Water-Resources Investigations Report

U.S. Department of the Interior

U.S. Geological Survey

Relation of shallow water quality in the 

Central Oklahoma aquifer to geology, soils, 

and land use

00–4241

Contribution from the National Water Quality Assessment Program
On the Cover: Photograph of the skyline of Oklahoma City; taken by U.S. Geological 

Survey employee, David Usher. 
U.S. Department of the Interior

U.S. Geological Survey

Water-Resources Investigations Report

Relation of shallow water quality in the 

Central Oklahoma aquifer to geology, soils, 

and land use

By Alan H. Rea, Scott C. Christenson, and William J. Andrews

00–4241

Contribution from the National Water Quality Assessment Program
The use of firm, trade, and brand names in this report is for identification purposes only and does

not constitute endorsement by the U.S. Geological Survey.

For additional information write to: Copies of this report can be purchased from:

U.S. Geological Survey

Information Services

Box 25286

Federal Center

Denver, CO 80225

UNITED STATES GOVERNMENT PRINTING OFFICE: OKLAHOMA CITY 2001

U.S. Department of the Interior

GALE A. NORTON, Secretary

U.S. Geological Survey

Charles G. Groat, Director

District Chief

U.S. Geological Survey

Water-Resources Division

202 NW 66 St., Bldg. 7

Oklahoma City, OK 73116
 III

FOREWORD

The mission of the U.S. Geological Survey 

(USGS) is to assess the quantity and quality of the 

earth resources of the Nation and to provide information that will assist resource managers and policy 

makers at Federal, State, and local levels in making 

sound decisions. Assessment of water-quality conditions and trends is an important part of this overall 

mission.

One of the greatest challenges faced by waterresources scientists is acquiring reliable information 

that will guide the use and protection of the Nation's 

water resources. That challenge is being addressed by 

Federal, State, interstate, and local water-resource 

agencies and by many academic institutions. These 

organizations are collecting water-quality data for a 

host of purposes that include: compliance with permits 

and water-supply standards; development of remediation plans for a specific contamination problem; operational decisions on industrial, wastewater, or watersupply facilities; and research on factors that affect 

water quality. An additional need for water-quality 

information is to provide a basis on which regional 

and national-level policy decisions can be based. Wise 

decisions must be based on sound information. As a 

society we need to know whether certain types of 

water-quality problems are isolated or ubiquitous, 

whether there are significant differences in conditions 

among regions, whether the conditions are changing 

over time, and why these conditions change from 

place to place and over time. The information can be 

used to help determine the efficacy of existing waterquality policies and to help analysts determine the 

need for and likely consequences of new policies.

To address these needs, the Congress appropriated funds in 1986 for the USGS to begin a pilot 

program in seven project areas to develop and refine 

the National Water-Quality Assessment (NAWQA) 

Program. In 1991, the USGS began full implementation of the program. The NAWQA Program builds 

upon an existing base of water-quality studies of the 

USGS, as well as those of other Federal, State, and 

local agencies. The objectives of the NAWQA 

Program are to:

• Describe current water-quality conditions for a 

large part of the Nation's freshwater streams, 

rivers, and aquifers.

• Describe how water quality is changing over 

time.

• Improve understanding of the primary natural 

and human factors that affect water-quality 

conditions.

This information will help support the development and evaluation of management, regulatory, and 

monitoring decisions by other Federal, State, and local 

agencies to protect, use, and enhance water resources.

The goals of the NAWQA Program are being 

achieved through ongoing and proposed investigations 

of 60 of the Nation's most important river basins and 

aquifer systems, which are referred to as study units. 

These study units are distributed throughout the 

Nation and cover a diversity of hydrogeologic settings. 

More than two-thirds of the Nation's freshwater use 

occurs within the 60 study units and more than twothirds of the people served by public water-supply 

systems live within their boundaries.

National synthesis of data analysis, based on 

aggregation of comparable information obtained from 

the study units, is a major component of the program. 

This effort focuses on selected water-quality topics 

using nationally consistent information. Comparative 

studies will explain differences and similarities in 

observed water-quality conditions among study units 

and will identify changes and trends and their causes. 

The first topics addressed by the national synthesis are 

pesticides, nutrients, volatile organic compounds, and 

aquatic biology. Discussions on these and other waterquality topics will be published in periodic summaries 

of the quality of the Nation's ground and surface water 

as the information becomes available.

This report is an element of the comprehensive 

body of information developed as part of the NAWQA 

Program. The program depends heavily on the advice, 

cooperation, and information from many Federal, 

State, interstate, Tribal, and local agencies and the 

public. The assistance and suggestions of all are 

greatly appreciated.

Robert M. Hirsch

Chief Hydrologist
CONTENTS V

CONTENTS

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

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

Methods of Data Compilation, Association, and Analysis ...................................................................................................................3

Compilation of areally-distributed attributes............................................................................................................................. 3

Geologic setting.................................................................................................................................................................... 3

Soil properties....................................................................................................................................................................... 3

Land use................................................................................................................................................................................. 8

Associating point data with areally-distributed attributes .....................................................................................................8

Stratification based on geology and land use.................................................................................................................8

Buffer-overlay analysis ..................................................................................................................................................... 11

Particle-tracking analysis................................................................................................................................................. 11

Statistical analysis....................................................................................................................................................................... 12

Hypothesis testing.............................................................................................................................................................. 12

Sampling biases ................................................................................................................................................................. 13

Relation of Geologic Setting to Ground-Water Quality....................................................................................................................... 14

Inorganic constituents ................................................................................................................................................................ 14

Organic compounds .................................................................................................................................................................... 17

Relation of Soil Properties to Ground-Water Quality.......................................................................................................................... 18

Inorganic constituents ................................................................................................................................................................ 18

Organic compounds .................................................................................................................................................................... 18

Relation of Land Use to Ground-Water Quality.................................................................................................................................... 18

Inorganic constituents ................................................................................................................................................................ 18

Organic compounds .................................................................................................................................................................... 22

Relation of Population Density to Ground-Water Quality................................................................................................................... 22

 Inorganic constituents ............................................................................................................................................................... 22

Organic compounds .................................................................................................................................................................... 22

Relation of Multiple Factors to Detection of Organic Compounds ...................................................................................................24

Possible Sources of Contaminants ........................................................................................................................................................ 25

Inorganic constituents ................................................................................................................................................................ 25

Organic compounds .................................................................................................................................................................... 27

Conclusions................................................................................................................................................................................................ 29

References................................................................................................................................................................................................. 29

ILLUSTRATIONS

Figures 1-7. Maps showing:

1. Location of the Central Oklahoma study unit and ground-water monitoring networks wells ................................... 2

2. Estimated soil runoff curve number in the Central Oklahoma study unit ...................................................................... 4

3. Soil profile clay content in the Central Oklahoma study unit .......................................................................................... 5

4. Soil profile vertical permeability in the Central Oklahoma study unit............................................................................ 6

5. Soil profile minimum permeability in the Central Oklahoma study unit ......................................................................... 7

6. Land use in the Central Oklahoma study unit..................................................................................................................... 9

7. Population density from 1990 Census in the Central Oklahoma study unit ................................................................. 10

Figure 8. Schematic diagram of wells with 1/4-mile buffers and particles overlain on land use................................................12

Figures 9.1-9.2. Boxplots of:

9.1. Physical parameters and inorganic constituents with significantly different concentrations, 

by geologic unit, Central Oklahoma aquifer.........................................................................................................16

9.2. Physical parameters and inorganic constituents with significantly different concentrations, 

by geologic unit, Central Oklahoma aquifer.........................................................................................................17

Figures 10.1-10.2. Scatter plots of:
VI CONTENTS

10.1. Inorganic constituent concentrations and soil runoff curve numbers, Central Oklahoma aquifer................... 20

10.2. Inorganic constituent concentrations and soil runoff curve numbers, Central Oklahoma aquifer................... 21

Figure 11. Boxplots of population density by sampling network....................................................................................................... 23

Figure 12 Graph showing observed and predicted cumulative VOC detection probabilities for logistic

regression models, including population density and the urban sampling network as explanatory 

variables .............................................................................................................................................................................. 26

TABLES

1. Kruskal-Wallis tests of inorganic constituent concentrations between wells completed in different 

geologic units............................................................................................................................................................................ 15

2. Spearman rank-correlation comparisons of inorganic constituent concentrations between groups of wells

based on average soil properties within 402 m (a quarter of a mile) of the wells......................................................... 19

CONVERSION FACTORS AND VERTICAL DATUM

Sea level: In this report, “sea level” refers to the National Geodetic Vertical Datum of 1929 (NGVD of 1929)--a geodetic datum 

derived from a general adjustment of the first-order level nets of both the United States and Canada, formerly called Sea Level 

Datum of 1929.

Multiply By To obtain

Length

inch (in) 25.4 millimeter

foot (ft) 0.3048 meter

mile (mi) 1.609 kilometer

Area

square mile (mi2) 2.590 square kilometer 

Flow rate

cubic foot per second (ft3/s) 0.02832 cubic meter per second 
 1

Relation of shallow water quality in the Central 

Oklahoma aquifer to geology, soils, and land use

By Alan H. Rea, Scott C. Christenson, and William J. Andrews

ABSTRACT

The purpose of this report is to identify, 

describe, and explain relations between natural 

and land-use factors and ground-water quality in 

the Central Oklahoma aquifer NAWQA study 

unit. Natural factors compared to water quality 

included the geologic unit in which the sampled 

wells were completed and the properties of soils 

in the areas surrounding the wells. Land-use 

factors included types of land use and population 

densities surrounding sampled wells. Groundwater quality was characterized by concentrations 

of inorganic constituents, and by frequencies of 

detection of volatile organic compounds and 

pesticides. Water-quality data were from samples 

collected from wells 91 meters (300 feet) or less 

in depth as part of Permian and Quaternary 

geologic unit survey networks and from an urban 

survey network.

Concentrations of many inorganic constituents were significantly related to geology. In addition, concentrations of many inorganic 

constituents were greater in water from wells 

from the Oklahoma City urban sampling network 

than in water from wells from low-density survey 

networks designed to evaluate ambient water 

quality in the Central Oklahoma aquifer study 

unit. However, sampling bias may have been 

induced by differences in hydrogeologic factors 

between sampling networks, limiting the ability to 

determine land-use effects on concentrations of 

inorganic constituents.

Frequencies of detection of pesticide and 

volatile organic compounds (VOC's) in groundwater samples were related to land use and population density, with these compounds being more 

frequently detected in densely-populated areas. 

Geology and soil properties were not significantly 

correlated to pesticide or VOC occurrence in 

ground water. Lesser frequencies of detection of 

pesticides in water from wells in rural areas may 

be due to low to moderate use of those 

compounds on agricultural lands in the study unit, 

with livestock production being the primary agricultural activity. There are many possible sources 

of pesticides and VOC's in the urban areas of 

Central Oklahoma. Because only existing watersupply wells were sampled, it is not clear from the 

data collected whether pesticides and VOC's: (1) 

occur in low concentrations throughout upper 

portions of the aquifer in urban areas, or (2) are 

present in ground water only in the immediate 

vicinity of the wells due to back-flow of those 

chemicals into the wells or to inflow around 

cement seals and through gravel packs 

surrounding well casings of surface runoff 

containing those compounds.

INTRODUCTION

The Central Oklahoma aquifer study unit 

encompasses about 8,000 square kilometers of Central 

Oklahoma (fig. 1), and is underlain by aquifers 

composed of sandstone of Permian-age or of unconsolidated sediments of Quaternary age. Those aquifers 

are used extensively to supply water for municipal,

industrial, commercial, and domestic needs. Several 

water-quality problems exist in these aquifers, 
2 Relation of shallow water quality in the Central Oklahoma aquifer to geology, soils, and land use

36O

34 45’ O

30’ 15’ 97O

96 45’ O

45’

30’

15’

35O

Base from U.S. Geological Survey digital data, 1:100,000, 1983

Universal Transverse Mercator Projection,

Zone 14

PONTOTOC CO

GARVIN CO

McCLAIN CO

GRADY CO CANADIAN CO KINGFISHER CO

LOGAN CO

OKLAHOMA CO

PAYNE CO

LINCOLN CO

POTTAWATOMIE CO

SEMINOLE CO

CLEVELAND CO

OKLAHOMA

CENTRAL OKLAHOMA

STUDY UNIT

EXPLANATION

Edmond

Midwest

City

Del City

Moore

Oklahoma

City

Lake

Hefner

Stanley

Draper

Lake

Norman

Lake

Thunderbird

Shawnee

Little River

Deep Fork

North

Canadian

River

Canadian River

River

Cimarron

Study Unit

Cities

Lakes

Rivers

County Boundaries

Lake

Overholser

Quaternary Alluvium and

Terrace Deposits Network

Well

Permian Geologic Units

Network Well

Urban Network Well

20 MILES

0 10 20 KILOMETERS

0 10

Figure 1. Location of the Central Oklahoma study unit and ground-water monitoring networks wells.
 3

including dissolved concentrations of arsenic, chromium, and selenium in exceedance of drinking-water 

standards, and the presence of synthetic organic 

compounds. These aquifers were sampled as part of 

the National Water-Quality Assessment (NAWQA) 

Program of the U.S. Geological Survey. The primary 

objective of the Central Oklahoma aquifer NAWQA 

project was to describe relations between groundwater quality and land use, hydrogeology, and other 

pertinent factors (Christenson and Parkhurst, 1987).

The purpose of this report is to identify, 

describe, and explain, to the extent possible, the relations between natural and land-use factors and groundwater quality in the Central Oklahoma aquifer study 

unit. Relations between those factors and groundwater quality were evaluated using statistical tests. 

Natural factors compared to ground-water quality 

include the geologic unit in which the sampled wells 

were completed, and the properties of soils in the areas 

surrounding the sampled wells. Land-use factors 

compared to ground-water quality include land-use 

types and population densities in the vicinity of 

sampled wells. Ground-water quality was characterized by concentrations of inorganic constituents, and 

by the presence or absence of volatile organic 

compounds and pesticides. Analyses of water samples 

collected as part of low-density survey and urban-area 

well networks composed of wells of depths less than 

91 meters (300 feet) are described in this report.

METHODS OF DATA COMPILATION, 

ASSOCIATION, AND ANALYSIS

The primary goals of the Central Oklahoma 

NAWQA project were to identify, describe, and 

explain relations between natural and land-use factors 

and ground-water quality in the Central Oklahoma 

aquifer study unit. Factors potentially affecting water 

quality were identified, based on knowledge of hydrogeologic processes, results of other investigations, and 

the feasibility of quantifying the factors. Some factors 

of possible relevance were not readily quantified and 

could not be evaluated in a statistical manner. For 

example, information about the quantities and 

geographic distributions of application of pesticides 

and fertilizers used would have been relevant for this 

report, but such information was not available. All the 

factors used in this investigation are spatially distributed, and are shown on maps. Each of those factors is 

described in the following sections.

Compilation of Areally-Distributed Attributes

Geologic Setting

The primary contributing geologic units for each 

sampled well were determined by field personnel, 

based on terrain, depth of well, and knowledge of the 

extent of the geologic units in the area from well logs 

and other sources. Bingham and Moore (1975) 

contains a map of bedrock geology of the study unit. 

Christenson (1998) contains a map of the surficial 

outcrops of geologic units in the study unit.

Soil Properties

Soil properties were derived from the six county 

soil-survey maps (Soil Conservation Service, 1960, 

1969, 1970, 1977, 1987a, and 1987b) that encompass 

the study unit. The soil survey maps were digitized in 

raster (grid) format, using a grid-cell size of 200 m by 

200 m (Soil Conservation Service, written commun., 

1991). Soils were not mapped in the central part of the 

Oklahoma City urban area in the Oklahoma County 

soil survey (Soil Conservation Service, 1969).

To evaluate the effects of different soil properties on ground-water quality, statistical comparisons 

were made between water quality and average soil 

properties in the areas around the wells. The soil 

survey of Oklahoma County (Soil Conservation 

Service, 1969) did not delineate soils in the central 

urbanized area of Oklahoma City (see figs. 2-5), therefore soils information is unavailable for 22 of 41 urban 

sampling network wells, and 3 of 42 wells in the 

Quaternary geologic units network. Because of lack of 

soils data, relations of water-quality data to soils characteristics were only tested for wells from the 

Permian- and Quaternary geologic unit networks 

having soils data. Soil properties used for these 

comparisons were soil-runoff curve number (fig. 2), 

soil-profile clay content (fig. 3), soil-profile vertical 

permeability (fig. 4), and soil-profile minimum permeability (fig. 5). For those parameters for which ranges 

were given in the soil survey, midpoints of the ranges 

were used.
4 Relation of shallow water quality in the Central Oklahoma aquifer to geology, soils, and land use

EXPLANATION

SOIL RUNOFF

CURVE NUMBER--

Larger numbers imply

increased runoff

No data

available

< 65

65 - 75

> 75

0

0 10

10 20

20

MILES

KILOMETERS

34°45 ́

35°00 ́

15 ́

30 ́

45 ́

36°00 ́

97°45 ́ 30 ́ 15 ́ 97°00 ́ 96°45 ́

Figure 2. Estimated soil runoff curve number in the Central Oklahoma study unit.
 5

EXPLANATION

SOIL-PROFILE CLAY

CONTENT--In grams clay/cm2

No data

available

< 30

30 - 60

> 60

0

0

10 20

10 20

MILES

KILOMETERS

34°45 ́

35°00 ́

15 ́

30 ́

45 ́

36°00 ́

97°45 ́ 30 ́ 15 ́ 97°00 ́ 96°45 ́

Figure 3. Soil profile clay content in the Central Oklahoma study unit.
6 Relation of shallow water quality in the Central Oklahoma aquifer to geology, soils, and land use

EXPLANATION

SOIL-PROFILE VERTICAL

PERMEABILITY--In in/hr

No data

available

< 1

1 - 2

> 2

0

0 10 20

10 20 MILES

KILOMETERS

34°45 ́

35°00 ́

15 ́

30 ́

45 ́

36°00 ́

97°45 ́ 30 ́ 15 ́ 97°00 ́ 96°45 ́

Figure 4. Soil profile vertical permeability in the Central Oklahoma study unit.
 7

0

0 10

10 20

20

MILES

KILOMETERS

34°45 ́

35°00 ́

15 ́

30 ́

45 ́

36°00 ́

97°45 ́ 30 ́ 15 ́ 97°00 ́ 96°45 ́

EXPLANATION

SOIL-PROFILE MINIMUM

PERMEABILITY--In in/hr

No data

available

< 1

1 - 2

> 2

Figure 5. Soil profile minimum permeability in the Central Oklahoma study unit.
8 Relation of shallow water quality in the Central Oklahoma aquifer to geology, soils, and land use

Soil-runoff curve numbers (fig. 2) were estimated for each soil component based on estimated 

typical cover types and hydrologic conditions, and 

average runoff conditions, as interpreted from the 

Engineering Field Manual (Soil Conservation Service, 

1989, tables 2-3). In general, the soil runoff curve 

number ranges from 40 to 100, with greater curve 

numbers indicating more runoff and consequently less 

infiltration.

The soil-profile clay content (fig. 3) was calculated by multiplying the thickness of each soil layer by 

the percentage of clay for that layer, multiplying the 

result by the bulk density of that layer, and summing 

the result for each layer in the soil profile. The end 

result is the number of grams of clay in the soil profile 

per square centimeter of land-surface area. The soilprofile vertical permeability (fig. 4) was calculated 

using the following relationship (adapted from 

Jorgensen, 1980):

in which P′

profile is the soil-profile vertical permeability, B is the thickness of the entire soil profile, and 

P′1,..., P′

n and b1,..., bn are the vertical permeabilities and thicknesses of soil horizons 1...n, respectively. 

The soil-profile minimum permeability (fig. 5) is the 

permeability of the soil horizon with the lowest permeability.

Many soil-mapping units were identified as soil 

complexes, which are made up of two or more soils 

grouped together because of the scale of mapping. Soil 

properties were averaged using the areal percentages 

of each soil component within complexes as a 

weighting factor, to arrive at an area-weighted average 

property for each soil-mapping unit. Some soils had no 

properties listed in the soil-property data base. Areal 

percentages of those soils were not included in soilcomplex averages.

Land Use

An important characteristic of the Central Oklahoma aquifer is that much of the aquifer is overlain by 

a large urban area composed of Oklahoma City and 

surrounding communities (fig. 6). Because chemical 

usage and discharges within the urban area may be 

important influences on the ground-water quality in 

the study unit, an urban survey well network was 

established to evaluate the effects of urban land uses 

on ground-water quality.

The Geographic Information Retrieval and 

Analysis System (GIRAS) Land-use and Land-cover 

Map (Fegeas and others, 1983) was used to characterize land use for this report. That map was compiled 

and digitized from 1975 and 1981 high-altitude aerial 

photography at a base scale of 1:250,000. For this 

report, GIRAS land-use categories were classified into 

three categories: urban, agricultural, and other (fig. 6).

Population density was calculated using data 

from the 1990 Census of Population and Housing. 

Population data at the block-group level were 

extracted from census data files (Bureau of the Census, 

1990a), and combined with block-group boundaries 

from the Topologically Integrated Geographically 

Referenced (TIGER/Line) (Bureau of the Census, 

1990b) digital data files. Population densities were 

calculated by dividing block-group population by the 

area of each block group (fig. 7).

Associating Point Data with Areally-Distributed Attributes

Several methods were used for associating wells 

with areally-distributed attributes, depending upon the 

types of attributes. Because there was no clear best 

association method, results of several methods are 

presented. If several of the methods indicated significant relations between an attribute and ground-water 

quality, then confidence in the validity of those relations is greater. Similarly, if relations of the different 

association methods do not agree, the association may 

be an artifact of the methods used, rather than being 

representative of relations in the hydrologic system. 

For statistical analyses, wells were classified based 

upon sampling networks, buffer-overlay analysis, and 

particle-tracking analysis.

Stratification based on Geology and Land Use

As discussed in Christenson and others (1998), 

sampling networks were stratified based on geology 

and land use. The urban network was designed to evaluate ground-water quality in the Oklahoma City urban 

area. The two low-density survey networks were 

designed to evaluate water quality in the two principal

P′profile

B

b1

P′1

------- b2

P′2

------- ... bn

P′n

+ ++ -------

= -------------------------------------------------
 9

0

0 10 20

10 20 MILES

KILOMETERS

34° 45 ́

35° 00 ́

15 ́

30 ́

45 ́

36° 00 ́

97° 45 ́ 30 ́ 15 ́ 97° 00 ́ 96° 45 ́

EXPLANATION

LAND USE

Urban

Agricultural

Other

Figure 6. Land use in the Central Oklahoma study unit.
10 Relation of shallow water quality in the Central Oklahoma aquifer to geology, soils, and land use

0

0 10 20

10 20 MILES

KILOMETERS

34°45 ́

35°00 ́

15 ́

30 ́

45 ́

36°00 ́

97°45 ́ 30 ́ 15 ́ 97°00 ́ 96°45 ́

EXPLANATION

POPULATION DENSITY--

In persons per km2

< 100

100 - 750

750 - 1500

> 1500

Figure 7. Population density from 1990 Census in the Central Oklahoma study unit.
 11

hydrogeologic units of the Central Oklahoma aquifer 

study unit--alluvial/terrace deposits of Quaternary age 

(Quaternary geologic units network), and sedimentary 

bedrock of Permian age (Permian geologic units 

network). Therefore, one method of characterizing the 

effects of land use on ground-water quality in the 

Oklahoma City area is to compare ground-water 

quality from the urban sampling network with those of 

the low-density survey-sampling networks.

Buffer-Overlay Analysis

The simplest method of associating wells with 

spatial factors is a point-in-polygon overlay. Using this 

method, the land use assigned to a well would be that 

of the polygon within which the well lies on the landuse map. However, errors in well locations or in 

boundaries on the land-use map may result in misclassification of land use for a well. Misclassification also 

can occur due to generalization inherent in any map, 

commonly a function of map scale. For example, in 

the GIRAS land-use map, polygons of some land-use 

categories having areas smaller than 4 hectares or 

widths less than 200 meters were not digitized. The 

minimum delineation area for other land-use categories was 16 hectares, with a minimum width of 400 

meters (Fegeas and others, 1983). This generalization 

eliminated polygons too small to distinguish easily at 

the base map scale of 1:250,000, so any well located in 

a land-use area smaller than the minimum delineation 

area could be misclassified when overlaid on the 

GIRAS land-use map.

A common approach for reduction of misclassification errors is to draw a circular buffer around the 

well location and overlay the buffer on the maps of the 

factors of interest. In addition to reducing misclassification errors, the buffer area also serves as a simplified 

representation of the recharge area for a well. By use 

of a Geographic Information System (GIS), buffers 

with a radius of 402 meters (0.25 mile) were generated 

around each well. The area of each land-use category 

within each buffer was calculated, and the predominant (largest area) land-use category within each 

buffer was determined. This summarization can reduce 

the effects of misclassification errors. As an example, 

figure 8 shows four wells in the urban network with 

buffers overlaid on the GIRAS land-use map. All of 

those buffer areas contain primarily urban land uses.

A buffer overlay method was used to determine 

soil properties in the vicinity of the sampled wells. 

Digital soil maps used in the analyses represent the 

soil mapping units on a grid of 200-meter by 200-

meter cells. A buffer was defined as a nine-cell array 

centered around each sampled well, approximately an 

area of a 402-meter buffer. The soil properties for the 

nine cells were used to assign a soil property for the 

well, using an area-weighted average technique. If soil 

properties were missing for a cell in the vicinity of a 

sampled well, that cell was not used in the areaweighted average.

In addition to land use, buffer-overlay analysis 

was used with the population-density map by applying 

an area-weighted averaging technique. Adaptations of 

these methods were also used with the maps of soil 

properties, mentioned earlier in this report. To the 

extent that such buffers do not correspond to the 

recharge areas for wells, misclassification can also be 

introduced by this method.

Particle-Tracking Analysis

A ground-water flow model of the study unit 

(Christenson, Parkhurst, and Breit, 1998) was developed to estimate recharge areas for each sampled well, 

using the finite-difference method (McDonald and 

Harbaugh, 1988). The Central Oklahoma aquifer study 

unit was discretized into three-dimensional cells 2 

kilometers on each side and 30 meters (100 feet) thick. 

Hypothetical particles of water were placed in each 

simulated well between the base of the well and the 

water table, as interpolated from the water-table map 

in Christenson, Morton, and Mesander (1992). A 

particle-tracking program (Pollock, 1989) was used in 

conjunction with the ground-water-flow model to trace 

the particles of water backward from each well to their 

recharge locations.

Particles shown on figure 8 represent recharge 

locations for ground water flowing to each well. Particles are aligned along ground-water flow lines because 

the finite-difference model is based on the assumption 

of steady-state conditions, with no pumping of the 

individual wells. Under these assumptions, the model 

simulates regional ground-water flow, rather than flow 

conditions near a pumped well. Modifying the model 

to simulate conditions around each well was beyond 

the scope of this report. In a manner similar to the 

buffer-overlay analysis, the use of a large number of 

particles leads to a summarization process, which 

reduces misclassification errors. Land use in the 

recharge area for each well was determined by the
12 Relation of shallow water quality in the Central Oklahoma aquifer to geology, soils, and land use

EXPLANATION

PARTICLE

WELL

LAND USE

Agricultural

Urban

Forest

Water

A B

C

D

97 26 28 97 26 27

35 26 30

35 26 29

0

0 .5

1

.25

.5

MILE

KILOMETER

O

O

O O

category associated with the largest number of particles. The population density associated with each well 

was the average of the population densities for all 

particle recharge locations related to each well. 

Because of computer programming complications, 

particle-tracking analysis was not used for associating 

wells with soil properties. The ground-water-flow 

model was calibrated to match flow conditions in the 

Permian geologic units, but calculated ground-water 

flow velocities did not correlate well with isotopic 

ground-water recharge age estimates for wells 

completed in the Quaternary units.

Statistical Analysis

Hypothesis Testing

A variety of statistical tests were used to evaluate the significance of relations between groundwater quality and potentially related factors. Statistical 

tests were chosen based on the type of data--continuous or categorical. Concentrations of inorganic 

constituents are continuous variables. Because most of 

the concentrations of detected organic constituents 

were near laboratory reporting levels, organic constituent detection was treated as a categorical variable 

(detected or not detected). The associated sampling 

network, geologic units, and land-use factors were also 

categorical variables. Estimated soil runoff curve 

numbers, soil-profile clay contents, soil-profile vertical 

permeabilities, soil-profile minimum permeabilities, 

and population densities were continuous variables.

Relations between two categorical variables 

were tested using contingency-table analyses with chisquare test statistics (P-STAT, Inc., 1989). The null 

hypothesis for each test was that the variables were 

independent. The alternate hypothesis was that the 

variables were dependent (Helsel and Hirsch, 1992). 

The Mann-Whitney test (P-STAT, Inc., 1989) 

was used to compare concentrations of inorganic 

constituents grouped by two categorical variables. A 

generalization of the Mann-Whitney test, the KruskalWallis one-way analysis of variance (P-STAT, Inc., 

Figure 8. Schematic diagram of wells with 1/4-mile buffers and particles overlain on land 

use.
 13

1989) was used to compare concentrations of inorganic constituents grouped by more than two categories. For both tests, the null hypothesis was that the 

constituent concentrations were the same between the 

groups. The alternate hypothesis was that the concentrations were different between groups.

If significant differences between groups were 

indicated by the Kruskal-Wallis tests, Tukey's Studentized Range test (Tukey, 1977) was applied to ranktransformed data to determine which group mean 

ranks differed. The mean ranked values are estimates 

of the median of distributions (Helsel and Hirsch, 

1992). Tukey's Studentized Range test was used to 

indicate which groups had significantly different 

median concentrations.

The Spearman rank correlation test (P-STAT, 

Inc., 1989) is a non-parametric test of correlation 

between two continuous variables. The null hypothesis 

was that the variables were independent. The alternate 

hypothesis was that a monotonic (increasing or 

decreasing, but not necessarily linear) relation 

between the variables existed.

Logistic regression (SAS Institute, Inc., 1990) 

was used to test the relation between a categorical 

response variable and one or more continuous or categorical independent variables. Logistic regression does 

not require an assumption of multivariate normality or 

equal variances of the independent variables (Helsel 

and Hirsch, 1992).

For all statistical tests, the null hypothesis was 

rejected if the achieved level of significance (p-value) 

was less than 0.05.

Sampling Biases

Although efforts were made to obtain a random, 

unbiased sampling of wells in each sampling network, 

some biases exist. One probable source of bias is that 

the sampling was limited to existing water-supply 

wells with installed pumps. If a known water-quality 

problem existed in an area, wells probably would not 

be in use in that area, meaning that the extent of 

contamination may have been underestimated. Only 

being able to sample wells for which owners have 

given permission also may have biased the sampling 

network.

The manner in which the sampled wells were 

constructed may have biased the water-quality data. 

Domestic supply wells completed in consolidated 

Permian geologic units in the study unit generally are 

constructed in the following manner: (1) drilled by 

rotary methods, (2) cased to the full depth of the well, 

screened or perforated at depths corresponding to the 

most permeable strata, (3) gravel-packed from the 

bottom of the well to 3 meters (10 feet) below land 

surface, and (4) sealed with cement to the land surface. 

This method of construction may effectively increase 

lengths of well screens and sampled intervals to the 

saturated thickness penetrated by each well. This type 

of construction also may increase the likelihood that 

water from a well can be contaminated from 

substances introduced at or near the land surface. Even 

if most of the water produced by a well is coming from 

deep strata, a contaminant introduced at or near the 

land surface could migrate down the gravel pack, 

affecting the quality of produced water. This bias 

could be particularly noticeable for organic constituents, as very small quantities of an organic compound 

would produce the relatively low concentrations of 

organic compounds that typically were detected.

Oklahoma City is located along the gradational 

contact between the Hennessey Group and the Garber 

Sandstone. Beneath much of the Oklahoma City urban 

area, a thin layer of the Hennessey Group overlies and 

confines the Garber Sandstone. Many wells are drilled 

through the Hennessey Group and completed in the 

confined Garber Sandstone. Outside of the urban area, 

few wells were sampled where the Hennessey Group 

overlies the Garber Sandstone, so the urban sampling 

network could be biased by that difference in geologic 

setting.

Christenson, Parkhurst, and Breit (1998) 

reported that water samples from the Hennessey 

Group and from the shallow (less than 93-meters deep) 

confined zones of the Garber Sandstone and Wellington Formation generally had greater concentrations 

of sodium, calcium, magnesium, and chloride than 

water samples from unconfined areas of the Garber 

Sandstone and Wellington Formation. Water samples 

from the Hennessey Group also had greater sulfate 

concentrations than shallow water samples from the 

Garber Sandstone and Wellington Formation.

Few of the wells sampled in the Permian 

geologic units network were in a geologic setting 

similar to that of the urban sampling network. Travel 

times from recharge areas to wells, calculated by the 

particle-tracking model, were greater for urbannetwork wells than for wells in the Permian geologic 

units network (Mann-Whitney test p-value of 0.0056). 
14 Relation of shallow water quality in the Central Oklahoma aquifer to geology, soils, and land use

Differences in water quality between the urban 

network and Permian geologic units network may be 

due, in part, to differences in geologic settings.

RELATION OF GEOLOGIC SETTING TO 

GROUND-WATER QUALITY

The primary contributing geologic unit in which 

a well is completed may affect the quality of water in 

the well substantially due to variations in physical and 

chemical properties of the minerals, stratigraphy, and 

topography, which influence flow rates, ground-water 

residence times, and chemical reactions. These 

geologic properties may also affect concentrations of 

organic compounds, due to differences in permeability, 

naturally-occurring organic compounds, oxidation 

states, and pH.

To analyze the effects of geology on groundwater quality, statistical comparisons were made of 

water-quality data grouped by primary contributing 

geologic units from the Permian and Quaternary 

geologic units networks. Because most wells in the 

urban network were completed in the same geologic 

unit (Garber Sandstone) and were in a limited 

geographical area, data from the urban-network wells 

were not used to evaluate the effects of geology on 

ground-water quality.

Inorganic Constituents

For statistical analysis, water-quality data were 

divided into three geologic-unit groups: (1) the Chase, 

Council Grove, and Admire Groups, undivided (Christenson, 1998); (2) the Garber Sandstone and Wellington Formation, which are geologically similar; and 

(3) alluvial and terrace deposits of Quaternary age.

Geology had significant effects on concentrations of inorganic constituents in ground water in the 

Central Oklahoma aquifer study unit. Kruskal-Wallis 

test p-values listed in table 1 indicate significant differences between the distributions of many inorganic 

constituent concentrations in water from wells 

completed in the three geologic-unit groups.

Generalizations about the concentrations of 

inorganic constituents in the geologic-unit groups may 

be made from the groups with the greatest mean ranks 

listed in table 1. The geologic-unit group with the 

greatest mean rank is indicated in table 1only for those 

constituents that were significantly different between 

geologic units. Figure 9 shows boxplots of the distributions of the constituents that had significant differences between geologic units. Boxplots with the same 

letter code represent groups with mean-rank (median) 

constituent concentrations that are not significantly 

different, according to Tukey's Studentized Range test 

on rank-transformed data. For cadmium, Tukey's 

Studentized Range test did not indicate significant 

differences in mean ranks, whereas the Kruskal-Wallis 

test indicated significant differences in the distributions of cadmium concentrations between the 

geologic-unit groups.

Water samples from Quaternary-age geologic 

unit wells had similar values of pH and alkalinity and 

similar concentrations of gross alpha radiation, nitrite 

plus nitrate, cadmium, sodium, bicarbonate, and 

carbonate as water samples from the Garber Sandstone 

and Wellington Formation (fig. 9). Water from Quaternary geologic units had significantly lesser concentrations of dissolved oxygen and significantly greater 

concentrations of radon, uranium, iron, manganese, 

and potassium than water from the Garber Sandstone 

and Wellington Formation (fig. 9). Water from Quaternary geologic units had similar values of alkalinity and 

similar concentrations of dissolved oxygen, radon, 

uranium, nitrite plus nitrate, cadmium, iron, manganese, sodium, potassium, and bicarbonate as water 

samples from the Chase, Council Grove, and Admire 

Groups (fig. 9). Water from Quaternary geologic units 

had significantly lesser values of pH, and significantly 

less gross alpha radiation than water samples from the 

Chase, Council Grove, and Admire Groups (fig. 9).

Water samples from wells completed in the 

Garber Sandstone and Wellington Formation had 

similar concentrations of radon, uranium, cadmium, 

iron, manganese, and potassium as water samples from 

the from the Chase, Council Grove, and Admire 

Groups (fig. 9). Water from the Garber Sandstone and 

Wellington Formation had significantly lesser values 

of pH and alkalinity, and lesser concentrations of gross 

alpha radiation, sodium, and bicarbonate than water 

from the from the Chase, Council Grove, and Admire 

Groups (fig. 9). Water from the Garber Sandstone and 

Wellington Formation had significantly greater 

concentrations of dissolved oxygen, nitrite plus 

nitrate; and carbonate than water from the Chase, 

Council Grove, and Admire Groups (fig. 9).
 15

Table 1. Kruskal-Wallis tests of inorganic constituent concentrations between wells completed in 

different geologic units

[Well group I, 102 low-density survey sampling network wells less than 91 m (300 ft) deep; Highest Mean Rank: C, 

Chase, Council Grove, and Admire Groups, undivided; G, Garber Sandstone and Wellington Formation, undivided; 

A, Alluvial and Terrace deposits; Highest Mean Rank values given only when p-value is significant; p-values less 

than 0.05 are underlined]

Geologic-Unit Groups

Well Group I

 Constituent Highest 

Mean Rank

Kruskal-Wallis

p-value

pH C .0058

Oxygen, dissolved G .0000

Alkalinity C .0064

Calcium .1965

Magnesium .8384

Sodium C .0018

Potassium A .0071

Bicarbonate C .0088

Carbonate C .0225

Sulfate .1737

Chloride .3218

Fluoride .5413

Nitrite plus nitrate G .0335

Arsenic .5477

Barium .2197

Cadmium A .0478

Chromium .9071

Copper .1238

Iron A .0001

Lead .0749

Manganese A .0001

Mercury .8172

Selenium .1300

Silver .2246

Gross alpha C .0034

Radon A .0000

Uranium C .0024
16 Relation of shallow water quality in the Central Oklahoma aquifer to geology, soils, and land use

10

5

6

7

8

9

ALLUVIAL

AND

TERRACE

DEPOSITS

GARBER SS

AND

WELLINGTON FM

CHASE, COUNCIL

GROVE, AND

ADMIRE GROUPS

700

0

100

200

300

400

500

600

ALLUVIAL

AND

TERRACE

DEPOSITS

GARBER SS

AND

WELLINGTON FM

CHASE, COUNCIL

GROVE, AND

ADMIRE GROUPS

TOTAL ALKALINITY, IN MILLIGRAMS

PER LITER

PH, IN STANDARD UNITS

12

0

2

4

6

8

10

DISSOLVED OXYGEN,

IN MILLIGRAMS PER LITER

ALLUVIAL

AND

TERRACE

DEPOSITS

GARBER SS

AND

WELLINGTON FM

CHASE, COUNCIL

GROVE, AND

ADMIRE GROUPS

250

0

50

100

150

200

ALLUVIAL

AND

TERRACE

DEPOSITS

GARBER SS

AND

WELLINGTON FM

CHASE, COUNCIL

GROVE, AND

ADMIRE GROUPS

DISSOLVED GROSS ALPHA RADIATION,

IN MICROGRAMS PER LITER AS URANIUM

250

0

50

100

150

200

ALLUVIAL

AND

TERRACE

DEPOSITS

GARBER SS

AND

WELLINGTON FM

CHASE, COUNCIL

GROVE, AND

ADMIRE GROUPS

DISSOLVED URANIUM,

IN MICROGRAMS PER LITER

5,000

0

1,000

2,000

3,000

4,000

ALLUVIAL

AND

TERRACE

DEPOSITS

GARBER SS

AND

WELLINGTON FM

CHASE, COUNCIL

GROVE, AND

ADMIRE GROUPS

DISSOLVED RADON-222,

IN PICOCURIES PER LITER

1,600

0

200

400

600

800

1,000

1,200

1,400

ALLUVIAL

AND

TERRACE

DEPOSITS

GARBER SS

AND

WELLINGTON FM

CHASE, COUNCIL

GROVE, AND

ADMIRE GROUPS

DISSOLVED SODIUM,

IN MILLIGRAMS PER LITER

20

0

5

10

15

ALLUVIAL

AND

TERRACE

DEPOSITS

GARBER SS

AND

WELLINGTON FM

CHASE, COUNCIL

GROVE, AND

ADMIRE GROUPS

DISSOLVED POTASSIUM,

IN MILLIGRAMS PER LITER

ALLUVIAL

AND

TERRACE

DEPOSITS

GARBER SS

AND

WELLINGTON FM

CHASE, COUNCIL

GROVE, AND

ADMIRE GROUPS

ALLUVIAL

AND

TERRACE

DEPOSITS

GARBER SS

AND

WELLINGTON FM

CHASE, COUNCIL

GROVE, AND

ADMIRE GROUPS

DISSOLVED CARBONATE,

IN MILLIGRAMS PER LITER

75th PERCENTILE

MEDIAN

25th PERCENTILE

EXPLANATION

VALUES WITHIN 1.5

TIMES THE INTERQUARTILE

RANGE

VALUES 1.5 TO 3.0

TIMES THE INTERQUARTILE

RANGE

VALUES GREATER THAN 3.0

TIMES THE INTERQUARTILE

RANGE

A Letter code identifying

significantly similar or

different data groups.

B B A B A B AB B A

B B A A B AB A B AB DISSOLVED BICARBONATE, IN MILLIGRAMS PER LITER

AB B A A B AB AB B A

B B A

0

200

400

600

800

1,000

0

10

20

30

40

Figure 9.1. Boxplots of physical parameters and inorganic constituents with significantly different concentrations, by 

geologic unit, Central Oklahoma aquifer.
 17

ALLUVIAL

AND

TERRACE

DEPOSITS

GARBER SS

AND

WELLINGTON FM

CHASE, COUNCIL

GROVE, AND

ADMIRE GROUPS

75th PERCENTILE

MEDIAN

25th PERCENTILE

EXPLANATION

VALUES WITHIN 1.5

TIMES THE INTERQUARTILE

RANGE

VALUES 1.5 TO 3.0

TIMES THE INTERQUARTILE

RANGE

VALUES GREATER THAN 3.0

TIMES THE INTERQUARTILE

RANGE

A Letter code identifying

significantly similar or

different data groups.

A B

DISSOLVED NITRITE PLUS NITRATENITROGEN, IN MILLIGRAMS PER LITER

0

20

40

60

80

100

AB

DISSOLVED CADMIUM,

IN MICROGRAMS PER LITER

0

1

2

3

4

5

6

ALLUVIAL

AND

TERRACE

DEPOSITS

GARBER SS

AND

WELLINGTON FM

CHASE, COUNCIL

GROVE, AND

ADMIRE GROUPS

A A A

ALLUVIAL

AND

TERRACE

DEPOSITS

GARBER SS

AND

WELLINGTON FM

CHASE, COUNCIL

GROVE, AND

ADMIRE GROUPS

DISSOLVED IRON,

IN MICROGRAMS PER LITER

0

500

1,000

1,500

2,000

2,500

3,500

3,000

A B AB

DISSOLVED MANGANESE

IN MICROGRAMS PER LITER

0

200

400

600

800

1,000

1,200

1,400

A B AB

ALLUVIAL

AND

TERRACE

DEPOSITS

GARBER SS

AND

WELLINGTON FM

CHASE, COUNCIL

GROVE, AND

ADMIRE GROUPS

Organic Compounds

Relations between the geologic-unit groups and 

detection of organic constituents were tested using 

contingency-table analysis with a chi-square test 

statistic (P-STAT, Inc., 1989) for data from 102 wells 

in the Permian and Quaternary geologic unit networks. 

Frequencies of detection of pesticides and volatile 

organic compounds (VOC's) were included for each 

analysis because most of the organic compounds were 

reported in only a few water samples. If the groups are 

too small, the expected value for some cells of a 

contingency table may be too small, and the assumptions of the contingency-table analysis would not be 

valid. The null hypothesis was that the frequencies of 

detection of organic compounds were the same for 

each geologic unit, whereas the alternate hypothesis 

was that the frequency of detection of organic 

compounds was different between geologic-unit 

groups.

For pesticides, the calculated p-value was 0.695, 

and for VOC's the p-value was 0.971—both much 

greater than 0.05. Thus, the frequencies of detection of 

these compounds were similar for these geologic-unit 

groups. The lack of significant differences in frequencies of detection of these compounds between 

geologic units was unexpected, because the Permian 

geologic units have substantially different hydrologic 

characteristics from the Quaternary geologic units. 

Well construction may have played a significant role in 

this similarity, by possibly facilitating seepage of these 

compounds in the vicinity of well gravel packs, as 

previously described in the Sampling Biases section of 

this report.

Figure 9.2. Boxplots of physical parameters and inorganic constituents with significantly different concentrations, by geologic 

unit, Central Oklahoma aquifer.
18 Relation of shallow water quality in the Central Oklahoma aquifer to geology, soils, and land use

RELATION OF SOIL PROPERTIES TO 

GROUND-WATER QUALITY

Soil permeability affects infiltration and runoff 

rates of precipitation, influencing the quantity and 

spatial distribution of water recharging aquifers in the 

study unit. Because much of the chemical reactivity in 

soils is associated with clay-sized particles (Brady, 

1974), clay content of soils in recharge areas may 

affect ground-water quality. 

Inorganic Constituents

The p-values from statistical tests of relations 

between inorganic constituent concentrations and soil 

properties are listed in table 2. A total of 99 wells from 

the low-density networks were used in the analyses.

Spearman's rank correlation tests indicated 

significant positive monotonic correlation between 

estimated soil runoff curve number and values of pH 

and alkalinity, and concentrations of sodium, bicarbonate, carbonate, sulfate, chloride, fluoride, gross 

alpha radiation, and uranium; and significant negative 

monotonic correlation with barium and cadmium 

concentrations (table 2). There was a marginally 

significant, positive, monotonic correlation (p-value 

0.0534) between estimated soil runoff curve number 

and radon concentrations. Scatter plots of the constituent concentrations that had significant correlations 

with estimated soil runoff curve number are shown in 

figure 10. Although the correlations are statistically 

significant, plots of the data indicate that several of the 

correlations (sulfate, sodium, chloride, cadmium, 

gross alpha radiation, barium, and uranium) are 

unlikely to be meaningful.

Spearman's rank correlation tests indicated 

significant negative monotonic correlation between 

soil-profile clay content and dissolved oxygen. Greater 

clay concentrations in soils may impede infiltration of 

oxygen into aquifers. Spearman's rank correlation tests 

indicated significant positive monotonic correlation 

between soil-profile clay content and radon concentration. As increased clay content impedes downward 

movement of oxygen into aquifers, it may impede 

upward seepage of radon gas into the atmosphere. A 

marginally significant, positive, monotonic correlation 

(p-value 0.0538) was observed between soil-profile 

clay content and selenium concentration.

Spearman's rank correlation tests indicated 

significant, negative, monotonic correlations between 

both soil-profile vertical permeability and soil-profile 

minimum permeability and pH, sodium, selenium, 

gross alpha radiation, radon, and uranium concentrations. Spearman's rank correlation tests indicated 

statistically significant, positive, monotonic correlations between both soil-profile vertical permeability 

and soil-profile minimum permeability and barium 

concentrations.

Organic Compounds

The estimated soil runoff curve number, soilprofile clay content, soil-profile vertical permeability, 

and soil-profile minimum permeability, all continuous 

variables, were evaluated as explanatory variables in 

logistic regression analyses of frequencies of detection 

of pesticides and VOC's. None of the resulting logistic 

regression model coefficients were significant, 

meaning that there were no significant relations 

between these soil properties and the frequencies of 

detection of pesticides or VOC's in ground water.

RELATION OF LAND USE TO 

GROUND-WATER QUALITY

Land use was classified into three categories: 

urban, agricultural, and other. Because land use affects 

the kinds and quantities of anthropogenic compounds 

used, spilled or discharged, land use can have a significant influence on ground-water quality.

Inorganic Constituents

No statistical comparisons of inorganic-constituent concentrations by land-use groups are presented 

because geology was a significant factor affecting 

inorganic-constituent concentrations. Because 

different geologic settings were represented by the 

urban and low-density survey networks, the effects of 

land use on water-quality would be biased by differences imparted by geologic settings. Only four wells 

from the two low-density networks were classified as 

having urban land use based on the GIRAS maps--an 

inadequate sample size for statistical tests.
 19

Table 2. Spearman rank-correlation comparisons of inorganic constituent concentrations between groups of wells based 

on average soil properties within 402 m (a quarter of a mile) of the wells

[Well Group II, 99 low-density survey sampling network wells with soils data available that are less than 91 m (300 ft) deep; Sign of rho given 

only for tests with significant p-values; p-values less than 0.05 are underlined; ( ), Sign of rho given even though p-value is not significant]

Soil Runoff Curve 

Number

Well Group II

Soil-Profile Clay 

Content

Well Group II

Soil-Profile Vertical 

Permeability

Well Group II

Soil-Profile 

Minimum Permeability

Well Group II

 Constituent Sign of 

rho p-value Sign of rho p-value Sign of rho p-value Sign of rho p-value

pH + .0088 .2078 − .0336 − .0420

Oxygen, dissolved .9742 − .0312 .8594 .8640

Alkalinity + .0006 .4526 .2350 .3998

Calcium .1990 .2600 .7784 .9632

Magnesium .1234 .9060 .9378 .6152

Sodium + .0004 .2402 − .0142 − .0304

Potassium .6412 .8888 .7392 .6952

Bicarbonate + .0008 .4670 .2284 .3938

Carbonate + .0182 .6908 .4348 .5320

Sulfate + .0002 .7534 .1848 .4446

Chloride + .0118 .5038 .0940 .2642

Fluoride + .0032 .6968 .0826 .1612

Nitrite plus nitrate .6224 .9016 .1426 .1082

Arsenic .0908 .8376 .3080 .5408

Barium − .0006 .8646 + .0010 + .0026

Cadmium − .0202 .4292 .1632 .4144

Chromium .7200 .8570 .3650 .2948

Copper .2660 .8818 .5200 .4592

Iron .5544 .3384 .8296 .5380

Lead .1474 .9738 .1456 .1828

Manganese .1316 .9378 .9574 .7684

Mercury .7658 .7918 .6646 .7944

Selenium .2262 (+) .0538 − .0044 − .0088

Silver .0996 .1684 .1636 .2922

Gross alpha + .0094 .2236 − .0020 − .0068

Radon (+) .0534 + .0084 − .0106 − .0030

Uranium + .0002 .0796 − .0002 − .0008
20 Relation of shallow water quality in the Central Oklahoma aquifer to geology, soils, and land use

40 50 60 70 80 90

SOIL RUNOFF CURVE NUMBER

9.5

5.5

6.5

7.5

8.5

P

H, IN STANDARD

UNITS

ALK

SOIL RUNOFF CURVE NUMBER

40 50 60 70 80 90

0

100

300

500

700

ALKALINI

T

Y, IN

MILLI

GRAMS

PER LI

TER

A

S CALCIUM

CARBO

N

T

E

DISSO

LVED SODIUM, IN

MILLI

GRAMS PER LI

TER

0

200

400

600

800

1,000

1,200

1,400

1,600

SOIL RUNOFF CURVE NUMBER

40 50 60 70 80 90 0

B

200

ICARBONAT

E, IN

MILLI

GRAMS PER LI

TER

400

600

800

1,000

rho = +0.2622

p-value = 0.0088

rho = +0.3393

p-value = 0.0006

rho = +0.3472

p-value = 0.0004 rho = +0.3343

p-value = 0.0008

CARBONAT

E, IN

MILLI

GRAMS PER LI

TER

0

40 50 60 70 80 90

SOIL RUNOFF CURVE NUMBER

10

20

30

40

rho = +0.2368

p-value = 0.0182

0

1,000

2,000

3,000

SULFAT

E, IN

MILLI

GRAMS PER LI

TER

SOIL RUNOFF CURVE NUMBER

40 50 60 70 80 90

SOIL RUNOFF CURVE NUMBER

40 50 60 70 80 90

rho = +0.3759

p-value = 0.0002

Figure 10.1. Scatter plots of inorganic constituent concentrations and soil runoff curve numbers, Central Oklahoma 

aquifer.
 21

CHL

ORIDE, IN

MILLI

GRAMS

PER LI

TER

100

0

300

500

700

SOIL RUNOFF CURVE NUMBER

40 50 60 70 80 90 0

1

2

3

4

FLUORIDE, IN

MILLI

GRAMS

PER LI

TER

SOIL RUNOFF CURVE NUMBER

40 50 60 70 80 90 0

200

400

600

800

BARIUM, IN

MICROGRAMS

PER LI

TER

SOIL RUNOFF CURVE NUMBER

40 50 60 70 80 90

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

CAD

MIUM, IN

MICROGRAMS

PER LI

TER

SOIL RUNOFF CURVE NUMBER

40 50 60 70 80 90 0

200

50

100

150

250

G

ROSS AL

PHA RADI

OAC

TIVI

T

Y,

IN

MICROGRAMS PER

LI

TER AS URANIUM

0

200

50

100

150

250

SOIL RUNOFF CURVE NUMBER

40 50 60 70 80 90

URANIUM, IN

MICROGRAMS PER

LI

TER

rho = +0.2524

p-value = 0.0118 rho = +0.2935

p-value = 0.0032

rho = -0.3380

p-value = 0.0006

rho = -0.2369

p-value = 0.0202

rho = +0.2598

p-value = 0.0094 rho = +0.3693

p-value = 0.0002

SOIL RUNOFF CURVE NUMBER

40 50 60 70 80 90

Figure10.2. Scatter plots of inorganic constituent concentrations and soil runoff curve numbers, Central Oklahoma aquifer.
22 Relation of shallow water quality in the Central Oklahoma aquifer to geology, soils, and land use

Organic Compounds

Because geology was not significantly related to 

the detection of organic compounds, differences in 

geologic settings of the sampling networks do not 

preclude use of data from the urban network wells in 

evaluations of correlations between land use and 

detection of organic compounds. Frequencies of detection of organic compounds in water samples from the 

sampling networks were compared using contingency 

tables with a chi-square test statistic. The null hypothesis was that the frequencies of detection of organic 

compounds were the same in water samples from the 

urban network and the low-density networks. The 

alternative hypothesis was that the frequencies of 

detection of organic compounds were different in 

those networks. A sample size of data from 143 wells 

was used for buffer-overlay analysis, and a sample size 

of data from 86 wells was used for particle-tracking 

analysis. Particle-tracking analysis was not used for 

wells completed in Quaternary geologic units.

Contingency-table analysis comparing frequencies of detection of pesticides between groups of wells 

based on the predominant GIRAS land-use category 

within 402-meter buffers had a p-value of 0.107, and 

for particle-tracking analyses had a p-value of 0.458, 

so the null hypothesis is accepted, meaning that land 

use was not significantly correlated to detection of 

pesticides in ground water in the study unit.

Contingency-table analysis of land use and 

frequency of detection of VOC's, based on bufferoverlay analysis had a p-value of <0.001, whereas the 

test based on particle-tracking analysis had a p-value 

of 0.002; indicating rejection of the null hypothesis, 

meaning that urban land use had a significant, positive 

correlation to frequencies of detection of VOC's in 

ground-water in the study unit.

RELATION OF POPULATION DENSITY 

TO GROUND-WATER QUALITY

The population density associated with wells 

was categorized using buffer-overlay and particletracking analyses with a map derived from the 1990 

Census and TIGER/Line files (U.S. Department of 

Commerce, 1990a and 1990b). Population density is 

related to land use, but is measured differently. Population density is a continuous numerical variable and 

thus provides more detailed information about the 

intensity of land use for residential purposes than the 

categorical variable land use.

 Inorganic Constituents

Because the geologic unit in which the sampled 

wells were completed was an important factor influencing inorganic constituent concentrations, and 

geologic settings varied between the urban and lowdensity networks, data from the urban network wells 

were not used in comparisons of inorganic concentrations by population density. By excluding data from 

the urban network, however, the range of population 

densities around the remaining wells is much reduced, 

and greater population densities are precluded. Figure 

11 shows boxplots of average population density 

within 402-meter buffers of wells in the three 

networks. Because any comparisons not including the 

urban network wells would not adequately represent 

the range of population densities in the study unit, no 

comparisons are presented of inorganic concentrations 

and population density.

Organic Compounds

Logistic regression was used to test the relation 

between population density and frequencies of detection of organic compounds. Wells from the urban and 

low-density networks were used, resulting in a sample 

size of 143 wells for the buffer-overlay analysis, and a 

sample size of 86 for the particle-tracking analysis. 

Population density was a significant (p-value = 

0.0036) predictor of pesticide occurrence probability. 

The resulting logistic regression model is:

where Ppest is the probability of a pesticide being 

reported in a water sample , e is 2.703, and Dbuff is the 

area-weighted population density in a 402-meter 

buffer around the well, expressed in persons per square 

kilometer. The positive value of the coefficient for 

Dbuff indicates that, the probability of detection of a 

pesticide increases with increasing population density. 

Ppest

e

2.0108 0.00101Dbuff ( ) – +

1 + e

– 0.00101 2.0108 Dbuff ( ) + = -----------------------------------------------------------
 23

PERSONS PER SQUARE KILOMETER

0

500

1,000

1,500

1,500

2,500

URBAN ALLUVIUM

AND

TERRACE

PERMIAN

GEOLOGIC

UNITS

SAMPLING NETWORK

XX

X

EXPLANATION

75th Percentile

50th Percentile

25th Percentile

Values within 1.5 times

the interquartile range

X Values 1.5 to 3.0 times

the interquartile range

Values greater than 3.0

times the interquartile

range

A logistic 

regression 

analysis using average 

p

opulation 

density 

at 

particle-recharge locations from the

particle-tracking analyses did not have a significant pvalue for the population-density regression coefficient. 

The lack of significance may have been the result of 

reduction of the sample size from 143 to 86, because it 

was not possible to use the particle-tracking model for 

the wells completed in Quaternary geologic units. If 

contaminant sources were in the immediate vicinity of 

the sample, then detection of contaminants would not 

be substantially affected by ground-water flow directions and upgradient population densities.

Logistic regression of population densities and 

frequencies of detection of VOC's resulted in the 

following logistic regression model:

where Pvoc is the probability of a VOC being detected 

in a well. The population-density variable was a significant (p-value of 0.0001) predictor of VOC detection 

probability. The positive value of the coefficient for 

Dbuff indicates that the probability of detection of a 

VOC increases with increasing population density. 

Logistic regression of population densities and 

frequencies of detection of VOC's using average population density at particle-recharge locations resulted in 

the following logistic regression model:

where Pvoc is the probability of a VOC being detected 

in a well, and Dpart is the average population density, 

in persons per square kilometer, at particle-recharge 

Pvoc

e

1.7806 0.00183Dbuff ( ) – +

1 + e

– 0.00183 1.7806 Dbuff ( ) + = -----------------------------------------------------------

Pvoc

e

1.6928 0.00131Dpart ( ) – +

1 + e

– 0.00131 1.6928 Dpart ( ) + = -----------------------------------------------------------

Figure 11. Boxplots of population density by sampling network
24 Relation of shallow water quality in the Central Oklahoma aquifer to geology, soils, and land use

locations. The population-density variable was significant (p-value of 0.0072). The positive value of the 

coefficient for Dpart indicates that the probability of 

detection of a VOC increases with increasing population density. For VOC's, a comparison of p-values and 

plots of predicted and observed cumulative detection 

probabilities versus Blom ranks (SAS Institute, 1990) 

indicated that the logistic regression model based on 

the buffer-overlay analysis fit the data better than the 

logistic regression model based on the particletracking analysis. Reduction of the sample size for the 

model using the particle-tracking analysis or very 

localized sources of contaminants may have caused 

the poorer fit of the particle-tracking model.

RELATION OF MULTIPLE FACTORS TO 

DETECTION OF ORGANIC 

COMPOUNDS

All of the comparisons discussed so far in this 

report have compared water quality with single 

factors. Multivariate statistical methods are useful for 

comparisons involving more than one factor, allowing 

interpretation of the interaction and relative importance of factors. Because of the bias problems previously discussed, no multivariate statistical 

comparisons were made for inorganic constituents. 

Multivariate tests are better suited for interpretation 

affecting frequencies of detection of organic constituents.

The logistic regression model is appropriate for 

comparing the occurrence of organic constituents, a 

categorical variable, with a combination of continuous 

and categorical factors (Helsel and Hirsch, 1992). 

Categorical variables such as land use and geologic 

setting were recoded into a series of binary variables, 

one for each category. For example, land use was 

regrouped to three categories: urban, agricultural, and 

other. The urban variable was coded “1” if land use 

was urban, and “0” if land use was agricultural or 

other. These binary variables may be used in logistic 

regression models, provided that at least one binary 

variable from each original categorical variable is not 

included in the model, to prevent problems caused by 

closure (Barringer and others, 1990). 

Numerous logistic regression models were 

tested, using many combinations of explanatory variables. The models were evaluated based on the pvalues of model coefficients, Akaike's Information 

Criterion (Helsel and Hirsch, 1992), and subjective 

evaluations of model fit using plots of predicted and 

observed organic constituent detection probabilities. 

The stepwise logistic regression procedure (SAS Institute, 1990) was used to evaluate the relations between 

pesticide occurrence and geologic unit, soil runoff 

curve number, soil-profile clay content, soil-profile 

vertical permeability, soil-profile minimum permeability, urban sampling network, buffer-overlay land 

use, and population density based both on bufferoverlay analysis and on particle-tracking analysis. 

None of the resulting logistic regression model coefficients were statistically significant, including the coefficient for population density, which had a significant 

coefficient when used in a single-variable logistic 

regression. The sample size used in the multiple 

logistic regression model was smaller than the sample 

size used in the single-variable logistic regression 

model because soil properties were not available for 

wells in the central part of the urban area, and because 

the particle-tracking analysis could not be used for all 

wells. The wells in the central part of the urban area 

also were the wells with the greatest surrounding 

population densities. The lack of statistical significance may have been a result of the reduction in 

sample size from 143 to 115 wells.

If the soil properties and the population-density 

variable based on the particle-tracking analyses were 

eliminated from the logistic regression analysis for 

pesticide occurrence, the stepwise procedure resulted 

in a model that used population density based on 

buffer-overlay analysis only, and thus was exactly the 

same model as was used in the single-variable logistic 

regression of buffer-overlay population density versus 

occurrence of pesticides. Using the stepwise procedure, none of the land-use, geologic-unit, or urbansampling network variables could be used to significantly increase the degree to which the logistic regression model fit the data.

A stepwise logistic regression model was used 

to evaluate relations between VOC detection and 

geologic unit, soil runoff curve number, soil-profile 

clay content, soil-profile vertical permeability, soilprofile minimum permeability, urban sampling 

network, buffer-overlay land use, and population 

density based both on buffer-overlay analysis and on 

particle-tracking analysis. As with pesticides, none of 

the resulting logistic regression model coefficients 
 25

were statistically significant if the soil properties were 

included in the analysis. If the soil properties were 

excluded, and the particle-tracking population density 

variable was included, the stepwise procedure resulted 

in the same model as resulted from the single-variable 

logistic regression of particle-tracking population 

density versus VOC frequency of detection.

When soil properties and the particle-tracking 

population density variables were left out of the analysis, the stepwise procedure resulted in a logistic 

regression model that produced significant relations 

for both buffer-overlay population density (p-value 

was 0.0328) and the binary variable for the urban 

sampling network (p-value was 0.0272). The 

following equation is the logistic regression model 

which produced significant relations for VOC frequencies of detection:

where Pvoc is the probability of a VOC being detected 

in the well, Dbuff is the area-weighted population 

density in a 402-meter buffer around the well, and U is 

a “1” if the well is in the urban network, and a “0” 

otherwise. The positive values of the coefficients for 

Dbuff and U indicate that, according to the model, the 

probability of detection of a VOC increases with 

increasing population density and the presence of the 

well in the urban sampling network. This model fits 

the data the best of all the multivariate logistic regression models tested. Figure 12 shows predicted and 

observed VOC detection probabilities for this model.

POSSIBLE SOURCES OF CONTAMINANTS

The term contaminant is used in this report as 

any substance in ground water that was introduced as a 

result of human activity. The following two sections 

identify some of the possible sources of contaminants, 

and how those sources may be related to the natural 

and anthropogenic factors. Available data were not 

adequate to test some hypotheses, and in some cases, 

relations were not significant.

Inorganic Constituents

Christenson and Rea (1993) reported that 

median concentrations of alkalinity, calcium, magnesium, sodium, potassium, bicarbonate, chloride, iron, 

gross-alpha radioactivity, and uranium were greater in 

water from urban wells than in water from other wells 

sampled for the Central Oklahoma NAWQA Project. 

However, because of the previously discussed possible 

bias in the urban sampling network, no clear relation 

has been established between land-use, population 

density, and the concentrations of inorganic constituents. The following factors are presented as possible 

sources of inorganic contaminants. Available data are 

not sufficient to determine whether greater concentrations of inorganic constituents in water from urban 

wells are due to land use or to natural hydrogeologic 

factors.

Paved surfaces in urban areas increase runoff 

and decrease the amount of recharge to the aquifer. 

Decreasing recharge from precipitation, which is low 

in dissolved constituents, may lead to greater concentrations of dissolved constituents in ground water. In 

contrast, lawn watering, a common practice in residential areas in the Oklahoma City urban area, and irrigation of agricultural land from surface-water sources 

could increase recharge to the aquifer, facilitating 

transport, but also dilution of constituents leaching 

from the land surface to the water table.

Treated water from municipal treatment plants 

seeping to the aquifer from lawn watering or from 

leaking water lines in the urban areas is a possible 

source of ground-water contamination. Municipal 

supplies for Oklahoma City and Norman are primarily 

from surface-water reservoirs, whereas most of the 

smaller municipal supplies in the study unit rely on 

ground water. Concentrations of sodium, potassium, 

chloride, and sulfate from two of Oklahoma City's 

three surface-water reservoirs (Betty Fox, City of 

Oklahoma City, written commun., 1991) tend to be 

greater than background concentrations in the aquifer.

Leakage from sewer lines and effluent from 

septic tanks may contaminate ground water in the area. 

Oklahoma City does not measure concentrations of 

major cations and anions in water entering its sewage 

treatment plants, but chemists at the City's waste-water 

treatment plants believe the major-ion concentrations 

would resemble treated public-supply water because 

municipal water is the major component of residential

Pvoc

e

1.997 0.00107Dbuff ( ) – + + 1.2993U

1 + e

– 0.00107 1.997 Dbuff ( ) + + 1.2993U = -----------------------------------------------------------------------------
26 Relation of shallow water quality in the Central Oklahoma aquifer to geology, soils, and land use

BLOM RANKS

CUMULATIVE

P

ROB

A

BILI

T

Y

EXPLANATION

Observed

0.2

0.4

0.6

0.8

1.0

-3 -2 -1 01 2 3

Predicted

sewage (Willard Keith, City of Oklahoma City, oral 

commun., 1991).

Road salt is another possible source of sodium 

and chloride in ground water underlying the urban 

area. Oklahoma City uses a mixture of salt and sand on 

roads for several days during a typical winter. In recent 

years, the road salt has been purchased from a mine in 

Hutchinson, Kansas. The chemical composition of the 

salt is 95 to 97 percent sodium chloride, 2 to 4 percent 

calcium sulfate, 1 to 2 percent water insolubles, and 

less than 1 percent magnesium chloride, iron oxide, 

calcium chloride, magnesium sulfate, and moisture 

(Max Liby, Hutchinson Salt Company, written 

commun., 1991).

Fertilizer is a possible source of inorganic 

constituents in ground water beneath the urban or agricultural areas. Lawns and gardens in the Oklahoma 

City area typically are fertilized with chemical fertilizers, which commonly contain calcium, magnesium, 

sodium, potassium, and chloride (Brady, 1974). Phosphate in some fertilizers contains substantial quantities 

of uranium (Spalding and Sackett, 1972), which might 

increase uranium and gross-alpha particle activity in 

ground water beneath areas where fertilizers are 

applied. Information on fertilizer application is incomplete. Fertilizer sales data are available by county 

(Oklahoma Department of Agriculture, 1990), but 

those data may include fertilizer purchased in one 

Figure 12. Observed and predicted cumulative VOC detection probabilities for logistic regression models, 

including population density and the urban sampling network as explanatory variables.
 27

county and used in another county. Fertilizer sales data 

also may not account for sales of fertilizer through 

department stores and garden stores to urban and 

suburban homeowners.

Petroleum production activities comprise an 

additional source of possible contaminants of ground 

water in the study area which are not necessarily 

related to the three main categories of land use previously described in this report. Brines from oil and gas 

wells may contaminate ground water in the area. Oil 

and gas have been produced from approximately 

13,000 wells in the study unit since the beginning of 

the 20th century. The Oklahoma City oil field, located 

in the southeastern part of the urban area, is one of the 

10 largest oil fields in the conterminous United States 

(Landes, 1970). Most oil wells produce brines as well 

as hydrocarbons. At present (2001), these brines are 

reinjected into deep strata, generally through old 

production wells that have been converted into injection wells. In the past, these brines have been spread 

on the land surface or dumped in holding pits, potentially contaminating ground water. Many wells in the 

Oklahoma City urban area were drilled before stricter 

regulations regarding casing requirements and brine 

disposal were enacted. Median concentrations of 

major ions in brines produced from some oil and gas 

wells in Oklahoma County (Parkhurst and Christenson, 1987) are: pH, 6.3; alkalinity, 46.4 mg/L as 

CaCO3; calcium, 13,000 mg/L; magnesium, 2,510 

mg/L; sodium, 75,500 mg/L; chloride, 146,000 mg/L; 

sulfate, 232 mg/L; and dissolved solids 237,000 mg/L. 

Snavely (1989) reported that brines produced during 

hydrocarbon production are enriched in radium 

isotopes. Radium-228 concentrations in 113 wells in 

Oklahoma, Texas, and along the Gulf Coast ranged 

from 19 to 1,507 pCi/L, and radium-226 concentrations ranged from 0.1 to 1,620 pCi/L. Radium-226 is 

an alpha-particle emitter and is part of the uranium238 decay series, of which radon-222 is a by-product. 

Disposal of brines from deeper units, therefore, may 

increase concentrations of major ions and radionuclides in shallow ground water in the area.

Organic Compounds

Local use of organic compounds may increase 

their frequencies of detection in ground water. Field 

observations indicated that the organic compounds 

detected in ground water commonly had been used or 

applied to the land immediately surrounding wells, or 

were being stored in well houses. During the first year 

of sampling (1988), all wells producing water with 

detectable concentrations of pesticides or VOC's were 

revisited by field personnel to identify possible 

sources of those compounds. At about half of these 

wells, a likely source of the detected organic 

compounds was identified in the immediate vicinity of 

the wells. In many cases, the well owners had used the 

compound near the well or was storing substances 

containing these compounds near the wellhead. As 

discussed in the Sampling Biases section, the manner 

in which most of the sampled wells were constructed 

may have provided conduits for organic constituents to 

seep to the water table. Because domestic supply wells 

in the study unit generally are gravel packed for the 

entire depth of the well except for a 3-meter (10-foot) 

cement seal at the top, small quantities of water 

containing man-made organic compounds may enter 

the well through the gravel pack. Some of the wells in 

which organic compounds were detected were handdug wells with plywood or concrete covers and 

unmortared stone linings. In general, organic 

compounds were detected only at very low concentrations (less than 1 μg/L), when they were detected. At 

least some of the occurrences of organic compounds in 

ground water may be due to localized use or to spills, 

rather than indicating widespread, low concentrations 

of these compounds in the aquifers. Because only 

existing wells were sampled, the data are not sufficient 

to determine whether the organic compounds are 

widespread or very localized in ground water. Deeper 

well seals or more careful management of pesticides 

and VOC's in the immediate vicinity of wells might 

decrease the frequency of detection of these 

compounds in water from domestic wells in the study 

unit.

As there are no natural sources of the pesticide 

compounds analyzed, detection of these compounds in 

ground water is due to human use of those compounds 

at the land surface. Detection of pesticides in general, 

and of chlordane, dieldrin, and prometon in particular, 

were most frequent in water from wells in the urban 

sampling network. All of the detected pesticides have 

been used in the study unit. Other studies in Nevada 

(Lawrence and Whitney, 1990), Minnesota (Andrews 

and others, 1998); New York, Connecticut and Massachusetts (Hanchar and Grady, 1994); and Long Island 

(Eckhardt, Siwiec, and Cauller, 1989) have reported 

detecting some of the same pesticides in ground water 
28 Relation of shallow water quality in the Central Oklahoma aquifer to geology, soils, and land use

in urban areas. Although much attention has been 

directed to the agricultural use of pesticides, many 

pesticides are used in urban areas on residential lawns 

and gardens, along roads and utility corridors, as 

termaticides around buildings, and in houses and other 

buildings. Pesticide use may be greater in urban areas 

because the cost of pesticides is a less important 

economic factor for a home owner than a farmer. No 

data are available concerning the amount of pesticides 

used in central Oklahoma, but it is possible that more 

pesticides have been applied per acre in the urban area 

than in outlying agricultural areas, particularly 

termaticides such as chlordane. The fact that the 

frequency of pesticide detection in ground water in 

central Oklahoma was greater in the urban area than in 

the surrounding rural areas also may be related to the 

relatively low use of agricultural chemicals in the area. 

Battaglin and Goolsby (1995) published maps 

showing the use of agricultural chemicals, harvested 

cropland, and amounts of selected crops and animals 

grown by county for the conterminous United States. 

The maps indicate that, relative to the rest of the 

country, use of agricultural chemicals in central Oklahoma is low to moderate. Cattle, sorghum and wheat 

account for much of the agricultural activity in the 

area.

The organochlorine insecticides reported in 

water samples collected as part of this investigation 

have been used widely in central Oklahoma, although 

their use was declining as of 1995. Chlordane and 

dieldrin have been used to control termites, and DDE 

is a breakdown product of DDT, which was used 

extensively as an insecticide before 1973 (Sine, 1988).

Phenoxy-acid herbicides are widely used for 

weed control. Of the triazine herbicides, atrazine is an 

agricultural herbicide used to control annual grasses in 

corn and sorghum (Winkelmann and Klaine, 1991). 

Atrazine use in the study unit in 1992 ranged from 1-

5.6 pounds per square mile, in the lower range of use 

nationwide (U.S. Geological Survey, 1999a). During 

the period of sampling and before, atrazine was 

applied in amounts exceeding 66 pounds per square 

mile to the west in the Texas Panhandle, and to the 

north in Kansas. Investigations in 1995 in Minnesota 

(Don Goolsby, U.S. Geological Survey, written 

commun., 1996) indicated concentrations of atrazine 

approaching the drinking-water standard of 3 μg/L can 

occur in rainfall in urban areas, from volatilization and 

particulates entrained in the atmosphere. Prometon is a 

nonselective, long-lasting, pre- and post-emergent 

herbicide used to control perennial broadleaf weeds 

and grasses on right-of-ways (Sine, 1993). Prometon is 

relatively slow to degrade in soils, with an estimated 

half-life of 200-500 days (Rao and Alley, 1993; 

Weber, 1994). Prometon has a relatively low sorption 

coefficient to organic carbon in soils, making it more 

likely to leach to ground water than many other pesticide compounds (Weber, 1994).

Volatile organic compounds detected in water 

samples from these aquifers also are unlikely to be 

natural. Most of the VOC's reported in water samples 

are in use or have been used as solvents, propellants, 

fumigants, or in manufacturing processes. Styrene is a 

compound used in the manufacturing of many 

different types of plastics (Verschueren, 1983). Trihalomethanes, such as bromoform, chlorodibromomethane, dichlorobromomethane, and, in 

particular, chloroform, were the VOC's most 

commonly detected in water from urban wells. One 

common source of trihalomethanes is the chlorination 

of drinking water for city water supplies, which may 

be a component of recharge water in urban areas. Trihalomethanes form when chlorine or bromine react 

with dissolved organic carbon (Thurman, 1985). Water 

in the Oklahoma City distribution system has had total 

concentrations of trihalomethanes as great as 262 

micrograms per liter (μg/L) (Judith Duncan, Oklahoma State Department of Health, written commun., 

1990). The drinking water standard for the total 

concentration of trihalomethanes is 100 μg/L. Trihalomethanes also are associated with manufacturing, 

particularly the manufacturing of chlorofluorocarbon 

refrigerants and fire extinguishers (Verschueren, 

1983). The chlorofluorocarbon refrigerants dichlorodifluoromethane and trichlorofluoromethane are used or 

occur in air conditioners, refrigerators, sewage, and the 

atmosphere.

Gasoline from leaking underground storage 

tanks was expected to be a common contaminant of 

ground water in the urban area. The composition of 

gasoline varies among refiners, and has changed over 

time. Some of the VOC's detected in water samples 

collected in the urban area are present in some gasoline formulations, including 1,2-dibromoethane; 1,1-

dichloroethane; 1,2-dichloroethane; and toluene, but 

these compounds have many other uses as solvents, 

fumigants, and manufacturing agents (Lucius and 

others, 1989). Toluene is a major component in gasoline, accounting for as much as 30 percent by weight 

of gasoline (Kreamer and Stetzenbach, 1990), but is 
 29

also a common constituent in paint, asphalt, and adhesives (Verschueren, 1983). Benzene, ethylbenzene, 

and xylene also are major components of gasoline, but 

those compounds were not detected in any samples. 

Thus, it is not possible to determine if the fuel-component VOC's detected in ground-water samples in the 

urban area were related to gasoline.

CONCLUSIONS

Numerous statistical tests were used to identify, 

describe, and explain relations between selected 

natural and land-use factors and shallow water quality 

in the Central Oklahoma aquifer NAWQA study unit 

well networks: urban, Permian geologic units, and 

Quaternary geologic units.

Concentrations of many inorganic constituents 

were significantly related to geologic settings. 

However, no significant relations were determined 

between geologic settings and the detection of organic 

constituents (pesticides and VOC's), which was unexpected because the Permian geologic units are lithologically and hydrologically quite different from the 

Quaternary geologic units.

Estimated soil runoff curve number, soil-profile 

clay content, soil-profile vertical permeability, and 

soil-profile minimum permeability were compared to 

ground-water quality. Of the four soil properties, the 

estimated soil runoff curve number was most closely 

related to the concentrations of inorganic constituents. 

None of the four soil properties had statistically significant relations to the detection of organic constituents. 

Lack of significant relations between soil properties 

and detection of organic constituents was unexpected, 

because large differences in the mobility of organic 

constituents should occur between soils having 

different properties, such as clay content or permeability.

Sampling networks were designed to evaluate 

the effects of land use on the ground-water quality in 

the Oklahoma City urban area. However, differences 

in geologic units sampled by the three well networks 

induced sampling bias for inorganic constituents in the 

urban sampling network. This sampling bias does not 

apply to the organic compounds. Land use from the 

GIRAS land-use map was significantly related to VOC 

detection, but not to pesticide detection. Bufferoverlay analyses and particle-tracking analyses 

produced similar results in comparing land use with 

ground-water quality.

Logistic regression models indicated the probability of detecting pesticides or VOC's in a well significantly increased with increasing population density in 

the area near the well. Multivariate, stepwise logistic 

regression procedures indicated that the probability of 

pesticide occurrence could best be modeled using 

population density as the only explanatory variable. 

Similar analyses for VOC's indicated that the probability of VOC occurrence could best be modeled using 

population density and the urban network as explanatory variables.

The lack of significant relations between 

geologic settings and soil properties to frequencies of 

detection of pesticides or VOC's may have been due to 

use or storage of compounds near wellheads, either 

through back-flows into the wells or by seepage of 

these compounds with recharging water around the top 

shallow cement seals and through the underlying 

gravel packs surrounding well casings. Because water 

samples were only collected from existing watersupply wells, it is not clear whether pesticides and 

VOC's occur in low concentrations in ground water 

throughout the urban area, or whether pesticides and 

VOC's preferentially seep to ground water only in the 

immediate vicinity of wells.

Detection of pesticides in ground water has been 

considered to be most likely in agricultural areas, but 

in Central Oklahoma, detection of those compounds is 

related with greater population densities of urban 

areas. Greater frequencies of detection of pesticides in 

water from urban wells may be due to low to moderate 

use of agricultural chemicals on agricultural areas in 

the study unit, with most agricultural activity centered 

around livestock production.

REFERENCES

Andrews, W.J., Fong, A.L., Harrod, L., and Dittes, M.E., 

1998, Water-quality assessment of part of the Upper 

Mississippi River Basin, Minnesota and Wisconsin--

ground-water quality in an urban part of the Twin 

Cities Metropolitan Area, Minnesota, 1996: U.S. 

Geological Survey, Water-Resources Investigations 

Report 97-4248, 54 p.

Barringer, T., Dunn, D., Battaglin, W.A., and Vowinkel, E., 

1990, Problems and methods involved in relating land 
30 Relation of shallow water quality in the Central Oklahoma aquifer to geology, soils, and land use

use to ground-water quality: Water Resources Bulletin 

V. 26, no. 1, p. 1-9.

Battaglin, W.A., and Goolsby, D.A., 1995, Spatial data in 

geographic information system format on agricultural 

chemical use, land use, and cropping practices in the 

United States: U.S. Geological Survey, WaterResources Investigations Report 94-4176, 87 p. 

Bingham, R.H., and Moore, R.L., 1975, Reconnaissance of 

the water resources of the Oklahoma City quadrangle, 

central Oklahoma: Oklahoma Geological Survey, 

Hydrologic Atlas 4, 4 sheets.

Brady, N.C., 1974, The nature and properties of Soils, 8th 

edition: New York, MacMillan, 639 p.

Bureau of the Census, 1990a, TIGER/line Precensus Files.

——, 1990b, 1990 Census of Population and Housing, 

Public Law 94-171.

Christenson, S.C., Morton, R.B., and Mesander, B.A., 1992, 

Hydrogeologic maps of the Central Oklahoma aquifer, 

Oklahoma: U.S. Geological Survey, Hydrologic Investigations Atlas HA-724, 3 sheets.

Christenson, S.C., Parkhurst, D.L., and Breit, G.N., 1998, 

Summary of the geochemical and geohydrologic investigations of the Central Oklahoma aquifer: in Christenson, S.C., and Havens, J.S., ed., 1994, Groundwater quality assessment of the Central Oklahoma 

aquifer, Oklahoma: Results of investigations. U.S. 

Geological Survey, Water-Supply Paper 2357-A, p. 

107-177.

Christenson, S.C., and Parkhurst, D.L., 1987, Groundwater-quality assessment of the Central Oklahoma 

aquifer, Oklahoma: Project Description: U.S. Geological Survey, Open-File Report 87-235, 30. p.

Christenson, S.C., and Rea, A.H., 1993, Ground-water 

quality in the Oklahoma City urban area: in Alley, 

W.M., ed., 1993, Regional ground-water quality, New 

York, Van Nostrand Reinhold, p. 589-611.

Eckhardt, D.A., Siwiec, S.F., and Cauller, S.J., 1989, 

Regional appraisal of ground-water quality in five 

different land-use areas, Long Island, New York: in 

U.S. Geological Survey Toxic Substances Hydrology 

Program-Proceedings of the Technical Meeting, 

Phoenix, Arizona, September 26-30, 1988: U.S. 

Geological Survey Water-Resources, Investigations 

Report 88-4220, p. 397-403.

Fegeas, R.G., Claire, R.W., Guptill, S.C., Anderson, K.E., 

and Hallam, C.A., 1983, Land use and land cover 

digital data, U.S. Geological Survey, Circular 895-E, 

21 p.

Hanchar, D.W., and Grady, S.J., 1994, Effects of urban land 

use on shallow ground-water quality in stratified-drift 

aquifers--comparison of data from the Hudson River 

basin, New York, and the Connecticut River basin, 

Connecticut and Massachusetts (abstract): Eos, vol. 75, 

no. 16, p. 150.

Helsel, D.R., and Hirsch, R. M., 1992, Statistical methods in 

water resources: Amsterdam, Elsevier, 522 p.

Jorgensen, D.G., 1980, Relationships between basic soilsengineering equations and basic ground-water flow 

equations: U.S. Geological Survey, Water-Supply 

Paper 2064, 40 p.

Kreamer, D.K., and Stetzenbach, K.J., 1990, Development 

of a standard, pure-compound base gasoline mixture 

for use as a reference in field and laboratory experiments: Ground Water Monitoring Review, v. 10, no. 2, 

p. 135-145. 

Landes, K.K., 1970, Petroleum geology of the United 

States: New York, John Wiley, 571 p.

Lawrence, S.J., and Whitney, Rita, 1990, Shallow groundwater quality in the vicinity of a small urban area in 

west-central Nevada: Paper read at Nevada Water 

Resources Annual Conference, February 1990 in Las 

Vegas, Nevada. 

Lucius, J.E., Olhoeft, G.R., Hill, P.L., and Duke, S.K., 1989, 

Properties and hazards of 108 selected substances: 

U.S. Geological Survey, Open-File Report 89-491, 538 

p.

McDonald, M.G., and Harbaugh, A.W., 1988, A modular 

three-dimensional finite-difference ground-water flow 

model: U.S. Geological Survey, Techniques of WaterResources Investigations, book 6, chap. A1, 586 p.

Oklahoma Department of Agriculture, 1990, Tonnage distribution of fertilizer in Oklahoma counties by grade and 

material, annual, for the period July 1, 1989 to July 1, 

1990: Oklahoma City, Oklahoma Department of Agriculture, 15 p.

P-STAT, Inc. 1989, P-STAT user's manual: Princeton, PSTAT, Inc. 

Parkhurst, R.S., and Christenson, S.C., 1987, Selected 

chemical analyses of water from formations of Mesozoic and Paleozoic age in parts of Oklahoma, northern 

Texas, and Union County, New Mexico: U.S. Geological Survey, Water-Resources Investigations Report 86-

4355, 222 p.

Pollock, D.W., 1989, Documentation of computer programs 

to compute and display pathlines using results from the 

U.S. Geological Survey modular three-dimensional 

finite-difference ground-water flow model: U.S. 

Geological Survey, Open-File Report 89-381, 188 p.

Rao, P.S.C., and Alley, W.M., 1993, Pesticides, in Alley, 

W.M., ed., Regional ground-water quality: Van 

Nostrand Reinhold, New York, p. 345-382.

SAS Institute, Inc., 1990, SAS/STAT Users Guide, Version 

6, Fourth Edition, volume 2: Cary, North Carolina, 795 

p. 

Sine, Charlotte, ed., 1988, Farm Chemicals Handbook `88: 

Meister Publishing Company, Willoughby, Ohio, variously paged.
 31

Sine, Charlotte, ed., 1993, Farm Chemicals Handbook `93: 

Meister Publishing Company, Willoughby, Ohio, variously paged.

Snavely, E.S., 1989, Radionuclides in produced water, a 

literature review: Dallas, American Petroleum Institute, 163 p.

Soil Conservation Service, 1960, Soil survey of Logan 

County, Oklahoma.

——, 1969, Soil survey of Oklahoma County, Oklahoma.

——, 1970, Soil survey of Lincoln County, Oklahoma.

——, 1977, Soil survey of Pottawatomie County, Oklahoma.

——, 1987a, Soil survey of Cleveland County, Oklahoma.

——, 1987b, Soil survey of Payne County, Oklahoma.

——, 1989, Engineering field manual, Chapter 2: Estimating runoff and peak discharges: U.S. Department of 

Agriculture, variously paged.

Spalding, R.F., and Sackett, W.M., 1972, Uranium in runoff 

from the Gulf of Mexico distributive province: Anomalous concentrations: Science v. 175, p. 629-631.

Thurman, E.M. 1985, Organic chemistry of natural waters: 

Dordrecht, Martinus Nijhoff/Dr. W. Junk Publishers, 

497 p.

Tukey, J.W., 1977, Exploratory Data Analysis: AddisonWesley Pub., Reading, Massachusetts, 506 p.

U.S. Geological Survey, 1999a, Atrazine--estimated annual 

agricultural use: U.S. Geological Survey, National 

Water-Quality Assessment Pesticide National 

Synthesis Project: accessed at 

http://water.wr.usgs.gov/pnsp/use92/atrazin.html on 

April 23, 1999. 

Verschueren, Karel, 1983, Handbook of environmental data 

on organic chemicals: New York, Van Nostrand Reinhold, 1,310 p.

Weber, J.B., 1994, Properties and behavior of pesticides in 

soil, in Honeycutt, R.C., and Schabacker, D.J., eds., 

Mechanisms of pesticide movement into ground water: 

Lewis Publishers, Boca Raton, Fla., p. 15-41.

Winkelmann, D.A., and Klaine, S.J., 1991, Atrazine metabolite behavior in soil-core microcosms--Formation, 

disappearance, and bound residues, in Somasundarum, 

L., And Coats, J.R., eds., Pesticide transformation 

products--Fate and significance in the environment: 

American Chemical Society, Symposium Series 59, 

Washington, D.C., p. 75-92.