Prepared in cooperation with the 

Pennsylvania Department of Health and the 

Pennsylvania Department of Environmental Protection

Arsenic Concentrations, Related Environmental Factors, and 

the Predicted Probability of Elevated Arsenic in Groundwater 

in Pennsylvania

Scientific Investigations Report 2012–5257

U.S. Department of the Interior

U.S. Geological Survey
Cover: See figure 5 for an explanation of arsenic concentrations in groundwater. 
Arsenic Concentrations, Related 

Environmental Factors, and the Predicted 

Probability of Elevated Arsenic in 

Groundwater in Pennsylvania

By Eliza L. Gross and Dennis J. Low 

Prepared in cooperation with the 

Pennsylvania Department of Health and the 

Pennsylvania Department of Environmental Protection

Scientific Investigations Report 2012–5257

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2013

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Suggested citation:

Gross, E.L., Low, D.J., 2013, Arsenic concentrations, related environmental factors, and the predicted probability 

of elevated arsenic in groundwater in Pennsylvania: U.S. Geological Survey Scientific Investigations 

Report 2012–5257, 46 p.
iii

Acknowledgments

This work was performed under the collaborative sub-grant 5U38EH000191-4 from the 

Pennsylvania Environmental Public Health Tracking Program (PA EPHT) of the Pennsylvania 

Department of Health, Bureau of Epidemiology, and the Pennsylvania Department of 

Environmental Protection.

Contents

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

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

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

Background on Arsenic Occurrence ................................................................................................2

Description of Study Area ...................................................................................................................3

Previous Studies ...................................................................................................................................5

Methods of Investigation..............................................................................................................................6

Groundwater-Quality Data ..................................................................................................................6

Spatial Data............................................................................................................................................6

Anthropogenic Factors ...............................................................................................................8

Natural Factors.............................................................................................................................8

Statistical Methods.............................................................................................................................11

Probability Maps ................................................................................................................................13

Arsenic Concentrations and Related Factors.........................................................................................13

Arsenic Concentrations.....................................................................................................................13

Significance of Groundwater-Quality Properties Related to Arsenic........................................15

Redox Conditions ................................................................................................................................16

Predicted Probability of Elevated Arsenic Concentrations in Groundwater ....................................18

Statewide .............................................................................................................................................19

Glacial Aquifer System.......................................................................................................................21

Gettysburg Basin.................................................................................................................................23

Newark Basin......................................................................................................................................25

Limitations and Uses of Arsenic Models and Probability Maps..........................................................27

Summary and Conclusions.........................................................................................................................29

References Cited..........................................................................................................................................30

Appendix 1. Summary of statewide and regional anthropogenic and natural factors used 

as explanatory variables in logistic regression models for elevated arsenic 

concentrations in groundwater in Pennsylvania and number of sample records..............35

Appendix 2. Summary of arsenic concentrations in groundwater (1969–2007) for the 193 

geologic units in Pennsylvania with major aquifer type and primary lithology ...................39

Appendix 3. Results of univariate logistic regression analyses with logistic regression 

standardized coefficients and individual p-values of independent variables related 

to the detection of elevated concentrations of arsenic in groundwater samples 

collected statewide and in three regions in Pennsylvania .....................................................44
iv

Figures

1. Map showing physiographic provinces and sections, major aquifer types, and the 

extent of the Wisconsin glaciation in Pennsylvania...............................................................4

2. Map showing intrastate regions modeled in Pennsylvania ..................................................7

3. Map showing location of sampling sites and associated reported arsenic concentrations in groundwater in Pennsylvania, 1969–2007............................................................14

4. Graphs showing percentage of observed detections of elevated arsenic concentrations in relation to the average predicted probability of detecting elevated arsenic 

concentrations in groundwater statewide and in three regions in Pennsylvania...........21

5. Map showing areas with predicted probability that arsenic concentrations in groundwater in surficial and bedrock aquifers statewide in Pennsylvania will be greater 

than or equal to 4.0 micrograms per liter and sampling sites with associated Pearson 

residual values showing predictive error for measured arsenic concentrations ...........22

6. Map showing areas with predicted probability that arsenic concentrations in groundwater in the glacial aquifer system in Pennsylvania will be greater than or equal to 

4.0 micrograms per liter and sampling sites with associated Pearson residual values 

showing predictive error for measured arsenic concentrations in wells.........................24

7. Map showing areas with predicted probability that arsenic concentrations in groundwater in the Gettysburg Basin in Pennsylvania will be greater than or equal to 

4.0 micrograms per liter and sampling sites with associated Pearson residual 

values showing predictive error for measured arsenic concentrations...........................26

8. Map showing areas with predicted probability that arsenic concentrations in groundwater in the Newark Basin in Pennsylvania will be greater than or equal to 4.0 micrograms per liter and sampling sites with associated Pearson residual values 

showing predictive error for measured arsenic concentrations in wells.........................28

Tables

1. Summary of arsenic concentrations in groundwater (1969–2007) for the four major 

aquifer types in Pennsylvania.....................................................................................................9

2. Summary of arsenic concentrations in groundwater (1969–2007) for the six physiographic provinces in Pennsylvania..........................................................................................15

3. Spearman’s rho correlations for the 17 groundwater-quality constituents that have 

the best correlations with arsenic in groundwater in Pennsylvania and major 

aquifer types................................................................................................................................16

4. Redox-pH matrix showing the frequency of arsenic in 5,023 groundwater samples 

collected statewide in Pennsylvania, by redox classification and range of arsenic 

concentrations. ..........................................................................................................................17

5. Summary of statistics for statewide and regional logistic regression models predicting 

the probability of arsenic exceeding 4.0 micrograms per liter statewide and in three 

regions in Pennsylvania.............................................................................................................20
v

Conversion Factors

SI to Inch/Pound

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in)

millimeter (mm) 0.03937 inch (in)

meter (m) 3.281 foot (ft)

kilometer (km) 0.6214 mile (mi)

Area

square centimeter (cm2) 0.001076 square foot (ft2)

square meter (m2) 10.76 square foot (ft2) 

square centimeter (cm2) 0.1550 square inch (ft2) 

square kilometer (km2) 0.3861 square mile (mi2)

Volume

liter (L) 0.2642 gallon (gal)

Mass

gram (g) 0.03527 ounce, avoirdupois (oz)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F=(1.8×°C)+32

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows:

°C=(°F-32)/1.8

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).

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

Specific conductance is given in microsiemens per centimeter at 25 degrees Celsius (μS/cm at 25 °C).

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

micrograms per liter (μg/L).

Arsenic Concentrations, Related Environmental Factors, 

and the Predicted Probability of Elevated Arsenic in 

Groundwater in Pennsylvania 

By Eliza L. Gross and Dennis J. Low

Abstract

Analytical results for arsenic in water samples from 

5,023 wells obtained during 1969–2007 across Pennsylvania 

were compiled and related to other associated groundwaterquality and environmental factors and used to predict the probability of elevated arsenic concentrations, defined as greater 

than or equal to 4.0 micrograms per liter (μg/L), in groundwater. Arsenic concentrations of 4.0 μg/L or greater (elevated 

concentrations) were detected in 18 percent of samples across 

Pennsylvania; 8 percent of samples had concentrations that 

equaled or exceeded the U.S. Environmental Protection Agency’s drinking-water maximum contaminant level of 10.0 μg/L. 

The highest arsenic concentration was 490.0 μg/L. 

Comparison of arsenic concentrations in Pennsylvania 

groundwater by physiographic province indicates that the Central Lowland physiographic province had the highest median 

arsenic concentration (4.5 μg/L) and the highest percentage 

of sample records with arsenic concentrations greater than 

or equal to 4.0 μg/L (59 percent) and greater than or equal to 

10.0 μg/L (43 percent). Evaluation of four major aquifer types 

(carbonate, crystalline, siliciclastic, and surficial) in Pennsylvania showed that all types had median arsenic concentrations less than 4.0 μg/L, and the highest arsenic concentration 

(490.0 μg/L) was in a siliciclastic aquifer. The siliciclastic and 

surficial aquifers had the highest percentage of sample records 

with arsenic concentrations greater than or equal to 4.0 μg/L 

and 10.0 μg/L. Elevated arsenic concentrations were associated with low pH (less than or equal to 4.0), high pH (greater 

than or equal to 8.0), or reducing conditions. For waters classified as anoxic (405 samples), 20 percent of sampled wells 

contained water with elevated concentrations of arsenic; for 

waters classified as oxic (1,530 samples) only 10 percent of 

sampled wells contained water with elevated arsenic concentrations. Nevertheless, regardless of the reduction-oxidation 

classification, 54 percent of samples with low pH (13 of 

24 samples) and 25 percent of samples with high pH (57 of 

230 samples) had elevated arsenic concentrations.

Arsenic concentrations in groundwater in Pennsylvania were correlated with concentrations of several chemical 

constituents or properties, including (1) constituents associated 

with redox processes, (2) constituents that may have a similar 

origin or be mobilized under similar chemical conditions as 

arsenic, and (3) anions or oxyanions that have similar sorption 

behavior or compete for sorption sites on iron oxides.

Logistic regression models were created to predict and 

map the probability of elevated arsenic concentrations in 

groundwater statewide in Pennsylvania and in three intrastate 

regions to further improve predictions for those three regions 

(glacial aquifer system, Gettysburg Basin, Newark Basin). 

Although the Pennsylvania and regional predictive models 

retained some different variables, they have common characteristics that can be grouped by (1) geologic and soils variables 

describing arsenic sources and mobilizers, (2) geochemical 

variables describing the geochemical environment of the 

groundwater, and (3) locally specific variables that are unique 

to each of the three regions studied and not applicable to statewide analysis. Maps of Pennsylvania and the three intrastate 

regions were produced that illustrate that areas most at risk 

are those with geology and soils capable of functioning as 

an arsenic source or mobilizer and geochemical groundwater 

conditions able to facilitate redox reactions. The models have 

limitations because they may not characterize areas that have 

localized controls on arsenic mobility. The probability maps 

associated with this report are intended for regional-scale use 

and may not be accurate for use at the field scale or when 

considering individual wells.

Introduction

In many areas worldwide, including Pennsylvania, drinking water is the primary route of human exposure to arsenic 

(Hopenhayn, 2006). Arsenic data are sparse for groundwater 

because statewide testing of private wells to determine where 

concentrations exceed the health-based maximum contaminant level (MCL) of 10.0 micrograms per liter (μg/L) for 

drinking water, established in 2001 by the U.S. Environmental Protection Agency (USEPA), is not required throughout 

Pennsylvania (U.S. Environmental Protection Agency, 2006). 

Domestic wells used for private water supplies in Pennsylvania are not required to be routinely tested for arsenic and other 
2 Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa.

contaminants, so homeowners may not know whether their 

well water has arsenic concentrations greater than the MCL.

Arsenic is a known carcinogen and consumption of 

arsenic in drinking water has been linked to multiple health 

problems, including bladder, lung, prostate, and skin cancers; 

cardiovascular disease; diabetes; and neurological disfunction (National Research Council, 1999, 2001; Hopenhayn, 

2006; Chen and others, 2007; Benbrahim-Tallaa and Waalkes, 

2007; Lin and others, 2008). Arsenic is also a potent endocrine disruptor that can cause problems with reproduction 

and embryotic development (Davey and others, 2007). In 

2001, the USEPA decreased the drinking water MCL from 

50.0 to 10.0 μg/L in recognition of the health risks associated 

with arsenic (U.S. Environmental Protection Agency, 2006). 

Although the USEPA regulates only public water-supply systems, the MCL has general applicability for the consumption 

of drinking water from private domestic wells. 

Arsenic concentrations in Pennsylvania groundwater are 

difficult to predict on a well-by-well basis because (1) there 

is considerable local- and regional-scale spatial variability 

in groundwater quality and (2) arsenic has multiple anthropogenic and natural sources. However, the risk of elevated 

arsenic concentrations in groundwater is greater in some areas 

of Pennsylvania than in others (Low and Galeone, 2006). If 

areas with increased probability for elevated arsenic concentrations could be identified, health monitoring, water-quality 

monitoring, and educational programs could then be directed 

where the need is greatest. To address these concerns, the 

U.S. Geological Survey, in cooperation with the Pennsylvania Department of Health and Pennsylvania Department of 

Environmental Protection, undertook a study in 2010 to determine areas in Pennsylvania that have increased probability of 

elevated arsenic concentrations in groundwater using available 

data describing arsenic concentrations, groundwater chemistry, 

geology, and other factors.

Purpose and Scope

This report (1) documents arsenic concentrations in 

groundwater samples collected in Pennsylvania during 

1969–2007, (2) describes the relation between arsenic concentrations and reduction-oxidation (redox) conditions and other 

groundwater-quality variables, and (3) documents the development of logistic regression models to represent the spatial 

relation between arsenic concentrations in groundwater and 

anthropogenic and natural factors. The models were developed 

using existing and constructed geographic information system 

(GIS) data for Pennsylvania and three intrastate regions 

(glacial aquifer system, Gettysburg Basin, and Newark Basin). 

Resulting model coefficients for selected spatial variables 

were used to produce maps displaying the predicted probability of elevated arsenic concentrations (greater than or equal 

to 4.0 μg/L) throughout the State and the selected intrastate 

regions. 

Background on Arsenic Occurrence

Arsenic is a naturally occurring trace element in rock, 

soil, plants, and the aquatic environment. A recent review 

of occurrence of arsenic in natural waters describes some 

principal sources and mechanisms of arsenic mobility in 

groundwater (Smedley and Kinniburgh, 2002). Concentrations 

of arsenic in groundwater vary greatly owing to the uneven 

distribution of source materials and dynamic geochemical 

controls on aqueous arsenic mobility. Although arsenic can 

be introduced to the environment from anthropogenic sources 

(such as contaminant releases from industrial facilities or 

usage as a pesticide for agriculture), it commonly is present 

as a trace component in naturally occurring minerals, such 

as sulfides (pyrite), hydrous metal oxides (iron oxides), coal, 

ironstones, clays, phosphates, silicates, and carbonates. Pyrite 

and iron oxides are important sources of elevated arsenic in 

groundwater because they are abundant in aquifers, leading to 

their dissemination throughout the aquifer matrix or accumulation in fractures, joints, or bedding planes (Smedley and 

Kinniburgh, 2002).

Arsenic, present as arsenic minerals or as a trace component in other naturally occurring minerals in the soil and aquifers, can be released to or removed from the groundwater as a 

result of oxidation and reduction, dissolution and precipitation, 

and surface complexation (sorption) reactions on mineral surfaces. Arsenian pyrite [Fe(S,As)2

], arsenopyrite (FeAsS), and 

(or) other unspecified sulfide minerals in bedrock and surficial 

sediments are common parent sources for naturally occurring 

arsenic in the environment (Foster and others, 2003). Substitution of arsenic for sulfur in sulfide minerals can increase their 

susceptibility to weathering and dissolution when exposed to 

oxidants (Savage and others, 2000). Arsenic released to solution by sulfide oxidation commonly has a valence state of V 

or III and forms the protonated oxyanion complexes, arsenate 

(HnAsO4

n-3) or arsenite (Hn

AsO3

n-3), respectively (Welch and 

others, 2000; Smedley and Kinniburgh, 2002; Stollenwerk, 

2003). Arsenite is considered the more toxic of the two major 

oxyanion forms.

Arsenate [As(V)] predominates in oxic groundwaters, 

whereas arsenite [As(III)] predominates in reducing sulfidic 

and methanic groundwaters (Welch and others, 2000; Smedley and Kinniburgh, 2002; Stollenwerk, 2003). In strongly 

reducing waters that are near saturation with sulfide minerals, 

arsenic sulfide complexes and minerals may form. Mueller 

and others (2001) noted that the prevalence of arsenite was 

correlated with low concentrations of dissolved oxygen that 

reflect strongly reducing conditions (dissolved oxygen less 

than 0.1 milligrams per liter (mg/L)); arsenate was associated with oxidizing conditions (dissolved oxygen greater than 

8 mg/L). The conversion of As(III) to As(V) in oxic waters 

may be relatively slow and can be measured in years (Eary 

and Schramke, 1990) with pH, ferric iron, manganese, and 

bacteria strongly affecting the rate of oxidation. The reduction 

of As(V) to As(III) under anaerobic conditions is generally 

much faster than the oxidation of As(III) to As(V). 
Introduction 3

In groundwater systems, arsenate and arsenite oxyanions commonly form surface complexes (adsorption) on 

iron oxides and other mineral surfaces (Stollenwerk, 2003). 

Although As(V) and As(III) adsorb over a wide pH range, 

As(V) is extensively adsorbed at low pH values and desorbs 

at alkaline pH; As(III) adsorption increases with pH and peaks 

at about pH 8 or 9 (Stollenwerk, 2003). In addition to iron 

oxides, a wide variety of minerals including aluminum oxides 

and oxyhydroxides, manganese oxides, silica, clays, and carbonates may sorb arsenic, and dissolved organic compounds, 

phosphate, and other dissolved ions can influence the adsorption of arsenic. 

Mobilization of adsorbed arsenic may occur through 

desorption or dissolution of the host mineral. Arsenic associated with iron oxides tends to be weakly bound on surface 

sites (adsorbed) and can be released to the groundwater by 

desorption or by dissolution of the iron oxides (Matisoff and 

others, 1982; Ayotte and others, 1998; Welch and others, 2000; 

Smedley and Kinniburgh, 2002; Stollenwerk, 2003; Thomas 

and others, 2008). Changes in pH and (or) redox conditions 

can result in the release of arsenic from minerals. Increases 

in pH can lead to the desorption of arsenate and arsenite. The 

development of reducing conditions can lead to the reductive 

dissolution of iron oxides and (or) the reduction of arsenate to 

arsenite and the consequent desorption of arsenite (reductive 

desorption) (Stollenwerk, 2003; Thomas, 2007). In general, 

fine-grained sediments tend to have higher arsenic concentrations than coarse-grained sediments because smaller-sized 

particles and (or) those with complex shapes have a higher 

surface-area-to-volume ratio and a more reactive surface area 

than larger, simply shaped particles (Parks, 1990). The density 

of sorption sites and potential exposure to reactive waters 

generally increase with the mineral surface area. 

Arsenic concentrations in groundwater may increase 

(accumulate) with the age of the water. Thomas (2007) 

reported that arsenic concentrations of 10.0 μg/L or greater 

were found more frequently in old waters (recharged before 

1953) as compared to younger waters (recharged since 1953). 

Geologic units that have high yields of water are, in general, 

highly permeable and transmissive, exhibit rapid recharge, 

and, as a result, consist of relatively young water. This young 

water will typically be predominantly oxic. In general, water 

in shallow wells is more likely to be affected by anthropogenic 

contaminants than water in deeper wells; however, exceptions are numerous because of complexities of groundwater 

flowpaths.

Description of Study Area

Pennsylvania is a physiographically, geologically, and 

hydrologically diverse State that covers about 139,859 square 

kilometers (54,000 square miles). Pennsylvania includes 

parts of six physiographic provinces, which are subdivided 

into 20 physiographic sections—(1) Appalachian Plateaus 

(Allegheny Mountain, Allegheny Plateau, Clarion Plateau, 

Deep Valleys, Glaciated High Plateau, Glaciated Low Plateau, 

High Plateau, Glaciated Pocono Plateau, Northwestern Glaciated Plateau, Pittsburgh Low Plateau, Waynesburg Hills), (2) 

Atlantic Coastal Plain (Lowland and Intermediate Upland), (3) 

Central Lowland (Eastern Lake), (4) New England (Reading 

Prong), (5) Piedmont (Gettysburg-Newark Lowland, Piedmont Lowland, Piedmont Upland), and (6) Ridge and Valley (Appalachian Mountain, Great Valley, South Mountain) 

(Fenneman and Johnson, 1946; Berg and others, 1989) (fig. 1). 

Land-surface elevations range from sea level (North American 

Verical Datum of 1988; NAVD 88) (Atlantic Coastal Plain) 

to 978 meters (3,210 feet) above NAVD 88 (Appalachian 

Plateaus). 

The topography of the Appalachian Plateaus Physiographic Province (hereafter province) varies from deep 

valleys to glaciated high plateaus with dominant rock types 

of sandstone, siltstone, and shale and abundant bituminous 

coal in places. The geologic structure of the Appalachian 

Plateaus province is complex, varying from horizontal beds 

to large-amplitude open folds. The Atlantic Coastal Plain 

province has little relief and consists of unconsolidated sand, 

gravel, and clay that overlie metamorphic rocks. The Central 

Lowland province also has little relief with considerable sand 

and gravel and beach deposits in the Lake Erie area. The New 

England province includes the steep hills and rounded ridges 

of the Reading Prong physiographic section (hereafter section), which consists of highly metamorphosed granitic rocks 

and quartzite. The Piedmont province topography consists of 

broad, rolling lowlands, narrow valleys, and broad, flat-topped 

hills. Shale, siltstone, sandstone, and diabase dominate the 

Gettysburg-Newark Lowland section of the Piedmont province; limestone and dolomite are common in the Piedmont 

Lowland section. The Piedmont Upland section is dominated 

by schist, gneiss and quartzite. The geologic structures of the 

Piedmont province are variable, ranging from half-grabens in 

the Gettysburg-Newark Lowland section to complex folds and 

faults elsewhere in the province. The topography of the Ridge 

and Valley province ranges from narrow to broad valleys with 

steep uplands or linear ridge and mountain tops. Dominant 

rock types in the Ridge and Valley province are sandstone, 

siltstone, and shale, except in the Great Valley section and valleys of the Appalachian Mountain section, which are underlain 

predominantly by limestone and dolomite rocks. The geologic 

structure within the Ridge and Valley province is complex 

with many folds, faults, thrust sheets, nappes, and a major 

anticlinorium with second- and third-order folds.

Pennsylvania has a complex geological history, which 

results in many different rock types within the State, with 

the Pennsylvania Geological Survey recognizing almost 200 

different geologic formations or members (Berg and others, 

1980; Pennsylvania Geological Survey, 2001). Despite the 

geologic diversity within the State, the groundwater system in 

Pennsylvania has been characterized as being representative 

of four major aquifer types—(1) carbonate bedrock (limestone 

and dolomite), (2) crystalline bedrock (igneous and metamorphic rocks), (3) siliciclastic bedrock (sandstone, siltstone, 
4 Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa. Appalachian Mountain Deep Valleys Pittsburgh Low Plateau Glaciated Low Plateau Great Valley High Plateau Allegheny Mountain Piedmont Upland Gettysburg-Newark Lowland Clarion Plateau Northwestern Glaciated Plateau Waynesburg Hills

Allegheny Plateau

Piedmont 

Lowland

Glaciated 

Pocono 

Plateau

South Mountain

Glaciated High Plateau

Lake Erie

Base from U.S. Bureau of the Census digital data, 1990b 1:100,000 Albers Equal-Area Conic projection: Standard Parallels 40° N and 42° N, Central Meridian -78° W, Latitude of Origin 39°

Physiographic provinces and sections modified from Pennsylvania Geological Survey, 1998 Major aquifer types from Lindsey and Bickford, 1999; Pennsylvania Geological Survey, 2001; Soller & Packard, 1998 Extent of Wisconsin glaciation from Pennsylvania Geological Survey, 1995

NEW JERSEY

MARYLAND WEST VIRGINIA

OHIO

NEW YORK

ATLANTIC 

COASTAL 

PLAIN 

PROVINCE

Lowland and Intermediate 

Upland

NEW ENGLAND

PROVINCE

Reading Prong

RIDGE AND VALLEY PROVINCE PIEDMONT PROVINCE

CENTRAL LOWLAND PROVINCE

Eastern Lake 

APPALACHIAN PLATEAUS PROVINCE

EXPLANATION

Physiographic sections

Major aquifer types

CarbonateCrystalline

SiliciclasticSurficial Physiographic provinces

Extent of Wisconsin glaciation

DELAWARE

75°00’ 76°00’ 77°00’ 79°00’ 80°00’ 78°00’

42°

00’

41°

00’

40°

00’

39°

00’

0 25 MILES 12.50 25 KILOMETERS 12.5

Figure 1. Physiographic provinces and sections, major aquifer types, and the extent of the Wisconsin glaciation in Pennsylvania.
Introduction 5

conglomerate, and shale), and (4) surficial (fig. 1) (Lindsey 

and Bickford, 1999; Pennsylvania Geological Survey, 2001; 

Soller and Packard, 1998). Surficial aquifers consist of 

unconsolidated material (sand and gravel) overlying bedrock aquifers in depths sufficient to serve as an aquifer, such 

as glacial outwash, alluvium, and beach deposits. Surficial 

aquifers shown in figure 1 consist of mapped areas of the State 

where surficial materials consist of coarse-grained sediment, 

and these were designated as surficial aquifers for this report. 

Despite this designation, it is still possible for wells located 

in other areas of the State within the extent of the Wisconsin 

glaciation (fig. 1) to be completed in glacial materials.

Carbonate bedrock aquifers are located in some valleys 

of the Pittsburgh Low Plateau section of the Appalachian 

Plateaus province, valleys of the Ridge and Valley province, 

and the Piedmont Lowland section of the Piedmont province. Crystalline bedrock aquifers make up the New England 

province, the Piedmont Upland section and diabase intrusions 

of the Gettysburg-Newark Lowland section of the Piedmont 

province, and South Mountain section of the Ridge and Valley 

province. The Central Lowland and Atlantic Coastal Plain 

provinces are predominantly composed of surficial aquifers 

resulting from beach deposits from Lake Erie and the Atlantic 

Ocean. Siliciclastic bedrock aquifers can be found throughout 

the rest of the State and are most prominent in the Appalachian 

Plateaus province, Gettysburg-Newark Lowland section of 

the Piedmont province, and the Ridge and Valley province. 

Surficial deposits are also mostly in the Northwestern Glaciated Plateau and Glaciated Low Plateau sections of the 

Appalachian Plateaus province and are within the extent of 

the Wisconsin glaciation. Some surficial deposits also extend 

into the Appalachian Mountain section of the Ridge and Valley 

province and the Pittsburgh Low Plateau section of the Appalachian Plateaus province. 

Temperature and precipitation vary across the State 

according to geography and topography. The average annual 

temperature is 11 degrees Celsius (oC) (52 degrees Fahrenheit) 

in southern Pennsylvania and 8 oC (46 degrees Fahrenheit) in 

the northern part of the State. The warmest areas correspond 

to the Atlantic Coastal Plain and Piedmont provinces. Progressive cooling occurs in the higher land-surface elevations of 

the Ridge and Valley province, and the Appalachian Plateaus 

province is the coolest area of the State (Cuff and others, 

1989). Heat and moisture circulate through the State from the 

south to the southeast, whereas most of the precipitation-producing weather fronts move from west to east. Precipitation 

increases from 102 centimeters (cm) (40 inches) at the western 

border of Pennsylvania to a maximum of 130 cm (51 inches) 

near the highest land-surface elevations of the Appalachian 

Plateaus province. The decreasing land-surface elevations in 

the Ridge and Valley province create rain shadow effects that 

reduce the average annual precipitation to 97 cm (38 inches). 

The eastern part of Pennsylvania is strongly affected by 

airflows coming directly off the Atlantic Ocean, and this 

contributes to the higher average annual precipitation of 102 to 

112 cm (40 to 44 inches), despite the lower land-surface elevation (Cuff and others, 1989). 

Land use is primarily forested (65 percent) followed by 

agricultural (27 percent) (Nakagaki and others, 2007). Forested is the dominant land use in the north-central, northeast, 

and rugged mountain slopes. Agricultural land use predominates in the valleys of the Ridge and Valley province and 

much of the Piedmont province. Urban (6 percent) land use is 

dominant in and around Pennsylvania’s major cities, especially those cities with populations greater than 40,000 (U.S. 

Bureau of the Census, 2010), of which most are located in the 

Piedmont and Ridge and Valley provinces.

Previous Studies

Arsenic concentrations in groundwater of Pennsylvania 

have been documented by a number of previous studies, typically a county-scale or regional-scale study. Cravotta (2008) 

reports that arsenic concentrations in groundwater discharged 

to 140 abandoned coal mines in the bituminous and anthracite coalfields of Pennsylvania ranged from less than 0.03 to 

64.0 μg/L. Arsenic concentrations were positively correlated 

with pH, chloride, bromide, and iodide and inversely correlated with dissolved oxygen and redox potential, indicating the 

potential for arsenic mobilization by desorption or reduction 

processes, possibly because of interactions with deep, saline 

groundwater. 

Williams and others (1998), who studied the glaciated 

valleys of Bradford, Potter, and Tioga Counties in Pennsylvania, found a correlation between arsenic and older (recharged 

before 1953) or briny water and found that arsenic concentrations varied by primary aquifer. Buckwalter and Moore (2007) 

concentrated their efforts in Warren County, which is also in 

a glaciated region of Pennsylvania, where almost one-third 

of the collected samples contained arsenic concentrations 

that exceeded the MCL of 10.0 μg/L; the maximum was 

490.0 μg/L. They also documented that arsenic concentrations 

exhibited seasonal fluctuations and that arsenic concentrations 

varied widely, even between adjacent (less than a 76-meter 

(250-foot) distance) wells. Low and Galeone (2006) collected 

groundwater samples for analysis for total arsenic in the glaciated region of Pennsylvania within eight counties and found 

that arsenic concentrations varied greatly over short distances 

but did not appear to be related to well depth. Thomas (2007) 

studied the association of arsenic with redox conditions in the 

glacial aquifer system of the northern United States, which 

includes the glaciated portion of Pennsylvania and concluded 

that elevated arsenic concentrations are more commonly 

detected in older, anoxic groundwaters (recharged before 

1953) and that arsenic correlated strongly with constituents 

linked to redox processes and anions or oxyanions that sorb to 

iron oxides.

Peters and Burkert (2008) examined groundwater-quality 

data from over 18,000 wells in the Newark Basin of Pennsylvania. They found that variations in pH were strongly 

correlated with arsenic concentrations, with the highest 
6 Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa.

concentrations of arsenic associated with pH values greater 

than 6.4. They concluded that the original source of arsenic in 

the study area was most likely black and gray shales containing arsenian pyrite and that groundwater concentrations of 

arsenic are most likely controlled by adsorption/desorption 

reaction with iron oxides in red mudstone aquifer materials. 

Senior and Sloto (2006) studied the Newark Basin, sampling 

58 wells within the study area to identify areas of elevated 

arsenic concentrations and characterize the geochemical environment associated with elevated concentrations of arsenic 

and various constituents. They found that arsenic correlated 

most strongly and positively with pH, boron, and molybdenum; correlated positively with selenium, uranium, nickel, 

lithium, fluoride, and strontium; and correlated negatively 

with total organic carbon, copper, and dissolved oxygen. They 

concluded that arsenic concentrations may be controlled partly 

by pH affecting adsorption of arsenate and that the correlation 

of arsenic with the presence of many trace elements indicates 

similar geochemical controls and (or) distribution in aquifer 

materials in the Newark Basin.

Methods of Investigation

Groundwater-quality data from 1969 to 2007 were 

obtained by the USGS from local, county, private, State, and 

Federal electronic databases. Spatial data consist of variables 

representing anthropogenic factors (such as land use and 

contamination sites) and natural factors (such as geology and 

climate) (appendix 1). Datasets for most factors were available in geographic information system (GIS) format from 

various sources, but additional GIS datasets were developed 

specifically for use as explanatory variables during statistical modeling. Some datasets listed in appendix 1 were not 

available statewide or pertinent to statewide analysis, so these 

datasets were only populated for selected intrastate regions 

(glacial aquifer system, Gettysburg Basin, Newark Basin) 

(fig. 2). Also, differences in the extent of explanatory variable 

data coverage caused different explanatory variables to have 

a different number of sample records associated with them. 

For example, a total of 5,023 sample records were available 

statewide, but appendix 1 shows 5,021 sample records available statewide for all of the soil characterization variables 

and 5,011 sample records available statewide for two of the 

groundwater geochemistry variables. These differences in data 

availability were due to the slightly different extents associated with the explanatory datasets, which caused these datasets 

to have slightly different coverage across Pennsylvania. 

Groundwater-Quality Data

A database consisting of 5,023 groundwater records 

with reported values for arsenic and various associated field 

and laboratory measured constituents was created (Low and 

Chichester, 2006; Low and others, 2008). More than 25,000 

groundwater-quality data records were examined initially. This 

number was reduced to create datasets containing values for 

arsenic (total and (or) dissolved, 12,781), pH (7,876), and specific conductance (5,931). The pH and specific conductance 

datasets were used to create explanatory spatial variables, and 

these datasets include data from areas where arsenic data are 

not necessarily available. The arsenic dataset of 12,781 was 

further reduced to 5,023 groundwater records by restricting 

values to one composite sample per well using three methods: 

(1) samples with reported censored arsenic data with detection 

levels greater than 4.0 μg/L were removed from the database; 

(2) duplicate sites and (or) samples were removed through 

verification of local well numbers, site identifiers, geographical coordinates (latitude and longitude) within the State 

boundary, and sample date; and (3) for sites that were sampled 

repeatedly, data from the analysis that was associated with 

the highest arsenic concentration were retained. If the arsenic 

concentrations were identical, then data from the most recent 

analysis were retained. 

Groundwater-quality data associated with the arsenic 

dataset of 5,023 groundwater records were handled in the 

following manner: (1) values with remark values coded as 

“estimated” were assumed to represent actual values; (2) total 

and dissolved constituent samples were not differentiated in 

regards to any statistical or chemical analyses, but if total 

and dissolved values were reported for the same sample, the 

dissolved value was retained; and (3) field measurements (pH, 

specific conductance) had precedence over laboratory results. 

Estimated dissolved solids represents the maximum reported 

value for residue on evaporation at 105 oC, residue dried 

at 180 °C, sum of dissolved constituents, or total dissolved 

solids. Although water use varied (commercial, monitor, 

industrial, public) no differentiation or statistical analysis was 

performed to distinguish among industrial, public, private, or 

other uses of water. 

Groundwater-quality data associated with the arsenic dataset of 5,023 groundwater records compiled for this 

investigation include up to 53 additional groundwater-quality 

constituents. These data were reduced to 31 groundwaterquality constituents for statistical analysis because of limited 

availability of data and differences in data quality among 

the records analyzed. Samples were analyzed at the USGS 

National Water Quality Laboratory, the Pennsylvania Department of Environmental Protection Laboratory, Pennsylvania 

Department of Agriculture, and Pennsylvania State University, 

as well as a number of private laboratories (Low and Chichester, 2006; Low and others, 2008). As a result, the groundwaterquality data represent multiple project designs and goals, 

site and well selection criteria, as well as sample collection, 

sample preservation, analyte detection levels, and qualityassurance/quality-control methods. 

Spatial Data

Variables representing anthropogenic and natural factors 

were compiled and evaluated for statewide and regional study 
Methods of Investigation 

7

Base from U.S. Bureau of the Census digital data, 1990b 1:100,000 Albers Equal-Area Conic projection: Standard Parallels 40° N and 42° N, Central Meridian -78° W, Latitude of Origin 39°

County boundaries from U.S. Bureau of the Census, 1990a Glacial aquifer system boundary from Pennsylvania Geological Survey, 1995; Soller & Packard, 1998Gettysburg and Newark Basin boundaries from Pennsylvania Geological Survey, 2001

NEW JERSEY

MARYLAND WEST VIRGINIA

OHIO

NEW YORK

EXPLANATION

Regional intrastate study areas

Glacial aquifer system Gettysburg Basin Newark Basin

DELAWARE

Lake Erie

75°00’ 76°00’ 77°00’ 79°00’ 80°00’ 78°00’

42°

00’

41°

00’

40°

00’

39°

00’

0 25 MILES 12.50 25 KILOMETERS 12.5

BUCKS

MONTGOMERY

ELK

TIOGA

ERIE

YORK

CENTRE

POTTER

BERKS

PIKE LYCOMING

MCKEAN

WAYNE

BEDFORD

BRADFORD

INDIANA

CLINTON

WARREN

BUTLER

LUZERNE

SOMERSET

CLEARFIELD

FAYETTE

BLAIR

CRAWFORD

LANCASTER

PERRY

CHESTER

MERCER

FRANKLIN

CAMBRIA

ADAMS

CLARION

MONROE

GREENE

VENANGO

HUNTINGDON

SCHUYLKILL

WASHINGTON

ALLEGHENY

DAUPHIN

WESTMORELAND

FULTON

BEAVER

JEFFERSON

SUSQUEHANNA

FOREST

MIFFLIN

ARMSTRONG

UNION

JUNIATA

SULLIVAN

COLUMBIA

LEHIGH

CARBON

WYOMING

SNYDER

CUMBERLAND

CAMERON

LEBANON

LAWRENCE

LACKAWANNA

NORTHUMBERLAND NORTHAMPTON

DELAWARE

MONTOUR

PHILADELPHIA

Figure 2. Intrastate regions modeled in Pennsylvania. (Glacial aquifer system, Gettysburg Basin, and Newark Basin)
8 Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa.

areas. Arsenic concentrations in groundwater from 5,023 wells 

within the State were combined with additional potential 

explanatory data consisting of anthropogenic and natural 

factors, and a GIS was used to produce a dataset in which 

each well with a measured arsenic concentration was associated with the explanatory geographic variables. Explanatory 

variables differed among statewide and regional study areas 

as a result of differences in anthropogenic activities (land use) 

and natural conditions (topography, geology, soils) throughout 

the State.

A raster dataset was created for each factor or variable 

using a GIS. Raster datasets represent a spatial data model 

defining space as an array of equally sized cells arranged in 

rows and columns with each cell containing an attribute value 

and location coordinates. Original data used for the study 

consist of previously existing raster or vector (point, line, or 

polygon) datasets of various resolutions (appendix 1). Previously converted, created, or existing raster datasets were 

clipped to the State of Pennsylvania’s political boundary (U.S. 

Bureau of the Census, 1990b) and snapped to a common 

dataset (Nakagaki and others, 2007) to ensure that the cell 

alignment of each output raster would be the same. The snap 

dataset was the dataset with the smallest resolution, which 

was the 30-meter resolution land-cover dataset (Nakagaki and 

others, 2007). This means that data for each factor or variable were compiled within 30-meter grid cells, thus, creating 

a spatial layer for each factor or variable lining up with a 

template representing the State of Pennsylvania consisting of 

9,493 rows and 16,508 columns and totaling 156,710,444 cells 

across the State.

Anthropogenic Factors

Data representing anthropogenic factors include proximity to known sources of contamination, disturbance, and 

land-cover variables. Variables describing proximity to known 

sources of contamination illustrate the distance to points or 

polygons representing areas that are known to be receiving 

treatment for arsenic contamination or that are in need of arsenic remediation. Disturbance variables describe the distance to 

points where humans are disturbing natural surroundings by 

mining or drilling operations. Land-cover variables describe 

land-use patterns that result from agricultural operations, 

urban development, or population density. 

Pennsylvania municipality boundaries were acquired 

from the Pennsylvania Department of Transportation (2008). 

A list of municipalities containing water suppliers receiving 

arsenic treatment and sites or groundwater in need of remediation owing to arsenic contamination was compiled from the 

Pennsylvania Bulletin (1997–2009), and only these municipalities were included in a new dataset describing the distance to the nearest municipality receiving arsenic treatment. 

Toxic chemical release inventory data were obtained from the 

USEPA (1994), and a similar dataset was created describing 

the distance to the nearest point representing toxic chemical 

release inventory sites.

Three datasets indicating anthropogenic disturbances to 

the landscape were acquired from the Pennsylvania Department of Environmental Protection (2008a, 2008b, 2008c). 

These datasets describe point locations of (1) underground 

and surface coal mining operations, (2) industrial mineral 

mining operations, and (3) drilled oil and gas wells. Spatial 

datasets were created to represent each of these three datasets 

by describing the distance to the point location located closest 

to each raster data cell across the State. Therefore, the raster 

cell is assigned a value that represents the distance from the 

raster cell to the closest data point in the dataset of interest 

(coal mining operations, mineral mining operations, oil and 

gas wells), whether the closest data point is 30 or 300 meters 

away. Because underground and surface coal mining operations and drilled oil and gas wells are concentrated in the 

northwestern and northeastern parts of the State, datasets 

associated with those parts of the State were considered only 

for analysis of conditions in the glacial aquifer system (fig. 2).

Land-cover classification data for Pennsylvania were 

compiled from the 1992 Enhanced National Land Cover Data 

(NLCD) (Nakagaki and others, 2007), a dataset that has a 

30-meter resolution. The agricultural land-cover classification used for this study was created by grouping data by the 

following classifications: Orchards/Vineyards/Other, Land 

Use Land Cover (LULC) Orchards/Vineyards/Other, Pasture/

Hay, and Row Crops. The urban land-cover classification was 

created by grouping data by the following classifications: Low 

intensity residential, High intensity residential, LULC residential, NLCD/LULC forested residential, and Urban/recreational 

grasses. Focal statistics were used to create agricultural and 

urban land-cover datasets by calculating the average amount 

of agricultural or urban land-cover cells within a 500-meter 

radius of each raster cell within the State. The resulting 

datasets describe the percentage of agricultural and urban land 

cover within a 500-meter radius of each raster cell. Population 

density data were compiled by block group from 1990 Census 

of Population and Housing data by Price (2003) in people per 

square kilometer.

Natural Factors

Spatial data describing natural factors that were compiled 

and evaluated are climate, geology, geophysical, groundwater geochemistry, land cover, identified mineral deposit, soils 

characterization, and topography variables. Climate variables 

describe precipitation, temperature, and groundwater recharge. 

Geology variables describe distance to geologic units with 

mineral properties that could affect arsenic concentrations 

in groundwater or the potential of geologic units to act as an 

arsenic source or mobilizer. Geophysical variables describe 

average residual total intensity of the earth’s magnetic field. 

Groundwater geochemistry variables describe groundwater 

corrosivity geologic groupings and pH and specific conductance of groundwater. Land-cover variables that include 

natural factors describe forested and wetland land-cover 

patterns. Metals and minerals variables describe the intensity 
Methods of Investigation 9

of the earth’s magnetic field resulting from the distribution of 

iron minerals and distance to mapped mineral deposits. Soils 

characterization variables describe soil water storage, compaction, texture, runoff potential, organic matter, permeability, 

thickness, and land-surface slope.

Average annual precipitation and minimum and maximum temperature data from 1971 to 2000 were compiled from 

the Parameter-Elevation Regressions on Independent Slopes 

Model (PRISM) Climate Group from Oregon State University 

(2006a, 2006b, 2006c). Minimum and maximum temperature 

datasets were averaged in order to create a dataset of average 

temperatures during 1971–2000. Groundwater recharge rates 

from 1951 to 1980 were taken from Wolock (2003).

Geologic units containing potentially substantial acidproducing sulfide minerals (Pennsylvania Geological Survey, 

2005) were obtained from the State bedrock geology spatial 

dataset (Pennsylvania Geological Survey, 2001). Diabase 

geologic units also were extracted from the State bedrock 

geology dataset to form a dataset. A dataset containing point 

locations for igneous rock samples (Grossman, 1999) was 

obtained from the USGS National Geochemical Database. 

Raster datasets were created to represent each of the three previously described datasets by calculating the shortest distance 

from each cell to the nearest polygon or point location in each 

dataset. Geophysical data describing average residual total 

intensity of the earth’s magnetic field resulting from variations 

in earth materials and structure were obtained from Bankey 

and others (2002). 

Hydrogeochemical stream-sediment data from the 

National Uranium Resource Evaluation (NURE) dataset were 

obtained from the USGS (2004) and used to create a dataset of estimated arsenic concentrations in stream sediments. 

Arsenic concentrations were estimated from point data using 

the inverse distance weighted (IDW) technique, which is an 

interpolation method used to estimate concentrations by averaging values for sample points within a defined neighborhood. 

Data on arsenic in stream sediments were not available for the 

entire State; therefore, this dataset was limited to the Newark 

Basin as this was the only regional study area within the State 

with a sufficient amount of data for analysis.

Major aquifers of Pennsylvania (carbonate, crystalline, 

siliciclastic, and surficial) (fig. 1) were used to create four geologic variables that could be modeled as discrete variables by 

performing a spatial intersection with mapped geologic units 

across the State to assign each well to a major aquifer. The 

discrete major aquifer variables were coded as “one” if a well 

was located in a particular major aquifer and coded as “zero” 

if the well was not located in an aquifer. For example, the 

carbonate major aquifer variable would code all wells spatially 

intersecting carbonate aquifers as “one” and all wells spatially 

intersecting the other major aquifers (crystalline, siliciclastic, 

and surficial) as “zero.” Mapped geologic units in Pennsylvania were divided by major aquifers according to their reported 

primary lithology descriptions (appendix 2) (Pennsylvania 

Geological Survey, 2001). Carbonate bedrock aquifers consist 

of primary lithologies of argillaceous dolomite, argillaceous 

limestone, dolomite, graphitic marble, high-calcium limestone, 

limestone, limestone conglomerate, marble, and shaly limestone. Crystalline bedrock aquifers consist of primary lithologies of albite-chlorite schist, andesite, anorthosite, chloritesericite schist, diabase, feldspathic quartzite, felsic gneiss, 

granitic gneiss, granitic pegmatite, graphitic felsic gneiss, 

graphitic gneiss, greenstone schist, mafic gneiss, metabasalt, 

metadiabase, metagabbro, metarhyolite, oligoclase-mica 

schist, phyllite, quartzite, serpentinite, and slate. Siliciclastic 

bedrock aquifers consist of primary lithologies of argillaceous 

sandstone, argillite, arkosic sandstone, black shale, calcareous sandstone, calcareous shale, graywacke, mudstone, quartz 

conglomerate, quartzite, sandstone, shale, siliceous sandstone, 

siltstone, and silty mudstone. Surficial aquifers for mapped 

geologic units consist of primary lithologies of feldspathic 

quartz sand, ferruginous clay, gravelly sand, and sand. Additionally, bedrock aquifers in some areas of the State are overlain by unconsolidated material (Lindsey and Bickford, 1999; 

Soller and Packard, 1998) of sufficient depths to serve as an 

aquifer. These additional areas of the State where surficial 

materials consist of coarse-grained sediments were designated 

as surficial aquifers for this report and are indicated in appendix 2. Table 1 gives a summary of arsenic concentrations in 

groundwater for the four major aquifer types in Pennsylvania. 

All four major aquifer types had median arsenic concentrations less than 4.0 μg/L, and the highest arsenic concentration 

(490.0 μg/L) was in a siliciclastic aquifer. The siliciclastic and 

surficial aquifers had the highest percentage of sample records 

with arsenic concentrations greater than or equal to 4.0 μg/L 

and 10.0 μg/L. Tukey’s multiple comparison tests were used 

to compare mean arsenic concentrations among the four major 

aquifers. Mean arsenic concentrations among all of the major 

Table 1. Summary of arsenic concentrations in groundwater (1969–2007) for the four major aquifer types in Pennsylvania.

[μg/L, micrograms per liter; <, less than]

Major aquifer 

type

Number 

of sample 

records

Median arsenic 

concentration, 

in μg/L

Maximum arsenic 

concentration, 

in μg/L

Sample records with arsenic 

concentrations greater than 

or equal to 4.0 μg/L, in percent

Sample records with arsenic 

concentrations greater than 

or equal to 10.0 μg/L, in percent

Carbonate 597 <4.0 217.5 9 3

Crystalline 852 <4.0 60.0 6 2

Siliciclastic 3,112 <4.0 490.0 20 8

Surficial 462 <4.0 293.0 34 20
10 Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa.

aquifer types were significantly different (alpha = 0.2), except 

for the comparison of mean arsenic concentrations between 

the carbonate and crystalline major aquifer types. Major aquifer variables were included in the statewide analysis but were 

not included in the analysis of the regional study areas because 

these consist of a broad characterization of lithologies that do 

not differentiate well among regions containing only a few 

geologic units and (or) geologic units categorized as the same 

major aquifer. 

Another dataset was developed for groundwater corrosivity. Geologic units across the State (Pennsylvania Geological 

Survey, 2001) were ranked according to estimated groundwater corrosivity to indicate the potential for groundwater 

to intereact with arsenic-bearing minerals and to recreate the 

dataset resulting from a 1996 study by Langland and Dugas 

(1996). They evaluated the relations among corrosive groundwater, water chemistry, and geology by the use of a modified 

version of the Langelier Saturation Index for 11 lithologic 

units based on the State map unit descriptions, then ranked the 

units from most to least corrosive according to color codes. 

Corrosivity rankings were assigned to each lithologic unit 

grouping according to the ranks used by Langland and Dugas 

(1996): (1) quartzite, (2) crystalline rocks excluding diabase 

and quartzite, (3) anthracite-bearing siliciclastic rocks, (4) 

unconsolidated sediments, (5) predominantly shale with other 

siliciclastic rocks, (6) predominantly sandstone with other 

siliciclastic rocks, (7) diabase, (8) mixed siliciclastic rocks 

with bituminous coal, (9) shale, (10) limestone-bearing siliciclastic rocks, and (11) carbonate rocks.

Groundwater-quality data for pH and specific conductance were obtained from data compilations reported by Low 

and Chichester (2006) and Low and others (2008) from areas 

where arsenic data were not necessarily available. The USGS 

(2004) NURE hydrogeochemical data were also used, which 

have additional values for specific conductance and pH for 

groundwater in the entire State; these data, in addition to the 

Low and Chichester (2006) and Low and others (2008) data 

compilations, provided additional spatial coverage. These 

groundwater-quality datasets were combined and used to 

estimate the continuous spatial distribution of pH and specific conductance across the State using the inverse distance 

weighted (IDW) technique. IDW is an interpolation technique 

used for predicting values for unmeasured locations using 

measured values surrounding the prediction location and 

is based on the assumption that things that are close to one 

another are more alike than those that are farther apart. A statewide pH dataset representing pH in groundwater in bedrock 

and surficial aquifers across the State was created from a total 

of 13,598 values with 7,876 (3,251 of these had associated 

arsenic concentrations) of these values from data compilations 

reported by Low and Chichester (2006) and Low and others (2008) and 5,722 of these values from the USGS (2004) 

NURE hydrogeochemical data. A statewide specific conductance dataset representing specific conductance in groundwater 

in bedrock and surficial aquifers across the State was created 

from a total of 11,652 values with 5,931 (2,514 of these had 

associated arsenic concentrations) of these values from data 

compilations reported by Low and Chichester (2006) and Low 

and others (2008) and 5,721 of these values from the USGS 

(2004) NURE hydrogeochemical data. The IDW technique 

was performed for each grouping of geologic units according to the groundwater corrosivity ranking dataset previously 

described (Langland and Dugas, 1996). Because there are 11 

different corrosivity rankings in the State, a separate IDW 

interpolation was calculated for each grouping of units to 

establish boundaries in the pH and specific conductance datasets, and each of the interpolated datasets were combined into a 

statewide raster dataset. Separate pH and specific conductance 

datasets were created to represent groundwater geochemistry 

in the glacial aquifer system. For this area, groundwaterquality data on pH and specific conductance from Low and 

Chichester (2006) and Low and others (2008) were used only 

if the wells were finished in unconsolidated aquifers and data 

from NURE hydrogeochemical data (U.S. Geological Survey, 

2004) were used if the well types were “dug” or “driven.” Of 

the data meeting these criteria, only those data from within the 

defined boundary of the glacial aquifer system (fig. 2) were 

included as estimates of pH and specific conductance. A pH 

dataset representing pH in groundwater in the glacial aquifer 

system in Pennsylvania was created from a total of 619 values 

with 349 (184 of these had associated arsenic concentrations) 

of these values from data compilations reported by Low and 

Chichester (2006) and Low and others (2008) and 270 of 

these values from the USGS (2004) NURE hydrogeochemical data. A specific conductance dataset representing specific 

conductance in groundwater in the glacial aquifer system in 

Pennsylvania was created from a total of 562 values with 293 

(192 of these had associated arsenic concentrations) of these 

values from data compilations reported by Low and Chichester (2006) and Low and others (2008) and 269 of these values 

from the USGS (2004) NURE hydrogeochemical data.

The forested land-cover classification used for this study 

consists of deciduous forest, evergreen forest, and mixed 

forest (Nakagaki and others, 2007). The wetland land-cover 

classification consists of woody wetlands and emergent herbaceous wetlands (Nakagaki and others, 2007). Focal statistics 

were used to create forested and wetland land-cover datasets 

by calculating the average value of each grouped land-cover 

type in cells within a 500-meter radius of each cell across the 

State.

Mineral resources data were obtained from the mineral resources data system of the USGS (2007). The mineral 

resources data were analyzed as a whole and according to 

metallic and nonmetallic minerals. Raster datasets were created to represent each of the three datasets (distance to all 

mineral resources, distance to metallic mineral resources, 

distance to nonmetallic mineral resources) by calculating the 

shortest distance from each cell to the nearest mineral resource 

point in each dataset.

Soil characterization criteria were obtained from Wolock 

(1997), who used the STATSGO soil database (U.S. Department of Agriculture, 1993). The soil data include available 
Methods of Investigation 11

water capacity, bulk density, hydrologic soil group, organic 

matter, permeability, thickness, slope, and texture. Available 

water capacity describes the amount of water that the soil is 

able to store. Bulk density is a measure of soil compaction. 

Hydrologic soil groups define the runoff potential of soil and 

are described according to the percentage of hydrologic soil 

group present. Hydrologic soil groups are described as Group 

A, Group B, Group C, and Group D, in order from low to high 

runoff potential, with dual hydrologic soil groups (AD, BC, 

CD) assigned on the basis of drained and undrained conditions. Soil organic matter represents the percentage of organic 

matter that a soil contains. Permeability is a measurement 

of the ability of water to flow through the soil. Thickness 

describes the distance from the surface of the soil to the underlying solid bedrock. Slope, the percentage of soil land-surface 

slope, describes the potential of precipitation to run off land 

surfaces or infiltrate into subsurfaces. Texture describes the 

percentage of sand, silt, or clay that a soil contains. 

Land-surface elevation data were retrieved from the 

USGS (2009) 1-arc second National Elevation Dataset. These 

data were used to create a slope dataset by calculating the 

maximum rate of change between each cell within the landsurface elevation dataset and its neighbors. Sinkhole location 

data were obtained from the Pennsylvania Geological Survey 

(2007) online sinkhole inventory and database (Kochanov and 

Reese, 2003). These point data were used to create a raster 

dataset by calculating the shortest distance from each cell 

to the nearest sinkhole location. Stream flowline data were 

obtained from the USGS (2005) high-resolution National 

Hydrography Dataset and were used to create a stream density 

raster dataset by calculating the total length of streams within 

a 500-meter search radius area of each cell.

Statistical Methods

Spearman’s rank correlation coefficient (rho) (Helsel and 

Hirsch, 2002), a nonparametric statistical test that uses the 

data ranks, was used to evaluate the significance of relations 

and differences among statewide data for arsenic concentrations and 31 groundwater-quality parameters (where sample 

size exceeded 30). Spearman’s rho also was used to test for 

statistical significance in relations among groundwater-quality 

parameter data (where sample size exceeded 30). Spearman’s 

rho is a monotonic correlation test in which a positive value 

of rho indicates that the response variable (Y) increases as 

the explanatory variable (X) increases, and a negative value 

of rho indicates that the response variable (Y) increases as 

the explanatory variable (X) decreases. High positive values (lower negative values) of rho indicate a strong monotonic correlation. Spearman’s rho was performed on ranktransformed arsenic concentrations and groundwater-quality 

parameter data, where ranks were assigned so that non-detects 

were ranked lower than the lowest value detected or estimated, 

following the methodology of Gilliom and others (2006). 

Reported values and censored values less than 4.0 μg/L were 

ranked as if they were all 3.9 μg/L, which is the lowest rank. 

Any associated groundwater-quality data constituent with a 

non-detect data value was ranked as if it had a value of 0 so 

that the non-detect values would be ranked lower than the 

lowest value detected or estimated. All other measured and 

estimated concentrations of arsenic and other groundwaterquality constituent data were ranked according to their nominal values.

Univariate and multivariate logistic regression analyses 

(Hosmer and Lemeshow, 1989; Helsel and Hirsch, 1992) were 

used to develop predictive models for the State and for three 

intrastate regions—glacial aquifer system, Gettysburg Basin, 

and Newark Basin—for which the model results were used to 

predict the probability of detecting concentrations of arsenic in 

groundwater greater than or equal to 4.0 μg/L on the basis of 

explanatory variables that could affect arsenic values. For the 

resulting logistic regression analyses, a binary response variable was defined by dividing the measured arsenic concentrations into two groups: concentrations greater than or equal to 

4.0 μg/L (exceedances) were classified as “one” and concentrations less than 4.0 μg/L (nonexceedances) as “zero”. The 

threshold of 4.0 μg/L was selected because this value represents the maximum common detection level for the censored 

arsenic concentration data. For the purposes of this report, 

arsenic concentrations greater than or equal to 4.0 μg/L are 

referred to as “elevated.” 

Univariate logistic regression was used as a first step to 

test the significance of individual explanatory variables as 

indicators of elevated arsenic concentrations. Standardized 

coefficients, which allow common unit comparisons among 

model variables, indicate the nature of the univariate relation, with positive relations indicated by values greater than 

zero and inverse relations indicated by values less than zero 

(Menard, 2002). An alpha level of 0.2 was chosen as the inclusion criteria for selecting explanatory variables to include in 

multivariate analysis rather than the traditional alpha level of 

0.10. Hosmer and Lemeshow (1989) suggest that a traditional 

alpha level of 0.10 has failed to identify variables known to be 

important during some logistic regression analyses, and other 

variables may not be considered important in a model until 

they are included with other complementary variables.

Stepwise logistic regression was used to create multivariate logistic regression models that predict the probability of 

elevated arsenic concentrations in groundwater in Pennsylvania. The logistic regression model begins with the intercept; 

then explanatory variables are added or eliminated through 

forward and backward selection procedures until changes in 

variables no longer change the log of the odds ratio (logit). 

The odds ratio is based on the probability of exceeding the 

given threshold value, and the log of the odds ratio (logit) 

transforms a variable constrained between 0 and 1 into a 

continuous, unbounded variable that is a linear function of the 

explanatory variables and converts the predicted values of the 

response variable into probability units (Hosmer and Lemeshow, 1989; Helsel and Hirsch, 1992).
12 Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa.

A number of statistical parameters are examined when 

evaluating multivariate logistic regression models to determine the success of a model (Menard, 2002). Predictive model 

performance was evaluated using measures such as (1) overall 

model significance, (2) values and probabilities associated 

with explanatory variables, (3) model fit statistics, (4) multicollinearity diagnostics, (5) linear regression, and (6) Pearson 

residuals.

Success and significance of a model is measured by 

the log-likelihood ratio, which compares observed values 

with predicted values (Hosmer and Lemeshow, 1989). The 

most significant model will have the highest log-likelihood 

ratio, but the degrees of freedom, or number of explanatory 

variables, are also taken into account. Explanatory variable 

significance is indicated by p-values less than 0.05 and shows 

how specific explanatory variables improve the ability of the 

model to predict the probability of elevated arsenic concentrations in groundwater.

Logistic regression model-fit statistics used in this study 

include the Hosmer-Lemeshow (H-L) goodness-of-fit test, 

generalized and maximum rescaled r-square, percent concordance, model sensitivity, and area under the Receiver Operating Characteristic (ROC) curve. The H-L statistic was used 

to evaluate model calibration by calculating the degree of 

correspondence between the predicted probabilities exceeding the threshold and the actual concentrations exceeding the 

threshold. For this test, p-values less than 0.05 indicate that 

the predicted probabilities are significantly different than the 

actual concentrations. Therefore, a higher H-L p-value will 

indicate a well-calibrated model (Hosmer and Lemeshow, 

1989). There is no r-square value that can be produced by 

the logistic regression model that is identical to the r-square 

value from linear regression; however, some substitutes for the 

r-square value have been calculated (Hosmer and Lemeshow, 

1989). The generalized r-square value (Cox and Snell, 1989) 

is based on maximizing the log-likelihood and is a generalized method of estimating an r-square value. The maximumrescaled r-square value (Nagelkerke, 1991) is another method 

that approximates the linear-regression r-square. Neither of 

these statistics can be interpreted as the percentage of variance explained by the model, but they can be used to compare 

one model with another. Logistic regression model results 

also are described in terms of percent concordance, which 

is the overall rate of correct classification. This value is the 

number of observed exceedances predicted by the model as 

exceedances, plus the number of observed nonexceedances 

predicted by the model as nonexceedances, divided by the 

combined number of observed exceedances and nonexceedances (Hosmer and Lemeshow, 1989). Model sensitivity is the 

number of observed exceedances predicted as exceedances, 

divided by the total number of observed exceedances. Higher 

percent concordance and sensitivity values indicate better 

fitting models. The area under the ROC curve is represented 

by the c statistic, which is a measure of the model’s ability to 

discriminate between groundwater samples that have arsenic 

concentrations greater than or equal to 4.0 μg/L and those that 

do not. The c statistic is a value that varies from 0.5 to 1.0 

with higher values indicating better discrimination. Hosmer 

and Lemeshow (1989) consider values from 0.7 to 0.8 to show 

acceptable discrimination and values from 0.8 to 0.9 to show 

excellent discrimination.

Multicollinearity in multivariate regression models is 

the result of strong correlations among two or more explanatory variables. Models with strongly correlated explanatory 

variables can produce incorrect signs and magnitudes of 

regression coefficients, which lead to incorrect conclusions 

about relations between explanatory and dependent variables 

(Allison, 2001). Multicollinearity was evaluated using Tolerance and Variance Inflation Factor (VIF) multicollinearity 

diagnostic statistics, which are based on linear regression 

analysis of explanatory variables (Allison, 2001). The Tolerance is defined as 1 – r2, where r2 is the coefficient of determination for the regression of one independent variable on 

all remaining independent variables (Allison, 2001; Menard, 

2002). The VIF is equal to the reciprocal of the Tolerance and 

describes how inflated the variance of coefficient is compared 

to what it would be if there were no multicollinearity (Allison, 2001). Although there are no formal thresholds to use for 

the Tolerance or VIF in detecting the presence of multicollinearity, Allison (2001) suggests that Tolerance values less 

than 0.4 (VIF greater than 2.5) may indicate the presence of 

multicollinearity.

Calibration for logistic regression predictive models 

(statewide, glacial aquifer system, Gettysburg Basin, Newark 

Basin) were evaluated using the degree of correspondence 

between observed elevated arsenic concentrations in groundwater and the predicted probabilities of elevated arsenic 

concentrations in groundwater. Data were compiled describing 

observed detections of elevated arsenic concentrations and the 

associated predicted probability of an elevated arsenic concentration. Observed detections of elevated arsenic concentrations 

were calculated by classifying arsenic concentrations less than 

4.0 μg/L as “zero” and arsenic concentrations greater than or 

equal to 4.0 μg/L as “one.” Predicted probabilities of elevated 

arsenic concentrations in groundwater were calculated according to the model results for each of the predictive models 

(statewide, glacial aquifer system, Gettysburg Basin, Newark 

Basin). Data were sorted according to the predicted probability 

of elevated arsenic in groundwater and divided according to 

deciles. The observed detections of elevated arsenic were averaged for each decile to calculate the percentage of observed 

detections, and predicted probabilities were also averaged to 

calculate the average predicted probability for each 10-percent decile. Linear regressions were constructed between the 

percentage of observed detections of elevated arsenic concentrations and the average predicted probability for each decile. 

R-square values associated with each regression were used to 

assess model calibration for the predictive models.

Pearson residuals were calculated for each individual predicted value to determine which predicted values were most 

poorly fit by the logistic regression models and spatially show 

the predictive error for each study area. The Pearson residual 
Arsenic Concentrations and Related Factors 13

statistic calculates the difference between observed and estimated probabilities and divides the result by the binomial standard deviation of the estimated probability (Menard, 2002). 

Cases where values are equal to zero indicate that the probability of arsenic exceeding 4.0 μg/L is exactly what would 

be expected based on the observation. Cases with positive 

residual values indicate that the model predicted a lower probability than what was observed (under prediction), whereas 

cases with negative values indicate that the model predicted a 

higher probability than what was observed (over prediction). 

Spatial evaluation of the highest and lowest residual values 

can indicate specific areas where the model does not do a good 

job of predicting the probability of elevated arsenic.

Probability Maps 

Maps showing the predicted probability of elevated arsenic in groundwater in Pennsylvania and three selected regions 

within the State were constructed using results from the logistic regression models. Model coefficients for each explanatory 

variable along with the variable’s value were used to calculate 

and map probabilities of elevated arsenic in groundwater 

within the State. 

Each variable was compiled for 30-meter grid cells to 

estimate the probability of elevated arsenic in groundwater in 

Pennsylvania or in selected regions within Pennsylvania. In 

order to create the statewide and regional probability maps 

depicting the predicted probability of elevated arsenic concentrations in groundwater, the values for the 30-meter grid cells 

for each explanatory variable were used in logistic regression 

equations within a map-algebra GIS application. For example, 

the equation for the statewide model was expressed as

PREDICTION = 1 / ( 1 + (EXP( - ( INT + ( PE1 * (1) 

[GRANK] ) + (PE2 * [SPCIDW]) + (PE3 * 

[AWCAVE] ) + (PE4 * [PHIDW]) )))) ,

where

PREDICTION is the output dataset describing the predicted 

probability of detecting elevated arsenic 

concentrations in groundwater in 

Pennsylvania, 

EXP is the base of the natural logarithm,

INT is the model intercept,

PE1, PE2, PE3, 

and PE4 represent the parameter estimates for each 

of the explanatory variables that were 

in the final statewide logistic regression 

model, and

GRANK,

SPCIDW,

AWCAVE,

and PHIDW are datasets describing the explanatory 

variables that were in the final statewide 

logistic regression model.

This process was used to calculate the probability that arsenic 

in groundwater exceeds 4.0 μg/L for each cell as a fraction. 

The units for the predicted probability datasets were then 

converted from fractions to percentages in order to produce 

probability datasets with units consistent with those units 

used for probability datasets produced by similar studies. The 

resolution of the predicted probability datasets was reduced 

by aggregating each dataset to keep dataset resolution consistent with the input explanatory dataset that had the lowest 

resolution for each model. The proportions of the predicted 

probability datasets were altered by resampling the datasets to 

correspond with the accuracy of the explanatory dataset having the lowest resolution for each model. For example, the soil 

characterization variables typically had the lowest resolution 

(1:250,000) of any variables ending up in the final models, 

so the predicted probability maps representing these models 

were aggregated to 1:250,000 because it is the same resolution as the original data used to create the soil characterization variables. The resulting dataset for each of the predicted 

probability maps represents the percent probability of arsenic 

concentrations exceeding or equal to 4.0 μg/L in groundwater.

Arsenic Concentrations and Related 

Factors

This section of the report documents arsenic concentrations and related factors in Pennsylvania using analyses that 

include only samples with arsenic concentrations. Later sections of the report consider subsets of the data to predict the 

probability of elevated arsenic concentrations in groundwater.

Arsenic Concentrations

Arsenic concentrations in 5,023 samples of Pennsylvania groundwater ranged from less than the detection level of 

0.01 μg/L to 490.0 μg/L. The median concentration was less 

than 4.0 μg/L. About 82 percent of groundwater samples had 

arsenic concentrations less than 4.0 μg/L, and about 18 percent 

of samples had concentrations greater than or equal to the 

model threshold of 4.0 μg/L. Almost 8 percent of groundwater 

samples had arsenic concentrations greater than or equal to the 

USEPA MCL of 10.0 μg/L. 

The statewide spatial distribution of the 5,023 samples 

with measured arsenic concentrations is shown in figure 3. 

Most of the data are concentrated in the southeastern part of 

the State because of the large number of groundwater-quality 

analyses conducted in that area. In most areas, there was a 

wide range of arsenic concentrations. Table 2 gives a summary 

of arsenic concentrations in groundwater for the six physiographic provinces in Pennsylvania. When analyzed according 

to physiographic province, the Central Lowland province 

had the highest median arsenic concentration (4.5 μg/L) 

and the highest percentage of sample records with arsenic 
14 Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa. Base from U.S. Bureau of the Census digital data, 1990b 1:100,000 Albers Equal-Area Conic projection: Standard Parallels 40° N and 42° N, Central Meridian -78° W, Latitude of Origin 39° County boundaries from U.S. Bureau of the Census, 1990a 1990a Glacial aquifer system boundary from Pennsylvania Geological Survey, 1995; Soller & Packard, 1998 Gettysburg and Newark Basin boundaries from Pennsylvania Geological Survey, 2001 NEW JERSEY MARYLAND WEST VIRGINIA OHIO NEW YORK EXPLANATION Arsenic concentrations, in micrograms per liter (n = 5,023)

DELAWARE

Greater than or equal to 4.0 (n = 909)Less than 4.0 (n = 4,114)

Lake Erie

75°00’ 76°00’ 77°00’ 79°00’ 80°00’ 78°00’

42°

00’

41°

00’

40°

00’

39°

00’ 0 25 MILES 12.5

0 25 KILOMETERS 12.5

Figure 3. Location of sampling sites and associated reported arsenic concentrations in groundwater in Pennsylvania, 1969–2007. (N, number of samples. Arsenic concentrations are shown as elevated if greater than or equal to 4.0 micrograms per liter. Data from Low and Chichester (2006) and Low and others (2008))
Arsenic Concentrations and Related Factors 15

Table 2. Summary of arsenic concentrations in groundwater (1969–2007) for the six physiographic provinces in Pennsylvania.

[μg/L, micrograms per liter; <, less than]

Physiographic 

province

Number 

of sample 

records

Median arsenic 

concentration, 

in μg/L

Maximum arsenic 

concentration, 

in μg/L

Sample records with arsenic 

concentrations greater than 

or equal to 4.0 μg/L, in percent

Sample records with arsenic 

concentrations greater than 

or equal to 10.0 μg/L, in percent

Appalachian Plateaus 1,870 <4.0 490.0 19 9

Atlantic Coastal Plain 52 <4.0 50.0 35 19

Central Lowland 76 4.5 293.0 59 43

New England 87 <4.0 20.0 14 6

Piedmont 1,637 <4.0 85.5 15 5

Ridge and Valley 1,301 <4.0 230.0 18 6

concentrations greater than or equal to 4.0 μg/L (59 percent) 

and greater than or equal to 10.0 μg/L (43 percent) (table 2). 

No formal statistical test was conducted to evaluate arsenic 

difference according to physiographic provinces or geologic 

units in Pennsylvania. Geologic units are listed in appendix 2.

Significance of Groundwater-Quality Properties 

Related to Arsenic

Spearman’s rho was used to investigate possible correlations among arsenic and 31 other groundwater-quality properties and constituents: pH, specific conductance, dissolved 

oxygen, carbon dioxide, alkalinity, major ions (calcium, 

magnesium, sodium, potassium, iron, manganese, chloride, 

sulfate, silica, and fluoride), nutrients [nitrate plus nitrite as 

nitrogen (N), ammonia, phosphate, total organic carbon], 

and trace elements (aluminum, barium, cadmium, chromium, 

cobalt, copper, lead, mercury, nickel, selenium, strontium, and 

zinc). Some of these constituents, such as dissolved oxygen, 

iron, manganese, sulfate, nitrate, ammonia, and total organic 

carbon, could be related to the redox environment as described 

in the next section. Others may have a common source or have 

mobility similar to that of arsenic, considering variations in 

redox and pH. Correlations between arsenic and groundwaterquality constituents were examined on a statewide basis, and 

those groundwater-quality constituents that were statistically 

significant (p<0.0001) and had Spearman’s rho values of at 

least 0.10 in the statewide analysis were also examined on a 

major aquifer basis to determine how correlations vary in the 

State according to major aquifer type. Correlations may differ 

by lithology if examined according to geologic unit because 

geochemical controls on arsenic occurrence and mobility in 

groundwater may differ locally across the State according to 

lithology.

For the statewide dataset, a total of 17 constituents and 

physical properties had correlations with arsenic that were 

statistically significant (p<0.0001), with Spearman’s rho

values of at least 0.10 (table 3). Out of these 17 constituents 

and physical properties, 1, 0, 10, and 7 had correlations with 

arsenic that were statistically significant (p<0.0001), with 

Spearman’s rho values of at least 0.10 for the carbonate, crystalline, siliciclastic, and surficial major aquifers, respectively. 

In order of decreasing magnitude of Spearman’s rho for the 

statewide analysis, cobalt, strontium, nickel, sodium, barium, 

specific conductance, aluminum, alkalinity, manganese, iron, 

silica, calcium, pH, sulfate, and total organic carbon were 

positively correlated with arsenic, whereas dissolved oxygen 

and nitrate plus nitrite (as N) were inversely correlated with 

arsenic. Positive and inverse correlations for the major aquifer 

analyses were the same as the statewide analysis. 

The statewide correlation of arsenic with cobalt, 

strontium, nickel, sodium, barium, aluminum, and calcium 

indicates that these constituents may occur with arsenic in 

the geologic environment, and in some cases, they may be 

mobilized under similar chemical conditions. Correlations of 

arsenic with these constituents also occurred in the siliciclastic 

major aquifer; correlations of arsenic with sodium and barium 

occurred in the surficial major aquifer. This finding indicates 

that these constituents occur with arsenic in the siliciclastic 

major aquifer geologic environment and may be mobilized 

under chemical conditions occurring in this major aquifer.

Statewide correlations with specific conductance, pH, 

alkalinity, and calcium may reflect greater releases of traces 

of arsenic with increasing dissolution of major minerals or 

the tendency for arsenic to be mobilized (desorbed) under 

alkaline conditions. In uncontaminated water, and except for 

carbonate aquifers, higher specific conductance values often 

are associated with older water, which has a higher residence 

time in fractures and other secondary openings than in soils 

and sediments and greater contact with minerals, leading to 

increased hardness, alkalinity, and arsenic concentrations in 

the water. Correlations with specific conductance and calcium 

occurred for the siliciclastic major aquifer, and correlations 

with specific conductance and alkalinity occurred for the surficial major aquifer.

Positive correlations of arsenic with iron, total organic 

carbon and negative correlations of arsenic with dissolved 

oxygen and nitrate plus nitrite (as N) are consistent with 

the observation that arsenic may be released by iron oxides 
16 Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa.

Table 3. Spearman’s rho correlations for the 17 groundwater-quality constituents that have the best correlations with arsenic in 

groundwater in Pennsylvania and major aquifer types.

[p less than 0.0001 and rho greater than or equal to 0.10; NS, not signficant (p equal to or greater than 0.0001 and (or) rho less than 0.10)]

Groundwater-quality 

constituent

Statewide Carbonate major 

aquifer

Crystalline major 

aquifer

Siliciclastic major 

aquifer

Surficial major 

aquifer

Number of 

samples

Spearman’s 

rho

Number of 

samples

Spearman’s 

rho

Number of 

samples

Spearman’s 

rho

Number of 

samples

Spearman’s 

rho

Number of 

samples

Spearman’s 

rho

Direct correlation

Cobalt 791 0.287 36 NS 102 NS 592 0.206 61 NS

Strontium 939 0.252 43 NS 78 NS 725 0.254 93 NS

Nickel 1,131 0.237 109 NS 129 NS 797 0.284 96 NS

Sodium 3,546 0.180 477 NS 685 NS 2,136 0.173 248 0.377

Barium 2,851 0.171 399 NS 467 NS 1,729 0.145 256 0.450

Specific conductance 2,514 0.155 222 NS 451 NS 1,561 0.150 280 0.275

Aluminum 1,794 0.155 185 NS 158 NS 1,285 0.225 166 NS

Alkalinity 2,411 0.155 346 NS 518 NS 1,390 NS 157 0.390

Manganese 3,541 0.153 477 NS 730 NS 2,086 NS 248 0.447

Iron 3,938 0.140 519 NS 789 NS 2,351 NS 279 0.418

Silica 2,378 0.134 301 NS 612 NS 1,330 0.230 135 NS

Calcium 3,445 0.121 456 NS 683 NS 2,079 0.119 227 NS

pH 3,251 0.113 421 NS 453 NS 2,109 NS 268 NS

Sulfate 3,837 0.103 502 0.183 742 NS 2,294 0.124 299 NS

Total organic carbon 2,096 0.101 352 NS 500 NS 1,146 NS 98 NS

Inverse correlation

Dissolved oxygen 639 -0.268 38 NS 205 NS 338 NS 58 NS

Nitrate plus nitrite 

(as N) 3,802 -0.108 509 NS 768 NS 2,256 NS 269 -0.235

under reducing conditions (Smedley and Kinniburgh, 2002). 

Specifically, the inverse correlation of arsenic with dissolved 

oxygen and nitrate plus nitrite (as N) and the direct correlation with iron and manganese indicate that reductive dissolution of iron and manganese oxides may be a mechanism that 

releases arsenic into groundwater. This may be especially 

true in the surficial major aquifer because arsenic had a direct 

correlation with iron and manganese and an inverse correlation with nitrate plus nitrite (as N), and these correlations were 

higher for the surficial major aquifer than those correlations 

that resulted from the statewide analysis (table 3). Smedley 

and Kinniburgh (2002) state that arsenic can be released or 

desorbed from mineral oxides in aquifer materials under alkaline (high pH) conditions, as can other anion-forming elements 

such as silica and sulfate, which explains the direct correlation 

of these constituents with arsenic statewide and in the siliciclastic major aquifer. 

Redox Conditions

Because arsenic can be mobilized in groundwater as 

a result of redox reactions and pH conditions, redox-pH 

matrices describing redox conditions and pH in groundwater 

were created (table 4) to further evaluate geochemical controls 

within the State. The frequency and number of samples with 

arsenic concentrations greater than or equal to 4.0 μg/L were 

summarized for specified redox and pH classifications. Redox 

conditions of groundwater were classified on the basis of concentrations of dissolved oxygen, nitrate, sulfate, manganese, 

and iron. This redox classification system, based on the work 

of McMahon and Chapelle (2008), used four redox categories for this report: (1) anoxic (dissolved oxygen, nitrate, or 

sulfate less than 0.5 mg/L, manganese greater than 50.0 μg/L, 

and iron greater than 100.0 μg/L), (2) mixed (dissolved 

oxygen, nitrate, or sulfate greater than or equal to 0.5 mg/L 

and either manganese greater than 50.0 μg/L or iron greater 

than 100.0 μg/L), (3) oxic (dissolved oxygen, nitrate, or 

sulfate greater than or equal to 0.5 mg/L, manganese less than 

50.0 μg/L, and iron less than 100.0 μg/L), and (4) unknown 
Arsenic Concentrations and Related Factors 17

Table 4. Redox-pH matrix showing the frequency of arsenic in 5,023 groundwater samples collected statewide in Pennsylvania, by redox classification and range of arsenic concentrations. [Values within colored blocks are the percentage and, in parentheses, the number of samples that exceed the threshold of 4 micrograms per liter relative to the total number of samples for the redox-pH class. Redox classifications are based on thresholds of McMahon and Chapelle (2008) and modified to four classes because of data limitations: (1) anoxic (dissolved oxygen, nitrate, or sulfate less than 0.5 milligrams per liter (mg/L), manganese greater than 50.0 micrograms per liter (μg/L), and iron greater than 100.0 μg/L), (2) mixed (dissolved oxygen, nitrate, or sulfate greater than or equal to 0.5 mg/Land either manganese greater than 50.0 μg/L or iron greater than 100.0 μg/L), (3) oxic (dissolved oxygen, nitrate, or sulfate greater than or equal to 0.5 mg/L, manganese less than 50.0 μg/L, and iron less than 100.0 μg/L), and (4) unknown (data for dissolved oxygen, nitrate, or sulfate and more than one of the other parameters were missing). %, percent; n, number]

Redox classification

pH, less than 4.0

pH, greater than or 

equal to 4.0 and less 

than 6.0

pH, greater than or 

equal to 6.0 and less 

than 8.0

pH, greater than or 

equal to 8.0 Unknown All samples in redox classification:

< 1 % (n = 24/5023) 5 % (n = 268/5023) 54 % (n = 2729/5023) 5 % (n = 230/5023) 35 % (n = 1772/5023)

Anoxic 8 % (n = 405/5023) 75 (n = 3/4) 18 (n = 2/11) 18 (n = 38/208) 30 (n = 13/43) 18 (n = 25/139) 20 (n = 81/405)Mixed 3 % (n = 129/5023) 0 (n = 0/2) 0 (n = 0/11) 15 (n = 4/27) 100 (n = 1/1) 8 (n = 7/88) 9 (n = 12/129)Oxic 30 % (n = 1530/5023) 0 (n = 0/1) 1 (n = 1/82) 13 (n = 107/809) 24 (n = 10/42) 7 (n = 42/596) 10 (n = 160/1530)Unknown 59 % (n = 2959/5023) 59 (n = 10/17) 14 (n = 23/164) 16 (n = 269/1685) 23 (n = 33/144) 21 (n = 204/949) 18 (n = 539/2959)All samples in pH classification 54 (n = 13/24) 10 (n = 26/268) 15 (n = 418/2729) 25 (n = 57/230) 16 (n = 278/1772)

EXPLANATION

Percentage of samples with arsenic concentrations greater than or equal to 4.0 micrograms per liter

No data0 to less than 11 to less than 1010 to less than 2020 to less than 5050 or more
18 Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa.

(data for dissolved oxygen, nitrate, or sulfate and more than 

one of the other constituents were missing). As a result of the 

small number of sites with measured dissolved oxygen values 

(12.7 percent of the dataset), much of the redox classifications 

(oxic, anoxic, mixed, and unknown) were dependent upon the 

presence and concentration of nitrate and (or) sulfate and manganese and (or) iron. Five general pH classifications were also 

considered: (1) pH less than 4.0, (2) pH greater than or equal 

to 4.0 and less than 6.0, (3) pH greater than or equal to 6.0 and 

less than 8.0, (4) pH greater than or equal to 8.0, and (5) pH 

concentration unknown. 

The frequency of redox conditions of groundwater samples in Pennsylvania was 8 percent of samples were anoxic, 

3 percent were mixed, 30 percent were oxic, and 59 percent 

were unknown (table 4). The frequency of pH classifications 

of groundwater samples was as follows: 1 percent of samples 

had pH values less than 4.0, 5 percent had pH values ranging 

from greater than or equal to 4.0 to less than 6.0, 54 percent 

had pH values ranging from greater than or equal to 6.0 to 

less than 8.0, 5 percent had pH values greater than or equal 

to 8.0, and 35 percent of samples had unknown pH values. In 

table 4, the percentage of samples within each pH and redox 

classification with arsenic concentrations greater than or equal 

to 4.0 μg/L is color coded to highlight those classifications 

with the greatest frequency of samples with elevated arsenic 

concentrations. 

As groundwater became more reducing, elevated arsenic 

concentrations were detected more frequently (table 4). For 

example, 10 percent of oxic waters had elevated arsenic 

concentrations, as compared to 20 percent of anoxic waters. 

Also, for a given pH range, more samples with anoxic redox 

classification more often had elevated arsenic concentrations 

than samples with oxic or mixed classification. This finding 

is consistent with the release of arsenic from iron oxides by 

reductive dissolution of iron oxides or reductive desorption 

(reduction of arsenate and consequent desorption of arsenite). 

For oxic and mixed redox classifications, the percentage of 

samples with elevated arsenic concentrations increased with 

pH, which is consistent with pH control of arsenate and arsenite adsorption by iron oxides. Regardless of redox classification, samples with pH of less than 4.0 or pH of greater than 

or equal to 8.0 constituted the highest percentages of samples 

with elevated arsenic concentrations, indicating that both high 

and low pH may be associated with elevated arsenic. At pH 

values less than 4.0, iron oxides may not be stable, which is 

indicated by elevated iron concentrations and low sulfate and 

iron ratios. Redox/pH matrices show that as waters become 

more strongly reduced, strongly acidic, or strongly alkaline, 

arsenic concentrations tend to increase. Elevated pH may 

provide a geochemical trigger to release arsenic from the iron 

oxides. Iron and manganese are highly soluble under reducing 

conditions, which are common in confined aquifers or where 

groundwater is in contact with organic material. In addition, 

dissolution of iron oxides may occur in strongly acidic waters.

Predicted Probability of Elevated 

Arsenic Concentrations in 

Groundwater 

Logistic regression models were created to predict the 

probability of elevated arsenic concentrations in groundwater in the State of Pennsylvania and to further improve those 

predictions for three regions within the State: (1) glacial 

aquifer system, (2) Gettysburg Basin, and (3) Newark Basin 

(fig. 2). For the purposes of this report, the predictions for the 

statewide map represent both surficial and bedrock aquifers, 

which may differ for any given area, because wells known 

to be completed in bedrock materials and wells known to be 

completed in glacial and (or) surficial materials were used for 

the statewide study. The glacial aquifer system consists of the 

portion of the State encompassed by the extent of the Wisconsin glaciation (Pennsylvania Geological Survey, 1995) (fig. 1) 

and those areas in the northern half of the State overlain by 

surficial material consisting of coarse-grained sediments 

(Soller and Packard, 1998) (fig. 1). Only wells known to be 

completed in glacial materials were used for the glacial aquifer 

system model, and these wells were also included in the 

statewide model analysis. The Gettysburg and Newark Basins 

are located in the Gettysburg-Newark Lowland section of the 

Piedmont province (fig. 1), with the Gettysburg Basin consisting of the southwestern part of the section and the Newark 

Basin consisting of the northeastern part of the section. The 

purpose of creating models for these three regions was to create a model specifically for the glaciated part of the State and 

to evaluate whether predictions could be improved in these 

areas of Pennsylvania by including locally specific variables 

that are not representative of or available across the entire 

State. These regional models were created using explanatory 

variables that were available or statistically significant only for 

certain regions within the State. 

Model development included univariate and multivariate logistic regression analyses. Univariate relations between 

elevated arsenic concentrations and explanatory variables 

were evaluated and are summarized in appendix 3; coefficients 

indicate the nature of the univariate relation. An alpha level of 

0.20 was chosen as the inclusion criterion for selecting explanatory variables for the multivariate analysis because Hosmer 

and Lemeshow (1989) indicate that a traditional alpha level of 

0.10 failed to identify variables known to be important during 

some logistic regression analyses. The number of explanatory 

variables selected for multivariate analyses varied according 

to study area: 30 out of 40 variables statewide, 32 out of 40 

variables for the glacial aquifer system, 23 out of 38 variables for the Gettysburg Basin, and 20 out of 40 variables for 

the Newark Basin. Coefficients from the final statewide and 

regional multivariate logistic regression models were used to 

construct maps illustrating the probability of arsenic concentrations equaling or exceeding 4.0 μg/L in groundwater in 

Pennsylvania and in selected regions within the State.
Predicted Probability of Elevated Arsenic Concentrations in Groundwater 19

Statewide

The results of the final statewide model are summarized 

in table 5 and show the overall fit for the statewide model is 

statistically significant with a Wald Chi-Square probability 

value of less than 0.0001. Explanatory variables included in 

the statewide model are the siliciclastic major aquifer type 

(SIL), surficial major aquifer type (SURF), estimated groundwater specific conductance (SPCIDW), estimated groundwater pH (PHIDW), and average soil available water capacity 

(AWCAVE). The standardized regression coefficient (table 5) 

allows for comparisons among variables in the model in 

common units where units differed among variables (Menard, 

2002). This statistic showed that presence or absence of the 

siliciclastic and surficial major aquifer types were the most 

significant variables in the model. SIL, SURF, SPCIDW, and 

PHIDW showed positive correlations with elevated arsenic 

concentrations. When the siliciclastic or surficial aquifer types 

are present, arsenic concentrations increase, which illustrates 

the potential of primarily siliciclastic and surficial geologic 

units to affect elevated arsenic concentrations. The positive 

correlation between elevated arsenic concentrations and the 

SPCIDW and PHIDW variables describes how groundwater 

geochemistry can be an indicator of elevated arsenic concentrations in groundwater. Also, the positive correlation 

between elevated arsenic concentrations and the SPCIDW and 

PHIDW variables illustrates how groundwater geochemical 

properties, such as groundwater specific conductance and pH, 

can increase the potential for arsenic mobilization, which is 

consistent with other studies where increased pH was shown 

to mobilize arsenic (Ayotte and others, 2006). Increases in pH 

can occur due to ion exchange with sodium, and high specific 

conductance in groundwater may indicate high amounts of 

salt (Schlottmann and others, 1998). The only variable with 

a negative relation with elevated arsenic concentrations was 

AWCAVE (appendix 3 and table 5), indicating that if a soil 

has the capability of storing smaller amounts of water, the 

potential for elevated arsenic concentrations in groundwater increases. This relation suggests that the probability of 

elevated arsenic concentrations increases beneath sandy and 

loamy soils, which are more prone to leaching and may show 

how soils that poorly store water may serve as pathways for 

arsenic to enter groundwater (U.S. Department of Agriculture, 

2008). This is a problem especially if arsenic is present on the 

land surface from contaminated soils (such as from land application of pesticides, mining and ore processing operations, or 

waste disposal) or mineral deposits because arsenic is more 

prone to leach from these soils into groundwater. Therefore, 

groundwater vulnerability to elevated arsenic concentrations 

in Pennsylvania may be characterized as a function of aquifer 

type and its associated groundwater geochemistry parameters 

(pH and specific conductance), along with the ability of overlying soils to store water.

Overall model fit is good with an H-L p-value of 0.2751 

(p-values greater than 0.05 indicate good model fit). The generalized r-square value was 0.0709, and the maximum-rescaled 

r-square value was 0.1161. Percent concordance was 

69.2 percent, and model sensitivity was 22.1. Model discrimination was acceptable, as indicated by a c statistic of 0.695. 

Diagnostic statistics indicated a lack of multicollinearity for all 

explanatory variables for the statewide model. Multicollinearity diagnostic statistics indicate no detection of multicollinearity among model variables. Tolerance values were greater than 

0.4 and VIF values were less than 2.5.

Linear regressions between the actual probability of 

elevated arsenic concentrations and the average predicted 

probabilities were constructed according to 10 percent deciles 

calculated with the statewide model calibration data. The 

results of the regression confirm good model calibration statewide in Pennsylvania, with an r-square of 0.9647 (fig. 4). 

Spatial distribution of predicted probabilities of elevated 

arsenic concentrations resulting from the statewide model varies across the State and is expressed as a percent probability 

on a map (fig. 5). The map shows the predicted probability of 

elevated arsenic in groundwater for both surficial and bedrock 

aquifers, which may differ in some areas of the State but are 

not differentiated in figure 5. Probabilities of elevated arsenic 

concentrations of 20 to 50 percent are more widespread in 

those parts of the State underlain by siliciclastic and surficial 

aquifers, which also have the highest potential to act as an 

arsenic source or mobilizer. Areas with less than 20 percent 

predicted probability of elevated arsenic concentrations, such 

as the southwestern part of the State, have soils with high 

percentages of available water capacity, which can also be 

indicative of other geologic factors because soil development 

is related to underlying bedrock lithology, except in glaciated areas. Within units having siliciclastic or surficial major 

aquifers, high predicted probabilities (greater than 30 percent) 

of elevated arsenic concentrations are most prominent in areas 

with high groundwater specific conductance and pH.

The Pearson residual statistic was calculated for each 

well to determine which observations were most poorly fit by 

the model and to spatially show the model’s predictive error. 

This statistic calculates the difference between observed and 

estimated probabilities and divides the result by the binomial 

standard deviation of the estimated probability (Menard, 

2002). Resulting Pearson residuals have a mean equal to 0 and 

a standard deviation equal to 1. For the 5,009 wells, Pearson 

residual values include 347 values greater than 2.0 and 3 values less than -2.0. Large positive values account for 7 percent 

of the dataset, whereas large negative values account for less 

than 1 percent of the dataset. Spatial locations of Pearson 

residual values associated with outliers indicate areas where 

the model is not entirely correct and does not fit the dependent 

data (fig. 5). The northeastern and north-central parts of the 

State have the fewest poor predictions, whereas the southeastern part of the State has a large cluster of poor predictions. 

Some poor predictions are spread through the northwestern 

tier and central parts of the State. These poor predictions 

can be attributed to the uneven spatial distribution of arsenic 

in rock and associated groundwater and the inability of the 

variables in the model to completely capture the diversity of 
20 Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa. Table 5. Summary of statistics for statewide and regional logistic regression models predicting the probability of arsenic exceeding 4.0 micrograms per liter statewide and in three regions in Pennsylvania. [--, no data; <, less than; m, meter; %, percent; μS, microsiemens; cm, centimeter; g, grams; cm3, cubic centimeter; hr, hour; H-L, Hosmer-Lemeshow goodness-of-fit test; VIF, variance inflation factor] Model Statistically significant explanatory variables Variable results Model fit Multicollinearity diagnostics Explanatory variable Description Parameter estimate Wald Chi-square probability (p-value) Standardized regression coefficient H-L (p-value) Generalized r-square Maximum rescaled r-square Percent concordant Sensitivity (%) c (area under the Receiver operating characteristic curve) Tolerance VIF Statewide Intercept Model intercept -4.1987 <0.0001 - 0.2751 0.0709 0.1161 69.2 22.1 0.695

- -

SIL Siliciclastic major aquifer type (1 = siliciclastic, 0 = all other) 0.8697 <0.0001 0.2327 0.62584 1.59785SURF Surficial major aquifer type (1 = surficial, 0 = all other) 1.4432 <0.0001 0.2289 0.7445 1.34319SPCIDW Estimated groundwater specific conductance (μS/cm) 0.000701 <0.0001 0.2071 0.95841 1.04339PHIDW Estimated groundwater pH 0.4189 <0.0001 0.1722 0.93767 1.06648AWCAVE Average soil available water capacity (%) -11.3568 <0.0001 -0.1417 0.74158 1.34847

Glacial 

aquifer 

system

Intercept Model intercept -15.8949 0.0004 -

0.4477 0.2314 0.3297 77.8 55.6 0.780

- -

BDAVE Average soil bulk density (g/cm3) 6.5455 0.0165 0.3576 0.95209 1.05032WETAVE Average wetlands land cover within a 500-m radius (%) 0.0392 0.0115 0.2896 0.76741 1.30309SPCIDWGLAC Estimated groundwater specific conductance using only glacial data (μS/cm) 0.00242 0.0013 0.2793 0.78779 1.26937PERMAVE Average soil permeability (cm/hr) 0.1318 0.0164 0.2039 0.82167 1.21703PHIDWGLAC Estimated groundwater pH using only glacial data 0.5392 0.0288 0.2017 0.88717 1.12718

Gettysburg 

Basin

Intercept Model intercept -12.4639 0.0056 -

0.9244 0.2428 0.3655 83.3 65.9 0.833

- -

SANDAVE Average soil sand content (%) 0.237 0.0406 0.6875 0.94178 1.06182ELEV Land surface elevation above sea level (m, North American Vertical Datum of 1988) -0.0182 0.0027 -0.4521 0.86271 1.15913TRI Distance to toxic chemical release inventory sites (m) -0.00025 0.0053 -0.3705 0.93638 1.06794PHIDW Estimated groundwater pH 1.0117 0.0096 0.3542 0.86822 1.15178SPCIDW Estimated groundwater specific conductance (μS/cm) 0.0029 0.0473 0.2439 0.75589 1.32295

Newark 

Basin

Intercept Model intercept -6.6069 0.0143 -

0.5738 0.1331 0.1860 72.7 74.1 0.729

- -

PHIDW Estimated groundwater pH 1.4425 <0.0001 0.3734 0.98324 1.01705SPCIDW Estimated groundwater specific conductance (μs/cm) 0.00115 0.0225 0.2809 0.97962 1.02080MUN

Distance to municipalities containing water 

suppliers with arsenic treatment and sites or groundwater in need of remediation because of arsenic contamination (m)

-0.00011 0.0113 -0.1846 0.87983 1.13659

AWCAVE Average soil available water capacity (%) -42.294 0.0141 -0.1737 0.89884 1.11254
Predicted Probability of Elevated Arsenic Concentrations in Groundwater 21

y = 0.9246x + 1.3574

R 2

 = 0.9647

0

10

20

30

40

50

60

Observed detections, in percent

Statewide n = 5,009

0 10 20 30 40 50 60

y = 0.9497x + 1.4624

R 2

 = 0.9487

0

10

20

30

40

50

60

70

80

90

100

Average predicted probability, in percent

0 10 20 30 40 50 60 70 80 90 100

Glacial aquifer system n = 307

y = 0.9887x + 0.3272

R 2

 = 0.9820

0

10

20

30

40

50

60

70

80

Average predicted probability, in percent

0 10 20 30 40 50 60 70 80

Gettysburg Basin n = 174

y = 0.9236x + 2.4701

R 2

 = 0.9295

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80

Newark Basin n = 455

Figure 4. Percentage of observed detections of elevated arsenic concentrations in relation to the average predicted probability of 

detecting elevated arsenic concentrations in groundwater statewide and in three regions in Pennsylvania.

the geologic, soil, and geochemical properties controlling 

arsenic in the State; additional variables or separate models for 

regions would be needed to seamlessly characterize elevated 

concentrations of arsenic across the entirety of Pennsylvania.

Glacial Aquifer System

The results of the glacial aquifer system model, which 

includes the late Wisconsinian glacial border (Pennsylvania 

Geological Survey, 1995) and areas in the northern half of 

Pennsylvania that are overlain by surficial material consisting of coarse-grained sediments (Soller and Packard, 1998) 

(fig. 2), are summarized in table 5. The overall fit for the 

model is statistically significant with a Wald Chi-Square probability value of 0.0004. Explanatory variables included in the 

glacial aquifer system model are average soil bulk density 

(BDAVE), average wetlands land cover within a 500-meter 

radius (WETAVE), specific conductance in groundwater 

estimated using only glacial data (SPCIDWGLAC), average 

soil permeability (PERMAVE), and groundwater pH estimated using only glacial data (PHIDWGLAC). Results of the 

standardized regression show that average soil bulk density 

is the most significant variable in the model, and all variables 

showed positive correlations with elevated arsenic concentrations. High soil bulk density is related to a greater degree 

of soil compaction and may be an indicator of sandy soils 

because total pore space in sandy soils commonly is less than 

total pore space in clay or silt (U.S. Department of Agriculture, 2008). Arsenic concentrations increase as average soil 

permeability increases ( appendix 3 and table 5), which indicates that areas of sandy soils with high permeability estimates 

tend to have higher concentrations of arsenic in groundwater 

than areas with other soil types. Areas in the glacial aquifer system with sandy or coarse-grained deposits are typically associated with glacial valley-fill deposits that overlay 

fine-grained deposits of till or clay in some places; sediment 

texture typically is not uniform with depth in glaciated regions 

(Thomas, 2007). The sandy soils may also exist as a thin 

veneer that overlies fine-grained till or clay deposits that form 

the valley walls and (or) terraces. Extensive clay deposits 

(glacial lake sediments) are found in many glacial valleys and 

where surface depressions exist the clay deposits encompass 

wetlands such as peat bogs. Average wetlands land cover 

within a 500-meter radius (WETAVE) has the second highest 
22 Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa. Base from U.S. Bureau of the Census digital data, 1990b 1:100,000 Albers Equal-Area Conic projection: Standard Parallels 40° N and 42° N, Central Meridian -78° W, Latitude of Origin 39° County boundaries from U.S. Bureau of the Census, 1990a 1990a Glacial aquifer system boundary from Pennsylvania Geological Survey, 1995; Soller & Packard, 1998 Gettysburg and Newark Basin boundaries from Pennsylvania Geological Survey, 2001 NEW JERSEY MARYLAND WEST VIRGINIA OHIO NEW YORK EXPLANATION Predicted probability of elevated arsenic concentrations in groundwater, in percent Less than -2.0; n = 3 -2.0 Greater than 2.0; n = 347

– 2.0; n = 4,659

Indicates that an elevated arsenic concentration in groundwater did not occur at the well despite the model predicting a high probability of an elevated concentrationIndicates that the predicted probabilities fit the dependent data observations wellIndicates that an elevated arsenic concentration in groundwater did occur at the well despite the model predicting a low probability of an elevated concentration

Pearson residual values n = 5,009 values less than -2.0 and greater than 2.0 indicate poor predictions (Menard, 2002)

0 – 1011 – 2021 – 30

31 – 4041 – 5051 – 100

Lake Erie

DELAWARE

75°00’ 76°00’ 77°00’ 79°00’ 80°00’ 78°00’

42°

00’

41°

00’

40°

00’

39°

00’

0 25 MILES 12.50 25 KILOMETERS 12.5

Figure 5.

Areas with predicted probability that arsenic concentrations in groundwater in surficial and bedrock aquifers statewide in Pennsylvania will be greater than or equal 

to 4.0 micrograms per liter and sampling sites with associated Pearson residual values showing predictive error for measured arsenic concentrations.
Predicted Probability of Elevated Arsenic Concentrations in Groundwater 23

standardized coefficient in the model, which means that the 

greater the percentage of wetlands within a 500-meter radius, 

the higher the probability of an elevated arsenic concentration 

in groundwater. Because many of the glacial valley-fill deposits identified by the bulk density and permeability of soils are 

overlain by wetlands, underlying till or clay may be creating a 

confining layer close to the land surface. This finding suggests 

that arsenic is being mobilized in these areas in part because 

of the confining layers that create the wetlands in the glacial 

valleys. Wetlands, which relate to low dissolved oxygen and 

redox, could be the result of the upward flow of groundwater. 

These poorly drained areas can lead to high pH and arsenic 

desorption. The positive correlation between elevated arsenic 

concentrations and SPCIDWGLAC and PHIDWGLAC variables describes groundwater geochemistry as an indicator of 

elevated arsenic concentrations in groundwater in the glacial 

aquifer system. Increases in pH can cause arsenic to become 

mobilized in water. Increases in specific conductance indicate 

older, less oxic water, which can result in the mobilization of 

arsenic. In inundated areas such as wetlands, groundwater is 

anoxic and organic rich, and flow is restricted, which allows 

arsenic to be released. The glacial aquifer system model 

illustrates that groundwater geochemical properties, such 

as high groundwater specific conductance and pH, may be 

acting as geochemical triggers and releasing arsenic from the 

aquifer matrix to the groundwater in the glacial aquifer system 

(Thomas, 2007).

Model fit was very good with an H-L p-value of 0.4477. 

The glacial aquifer system model had a generalized r-square 

value of 0.2314 with a maximum-rescaled r-square value of 

0.3297. According to the percent concordant, 77.8 percent 

of responses were correctly predicted, and the sensitivity 

value was 55.6. Model discrimination was acceptable with a 

c statistic of 0.780. Diagnostic statistics indicated a lack of 

multicollinearity for all explanatory variables because the 

lowest Tolerance value was 0.76741 (greater than 0.4), and the 

highest VIF value was 1.30309 (less than 2.5). 

Linear regressions between the actual probability of 

elevated arsenic concentrations and the average predicted 

probabilities were constructed according to 10 percent deciles 

calculated with the glacial aquifer system model calibration 

data. Regression results confirm good model calibration with 

an r-square of 0.9487 (fig. 4). 

Predicted probabilities of elevated arsenic concentrations 

produced by the glacial aquifer system model were calculated 

using arsenic concentration data that were known to represent the glacial units in order to create a model distinguishing 

between the glacial units and the underlying bedrock. Predicted probabilities for the glacial aquifer system model have 

a different spatial distribution than probabilities of elevated 

arsenic resulting from the statewide model that represents 

underlying bedrock (figs. 5–6). The predicted probabilities of 

elevated arsenic in groundwater are much greater in the northwestern than in the northeastern glacial tier. High predicted 

probabilities (greater than 50 percent) of elevated arsenic concentrations are most prominent in areas with high estimated 

groundwater specific conductance and pH, which were calculated only for those wells finished in the glacial aquifer system. These areas are located along Lake Erie in the northwestern glacial tier and in areas with glacial valley-fill deposits that 

correspond to high percentages of wetlands. Probabilities of 

elevated arsenic concentrations of 20 to 50 percent are more 

widespread in those areas with high average soil bulk density 

and high soil permeability; most of these areas are located in 

the northwestern glacial tier. Areas with the lowest estimated 

specific conductance and pH, such as the northeastern glacial 

tier, have less than 20 percent predicted probability of elevated 

arsenic concentrations. 

The Pearson residual statistic was calculated for each 

well and plotted on a map to determine which observations 

and spatial areas were most poorly fit by the model and to spatially show the model’s predictive error. Results indicate Pearson residuals have a mean equal to 0 and standard deviation 

equal to 1. For the 307 wells, Pearson residual values include 

16 values greater than 2.0 and two values less than -2.0. Large 

positive values account for 5 percent of the dataset, whereas 

large negative values account for less than 1 percent of the 

dataset. Spatial locations of Pearson residual outliers appear to 

be randomly distributed throughout the region (fig. 6).

Gettysburg Basin

Results for the Gettysburg Basin model, which represents 

south-central Pennsylvania, show the overall model fit is statistically significant with a Wald Chi-Square probability value 

of 0.0056 (table 5). Explanatory variables in the model include 

average soil sand content (SANDAVE), land-surface elevation 

above sea level (NAVD 88) (ELEV), distance to toxic chemical release inventory sites (TRI), average estimated groundwater pH (PHIDW), and estimated groundwater specific conductance (SPCIDW). Standardized regression coefficients show 

that average soil sand content is the most significant variable 

in the model. SANDAVE, PHIDW, and SPCIDW were positively correlated with elevated arsenic concentrations, whereas 

ELEV and TRI were negatively correlated. As average soil 

sand content increases, arsenic concentrations also increase, 

and as land-surface elevation above NAVD 88 decreases, 

arsenic concentrations increase. Soils with high sand content 

seem to be found mostly in the parts of the region with lower 

land-surface elevations, which seems to correspond to hornfels 

bordering diabase intrusions with associated mineral deposits 

of hydrothermal origin, which Senior and Sloto (2006) also 

found to have locally elevated arsenic concentrations. Sandy 

soils and low land-surface elevations may be indicators of 

the underlying geologic environment acting as an arsenic 

source or mobilizer, causing elevated arsenic concentrations 

in groundwater within the Gettysburg Basin. The positive correlation between elevated arsenic concentrations and SPCIDW 

and PHIDW variables indicate groundwater geochemistry 

conditions that facilitate arsenic mobilization in groundwater. 

Geochemical conditions may be further mobilizing arsenic 
24 Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa. Base from U.S. Bureau of the Census digital data, 1990b 1:100,000 Albers Equal-Area Conic projection: Standard Parallels 40° N and 42° N, Central Meridian -78° W, Latitude of Origin 39° County boundaries from U.S. Bureau of the Census, 1990a Glacial boundary from Pennsylvania Geological Survey, 1995 and Soller & Packard, 1998NEW JERSEY MARYLAND WEST VIRGINIA OHIO NEW YORK EXPLANATION Predicted probability of elevated arsenic concentrations in groundwater, in percent Less than -2.0; n = 2 -2.0 Greater than 2.0; n = 16

– 2.0; n = 289

Indicates that an elevated arsenic concentration in groundwater did not occur at the well despite the model predicting a high probability of an elevated concentrationIndicates that the predicted probabilities fit the dependent data observations wellIndicates that an elevated arsenic concentration in groundwater did occur at the well despite the model predicting a low probability of an elevated concentration

Pearson residual values n = 307 values less than -2.0 and greater than 2.0 indicate poor predictions (Menard, 2002)

0 – 1011 – 2021 – 30

31 – 4041 – 5051 – 100

Lake Erie

DELAWARE

75°00’ 76°00’ 77°00’ 79°00’ 80°00’ 78°00’

42°

00’

41°

00’

40°

00’

39°

00’

0 25 MILES 12.50 25 KILOMETERS 12.5

Figure 6.

Areas with predicted probability that arsenic concentrations in groundwater in the glacial aquifer system in Pennsylvania will be greater than or equal to 

4.0 micrograms per liter and sampling sites with associated Pearson residual values showing predictive error for measured arsenic concentrations in wells.
Predicted Probability of Elevated Arsenic Concentrations in Groundwater 25

from aquifer materials because high predicted probabilities 

of elevated arsenic in groundwater also correspond to high 

estimated groundwater specific conductance and pH. This 

finding is consistent with the results of the study by Senior and 

Sloto (2006) in the Newark Basin. With decreased distances to 

toxic chemical release inventory sites, arsenic concentrations 

increase. This relation indicates elevated arsenic concentrations occur close to facilities that have been known to release 

toxic chemicals directly to air, water, or land. These facility 

locations represent anthropogenic contamination sites that 

could have released chemicals containing arsenic or chemicals 

containing constituents that affect redox environment or pH 

and, hence, promote arsenic mobilization. Senior and Sloto 

(2006) also noted elevated arsenic concentrations in groundwater near a few contamination sites but did not directly 

attribute elevated arsenic concentrations in groundwater to 

contaminant sources. The Gettysburg Basin model illustrates 

that predictions of elevated arsenic concentrations in groundwater can be refined by considering soils prone to leaching, 

proximity to contaminated sites, and topography in conjunction with previously associated groundwater geochemical 

properties (pH, redox, specific conductance). 

An H-L p-value of 0.9244 indicates that model fit was 

very good. The generalized r-square value was 0.2428, and 

the maximum-rescaled r-square value was 0.3655. These were 

the highest r-square values for any of the models, indicating 

that these data fit the Gettysburg Basin model better than data 

for the other models fit those models. The percent concordant 

value indicates that 83.3 percent of responses were correctly 

predicted, and model sensitivity was 65.9. Model discrimination was excellent with a c statistic of 0.833. No multicollinearity was detected among model variables because the 

lowest Tolerance value was greater than 0.4 and the highest 

VIF value was less than 2.5. Linear regressions between the 

percentage of observed detections of elevated arsenic concentrations and the average predicted probabilities confirm good 

model calibration, with an r-square of 0.9820 (fig. 4). 

Elevated arsenic concentration predicted probabilities 

resulting from the Gettysburg Basin model (fig. 7) were higher 

for the southern part of the region than the probabilities that 

resulted from the statewide model (fig. 5). Predicted probabilities of elevated arsenic concentrations greater than 50 percent 

are focused in areas with high sand soil content and low 

land-surface elevation; many of these areas are located along 

the southern boundary of the region. Probabilities of elevated 

arsenic concentrations between 20 to 50 percent surround 

the areas that represent probabilities greater than 50 percent, 

which is mainly attributed to the 122 toxic chemical release 

inventory sites within the basin because as distances to these 

sites decrease, predicted probabilities of elevated arsenic 

concentrations increase. Areas that have under 20 percent 

predicted probability of elevated arsenic concentrations largely 

correspond to those parts of the region with the lowest estimated groundwater specific conductance and pH. 

Results for the Pearson residual statistic include a 

mean equal to 0 and a standard deviation equal to 1. For the 

174 wells, Pearson residual values include 8 values greater 

than 2.0 and 0 values less than -2.0; large positive values 

account for 5 percent of the dataset. The spatial locations for 

the eight Pearson residual values associated with outliers are 

randomly spread throughout the region (fig. 7).

Newark Basin

The Newark Basin model results, which represent part of 

southeastern Pennsylvania northeast of the Gettysburg Basin, 

have geologic variables that are similar to the Gettysburg 

Basin, which means that these two models have similar model 

variables. These two areas were considered separately for 

this study because the Newark Basin has often been considered separately for other studies and has similar but different 

geology (Peters and Burkert, 2008; Senior and Sloto, 2006). 

The Newark Basin has an overall model fit that is statistically significant with a Wald Chi-Square probability value of 

0.0143 (table 5). Explanatory variables for the Newark Basin 

model include estimated groundwater pH (PHIDW), estimated 

groundwater specific conductance (SPCIDW), distance to 

municipalities containing water suppliers receiving arsenic 

treatment and sites or groundwater in need of remediation 

because of arsenic contamination (MUN), and average soil 

available water capacity (AWCAVE). Standardized regression coefficients show that estimated groundwater pH was the 

most significant variable in the model. PHIDW and SPCIDW 

had positive correlations with elevated arsenic concentrations, whereas MUN and AWCAVE had negative correlations 

(table 5). Correlations between arsenic and PHIDW, SPCIDW, 

and AWCAVE indicate relations are similar to those for the 

statewide and Gettysburg Basin models. High specific conductance and pH in groundwater are more than likely providing a 

geochemical environment that facilitates the mobilization of 

arsenic from mineral deposits bordering diabase intrusions. 

Senior and Sloto (2006) found in their study of the Newark 

Basin that wells completed near diabase in the hornfels commonly had elevated groundwater concentrations of arsenic 

and that areas with pH of 8 or greater were associated with a 

geochemical environment favorable for arsenic mobilization. 

Also, the lowest predicted probabilities of arsenic correspond 

to those parts of the region with the highest soil available 

water capacity, which are underlain by the diabase intrusions. 

The MUN variable was not part of the statewide model; it 

represents areas with known arsenic contamination in groundwater and soils. The MUN variable’s negative correlation with 

arsenic shows that arsenic concentrations increase as distance 

to municipalities with treatment decreases. These municipalities not only represent areas containing geochemical and 

geologic properties favorable for arsenic mobilization, but 

they also contain sites that may have been affected by anthropogenic contamination. As with the toxic chemical release 

inventory sites, it is possible for contaminants, such as arsenic, 

to enter soils and groundwater from these sites that are in need 

of remediation. As with the Gettysburg Basin, results of the 
26 Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa. Base from U.S. Bureau of the Census digital data, 1990b 1:100,000 Albers Equal-Area Conic projection: Standard Parallels 40° N and 42° N, Central Meridian -78° W, Latitude of Origin 39° County boundaries from U.S. Bureau of the Census, 1990a Gettysburg Basin from Pennsylvania Geological Survey, 2001 EXPLANATION Predicted probability of elevated arsenic concentrations in groundwater, in percent Less than -2.0; n = 0 -2.0 Greater than 2.0; n = 8

– 2.0; n = 166

Indicates that an elevated arsenic concentration in groundwater did not occur at the well despite the model predicting a high probability of an elevated concentrationIndicates that the predicted probabilities fit the dependent data observations wellIndicates that an elevated arsenic concentration in groundwater did occur at the well despite the model predicting a low probability of an elevated concentration

Pearson residual values n = 174 values less than -2.0 and greater than 2.0 indicate poor predictions (Menard, 2002)

0 – 1011 – 2021 – 30

31 – 4041 – 5051 – 100

0 10 MILES 50 10 KILOMETERS 5

ADAMS

CHESTER

MONTGOMERY

LANCASTER

YORK

BERKS PERRY LEBANON

CUMBERLAND

DAUPHIN

PENNSYLVANIA

DELAWARE MARYLAND

76°00’ 77°00’

40°

00’

39°

30’

76°30’

Figure 7.

Areas with predicted probability that arsenic concentrations in groundwater in the Gettysburg Basin in Pennsylvania will be greater than or equal to 4.0 micrograms 

per liter and sampling sites with associated Pearson residual values showing predictive error for measured arsenic concentrations.
Limitations and Uses of Arsenic Models and Probability Maps 27

Newark Basin model illustrate that geochemical properties of 

groundwater, soils properties, and proximity to areas containing water suppliers with arsenic treatment or sites in need of 

remediation because of arsenic contamination describe an 

environment favorable for arsenic mobilization.

Overall model fit was very good, indicated by an H-L 

p-value of 0.5738. The generalized r-square value was 0.1331, 

and the maximum-rescaled r-square value was 0.1860. The 

percent concordant value indicates that 72.7 percent of 

responses were correctly predicted, and model sensitivity was 

74.1. A c statistic of 0.729 shows acceptable model discrimination. Diagnostic statistics indicate a lack of multicollinearity for all explanatory variables for the Newark Basin model. 

Because the lowest Tolerance value was 0.87983 (all values 

are greater than 0.4) and the highest VIF value was 1.13659 

(all values were less than 2.5), these multicollinearity diagnostic statistics indicate that there is no multicollinearity among 

model variables. Linear regressions between the percentage of 

observed detections of elevated arsenic concentrations and the 

average predicted probabilities confirm good model calibration, with an r-square of 0.9295 (fig. 4). 

Predicted probabilities of elevated arsenic concentrations 

resulting from the Newark Basin model (fig. 8) are higher 

than those probabilities that resulted from the statewide model 

(fig. 5). Predicted probabilities of elevated arsenic concentrations greater than 30 percent are distributed mainly in the 

central and northern parts of the region and correspond to high 

estimated groundwater specific conductance and pH and to 

proximity to municipalities containing water suppliers with 

arsenic treatment and sites or groundwater in need of remediation because of arsenic contamination. Predicted probabilities 

less than 30 percent primarily correspond to high percentages 

of soil available water capacity. These areas are located in the 

southeastern part of the Newark Basin and in the northern part 

of the basin where they correspond to diabase intrusions. 

Pearson residual results show a mean equal to 0 and standard deviation equal to 1. For the 455 wells, Pearson residual 

values include 0 values less than -2.0 and 14 values greater 

than 2.0; large positive values account for 3 percent of the 

dataset. Spatial locations of Pearson residual values associated 

with outliers indicate that most poor predictions are located in 

the southeastern part of the region (fig. 8).

Limitations and Uses of Arsenic 

Models and Probability Maps

Probability maps developed for this study show the 

predicted probability of arsenic concentrations greater than or 

equal to 4.0 μg/L in groundwater in Pennsylvania and three 

regions within the State, including the glacial aquifer system, 

the Gettysburg Basin, and the Newark Basin (figs. 5–8). The 

probability maps do not show actual arsenic contamination 

of groundwater but rather depict areas that have the potential 

or likelihood of having groundwater with elevated arsenic 

concentrations. There is inherent uncertainty associated with 

each of the maps, which stems from data quality and availability in the well database and GIS-based explanatory variables 

and the estimation errors in the logistic regression coefficients. 

Certain areas of the predictive maps may have elevated 

arsenic that are not characterized by the statewide or regional 

models and are indicated by areas with many Pearson residuals indicating poor predictions (values less than -2.0 and 

greater than 2.0). These areas have greater uncertainty associated with their relative predicted probabilities. The occurrence 

of Pearson residuals representing poor predictions resulting 

from the statewide and regional models supports the concept that regional models across the entire State or regional 

models encompassing smaller regions than those analyzed in 

this study may be needed to improve predicted probabilities. 

Higher resolution spatial data and additional data on arsenic 

concentrations in groundwater and associated variables would 

improve the predictive power of the models. Further study and 

additional independent data are also needed to validate the 

results of each logistic regression model.

In addition, regional differences in numerous factors, 

such as lithology and geochemical controls, affecting arsenic 

mobility are not accounted for in the statewide model and 

may reduce the predictive power of the statewide model. 

For example, the major aquifer type variable present in the 

statewide analysis is a broad characterization of dominant 

lithologies and may mask localized mineralogical differences 

in geologic units within the major aquifers. An example of a 

geochemical control not accounted for can be seen in table 4 

where it shows how elevated concentrations of arsenic tend 

to occur at low pH (less than 4.0) and high pH (greater than 

or equal to 8.0), but the statewide Spearman’s rho correlation 

indicates a positive correlation between arsenic concentrations and pH values. This positive correlation between arsenic 

concentrations and pH values masks the relation of elevated 

arsenic concentrations to low pH, which may occur in fewer 

places statewide but still results in arsenic concentrations of 

concern. Differences in variables that were significant among 

statewide and regional models also illustrate factors that are 

not able to be accounted for in the statewide model. It is likely 

that the regional models for geologic units and major aquifers 

in other physiographic provinces, such as the Appalachian 

Plateaus, Atlantic Coastal Plain, Piedmont Uplands, or Ridge 

and Valley, that were not separately analyzed for this study, 

would also differ and provide better predictive ability in those 

regions than the statewide model.

By providing an improved understanding of spatial 

controls on arsenic statewide and in three regions in Pennsylvania, the probability maps developed for this report may help 

resource managers to prioritize areas for groundwater-quality 

monitoring or implement alternative management practices. 

However, the probability maps associated with this report are 

intended for regional-scale use and have limitations for use 

at the field-scale or when considering individual wells. The 

maps are not appropriate at any scale larger than 1:250,000, 

which is the smallest scale of any of the explanatory variables 
28 Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa. LANCASTER DELAWARE PHILADELPHIA MONTGOMERY BUCKS SCHUYKILL NORTHAMPTON BERKS LEHIGH PENNSYLVANIA NEW JERSEY CHESTER

0 5 MILES0 5 KILOMETERS

Base from U.S. Bureau of the Census digital data, 1990b 1:100,000 Albers Equal-Area Conic projection: Standard Parallels 40° N and 42° N, Central Meridian -78° W, Latitude of Origin 39°

County boundaries from U.S. Bureau of the Census, 1990aNewark Basin from Pennsylvania Geological Survey, 2001

EXPLANATION

Predicted probability of elevated arsenic 

concentrations in groundwater, in percent

Less than -2.0; n = 0-2.0 Greater than 2.0; n = 14

– 2.0; n = 441

Indicates that an elevated arsenic concentration in groundwater did not occur at the well despite the model predicting a high probability of an elevated concentrationIndicates that the predicted probabilities fit the dependent data observations wellIndicates that an elevated arsenic concentration in groundwater did occur at the well despite the model predicting a low probability of an elevated concentration

Pearson residual values n = 455 values less than -2.0 and greater than 2.0 indicate poor predictions (Menard, 2002)

0 – 1011 – 2021 – 30

31 – 4041 – 5051 – 100

75°00’ 76°00’

40°

30’

40°

00’

75°30’

Figure 8.

Areas with predicted probability that arsenic concentrations in groundwater in the Newark Basin in Pennsylvania will be greater than or equal to 4.0 micrograms per 

liter and sampling sites with associated Pearson residual values showing predictive error for measured arsenic concentrations in wells.
Summary and Conclusions 29

used in the models. The threshold accuracy value for a scale of 

1:250,000, as established by National Map Accuracy Standards, is 127 meters (417 feet) in ground units. Concentrations 

of arsenic in groundwater and in a well are affected by many 

field-scale complexities that are not accounted for in the models. For example, the models do not account for preferential 

groundwater flow paths in bedrock units. As a result, although 

a well may be located in a region with a high predicted probability of elevated arsenic, the well may yield water with 

low arsenic concentrations as a result of complexities that 

cannot be represented in the regional-scale models that were 

developed for this study. Therefore, the models and maps are 

not appropriate for predicting the risk of elevated arsenic in 

individual wells. 

In addition to the explanatory variables included in each 

of the four models, there may be other explanatory variables 

that can affect concentrations of arsenic in groundwater. 

For example, the regional models do not take geology into 

account, which means that these regions need to be studied 

in greater detail. Also, exclusion of an explanatory variable 

from the model does not mean that the variable does not affect 

concentrations of arsenic. For example, data on dissolved 

oxygen concentrations could have been used to define the 

redox environment, but sufficient data were not available to 

create a dissolved oxygen dataset for use in the analysis. In 

addition, for two or more explanatory variables that exhibit 

strong correlations with one another, all of these variables 

were not included in the same model because including one 

variable usually accounts for the effects of the other correlated 

variables. For example, soil available water capacity and soil 

thickness have a strong positive correlation, so only one of 

the variables was included. Additionally, characterization of 

lithologies other than by the selected major aquifer types may 

result in different correlations between elevated arsenic concentrations in groundwater and geologic units. Some geologic 

units from each major aquifer type may be more likely than 

other geologic units to have elevated arsenic concentrations, 

and so the more subtle differences in mineralogy of these units 

that affects arsenic occurrence and mobility are not captured in 

the models. 

Summary and Conclusions

Data on arsenic concentrations in groundwater were 

available for 5,023 wells—monitoring, domestic, public 

supply, commercial, irrigation, and industrial wells—across 

Pennsylvania. Arsenic was detected at a concentration of 

4.0 micrograms per liter (μg/L) or greater in 18 percent of 

samples. Arsenic concentrations that met or exceeded the 

U.S. Environmental Protection Agency maximum contaminant level of 10.0 μg/L were measured in about 8 percent of 

samples; the highest arsenic concentration was 490.0 μg/L. 

Comparison of arsenic concentrations in groundwater 

by physiographic province indicates that the Central Lowland 

province in northwestern Pennsylvania has the highest median 

arsenic concentration (4.5 μg/L) and highest percentage of 

sample records with arsenic concentrations greater than or 

equal to 4.0 μg/L (59 percent) and greater than or equal to 

10.0 μg/L (43 percent). Evaluation of arsenic concentrations 

in groundwater of four major aquifer types in Pennsylvania 

(carbonate, crystalline, siliciclastic, and surficial) showed that 

all aquifer types have median arsenic concentrations less than 

4.0 μg/L, and the highest arsenic concentration (490.0 μg/L) 

was in a siliciclastic aquifer. The siliciclastic and surficial 

aquifers have the highest percentage of sample records with 

arsenic concentrations greater than or equal to 4.0 μg/L and 

10.0 μg/L. Elevated arsenic concentrations, which are those 

arsenic concentrations greater than or equal to 4.0 μg/L, were 

most commonly found in strongly reducing groundwater statewide. Arsenic concentrations were elevated in 10 percent of 

oxic waters, whereas 20 percent of anoxic waters had elevated 

arsenic concentrations. Also, for a given pH range, samples 

classified as anoxic reductive-oxidative (redox) water more 

typically had elevated arsenic concentrations than samples 

classified as oxic or mixed. The relatively large percentage of 

anoxic and high pH waters with elevated concentrations of 

arsenic indicates that mechanisms such as reductive desorption or dissolution are occurring, releasing arsenic from iron 

oxides. However, elevated arsenic concentrations were also 

found in reducing waters with low pH, indicating that other 

mechanisms, such as reductive dissolution of iron oxides, also 

may result in mobilization of arsenic in groundwater.

Arsenic concentrations were correlated with concentrations of several chemical constituents, including (1) constituents linked to redox processes (specific conductance, 

alkalinity, iron, pH, total organic carbon, dissolved oxygen, 

and nitrate plus nitrite (as N)), (2) constituents that may be 

mobilized under chemical conditions similar to those that 

mobilize arsenic (cobalt, strontium, nickel, sodium, barium, 

aluminum, and calcium), and (3) anions or oxyanions that sorb 

to iron oxides (silica and sulfate).

Logistic regression was used to develop spatial statistical models predicting the probability of detecting arsenic 

concentrations greater than or equal to 4.0 μg/L in groundwater statewide and in three intrastate regions. Although, the 

statewide and regional (glacial aquifer system, Gettysburg 

Basin, and Newark Basin) models consisted of slightly different variables, the results have common characteristics that can 

be grouped as (1) geologic and soils variables that describe 

arsenic sources and mobilizers (major aquifer types, soil 

available water capacity, soil bulk density, soil permeability, 

and soil sand content), (2) geochemical variables that describe 

the geochemical environment of the groundwater (specific 

conductance and pH), and (3) locally specific variables that are 

unique to each of the three regions studied and are not applicable to the statewide analysis (wetlands land cover, land-surface elevations above the North American Vertical Datum of 

1988 (NAVD 88), distance to toxic chemical release inventory 

sites, and distance to municipalities containing water suppliers with arsenic treatment and sites or groundwater in need of 
30 Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa.

remediation because of arsenic contamination). These models 

have limitations because they may not characterize areas that 

have different controls on arsenic mobility and should not be 

used to estimate probabilities of elevated arsenic in groundwater at the field-scale or when considering individual wells.

The occurrence of arsenic in groundwater of Pennsylvania is largely the result of mobilization of arsenic from 

natural sources in the aquifer and the geochemical conditions 

of groundwater. For this investigation, the potential for natural 

geologic features to be sources of arsenic was established on 

the basis of selected geologic characteristics (primary lithology to determine major aquifer type). Also, the data reviewed 

for this investigation indicate that variations in arsenic concentrations in Pennsylvania groundwater generally are related to 

variations in redox conditions, pH, and specific conductance 

at State and regional scales. As demonstrated in this report, 

these groundwater-quality variables, which affect the release, 

transport, and attenuation of arsenic in the aquifer, may be 

considered in conjunction with siliciclastic and surficial major 

aquifer types to indicate geologic conditions where groundwater may contain elevated arsenic. Health officials may 

consider testing or advising homeowners in geologic settings 

within siliciclastic or surficial major aquifer types to test their 

groundwater for arsenic, particularly if other available data, 

such as proximity to known areas of elevated arsenic or arsenic contaminant release sites or geochemical conditions (low 

redox, high pH, high specific conductance) indicate a geochemical environment that favors the mobilization of arsenic.

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Appendixes 1, 2, and 3

Appendix 1. Summary of statewide and regional anthropogenic and natural factors used as explanatory variables in logistic 

regression models for elevated arsenic concentrations in groundwater in Pennsylvania and number of sample records.

Appendix 2. Summary of arsenic concentrations in groundwater (1969–2007) for the 193 geologic units in Pennsylvania 

with major aquifer type and primary lithology. 

Appendix 3. Results of univariate logistic regression analyses with logistic regression standardized coefficients and individual p-values of independent variables related to the detection of elevated concentrations of arsenic in groundwater samples 

collected statewide and in three regions in Pennsylvania.
Appendix 1 35

Appendix 1. Summary of statewide and regional anthropogenic and natural factors used as explanatory variables in logistic regression models for elevated arsenic concentrations in groundwater in Pennsylvania and number of sample records.—Continued[-, variable not assessed for this study area; GIS, geographic information system; m, meter; km2, square kilometer; %, percent; oF, degrees Fahrenheit; μg/L, micrograms per liter; μS, microsiemens; cm, centimeter; nT, nanoteslas; g, grams; cm3, cubic centimeter; hr, hour; μS/cm, microsiemens per centimeter at 25 degrees Celsius; mm/yr, millimeters per year]

Explanatory 

variable Description Data source(s)

Original 

data GIS 

format

Original 

data resolution

Number of sample records

Statewide Glacial aquifer system Gettys- burg Basin New- ark Basin

Anthropogenic factors

Contamination

MUN Distance to municipalities containing water suppliers with arsenic treatment and sites or groundwater in need of remediation because of arsenic 

contamination (m)

Developed for this study from Pennsylvania 

Bulletin (1997 to 2009) and Pennsylvania Department of Transportation (2008)

Polygon 1:24,000 5,023 309 175 455

TRI Distance to toxic chemical release inventory 

sites (m)

U.S. Environmental Protection Agency 

(1994)

Point 1:100,000 5,023 309 175 455

Disturbance

COAL Distance to underground and surface coal mining 

operations (m)

Pennsylvania Department of Environmental 

Protection (2008a)

Point 1:24,000 - 309 - -

INDMIN Distance to industrial mineral mining operations (m) Pennsylvania Department of Environmental 

Protection (2008b)

Point 1:24,000 5,023 309 175 455

OILGAS Distance to oil and gas locations (m) Pennsylvania Department of Environmental 

Protection (2008c)

Point 1:24,000 - 309 - -

Land cover

AGAVE Average agricultural land cover within a 500-m 

radius (%)

Nakagaki and others (2007) Raster 1:24,000 5,023 309 175 455

POP Population density (people/km2) Price (2003) Raster 1:250,000 5,023 309 175 455URBAVE Average urban land cover within a 500-m radius (%) Nakagaki and others (2007) Raster 1:24,000 5,023 309 175 455

Natural factors

Climate

PRE Average precipitation from 1971 to 2000 (mm/yr) PRISM Group at Oregon State University 

(2006c)

Raster 1:250,000 5,023 309 175 455

RECH Average groundwater recharge from 1951 to 1980 

(mm/yr)

Wolock (2003) Raster 1:250,000 4,995 281 175 455

TEMP Average temperature from 1971 to 2000 (oF) PRISM Group at Oregon State University 

(2006a, 2006b)

Raster 1:250,000 5,023 309 175 455
36 Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa. Appendix 1. Summary of statewide and regional anthropogenic and natural factors used as explanatory variables in logistic regression models for elevated arsenic concentrations in groundwater in Pennsylvania and number of sample records.—Continued [-, variable not assessed for this study area; GIS, geographic information system; m, meter; km2, square kilometer; %, percent; oF, degrees Fahrenheit; μg/L, micrograms per liter; μS, microsiemens; cm, centimeter; nT, nanoteslas; g, grams; cm3, cubic centimeter; hr, hour; μS/cm, microsiemens per centimeter at 25 degrees Celsius; mm/yr, millimeters per year] Explanatory variable Description Data source(s) Original data GIS format Original data reso- lution Number of sample records State- wide Glacial aquifer system Gettys- burg Basin New- ark Basin Natural factors—Continued Geology ACID Distance to geologic units containing acid-produc- ing sulfide minerals (m) Pennsylvania Geological Survey (2001, 2005) Polygon 1:250,000 5,023 309 - 455 CARB Carbonate major aquifer type (1 = carbonate, 0 = all other) Pennsylvania Geological Survey (2001) Polygon 1:250,000 5,023 - - - CRYST Crystalline major aquifer type (1 = crystalline, 0 = all other) Pennsylvania Geological Survey (2001) Polygon 1:250,000 5,023 - - - DIA Distance to diabase geologic units (m) Pennsylvania Geological Survey (2001) Polygon 1:250,000 - - 175 455 PLUTIG Distance to igneous rock locations (m) Grossman (1999) Point 1:62,500 - - 175 455 SIL Siliciclastic major aquifer type (1 = siliciclastic, 0 = all other) Pennsylvania Geological Survey (2001) Polygon 1:250,000 5,023 - - - SURF Surficial major aquifer type (1 = surficial, 0 = all other) Pennsylvania Geological Survey (2001) Polygon 1:250,000 5,023 - - - Geophysical MAG Average residual total intensity of the earth’s mag- netic field (nT) Bankey and others (2002) Raster 1:250,000 5,023 309 175 455 Groundwater geochemistry CORR Grouping of geologic units according to estimated groundwater corrosivity (1 to 11 based on Lange- lier Saturation Index) Developed for this study from Langland and Dugas (1996) and Pennsylvania Geologi- cal Survey (2001) Polygon 1:250,000 5,023 309 175 455 NUREAS Estimated arsenic concentration derived from stream-sediment data (μg/L) U.S. Geological Survey (2004) Point 1:24,000 - - - 455 PHIDW Estimated groundwater pH Developed for this study from Langland and Dugas (1996), Low and Chichester (2006), Pennsylvania Geological Survey (2001), and U.S. Geological Survey (2004) Point 1:24,000 5,011 - 174 455
Appendix 1 37

Appendix 1. Summary of statewide and regional anthropogenic and natural factors used as explanatory variables in logistic regression models for elevated arsenic concentrations in groundwater in Pennsylvania and number of sample records.—Continued[-, variable not assessed for this study area; GIS, geographic information system; m, meter; km2, square kilometer; %, percent; oF, degrees Fahrenheit; μg/L, micrograms per liter; μS, microsiemens; cm, centimeter; nT, nanoteslas; g, grams; cm3, cubic centimeter; hr, hour; μS/cm, microsiemens per centimeter at 25 degrees Celsius; mm/yr, millimeters per year]

Explanatory 

variable Description Data source(s)

Original 

data GIS 

format

Original 

data resolution

Number of sample records

Statewide Glacial aquifer system Gettys- burg Basin New- ark Basin

Natural factors—Continued

Groundwater geochemistry—ContinuedPHIDWGLAC Estimated groundwater pH using only glacial data Developed for this study from Langland 

and Dugas (1996),Low and Chichester (2006), Pennsylvania Geological Survey (1995, 2001), Soller and Packard (1998), and U.S. Geological Survey (2004) 

Point 1:24,000 - 309 - -

SPCIDW Estimated groundwater specific conductance 

(μS/cm)

Developed for this study from Langland 

and Dugas (1996), Low and Chichester (2006), Pennsylvania Geological Survey (2001), and U.S. Geological Survey (2004)

Point 1:24,000 5,011 - 174 455

SPCIDWGLAC Estimated groundwater specific conductance using 

only glacial data (μS/cm)

Developed for this study from Langland 

and Dugas (1996), Low and Chichester (2006), Pennsylvania Geological Survey (1995, 2001), Soller and Packard (1998), and U.S. Geological Survey (2004) 

Point 1:24,000 - 309 - -

Land cover

FORAVE Average forested land cover within a 500-m 

radius (%)

Nakagaki and others (2007) Raster 1:24,000 5,023 309 175 455

WETAVE Average wetlands land cover within a 500-m 

radius (%)

Nakagaki and others (2007) Raster 1:24,000 5,023 309 175 455

Metals and minerals

METMIN Distance to metallic mineral resources (m) U.S. Geological Survey (2007) Point 1:24,000 5,023 309 175 455MIN Distance to metallic and nonmetallic mineral 

resources (m)

U.S. Geological Survey (2007) Point 1:24,000 5,023 309 175 455

NONMETMIN Distance to nonmetallic mineral resources (m) U.S. Geological Survey (2007) Point 1:24,000 5,023 309 175 455

Soil characterization

AWCAVE Average soil available water capacity (%) Wolock (1997) Raster 1:250,000 5,021 307 175 455BDAVE Average soil bulk density (g/cm3) Wolock (1997) Raster 1:250,000 5,021 307 175 455CLAYAVE Average soil clay content (%) Wolock (1997) Raster 1:250,000 5,021 307 175 455
38 Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa. Appendix 1. Summary of statewide and regional anthropogenic and natural factors used as explanatory variables in logistic regression models for elevated arsenic concentrations in groundwater in Pennsylvania and number of sample records.—Continued [-, variable not assessed for this study area; GIS, geographic information system; m, meter; km2, square kilometer; %, percent; oF, degrees Fahrenheit; μg/L, micrograms per liter; μS, microsiemens; cm, centimeter; nT, nanoteslas; g, grams; cm3, cubic centimeter; hr, hour; μS/cm, microsiemens per centimeter at 25 degrees Celsius; mm/yr, millimeters per year] Explanatory variable Description Data source(s) Original data GIS format Original data reso- lution Number of sample records State- wide Glacial aquifer system Gettys- burg Basin New- ark Basin Natural factors—Continued Soil characterization—Continued HGA Average soil hydrologic soil group A content (%)* Wolock (1997) Raster 1:250,000 5,021 307 175 455 HGAD Average soil hydrologic soil group AD content (%)* Wolock (1997) Raster 1:250,000 5,021 307 175 455 HGB Average soil hydrologic soil group B content (%)* Wolock (1997) Raster 1:250,000 5,021 307 175 455 HGBC Average soil hydrologic soil group BC content (%)* Wolock (1997) Raster 1:250,000 5,021 307 175 455 HGBD Average soil hydrologic soil group BD content (%)* Wolock (1997) Raster 1:250,000 5,021 307 175 455 HGC Average soil hydrologic soil group C content (%)* Wolock (1997) Raster 1:250,000 5,021 307 175 455 HGCD Average soil hydrologic soil group CD content (%)* Wolock (1997) Raster 1:250,000 5,021 307 175 455 HGD Average soil hydrologic soil group D content (%)* Wolock (1997) Raster 1:250,000 5,021 307 175 455 OMAVE Average soil organic matter content (% by weight) Wolock (1997) Raster 1:250,000 5,021 307 175 455 PERMAVE Average soil permeability (cm/hr) Wolock (1997) Raster 1:250,000 5,021 307 175 455 ROCKDEPAVE Average soil thickness (cm) Wolock (1997) Raster 1:250,000 5,021 307 175 455 SANDAVE Average soil sand content (%) Wolock (1997) Raster 1:250,000 5,021 307 175 455 SILTAVE Average soil silt content (%) Wolock (1997) Raster 1:250,000 5,021 307 175 455 SLOPEAVE Average soil land-surface slope (%) Wolock (1997) Raster 1:250,000 5,021 307 175 455 Topography ELEV Land-surface elevation above sea level (m, North American Vertical Datum of 1988) U.S. Geological Survey (2009) Raster 1:24,000 5,023 309 175 455 SINK Distance to nearest sinkhole (m) Kochanov and Reese (2003) and Pennsylva- nia Geological Survey (2007) Point 1:24,000 - - 175 455 SLOPE Slope derived from elevation (%) U.S. Geological Survey (2009) Raster 1:24,000 5,023 309 175 455 STREAMD Average stream density within a 500-m radius (km2) U.S. Geological Survey (2005) Line 1:100,000 5,023 309 175 455

*Hydrologic soil groups are described as Group A, Group B, Group C, and Group D, in order from low to high runoff potential, with dual hydrologic soil groups (AD, BC, CD) assigned on the basis of 

drained and undrained conditions.
Appendix 2 39

Appendix 2. Summary of arsenic concentrations in groundwater (1969–2007) for the 193 geologic units in Pennsylvania with major aquifer type and primary lithology.—Continued[* indicates geologic unit partially overlain by surficial materials consisting of coarse grained sediments (Soller and Packard, 1998; Lindsey and Bickford, 1999) and that some sample records associated with these geologic units may be categorized with the surficial major aquifer type; μg/L, micrograms per liter; <, less than; --, geologic unit with no sample records]

Major 

aquifer 

type

Primary lithology1 Geologic unit name1

Number of 

sample 

records

Median 

arsenic 

concentration, 

in μg/L

Maximum 

arsenic 

concentration, 

in μg/L

Number of sample 

records with arsenic 

concentrations greater 

than or equal to 4.0 μg/L

Number of sample 

records with arsenic 

concentrations greater 

than or equal to 10.0 μg/L

Carbonate Argillaceous dolomite Vintage Formation 8 <4.0 <4.0 0 0Carbonate Argillaceous limestone Chambersburg Formation 4 <4.0 4.4 1 0Carbonate Argillaceous limestone Hershey and Myerstown Formations, undivided 3 <4.0 12.0 1 1Carbonate Argillaceous limestone Hershey Formation through Annville Formation, undivided 0 -- -- 0 0Carbonate Argillaceous limestone Limestone of Martinsburg Formation 3 <4.0 8.7 1 0Carbonate Argillaceous limestone Pleasant Hill Formation 1 <4.0 <4.0 0 0Carbonate Dolomite Allentown Formation 39 <4.0 10.0 4 1Carbonate Dolomite Bellefonte and Axemann Formations, undivided 0 -- -- 0 0Carbonate Dolomite Bellefonte Formation 17 <4.0 6.4 1 0Carbonate Dolomite Gatesburg Formation 7 <4.0 <4.0 0 0Carbonate Dolomite Ledger Formation 54 <4.0 20.0 4 4Carbonate Dolomite Leithsville Formation 24 <4.0 40.0 1 1Carbonate Dolomite Lower (Middle?) Cambrian rocks, undivided 0 -- -- 0 0Carbonate Dolomite Mines Member of Gatesburg Formation 8 <4.0 5.9 1 0Carbonate Dolomite Nittany and Stonehenge/Larke Formations, undivided 12 <4.0 10.0 3 2Carbonate Dolomite Nittany Formation 7 <4.0 <4.0 0 0Carbonate Dolomite Ontelaunee Formation 6 <4.0 10.0 2 1Carbonate Dolomite Pinesburg Station Formation 6 <4.0 5.0 2 0Carbonate Dolomite Richland Formation 3 <4.0 <4.0 0 0Carbonate Dolomite Rickenbach Formation 9 <4.0 <4.0 0 0Carbonate Dolomite Snitz Creek Formation 6 <4.0 <4.0 0 0Carbonate Dolomite Tomstown Formation 7 <4.0 <4.0 0 0Carbonate Dolomite Zooks Corner Formation 25 <4.0 <4.0 0 0Carbonate Graphitic marble Franklin Marble 0 -- -- 0 0Carbonate High-calcium limestone Annville Formation 0 -- -- 0 0Carbonate High-calcium limestone Valentine Member of Benner Formation 0 -- -- 0 0Carbonate Limestone Axemann Formation 6 <4.0 <4.0 0 0Carbonate Limestone Beekmantown Group 14 <4.0 40.0 1 1Carbonate Limestone Benner Formation through Loysburg Formation, undivided 13 <4.0 4.0 1 0Carbonate Limestone Buffalo Springs Formation 12 <4.0 7.0 2 0Carbonate Limestone Coburn Formation through Loysburg Formation, undivided 4 <4.0 10.0 1 1Carbonate Limestone Coburn Formation through Nealmont Formation, undivided 20 <4.0 <4.0 0 0Carbonate Limestone Conestoga Formation 59 <4.0 67.0 6 3Carbonate Limestone Epler Formation 42 <4.0 5.0 3 0Carbonate Limestone Keyser and Tonoloway Formations, undivided* 46 <4.0 110.0 8 4Carbonate Limestone Keyser Formation through Clinton Group, undivided 0 -- -- 0 0Carbonate Limestone Keyser Formation through Mifflintown Formation, undivided* 9 <4.0 217.5 1 1Carbonate Limestone Limestone of Hamburg sequence 4 <4.0 <4.0 0 0Carbonate Limestone Millbach and Schaefferstown Formations, undivided 2 <4.0 <4.0 0 0
40 Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa. Appendix 2. Summary of arsenic concentrations in groundwater (1969–2007) for the 193 geologic units in Pennsylvania with major aquifer type and primary lithology.—Continued [* indicates geologic unit partially overlain by surficial materials consisting of coarse grained sediments (Soller and Packard, 1998; Lindsey and Bickford, 1999) and that some sample records associated with these geologic units may be categorized with the surficial major aquifer type; μg/L, micrograms per liter; <, less than; --, geologic unit with no sample records] Major aquifer type Primary lithology1 Geologic unit name1 Number of sample records Median arsenic concentration, in μg/L Maximum arsenic concentration, in μg/L Number of sample records with arsenic concentrations greater than or equal to 4.0 μg/L Number of sample records with arsenic concentrations greater than or equal to 10.0 μg/L Carbonate Limestone Millbach Formation 5 <4.0 <4.0 0 0 Carbonate Limestone Monongahela Group* 19 <4.0 24.1 1 1 Carbonate Limestone Rockdale Run Formation 16 <4.0 6.1 1 0 Carbonate Limestone Shadygrove Formation 6 <4.0 <4.0 0 0 Carbonate Limestone Shriver, Mandata, Corriganville, and New Creek Members of Old Port Formation, undivided 0 -- -- 0 0 Carbonate Limestone Snitz Creek and Buffalo Springs Formations, undivided 4 <4.0 <4.0 0 0 Carbonate Limestone St. Paul Group 9 <4.0 <4.0 0 0 Carbonate Limestone Stonehenge Formation 29 <4.0 5.8 3 0 Carbonate Limestone Stonehenge/Larke Formation 4 <4.0 8.8 1 0 Carbonate Limestone Warrior Formation 2 <4.0 <4.0 0 0 Carbonate Limestone Zullinger Formation 7 <4.0 <4.0 0 0 Carbonate Limestone conglomerate Limestone fanglomerate 7 <4.0 4.3 1 0 Carbonate Marble Cockeysville Marble 19 <4.0 <4.0 0 0 Carbonate Marble Wakefield Marble 0 -- -- 0 0 Carbonate Shaly limestone Jacksonburg Formation 8 6.2 20.7 5 2 Crystalline Albite-chlorite schist Octoraro Formation 66 <4.0 10.0 3 2 Crystalline Andesite Jonestown Volcanic Suite 3 <4.0 <4.0 0 0 Crystalline Anorthosite Anorthosite 7 <4.0 <4.0 0 0 Crystalline Chlorite-sericite schist Peters Creek Schist 47 <4.0 7.0 3 0 Crystalline Diabase Diabase 92 <4.0 60.0 17 7 Crystalline Feldspathic quartzite Setters Quartzite 28 <4.0 6.1 1 0 Crystalline Felsic gneiss Felsic and intermediate gneiss 106 <4.0 <4.0 0 0 Crystalline Felsic gneiss Felsic gneiss 34 <4.0 23.9 2 1 Crystalline Felsic gneiss Felsic to mafic gneiss 58 <4.0 20.0 4 1 Crystalline Granitic gneiss Granitic gneiss and granite 0 -- -- 0 0 Crystalline Granitic pegmatite Pegmatite 1 <4.0 <4.0 0 0 Crystalline Graphitic felsic gneiss Graphitic felsic gneiss 18 <4.0 <4.0 0 0 Crystalline Graphitic gneiss Graphitic felsic gneiss 28 <4.0 5.0 2 0 Crystalline Greenstone schist Greenstone schist 0 -- -- 0 0 Crystalline Mafic gneiss Banded mafic gneiss 16 <4.0 <4.0 0 0 Crystalline Mafic gneiss Hornblende gneiss 21 <4.0 20.0 6 2 Crystalline Mafic gneiss Mafic gneiss 15 <4.0 <4.0 0 0 Crystalline Metabasalt Metabasalt 1 <4.0 <4.0 0 0 Crystalline Metabasalt Sams Creek Metabasalt 2 <4.0 <4.0 0 0 Crystalline Metadiabase Metadiabase 0 -- -- 0 0

Crystalline Metagabbro Metagabbro 0 -- -- 0 0Crystalline Metarhyolite Metarhyolite 5 <4.0 <4.0 0 0Crystalline Oligoclase-mica schist Glenarm Wissahickon formation 134 <4.0 4.0 1 0Crystalline Oligoclase-mica schist Wissahickon Formation 28 <4.0 4.0 1 0
Appendix 2 41

Appendix 2. Summary of arsenic concentrations in groundwater (1969–2007) for the 193 geologic units in Pennsylvania with major aquifer type and primary lithology.—Continued[* indicates geologic unit partially overlain by surficial materials consisting of coarse grained sediments (Soller and Packard, 1998; Lindsey and Bickford, 1999) and that some sample records associated with these geologic units may be categorized with the surficial major aquifer type; μg/L, micrograms per liter; <, less than; --, geologic unit with no sample records]

Major 

aquifer 

type

Primary lithology1 Geologic unit name1

Number of 

sample 

records

Median 

arsenic 

concentration, 

in μg/L

Maximum 

arsenic 

concentration, 

in μg/L

Number of sample 

records with arsenic 

concentrations greater 

than or equal to 4.0 μg/L

Number of sample 

records with arsenic 

concentrations greater 

than or equal to 10.0 μg/L

Crystalline Phyllite Harpers Formation 1 <4.0 <4.0 0 0Crystalline Phyllite Marburg Schist 16 <4.0 <4.0 0 0Crystalline Quartzite Antietam and Harpers Formations, undivided 24 <4.0 10.0 1 1Crystalline Quartzite Antietam Formation 2 <4.0 <4.0 0 0Crystalline Quartzite Chickies Formation 55 <4.0 40.0 9 3Crystalline Quartzite Hardyston Formation 14 <4.0 20.0 3 3Crystalline Quartzite Montalto Member of Harpers Formation 1 <4.0 <4.0 0 0Crystalline Quartzite Weverton and Loudoun Formations, undivided 0 -- -- 0 0Crystalline Serpentinite Ultramafic rocks 27 <4.0 <4.0 0 0Crystalline Slate Peach Bottom Slate and Cardiff Conglomerate, undivided 2 <4.0 10.0 1 1Siliciclastic Argillaceous sandstone Rockwell Formation 1 <4.0 <4.0 0 0Siliciclastic Argillaceous shale Girard Shale* 41 <4.0 293.0 16 4Siliciclastic Argillite Lockatong Formation 54 <4.0 40.0 19 4Siliciclastic Arkosic sandstone New Oxford Formation 45 <4.0 85.5 15 10Siliciclastic Arkosic sandstone Sedimentary strata at Jacksonwald and Aspers 0 -- -- 0 0Siliciclastic Arkosic sandstone Stockton Formation 141 <4.0 69.0 23 4Siliciclastic Black shale Marcellus Formation* 4 <4.0 7.0 1 0Siliciclastic Calcareous sandstone Decker Formation through Poxono Island Formation, undivided* 2 <4.0 <4.0 0 0Siliciclastic Calcareous shale Elbrook Formation 25 <4.0 <4.0 0 0Siliciclastic Calcareous shale Onondaga and Old Port Formations, undivided* 37 <4.0 20.0 5 2Siliciclastic Calcareous shale Onondaga Formation 1 <4.0 <4.0 0 0Siliciclastic Calcareous shale Wills Creek Formation through Mifflintown Formation, undivided 2 <4.0 <4.0 0 0Siliciclastic Calcareous shale Wills Creek Formation* 36 <4.0 22.0 6 1Siliciclastic Graywacke Graywacke and shale of Martinsburg Formation 22 <4.0 20.0 7 1Siliciclastic Graywacke Graywacke of Hamburg sequence 10 <4.0 <4.0 0 0Siliciclastic Mudstone Brunswick Formation 257 <4.0 60.9 104 43Siliciclastic Mudstone Lock Haven Formation* 197 <4.0 178.0 59 30Siliciclastic Mudstone Sherman Creek Member of Catskill Formation* 47 4.0 49.8 24 12Siliciclastic Quartz conglomerate Gettysburg conglomerate 6 <4.0 <4.0 0 0Siliciclastic Quartz conglomerate Hammer Creek conglomerate 19 <4.0 7.6 4 0Siliciclastic Quartz conglomerate New Oxford conglomerate 3 <4.0 <4.0 0 0Siliciclastic Quartz conglomerate Quartz fanglomerate 10 <4.0 13.6 2 1Siliciclastic Quartz conglomerate Stockton conglomerate 0 -- -- 0 0Siliciclastic Quartzite Tuscarora Formation* 1 <4.0 <4.0 0 0Siliciclastic Sandstone Allegheny and Pottsville Formations, undivided* 1 <4.0 <4.0 0 0Siliciclastic Sandstone Allegheny Formation* 233 <4.0 490.0 30 12Siliciclastic Sandstone Bald Eagle Formation 8 <4.0 6.0 2 0Siliciclastic Sandstone Berea Sandstone through Riceville Formation, undivided* 8 <4.0 180.0 2 1Siliciclastic Sandstone Berea Sandstone through Venango Formation, undivided* 17 16.0 58.0 14 10
42 Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa. Appendix 2. Summary of arsenic concentrations in groundwater (1969–2007) for the 193 geologic units in Pennsylvania with major aquifer type and primary lithology.—Continued [* indicates geologic unit partially overlain by surficial materials consisting of coarse grained sediments (Soller and Packard, 1998; Lindsey and Bickford, 1999) and that some sample records associated with these geologic units may be categorized with the surficial major aquifer type; μg/L, micrograms per liter; <, less than; --, geologic unit with no sample records] Major aquifer type Primary lithology1 Geologic unit name1 Number of sample records Median arsenic concentration, in μg/L Maximum arsenic concentration, in μg/L Number of sample records with arsenic concentrations greater than or equal to 4.0 μg/L Number of sample records with arsenic concentrations greater than or equal to 10.0 μg/L Siliciclastic Sandstone Berry Run and Sawmill Run Members of Catskill Formation, undivided 0 -- -- 0 0 Siliciclastic Sandstone Burgoon Sandstone through Cuyahoga Group, undifferentiated* 1 4.0 4.0 1 0 Siliciclastic Sandstone Burgoon Sandstone* 19 <4.0 9.3 3 0 Siliciclastic Sandstone Catskill Formation* 247 <4.0 188.0 82 37 Siliciclastic Sandstone Clarks Ferry Member of Catskill Formation 0 -- -- 0 0 Siliciclastic Sandstone Corry Sandstone through Riceville Formation, undivided* 28 <4.0 64.4 5 3 Siliciclastic Sandstone Duncannon Member of Catskill Formation* 20 <4.0 4.0 1 0 Siliciclastic Sandstone Foreknobs Formation 15 6.3 12.6 8 3 Siliciclastic Sandstone Greene Formation 7 <4.0 10.0 2 1 Siliciclastic Sandstone Hammer Creek Formation 43 <4.0 20.7 8 3 Siliciclastic Sandstone Huntley Mountain Formation* 30 <4.0 10.0 14 1 Siliciclastic Sandstone Juniata and Bald Eagle Formations, undivided 1 <4.0 <4.0 0 0 Siliciclastic Sandstone Juniata Formation 9 <4.0 10.0 3 1 Siliciclastic Sandstone Llewellyn Formation* 20 <4.0 11.4 2 1 Siliciclastic Sandstone Long Run and Walcksville Members of Catskill Formation, undivided* 43 <4.0 15.0 7 1 Siliciclastic Sandstone Long Run Member of Catskill Formation* 7 <4.0 10.0 2 1 Siliciclastic Sandstone Lower members of Gatesburg Formation, undivided 2 <4.0 <4.0 0 0 Siliciclastic Sandstone Packerton Member of Catskill Formation 0 -- -- 0 0 Siliciclastic Sandstone Pocono and Rockwell Formations, undivided 0 -- -- 0 0 Siliciclastic Sandstone Pocono Formation* 5 <4.0 <4.0 0 0 Siliciclastic Sandstone Poplar Gap and Packerton Members of Catskill Formation, undivided 18 <4.0 20.0 3 1 Siliciclastic Sandstone Poplar Gap Member of Catskill Formation 4 <4.0 10.0 1 1 Siliciclastic Sandstone Pottsville Formation* 89 <4.0 210.0 23 11 Siliciclastic Sandstone Ridgeley Formation through Coeymans Formation, undivided* 0 -- -- 0 0 Siliciclastic Sandstone Ridgeley Member of Old Port Formation 0 -- -- 0 0 Siliciclastic Sandstone Shawangunk Formation* 0 -- -- 0 0 Siliciclastic Sandstone Shenango Formation through Cuyahoga Group, undivided* 11 <4.0 4.0 1 0 Siliciclastic Sandstone Shenango Formation through Oswayo Formation, undivided* 7 <4.0 6.0 1 0 Siliciclastic Sandstone Shenango Formation through Riceville Formation, undivided* 13 <4.0 17.0 5 3 Siliciclastic Sandstone Shenango Formation* 8 <4.0 11.0 2 2 Siliciclastic Sandstone Spechty Kopf Formation* 2 <4.0 <4.0 0 0 Siliciclastic Sandstone Towamensing Member of Catskill Formation* 8 <4.0 <4.0 0 0 Siliciclastic Sandstone Walcksville and Towamensing Members of Catskill Formation, undivided 2 <4.0 <4.0 0 0 Siliciclastic Sandstone Walcksville Member of Catskill Formation* 2 <4.0 4.0 1 0 Siliciclastic Sandstone Washington Formation* 14 <4.0 6.0 3 0

Siliciclastic Sandstone Waynesburg Formation* 16 <4.0 21.0 4 2Siliciclastic Shale Bloomsburg and Mifflintown Formations, undivided* 37 <4.0 57.0 18 8Siliciclastic Shale Bloomsburg Formation* 2 4.3 4.7 2 0Siliciclastic Shale Casselman Formation* 136 <4.0 9.0 5 0
Appendix 2 43

Appendix 2. Summary of arsenic concentrations in groundwater (1969–2007) for the 193 geologic units in Pennsylvania with major aquifer type and primary lithology.—Continued[* indicates geologic unit partially overlain by surficial materials consisting of coarse grained sediments (Soller and Packard, 1998; Lindsey and Bickford, 1999) and that some sample records associated with these geologic units may be categorized with the surficial major aquifer type; μg/L, micrograms per liter; <, less than; --, geologic unit with no sample records]

Major 

aquifer 

type

Primary lithology1 Geologic unit name1

Number of 

sample 

records

Median 

arsenic 

concentration, 

in μg/L

Maximum 

arsenic 

concentration, 

in μg/L

Number of sample 

records with arsenic 

concentrations greater 

than or equal to 4.0 μg/L

Number of sample 

records with arsenic 

concentrations greater 

than or equal to 10.0 μg/L

Siliciclastic Shale Clinton Group* 25 <4.0 7.0 4 0Siliciclastic Shale Cocalico Formation 20 <4.0 30.5 5 1Siliciclastic Shale Conemaugh Group 7 <4.0 <4.0 0 0Siliciclastic Shale Glenshaw Formation* 426 <4.0 200.0 20 11Siliciclastic Shale Hamburg sequence rocks 86 <4.0 12.0 8 1Siliciclastic Shale Hamilton Group* 109 <4.0 28.0 18 4Siliciclastic Shale Heidlersburg Member of Gettysburg Formation 6 <4.0 6.0 1 0Siliciclastic Shale Kinzers Formation 9 <4.0 <4.0 0 0Siliciclastic Shale Mahantango Formation* 18 <4.0 4.5 2 0Siliciclastic Shale Martinsburg Formation 107 <4.0 161.0 16 9Siliciclastic Shale Mauch Chunk Formation* 91 <4.0 230.0 16 7Siliciclastic Shale Northeast Shale* 3 <4.0 <4.0 0 0Siliciclastic Shale Onondaga Formation through Poxono Island Formation, undivided 0 -- -- 0 0Siliciclastic Shale Reedsville Formation 21 <4.0 4.2 2 0Siliciclastic Shale Shale and graywacke of Hamburg sequence 31 <4.0 20.0 4 2Siliciclastic Shale Waynesboro Formation 5 <4.0 <4.0 0 0Siliciclastic Siliceous sandstone Buttermilk Falls Limestone through Esopus Formation, undivided* 1 <4.0 <4.0 0 0Siliciclastic Siltstone Beaverdam Run Member of Catskill Formation* 2 4.5 5.0 2 0Siliciclastic Siltstone Brallier and Harrell Formations, undivided* 15 <4.0 23.0 3 1Siliciclastic Siltstone Buddys Run Member of Catskill Formation* 11 <4.0 19.0 4 1Siliciclastic Siltstone Chadakoin Formation* 129 <4.0 230.0 49 32Siliciclastic Siltstone Cuyahoga Group* 8 <4.0 21.0 3 1Siliciclastic Siltstone Irish Valley Member of Catskill Formation* 20 <4.0 85.0 5 2Siliciclastic Siltstone Scherr Formation 8 <4.0 5.0 2 0Siliciclastic Siltstone Trimmers Rock Formation* 72 <4.0 7.0 6 0Siliciclastic Siltstone Venango Formation* 117 <4.0 68.0 31 19Siliciclastic Silty mudstone Gettysburg Formation 47 <4.0 14.0 14 3Surficial Feldspathic quartz sand Pensauken and Bridgeton Formations, undifferentiated 3 <4.0 11.0 1 1Surficial Ferruginous clay Patapsco(?) Formation 0 -- -- 0 0Surficial Gravelly sand Bryn Mawr Formation 0 -- -- 0 0Surficial Gravelly sand Trenton Gravel 38 <4.0 50.0 16 9Surficial Sand Sands of Presque Isle 27 103.0 216.0 27 27

1Data from Pennsylvania Geological Survey, 2001.
44 Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa. Appendix 3. Results of univariate logistic regression analyses with logistic regression standardized coefficients and individual p-values of independent variables related to the detection of elevated concentrations of arsenic in groundwater samples collected statewide and in three regions in Pennsylvania.—Continued [NS, variable not selected for multivariate analysis; --, variable not assessed for this study area; <, less than; values not enclosed in parentheses are logistic regression standardized coefficients; values enclosed in parentheses are p-values; m, meter; km2, square kilometer; %, percent; oF, degrees Fahrenheit; μg/L, micrograms per liter; μS, microsiemens; cm, centimeter; μS/cm, microsiemens per centimeter at 25 degrees Celsius; nT, nanoteslas; g, grams; cm3, cubic centimeter; mm/yr, millimeters per year; hr, hour] Explanatory variable Description Univariate logistic regression results Statewide Glacial aquifer system Gettysburg Basin Newark Basin Anthropogenic factors MUN Distance to municipalities containing water suppliers with arsenic treatment and sites or groundwater in need of remediation because of arsenic contamination (m) -0.0505 (0.0160) -0.1297 (0.0755) 0.2747 (0.0057) -0.2951 (<0.0001) TRI Distance to toxic chemical release inventory sites (m) NS -0.3107 (0.0006) -0.2893 (0.0100) NS COAL Distance to underground and surface coal mining operations (m) -- NS -- -- INDMIN Distance to industrial mineral mining operations (m) -0.0540 (0.0106) 0.3967 (<0.0001) NS NS OILGAS Distance to oil and gas locations (m) -- -0.3066 (0.0068) -- -- AGAVE Average agricultural land cover within a 500-m radius (%) -0.1037 (<0.0001) -0.2530 (0.0006) -0.1502 (0.1332) 0.1044 (0.0619) POP Population density (people/km2) NS NS 0.1873 (0.0365) NS URBAVE Average urban land cover within a 500-m radius (%) 0.0571 (0.0028) NS 0.4095 (<0.0001) NS Natural factors PRE Average precipitation from 1971–2000 (mm/yr) -0.1073 (<0.0001) NS -0.1905 (0.0619) NS RECH Average groundwater recharge from 1951–1980 (mm/yr) -0.1292 (<0.0001) NS NS NS TEMP Average temperature from 1971–2000 (oF) -0.1066 (<0.0001) 0.2391 (0.0006) 0.2950 (0.0061) -0.1130 (0.0423) ACID Distance to geologic units containing acid-producing sulfide minerals (m) 0.1041 (<0.0001) 0.4306 (<0.0001) -- 0.1099 (0.0451) CARB Carbonate major aquifer type (1 = carbonate, 0 = all other) -0.1647 (<0.0001) -- -- -- CRYST Crystalline major aquifer type (1 = crystalline, 0 = all other) -0.2768 (<0.0001) -- -- -- DIA Distance to diabase geologic units (m) -- -- 0.2645 (0.0039) -0.1460 (0.0133)

PLUTIG Distance to igneous rock locations (m) -- -- NS -0.1999 (0.0009)SIL Siliciclastic major aquifer type (1 = siliciclastic, 0 = all other) 0.1344 (<0.0001) -- -- --
Appendix 3 45

Appendix 3. Results of univariate logistic regression analyses with logistic regression standardized coefficients and individual p-values of independent variables related to the detection of elevated concentrations of arsenic in groundwater samples collected statewide and in three regions in Pennsylvania.—Continued[NS, variable not selected for multivariate analysis; --, variable not assessed for this study area; <, less than; values not enclosed in parentheses are logistic regression standardized coefficients; values enclosed in parentheses are p-values; m, meter; km2, square kilometer; %, percent; oF, degrees Fahrenheit; μg/L, micrograms per liter; μS, microsiemens; cm, centimeter; μS/cm, microsiemens per centimeter at 25 degrees Celsius; nT, nanoteslas; g, grams; cm3, cubic centimeter; mm/yr, millimeters per year; hr, hour]

Explanatory 

variable Description

Univariate logistic regression results

Statewide Glacial aquifer system Gettysburg Basin Newark Basin

SURF Surficial major aquifer type (1 = surficial, 0 = all other) 0.1509 (<0.0001) -- -- --MAG Average residual total intensity of the earth’s magnetic field (nT) NS -0.1232 (0.0963) -0.1723 (0.1119) 0.1418 (0.0093)CORR Grouping of geologic units according to estimated groundwater corrosivity (1 to 11 based on Langelier Saturation Index) 0.0805 (0.0001) -0.3546 (<0.0001) 0.2126 (0.0422) 0.2975 (<0.0001)NUREAS Estimated arsenic concentration derived from stream sediment data (μg/L) -- -- -- 0.1056 (0.0550)PHIDW Estimated groundwater pH 0.1283 (<0.0001) -- 0.3748 (0.0016) 0.3500 (<0.0001)PHIDWGLAC Estimated groundwater pH using only glacial data -- 0.2958 (0.0002) -- --SPCIDW Estimated groundwater specific conductance (μS/cm) 0.1752 (<0.0001) -- 0.3619 (0.0006) 0.3624 (0.0037)SPCIDWGLAC Estimated groundwater specific conductance using only glacial data (μS/cm) -- 0.5250 (<0.0001) -- --FORAVE Average forested land cover within a 500-m radius (%) NS -0.2439 (0.0013) -0.2359 (0.0337) -0.0740 (0.1908)WETAVE Average wetlands land cover within a 500-m radius (%) 0.1409 (<0.0001) 0.5217 (<0.0001) NS 0.1171 (0.0414)METMIN Distance to metallic mineral resources (m) 0.0700 (0.0003) -0.2371 (0.0012) 0.1825 (0.0674) NSMIN Distance to metallic and nonmetallic mineral resources (m) NS -0.1354 (0.0727) NS NSNONMETMIN Distance to nonmetallic mineral resources (m) NS NS -0.1620 (0.1335) NSAWCAVE Average soil available water capacity (%) -0.2133 (<0.0001) -0.2979 (0.0003) NS -0.2028 (0.0015)BDAVE Average soil bulk density (g/cm3) 0.1268 (0.0001) 0.5296 (0.0001) NS NSCLAYAVE Average soil clay content (%) -0.2421 (<0.0001) -0.1699 (0.0206) NS NSHGA Average soil hydrologic soil group A content (%)1 0.0784 (<0.0001) 0.1009 (0.1402) NS -0.1833 (0.1339)HGAD Average soil hydrologic soil group AD content (%)1 0.0714 (<0.0001) 0.1180 (0.0721) -- --
46 Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa. Appendix 3. Results of univariate logistic regression analyses with logistic regression standardized coefficients and individual p-values of independent variables related to the detection of elevated concentrations of arsenic in groundwater samples collected statewide and in three regions in Pennsylvania.—Continued [NS, variable not selected for multivariate analysis; --, variable not assessed for this study area; <, less than; values not enclosed in parentheses are logistic regression standardized coefficients; values enclosed in parentheses are p-values; m, meter; km2, square kilometer; %, percent; oF, degrees Fahrenheit; μg/L, micrograms per liter; μS, microsiemens; cm, centimeter; μS/cm, microsiemens per centimeter at 25 degrees Celsius; nT, nanoteslas; g, grams; cm3, cubic centimeter; mm/yr, millimeters per year; hr, hour] Explanatory variable Description Univariate logistic regression results Statewide Glacial aquifer system Gettysburg Basin Newark Basin HGB Average soil hydrologic soil group B content (%)1 -0.1696 (<0.0001) 0.2881 (<0.0001) NS -0.0777 (0.1582) HGBC Average soil hydrologic soil group BC content (%)1 NS -- -- -- HGBD Average soil hydrologic soil group BD content (%)1 0.1021 (<0.0001) -0.2740 (0.0010) NS NS HGC Average soil hydrologic soil group C content (%)1 0.0724 (0.0003) -0.1702 (0.0195) 0.4309 (0.0166) 0.1114 (0.0554) HGCD Average soil hydrologic soil group CD content (%)1 0.0940 (<0.0001) NS -- -- HGD Average soil hydrologic soil group D content (%)1 NS NS -0.2575 (0.0638) NS OMAVE Average soil organic matter content (% by weight) 0.0726 (<0.0001) -0.2107 (0.0068) -0.0705 (0.4501) NS PERMAVE Average soil permeability (cm/hr) 0.1595 (<0.0001) 0.3858 (<0.0001) 0.2934 (0.0305) NS ROCKDEPAVE Average soil thickness (cm) -0.0373 (0.0633) 0.3020 (0.0056) NS -0.1159 (0.0619) SANDAVE Average soil sand content (%) 0.2124 (<0.0001) 0.2967 (0.0002) 0.8370 (0.0118) NS SILTAVE Average soil silt content (%) -0.0942 (<0.0001) -0.2157 (0.0073) NS NS SLOPEAVE Average soil land-surface slope (%) -0.1324 (<0.0001) -0.2921 (0.0009) NS NS ELEV Land-surface elevation above sea level (m, North American Vertical Datum of 1988) NS -0.1933 (0.0059) -0.3632 (0.0016) 0.0843 (0.1245) SINK Distance to nearest sinkhole (m) -- -- NS NS SLOPE Slope derived from elevation (%) -0.0512 (0.0182) -0.2902 (0.0010) -0.1555 (0.1817) NS STREAMD Average stream density within a 500-m radius (km2) NS -0.2598 (0.0008) 0.1510 (0.1155) 0.1137 (0.0378)

1Hydrologic soil groups are described as Group A, Group B, Group C, and Group D, in order from low to high runoff potential, with dual hydrologic soil groups (AD, BC, CD) assigned on the basis of 

drained and undrained conditions.
Prepared by the West Trenton Publishing Service Center

For additional information, write to:

Director

U.S. Geological Survey

Pennsylvania Water Science Center

215 Limekiln Road

New Cumberland, Pa. 17070

or visit our Web site at:

http://pa.water.usgs.gov/
Gross and Low—Arsenic Concentrations, Environmental Factors, and the Probability of Elevated Arsenic in Groundwater in Pa.—Scientific Investigations Report 2012–5257