminants may also affect 

arsenic mobility. These factors were characterized in terms of 

the distance to features such as roads (Dennis Fowler, New 

Hampshire Department of Transportation, written commun., 

2005) and to waste sites (such as fuel and volatile organic 

compounds waste sites) (Ellen D’Amico, New Hampshire 

Department of Environmental Services, written commun., 

2006). Demographic features such as population density 

(Environmental Systems Research Institute, 2000); landcover 

classes such as developed, agricultural, forested, and wetlands 

(Homer and others, 2007; Complex Systems Research Center, 

2001); and historic agricultural land use (Robinson and Ayotte, 

2006) were evaluated as percentages within a 500-meter 

(m) radius around the well. Larger buffers (1,000-m radius) 

were evaluated but variables based on such buffers were not 

significant in the models. 
6 Estimated Probability of Arsenic in Groundwater from Bedrock Aquifers in New Hampshire, 2011

Probability of Arsenic in Groundwater 

from Bedrock Aquifers

The probability is high (greater than 50 percent) 

that groundwater from bedrock aquifers in much of New 

Hampshire has arsenic concentrations greater than or equal to 

1 μg/L (fig. 2). High probabilities of arsenic greater than or 

equal to 5 and 10 μg/L are not widespread across the State but 

rather are focused in the southeastern counties of Merrimack, 

Strafford, Hillsborough, and Rockingham (fig. 2). Variables 

that were significant predictors of arsenic in groundwater from 

bedrock aquifers included geologic, geochemical, land use, 

hydrologic, and topographic and demographic factors (table 2 

at back of report). There were some differences in explanatory 

variables among the three thresholds evaluated but many of 

the variables were the same among the three models.

Probability of Arsenic Greater Than or Equal to 

1 μg/L in Groundwater

The probability model for arsenic concentrations greater 

than or equal to 1 μg/L in groundwater from bedrock aquifers 

in New Hampshire contained 23 significant independent 

variables, 12 of which were binary geologic variables 

(table 3). This model accurately predicted whether arsenic 

was greater than or equal to 1 μg/L or whether it was less than 

1 μg/L in 74.8 percent of the cases (table 4).

Although there were many geologic variables in this 

model, most model coefficients had a negative sign, indicating 

an inverse relation with arsenic greater than or equal to 

1 μg/L. For example, groundwater from wells drilled in the 

Massabesic Gneiss Complex (GON_Zmz) are known to have 

little or no arsenic (Montgomery and others, 2003). Two 

granitic formations—the Kinsman Granodiorite (GON_Dk2x) 

and the Winnipesaukee Tonalite (GON_Dw3A)— that are 

part of the New Hampshire Plutonic Suite, however, were 

associated with increased probability of arsenic concentrations 

greater than or equal to 1 μg/L.

Stream-sediment concentrations of arsenic and of barium, 

in addition to stream alkalinity, were positively related to 

increased probability of arsenic greater than or equal to 

1 μg/L in groundwater. Factors associated with high-yielding 

wells, including probability estimates of yield (the variable 

“probyield”) and an indicator of recharge to the land surface 

(“rechbfi”), also were positively associated with arsenic 

concentrations greater than 1 μg/L. Rainfall amounts were 

negatively related to increased probability of arsenic greater 

than or equal to 1 μg/L. Multicollinearity metrics for rainfall, 

recharge, and yield probability are close to traditionally 

acceptable limits of tolerance (< 0.4) (table 3), indicating 

somewhat strong correlation between these variables.

The model identified about 39 percent of New Hampshire 

bedrock groundwater as having a 50-percent or greater 

likelihood that arsenic concentrations are greater than or equal 

to 1 μg/L (fig. 2A). The results of this model indicate that it is 

common for concentrations of arsenic in bedrock-well water to 

be equal to or greater than 1 μg/L, and that high probabilities 

are widespread in the State, implying that no part of the State 

is without risk for arsenic at some concentration in water from 

bedrock wells.

Probability of Arsenic Greater Than or Equal to 

5 μg/L in Groundwater

The probability model for arsenic concentrations greater 

than or equal to 5 μg/L in groundwater from bedrock aquifers 

in New Hampshire contained 22 significant independent 

variables, 10 of which were binary geologic variables 

(table 3). This model accurately predicted whether arsenic 

was greater than or equal to 5 μg/L or whether it was less than 

5 μg/L in 72 percent of the cases (table 4).

Of the 10 binary geologic variables in this model, three 

(mostly granites, including the Massabesic Gneiss Complex) 

had a negative relation with arsenic greater than or equal 

to 5 μg/L. Conversely, rocks of the Berwick (CPN_SObc) 

and Eliot Formations (SSN_SOec) were associated with 

concentrations greater than or equal to 5 μg/L, similar to 

findings from the New England arsenic model (Ayotte and 

others, 2006). Other granitic rocks, such as the Kinsman 

Granodiorite (GON_Dk2x) and the Winnipesaukee Tonalite 

(GON_Dw3A) that appeared in the model for the 1 μg/Lthreshold, were also significant positive predictors for the 

5 μg/L-threshold model. Pelitic rocks of the Perry Mountain 

Formation (PRN_Sp) and of the Littleton Formation 

(PRN_Dll) also predicted arsenic greater than or equal to 

5 μg/L in groundwater from bedrock aquifers (table 2 at back 

of report and table 3).

Stream-sediment concentrations of arsenic, silica, and 

barium, in addition to stream alkalinity, were associated with 

increased probability of arsenic greater than or equal to 5 μg/L 

in groundwater from bedrock aquifers. Similarly associated 

was the part of seacoast New Hampshire that was within the 

area of Pleistocene marine inundation (MARINELIM) (table 2 

at back of report).

Factors associated with land use and land development, 

such as density of agriculture (AG_DENS), residential, 

commercial, or industrial land (gdevel), and moderately 

intense development (LU_01_DVM_5) also were positively 

related with increased probabilities of arsenic. Additionally, 

areas identified as having available public water supply were 

inversely related to arsenic greater than or equal to 5 μg/L. 

Rainfall was inversely related, as it was in the 1 μg/Lthreshold model, but multicollinearity metrics were well below 

levels that would indicate that recharge and yield probability 

were nonindependent (table 3) in this model.

About 7 percent of New Hampshire is identified by the 

model as having at least a 50 percent chance of an arsenic 

concentration in bedrock groundwater equaling or exceeding 

5 μg/L (fig. 2B). In the regional New England arsenic model 
Probability of Arsenic in Groundwater from Bedrock Aquifers 

7

Ayotte_fig 02

EXPLANATION

0 40 60 MILES 20

0 40 60 KILOMETERS 20

Predicted probability

High : 1.00Low : 0.00

A. Arsenic ≥ 1 μg/L model B. Arsenic ≥ 5 μg/L model C. Arsenic ≥ 10 μg/L model

Town boundaries

Figure 2. Model-predicted probabilities of arsenic concentrations in groundwater from bedrock aquifers in New Hampshire: A, greater than or equal to 

1 microgram per liter (μg/L); B, greater than or equal to 5 μg/L; and C, greater than or equal to 10 μg/L.
8 Estimated Probability of Arsenic in Groundwater from Bedrock Aquifers in New Hampshire, 2011

Table 3. Summary of model coefficients, Wald p-values, exponentiated coefficients, and standardized coefficients for the 1, 5, and 

10 micrograms per liter (μg/L) -threshold multivariate logistic regression models.—Continued

[Exp, exponentiated; <, less than; --, no data available]

 Variable Parameter estimate 

(B)

Wald probability 

(p-value)

Exp

(B)

Standardized 

coefficient Tolerance

Arsenic greater than or equal to 1 micrograms per liter

Intercept -9.213 0.024 -- -- --

PRN -6.208 <.0001 0.002 -0.189 0.769

GOB -3.055 <.0001 0.047 -0.168 0.883

PGN_P1m -3.677 <.0001 0.025 -0.154 0.907

GON_Zmz -3.188 <.0001 0.041 -0.247 0.833

GON_Ds1_6 -0.565 0.027 0.568 -0.051 0.760

GON_Dk2x 1.228 <.0001 3.415 0.121 0.712

GON_Dw3A 1.820 <.0001 6.172 0.139 0.749

CGN_S0b -0.632 0.001 0.531 -0.083 0.681

PGN_D1m -1.650 0.004 0.192 -0.054 0.958

CGN_S0e -0.763 0.039 0.466 -0.067 0.589

CGN_S0k -1.448 0.017 0.235 -0.080 0.587

CGN_Sobc -1.125 0.006 0.325 -0.055 0.914

P0P00DEN_K -0.0005 0.034 1.000 -0.045 0.905

PROBYIELD 0.070 0.001 1.072 0.133 0.388

RAIN7100MM -0.004 0.041 0.966 0.834 0.329

STR_ALK 0.391 <.0001 1.478 0.140 0.570

lnsscu -1.847 <.0001 0.158 -0.150 0.635

lnsssr -2.593 <.0001 0.075 -0.135 0.551

lnssas 1.060 <.0001 2.887 0.204 0.512

lnssba 4.787 <.0001 119.905 0.196 0.553

NEARUSTAST -0.0004 0.011 1.000 -0.059 0.852

RECHBFI 0.004 0.069 1.005 0.071 0.394

gtrans -0.527 0.002 0.591 -0.067 0.976

Arsenic greater than or equal to 5 micrograms per liter

Intercept -30.833 0.006 -- -- --

GOB -2.276 0.028 0.103 -0.115 0.848

PGN_P1m -2.522 0.016 0.080 -0.097 0.938

GON_Zmz -3.356 <.0001 0.035 -0.240 0.875

PRN_D11 1.697 0.009 5.457 0.047 0.956

PRN_Sp 0.779 0.015 2.180 0.042 0.936

PRN_Srl 0.648 0.002 1.911 0.057 0.909

CPN_S0bc 0.674 0.036 1.962 0.037 0.916

GON_Dk2x 1.292 <.0001 3.639 0.117 0.799

GON_Dw3A 1.721 <.0001 5.588 0.121 0.840

SSN_S0ec 1.819 0.023 6.166 0.049 0.968

RAIN7100MM -0.005 0.000 0.995 -0.094 0.690

MARINELIM 1.040 <.0001 2.830 0.109 0.514

STR_ALK 0.203 0.009 1.225 0.067 0.594

AG_DENS 0.049 0.003 1.050 0.069 0.613
Probability of Arsenic in Groundwater from Bedrock Aquifers 9

Table 3. Summary of model coefficients, Wald p-values, exponentiated coefficients, and standardized coefficients for the 1, 5, and 

10 micrograms per liter (μg/L) -threshold multivariate logistic regression models.—Continued

[Exp, exponentiated; <, less than; --, no data available]

 Variable Parameter estimate 

(B)

Wald probability 

(p-value)

Exp

(B)

Standardized 

coefficient Tolerance

Arsenic greater than or equal to 5 micrograms per liter—Continued

PUBWAT -0.429 0.011 0.651 -0.052 0.928

lnssas 0.841 <.0001 2.318 0.149 0.558

lnsssi 5.704 0.011 299.933 0.065 0.592

lnssba 1.988 0.002 7.302 0.075 0.803

gdevel 0.481 0.009 1.618 0.052 0.862

LU01_DVM_5 0.021 0.065 1.021 0.040 0.698

NURE_PH -0.689 0.002 0.502 -0.079 0.674

ratiopbcu 1.177 0.008 3.246 0.065 0.713

Arsenic greater than or equal to 10 micrograms per liter

Intercept 1.489 0.433 -- -- --

GON_Ds1_6 5.403 <.0001 222.000 0.109 0.797

CPN_S0bc 1.229 0.001 3.417 0.057 0.891

PRN_D11 3.339 <.0001 28.196 0.067 0.963

PGN_Dc1m 5.357 <.0001 212.164 0.113 0.870

PRN_Sp 1.967 <.0001 7.146 0.103 0.898

PRN_Srl 1.794 <.0001 6.015 0.121 0.839

GON_Dk2x 2.505 <.0001 12.242 0.155 0.786

GON_Dw3A 6.384 <.0001 592.296 0.160 0.835

SSN_S0ec 2.205 0.001 9.073 0.052 0.981

RAIN7100MM -0.006 0.000 0.994 -0.112 0.849

STRAT_DRIF 0.330 0.027 1.390 0.054 0.913

MARINELIN 0.849 0.001 2.337 0.082 0.637

NURE_COND 0.012 0.000 1.012 0.085 0.473

lnssas 2.195 <.0001 8.979 0.124 0.533

gdevel 0.347 0.078 1.415 0.043 0.950

SSN_Sru 1.183 0.001 3.265 0.071 0.844

AG_CLASS 0.296 0.003 1.345 0.074 0.658

GON_Zmz -2.036 0.006 0.131 -0.128 0.868

GON_Ds1_6*lnssas -2.210 <.0001 0.110 -- --

PGN_Dc1m*lnssas -2.303 <.0001 0.100 -- --

GON_Dw3A*lnssas -2.865 0.014 0.057 -- --

NURE_COND*lnssas -0.005 0.005 0.995 -- --

lnssfe -1.565 0.001 0.209 -0.090 0.903

nr500 -0.704 0.001 0.495 -0.062 0.980
10 Estimated Probability of Arsenic in Groundwater from Bedrock Aquifers in New Hampshire, 2011

Table 4. Classification tables for predicted probabilities of arsenic greater than or equal to 1, 5, and 10 micrograms per liter in 

groundwater from bedrock aquifers.

Classification criteria, for the 50 percent probability cut point

Data set Total correct predictions Model sensitivity Model specificity Number of observations

Arsenic greater than or equal to 1 microgram per liter

Calibration 74.8 92.2 41.8 1,327

Validation 74.5 90.6 38.8 216

Combined 74.8 92.3 40.9 1,543

Arsenic greater than or equal to 5 micrograms per liter

Calibration 72.1 39.7 88.8 1,443

Validation 65.0 35.2 82.6 246

Combined 71.5 38.3 88.4 1,689

Arsenic greater than or equal to 10 micrograms per liter

Calibration 79.6 15.5 96.9 1,427

Validation 80.7 21.2 95.3 264

Combined 80.4 19.2 96.6 1,691

described by Ayotte and others (2006), which also used a 

5 μg/L threshold, about 5.3 percent of the New Hampshire 

portion of the model had at least a 50 percent chance of 

equaling or exceeding 5 μg/L. The difference in these results 

is due to having new data for dependent and independent 

variables for the model that are specific to New Hampshire. 

Additionally, the modeling domain differs for the New 

England region and for the State alone. The results of the 

newer model indicate that concentrations of arsenic in bedrock 

well water equaling or exceeding 5 μg/L occur primarily 

in the southeastern and south-central portions of the State. 

However, no part of the State is without risk for arsenic at 

some concentration level (greater than or equal to 1 μg/L) in 

water from bedrock wells.

Probability of Arsenic Greater Than or Equal to 

10 μg/L in Groundwater

The probability model for arsenic concentrations greater 

than or equal to 10 μg/L in groundwater from bedrock aquifers 

in New Hampshire contained 24 significant independent 

variables, 11 of which were binary geologic variables 

(table 2 at back of report). This model accurately predicted 

whether arsenic was greater than or equal to 10 μg/L or 

whether it was less than 10 μg/L in 80.4 percent of the cases 

(table 4).

Ten of the 11 geologic variables in this model (a mix 

of granites and metamorphic rocks) had a positive relation 

with arsenic greater than or equal to 10 μg/L. Many of these 

geologic variables also appear in the models for the 1 and 

5 μg/L thresholds (table 3). The Massabesic Gneiss Complex 

was the only lithology that was inversely related with high 

arsenic in groundwater, similar to the results from the previous 

two threshold models (table 3).

Stream-sediment concentrations of arsenic and stream 

conductivity also were associated with increased probability 

of arsenic concentrations greater than or equal to 10 μg/L in 

groundwater. Concentrations of iron in stream sediments were 

inversely related to the probability of high arsenic. The area 

of seacoast New Hampshire that was inundated by the ocean 

just after the retreat of Pleistocene glaciers and areas underlain 

by glacial stratified drift deposits also had an increased 

probability of having arsenic greater than or equal to 10 μg/L.

Factors associated with land use and land development, 

such as agricultural land use (AG_CLASS), residential, 

commercial, or industrial land (gdevel), and the presence of 

roads near wells (nr500) also were associated with increased 

probabilities of high arsenic concentrations. Additionally, 

areas identified as having available public water supply 

were inversely related to arsenic concentrations greater 

than or equal to 10 μg/L. Rainfall was inversely related, as 

it was in the models for the 1 and 5 μg/L thresholds, and 

multicollinearity metrics were well below levels that would 

indicate nonindependence (table 3).

About 5 percent of New Hampshire is identified by the 

model as having at least a 50 percent chance of an arsenic 

concentration in bedrock groundwater equaling or exceeding 

10 μg/L (fig. 2C). The results of this model indicate that it is 

common for concentrations of arsenic in bedrock well water to 

equal or to exceed 10 μg/L, but that most of the high (greater 

than 50 percent) probabilities are located in the southeastern 

and south-central portions of the State. This suggests that 

high (greater than or equal to 10 μg/L) arsenic concentrations 
Probability of Arsenic in Groundwater from Bedrock Aquifers 11

follow some spatial pattern (similar to that for the 5 μg/Lthreshold model) and that many areas of the State have some 

risk for arsenic concentrations greater than or equal to 10 μg/L 

in water from bedrock wells.

Evaluation of Model Performance

An evaluation of how reliably the three probability 

models can predict the dependent variable is needed in order 

to understand model performance. Part of this evaluation is to 

determine how well the model predicts the dependent variable 

using data that were not used (withheld as validation data) 

when developing the model. After calibration and validation, a 

final model was developed using the combined calibration and 

validation data.

Calibration

During the calibration step, the predictor variables used 

in each of the models were assessed to determine whether each 

was a significant predictor based on the Wald p-value. Most 

Wald p-values for significant variables were < 0.05, although 

the threshold for acceptance was 0.1 (table 3). In order to 

determine whether the models fit the overall data, the HosmerLemeshow test was used, for which higher p-values indicate 

better model fit and that the predictions agreed on average 

with the observed probabilities (table 5). The c statistic for the 

final probability model at each of the three thresholds ranged 

from 0.757 to 0.772 (table 5), which is indicative of acceptable 

discrimination (Hosmer and Lemeshow, 2000).

Validation

Model validation can be assessed in part by examining 

model diagnostic metrics developed with the validation 

dataset (15 percent of the combined data) and then 

determining whether the model results are similar to those 

of the calibration model and of the final model. For all of the 

modeled thresholds (1, 5, and 10 μg/L), the percentages of 

correctly predicted events and non-events for the validation 

datasets were not substantially unlike those for the calibration 

datasets (table 4). For the thresholds of 1 and 5 μg/L, the 

validation model sensitivity was within 11 percent of the 

calibration model sensitivity; this increased to 27 percent 

for the 10 μg/L threshold. For specificity and total correct 

predictions, no differences were greater than 9 percent and 

most were less than 5 percent (table 4).

Pearson residuals indicate that the models for the 5 

and 10 μg/L thresholds predicted reasonably well (residuals 

ranging from -2 to 2). However, some predictions fell in 

the highest residual category, indicating underprediction 

of probabilities in some cases (figs. 3B and C). The 

model predicting the probability of arsenic occurring at 

concentrations greater than or equal to 1 μg/L in water from 

bedrock wells also predicted well, but it had noticeably 

more residuals in the lowest category. This indicates that, 

in some cases, the model overpredicted the probability 

(fig. 3A). Overall, there were few observations (only 0.58 to 

0.95 percent) where the absolute value of the Pearson residual 

exceeded 3 for each model. These appeared to be randomly 

located across the State, but most of the observations that 

were identified were located in geologic formations described 

as granite, which otherwise did not seem to be related to the 

occurrence of or high concentrations of arsenic.

Limitations of Models

The probability models developed and presented in this 

report show how the distribution of high probabilities of 

having groundwater with concentrations of arsenic exceeding 

1, 5, and 10 μg/L vary across the State of New Hampshire. 

The maps produced from the probability models do not 

predict actual concentrations nor do they accurately portray 

concentrations of arsenic in water from any given bedrock 

well. Thus, the models and maps presented here are intended 

to provide a statistical estimate of the probability that well 

water from randomly selected bedrock aquifers contains 

arsenic at various levels.

It is important for users of the probability models to 

understand that the scales of the data that went into making 

the models and maps vary from 1:24,000 to 1:500,000; 

therefore, the use of the maps at larger scales may not 

represent conditions at specific locations or at individual 

Table 5. Summary of evaluation statistics for the 1, 5, and 10 micrograms per liter (μg/L) -threshold 

logistic regression models. 

Model Generalized 

r-square

Maximum 

rescaled 

r-square

Percent 

concordant

c statistic 

(area under 

receiver operating 

characteristics 

curve)

Hosmer-Lemeshow 

(p-value)

Arsenic ≥ 1 0.2157 0.2984 77.1 0.772 0.6541

Arsenic ≥ 5 0.1776 0.2460 75.6 0.757 0.7148

Arsenic ≥ 10 0.1573 0.2449 76.9 0.770 0.3131
12 Estimated Probability of Arsenic in Groundwater from Bedrock Aquifers in New Hampshire, 2011

Ayotte_fig 03

EXPLANATION

 Less than -2-2 to less than -1-1 to less than 11 to less than 22 or greater

Pearson residual of predicted probabilityat each wellTown boundaries

A. Arsenic ≥ 1 μg/L model B. Arsenic ≥ 5 μg/L model C. Arsenic ≥ 10 μg/L model

0 40 60 MILES 20

0 40 60 KILOMETERS 20

Figure 3. Pearson residuals for model-predicted probabilities of arsenic concentrations in groundwater from bedrock aquifers in New Hampshire: A, greater than 

or equal to 1 microgram per liter (μg/L); B, greater than or equal to 5 μg/L; and C, greater than or equal to 10 μg/L.
Summary and Conclusions 13

wells. Although the probability maps (fig. 2) can be useful 

to water-resource managers to identify areas that can benefit 

from increased monitoring or to identify populations at risk, 

the models cannot determine which individual wells will be 

at risk. Only testing of individual wells for concentrations 

of arsenic in the groundwater can reliably provide that level 

of information.

The maps that were derived from the models can be used 

as tools for resource decision-making and for determining 

potential risk assessments; they may also have value for 

ecological-level analysis of disease outcomes. In this light, 

these maps are intended to be available through the databases 

of the CDC and the NHEPHT programs that are available on 

the Internet. In addition, these models represent probabilities 

based on available mapped data that relate to concentrations 

of arsenic in groundwater from bedrock aquifers. Some 

explanatory variables that are known to relate to arsenic—such 

as regional groundwater redox information, groundwater pH, 

well depth, fracture location and depth information, and other 

groundwater chemistry—were not used because they are not 

available in map form. To the extent that these features can 

be mapped in the future, it is likely that models of arsenic 

concentrations in groundwater can be improved.

Summary and Conclusions

Arsenic concentrations above the Federal and State 

human-health benchmark of 10 micrograms per liter (μg/L) 

for public drinking-water supplies affect 20 to 30 percent of all 

private bedrock wells in New Hampshire. Increased reliance 

on private bedrock wells for water supply will continue as the 

State’s population grows, thereby exposing more residents 

to groundwater having concentrations of arsenic greater 

than 10 μg/L. The New Hampshire Department of Health 

and Human Services (NHDHHS) and the New Hampshire 

Department of Environmental Services (NHDES) developed 

the New Hampshire Environmental Public Health Tracking 

(NHEPHT) Program which is supported by the Centers for 

Disease Control and Prevention (CDC) to improve public 

health by providing science-based information about the 

presence of and trends in environmentally related diseases. 

A focus area for the NHEPHT Program is understanding the 

occurrence of arsenic in both public and private drinkingwater supplies throughout the State. To assist in this goal, 

the NHDHHS, NHDES, and the U.S. Geological Survey 

(USGS) conducted a study to develop statistical models 

of the probability of arsenic in groundwater from wells in 

bedrock aquifers. These probability models are similar to one 

developed for the entire New England region (Ayotte and 

others, 2006), but the newer models incorporate additional 

data specific to New Hampshire in order to improve the 

probability assessments of arsenic for the State.

Probabilities of arsenic occurrence in bedrock 

groundwater at concentrations greater than or equal to 1, 5, 

and 10 μg/L were estimated using multivariate logistic 

regression modeling (“probability models”). The probability 

models were developed from arsenic measurements in 

water from public and private wells as the dependent 

(or predicted) variable, and from a variety of geologic, 

geochemical, hydrologic, and land use data as the independent 

(predictor) variables. The study used a total of 1,715 arsenic 

concentrations from four data sources for the dependent 

variable. Arsenic concentrations from public water supply 

wells comprise 56 percent of these 1,715 sample data, with the 

remaining 44 percent of these data from private wells. More 

than 250 individual predictor variables were assembled for this 

study and tested as potential model predictors.

About 39 percent of the land area of New Hampshire 

is identified as having at least a 50 percent chance of arsenic 

concentrations in bedrock groundwater greater than or equal 

to 1 μg/L. About 7 percent of New Hampshire is identified as 

having at least a 50 percent chance of arsenic concentrations 

in bedrock groundwater equaling or exceeding 5 μg/L, and 

about 5 percent of the State is identified as having at least a 

50 percent chance for concentrations greater than or equal to 

10 μg/L. The southeastern counties of Merrimack, Strafford, 

Hillsborough, and Rockingham have the greatest potential for 

having arsenic concentrations greater than or equal to 5 μg/L 

and 10 μg/L.

Significant predictors of arsenic in groundwater from 

bedrock aquifers for all three thresholds analyzed included 

geologic, geochemical, land use, hydrologic, topographic, 

and demographic factors. There were some differences in 

explanatory variables among the three thresholds evaluated 

but many were the same among the three models. The 

explanatory variables were both positively and negatively 

related to the probability of arsenic occurrence. For example, 

the Massabesic Gneiss Complex in south central New 

Hampshire was negatively related to arsenic occurrence. 

Predictor variables that were positively related to arsenic 

in groundwater included stream-sediment concentrations 

of arsenic, stream alkalinity, and the area of seacoast New 

Hampshire that was inundated by the ocean just after the 

retreat of Pleistocene glaciers.

The maps of arsenic probability at the three thresholds 

can be used as a tool for resource decision-making and for 

determining potential risk. They may also have value for 

ecological-level analysis of disease outcomes. Although the 

maps are not designed for predicting arsenic in any single 

well—only actual water sampling and analysis can thus 

identify arsenic—they also provide information about areas 

of the State that currently contain little to no data about 

arsenic concentrations. The approach for modeling arsenic 

in groundwater could also be applied to other environmental 

contaminants—such as uranium, radon, fluoride, manganese, 

volatile organic compounds, nitrate, and bacteria—that have 

potential implications for human health.
14 Estimated Probability of Arsenic in Groundwater from Bedrock Aquifers in New Hampshire, 2011

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Chemistry of stream sediments and surface waters in 

New England: U.S. Geological Survey Open-File Report 

04–1026, 18 p.

Rogers, R.J., 1989, Geochemical comparison of ground water 

in areas of New England, New York, and Pennsylvania: 

Ground Water, v. 27, p. 690–712.

SAS Institute, Inc., 2008, SAS OnlineDoc 9.1.3: SAS 

Institute, Inc., accessed October 31, 2011, at http://support.

sas.com/onlinedoc/913/docMainpage.jsp.

Smedley, P.L., 2003, Arsenic in groundwater—South and east 

Asia, in Welch, A.H., and Stollenwerk, K.G., eds., Arsenic 

in groundwater—Geochemistry and occurrence: Boston, 

Kluwer Academic Publishers, p. 179–209.

U.S. Census Bureau, 1999, Historical census of housing 

tables—Sources of water: U.S. Census Bureau, accessed 

October 2002, at http://www.census.gov/hhes/www/

housing/census/historic/water.html.

U.S. Department of Agriculture, Natural Resources 

Conservation Service, 2006, Soil survey geographic 

(SSURGO) database for New Hampshire: U.S. Department 

of Agriculture, Natural Resources Conservation Service, 

accessed October 31, 2006, at http://soildatamart.nrcs.

usda.gov.

U.S. Environmental Protection Agency, 2006, National 

hydrography dataset plus: U.S. Environmental Protection 

Agency, accessed September 6, 2007, at http://www.epa.

gov/waters.

U.S. Geological Survey, 2003, 1:250,000-scale digital 

elevation model: U.S. Geological Survey National Elevation 

Dataset, accessed April 23, 2003, at http://eros.usgs.gov/#/

Guides/dem.

U.S. Geological Survey, 2004, 1:24,000-scale digital elevation 

model: U.S. Geological Survey National Elevation Dataset, 

accessed October 5, 2006, at http://seamless.usgs.gov/

website/seamless/viewer.php.

Wolock, D.M., 1997, STATSGO soil characteristics for the 

conterminous United States: U.S. Geological Survey OpenFile Report 97–656, at http://water.usgs.gov/GIS/metadata/

usgswrd/XML/muid.xml.

Wolock, D.M., 2003, Hydrologic landscape regions of the 

United States: U.S. Geological Survey Open-File Report 

03–145, available at http:// pubs.er.usgs.gov/publication/

ofr03145.
16 Estimated Probability of Arsenic in Groundwater from Bedrock Aquifers in New Hampshire, 2011 Table 2. Summary of predictor variables tested in the development of the multivariate arsenic models.—Continued [N, number of samples] Variable Description Type

N Minimum Median Maximum Source

Geology

AGB Bronson Hill sequence; alkali granite Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

AGN NH-Maine sequence province; alkali granite Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

AGN_J1r NH-Maine sequence province; alkali granite; 

J1r

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

AGN_J4hx NH-Maine sequence province; alkali granite; 

J4hx

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

AGN_J7x NH-Maine sequence province; alkali granite; 

J7x

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

AGN_Jc1b NH-Maine sequence province; alkali granite; 

Jc1b

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

AGN_Jo1h NH-Maine sequence province; alkali granite; 

Jo1h

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

AGN_Kc1b NH-Maine sequence province; alkali granite; 

Kc1b

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

AGW Waits River-Gile Mountain sequence; alkali 

granite

Binary 1715 0.00 0.00 0.00 Robinson and Kapo, 2003, USGS OFR 2003–225

B Bronson Hill sequence (geologic province) Binary 1715 0.00 0.00 0.00 Robinson and Kapo, 2003, USGS OFR 2003–225

CGN NH-Maine sequence province; calcgranofels Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

CGN_SOb NH-Maine sequence province; calcgranofels; 

Sob

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

CGN_SObc NH-Maine sequence province; calcgranofels; 

Sobc

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

CGN_SOe NH-Maine sequence province; calcgranofels; 

Soe

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

CGN_SOk NH-Maine sequence province; calcgranofels; 

Sok

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

CGN_Sm NH-Maine sequence province; calcgranofels; 

Sm

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

CGN_Srlp NH-Maine sequence province; calcgranofels; 

Srlp

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

CPN NH-Maine sequence province; calcpelite Binary 1715 0.00 0.00 0.00 Robinson and Kapo, 2003, USGS OFR 2003–225

CPN_SObc NH-Maine sequence province; calcpelite; 

Sobc

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225
Table 2 17

Table 2. Summary of predictor variables tested in the development of the multivariate arsenic models.—Continued[N, number of samples]

Variable Description Type N Minimum Median Maximum Source

Geology—Continued

FVN NH-Maine sequence province; felsic volcanics Binary 1715 0.00 0.00 0.00 Robinson and Kapo, 2003, USGS OFR 2003–225

GOB Bronson Hill sequence; granite, other Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

GOB_Db2b Bronson Hill sequence; granite, other; Db2b Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

GOB_Dk2x Bronson Hill sequence; granite, other; Dk2x Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

GOB_Oh2h Bronson Hill sequence; granite, other; Oh2h Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

GOB_Oo1h Bronson Hill sequence; granite, other; Oo1h Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

GON NH-Maine sequence province; granite, other Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

GON_D1b NH-Maine sequence province; granite, other; 

D1b

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

GON_Db2b NH-Maine sequence province; granite, other; 

Db2b

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

GON_Dk2x NH-Maine sequence province; granite, other; 

Dk2x

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

GON_Ds1_6 NH-Maine sequence province; granite, other; 

Ds1_6

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

GON_Dw3A NH-Maine sequence province; granite, other; 

Dw3A

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

GON_OZrb NH-Maine sequence province; granite, other; 

Ozrb

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

GON_Sa2x NH-Maine sequence province; granite, other; 

Sa2x

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

GON_Zmz NH-Maine sequence province; granite, other; 

Zmz

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

GOW Waits River - Gile Mountain sequence; 

granite, other

Binary 1715 0.00 0.00 0.00 Robinson and Kapo, 2003, USGS OFR 2003–225

MAB Bronson Hill sequence; mafic rocks Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

MAB_Dlv Bronson Hill sequence; mafic rocks; Dlv Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

MAB_Oalx Bronson Hill sequence; mafic rocks; Oalx Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

MAB_Oaux Bronson Hill sequence; mafic rocks; Oaux Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

MAN NH-Maine sequence province; mafic rocks Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

MAN_De9 NH-Maine sequence province; mafic rocks; 

De9

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225
18 Estimated Probability of Arsenic in Groundwater from Bedrock Aquifers in New Hampshire, 2011 Table 2. Summary of predictor variables tested in the development of the multivariate arsenic models.—Continued [N, number of samples] Variable Description Type

N Minimum Median Maximum Source

Geology—Continued

MAN_J5 NH-Maine sequence province; mafic rocks; 

J5

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

MAN_J8 NH-Maine sequence province; mafic rocks; 

J8

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

MAN_J9A NH-Maine sequence province; mafic rocks; 

J9A

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

MAW Waits River-Gile Mountain sequence; mafic 

rocks

Binary 1715 0.00 0.00 0.00 Robinson and Kapo, 2003, USGS OFR 2003–225

MEB Bronson Hill sequence; metamorphic rocks, 

other

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

MEN NH-Maine sequence province; metamorphic 

rocks, other

Binary 1715 0.00 0.00 0.00 Robinson and Kapo, 2003, USGS OFR 2003–225

MEN_Sp NH-Maine sequence province; metamorphic 

rocks, other; Sp

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

N New Hampshire - Maine sequence (geologic 

province)

Binary 1715 0.00 0.00 0.00 Robinson and Kapo, 2003, USGS OFR 2003–225

OtherB1 All other bedrock units, undifferentiated Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

PGN NH-Maine sequence province; peraluminous 

granite

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

PGN_D1m NH-Maine sequence province; peraluminous 

granite; D1m

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

PGN_Dc1m NH-Maine sequence province; peraluminous 

granite; Dc1m

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

PGN_P1m NH-Maine sequence province; peraluminous 

granite; P1m

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

PGN_PM1m NH-Maine sequence province; peraluminous 

granite; PM1m

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

PRB Bronson Hill sequence; pelitic rocks Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225PRB_Dl Bronson Hill sequence; pelitic rocks; Dl Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225PRB_Sfr Bronson Hill sequence; pelitic rocks; Sfr Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225PRB_Spr Bronson Hill sequence; pelitic rocks; Spr Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225PRN NH-Maine sequence province; pelitic rocks Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225PRN_DSlr NH-Maine sequence province; pelitic rocks; 

DSlr

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225
Table 2 19

Table 2. Summary of predictor variables tested in the development of the multivariate arsenic models.—Continued[N, number of samples]

Variable Description Type N Minimum Median Maximum Source

Geology—Continued

PRN_Dll NH-Maine sequence province; pelitic rocks; 

Dll

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

PRN_Dlu NH-Maine sequence province; pelitic rocks; 

Dlu

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

PRN_SObg NH-Maine sequence province; pelitic rocks; 

Sobg

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

PRN_Sp NH-Maine sequence province; pelitic rocks; 

Sp

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

PRN_Srl NH-Maine sequence province; pelitic rocks; 

Srl

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

PRW Waits River-Gile Mountain sequence; pelitic 

rocks

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

PRW_Sfr Waits River-Gile Mountain sequence; pelitic 

rocks; Sfr

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

SSB Bronson Hill sequence; sulfidic schists Binary 1715 0.00 0.00 0.00 Robinson and Kapo, 2003, USGS OFR 2003–225

SSB_Op Bronson Hill sequence; sulfidic schists; Op Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

SSB_Ssf Bronson Hill sequence; sulfidic schists; Ssf Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

SSN NH-Maine sequence province; sulfidic 

schists

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

SSN_SOec NH-Maine sequence province; sulfidic 

schists; Soec

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

SSN_Sru NH-Maine sequence province; sulfidic 

schists; Sru

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

SSN_Ssf NH-Maine sequence province; sulfidic 

schists; Ssf

Binary 1715 0.00 0.00 1.00 Robinson and Kapo, 2003, USGS OFR 2003–225

W Waits River-Gile Mountain sequence 

(geologic province)

Binary 1715 0.00 0.00 0.00 Robinson and Kapo, 2003, USGS OFR 2003–225

Hydrology

CLAYAVE Average clay content, in percent Numeric 1715 0.00 5.21 18.31 NRCS Soil survey STATSGO (via D. Wolock, 

USGS)

DIST2PLUT Distance to a pluton Numeric 1715 0.00 390.00 11217.00 Derived from Robinson and Kapo, 2003, USGS OFR 

2003–225

DIST2STRM Distance to nearest stream, in meters Numeric 1715 0.00 536.99 7803.22 The downslope distance along the flow path (using 

a flow direction grid) from the well to the nearest stream segment in NHDPlus (1:100,000-scale).
20 Estimated Probability of Arsenic in Groundwater from Bedrock Aquifers in New Hampshire, 2011 Table 2. Summary of predictor variables tested in the development of the multivariate arsenic models.—Continued [N, number of samples] Variable Description Type

N Minimum Median Maximum Source

Hydrology—Continued

MARINELIM Within Pleistocene marine inundation limit Binary 1715 0.00 0.00 1.00 Modified from Olcott, P.G., 1995, USGS HA730–M; 

http://pubs.usgs.gov/ha/ha730/ch_m/

NEARLIN20K Distance to nearest lineament (low altitude) Numeric 1715 1.34 845.78 5294.53 Moore, R.B., and others, 2002, USGS PP 1660; 

http://pubs.usgs.gov/pp/pp1660/

NEARLIN80K Distance to nearest lineament (high altitude) Numeric 1715 0.28 1024.27 5743.19 Moore, R.B., and others, 2002, USGS PP 1660; 

http://pubs.usgs.gov/pp/pp1660/

NEARLINSAT Distance to nearest lineament (satellite) Numeric 1715 3.81 2330.64 14690.92 Moore, R.B., and others, 2002, USGS PP 1660;

 http://pubs.usgs.gov/pp/pp1660/

OMAVE Average organic matter content, in percent 

by weight.

Numeric 1715 0.01 0.90 16.06 NRCS Soil survey STATSGO (via D. Wolock, 

USGS)

PROBYIELD Well yield probability Numeric 1714 1.88 12.02 51.58 USGS Professional Paper 1660 at http://nh.water.

usgs.gov/projects/nhwellyieldprob/data.htm

RAIN7100MM Mean annual precipitation from 1971 to 

2000, in millimeters

Numeric 1710 921.20 1151.78 1622.55 PRISM climate data: http://www.prism.oregonstate.

edu/

RECHBFI Recharge base-flow index Numeric 1705 163.00 274.00 826.00 Wolock, D.M., 2003, USGS OFR 03–311; 

http://water.usgs.gov/lookup/getspatial?rech48grd.

SANDAVE Average sand content, in percent Numeric 1715 0.00 50.83 81.31 NRCS Soil survey STATSGO (via D. Wolock, 

USGS)

SILTAVE Average silt content, in percent Numeric 1715 0.00 41.61 70.87 NRCS Soil survey STATSGO (via D. Wolock, 

USGS)

STRAT_DRIF Within stratified drift boundary Binary 1715 0.00 0.00 1.00 New England Governor's conference

TEMP6190CX Mean annual temperature from 1961 to 1990, 

in degrees celsius multiplied by 10

Numeric 1715 28.00 74.00 92.00 PRISM climate data: http://www.prism.oregonstate.

edu/

WTDEPAVE Average depth to seasonally high water table, 

in feet

Numeric 1715 0.00 4.63 5.48 NRCS Soil survey STATSGO (via D. Wolock, 

USGS)

Geochemistry

NURE_ALK NURE stream alkalinity Numeric 1706 0.04 0.28 1.51 Robinson, G.R., and others, 2004, USGS OFR 

2004–1026; http://pubs.usgs.gov/of/2004/1026/

NURE_COND NURE stream conductance Numeric 1706 13.58 107.93 601.78 Robinson, G.R., and others, 2004, USGS OFR 

2004–1026; http://pubs.usgs.gov/of/2004/1026/

STR_ALK Stream alkalinity Numeric 1714 0.00 3.00 5.00 Omernik, J.M., and others 1988; http://water.usgs.

gov/owq/alkus.pdf

lnssas log NURE stream sediment arsenic concentration Numeric 1706 -0.45 1.60 3.70 Robinson, G.R., and others, 2004, USGS OFR 2004–1026; http://pubs.usgs.gov/of/2004/1026/
Table 2 21

Table 2. Summary of predictor variables tested in the development of the multivariate arsenic models.—Continued[N, number of samples]

Variable Description Type N Minimum Median Maximum Source

Geochemistry—Continued

lnssba log NURE stream sediment barium concentration Numeric 1706 5.26 5.81 6.33 Robinson, G.R., and others, 2004, USGS OFR 2004–1026; http://pubs.usgs.gov/of/2004/1026/

lnssca log NURE stream sediment calcium concentration Numeric 1701 -0.27 0.49 1.72 Robinson, G.R., and others, 2004, USGS OFR 2004–1026; http://pubs.usgs.gov/of/2004/1026/

lnsscu log NURE stream sediment copper concentration Numeric 1706 1.13 2.27 3.42 Robinson, G.R., and others, 2004, USGS OFR 2004–1026; http://pubs.usgs.gov/of/2004/1026/

lnssfe log NURE stream sediment iron concentration Numeric 1702 0.03 1.05 1.96 Robinson, G.R., and others, 2004, USGS OFR 2004–1026; http://pubs.usgs.gov/of/2004/1026/

lnssk log NURE stream sediment potassium concentration Numeric 1702 -0.24 0.57 1.17 Robinson, G.R., and others, 2004, USGS OFR 2004–1026; http://pubs.usgs.gov/of/2004/1026/

lnssmg log NURE stream sediment magnesium 

concentration

Numeric 1702 -1.81 -0.53 0.72 Robinson, G.R., and others, 2004, USGS OFR 

2004–1026; http://pubs.usgs.gov/of/2004/1026/

lnssna log NURE stream sediment sodium concentration Numeric 1701 0.44 0.72 1.19 Robinson, G.R., and others, 2004, USGS OFR 2004–1026; http://pubs.usgs.gov/of/2004/1026/

lnsspb log NURE stream sediment lead concentration Numeric 1706 2.10 3.69 5.35 Robinson, G.R., and others, 2004, USGS OFR 2004–1026; http://pubs.usgs.gov/of/2004/1026/

lnsssi log NURE stream sediment silica concentration Numeric 1701 3.99 4.26 4.41 Robinson, G.R., and others, 2004, USGS OFR 2004–1026; http://pubs.usgs.gov/of/2004/1026/

lnsssr log NURE stream sediment strontium concentration Numeric 1701 4.56 5.06 6.32 Robinson, G.R., and others, 2004, USGS OFR 2004–1026; http://pubs.usgs.gov/of/2004/1026/

lnssv log NURE stream sediment vanadium concentration Numeric 1701 2.38 3.65 4.59 Robinson, G.R., and others, 2004, USGS OFR 2004–1026; http://pubs.usgs.gov/of/2004/1026/

lnsszn log NURE stream sediment zinc concentration Numeric 1706 2.62 3.63 4.88 Robinson, G.R., and others, 2004, USGS OFR 2004–1026; http://pubs.usgs.gov/of/2004/1026/

Land use

AG_CLASS Agricultural class Numeric 1713 1.00 2.00 4.00 Robinson, G.R., Jr., and Ayotte, J.D., 2006. 

http://nh.water.usgs.gov/Publications/2006/appgeochem.092006.pdf

AG_DENS Agricultural density Numeric 1713 0.00 4.97 27.12 Robinson, G.R., Jr., and Ayotte, J.D., 2006. 

http://nh.water.usgs.gov/Publications/2006/appgeochem.092006.pdf

AG_INT Agricultural density integer value Numeric 1713 0.00 5.00 27.00 Robinson, G.R., Jr., and Ayotte, J.D., 2006. 

http://nh.water.usgs.gov/Publications/2006/appgeochem.092006.pdf
22 Estimated Probability of Arsenic in Groundwater from Bedrock Aquifers in New Hampshire, 2011 Table 2. Summary of predictor variables tested in the development of the multivariate arsenic models.—Continued [N, number of samples] Variable Description Type

N Minimum Median Maximum Source

Land use—Continued

CKNOWN_SQK Density of known waste sites Numeric 1715 0.00 0.00 13.00 Polygon and point contaminated sites from NH DES 

with proj_type = COMPLAINTS, ETHER, FUEL, IRSPILL, LAST, LUST, MOST, OPUF, or SPILL/RLS

NEARANYUST Distance to nearest underground storage tank 

(active, inactive, removed)

Numeric 1715 40.21 3427.67 40870.57 NH DES Underground Storage Tank Facilities 

(UST_SITE, 1:25,000, March 2003)

NEARCKNOWN Distance to nearest known contamination site Numeric 1715 0.01 2780.89 35427.70 Polygon and point contaminated sites from NH DES 

with proj_type = COMPLAINTS, ETHER, FUEL, IRSPILL, LAST, LUST, MOST, OPUF, or SPILL/RLS

NEARROADS Distance to nearest roadway Numeric 1715 0.08 139.84 1982.37 NHDOT, 2004, downloaded Anchor_sections_04 

and Private_Roads_04 from NHDOT's FTP site 2/3/2005; combined into one file "Nhdot_rds04"

NEARROUTES Distance to nearest route Numeric 1715 1.80 1485.90 31236.40 NHDOT, 2004, downloaded Routes_04 from 

NHDOT's FTP site 2/3/2005

NEARUSTAST Distance to nearest above or underground 

storage tank (active, inactive, removed)

Numeric 1715 40.21 3126.19 32349.11 NH DES Underground Storage Tank Facilities 

(UST_SITE) and Aboveground Storage Tabk Facilities (AST_SITE, 1:25,000, March 2003) combined

PUBSEW Areas of public sewer Binary 1715 0.00 0.00 1.00 Water Distribution Areas (Town_SW) from NHDES, 

Water Supply Engineering Bureau, 2003

PUBWAT Areas of public water supply Binary 1715 0.00 0.00 1.00 Water Distribution Areas (Town_SW) from NHDES, 

Water Supply Engineering Bureau, 2003

ROADS_SQKM Road density Numeric 1715 0.00 3.12 10.80 NHDOT, 2004, downloaded Anchor_sections_04 

and Private_Roads_04 from NHDOT's FTP site 2/3/2005; combined into one file "Nhdot_rds04"

ROUTES_SQK Route density Numeric 1715 0.00 0.63 4.28 NHDOT, 2004, downloaded Routes_04 from 

NHDOT's FTP site 2/3/2005

gagric Row crops & fruit orchards Binary 1715 0.00 0.00 1.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001

galpin Alpine (Krumholz) Binary 1715 0.00 0.00 0.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001

gbarren Disturbed or cleared/other open or 

bedrock/vegetated

Binary 1715 0.00 0.00 1.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001

gbeech Beech/oak Binary 1715 0.00 0.00 1.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001
Table 2 23

Table 2. Summary of predictor variables tested in the development of the multivariate arsenic models.—Continued[N, number of samples]

Variable Description Type N Minimum Median Maximum Source

Land Use—Continued

gbirch Paper birch/aspen Binary 1715 0.00 0.00 1.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001

gbrock Bedrock/vegetated Binary 1715 0.00 0.00 0.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001

gclear Cleared/other open Binary 1715 0.00 0.00 1.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001

gdevel Residential, commercial, or industrial Binary 1715 0.00 0.00 1.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001

gdistu Disturbed Binary 1715 0.00 0.00 1.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001

gdunes Sand dunes Binary 1715 0.00 0.00 0.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001

gfowet Forested wetlands Binary 1715 0.00 0.00 1.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001

ghardw Other hardwoods Binary 1715 0.00 0.00 1.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001

ghemlo Hemlock Binary 1715 0.00 0.00 1.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001

gmixed Mixed forest Binary 1715 0.00 0.00 1.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001

gnfwet Non-forested wetlands Binary 1715 0.00 0.00 1.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001

gopenw Open water Binary 1715 0.00 0.00 1.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001

gpines White/red pine Binary 1715 0.00 0.00 1.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001

gpitch Pitch pine Binary 1715 0.00 0.00 1.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001

gspruc Spruce/fir Binary 1715 0.00 0.00 1.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001

gtiwet Tidal wetlands Binary 1715 0.00 0.00 0.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001

gtrans Transportation Binary 1715 0.00 0.00 1.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001

gtundr Tundra Binary 1715 0.00 0.00 0.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001
24 Estimated Probability of Arsenic in Groundwater from Bedrock Aquifers in New Hampshire, 2011 Table 2. Summary of predictor variables tested in the development of the multivariate arsenic models.—Continued [N, number of samples] Variable Description Type

N Minimum Median Maximum Source

Land Use—Continued

gwetlan Open water or forested or non-forested 

wetlands

Binary 1715 0.00 0.00 1.00 Complex Systems Research Center, NH Land Cover 

Assessment 2001

lu01dvm500ge30 500-meter-radius circle around well is ≥ 

30 percent medium-intensity developed

Binary 1715 0.00 0.00 0.00 Homer and others, 2007; http://www.mrlc.gov/

nlcd2001.php

lu01dvh500ge30 500-meter-radius circle around well is 

≥ 30 percent high-intensity developed

Binary 1715 0.00 0.00 0.00 Homer and others, 2007; http://www.mrlc.gov/

nlcd2001.php

lu01wat500ge30 500-meter-radius circle around well is 

≥ 30 percent water

Binary 1715 0.00 0.00 1.00 Homer and others, 2007; http://www.mrlc.gov/

nlcd2001.php

lu01wh500ge30 500-meter-radius circle around well 

is ≥ 30 percent emergent herbaceous wetlands

Binary 1715 0.00 0.00 1.00 Homer and others, 2007; http://www.mrlc.gov/

nlcd2001.php

lu01ww500ge30 500-meter-radius circle around well is 

≥ 30 percent woody wetlands

Binary 1715 0.00 0.00 1.00 Homer and others, 2007; http://www.mrlc.gov/

nlcd2001.php

Topograpy and demographics

CURVE24 Curvature at each cell center, in 1/100 meters. 

(Hilly areas may differ from about -0.5 to 0.5, while steep rugged mountains have values between -4 and 4.)

Numeric 

(range of -4 to 4)

1715 -1.94 0.01 2.49 Digital elevation model (DEM) 1: 24,000

CURVE250 Curvature at each cell center, in 1/100 meters. 

(Hilly areas may differ from about -0.5 to 0.5, while steep rugged mountains have values between -4 and 4.)

Numeric 

(range of -4 to 4)

1715 -0.84 0.00 0.39 Digital elevation model (DEM) 1: 250,000

ELEV24_M Elevation in meters Numeric 1715 0.20 143.07 692.86 Digital elevation model (DEM) 1: 24,000

ELEV250_M Elevation in meters Numeric 1715 0.00 145.00 671.00 Digital elevation model (DEM) 1: 250,000

POP00DEN16 Mean Census 2000 population density within 

500-meter radius (488-meter = 1,600-foot radius)

Numeric 1715 0.00 82.79 1301.34 Environmental Systems Research Institute, Census 

2000

POP00DEN_K Census 2000 population density in people per 

square kilometer

Numeric 1715 0.00 63.00 5305.00 Environmental Systems Research Institute, Census 

2000

SLOPE24 Slope (in degrees, 0 to 90) = rate of maximum change in elevation at this location = 

first derivative of the surface.

Numeric 

(range 0 to 90)

1715 0.00 2.84 27.86 Digital elevation model (DEM) 1: 24,000

SLOPE250 Slope (in degrees, 0 to 90) = rate of maximum change in elevation at this location = 

first derivative of the surface.

Numeric 

(range 0 to 90)

1715 0.00 1.38 17.77 Digital elevation model (DEM) 1: 250,000
References Cited 25

Table 2. Summary of predictor variables tested in the development of the multivariate arsenic models.—Continued[N, number of samples]

Variable Description Type N Minimum Median Maximum Source

Topograpy and demographics—Continued

c24 Curvature at each cell center, in 1/100 meters. 

(Range of -4 to 4 converted to binary. Greater than 0 = 1. Otherwise = 0.)

Binary 1715 0.00 1.00 1.00 Digital elevation model (DEM) 1: 24,000

c250 Curvature at each cell center, in 1/100 meters. 

(Range of -4 to 4 converted to binary. Greater than 0 = 1. Otherwise = 0.)

Binary 1715 0.00 0.00 1.00 Digital elevation model (DEM) 1: 250,000

pc24 Profile curvature = Curvature of the surface 

in the direction of slope, in 1/100 meters. (Positive indicates upwardly convex at that cell; negative indicates upwardly concave at that cell; 0 indicates flat). (Range of -4 to 4 converted to binary. Greater than 0 = 1. Otherwise = 0.)

Binary 1715 0.00 0.00 1.00 Digital elevation model (DEM) 1: 24,000

pc250 Profile curvature = Curvature of the surface 

in the direction of slope, in 1/100 meters. (Positive indicates upwardly convex at that cell; negative indicates upwardly concave at that cell; 0 indicates flat). (Range of -4 to 4 converted to binary. Greater than 0 = 1. Otherwise = 0.)

Binary 1715 0.00 0.00 1.00 Digital elevation model (DEM) 1: 250,000
This page has been left blank intentionally.
Prepared by the Pembroke Publishing Service Center.

For more information concerning this report, contact:

Office Chief

U.S. Geological Survey

New England Water Science Center 

New Hampshire-Vermont Office

331 Commerce Way, Suite 2

Pembroke, NH 03275

dc_nh@usgs.gov

or visit our Web site at:

http://nh.water.usgs.gov
Ayotte and others—Estimated Probability of Arsenic in Groundwater from Bedrock Aquifers in New Hampshire, 2011—Scientific Investigations Report 2012–5156