Determining Sources of Water and 

Contaminants to Wells in a Carbonate 

Aquifer Near Martinsburg, Blair County, 

Pennsylvania, By Use of Geochemical 

Indicators, Analysis of Anthropogenic 

Contaminants, and Simulation of 

Ground-Water Flow

By Bruce D. Lindsey and Michele L. Koch

U.S. Department of the Interior 

U.S. Geological Survey

In cooperation with the Southern Alleghenies Conservancy and the Borough of Martinsburg, Pennsylvania 

Scientific Investigations Report 2004-5124
U.S. Department of the Interior

Gale A. Norton, Secretary

U.S. Geological Survey

Charles G. Groat, Director

U.S. Geological Survey, Reston, Virginia: 2004

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

Box 25286, Denver Federal Center 

Denver, CO 80225

For more information about the USGS and its products: 

Telephone: 1-888-ASK-USGS 

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

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

endorsement by the U.S. Government.

Although this report is in the public domain, permission must be secured from the individual copyright owners to reproduce any copyrighted materials contained within this report.
 iii

Contents

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

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Purpose and Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Description of Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Geologic Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Hydrogeology and Aquifer Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Well-Field Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Land Use and Sources of Nitrogen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Study Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Field Data Collection and Analytical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Methods to Determine Sources of Contaminants to Ground-Water Wells . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Natural Geochemistry of Water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Anthropogenic Contaminants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Microbial Source Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Simulation of Ground-Water Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Sources of Water and Contaminants to Martinsburg Municipal Wells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Geochemical Indicators. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Source of Water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Type of Permeability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Sources Based on Analysis of Anthropogenic Contaminants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Nitrate-Nitrogen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Nitrogen Isotopes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Chloride/Bromide Ratio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Boron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Wastewater Compounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Microbial Source Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Sources Based on Simulation of Ground-Water Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Conceptual Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Model Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Model Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Simulation Results and Sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Zone of Contribution to Municipal Wells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Relating Simulation Results, Natural Geochemistry, 

and Anthropogenic Contaminants to Determine Source Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Limitations of Data and Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Summary and Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

References Cited. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
iv 

Figures

1-3. Maps showing:

1. Location of the Ridge and Valley Physiographic Province, carbonate bedrock in 

Pennsylvania, and the Borough of Martinsburg, Pennsylvania. . . . . . . . . . . . . . . . . . . . . . . . . . . 2 

2. Location of the Morrison Cove Valley, Martinsburg, Pennsylvania, boundary of 

ground-water model area, and land use 3

3. Geologic formations, structure, and geologic section in the study area near

Martinsburg, Pennsylvania. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

4. Shaded-relief map of the Martinsburg, Pennsylvania Quadrangle showing topography, 

lithology, and stream drainage near Martinsburg, Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

5. Conceptual model of ground-water-flow system near Martinsburg, Pennsylvania . . . . . . . . . . . . 9

6. Map showing basic soil types and their relation to bedrock lithology near Martinsburg,

Pennsylvania. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

7. Conceptual model of hypothetical orientation of major fracture sets in study area near

Martinsburg, Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

8-12. Maps showing:

8. Karst features within ground-water model boundary near Martinsburg, 

Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

9. Water levels near Martinsburg, Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

10. Generalized land use in the area of Martinsburg, Pennsylvania 14

11. Types of nitrogen applied to fields and areas served by public sewer lines near 

Martinsburg, Pennsylvania. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

12. Location of wells, springs, and streams sampled or measured in and near

Martinsburg, Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

13. Graph showing δ15N relative to organic carbon: total nitrogen for (A) nitrogen-source

materials; and (B) suspended particulates in water, Susquehanna River Basin,

Pennsylvania 21

14. Piper diagram showing concentrations of major ions in water from wells near

Martinsburg, Pennsylvania. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

15. Graph showing binary mixing curves for chloride to bromide ratios for water from wells 

 and streams near Martinsburg, Pennsylvania. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

16. Map showing location of model grid, boundaries, streams, drain nodes, and wells near 

Martinsburg, Pennsylvania. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

17. Hypothetical cross section of model layers used for simulation of ground-water flow. . . . . . . . . 29

18-19. Graphs showing:

18. Composite scaled sensitivities for parameters used in model calibration . . . . . . . . . . . . . . . 31

19. Calibration results showing simulated and measured water levels near Martinsburg, 

Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

20. Diagram showing simulated water-budget summary for layers 1 and 2 of hydrologic model . . 34

21-22. Maps showing:

21. Simulated water table and zone of contribution to wells near Martinsburg, 

Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

22. Areal extent of zone of contribution to wells, pathlines, and source areas near 

Martinsburg, Pennsylvania, under normal and low recharge conditions. . . . . . . . . . . . . . . . 36

23-24. Model sections showing:

23. Simulated water table and pathlines to municipal wells from south to north along 

model column 33. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

24. Simulated water table from west to east along model row 20. . . . . . . . . . . . . . . . . . . . . . . . . . . 37
 v

Figures—Continued

25-28. Maps showing:

25. Areal extent of zone of contribution to wells near Martinsburg, Pennsylvania,

illustrating simulation with horizontal anisotropy and without horizontal 

anisotropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

26. Geology and karst features in the areal extent of the zone of contribution to well

BA 332 and well BA 545 near Martinsburg, Pennsylvania. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

27. Land use in the areal extent of the zone of contribution to well BA 332 and BA 545

near Martinsburg, Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

28. Sources of nitrogen, nitrate concentration, and δ15N in municipal wells near

Martinsburg, Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Tables

1. Geologic-stratigraphic and hydrologic units column near Martinsburg, Blair County,

Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

2. Hydrogeologic properties of aquifers in the study area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8

3. Land covers included in the land-use groupings in the study area near Martinsburg,

Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

4. Wells sampled and/or used for model calibration near Martinsburg, Pennsylvania. . . . . . . . . . 17

5. Sampling requirements and analytical methods for water analyses from wells and

streams near Martinsburg, Pennsylvania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

6. Data from previous studies of water from Ridge and Valley carbonate aquifers in

Pennsylvania showing geochemical indicators of source of water . . . . . . . . . . . . . . . . . . . . . . . . . . 19

7. Data on geochemical indicators of type of permeability from previous studies of Ridge

and Valley carbonate aquifers in Pennsylvania. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

8. Geochemical composition of water from wells sampled near Martinsburg, Pennsylvania . . . 23

9. Selected chemical analyses from wells, springs, and a spring sampled near 

Martinsburg, Pennsylvania. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

10. Optimum values of aquifer properties estimated near Martinsburg, Pennsylvania . . . . . . . . . . . 33

11. Water budget for steady-state simulation near Martinsburg, Pennsylvania . . . . . . . . . . . . . . . . . . 33
vi 

Conversion Factors and Abbreviations

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).

Vertical coordinate information is referenced to National Geodetic Vertical Datum of 1929 (NGVD 29); horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Per mil: A unit expressing the ratio of stable isotopic abundance of an element in a sample to those of a standard 

material. Per mil units are equivalent to parts per thousand. Stable-isotopic ratios are computed as follows (Kendall 

and Caldwell, 1998):

δX = (Rsample / Rstandard - 1) × 1,000

where X is the heavier stable isotope and R is the ratio of the heavier, less abundant isotope to the lighter stable isotope in a sample or standard. The δ values for nitrogen stable-isotopic ratios discussed in this report are referenced to 

the following standard materials: Nitrogen-15: nitrogen-14, Standard atmospheric nitrogen, referenced to the 

National Bureau of Standards, NBS-14 nitrogen gas. 

Multiply By To obtain

Length

inch (in.) 0.0254 meter (m)

foot (ft) 0.3048 meter (m)

mile (mi) 1609.3 meter (m)

Area

square mile (mi2) 2.590 square kilometer (km2)

Volume

gallon (gal) 0.00022 milliliter (mL)

gallon (gal) 3.785 liter (L)

Flow Rate

foot per day (ft/d) 0.3048 meter per day (m/d)

gallon per minute (gal/min) 0.06309 liter per second (L/s)

gallon per day (gal/d) 0.003785 cubic meter per day (m3/d)

cubic foot per second (ft3

/s) 0.0283 cubic meters per second (m3/s)

inch per year (in/yr) 25.4 millimeter per year (mm/yr)

Specific capacity

gallon per minute per foot 

[(gal/min)/ft)] 0.2070 liter per second per meter (L/s/m2

)

Temperature

degree Fahrenheit (°F) °C=5/9 (°F-32) degree Celsius
Determining Sources of Water and Contaminants to Wells 

in a Carbonate Aquifer near Martinsburg, Blair County, 

Pennsylvania, by Use of Geochemical Indicators, Analysis of 

Anthropogenic Contaminants, and Simulation of Ground-Water 

Flow

by Bruce D. Lindsey and Michele L. Koch

Abstract

Water supply for the Borough of Martinsburg, Pa., is from 

two well fields (Wineland and Hershberger) completed in carbonate-bedrock aquifers in the Morrison Cove Valley. Water 

supply is plentiful; however, waters with high concentrations of 

nitrate are a concern. This report describes the sources of water 

and contaminants to the supply wells. A review of previous 

investigations was used to establish the aquifer framework and 

estimate aquifer hydraulic properties. Aquifer framework and 

simulation of ground-water flow in a 25-square-mile area using 

the MODFLOW model helped to further constrain aquifer 

hydraulic properties and identify water-source areas in the zone 

of contribution of ground water to the well fields. Flow simulation identified potential contaminant-source areas. Data on contaminants and geochemical characteristics of ground water at 

the well fields were compared to the results of flow simulation.

The Woodbury Anticline controls the aquifer framework 

near the well fields and four carbonate-bedrock formations contain the primary aquifers. Three carbonate-bedrock aquifers of 

Ordovician age overlie the Gatesburg aquifer of Cambrian age 

on the flanks of the anticline. Fracture, not conduit, permeability was determined to be the dominant water-bearing characteristic of the bedrock. The horizontal hydraulic conductivity of 

the Gatesburg aquifer is about 36 feet per day. The other carbonate aquifers (Nittany/Stonehenge, Bellefonte/Axemann, and 

Coburn through Loysburg aquifers) overlying and flanking the 

Gatesburg aquifer have horizontal hydraulic conductivities of 

about 1 foot per day. Regional directions of ground-water flow 

are toward the major streams with Clover Creek as the major 

discharge point for ground water in the east. Ground-water flow 

to the well fields is anisotropic with a 5:1 preferential horizontal 

direction along strike of the axial fold of the anticline. Thus, the 

zone of contribution of ground water to the well fields is elongate in a north-south direction along the anticline axis, with the 

majority of the flow to the well fields originating from the 

south.

Human activity in the areal extent of the zone of contribution to the well fields was the source of contaminants. The areal 

extent of the zone of contribution included both urban areas in 

the Borough and a large amount of agricultural land. By relating 

results of flow simulation, natural geochemistry, and analyses 

of anthropogenic (human-made) contaminants, the source areas 

for water and contaminants were determined with more confidence than by using only flow simulation. Analysis of natural 

geochemistry identified water sources from both limestone and 

dolomite aquifers. Geochemistry results also indicated fractures, not conduits, were the dominant source of water from 

aquifers; however, quantitative source identification was not 

possible. Chemical ratios of chloride and bromide were useful 

to show that all samples of ground water had sources with 

chemical contributions from land surface. Nitrogen isotope 

ratio analysis indicated animal manure as the possible primary 

source of nitrate in most ground water. Some of the nitrate in 

ground water had chemical fertilizer as a source. At the Wineland well field, chemical fertilizer was likely the source of 

nitrate. The nitrate in water from the Hershberger well field was 

from a mixture of fertilizer and animal-manure sources. Human 

sewage was ruled out as a major source of nitrate in water from 

the municipal wells by results showing 1) wastewater compounds in sewage were rarely detected and 2) a mass-balance 

calculation indicating the small contribution of nitrogen that 

could be attributed to septic systems.
2 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

Introduction

Ground water is an essential resource for residents of 

Pennsylvania. In many rural areas, ground water is the only 

source of water for domestic supply. Ground water also can be 

the major source for municipal water supply in rural communities underlain by carbonate bedrock. Because of the karst topography characteristics in these carbonate-bedrock areas, the network of streams can be less dense than in other areas, making 

ground water the only available source of supply. The Borough 

of Martinsburg is a rural community in Blair County, Pa. 

(fig. 1), that relies on ground-water supply from a carbonatebedrock aquifer, hereinafter termed the carbonate aquifer. Martinsburg has four wells that supply the local population of 

approximately 3,140 persons, with 1,200 service connections 

(Randy Stoltz, Martinsburg Municipal Authority, written commun., 2002). The daily water usage is about 300,000 gal/d from 

two well fields (Wineland and Hershberger). The supply of 

water from the four municipal wells is plentiful; however, the 

quality of the water is a concern. On a few occasions, concentrations of nitrate in water from the municipal wells have 

exceeded the U.S. Environmental Protection Agency (USEPA) 

Maximum Contaminant Level (MCL) of 10 mg/L as nitrogen 

(U.S. Environmental Protection Agency, 2002) (Randy Stoltz, 

Martinsburg Municipal Authority, written commun., 2002). If 

the water from these wells continues to exceed the USEPA 

MCL, the Martinsburg Municipal Authority will be obligated to 

mitigate the problem by either finding another source of water 

or treating the water by installing a nitrate-removal system. 

Other sources of water, including surface water, are not readily 

available. Water from new wells installed in the carbonate aquifer would be likely to have similar nitrate concentrations. A 

treatment system for nitrate removal is likely to be an expensive 

option (Randy Stoltz, Martinsburg Municipal Authority, oral 

commun., 2002). Therefore, another alternative would be to 

determine the source of nitrate contamination and to attempt to 

mitigate the problem by reducing or eliminating the source.

 High concentrations of nitrate in ground water are not isolated to the Martinsburg municipal supply wells. The U.S. Geological Survey (USGS) conducted a study of ground-water 

quality in the Lower Susquehanna River Basin that included the 

Martinsburg area (Lindsey and others, 1997; Siwiec and others, 

1997, p. 92-93). The USGS reported the median nitrate concentration at 9 mg/L as nitrogen in the area that included the Martinsburg area. The Pennsylvania Department of Agriculture 

EXPLANATION

RIDGE AND VALLEY PHYSIOGRAPHIC PROVINCE

CARBONATE ROCKS IN PENNSYLVANIA

MARTINSBURG

BLAIR

COUNTY

40°

80°

42°

78° 76°

0 50 100 25 MILES

0 50 100 25 KILOMETERS

CENTER

COUNTY

MIFFLIN

COUNTY

BEDFORD

COUNTY

Figure 1. Location of the Ridge and Valley Physiographic Province (Sevon, 2000), carbonate bedrock in 

Pennsylvania (Berg and others, 1980), and the Borough of Martinsburg, Pennsylvania.
Introduction 3

conducted a study of pesticides, nitrate, and bacteria in rural 

wells (Coyle, 1998). During the Pennsylvania Department of 

Agriculture study, 189 wells were sampled in southern Blair 

County and northern Bedford County (Morrison Cove Valley, 

fig. 2), and water from about 35 percent of the wells sampled 

exceeded the USEPA MCL for nitrate. To address concerns 

about high nitrate concentrations in ground water, the USGS, in 

cooperation with the Southern Alleghenies Conservancy and 

the Borough of Martinsburg, conducted a study of the sources 

of water and contaminants to the Martinsburg municipal supply 

wells. 

The Borough of Martinsburg is surrounded by agricultural 

and low-density residential land use. Fertilizers, animal 

manure, and on-lot septic systems are all potential sources of 

nitrate that could cause concentrations in ground water to 

approach or exceed the USEPA MCL. Because these potential 

sources are found in virtually every direction around the well 

fields, it is necessary to delineate the areal extent of the zone of 

contribution that supplies water to the wells in order to identify 

the nitrate source. 

Purpose and Scope

This report defines sources of water and contaminants to 

wells in a carbonate aquifer near Martinsburg, Pa., by use of 

geochemical indicators, analysis of anthropogenic contaminants, and simulation of ground-water flow. The areal extent of 

the zone of contribution of water to the four supply wells used 

by the Martinsburg Municipal Authority, Blair County, Pa., is 

estimated. This report includes interpretations of water-quality 

analyses collected in September 2002, water levels measured in 

16 wells in September 2002, streamflow discharge measured in 

1 spring and 4 streams during September and November 2002, 

and simulation of ground-water flow under steady-state conditions. The results of water-quality sampling are compared to 

results of ground-water modeling and are used to assess the 

accuracy of the areal extent of the zone of contribution delineated by the model. The study focused on a 25-mi2 area around 

the Borough of Martinsburg. 

The areal extent of the zone of contribution, assuming no 

induced infiltration from streams, is the aquifer volume through 

which water is drawn to a well, projected to the land surface 

(Risser and Barton, 1995). A rigorous approach to determine 

the areal extent of the zones of contribution to wells commonly 

includes ground-water-flow modeling. In areas of fractured 

bedrock and, in particular, areas underlain by carbonate bedrock, predictions of ground-water-flow direction using groundwater-flow models can be uncertain. This uncertainty results 

because some of the underlying assumptions of the model used 

here, such as a porous medium and a homogeneous, isotropic 

aquifer, are not entirely accurate. 

Woodbury

Martinsburg

Roaring

Spring

BLAIR COUNTY

BEDFORD COUNTY

HUNTINGDON

COUNTY

40°20'

40°10'

78°20' 78°10'

EXPLANATION

MORRISON COVE VALLEY

GROUND-WATER MODEL BOUNDARY

LAKE

FORESTED AREAS

MUNICIPALITIES

COUNTY BOUNDARY

STREAM

Clover Creek

Cove

Creek

Piney

Creek

Yellow Creek

Raystown Lake

0 2 4 8KILOMETERS

0 2 4 8 MILES

Figure 2. Location of the Morrison Cove Valley, Martinsburg, Pennsylvania, boundary 

of ground-water model area, and land use (Vogelmann and others, 1998a, 1998b). 
4 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

Acknowledgments

The authors would like to thank the following individuals 

for assisting in developing the project: Randy Stoltz, Martinsburg Borough; Len Lichvar, Southern Alleghenies Conservancy; Ron Donlan, Southern Alleghenies Resource Conservation and Development; Janie French, Canaan Valley Institute; 

and Amanda Ritchy, U.S. Department of Agriculture, National 

Resources Conservation Service. The authors also would like to 

express their thanks to all the homeowners who participated in 

the project by allowing their wells to be sampled. 

Description of Study Area

Martinsburg is in the Ridge and Valley Physiographic 

Province (fig. 1) (Sevon, 2000). The Ridge and Valley Physiographic Province is a series of long, narrow, resistant ridges and 

broad to narrow valleys. Martinsburg is in a valley known as 

Morrison Cove (fig. 2). The 25-mi 2 area around the municipal 

wells that was the focus of this study is referred to as the 

ground-water model area (fig. 4). 

Geologic Setting 

Morrison Cove Valley is underlain by layers of folded bedrock that consist predominantly of Cambrian- and Ordovicianage limestone and dolomite. The flanking ridges consist of 

Ordovician- and Silurian-age sandstones, siltstones, and shales 

(figs. 3 and 4). The limestone and dolomite in the valley are 

weathered easily by acidic water. Because of its mineral composition, carbonate bedrock commonly is highly weathered in 

the subsurface as well. The ridges in the Martinsburg area are 

underlain by sandstone that is more resistant to weathering. 

Shale units also are present in the valley. Shale is more resistant 

to weathering than the carbonate rocks, but less resistant than 

sandstone.

Two major structural features are in the study area. The 

Woodbury Anticline runs generally north and south through the 

valley with the axis of the anticline being east of Martinsburg 

(fig. 3). The Scotch Valley Syncline parallels this structure further west in the valley. Bedding has a strike of about N. 30° E., 

and dip angles range from 60° to 80° NW., between the anticline and the syncline (Butts, 1945).

Hydrogeology and Aquifer Characteristics

A study of ground water in the Cambrian and Ordovician 

carbonate rocks in the Ridge and Valley Physiographic Province by Becher (1996) provided the basic information about 

hydrogeology and aquifer characteristics used in this study. 

Becher noted that municipalities in this area were highly dependent on ground water as a source of supply and that wells in the 

Gatesburg and Nittany/Stonehenge aquifers (the aquifers in 

which the Martinsburg wells in the Wineland and Hershberger 

well fields are completed) had the highest yields of all the aquifers studied, with yields as great as 1,000 gal/min reported. 

Additional information on geologic and aquifer characteristics 

from Becher (1996) is summarized in tables 1 and 2. Not listed 

in table 2 are the Juniata, Bald Eagle, and Waynesboro aquifers. 

The Tuscarora, Juniata, and Bald Eagle aquifers typically 

underlie ridges, and generally are considered low-yielding aquifers (Taylor and others, 1982). The Reedsville and Waynesboro 

aquifers also are considered to yield small to moderate amounts 

of water (Taylor and others, 1982). 

The term 'aquifer,' as used in this report, refers to that part 

of the geologic formation that is close enough to the land surface to be in the active part of the ground-water-flow system. 

Most of the geologic formations are tilted on the flanks of an 

anticlinal or synclinal structure (fig. 3), and, therefore, the 

water-bearing part of the aquifer includes only that part of the 

geologic formation in the active ground-water-flow system 

(fig. 5). Also, because of the tilted nature of the bedrock, the 

thicknesses, or sum of the thicknesses of the geologic formations given in table 1 may not be equal to the depth of the geologic formation or aquifer. 

The conceptual model of a flow system controlled by 

depth and lithology proposed by Gerhart and Lazorchick (1988) 

for ground-water flow in fractured bedrock was used as a basis 

for this study. Gerhart and Lazorchick (1988) found that the 

density of fractures and, subsequently, the hydraulic conductivity decreased with depth as illustrated schematically in fig. 5. 

The water-bearing capacity of a given aquifer, therefore, would 

be greater near the land surface and decrease with depth to a 

point where ground-water flow is negligible. Because of the 

decreasing fracture density with depth, water flowing in the 

deep part of the aquifer would follow a more tortuous flow path 

(fig. 5) through a medium with a lower transmissivity than 

water flowing in the shallow part of the aquifer. Thus, water 

flowing through the deeper part of the system would make up a 

smaller proportion of the total water budget than water flowing 

in the shallow part. Another basic assumption in the conceptual 

model was that ground-water flow varies on the basis of lithology because of variations that affect hydraulic conductivity 

(such as porosity, permeability, and weathering of fractures) 

among the different rock types. The four basic lithologies in the 

study area are shale, sandstone, limestone, and dolomite. Most 

of the carbonate aquifers as mapped locally include limestone 

and dolomite members; for example, the Nittany/Stonehenge 

aquifer is mapped as one unit but includes the Nittany aquifer 

(dolomite) and the Stonehenge aquifer (limestone). Therefore, 

Nittany/Stonehenge, Bellefonte/Axemann, and Coburn through 

Loysburg aquifers were considered as mixed limestone and 

dolomite and not separated in the conceptual model. The exception to this was the Gatesburg aquifer, which has distinct hydrogeologic properties (Becher, 1996) and geochemical properties 

(Langmuir and White, 1971) and was therefore considered a 

separate hydrogeologic unit in the conceptual model (fig. 5). 

The four hydrogeologic units in this study were sandstone, 

shale, mixed limestone/dolomite, and dolomite (the Gatesburg 

aquifer).
Introduction 5

Figure 3. Geologic formations (Berg and others, 1980), structure, and geologic section (modified from 

Butts, 1945) in the study area near Martinsburg, Pennsylvania.
6 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

Figure 4. Shaded-relief map of the Martinsburg, Pennsylvania Quadrangle showing topography, lithology, and stream drainage 

near Martinsburg, Pennsylvania. (vertical exaggeration X 2.5)
Introduction 7

Table 1. Geologic-stratigraphic and hydrologic units column near Martinsburg, Blair County, Pennsylvania.

[Descriptions from Becher, 1996]

Geologic

age

Geologic formation 

and abbreviation 

(units as mapped locally)

Geologic description Thickness 

(feet)

Hydrologic unit

(lithology)

Silurian Tuscarora 

St

Light- to medium-gray sandstone and minor 

interbedded shale.

400-700 Tuscarora, Juniata, and Bald 

Eagle aquifers (sandstone)

Ordovician Juniata 

Oj

Brownish- to grayish-red sandstone and some 

siltstone and shale.

850

Bald Eagle 

Obe

Gray to olive-gray and grayish-red fine- to 

coarse-grained sandstone and some 

conglomerate.

600-900

Reedsville 

Or

Medium dark-gray to brownish or greenishgray shale with some interbedded siltstone 

and sandstone beds.

1,000-2,000 Reedsville and Waynesboro 

aquifer (shale)

Coburn through Loysburg, 

Undivided 

Ocl

Coburn: Medium and medium- to very darkgray thin-bedded, argillaceous, and 

fossiliferous limestone containing interbeds 

of calcareous shale.

325 Coburn through Loysburg 

aquifers 

(mixed limestone /dolomite)

Salona: very dark-gray to black, medium- to 

coarse-grained, sparsely fossiliferous 

limestone with some shale partings. 

170

Nealmont: medium- to dark-gray, coarsely 

crystalline, limestone and medium- to darkgray, finely crystalline limestone. 

30-75

Benner: light- to dark-gray, mostly fissile to 

flaggy and thick-bedded, very-finely to finely 

crystalline limestone. 

150

Snyder: Medium- to dark-gray coarselycrystalline limestone and limestone 

conglomerate containing interbeds of finely 

crystalline dolomitic limestone.

80

Hatter: Medium- to dark-gray fine-grained, 

argillaceous, laminated, dolomitic and oolitic 

limestone.

75

Loysburg: Light- to medium-gray medium- to 

thick-bedded, fine-grained, shaly limestone 

and dark gray dolomite and dolomitic 

limestone. 

150

Bellefonte and Axemann, 

Undivided 

Oba

Bellefonte: light- to medium-gray, very finegrained dolomite and medium-gray dolomite 

with sandstone beds and chert

1,400 Bellefonte / Axemann aquifer 

(mixed limestone dolomite)

Axemann: Light- to dark-gray, coarsely 

crystalline, fossiliferous limestone 

interbedded with silty, fine-grained limestone 

and silty, fine-grained dolomitic limestone. 

400-700
8 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

Ordovician 

(continued)

Nittany and Stonehenge, 

Undivided 

Ons

Nittany: Finely to coarsely crystalline, 

alternating light- and dark-gray beds of 

dolomite that contains siliceous oolites and 

some sandy and cherty beds.

1,200 Nittany / Stonehenge aquifer 

(mixed limestone / dolomite)

Stonehenge: Medium and dark-gray, finely 

crystalline limestone that contains thin 

laminae, bands, or as much as 6-foot thick 

interbeds of dolomite.

250-600

Cambrian Gatesburg _

Cg

Dark-gray, coarse-grained dolomite. Darkgray, thin bedded, microcrystalline, silty 

dolomite; finely crystalline, shaly dolomite, 

and coarse-grained quartzose sandstone beds 

grading from conglomerate to sandstone. 

Interbedded limestone and sandstone. 

Massively bedded, coarsely crystalline 

dolomite. Dark-gray medium and coarsely 

crystalline dolomite interbedded with oolitic 

and cryptozoan-bearing dolomite.

1,980-2,160 Gatesburg aquifer (dolomite)

Waynesboro1 

_

Cwb

Greenish-gray and grayish-purple shale 

interbedded with greenish-gray sandstone 

and conglomerate. 

1,000-1,500 Reedsville and Waynesboro 

aquifer (shale)

1 Geologic description from Taylor and others, 1982.

Table 2. Hydrogeologic properties of aquifers in the study area (from Becher, 1996). 

[Intended use of well: H, High capacity; L, Low capacity; (gal/min)/ft, gallons per minute per foot of drawdown; --, no data]

Aquifer name

Median reported 

yield 

(gallons per minute)

Specific capacity

[(gal/min)/ft] - 24 hour test

Transmissivity

(feet squared per day)

Average well depth

(feet)

Coburn through Loysburg 8 (L) 0.12 (L) 1

 25

2 680

170

Bellefonte and Axemann 10 (L) .18 (L) 1 40 180

Nittany Stonehenge 11 (L) .19 (L) 1 40 175

Gatesburg 16 (L)

325 (H)

.30 (L)

9.1 (H)

1 2,000

2 2,700

2 5,000

300

Reedsville 20 (L) .4 (L) -- --

1 Based on median specific-capacity data.

2 Based on aquifer-test data at well field. 

Table 1. Geologic-stratigraphic and hydrologic units column near Martinsburg, Blair County, Pennsylvania.—Continued

[Descriptions from Becher, 1996]

Geologic

age

Geologic formation 

and abbreviation 

(units as mapped locally)

Geologic description Thickness 

(feet)

Hydrologic unit

(lithology)
Introduction 9

That part of the aquifer system between the land surface 

and competent bedrock is unconsolidated (fig. 5). Water in this 

part of the ground-water system travels through voids between 

individual grains of material. The differences in hydrologic 

properties of the unconsolidated material vary on the basis of 

the properties of the parent material (fig. 6). In the case of soils 

on mountain slopes, the unconsolidated material overlying a 

given geologic formation may be material transported downslope from other geologic formations; however, for the majority 

of the study area, the unconsolidated material is residuum from 

weathering of the underlying bedrock formation. Soils overlying sandstone lithology include silt loam, but also have soils 

with a higher sand content such as loam and sandy loam as well 

as loose stones or rubble (fig. 6). Major soil types overlying 

shale lithologies include loam, silt loam, and clay loam. Soil 

types overlying the mixed limestone and dolomite lithologies 

are predominantly silt loam. Other soils that overlie these mixed 

limestone and dolomites are near the boundaries with other rock 

types and appear to have been transported downslope from their 

location of origin. The soils overlying the Gatesburg aquifer are 

classified as sand or sandy loam. Becher (1996) also has noted 

that the unconsolidated material overlying the Gatesburg aquifer consists of a thick sandy residuum. The upper portion of the 

unconsolidated material is typically above the water table; however, some of the unconsolidated material can be saturated. 

Those parts of the unconsolidated material that are saturated are 

part of the aquifer system. The thickness of the unconsolidated 

material overlying the other carbonate rocks in this study is 

highly variable. The boundary between the unconsolidated 

material and the highly weathered bedrock is not well defined, 

and the highly weathered part of the aquifer may maintain some 

of the structural orientation of the original bedrock.

The bedrock has three potential types of permeability to 

water—primary, fractures, and conduits. Primary permeability 

could occur in interconnected spaces between grains; however, 

in the study area, primary permeability is not considered to be a 

major factor affecting ground-water flow. Water that travels 

through fractures generally moves slowly (in the range of tens 

of feet per day or less). Fractures enlarged by chemical dissolution can become conduits. Water that travels through large solution conduits moves rapidly (in the range of hundreds of feet per 

day or more). The flow regime in conduits becomes turbulent, 

as opposed to laminar flow regimes in unconsolidated materials 

and fracture flow (White, 1988).

Figure 5. Conceptual model of ground-water-flow system near Martinsburg, Pennsylvania. Generalized hydrogeologic 

section near A - A’ from figure 3.
10 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

The fracture patterns in the Martinsburg area were not 

determined from field measurements, but the probable patterns 

can be deduced because they typically form along weaknesses 

in the rock caused by initial sedimentary structures in the rock 

and the tectonic forces that deformed the rocks. These fractures 

may be related to bedding and cleavage (both parallel to the 

strike of the axial fold in map view) or they may be perpendicular to the strike of the axial fold (fig. 7). Bedding planes were 

identified by Butts (1945); the orientation of cleavage and 

cross-strike fractures have been inferred from the orientation of 

bedding planes. Bedding is a depositional structure in sedimentary rocks that would have been planar and horizontal at the 

time of deposition. During tectonic deformation, weaknesses 

between bedding planes may have been enhanced as beds 

slipped along each other to accommodate the deformation. Dip 

directions of bedding vary due to folding, but the strike in the 

vicinity of the Martinsburg wells is N. 30° E., parallel to the fold 

axis (Butts, 1945). Axial cleavage fractures are created as a 

result of the stress during deformation and create a fan pattern 

over the axis of a fold in geologic section (fig. 7). Cleavage is 

nearly perpendicular to bedding and varies with the dip of the 

beds. Cleavage also is parallel to the fold axis; therefore, bedding and cleavage planes intersect in lines parallel to the fold 

Figure 6. Basic soil types (color coded) and their 

relation to bedrock lithology (labeled) near 

Martinsburg, Pennsylvania. Soil data from U.S. 

Department of Agriculture (1981); geology from 

Berg (1980).

Figure 7. Conceptual model of hypothetical orientation of major 

fracture sets in study area near Martinsburg, Pennsylvania.
Introduction 11

axis. Tectonic stresses also have been shown to create near-vertical fractures in the direction of the compressional force perpendicular to the fold. In this case, such fractures (cross-strike 

fractures-fig. 7) would be oriented perpendicular to the fold 

axis or N. 60° W. 

Typically, not all fracture sets have the same capacity to 

transmit water; thus understanding the orientation of the dominant water-bearing fractures may be helpful for predicting 

ground-water-flow directions. Bedding-plane partings were 

found by Burton and others (2002) to be the dominant pathway 

of ground-water flow in a fractured-bedrock aquifer imparting 

anisotropy (hydraulic conductivity is not the same in every 

direction) in the ground-water-flow system. Conduits can 

develop along existing fractures; therefore, the orientation of 

conduits is likely to be similar to that of the dominant fracture 

set. Because of the preferred orientation of fractures and conduits, carbonate aquifers are likely to be anisotropic (White, 

1988). Although the abundance of each of these fracture sets 

and their importance in the local flow system near Martinsburg 

is unknown, the orientation of the fractures sets deduced from 

bedding and structure can be used in determining the orientation 

of the ground-water model as well as developing the conceptual 

model of the ground-water flow. 

A major geologic factor that can have an appreciable effect 

on ground-water flow in this area is the presence of karst features such as sinkholes, closed depressions, vertical shafts, 

caves, and conduits. Surface water can enter sinkholes directly, 

allowing contaminants to enter the aquifer that may have otherwise been attenuated in the soil. Closed depressions are topographically low areas where water can accumulate during wet 

periods. Closed depressions can have vertical dissolutional features called shafts that drain the accumulated water. Caves are 

naturally occurring openings in the subsurface large enough for 

human entry. Although caves can be related to ground-water 

flow, a more relevant feature would be a conduit or a subsurface 

fissure or tunnel that is filled with water. 

Some of these karst features in the Martinsburg area were 

mapped by the Pennsylvania Topographic and Geologic Survey 

(William Kochanov, Pennsylvania Topographic and Geologic 

Survey, written commun., 2002) and are shown on figure 8. The 

orientation of caves was used to infer possible preferential flow 

directions of ground water. Three caves have been identified in 

the study area (Speece and Cullinan, 1972) (fig. 8). The cave 

approximately 0.5 mi south of Martinsburg is Martinsburg 

Cave, classified as a small cave with branches trending due 

north and due east with a total length of 85 ft. Most of the cave 

has collapsed, forming numerous sinkholes that have since been 

filled. The other two caves are a pair known as Drake Cave #1 

and Drake Cave #2 (Speece and Cullinan, 1972) and are 

approximately 1 mi west of Martinsburg. Drake Cave #1 is 

about 25 ft long trending north-south and ending in a blockage 

of rock. Drake Cave #2 is about 300 ft to the east of Drake Cave 

#1 and trends northwest-southeast. It also is about 25 ft long and 

pinches out at the end. All three caves are above the water table. 

It is interesting to note that the trends of the cave passages are 

approximately aligned with the bedding (Drake Cave #2 and 

Martinsburg Cave), cleavage (Drake Cave #1), or cross-strike 

fractures (Martinsburg Cave) (fig. 8); however, this small number of caves does not provide sufficient evidence to draw conclusions about whether or not caves or conduits would always 

be oriented in these directions. Conduits probably are the most 

difficult feature to identify because they have no easily identified expression on the land surface; however, areas with a high 

density of the other karst features may have a higher probability 

of having conduits. The non-uniform density of karst features 

on figure 8 could be related to geologic structure (intersection 

of major fracture sets) or differences in lithology. 

The Nittany/Stonehenge Formation appears to have a 

higher density of karst features than other formations in 

figure 8, which may lead to the conclusion that lithology is the 

key to development of karst features. Lithology alone, however, 

does not explain the relative density of karst features. In the 

southwestern part of the ground-water model area, the density 

of karst features in the Bellefonte/Axemann Formation is similar to that found in the Nittany/Stonehenge Formation; however, in the central part of the ground-water model area, karst 

features are not present in the Bellefonte/Axemann Formation. 

The orientation and density of the major fracture sets (fig. 7) 

may vary on the basis of their position relative to the fold axis. 

Stress during folding events may have been more intense in 

some areas than in other areas based on their positions along the 

fold; fracture sets in those areas may allow preferential development of karst features. 

In some karst areas, point recharge is significant. Entire 

streams can disappear into sinkholes and be a major component 

of recharge. Despite numerous karst features in this area, this 

type of recharge was not considered to be an important component of recharge because the model area is located at headwaters 

where no regional stream brings in flow from outside of this 

area. Also, measurement of stream discharge at successive 

points along streams exhibited no areas where these local 

streams were losing water to the aquifer. Although the sinkholes and closed depressions probably direct water into the 

aquifer in concentrated zones from vertical pipes or shafts in the 

bedrock, this water is from precipitation in the immediate area 

near that sinkhole or closed depression. Therefore, recharge 

was considered to be areally distributed.

The topographic position of the well fields is an important 

factor in determining the boundaries for the study area. The well 

fields are near the headwaters of three stream drainages: 

1) Cove Creek to the west, 2) Piney Creek to the north, and 

3) an unnamed tributary to Clover Creek to the east (fig. 4). The 

boundary of the study area includes the surface drainage basins 

of these three watersheds to allow the model to simulate the 

configuration of the water table near the watershed divide. The 

study by Becher (1996) produced a water-table map for the area 

that includes most of the current study area. Becher's watertable map indicated the water table was smooth and generally 

followed the land-surface contours (fig. 9).
12 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

Well-Field Characteristics

Martinsburg Borough uses four wells for municipal supply. The well known as the Hershberger Well (USGS well number BA 332) is in the borough, referred to as the Hershberger 

well field, and the other three wells (Well #1, BA 329; Well #2, 

BA 330; and Well #3, BA 545) are to the east of the borough in 

the Wineland Well field (fig. 3). Well BA 332 is completed in 

the Nittany/Stonehenge aquifer and is 100 ft deep with approximately 80 ft of 12-in. casing; static water levels are typically 

about 12 ft below land surface. Wells BA 545, BA 329, and 

BA 330 are completed in the Gatesburg aquifer. BA 329 is 

255 ft deep with 134 ft of 6-in. casing; static water levels are 

typically about 80 ft below land surface. BA 330 is 224 ft deep 

with 195 ft of 6-in. casing; static water levels are typically about 

80 ft below land surface. BA 545 is 308 ft deep with 235 ft of 

8-in. casing. Static water levels in well BA 545 range from 85 to 

95 ft below land surface (Randy Stoltz, Martinsburg Municipal 

Authority, written commun., 2002). All four wells draw water 

from the open borehole between the bottom of the casing and 

Figure 8. Karst features within 

ground-water model boundary 

near Martinsburg, 

Pennsylvania. (Cave locations, 

Speece and Cullinan (1972); 

Sinkhole and closed 

depression data from William 

Kochanov, Pennsylvania 

Topographic and Geologic 

Survey, written commun., 

2002).

MILES

KILOMETERS
Introduction 13

the bottom of the well. Driller's logs from well BA 545 indicate 

an interval of medium-grained sand from below the soil layer to 

approximately 90 ft and that the well was completed in competent bedrock without encountering voids in the open interval 

(Crystal Newcomer, Moody and Associates, written commun., 

1986). The wells are pumped on alternate days. Well BA 332 is 

pumped at a rate of 300,000 gal/d on 1 day. BA 545 and BA 329 

are pumped on the alternate day at rates of 210,000 and 

90,000 gal/d, respectively, to supply the daily requirement of 

300,000 gal/d. BA 330 currently (2003) is not used. 

Figure 9. Water levels near 

Martinsburg, Pennsylvania 

(Based on Becher, 1996, 

plate 2).
14 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

Land Use and Sources of Nitrogen

Potential sources of ground-water contaminants are related 

to the land use. General classifications of land use in the study 

area are shown in figure 10. The areas within the ground-water 

model boundary shown in figure 10 include 1) forested areas 

with virtually no source of nitrogen other than precipitation, 

2) agricultural areas with manure (either applied to fields or as 

runoff from pastures and feedlots) and chemical fertilizers as 

potential sources of nitrogen, and 3) low-density residential 

(urban) with potential nitrogen sources from sewage and fertilizers. In areas served by public sewers, leaking sewer lines may 

be a source of nitrogen to the ground water. Residences not 

served by public sewer lines have on-lot septic systems for sewage disposal. In areas with on-lot septic systems, nitrogen may 

leach into the ground water directly from the systems. The areas 

served by public sewer lines are shown in figure 11. Homeowners in residential areas may use lawn fertilizers, which are a 

potential source of nitrogen as well. A summary of the land use 

in the area around Martinsburg used for the simulation of 

ground-water flow is shown in table 3.

Figure 10. Generalized land use in the 

area of Martinsburg, Pennsylvania 

(Vogelman and others, 1998a, 1998b).
Introduction 15

Figure 11. Types of nitrogen applied 

to fields and areas served by public 

sewer lines near Martinsburg, 

Pennsylvania (Manure and fertilizer 

application from Amanda Ritchy, Blair 

County Conservation District, written 

commun., 2003; Sewer line locations 

from Randy Stoltz, Martinsburg 

Borough, written commun., 2003).

Table 3. Land covers included in the land-use groupings in the 

study area near Martinsburg, Pennsylvania (Modified from 

Vogelman and others, 1998a, 1998b).

Land-use grouping 

shown on figure 10 Land cover Percentage of area

Agricultural Row crop 61.6

Hay pasture 18.6

Forested Deciduous forest 15.4

Mixed forest 1.6

Evergreen forest .2

Emergent herbaceous wetland .1

Urban High-intensity commercial .5

High-intensity development .4

Low-intensity residential 1.6

Water Water .2
16 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

Study Methods

The description of study methods includes those techniques used in the field for collection of data, the methods used 

in laboratories to analyze the data, and the methods used to 

interpret the data.

Field Data Collection and Analytical Methods

Field data were collected to provide streamflow and 

ground-water levels needed for delineating the areal extent of 

the zone of contribution to the Martinsburg municipal wells by 

use of a numerical ground-water-flow model. Water-quality 

data also were collected to determine the source of contaminants in the municipal wells. Data were collected at eight private homeowner wells, two municipal wells, one spring used 

for domestic supply, and four streams draining the area around 

Martinsburg, Pa. Water-quality samples were collected and 

water levels were measured in September 2002; water levels 

and streamflow were measured in November 2002 (fig. 12, 

table 4). Water levels from 11 additional wells were obtained 

from the USGS Ground Water Site Inventory database for use 

in model calibration (table 4).

The field data collected at the 10 wells and the spring consisted of volumetric measurements of discharge, site characteristics, and water levels (wells only). Samples of untreated rawwater also were collected at selected sites. The data were collected according to USGS protocols (U.S. Geological Survey, 

1997 to present). For the wells with removable well caps, the 

water level was measured using an electric tape cleaned with 

Figure 12. Location of wells, 

springs, and streams sampled or 

measured in and near 

Martinsburg, Pennsylvania. 
Study Methods 17

alcohol between uses. The water then was purged (usually from 

an outside garden hose) for 10 to 15 minutes and field constituents (temperature, specific conductance, dissolved oxygen, and 

pH) were recorded. The volumetric flow was determined by 

recording the amount of time needed to fill a standard 5-gal 

bucket. The fluctuations in measurements of field constituents 

tended to stabilize when the standing water in the well was 

purged. Stability was defined as a specific conductance change 

of less than 10 μS/cm, a temperature change of less than 0.5°C, 

and a pH change of less than 0.2 pH units in a 5-minute period. 

Water samples were collected from a spigot at the pressure tank 

prior to any form of water treatment or at an outside spigot if 

there was no treatment system. The spring was a domestic water 

supply and was sampled through the homeowner’s plumbing. 

Latex gloves were worn during sample collection to prevent 

contamination. The sampling requirements for each analysis are 

shown in table 5. The water samples were placed on ice and 

transported to the appropriate location for analysis.

Surface-water sites were sampled according to standard 

USGS procedures (U.S. Geological Survey, 1997 to present). 

Volumetric flow was measured at each site using a Pygmy current meter. The meter-type selection was based on stream depth 

and velocity. The water sample was collected as a single depthintegrated sample in the center of the stream. The bottle was 

lowered and raised through the water column, capped immediately, and placed on ice. Field constituents were recorded when 

previously defined stability criteria were met. Surface-water 

samples and flow measurements were intended to represent 

base-flow conditions. Comparison of the streamflow at the 

nearest USGS streamflow-measurement station on the sampling dates to annual streamflow statistics at those sites helps 

put the sampling conditions in perspective. Water-chemistry 

samples were collected on September 11, 2002. On this date, 

the daily mean streamflow at Frankstown Branch Juniata River 

(USGS station number 01556000) was 47 ft3/s, which is a flow 

exceeded 90 percent of the time during the entire period of 

record (1916 to 2003) and is representative of very low base 

flow. Streamflow was measured on November 20, 2002. On 

Table 4. Wells sampled and/or used for model calibration near Martinsburg, Pennsylvania.

[ft, feet; n/a, not applicable; --, no data; NAD 83, North American Datum of 1983; NGVD 29, National Geodetic Vertical Datum of 1929]

County well 

or spring 

number

Aquifer

Latitude 

(degrees, 

minutes, 

seconds, 

NAD 83)

Longitude 

(degrees, 

minutes, 

seconds, 

NAD 83)

Elevation of 

land surface 

(ft above 

NGVD 29)

Well 

depth 

(ft)

Casing 

length

(ft)

Water 

level 

below land 

surface 

(ft)

Model 

layer

Date of

water level

BA 2481 Nittany/ Stonehenge 401857 781730 1,334 145 60 58 2 05/01/1980

BA 3322, 3 Nittany/ Stonehenge -- -- 1,393 100 -- 30 2 09/10/2002

BA 431

 Bellefonte/ Axemann 401711 782119 1,445 325 21 38 1-2 05/01/1980

BA 4334 Coburn through Loysburg 401808 782021 1,363 160 42 2 1-2 09/10/2002

BA 4371 Bellefonte/ Axemann 401724 781958 1,439 105 21 15 1-2 10/23/1984

BA 4531 Nittany/ Stonehenge 401748 781833 1,475 180 -- 84 1-2 05/01/1980

BA 4571 Coburn through Loysburg 401931 782134 1,361 325 37 35.65 1-2 06/14/1984

BA 4591 Coburn through Loysburg 401845 782044 1,349 145 45 27.2 1-2 05/15/1984

BA 4711 Bellefonte/ Axemann 401955 781901 1,409 60 -- 15 2 05/15/1984

BA 4731 Bellefonte/ Axemann 401848 782152 1,369 180 20 7.98 1-2 05/15/1984

BA 4751 Nittany/ Stonehenge 401918 781837 1,506 195 186 138.4 2 05/16/1984

BA 4871 Reedsville 401858 781953 1,419 107 19 11.59 1-2 06/14/1984

BA 4991 Nittany/ Stonehenge 401913 781757 1,419 170 135 71.1 2 05/14/1984

BA 5434 Reedsville 402013.5 781927.2 1,473 145 28 22.9 1-2 09/09/2002

BA 5444 Bellefonte/ Axemann 401938.6 781903.6 1,437 125 50 43.3 1-2 09/09/2002

BA 5452, 3 Gatesburg -- -- 1,405 308 90 113 2 09/09/2002

BA 5464 Bellefonte/ Axemann 401813.8 781728.4 1,336 180 40 70 1-2 09/10/2002

BA 5474 Nittany/ Stonehenge 401923.1 781848.1 1,473 200 50 138 1-2 09/11/2002

BA 5484 Nittany/ Stonehenge 401726.5 781945.4 1,461 185 130 44 2 09/11/2002

BA 5495 Bellefonte/ Axemann 401746.8 781951.3 1,449 420 21 32.45 1-2 09/11/2002

BA 5505 Bellefonte/ Axemann 401718 782115.5 1,440 310 40 108 1-2 09/11/2002

BA Sp 235 Coburn through Loysburg 402028 781550 1,334 n/a n/a n/a n/a 09/09/2002

1 Well not sampled, water level used for model calibration only.

2 Municipal supply well: sampled for chemical and microbiological indicators, pumping simulated in model, water level not used for calibration. 3 Water level affected by recent pumping. 

4 Well sampled for chemical and microbiological indicators, water level used for model calibration.

5 Well or spring sampled for chemical and microbiological indicators, water level not used for model calibration.
18 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

this date, the daily mean streamflow at Frankstown Branch 

Juniata River was 527 ft3/s, which is a flow exceeded 25 percent 

of the time and probably is representative of high base-flow discharge.

The water samples were analyzed for a variety of chemicals useful in determining the potential sources of contaminants 

in the aquifer and streams. The analyses included major ions, 

nutrients, 15N/14N isotope ratio (hereafter referred to as δ15N), 

wastewater compounds, and bacteria. Quality-assurance sampling was limited to replicate samples for nitrate, the major contaminant of concern; blanks were not collected for any sample 

types. Replicate samples for nitrate analysis indicated good precision in determination of nitrate concentrations. Bacteria samples underwent additional analysis for source tracking at the 

Pennsylvania State University, Environmental Microbiology 

Laboratory at the Capitol Campus, Harrisburg, Pa. (Dr. 

Katharine Baker, Pennsylvania State University, written commun., 2003).

Methods to Determine Sources of Contaminants to 

Ground-Water Wells

Multiple methods were used in this study to determine the 

potential sources of contaminants to the Martinsburg municipal-supply wells. First, the geochemistry of water was analyzed 

to determine spatial patterns of naturally occurring chemicals in 

ground water and evolution of geochemical constituents relative to rock formations. Second, anthropogenic contaminants 

were analyzed to determine their spatial distribution in ground 

water and their relation to surface processes. A component of 

the study of anthropogenic contaminants included tracking 

sources of bacteria detected in ground water. Finally, a groundwater model was used to determine probable directions of 

ground-water flow and the areal extent of the zones of contribution to wells.

Natural Geochemistry of Water 

The analysis of the geochemistry of ground water was 

based primarily on physical properties and major-ion chemistry. The major-ion analysis is an important step in determining 

the water type and can help indicate sources of ground water. 

Some specific calculations were made on the basis of previous 

studies of natural ground-water geochemistry in carbonate aquifers (Langmuir and White, 1971). That study used chemical 

indicators, such as calcite saturation, calcium-magnesium 

(Ca2+/Mg2+) ratios, CO2 partial pressure, and seasonal pH variations, to determine major sources of water (table 6) and types 

of permeability (table 7) in the nearby Nittany Valley carbonate 

aquifers in Center County, Pa. (fig. 1). The samples in the Langmuir and White study (1971) included water from wells in the 

Gatesburg, Nittany, Stonehenge, and Axemann aquifers, all of 

which are present in the Martinsburg area. The Ca2+/Mg2+ ratio 

is related to the type of bedrock in which the water originated. 

Water drawn from a pure limestone aquifer will have little or no 

magnesium, and water drawn from a pure dolomite aquifer will 

have a Ca2+/Mg2+ ratio of 1. Limestone commonly contains 

some magnesium, and water drawn from limestone aquifers 

will have a Ca2+/Mg2+ ratio near 6 with a range from 2 to 10 

(White, 1988). 

The partial pressure of CO2 in water is related to the connection of the ground water with the atmosphere and can be an 

indicator of potential for conduit flow. Partial pressure of CO2

(PCO2) depends on whether the system is open to atmospheric 

air (open system) or isolated from the atmosphere (closed system) as well as the amount of soil organic matter and mixing of 

water from various sources. Although numerous factors affect 

CO2 concentrations in water, data from karst aquifers in the 

North Central Appalachian Mountains show that water discharged from diffuse springs and wells has CO2 partial pressures (PCO2 water /PCO2 atmosphere = 20 and 33, respectively) 

that are higher than water discharged from conduit springs 

(PCO2 water /PCO2 atmosphere = 14) (White, 1988). 

Because several factors can affect PCO2, a better indicator 

of conduit flow is calcite saturation. The calcite saturation index 

Table 5. Sampling requirements and analytical methods for water analyses from wells and streams near Martinsburg, Pennsylvania.

[All analyses conducted by U.S. Geological Survey (USGS), National Water-Quality Laboratory; mL, milliliter; L, liter; HNO3, nitric acid; H2SO4,sulfuric acid]

Analysis Bottle Sampling specifications Analytical method

Major ions1 250 mL (anions) Rinse, filter, unacidified Ion chromatography (Fishman and Friedman, 1989)

250 mL (cations) Rinse, filter, acidify with HNO3 Inductively coupled plasma, (Fishman, 1993)

250 mL (lab alkalinity) Rinse, raw, unacidified Titration (Fishman and Friedman, 1989)

Wastewater compounds1 1-L glass amber Do not rinse, fill to shoulder Polystyrene-divinylbenzene solid-phase extraction 

and capillary-column gas chromatography/mass 

spectrometry (Zaugg and others, 2002)

Nitrate /nitrite1 125 mL brown polyethylene Rinse, filter, acidify with H2SO4 Cadmium reduction colorimetry (Fishman, 1993)

Nitrogen isotope2 1-L polyethylene bottle with 

poly-seal cap

Rinse, filter, unacidified Mass spectrometry

1 Analyzed by USGS National Water-Quality Laboratory, Denver, Colo.

2 Analyzed by Zymax Laboratory, San Luis Obispo, Calif., on contract with USGS National Water-Quality Laboratory.
Study Methods 19

(SIc) is the ratio between the ion activity product and the solubility product for calcite. Waters with indices less than 1 are 

undersaturated, an index of 1 is at saturation, and indices greater 

than 1 are supersaturated. The SIc is affected by lithology, and 

waters with the longer transport times associated with diffuse 

flow and fracture permeability typically are more saturated with 

respect to calcite than waters moving rapidly through a conduit 

system with much less contact with the bedrock. Data from 

karst waters in the North Central Appalachian Mountains show 

that diffuse-flow springs and wells have a higher SIc (-0.24 and 

-0.13, respectively) than conduit-flow springs (-0.89) (White, 

1988).

Anthropogenic Contaminants 

The sampling for anthropogenic contaminants included 

nutrients, δ15N, and compounds indicative of wastewater. The 

nutrient analyses included nitrogen and phosphorus species. 

Nitrate is the main contaminant of concern in the ground water; 

however, occurrence of phosphorus also is considered important. The δ15N ratio is considered important in determining 

whether the source of nitrogen is chemical fertilizer or organic 

waste such as manure or septic systems. The wastewater analysis included multiple indicators of human wastewater, such as 

caffeine and cholesterol, hormones, pesticides, and volatile 

organic compounds (VOCs). The analysis was developed by the 

USGS to detect chemicals commonly expected in wastewater 

(Zaugg and others, 2002). Other possible indicators of contamination by on-lot sewage disposal systems (septic tanks), such 

as boron concentration and comparisons of chloride-bromide 

(Cl/Br) ratios, also were analyzed.

To further classify possible sources of nitrogen in agricultural areas, the origin of fertilizer (inorganic chemical or 

manure) was classified as well. This classification is important 

Table 6. Data from previous studies of water from Ridge and Valley carbonate aquifers in Pennsylvania (Langmuir and Jacobson, 1971) 

showing geochemical indicators of source of water. 

[mg/L, milligrams per liter; atm, atmosphere]

Source of water

Geochemical indicators

Specific 

conductance

(microsiemens 

per centimeter 

at 25°C)

pH

Alkalinity

(mg/L as 

CaCO3)

Partial 

pressure

CO2 (atm)1

Calcite 

saturation 

index

Dolomite 

saturation 

index

Calciummagnesium 

molar ratio

Limestone springs 315 7.29 134.0 -2.17 -0.52 -0.86 5.23

Dolomite springs 

(non-Gatesburg)

416 7.49 183.0 -2.23 -.2 -.22 1.04

Limestone wells 569 7.31 234.0 -1.95 -.07 -.27 2.97

Dolomite wells 

(non-Gatesburg)

542 7.34 237.0 -1.92 -.18 -.17 .94

Gatesburg wells and springs 276 7.93 132.0 -2.82 -.1 -.11 1.06

1 Log of atmospheric pressure.

Table 7. Data on geochemical indicators of type of permeability from previous studies of Ridge and Valley carbonate aquifers in 

Pennsylvania.

[mg/L, milligrams per liter; atm, atmosphere; --, no data]

Type of permeability

Geochemical indicator

Specific 

conductance 

(microsiemens 

per centimeter 

at 25°C)

pH

Alkalinity 

(mg/L as 

CaCO3)

Partial 

pressure 

CO2 (atm)1

Calcite 

saturation

index

Dolomite 

saturation 

index

Calciummagnesium 

molar ratio

Data source

Primary -- High -- Low Saturated Saturated Low Langmuir and White 

(1971) values by 

type of 

permeability

Fracture zones -- Low -- High Near Saturation Near Saturation Low

Conduits -- Low -- Low Undersaturated Undersaturated High

Conduits (springs) 112 -- -- -2.38 -0.89 -- -- White (1988) values 

for types of 

permeability

Diffuse flow (springs) 200 -- -- -2.22 -.24 -- --

 Fracture zones (wells) 288 -- -- -2.00 -.13 -- --

Conduits (springs) -- -- 108 -2.41 -.52 -- 4.5 Scanlon and 

Diffuse flow (springs) -- -- 175 -- -- -- 2.4 Thrailkill (1987)

1 Log of atmospheric pressure.
20 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

information because the isotopic signature of nitrogen can vary 

on the basis of the source of that nitrogen (Cravotta, 1997; 

Böhlke, 2003). A summary of the organic (manure) or inorganic 

(chemical fertilizer) origin of nitrogen in the study area is illustrated in figure 11. Data from the U.S. Department of Agriculture (USDA), Natural Resources Conservation Service (NRCS) 

(Amanda Ritchy, U.S. Department of Agriculture, written commun., 2003) were used to classify cropland nutrient sources in 

the vicinity of the well fields as 1) predominantly manure (91 to 

100 percent manure), 2) predominantly chemical fertilizer (0 to 

10 percent manure), or 3) a mixture of chemical and manurebased fertilizers (fig. 11). This illustration shows that the predominant source of nitrogen applied in most areas is animal 

manure. The uptake of nitrogen by crops is not considered, and 

the figure is not meant to imply that any specific field has excess 

nitrogen applied or that nitrogen leaches to the ground water 

from any specific field. Areas where septic systems are used 

also are shown in figure 11 (any area not served by public 

sewer). 

Determining the possible sources of nitrate in ground 

water by delineating land use may not be helpful in an area 

where agricultural activity is widespread; however, a nutrient 

mass-balance analysis can be used to help determine the 

sources. For example, if the areal extent of the zone of contribution to a well includes residences with on-lot septic systems and 

agricultural land, the amount of nitrogen generated by each can 

be estimated. The number of septic systems can be multiplied 

by a typical amount of nitrogen generated by a household to calculate the contribution by septic systems. The amount of agricultural nitrogen applied per acre (with allowances for nitrogen 

uptake by crops) can be computed as well. The proportion of 

nitrogen from each source can help determine which source is 

contributing the greatest amount of nitrogen to the ground 

water. Although specific amounts of nitrogen applied to cropland was not available to make this calculation, the number of 

septic systems could be estimated to allow a calculation of the 

part of the nitrogen contributed by septic systems by (eqn. 1):

Cw = (Ce × Ve × n) / Vw (1)

where 

Cw is the concentration of nitrogen in the municipal 

well due to septic effluent;

Ce is concentration of nitrogen in septic effluent

(85 mg/L - Miller, 1980);

Ve is the volume of septic effluent per person per day

(170 L/day - Miller, 1980);

n is the number of people using septic systems in the

areal extent of the zone of contribution to the

well;

and

Vw is the volume of water pumped per day by the

municipal well.

Assuming that the system is in a steady state where the volume 

of water withdrawn and the nitrogen mass input are the same 

over a period of several years, and that there is no component of 

old, nitrogen-free water diluting the nitrogen, the storage of 

water can be ignored and the concentration in the water in the 

zone of contribution to a well can be calculated by dividing the 

nitrogen mass input by the volume pumped from the municipal 

well in a given time period. This concentration will result in the 

highest possible nitrogen concentration that the septic systems 

could contribute, because there is probably some component of 

dilution by older water and there are losses to volatilization, 

denitrification, and plant uptake that are not considered in this 

calculation. 

Nitrogen occurs as two stable isotopes, 15N and 14 N, and 

the ratio 15N: 14N is reported relative to a standard, in per mil 

(see table of contents for further description of notation, standards and units). The δ15N can provide information on sources 

of nitrogen in nitrate. Cravotta (1997) compared δ15N values in 

surface water with animal manure, fertilizer, and human waste 

and found a wide range within a nitrogen source and some overlap among sources. The range of the 15th to 85th percentile of 

δ15N was about +4 to +21 per mil for animal manure with a 

median near +10, fertilizer had a range from -2 to +3 per mil 

with a median near 0, septic-tank effluent ranged from -2 to 

+4 per mil with a median of near 1 per mil, and sewage sludge 

had a range of +1 to +11 per mil with a median of 8 (fig. 13). 

Cravotta found that fractionation caused significant changes in 

the isotope ratios during transport from source areas; processes 

that transform nitrogen affect the lighter isotope disproportionally, resulting in higher δ15N than would be expected on the 

basis of the source material. Processes such as volatilization, 

residual nitrogen in soils, and denitrification can obscure the 

relations between δ15N in sources and environmental samples 

(Böhlke, 2003), and the potential for these transformation processes must be scrutinized for each sample before using δ15N to 

infer nitrogen sources. In findings consistent with those of Cravotta (1997), Böhlke (2003) found that δ15N was lower in water 

recharged beneath fields receiving chemical fertilizers (+2 to 

+6 per mil) than in water recharged beneath fields receiving 

manure (+10 to +20 per mil) or water recharged through septic 

systems (+8 to +11 per mil).

Microbial Source Tracking 

Microbial source tracking was conducted to help differentiate among human, livestock, and other possible sources of 

bacteria in the municipal water supply. Microbial source tracking is a term used for a variety of microbiological methods to 

attempt to identify probable sources of fecal bacteria identified 

in the environment. Antibiotic resistance patterns (ARPs) of 

fecal-coliform bacteria were analyzed by the Environmental 

Microbiology Laboratory at the Pennsylvania State University 

Capitol Campus in order to determine whether potential sources 

of nitrate originate from human or livestock waste. ARPs of 

fecal coliforms were used to evaluate the importance of the 

agricultural activity as a source of fecal contamination in Morrison Cove. ARP analysis requires that samples from all likely 

fecal sources are collected, and the bacteria isolated from these 

source samples are tested for resistance against a broad spec-
Study Methods 21

trum of antibiotics. Samples of bacteria isolated from environmental samples are tested for resistance against that same spectrum of antibiotics, and a discriminant analysis is conducted to 

determine if the environmental samples are in the same statistical cluster of resistance patterns as one of the known source 

samples. Samples of dairy cattle feces (the only major type of 

animal livestock in this area) and untreated samples from the 

Martinsburg Wastewater Treatment Facility were collected, 

and the fecal coliforms isolated were characterized in terms of 

their ARPs using the methods described by Hagedorn and others (1999), Harwood and others (2000), and Wiggins and others 

(1999). The ARPs from data collected in the Martinsburg area 

were determined by using the methods outlined in these previous studies (Dr. Katharine Baker, Pennsylvania State University, written commun., 2003). 

Two sets of environmental samples from wells were collected from September through October 2002. These samples 

were used to isolate fecal coliforms for the ARPs. The ARPs of 

the isolates were compared to the database developed to determine if the dairy-cattle population is the major source of fecal 

contamination to the area. Quality assurance of the ARPs was 

performed by analyzing replicate isolates (approximately 1 percent of the total isolates) and known cultures of fecal coliforms 

obtained from commercial culture collections. In addition, an 

uninoculated control was run with every set of antibiotic sensitivity determinations.

Simulation of Ground-Water Flow

The areal extent of the zone of contribution is the volume 

of the aquifer that contributes water to a well and all pathlines 

from the recharge area to the well, projected to the land surface. 

The zone of contribution cannot be measured directly, however, 

the ground-water-flow model MODFLOW (Harbaugh and others, 2000) was used to simulate ground-water-flow directions 

and the areal extent of the zone of contribution. The details of 

the ground-water modeling are presented in detail later in this 

report. 

Figure 13. δ15N relative to organic carbon: total nitrogen for 

(A) nitrogen-source materials, and (B) suspended particulates in 

water, Susquehanna River Basin, Pennsylvania (from 

Cravotta,1997).
22 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

Sources of Water and Contaminants to 

Martinsburg Municipal Wells 

Various methods were used to determine the sources of 

water and contaminants to the Martinsburg municipal supply 

wells. The methods were analysis of the geochemical indicators, anthropogenic contaminants, and the simulation of 

ground-water-flow. These approaches were analyzed independently and then were compared to each other to determine if the 

various methods indicated similar conclusions.

Geochemical Indicators

Various water-quality analyses were conducted to assist in 

classifying the source of water and contaminants to wells. 

These analyses included major ions, nitrate and other nutrients, 

wastewater compounds, and microbiological contaminants. 

Results of water-quality analyses collected during this study are 

published in Durlin and Schaffstall (2003).

Source of Water

Ground water contains dissolved minerals that vary on the 

basis of the type of bedrock it has been in contact with. 

Although bedrock in the study area has similar chemical compositions, geochemistry of water still can be used to help determine the source of water to a given well. As previously discussed, water from a dolomite aquifer would be expected to 

have a slightly different geochemical composition than water 

from a limestone aquifer. Water from a well completed in one 

aquifer may have traveled through other aquifers to get to that 

well and, therefore, has a mixed chemical composition. 

Well BA 543 (fig. 12) was completed in shale, and the 

water from this well had a different geochemical composition 

than water from the other wells sampled during this study. 

Results from this well are not included in most geochemical 

comparisons where ranges are given and results from carbonate 

aquifers are compared to each other. 

Langmuir and White (1971) found many comparisons of 

geochemical constituents that were useful to distinguish water 

with five distinct sources: limestone springs, limestone wells, 

dolomite springs (other than the Gatesburg aquifer), dolomite 

wells (other than the Gatesburg aquifer), and water from wells 

and springs in the Gatesburg aquifer. Water from the Gatesburg 

aquifer was found in most cases to be appreciably different 

from water in other carbonate rocks (Langmuir and White, 

1971). The data from the wells sampled for this study were 

compared to their results. Some of these comparisons were 

based on field measurements, others were based on laboratory 

results, and others were values calculated for comparison. 

The calcium/magnesium molar ratios in ground water 

from this study ranged from 1.04 to 9.18. When compared to 

Langmuir and White (1971), samples from wells and the spring 

in the Coburn-Loysburg aquifers had ratios that indicated a 

limestone source; the Bellefonte/Axemann, Nittany/Stonehenge, and Gatesburg aquifers had ratios that indicated a dolomite source (fig. 14). The geochemical indications for a limestone source in the Coburn-Loysburg aquifers suggested that 

the water-bearing zones in these wells may have been in the 

Upper Ordovician limestone lithologies (table 1) of this aquifer. 

The only exception to this pattern was well BA 433, which had 

an abnormally high sodium concentration (fig. 14). This may 

have been a result of anthropogenic contamination or a highly 

mineralized zone. In general, the geochemical composition of 

water matched values that would be expected on the basis of the 

lithology of the aquifers where samples were collected. Those 

wells in aquifers with more pure limestone compositions had 

water that geochemically was similar to ground water from 

limestone wells as reported by Langmuir and White (1971). 

Some wells were completed in aquifers that could contain limestone or dolomite, and water from those wells indicated mixed 

geochemical signatures. Municipal well BA 545 had a 

geochemical signature that indicated a dolomite source, and 

well BA 332 had a geochemical signature that indicated a 

mixed limestone and dolomite source.

Type of Permeability

The type of permeability (primary, fracture, and conduit) 

is one factor affecting the geochemical composition of waters. 

As previously stated, primary permeability was considered to 

be minimal in the study area. The geochemistry of water that 

travels through fractures generally is affected by the large 

amount of contact time with the rock matrix and isolation from 

the atmosphere. The geochemistry of water that travels through 

large solution conduits and caverns is affected by less contact 

with the rock matrix and more contact with atmospheric air than 

water traveling through fractures. Geochemical data collected 

in the current study were compared to the findings of previous 

studies (table 7) to determine which of the three types of permeability was present in this ground-water system. Calcite saturation, PCO2, alkalinity, and specific conductance values of samples from wells indicated that fractures were the dominant 

pathway of ground-water flow.

SIc in ground water ranged from -0.27 to 0.03 (table 8). 

These data were all in the range reported by previous studies in 

fracture-flow systems. Values of -0.52 (Scanlon and Thraillkill, 

1987) to -0.87 (White, 1988) would be indicators of conduit 

flow. Ground water in this system appeared to have sufficient 

amount of contact with the aquifer to have higher SIc than 

expected in conduit-flow systems consistent with longer residence times or a tortuous fracture flow path.

All of the log PCO2 values in ground water were greater 

than -2.4 (table 8), a value shown by previous studies to indicate 

conduit flow (Scanlon and Thraillkill, 1987; White, 1988). The 

two wells that had log PCO2 values less than -2 were wells 

BA 545 and BA 547, which had PCO2 values of -2.03 and -2.21,
Sources of Water and Contaminants to Martinsburg Municipal Wells 23

Table 8. Geochemical composition of water from wells sampled near Martinsburg, Pennsylvania. 

[atm, atmospheres]

County 

well 

number

Aquifer Lithology

Specific 

conductance 

(microsiemens per 

centimeter at 

25°C)

pH

Alkalinity 

(mg/L as 

CaCO3)

Log partial 

pressure of 

CO2

(atm)

Calcite 

saturation 

index

Dolomite 

saturation 

index

Calciummagnesium 

molar ratio

BA 544 Bellefonte/ Axemann Dolomite/ Limestone 1,084 6.80 313 -1.33 -0.27 -0.46 1.14

BA 546 Bellefonte/ Axemann Dolomite/ Limestone 873 6.70 421 -1.04 -.12 -.46 1.31

BA 549 Bellefonte/ Axemann Dolomite/ Limestone 783 6.98 294 -1.53 -.19 -.28 1.17

BA 550 Bellefonte/ Axemann Dolomite/ Limestone 960 6.94 349 -1.43 -.14 -.22 1.04

BA 5451 Gatesburg Dolomite 469 7.30 189 -2.03 -.24 -.43 1.23

BA 3321 Nittany/Stonehenge Dolomite/ Limestone 557 7.13 267 -1.67 .03 -.30 1.78

BA 547 Nittany/Stonehenge Dolomite/ Limestone 499 7.43 170 -2.21 -.18 -.32 1.02

BA 548 Nittany/Stonehenge Dolomite/ Limestone 682 7.30 222 -1.96 -.10 -.16 1.10

BA 433 Coburn through 

Loysburg Undivided

Limestone 2,440 6.80 324 -1.32 -.15 -1.17 9.18

BA Sp 23 Coburn through 

Loysburg Undivided

Limestone 519 7.21 203 -1.92 -.10 -.73 4.66

BA 5432 Reedsville Shale 277 7.55 96 -2.56 -.46 -1.27 2.12

1 Municipal supply wells (dark shading).

2 Well completed in shale was not used in comparisons in text (light shading).

Figure 14. Concentrations of major ions 

in water from wells near Martinsburg, 

Pennsylvania.
24 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

respectively. The value in well BA 545 was much closer to the 

values associated with fracture-flow in limestone wells (-2.00) 

than the value associated with conduit-flow springs (-2.38) 

(table 7). Well BA 547 is in an area with a large number of 

closed depressions and water chemistry may be influenced by 

conduit flow. These wells are in or near the Gatesburg aquifer, 

which according to Langmuir and Jacobson (1971) has log 

PCO2 values in the range of -2.82.

Specific conductance and alkalinity values also are indicators of residence times or the amount of contact that water has 

with the bedrock. The values of specific conductance in ground 

water ranged from 469 to 2,440 μS/cm. Some of the higher values may be affected by anthropogenic contaminants. High specific-conductance values also could indicate long residence 

times or wells completed in zones of unusually high mineral 

content. None of the values were near the mean value of 

112 μS/cm measured in conduit-flow springs by White (1988). 

The values of alkalinity in ground water ranged from 170 to 

421 mg/L as CaCO3. Alkalinity values were all near or greater 

than the values of alkalinity measured in diffuse-flow springs

(175 mg/L, Scanlon and Thraillkill, 1987) and none were as low 

as the values measured in conduit-flow springs (108 mg/L, 

Scanlon and Thraillkill, 1987).

Sources Based on Analysis of Anthropogenic 

Contaminants

Anthropogenic contaminants were measured in ground 

water and streams to determine their occurrence and distribution as well as to indicate potential sources of ground water. 

Some indicators, such as δ15N, are useful in distinguishing 

chemical-fertilizer sources from manure sources. Wastewater 

compounds such as caffeine, cholesterol, and human steroids 

can help determine whether or not human sewage is a likely 

source of ground-water contamination. Cl/Br ratios also can 

indicate anthropogenic effects on ground-water quality.

Nitrate-Nitrogen

The predominant form of nitrogen detected in samples was 

nitrate. Concentrations of nitrite plus nitrate in ground water 

from the carbonate aquifers ranged from 5.73 to 36.9 mg/L as 

nitrogen (table 9). Well BA 543 was completed in shale and had

Table 9. Selected chemical analyses from wells, streams, and a spring sampled near Martinsburg, Pennsylvania.

[mg/L, milligrams per liter; μg/L, micrograms per liter; δ15 N, Nitrogen isotope ratio N15/N14, in per mil; E, value is estimated but below the level that can be 

quantified accurately with the analytical instruments; M, substance was identified positively, but no value can be assigned to the detection because of the uncertainty of the measurement; NS, insufficient bacteria for analysis;--, not analyzed]

County well number, 

spring number,

of stream name

Oxygen, 

dissolved 

(mg/L)

Nitrogen,

nitrite plus nitrate,

dissolved 

(mg/L 

as nitrogen)

Nitrogen,

nitrate plus

nitrite, 

dissolved δ 15N 

(per mil)

Boron, 

dissolved 

(μg/L as 

boron)

Chloride - 

bromide 

ratio

Wastewater 

compounds

(number detected

out of

67 compounds1

)

Percentage of bacteria 

isolates matching 

source2

Well and Spring Sites

BA 544 4.0 36.9 9.4 E 10 1,186 1 NS

BA 546 2.4 5.46 8.8 20 440 1. (metolachlor) Wastewater - 100%

BA 549 3.7 10.5 6.4 10 1,350 3 NS

BA 550 4.4 5.73 -- E 10 -- -- NS

BA 5453 9.2 8.92 4.1 M 710 1. (metolachlor) NS

BA 3323 7.1 9.16 6.0 20 1,355 4 NS

BA 547 8.2 16.8 7.4 E 10 427 1 Cattle - 100%

BA 548 7.0 7.88 -- 20 -- -- NS

BA 433 1.7 7.69 -- 20 3,219 -- Cattle - 88%

BA SP23 1.4 8.81 -- 20 -- -- Cattle - 68%

BA 543 .2 .11 -- 20 -- -- Cattle - 83%

Stream Sites

Piney Creek 5.5 7.92 8.3 20 4-- 2 Cattle - 93%

Clover Creek 8.3 5.35 5.2 E 10 4-- 1 Cattle - 86%

Tributary to Plum Run 6.0 .80 6.6 20 4-- 6 Cattle - 88%

Cove Creek 3.0 1.65 11.3 220 7,666 20 Cattle - 88%

1 Includes estimated and unquantified values, entire list of analytes is available in Durlin and Schaffstall (2003).

2 Data summarized from Dr. Katharine Baker, Pennsylvania State University, written commun., 2003.

3 Municipal supply wells (shading).

4 Bromide measured but not detected, therefore, no ratio shown.
Sources of Water and Contaminants to Martinsburg Municipal Wells 25

a concentration of 0.11 mg/L. These results were consistent 

with previous studies conducted in this area (Lindsey and others, 1997; Coyle, 1998). Municipal supply wells BA 545 and 

BA 332 had concentrations of 8.92 and 9.16 mg/L, respectively. 

The USGS results for water from these wells were consistent 

with the concentrations typically measured by Martinsburg 

Borough for routine sampling in these wells (Randy Stoltz, 

Martinsburg Borough, written commun., 2003). 

Nitrogen Isotopes

The δ15N values in ground water in the study area ranged 

from + 4.1 to +9.4 per mil (table 9). Although δ15N values can 

be affected by denitrification, the relatively high dissolved oxygen concentrations (2.4 to 9.2 mg/L) in samples from wells 

where isotopes were measured suggested that denitrification 

was minimal (Böhlke, 2003). The δ15N values were in the range 

that could indicate nitrate in ground water originates from either 

organic-nitrogen sources (human sewage or animal manure) or 

chemical fertilizer. Values close to +10 per mil in samples from 

BA 546 and BA 548 (table 9) were more likely derived from 

animal sources, while values lower than +6 per mil were likely 

derived from chemical fertilizer. The sample collected on Cove 

Creek downstream from the Martinsburg Wastewater Treatment Plant had a value (+11.3 per mil) in the range that would 

be expected for human sewage or animal manure. Water from 

well BA 545 had a value of +4.1 per mil, and well BA 332 had 

a value of +6.0 per mil. The value in water from well BA 332 

indicated a mixture of chemical fertilizer and organic (human 

sewage or animal manure) sources of nitrogen. The high end of 

the range of δ15N values for septic effluent is about +4.0 per mil 

(fig. 13), therefore; septic systems cannot be ruled out as a 

potential source of nitrogen to well BA 545 on the basis of the 

nitrogen isotope results.

Chloride/Bromide Ratio

Chloride and bromide are useful indicators of anthropogenic contamination because they are 1) highly soluble, 2) minimally affected by adsorption once dissolved in water, and 

3) not altered by oxidation-reduction reactions (Hem, 1985). 

High concentrations (greater than 10,000 mg/L) of chloride can 

be found in evaporite deposits in carbonate rocks (Hem, 1985); 

however, evaporite deposits causing elevated chloride concentrations are not likely in the study area. Sources of chloride 

included septic systems, animal waste, fertilizer, and road salts. 

The chloride present in fertilizer commonly is in the form of 

potassium chloride, also known as potash (Lamond and 

Leikam, 2002). Concentration ratios of chloride to bromide 

vary because bromide has a higher solubility than chloride. 

Evaporation of sea water results in halite deposits with a high 

chloride-bromide ratio relative to the brine that remains. This 

halite applied as road salt and dissolved in freshwater will subsequently have a high chloride-bromide ratio as well. Chloridebromide ratios in precipitation are typically in the range of 50 to 

100; sewage ratios are in the 300 to 600 range, and halite ratios 

are in the range of 1,000 to 10,000 (Davis and others, 1998). 

Mixing ratios of chloride-bromide plotted against chloride for 

septic effluent, halite, and a combination of septic effluent and 

halite were developed by Jagucki and Darner (2001) (fig. 15).

End members of chloride-bromide ratios for halite (A) are from 

Knuth and others (1990), the domestic sewage end member (B) 

is from Jagucki and Darner (2001), and the dilute ground-water 

end member (C) is from Low and Conger (2002). Chloride-bromide ratios from ground-water samples collected in this study 

fell between mixtures indicating domestic sewage and halite, 

and all were above the ratio of 400 that Jagucki and Darner 

(2001) established as a threshold to indicate influence from 

anthropogenic sources. It was difficult to assess the relative 

magnitude of these sources of water on the basis of these data 

because all the potential sources could have contributed to the 

observed ratios, and end members for chemical fertilizer and 

animal manure are unknown.

Boron 

Boron indicates possible sewage contamination because it 

is found in human sewage as a residue of detergents. Boron was 

used by Bassett and others (1995) to identify municipal wastewater that had been injected in an aquifer. Concentrations of 

boron in ground-water samples in the study ranged from 10 to 

20 μg/L indicating a low likelihood of sewage or industrial contamination (Hem, 1985). In contrast, the surface-water sample 

collected in Cove Creek downstream from the Martinsburg 

Wastewater Treatment Plant had a boron concentration of 

220 μg/L, near the range found by Bassett and others (1995) in 

their study of treated sewage effluent (270 to 300 μg/L).

Wastewater Compounds 

Samples were analyzed for 67 organic chemicals typically 

found in domestic and industrial wastewater (Zaugg and others, 

2002) collectively referred to herein as wastewater compounds. 

The compounds analyzed include surfactants, food additives, 

antioxidants, flame retardants, plasticizers, industrial solvents, 

fecal sterols, polycyclic aromatic hydrocarbons, and high-use 

pesticides (Zaugg and others, 2002). Seven compounds on the 

wastewater-analysis schedule were detected in wells (pyrene, 

fyrol, bromoform, isophorone, metolachlor, DEET, and Tri 

(2-butoxylethyl) phosphate). The sites where wastewater compounds were detected are given in table 9. All these detections 

were either at or below the detection limit. The values were 

reported as “estimated” or “unquantified.” Estimated indicates 

the value is below the level that can be quantified accurately 

with the analytical instruments. Unquantified indicates a substance was identified positively, but no value can be assigned to 

the detection because of the uncertainty of the measurement. 

The compounds detected have the following uses—pyrene: coal 

tar and asphalt; fyrol: plasticizer and flame retardant; bromoform: wastewater ozonation by-product; isophorone: solvent; 
26 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

metolachlor: agricultural (corn) herbicide; DEET: insecticide; 

Tri (2-butoxylethyl) phosphate: flame retardant. Metolachlor is 

an herbicide commonly detected in ground water in this area 

(Hainly and others, 2001). The other compounds detected were 

indicative of urban/industrial contamination rather than specific 

sewage-related contamination. Municipal supply well BA 332 

had the greatest number of detections in ground water and is 

near the center of Martinsburg Borough. Low concentrations 

and few detections of wastewater compounds indicated that 

sewage was not entering ground water in substantial quantities, 

and therefore, domestic and municipal sewage were an unlikely 

source of the elevated nitrate in the municipal wells. None of the 

wastewater compounds detected were above USEPA MCLs.

Microbial Source Tracking

Fecal-coliform bacteria were detected in all wells and 

streams sampled (Dr. Katharine Baker, Pennsylvania State University, written commun., 2003); however, ARP analysis could 

not be conducted in four wells including wells BA 545 and

BA 332 because the concentrations of bacteria were too low. 

For those sites with sufficient numbers of bacteria, ARP analysis of bacteria from environmental samples was compared statistically with ARP analysis of bacteria from either municipal 

sewage or cattle. The ARP analysis resulted in the ability to 

identify distinct patterns for the human-sewage and dairy-cattle 

source samples. Of the 13 samples collected, 9 had sufficient 

numbers of bacteria for the statistical analysis (4 streams and 

5 ground-water samples). The statistical association between 

the environmental samples and the known sources is given as a 

percentage; higher percentages indicate a closer statistical probability that an environmental sample is associated with (or in 

the same ‘cluster’ as) one of the known sources (table 9). All 

four samples from surface water were related closely to the 

dairy-cattle sources (table 9). Of the five ground-water samples 

with sufficient isolates for analysis, three wells and the spring 

were associated predominantly with dairy-cattle bacteria; 68 to 

100 percent of the isolates in these samples were in the same 

cluster with dairy cattle. One of the wells (BA 546) was associated with human sewage; 100 percent of the isolates in the sample were in the same cluster with the human-sewage source 

sample (Dr. Katharine Baker, Pennsylvania State University, 

written commun., 2003). Although Dr. Baker did not make a 

positive conclusion that all bacteria were from the dairy cattle, 

the implication was that dairy cattle were the most likely source 

of the fecal bacteria detected in all streams and all but one well.

Figure 15. Binary mixing curves for 

chloride to bromide (Cl:Br) ratios for 

water from wells and streams near 

Martinsburg, Pennsylvania (mixing ratio 

curves modified from Jagucki and 

Darner, 2001). End member for halite (A) 

from Knuth and others, (1990); end 

member for domestic sewage (B) from 

Jagucki and Darner (2001); end member 

for dilute ground water (C) from Low 

and Conger (2002).
Sources of Water and Contaminants to Martinsburg Municipal Wells 27

Sources Based on Simulation of Ground-Water Flow

Ground-water flow in the area around the Martinsburg 

Municipal Authority wells was simulated using a three-dimensional, finite-difference modular model known as MODFLOW2000 (Harbaugh and others, 2000). The model was used to simulate recharge, movement through the aquifer, withdrawals 

from water-supply wells, and discharge to streams. 

In carbonate aquifers, some justification needs to be made 

concerning whether or not a numerical model such as MODFLOW is a valid tool to simulate the ground-water-flow system. 

One of the basic assumptions of MODFLOW is that the dominant pathway of ground-water flow is laminar and has characteristics of flow through a porous-media aquifer such as sand. 

The aquifers in this study are predominantly fractured carbonate-rock aquifers. If the fracture system approximates a series of 

interconnected fractures with sufficient density, it can be treated 

as a porous-media continuum. Scanlon and others (2003) indicated that in karst aquifers, MODFLOW can be used to simulate 

hydraulic conductivity, flow directions, and flow volumes if the 

cell size is large enough to simulate an equivalent porous media. 

Risser and Barton (1995) list criteria that can be used to provide 

an indication of whether the system approximates this porousmedia continuum allowing use of numerical models such as 

MODFLOW. These criteria include a smooth water-level surface in the vicinity of the well field and water that is close to saturation with respect to calcite. The water-level map by Becher 

(1996) (fig. 8) showed no abrupt changes in the water-table-surface contours, although this map was not detailed enough to 

show discontinuities if they existed. Samples from all the wells 

were near saturation with respect to calcite. As discussed previously, geochemical data (calcite saturation, specific conductance, alkalinity, and PCO2) from all the wells sampled indicated 

the water was likely to be from a fracture-flow system rather 

than a conduit-flow system. Also, the water levels in the municipal wells do not respond rapidly to precipitation events, but 

show seasonal fluctuations over a span of months (Randy 

Stoltz, Martinsburg Borough, oral commun., 2003). These factors indicated that the system was dominated by flow through 

an interconnected network of fractures rather than conduits, 

making the use of the porous-media continuum model an 

acceptable approach to simulating ground-water flow in this 

area.

All four of the wells used by Martinsburg Borough passed 

the Microscopic Particulate Analysis (MPA) (Susan Boutros, 

Environmental Associates, Ltd., written commun., 2003). The 

MPA (U.S. Environmental Protection Agency, 1992) requires 

sampling for surface-water indicators such as plant debris, 

algae, diatoms, insects, rotifers, giardia, and coccidia, which are 

characteristic of surface waters. The results of this sampling are 

used to determine ground waters under the direct influence of 

surface water. Although not conclusive, passing the MPA 

would be less likely if conduit flow was the predominant pathway for ground-water flow.

Conceptual Model

The initial step in simulating ground-water flow is to formulate a conceptual model of the ground-water-flow system 

that incorporates aquifer characteristics, recharge, and boundary conditions and presumes a dominant pathway of groundwater flow. A conceptual flow model based on a porous-media 

continuum does not suggest the absence of conduit-flow features in the system, but it assumes these features are not the predominant pathway at the scale of the system simulated. The 

conceptual model is based on available information on the 

hydrogeologic properties of the aquifer and the assumed boundaries of the ground-water-flow system and is used to construct 

the numerical model. If the results of the numerical model are 

aligned closely with observations measured in the field, such as 

water levels and streamflow, the model is assumed to be an 

accurate representation of the ground-water system. If the 

model results do not closely resemble the field data, the 

assumptions of the conceptual model are revisited, and the 

numerical model subsequently is revised. 

Recharge from precipitation enters the aquifer from the 

land surface and then moves through unconsolidated material in 

the near-surface, fracture networks in the bedrock, and possibly 

some conduits in the bedrock. Ground water discharges to 

streams and wells, and some ground water may exit the model 

area as underflow to discharge at streams further downgradient. 

Although bedrock units are folded, the model approach is to 

simulate with horizontal layers that have aquifer properties that 

differ with depth and lithology. The properties of the highly 

weathered bedrock and unconsolidated material are assumed to 

vary on the basis of the parent material from which they were 

derived, which, in this area, is generally the bedrock directly 

below that material (fig. 6).

Structure, fractures, and conduits may impart horizontal 

anisotropy with respect to hydraulic conductivity such that conductivity is larger along the strike of beds. As shown in figure 7, 

bedding and cleavage are near-vertical, on a fold axis oriented 

N. 30° E. Fractures oriented in these directions could facilitate 

flow in those planes. Fractures at the intersection of bedding 

and cleavage planes also are oriented N. 30° E., but would be 

horizontal, facilitating flow parallel to the axial fold, but not in 

the vertical direction. The cross-strike fractures would have a 

strike of N. 60° W. and also be near vertical. These regularly 

oriented fractures could cause horizontal anisotropy, which 

could affect the highly weathered upper aquifer as well as the 

competent fractured-bedrock aquifer. The near-vertical orientation of most of the fractures, in addition to the presence of many 

karst features, could allow water to move downward rapidly 

and cause a component of vertical anisotropy. Ground-water 

flow generally follows topography to discharge at local 

streams; however, the regional direction of ground-water flow 

to the east crosses minor topographic divides and discharges to 

Clover Creek. 
28 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

Model Design

A MODFLOW model was constructed using a grid of 

52 columns and 61 rows with each grid cell being 490 ft by 

490 ft (fig. 16). The boundaries of the area were based on surface-watershed boundaries and included the headwaters of 

Cove Creek, Piney Creek, and an unnamed tributary to Clover 

Creek (fig. 8). Boundary delineations were based on topographic divides. The municipal wells were near the headwaters 

of all these streams. An area of this size was chosen to allow 

determination of the areal extent of the zone of contribution 

without being constrained by the boundary conditions. No-flow 

boundaries represented watershed divides and drains represented streams. Constant-head boundaries represented underflow from the watershed divide east toward Clover Creek, the 

regional drain. The model grid was aligned at an angle approximately parallel to the axis of the Woodbury Anticline 

(N. 30° E.) to simulate potential anisotropy imparted by bedding, cleavage, and cross-strike fractures.

A two-layer model was used, with layer 1 representing 

ground water in the highly weathered bedrock and unconsolidated material. The thickness of layer 1 was established from 

land-surface altitude and the estimated altitude of the top surface of competent fractured bedrock based on well casing 

lengths. Layer 2 represented that part of the active groundwater-flow system in fractured bedrock and was assumed to be 

a confined aquifer. The geologic contacts were near-vertical in 

this area; therefore, the geologic formations as mapped on the 

surface (Berg and others, 1980) were translated downward to 

define the zones of hydraulic conductivity for layer 1 and layer 

2 (figs. 16 and 17). The depth of layer 2 was established on the

Figure 16. Location of model grid, boundaries, streams, 

drain nodes, and wells near Martinsburg, Pennsylvania. 
Sources of Water and Contaminants to Martinsburg Municipal Wells 29

basis of well-construction information. The length of casing 

approximated the depth to competent bedrock and well depths 

were used to infer the depths of the active part of the fracturedbedrock aquifer. The 75th percentile of casing length of wells 

completed in carbonate bedrock in Blair County was 120 ft, 

which was the depth used as the interface between layer 1 and 

layer 2. The 75th percentile of well depths was approximately 

400 ft, which was assumed to be near the bottom of the active 

ground-water-flow system and was used as the bottom of layer 

2. These layer depths were used for the sandstone and shale 

aquifers as well.

Streams were represented in the model by drains (fig. 16) 

with conductance values (3,200 ft/d) much greater than the conductance of aquifer layers to allow water to enter drains with 

minimal resistance. The drain cells allowed ground-water discharge but not recharge; this was because although the water 

table may be below the stream in some locations, the streams 

were small headwaters streams and did not have an infinite supply of water from upstream to provide water to the aquifer at 

those locations. Stream width that varied from 5 to 10 ft, and 

streambed thickness of 3 ft were used in the conductance calculation (Harbaugh and others, 2000). A single drain per cell was 

used to represent all stream locations. 

Pumping from model layer 2 was simulated at the locations of the two Martinsburg municipal well fields: well 

BA 332, in the Hershberger well field in the borough and well 

BA 545 known as Well #3 in the Wineland Well Field. Well 

BA 545 currently (2003) is the most productive well in the 

Wineland Well Field and was used to represent the entire well 

field. Although well BA 332 is only 100 ft deep, it was simulated in layer 2 because its proximity to the layer boundary 

caused instability (dry cells) when simulated in layer 1. Numerous wells in the area are used for purposes such as domestic supply and livestock; however, these wells did not pump as much 

water as the municipal supply wells and were not considered in 

model simulation.

Values of horizontal hydraulic conductivity initially were 

assigned to both model layers for four aquifers—sandstone 

aquifers, shale aquifers, the Gatesburg aquifer, and other carbonate aquifers. The initial values assigned to these aquifers 

were based on the values for hydraulic conductivity from Fleeger and others (2004). The horizontal hydraulic conductivity 

values assigned initially to the upper layer were higher than 

those assigned to the lower layer to simulate decreasing hydraulic conductivity with depth. Vertical leakance controls the rate 

of ground-water flow between model layers, and is calculated 

from the thickness of each layer between its node and the comFigure 17. Hypothetical cross section of model layers used for simulation of ground-water flow. Cross section near A-A’ 

from figure 3.
30 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

mon layer contact and the vertical hydraulic conductivity of 

each layer (McDonald and Harbaugh, 1988). Vertical and horizontal anisotropy initially were assigned a value of 1 and 

adjusted as the model was calibrated. This indicates that for initial simulations, the value of horizontal hydraulic conductivity 

within each aquifer was the same along rows and columns, and 

was also equal to the value of vertical hydraulic conductivity. 

Zones were assigned to hydrogeologic units that had similar hydrologic characteristics (fig. 16) on the basis of the surface 

expression of the four basic lithologies present in the model 

area. Zone 1 represented the predominantly sandstone aquifers: 

Juniata, Bald Eagle, and Tuscarora. Zone 2 represented the predominantly shale aquifers: Reedsville and Waynesboro. Zone 3 

represented the mixed carbonate aquifers: Coburn through 

Loysburg, Bellefonte /Axemann, and the Nittany/Stonehenge. 

Zone 4 represented the Gatesburg aquifer. Zones for layer 2 

were directly below zones for layer 1 (fig. 17).

The value for recharge used for model calibration was 

obtained from Becher (1996). Becher used a computerized 

hydrograph-separation program (HYSEP, Sloto and Crouse, 

1996) to separate the surface runoff and stream base-flow components for the USGS streamflow-measurement station at 

Kishacoquillas Creek at Reedsville. Using the fixed-interval 

method, a value of 13 in. of annual base flow discharge was calculated for Kishacoquillas Creek in Mifflin County, Pa. (fig. 1), 

a stream in a similar setting underlain by carbonate bedrock. 

Using the assumption that long-term base flow is equivalent to 

recharge, the value of 13 in. was used in the MODFLOW model 

for recharge. An alternate value for recharge was estimated for 

two nearby USGS streamflow-measurement stations using the 

local-minimum method of hydrograph separation (Sloto and 

Crouse, 1996). The long-term median base flow for Dunning 

Creek near Belden in Bedford County, Pa. (fig. 1), from 1940 to 

2001 was 9.1 in/yr. The long-term median base flow for Frankstown Branch Juniata River at Williamsburg (Blair County, Pa.) 

from 1917 to 2001 was 9.7 in/yr. Although Dunning Creek and 

Frankstown Branch Juniata River are both closer to Martinsburg than Kishacoquillas Creek, Kishacoquillas is geologically 

more similar to the Martinsburg area. The high percentage of 

non-carbonate rock in the Dunning Creek and Frankstown 

Branch Juniata River watersheds would tend to make estimates 

of base flow in those watersheds lower than would be expected 

in a watershed with mostly carbonate bedrock. Also, the localminimum method of hydrograph separation is a lower estimate 

of base-flow discharge (or recharge) than the fixed interval 

method. Therefore, the value of 13 in. of recharge was used for 

model calibration, and a value of 9.5 in. of recharge was used to 

simulate the zone of contribution in a sustained dry period. 

Model Calibration

Model calibration is a process by which aquifer properties 

are adjusted and results of model simulation are compared to 

observations of water levels and discharge from the aquifer system. The purpose of this is to estimate the optimal hydraulic 

properties of aquifers and demonstrate the capability of the 

model to reliably simulate a measured value such as a waterlevel altitude, with the implication that it will then also accurately predict other unmeasurable response such as the zone of 

contribution to a well. MODFLOW-2000 has the capability to 

estimate the optimum values for parameters such as recharge, 

anisotropy, and hydraulic conductivity that minimize the difference between measured and simulated aquifer characteristics 

(Harbaugh and others, 2000). Only water-level altitude measurements were used for model calibration (table 4). The 

parameter-estimation process used an iterative method to adjust 

the values of parameters to minimize the value of a leastsquared objective function that measures how close the numerical model simulates field measurements of water-level altitudes. The process computes parameters that were the most sensitive and caused the largest change in head per change in 

parameter value. This sensitivity can be used to guide data collection and revise the conceptual model and the numerical 

model. 

A steady-state calibration was conducted to represent average ground-water-flow conditions in the study area. Water levels from 16 wells were used in model calibration. Water levels 

were measured in 2002 at six wells where water levels were 

static and suitable for model calibration. To supplement the 

number of wells with water levels available for model calibration, water levels from wells reported in Becher (1996) also 

were used (table 4). Streamflow was measured under base-flow 

conditions, but at an abnormally high base flow, and therefore 

was not used in model calibration. When streamflow measurements were made, the flow at the nearest USGS streamgage 

(Frankstown Branch Juniata River) was near the 75th percentile, a value exceeded only 25 percent of all days during the 

period of record. 

Eight parameter values were initially assigned to represent 

horizontal hydraulic conductivity in each of the four aquifers in 

the upper and lower layers. Together with recharge, horizontal 

anisotropy, and vertical anisotropy, the resulting number of 

parameters (11) was more than could accurately be estimated on 

the basis of the number of observations. Initial model runs, 

therefore, were conducted to determine which parameters were 

most sensitive and could be estimated. Composite sensitivity is 

calculated as follows: the sensitivities are scaled by multiplying 

them by the product of the parameter value and the square root 

of the weight of the observation to obtain dimensionless values. 

The scaled sensitivities for each parameter are then squared and 

the sum of these values is divided by the number of observations. The composite scaled sensitivities equal the square root 

of these values. Sensitivity analysis indicated the model was 

highly sensitive to recharge; however, using recharge as a 

parameter caused statistically significant correlations (0.99 or 

higher) among all other parameters suggesting a high probability of a non-unique solution in which an infinite number of combinations of recharge values and hydraulic conductivity values 

could match the water levels used for calibration. Therefore, 
Sources of Water and Contaminants to Martinsburg Municipal Wells 31

recharge was assigned a constant value (13.5 in/yr) during 

model calibration. 

The sensitivity analysis also indicated the hydraulic-conductivity values of the upper and lower layers of the sandstone 

aquifer were the least sensitive parameters in the model, had 

statistically significant correlations with each other, and were 

near the same value. The upper and lower sandstone units were 

combined and simulated as one parameter; this resulted in significant improvements with closer agreement between simulated and measured water levels in the model. The combined 

sandstone unit was still highly insensitive (fig. 18), indicating it 

was unlikely to be predicted with any degree of certainty and 

was specified as 0.33 ft/d, a value obtained from a previous 

study of ground water in sandstone aquifers (Gburek and others, 

1998). Analysis of sensitivity from subsequent model runs indicated the upper and lower shale unit and the upper and lower 

Gatesburg unit also could be combined, leaving the mixed limestone and dolomite aquifers as the only unit simulated with a 

different hydraulic conductivity in layer 1 and layer 2. Specifying a value of recharge and the hydraulic conductivity of the 

sandstone aquifer and combining the upper and lower units into 

a single parameter left six parameters to estimate: horizontal 

hydraulic conductivity in four units, vertical anisotropy, and 

horizontal anisotropy. Among these six parameters, the three 

most sensitive were the horizontal anisotropy, hydraulic conductivity of the upper limestone and dolomite unit, and the 

hydraulic conductivity of the lower limestone and dolomite unit 

(fig 18). Obtaining optimum parameter estimates for these three 

values resulted in significant improvement of the model. The 

values of the three remaining parameters, hydraulic conductivity of the shale aquifer, hydraulic conductivity of the dolomite 

aquifer, and vertical anisotropy, were estimated during the final 

parameter-estimation run. No correlation greater than 0.85 was 

found among the six parameters estimated.

Figure 18. Composite scaled sensitivities (Hill and others, 2000) for parameters used in model calibration. 
32 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

Uncertainty of the estimated parameters is related to 

parameter sensitivity. Those parameters that are highly sensitive usually are estimated with a higher level of confidence than 

those parameters that are not highly sensitive. The relation 

between the measured water levels in the 16 wells used to calibrate the model and the simulated water levels in those wells is 

shown in figure 19 (one of the wells was omitted during calibration because the cell where it was located went dry). Measurements of head were equally weighted during model calibration. 

Plots of residuals (the difference between the simulated value 

and the observed value) and weighted simulated equivalents 

showed no bias in the prediction of heads, and the geographical 

distribution of the residuals showed no spatial pattern. Three 

streamflow measurements were available within the area of the 

ground-water model boundary. Streamflow measurements were 

not used for actual calibration; however, the values are given 

here for comparison purposes. The measured streamflows at 

Piney Creek and the Unnamed Tributary to Clover Creek were 

very small (0.1 ft3/s), even when measured under the high baseflow conditions, and the simulated streamflows were 0.20 and 

0.08 ft3/s, respectively. In absolute terms, the measured and 

simulated values were quite close; however, in relative terms, 

the error was 100 and 20 percent, respectively. The discharge 

measured at Cove Creek was 6.75 ft3/s, and the simulated 

equivalent streamflow was 3.44 ft3/s. 

Simulation Results and Sensitivity

Measures of model results given herein are numerical indications of the level of confidence in the properties estimated. 

Final calibrated parameter values are shown in table 10. One 

important measure of the model fit is the sum of squared residuals, which is based on the fit between measured and simulated 

head. For this regression, the sum of squared residuals was 76.2. 

The root-mean-square error for the simulation was 2.2 ft. The 

largest positive residual value (simulated minus measured) was 

19 ft (BA 437), and the most negative residual value was -10 ft 

(BA 549) (fig. 19).

The values given on table 10 are based on the assumption 

that the optimization model is linear; therefore, if the model is 

highly non-linear, these statistics are not reliable. One measure 

of model linearity is the correlation between weighted residuals 

and normal order statistics, which, for this model, was 0.94. 

This indicated a 90-percent probability that the weighted residuals were independent and normally distributed. Beale's measure of linearity (Hill and others, 2000) was 0.73, a value higher 

than the threshold for this model of 0.31 indicating that the 

model regression was moderately non-linear. Therefore, the 

confidence intervals shown on table 10 based on linear theory 

are considered approximate values.

The RESAN program (Hill and others, 2000) was used to 

calculate Cook's D and DFBeta statistics, which indicated the 

influence of individual observations on the estimated parameter 

values. These statistics indicated that heads in wells BA 248, 

BA 548, BA 544, and BA 543 were the observations that had

Figure 19. Calibration results

showing simulated and 

measured water levels near 

Martinsburg, Pennsylvania.
Sources of Water and Contaminants to Martinsburg Municipal Wells 33

the most influence on the estimated parameter values. The purpose of these measures were 1) to ensure data were accurate for 

those observations having the most influence on the model 

results, and 2) to see if there were spatial patterns in the distribution of these observation wells. The accuracy of the input data 

from each of these wells was verified, and no spatial patterns 

were evident from the geographical distribution of the most 

influential wells. 

The water budget determined by simulation of groundwater flow indicated quantities of flow for the various components of the ground-water system. All inflow to the system was 

recharge from precipitation (table 11), as previously discussed. 

The outflow was distributed among discharge to streams 

(drains), underflow (constant-head boundaries), and groundwater pumpage (wells). The vast majority of the water (75 percent) discharged to the drains within the ground-water model 

boundary. An additional 22 percent of the water discharged 

through constant-head boundaries, representing ground water 

that traveled under the ground-water model boundary and 

moved toward Clover Creek to the east. The amount pumped 

from the municipal-supply wells was about 3 percent of the 

total water budget for the area simulated. About 45 percent of 

the water traveled into the deeper parts (layer 2) of the groundwater system (fig. 20) where some water was withdrawn from 

the municipal wells and the remainder eventually returned to 

layer 1 to discharge at the streams or exit the system as underflow.

Table 10. Optimum values of aquifer properties estimated near Martinsburg, Pennsylvania.

[n/a, value not computed; ft/d, feet per day]

Property Layer Zone

(fig. 16)

Final value 

(ft/d) 

Approximate

95-percent 

confidence

interval1

Horizontal hydraulic 

conductivity of: 

Sandstone aquifer 1 and 2 1 0.33 n/a

Shale aquifer 1 and 2 2 .15 0.02 - 0.39

Upper limestone / dolomite aquifer 1 3 .6 .2 - 1.9

Lower limestone/ dolomite aquifer 2 3 1.2 .2 - 2.3

 Gatesburg aquifer 1 and 2 4 35.7 10.2 - 125

Anisotropy Horizontal (hydraulic conductivity 

along model columns/ hydraulic 

conductivity along model rows)

1 and 2 All 5.3 1.8 - 15.8

Vertical 1 and 2 All 24.4 2.3 - 259

1 Values are given as approximate ranges because the model is moderately non-linear.

Table 11. Water budget for steady-state simulation near Martinsburg, Pennsylvania.

[ft3/d, cubic feet per day; ft3/s, cubic feet per second]

Source of inflow 

to model

Inflow volume

(million ft3

/d)

Inflow volume

(ft3

/s)

Source of outflow 

from model

Outflow volume

(million ft3

/d)

Outflow volume 

(ft3

/s)

Recharge (precipitation) 1.17 13.59 Discharge to streams 0.88 10.17

Underflow .26 2.96

Ground-water pumpage .04 .46

TOTAL INFLOW 1.17 13.59 TOTAL OUTFLOW 1.18 13.59
34 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

Zone of Contribution to Municipal Wells

The simulated areal extent of the zone of contribution for 

well BA 545 in the Wineland Well Field and well BA 332 in the 

Hershberger Well Field combined is shown in figure 21. Also 

shown in figure 21 is the simulated water table. The simulated 

water table can be compared to the water table based on waterlevel measurements from Becher (1996) in figure 9. The simulation was conducted with both wells pumping full time at halfcapacity (each pumping 150,000 gal/d simultaneously). 

Ground-water flow not captured by pumping discharges to local 

streams. 

The simulation showed the ground-water table did not necessarily mirror surface topography. For example, there were 

three topographic high areas near the Wineland Well Field. The 

upgradient directions topographically were to the northeast, 

west toward the Borough of Martinsburg, and to the south. The 

water-level contours from Becher (1996) and the simulated 

water levels showed a regional gradient of the water levels 

causing the ground-water-flow direction to be from the south 

and west to both well fields. The simulated flow lines nearly 

parallel to the strike of the fold axis (fig. 22) were a result of the 

approximate 5 to 1 anisotropy (table 10) estimated by the 

regression, which physically represented that hydraulic conductivity in the ‘y’ direction (along model columns) in the model 

was five times higher than hydraulic conductivity in the ‘x’ 

direction (along rows) so the majority of the flow to both well 

fields was from the south (figs. 22 and 23). Fractures at the 

intersection of bedding and cleavage planes oriented in this 

direction may have been the cause of the anisotropy. The deep, 

relatively flat water table in the Gatesburg subcrop area illustrated in figure 24 was one of the factors influencing the 

ground-water-flow field. The deep water table underlying the 

Gatesburg aquifer has been documented in previous studies 

(Becher, 1996; Chin, 1996) suggesting that the topographic 

high area underlain by the Gatesburg aquifer did not influence 

the direction of ground-water flow. Instead, water passed from 

west to east under the topographic high area of the Gatesburg 

aquifer and likely discharged to Clover Creek, which is situated 

on the topographically low part of the valley in this area and acts 

as a regional drain for the system. 

Several model simulations were conducted to determine 

the affect of changing parameter values on the zone of contribution. The areal extent of the zone of contribution that may be 

expected under low recharge conditions, lowering recharge 

from 13 to 9.5 in/yr, is illustrated in fig. 22. Note the larger areal 

extent of the zone of contribution predicted when the lower 

recharge value is used compared to when the normal recharge 

(steady-state conditions) is used. The sum of squared residuals 

for this simulation was 486. Simulations also were run with no 

horizontal anisotropy, which resulted in a sum of squared residuals of 837. The zone of contribution in this scenario was only 

slightly different than that using the calibrated value of horizontal anisotropy (fig. 25). A simulation with no vertical anisotropy 

resulted in a sum of squared residuals of 576, but the zone of 

contribution was virtually identical to that shown in figure 21. 

Model sections were evaluated for each of these scenarios and 

showed only minor differences in the water table from that 

illustrated in figures 23 and 24.

LAYER 1

LAYER 2

1.17 RECHARGE

CONSTANT- HEAD BOUNDARY

(UNDERFLOW) 0.26

0.49 LAYER 2 TO LAYER 1

0.88 OUT TO DRAINS (STREAMS)

0.53 LAYER 1 TO LAYER 2

0.04 OUT TO WELLS (PUMPAGE)

UNITS IN MILLIONS OF CUBIC FEET PER DAY

MUNICIPAL

WELL

Figure 20. Simulated 

water-budget 

summary for layers 1 

and 2 of hydrologic 

model.
Sources of Water and Contaminants to Martinsburg Municipal Wells 35

Figure 21. Simulated water table and zone of contribution to wells near Martinsburg, Pennsylvania (vertical 

exaggeration X 3). 
36 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

Figure 22. Areal extent of zone of contribution to wells, pathlines, and source areas near 

Martinsburg, Pennsylvania, under normal and low recharge conditions. Dividing line between 

zones is approximate, actual boundaries overlap.
Sources of Water and Contaminants to Martinsburg Municipal Wells 37

EXPLANATION

PATHLINE TO WELL BA 545

SIMULATED WATER TABLE

CELL BOUNDARY

PATHLINE TO WELL BA 332

0

0

1,000

1,000 METERS

2,000 FEET

VERTICAL EXAGGERATION X 3

BA 332

BA 545

Pumping

N S

LAYER 1

LAYER 2

BA 332

Pumping BA 545 Land Surface

Note: Both wells are

projected into section

Land Surface

N S

LAYER 1

LAYER 2

500

Figure 23. Simulated water table and pathlines to municipal wells from south to north along model column 33 

(location of model column shown on figure 16).

Figure 24. Simulated water table from west to east along model row 20 (location of model row shown on figure 16).
38 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

Figure 25. Areal extent of zone of contribution to wells near Martinsburg, Pennsylvania, illustrating 

simulation with horizontal anisotropy and without horizontal anisotropy. 
Sources of Water and Contaminants to Martinsburg Municipal Wells 39

Relating Simulation Results, Natural Geochemistry, 

and Anthropogenic Contaminants to Determine 

Source Areas

Simulation results from the MODFLOW model indicated 

the areal extent of the zone of contribution to wells BA 545 and 

BA 332. Water-quality sampling was used to classify the samples as having sources in limestone aquifers or dolomite aquifers. As previously stated, the Gatesburg aquifer is predominantly dolomite, the Nittany/Stonehenge and the Bellefonte/

Axemann aquifers are mixed limestone and dolomite aquifers, 

and the Coburn through Loysburg aquifer is predominantly 

limestone. The modeling and geochemical approaches can be 

combined to determine whether or not the findings are in accordance with each other.

The simulation results from the MODFLOW model indicated the areal extent of the zone of contribution for the source 

of water to well BA 545 included a combination of the Gatesburg, Nittany/Stonehenge, Bellefonte/Axemann, and Coburn 

through Loysburg aquifers (fig. 26). Although the well was 

completed in the Gatesburg aquifer, the areal extent of the zone 

of contribution was not restricted to that aquifer. Well BA 332 

is completed in the Nittany/Stonehenge aquifer, and the areal 

extent of the zone of contribution included part of the Nittany/

Stonehenge, Gatesburg, Bellefonte/Axemann, and Coburn 

through Loysburg aquifers (fig. 26). The majority of the zone of 

contribution was within the Nittany/Stonehenge aquifer. 

Results of geochemical analysis of water from well BA 545 and 

BA 332 indicated water from a dolomite and a mixed limestone 

and dolomite aquifer, respectively, which agreed with the 

results of the MODFLOW model.

The relation of karst features to the areal extent of the 

zones of contribution is illustrated in figure 26. The model did 

not account for the potential changes in hydraulic characteristics of ground-water flow caused by these features, such as high 

volumes of rapid recharge through closed depressions or sinks 

and high hydraulic conductivity along fracture traces. The 

actual effects of these features is unknown. It is important to 

note, however, that some of these features were in the areal 

extent of the zone of contribution to the wells. Although the 

geochemical results and MPA did not indicate conduit flow or 

direct recharge through karst features, the Martinsburg Borough 

Authority may want to consider the closed depressions in or 

near the areal extent of the zone of contribution to the well to be 

potential points of entry of contaminants to the aquifer by virtue 

of their proximity to the municipal supply wells. The closed 

depressions within and just to the north of the Wineland Well 

Field (BA 545) are probably the most likely to have an immediate and direct effect on the wells.

Potential anthropogenic contaminants in the wells of interest also can be compared to the results of the MODFLOW 

model. Human activity at the land surface in the areal extent of 

the zone of contribution to a well is, by definition, the source of 

anthropogenic contaminants to wells. The primary contaminant 

of concern for these wells is nitrate. The analysis of land use, 

nitrate concentrations, nitrogen isotopes, and other contaminants indicated the following: 1) the primary source of the 

nitrate in the municipal supply wells was from agricultural 

activity, and 2) the source of the nitrate in the municipal supply 

wells was predominantly chemical fertilizer in well BA 545 and 

a mixture of chemical fertilizer and manure in well BA 332. 

Land use in the areal extent of the zone of contribution to 

wells BA 545 and BA 332 is shown in figure 27. The areal 

extent of the zone of contribution to well BA 545 included 

urban land use in the Borough of Martinsburg; however, the 

areal extent of the zone of contribution had a large amount of 

agricultural land use, both to the north and southeast of Martinsburg (fig. 27). Approximately 82 percent of the land use in the 

areal extent of the zone of contribution to well BA 545 was agricultural, 12 percent was urban, and the remaining 6 percent was 

forested land use. The areal extent of the zone of contribution to 

well BA 332 included 81 percent agricultural land use, 18 percent urban land use, and 1 percent forested land use (fig. 27).

In the areas where the land use was agricultural, the nitrate 

sources were chemical fertilizer and manure. There were several potential sources in residential areas. Almost all residences 

in the areal extent of the zone of contribution were served by 

public sewer. In those areas, the potential sources of nitrate to 

ground water were limited to home lawn-care fertilizer or possibly leaking sewer lines. The amount of this input was 

unknown. In those residential areas not on public sewer (fig. 6), 

septic systems may have been a source of nitrate as well; however, there are very few of these residences in the areal extent of 

the zone of contribution. The zone of contribution to both wells 

contained about 30 homes using on-lot septic systems; however, a nitrogen mass-balance calculation indicates that septic 

systems could not be the source of most of the nitrogen. Using 

equation (1) and values from Miller (1980) for nitrogen production from septic systems (effluent concentration of 85 mg/L of 

nitrogen and effluent volume of 45 gal/d per person) and assuming 4 persons per household, the maximum concentration in 

water in the zone of contribution would be 1.5 mg/L if septic 

systems were the only nitrogen source. This estimate assumes 

that 100 percent of the nitrogen generated in these homes 

leached into the ground water, allowing no losses to biological 

processes, septic system pumpage, plant uptake, or any other 

loss. In reality, many processes affect nitrogen in septic systems; therefore, the contribution of septic system-nitrogen to 

the municipal-supply wells likely is even lower than the value 

calculated. The concentrations of nitrate in water samples from 

wells BA 545 and BA 332 were 8.9 and 9.1 mg/L, respectively, 

suggesting a large contribution of nitrogen from sources other 

than septic systems.

The results of water-quality sampling in the municipal 

supply wells can be compared to the sources of nitrogen in the 

areal extent of the zone of contribution. Well BA 332 had a 

δ15N ratio of 6.0 per mil, and well BA 545 had a δ15N ratio of 

4.1 per mil. The higher value in well BA 332 is at the high end 

of the range for chemical fertilizer (+2 to +6 per mil) and could 

include an influence of animal manure (+10 to +20 per mil). 
40 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

Figure 26. Geology and karst features in the areal extent of the zone of contribution to well BA 332 and well 

BA 545 near Martinsburg, Pennsylvania.
Sources of Water and Contaminants to Martinsburg Municipal Wells 41

Figure 27. Land use in the areal extent of the zone of contribution to well BA 332 and BA 545 near 

Martinsburg, Pennsylvania.
42 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

The value in well BA 545 indicated the source of nitrogen in 

water from the wells was likely to be a result of chemical fertilizer. Because the δ15N ratio values resulting from various 

sources of nitrogen are represented by ranges rather than a single value, it is not possible to use a mixing model to determine 

the percentage of water from a given source with any amount of 

accuracy. For example, using the median δ15N ratio values from 

Cravotta (1997) of 4 per mil for runoff from fertilizer and 8 per 

mil for runoff from manure, a binary mixture of water with an 

equal component from each source would be near 6 per mil; 

however, there are numerous other combinations of source 

waters with other δ15N concentrations that could result in that 

same value. Therefore, individual measurements of δ15N that 

are in the area where there is overlap in the signature of the 

sources cannot be used to make conclusive statements about 

which source is predominant. 

The type of nitrogen applied in the areal extent of the zone 

of contribution to wells BA 332 and BA 545 probably relates to 

the source of nitrate in ground water. In the areal extent of the 

zone of contribution to well BA 545, nearly half the agricultural 

land can be classified with respect to the type of nitrogen 

applied (fig. 28). In this part of the zone of contribution, 31 percent had chemical fertilizer as the predominate nitrogen source 

and 46 percent had manure as the predominant nitrogen source; 

the remaining area had a mixture of chemical fertilizer and 

manure as the nitrogen source. The areas immediately around 

well BA 545 have a number of karst features and were the areas 

where chemical fertilizer was used (fig. 26). Areas that have 

predominantly animal manure were at the farthest extent of the 

zone of contribution. About 60 percent of the agricultural fields 

in the areal extent of the zone of contribution to well BA 332 

could be classified with respect to nitrogen source, and all these 

fields had manure as the predominant source of nitrogen 

(fig. 28). The urban/residential area in the zone of contribution 

to both wells underlying the Borough of Martinsburg was likely 

to have chemical fertilizer as the predominant nitrogen source. 

Neither municipal well had sufficient numbers of fecal coliform 

bacteria to do the ARP analysis, but well BA 332 had a higher 

count of bacteria. Well BA 332 consistently had detectable concentrations of bacteria in samples collected by Martinsburg 

Borough during routine sampling, whereas the samples collected in well BA 545 rarely had bacteria detected (Randy 

Stoltz, Martinsburg Borough, written commun., 2002). This 

seemed to indicate a higher likelihood that water from well 

BA 332 was affected by animal manure and water from well 

BA 545 was affected by chemical fertilizer.

Another way to help determine whether human sewage is 

a possible source of contaminants to the municipal wells is to 

compare some of the contaminants measured in the municipal 

wells to values of those contaminants measured downstream 

from the wastewater treatment plant. In the sample collected 

downstream from the wastewater treatment plant, 20 of the 

63 compounds on the wastewater analysis schedule were 

detected; whereas only 1 of these compounds was detected in 

well BA 545 and 4 were detected in well BA 332. The boron 

concentration downstream from the wastewater treatment plant 

was 220 μg/L, as compared to 20 μg/L in both BA 545 and 

BA 332. The specific conductance was over 1,300 μS/cm 

downstream from the wastewater treatment plant; in wells 

BA 545 and BA 332, the specific conductance was 469 μS/cm 

and 557 μS/cm, respectively. The δ15N ratio was about 11 per 

mil downstream from the sewage treatment plant; in wells 

BA 545 and BA 332, the δ15N ratio was 4.0 and 6.1 per mil, 

respectively. All these factors indicate human sewage was the 

least likely major source of nitrogen in wells BA 332 and 

BA 545.

Limitations of Data and Findings

The findings herein are limited by the assumptions that 

were made in the ground-water model and the amount of data 

available to make interpretations. The model was designed with 

a 490-ft by 490-ft grid, which provides information on groundwater flow at a large scale. The accuracy of the model in determining water levels and the areal extent of the zone of contribution could be improved by a model with a finer grid. Model 

input parameters, such as horizontal and vertical hydraulic conductivity and recharge, are applied as an average value for each 

cell. This assumption of uniformity introduces inaccuracies 

because of the heterogeneity of the geologic materials and variability of climatic conditions. The model also was created for 

steady-state conditions, which assume that inflows equal outflows. If the water levels were rising or falling during the time 

period when the measurements were taken, this would introduce error into the model as well. As previously stated, the 

MODFLOW model is made for a porous medium. The assumption that the fracture system was equivalent to a porous medium 

is another potential source of error. 

The confidence level associated with the findings could be 

improved by additional data. The ground-water model could be 

improved by additional water levels, water levels measured 

under various hydrologic conditions, as well as additional 

streamflow measurements to calibrate the model to stream discharge. Aquifer tests could be conducted to improve the parameters estimated by the model and test theories such as the anisotropy indicated by the ground-water model. The significance of 

the karst features, including the locations of major fracture 

traces or conduits, and their potential affect on water quality in 

the municipal wells could be tested by dye-tracer tests or geophysical investigations. Findings based on a single chemical or 

microbiological analysis could be strengthened by additional 

sampling. In particular, additional δ15N sampling and ARP 

analysis could be conducted to clarify the important issue of the 

sources of nitrogen to the municipal wells. 
Sources of Water and Contaminants to Martinsburg Municipal Wells 43

Figure 28. Sources of nitrogen, nitrate concentration, and δ15N in municipal wells near Martinsburg, Pennsylvania.
44 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

Summary and Conclusions

The U.S. Geological Survey, in cooperation with the 

Southern Alleghenies Conservancy and the Borough of Martinsburg, conducted a study of the sources of water and contaminants to the Martinsburg municipal supply wells. The wells 

have a history of nitrate concentrations that approach and occasionally exceed the USEPA MCL. Multiple lines of evidence 

were used to delineate the areal extent of the zone of contribution to the Martinsburg municipal wells. Ground-water-flow 

modeling was used to estimate the potential areal extent of the 

zone of contribution on the land surface. Because of the complexities of modeling in carbonate terrain, geochemical indicators, analyses for anthropogenic contaminants, and microbial 

source tracking were used to assess the validity of the simulation results.

The simulation of ground-water flow was based on a conceptual model of areal recharge, flow through fractures that are 

equivalent to a porous media, and discharge to streams and 

pumping wells. The conceptual model included a highly fractured upper layer overlying a layer of more competent bedrock. 

The Woodbury Anticline controls the potential fracture patterns 

in the area, and therefore was assumed to control the aquifer 

framework near the well fields. Four carbonate-bedrock formations contain the primary aquifers. Three carbonate-bedrock 

aquifers of Ordovician age overlie the Gatesburg aquifer of 

Cambrian age on the flanks of the anticline. Sandstone and 

shale aquifers also were simulated in the ground-water model. 

Geochemical data used to determine possible sources of 

water indicated well water representing a dolomite source for 

well BA 545 and a mixed limestone and dolomite source for 

well BA 332. The water-source types were in accordance with 

what would be expected on the basis of the geographic locations 

and primary aquifer of the wells. The results of geochemical 

analyses also indicated that conduits were not likely to be the 

dominant pathway for ground-water flow to most wells. Modeling was conducted assuming that conduit flow was not the 

dominant type of ground-water flow. Calcite saturation, specific conductance, alkalinity, and PCO2 all indicated fracture 

flow was the dominant pathway for ground water. Nevertheless, 

karst features are present and may have had a significant effect 

on localized patterns of ground-water flow. 

Sampling for anthropogenic contaminants indicated 

ground water was significantly affected by human activities. 

Nitrate concentrations in water samples from wells in carbonate 

bedrock ranged from 5.7 to 36.9 mg/L as nitrogen. Both wells 

BA 545 and BA 332 had chloride/bromide ratios above the 

level indicating anthropogenic influence. Results of sampling 

for boron were inconclusive. Results of wastewater analyses 

and fecal-source tracking indicated human sewage was probably not the primary source of nitrogen in the ground water. 

Although not conclusive, the indications of the fecal-source 

tracking study showed the bacteria in most wells and streams to 

be closely aligned with bacteria from dairy manure rather than 

human sewage. Most of the evidence indicated bacteria and 

nitrate concentrations in wells in this study were from animalmanure sources; however, the areal extent of the zone of contribution and δ15N values indicated chemical fertilizer was a 

likely source of much of the nitrogen in wells BA 332 and 

BA 545. Organic nitrogen sources (human sewage or animal 

manure) may have contributed some nitrogen to each of these 

wells and was a more likely source of nitrogen in well BA 332. 

The MODFLOW three-dimensional finite-difference 

model was used to simulate ground-water flow in the area near 

the Martinsburg well fields. Model results indicated the areal 

extent of the zone of contribution to wells BA 332 and BA 545. 

Parameter estimation was used to obtain the best fit between 

measured and simulated water levels. Measured water levels 

were generally in agreement with the simulation results. For 

this regression, the sum of squared residuals was 76.2. The rootmean-square error for the simulation was 2.2 ft. The relative 

agreement between simulated and measured water levels supported the assumption that flow through conduits was not a 

dominant pathway for ground-water flow. Fracture, not conduit, permeability was determined to be the dominant waterbearing characteristic of the bedrock. The horizontal hydraulic 

conductivity of the Gatesburg aquifer is about 36 feet per day. 

The other carbonate aquifers (Nittany/Stonehenge, Bellefonte/

Axemann, and Coburn through Loysburg aquifers) overlying 

and flanking the Gatesburg aquifer have horizontal hydraulic 

conductivities of about 1 foot per day. Regional directions of 

ground-water flow are toward the major streams with Clover 

Creek as the major discharge point for ground water in the east. 

The flow toward Clover Creek crosses minor topographic 

divides. Ground-water flow to the well fields is anisotropic with 

a 5:1 preferential horizontal direction that is oriented along the 

strike of the axial fold of the anticline, making the shape of the 

zone of contribution of ground water to the well fields elongated in a north-south direction along the anticline axis. The 

majority of the flow to the well fields originates from the south. 

Results of the ground-water flow-model were supported 

by the other lines of evidence. Geochemical data used to determine possible sources of water indicated a mixture of ground 

waters consistent with that which would result if the model had 

correctly identified the aquifers producing the water to the 

municipal-supply wells. The comparison of the land use and the 

areal extent of the zone of contribution indicated that much of 

the zone of contribution was under agricultural or urban land. 

The substantial amount of agricultural land use in the areal 

extent of the zone of contribution explained the consistent, high 

nitrate concentrations detected in these wells. The δ15N values, 

in well BA 545 particularly, indicated a source area with a high 

percentage of chemical fertilizer and a lower percentage of 

manure as a source of nitrogen. The areal extent of the zone of 

contribution to well BA 545 delineated by the model included 

several fields that have chemical fertilizer as the predominant 

source of nitrogen. 
References Cited 45

References Cited

Bassett, R.L., Buszka, G.R., Davidson, G.R., and Chong-Diaz, 

D., 1995, Identification of ground water solute sources using 

boron isotopic composition: Environmental Science and 

Technology, v. 29, no. 12, p. 2,915-2,922. 

Becher, A.E., 1996, Ground-water resources of Cambrian and 

Ordovician carbonate rocks in the Valley and Ridge Physiographic Province of Pennsylvania: U.S. Geological Survey 

Open-File Report 90-109, 134 p.

Berg, T.M., Edmunds, W.E., Geyer, A.R., Glover, A.D., Hoskins, D.M., MacLachlan, D.B., Root, S.I., Sevon, W.D., and 

Socolow, A.A., comps., 1980, Geologic map of Pennsylvania: Pennsylvania Geological Survey, 4th ser., scale 

1:250,000, 2 sheets.

Böhlke, J.F., 2003, Sources, transport, and reaction of nitrate, in

Lindsey, B.D., Phillips, S.W., Donnelly, C.A., Speiran, G.K., 

Plummer, L.N., Böhlke, John-Karl, Focazio, M.J., Burton, 

W.J., and Busenberg, Eurybiades, Residence time and nitrate 

transport in ground water discharging to streams in the Chesapeake Bay Watershed: U.S. Geological Survey WaterResources Investigations Report 03-4035, 201 p.

Burton, W.C., Plummer, L.N., Busenberg, Eurybiades, Lindsey, B.D., and Gburek, W.J., 2002, Influence of fracture 

anisotropy on ground water ages and chemistry, Valley and 

Ridge Province, Pennsylvania: Ground Water, v. 40, no. 3, 

p. 242-257.

Butts, Charles, 1945, Hollidaysburg-Huntingdon folio, Pennsylvania: U.S. Geological Survey Geologic Atlas of the 

United States, Folio 227, 20 p.

Chin, C.V., 1996, Application and comparison of EPA wellhead protection delineation methods for karst aquifers, 

Centre County, Pennsylvania (Pennsylvania State University 

dissertation): Ann Arbor, Mich., UMI Dissertation 

Services, 328 p.

Coyle, Elizabeth, 1998, Morrisons Cove wells high in nitrates: 

Bedford, Pa., Gazette, April 10, 1998, p. 1.

Cravotta, C.A., III, 1997, Use of stable isotopes of carbon, 

nitrogen, and sulfur to identify sources of nitrogen in surface 

waters in the Lower Susquehanna River Basin, Pennsylvania: 

U.S. Geological Survey Water-Supply Paper 2497, 99 p.

Davis, S.N., Whittemore, D.O., and Fabryka-Martin, J., 1998, 

Uses of chloride/bromide ratios in studies of potable water: 

Ground Water, v. 36, p. 338-350.

Durlin, R.R., and Schaffstall, W.P., 2003, Water Resources 

Data, Pennsylvania, Water Year 2002, Volume 2. Susquehanna River Basin: U.S. Geological Survey Water-Data 

Report PA-02-2, 536 p.

Fishman, M.J., ed., 1993, Methods of analysis by the U.S. Geological Survey National Water Quality Laboratory—Determination of inorganic and organic constituents in water and 

fluvial sediments: U.S. Geological Survey Open-File 

Report 93-125, 217 p.

Fishman, M.J., and Friedman, L.C., 1989, Methods for determination of inorganic substances in water and fluvial sediments: U.S. Geological Survey Techniques of WaterResources Investigations, book 5, chap. A1, 545 p.

Fleeger, G.M., McElroy, T.A., and Moore, M. E., 2004, Hydrogeologic and well construction characteristics of the rocks of 

Pennsylvania: Pennsylvania Geological Survey, 4th ser., 

Water Resource Report 69, CD-ROM.

Gburek, W.J., Folmar, G.J., and Urban, J.B., 1998, Field data 

and ground-water modeling in a layered fractured aquifer: 

Ground Water, v. 37, no. 2, p. 175-184.

Gerhart, J.M., and Lazorchick, G.J., 1988, Evaluation of the 

ground-water resources of the Lower Susquehanna River 

Basin, Pennsylvania and Maryland: U.S. Geological 

Survey Water-Supply Paper 2284, 128 p.

Hagedorn, Charles, Robinson, S.L., Filtz, J.R., Grubbs, S.M., 

Angier, T.A., and Reneau, R.B., Jr., 1999, Determining 

sources of fecal pollution in a rural Virginia watershed with 

antibiotic resistance patterns in fecal streptococci: Applied 

and Environmental Microbiology, v. 65, p. 5,522-5,531.

Hainly, R.A., Zimmerman, T.M., Loper, C.A., and Lindsey, 

B.D., 2001, Summary of and factors affecting pesticide concentrations in streams and shallow wells of the Lower Susquehanna River Basin, Pennsylvania and Maryland, 1993-

95: U.S. Geological Survey Water-Resources Investigations Report 01-4012, 75 p.

Harbaugh, A.W., Banta, E.R., Hill, M.C., and McDonald, M.G., 

2000, MODFLOW-2000, The U.S. Geological Survey modular ground-water model—User guide to modularization 

concepts and the ground-water flow process: U.S. Geological Survey Open-File Report 00-92, 121 p.

Harwood, V.J., Whitlock, John, and Withington, Victoria, 

2000, Classification of antibiotic resistance patterns of indicator bacteria by discriminant analysis—Use in predicting 

the source of fecal contamination in subtropical waters: 

Applied and Environmental Microbiology, v. 66, p. 3,698-

3,704.

Hem, J.D., 1985, Study and interpretation of the chemical characteristics of natural water: U.S. Geological Survey WaterSupply Paper 2254, 263 p.

Hill, M.C., Banta, E.R., Harbaugh, A.W., and Anderman, E.R., 

2000, MODFLOW-2000, The U.S. Geological Survey modular ground-water model—User guide to the observation, 

sensitivity, and parameter estimation processes and three 

post-processing programs: U.S. Geological Survey OpenFile Report 00-184, 209 p. 

Jagucki, M.L., and Darner, R.A., 2001, Ground-water quality in 

Geauga County, Ohio-Review of previous studies, status in 

1999, and comparison of 1986 and 1999 data: U.S. Geological Survey Water-Resources Investigations Report 01-4160, 

60 p.

Kendall, C., and Caldwell, E.A., 1998, Fundamentals of isotope 

geochemistry in Kendall, C., and McDonnell, J.J. (eds.), Isotope tracers in catchment hydrology: Amsterdam, Elsevier 

Science, p. 51-86.
46 Determining Sources of Water and Contaminants to Wells in a Carbonate Aquifer near Martinsburg, Pennsylvania

References Cited—Continued

Knuth, M., Jackson, J.L., and Whittemore, D.O., 1990, An integrated approach to identifying the salinity source contaminating a ground-water supply: Ground Water, v. 28, no. 2, 

p. 207-214. 

Lamond, R.E., and Leikam, D.F., 2002, Chloride in Kansas— 

Plant, soil, and fertilizer considerations: Kansas State University MF-2570, 4 p. 

Langmuir, D., and Jacobson, R.L., 1971, Hydrochemistry of 

ground waters in carbonate rocks near State College, Pennsylvania, in Parizek, W.B., White, W.B., and Langmuir, D., 

Hydrogeology and geochemistry of folded and faulted rocks 

of the Central Appalachian type and related land-use problems: University Park, Pa., Pennsylvania State University 

Circular 82, p. 126-133.

Langmuir, D., and White, W.B., 1971, Natural ground-water 

geochemistry, in Parizek, W.B., White, W.B., and Langmuir, 

D., Hydrogeology and geochemistry of folded and faulted 

rocks of the Central Appalachian type and related land-use 

problems: University Park, Pa., Pennsylvania State University Circular 82, p. 115-134.

Lindsey, B.D., Loper, C.A., and Hainly, R.A., 1997, Nitrate in 

ground water and stream base flow in the Lower Susquehanna River Basin, Pennsylvania and Maryland: U.S. Geological Survey Water-Resources Investigations Report 97-

4146, 66 p.

Low, D.J., and Conger, R.W., 2002, Ground-water availability 

in part of the Borough of Carroll Valley, Adams County, 

Pennsylvania, and the establishment of a drought-monitor 

well: U.S. Geological Survey Water-Resources Investigations Report 02-4273, 78 p. 

McDonald, M.G., and Harbaugh, A.W., 1988, A model of 

three-dimensional finite-difference ground-water flow 

model: U.S. Geological Survey Techniques of WaterResources Investigations, book 6, chap. A1, 586 p.

Miller, D.W., 1980, Waste disposal effects on ground water— 

A comprehensive survey of the occurrence and control of 

ground-water contamination resulting from waste-disposal 

practices: Berkeley, Calif., Premier Press, 512 p.

Risser, D.W., and Barton, G.J., 1995, A strategy for delineating 

the area of ground-water contributing to wells completed in 

fractured bedrock aquifers in Pennsylvania: U.S. Geological Survey Water-Resources Investigations Report 95-4033, 

30 p.

Scanlon, B.R., Mace, R.E., Barrett, M.E., and Smith, Brian, 

2003, Can we simulate regional ground water flow in a karst 

system using equivalent porous media models? Case study, 

Barton Springs Edwards aquifer, USA: Journal of Hydrology, v. 276, p. 137-158. 

Scanlon, B.R., and Thrailkill, J., 1987, Chemical similarities 

among physically distinct spring types in a karst terrain: 

Journal of Hydrology, v. 89, p. 259-279. 

Sevon, W.D., 2000, Physiographic provinces of Pennsylvania: 

Pennsylvania Geological Survey, 4th ser., scale 1:2,000,000.

Siwiec, S.F., Hainly, R.A., Lindsey, B.D., Bilger, M.D., and 

Brightbill, R.A., 1997, Water-quality assessment of the 

Lower Susquehanna River Basin, Pennsylvania and Maryland—Design and implementation of water-quality studies, 

1992-95: U.S. Geological Survey Open-File Report 97-583, 

121 p.

Sloto, R.A., and Crouse, M.Y., 1996, HYSEP—A computer 

program for streamflow hydrograph separation and analysis: 

U.S. Geological Survey Water-Resources Investigations 

Report 96-4040, 46 p.

Speece, Jack, and Cullinan, Mike, 1972, The caves of Blair 

County: Mid-Appalachian region of the National Speliological Society, Bulletin no. 8, 90 p. 

Taylor, L.E., Werkheiser, W.H., duPont, N.S., and Kriz, M.L., 

1982, Groundwater resources of the Juniata River Basin, 

Pennsylvania: Pennsylvania Geological Survey, 4th ser., 

Water-Resource Report 54, 131 p. 

U.S. Department of Agriculture, 1981, Soil survey of Blair 

County, Pennsylvania: Washington, D.C., United States 

Department of Agriculture, Soil Conservation Service, scale 

1:20,000. 

U.S. Environmental Protection Agency, 1992, Consensus 

method for determining groundwaters under the direct influence of surface water using microscopic particulate analysis 

(MPA): EPA 910/9-92-029.

U.S. Environmental Protection Agency, 2002, National Primary Drinking-Water Standards: EPA 816-F-02-013, 7 p. 

U.S. Geological Survey, 1997 to present, National field manual 

for the collection of water-quality data: U.S. Geological 

Survey Techniques of Water-Resources Investigations, book 

9, chap. A4, 2 v., variously paged.

Vogelmann, J.E., Sohl, T.L., Campbell, P.V., and Shaw, D.M., 

1998a, Regional land cover characterization using Landsat 

Thematic Mapper data and ancillary data sources: Environmental Monitoring and Assessment 51, p. 415-428.

Vogelmann, J.E., Sohl, T.L., and Howard, S.M.,1998b, 

Regional characterization of land cover using multiple 

sources of data: Photogrammetric Engineering and Remote 

Sensing 64, p. 45-57.

White, W.B., 1988, Geomorphology and hydrology of karst 

terrains: New York, Oxford University Press, 464 p.

Wiggins, B.A., Andrews, R.W., Conway, R.A., Corr, C.L., 

Dobratz, E.J., Dougherty, D.P., Eppard, J.R., Knupp, S.R., 

Limjoco, M.C., Mettenburg, J. M., Rinehardt, J.M., Sonsino, 

J., Torrijos, R.L., and Zimmerman, M.E., 1999, Use of antibiotic resistance analysis to identify nonpoint sources of fecal 

pollution: Applied and Environmental Microbiology, v. 65, 

p. 3,483-3,486.

Zaugg, S.D., Smith, S.G., Schroeder, M.P., Barber, L.B., and 

Burkhardt, M.R., 2002, Methods of analysis by the U.S. Geological Survey National Water Quality Laboratory—Determination of wastewater compounds by polystyrene-divinylbenzene solid-phase extraction and capillary-column gas 

chromatography/mass spectrometry: U.S. Geological 

Survey Water-Resources Investigations Report 01-4186, 

37 p.