U.S. Department of the Interior

U.S. Geological Survey

Estimating the Susceptibility of Surface Water in

Texas to Nonpoint-Source Contamination

by Use of Logistic Regression Modeling

Water-Resources Investigations Report 03–4205 
Estimating the Susceptibility of Surface Water 

in Texas to Nonpoint-Source Contamination 

by Use of Logistic Regression Modeling

By William A. Battaglin, Randy L. Ulery, Thomas Winterstein, and Toby Welborn

Water-Resources Investigations Report 03–4205

U.S. Department of the Interior

U.S. Geological Survey
Any use of trade, product, or firm names in this publication

is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

U.S. Department of the Interior

Gale A. Norton, Secretary

U.S. Geological Survey

Charles G. Groat, Director
Contents iv

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

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

Texas Source Water Assessment Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

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

Surface-Water Nonpoint-Source Component . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Surface-Water Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Water-Quality Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Watershed-Characteristics Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Logistic Regression Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Estimating the Susceptibility of Surface Water to Nonpoint-Source Contamination. . . . . . . . . . . . . . . . . . . . . . . . . . 12

Summary of Modeled Contaminants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Examples of Modeling Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Figures

1. Map showing summary of (A) dissolved manganese monitoring data and 

(B) the results of a logistic regression model preducting the probability of manganese 

occurrence at or greater than 75 micrograms per liter in Texas surface water . . . . . . . . . . . . . . . . . . . . 13

2. Map showing summary of (A) dissolved simazine monitoring data and 

(B) the results of a logistic regression model predicting the probability of simazine 

occurrence at or greater than 0.01 microgram per liter in Texas surface water. . . . . . . . . . . . . . . . . . . . 14

Tables

1. Chemical class, threshold concentration, and model status for 227 surface-water 

contaminants and water-quality properties in Texas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

2. Watershed characteristics used in surface-water contaminant occurrence logistic 

regression models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3. Logistic regression-model equations and model-fit statistics for selected surface-water 

nonpoint-source contaminants in Texas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

CONVERSION FACTORS, VERTICAL DATUM, AND ABBREVIATIONS

MISCELLANEOUS ABBREVIATIONS AND ABBREVIATED WATER-QUALITY UNITS

meter (m) 

microgram per liter (μg/L)

microsiemens per centimeter at 25 degrees Celsius (μS/cm at 25o C)

milligram per liter (mg/L)

milliliter (mL)

picocuries per liter (pCi/L)

MISCELLANEOUS DEFINITIONS

Point source - discharge of a known contaminant from a specific location such as a pipe or smokestack.

Nonpoint source - discharge of a known contaminant from a diffuse source such as a field or urban area.

Relative susceptibility rating - a categorical rating of high, medium, or low that is assigned after analysis of the 

risk data or other information.

Multiply By To obtain

foot (ft) 0.3048 meter

mile (mi) 1.609 kilometer

square mile (mi2) 2.590 square kilometer (km2)
Estimating the Susceptibility of Surface Water in Texas to 

Nonpoint-Source Contamination by Use of Logistic 

Regression Modeling

By William A. Battaglin, Randy L. Ulery, Thomas Winterstein, and Toby Welborn

Abstract

In the State of Texas, surface water (streams, canals, and 

reservoirs) and ground water are used as sources of public water 

supply. Surface-water sources of public water supply are susceptible to contamination from point and nonpoint sources. To 

help protect sources of drinking water and to aid water managers in designing protective yet cost-effective and risk-mitigated 

monitoring strategies, the Texas Commission on Environmental 

Quality and the U.S. Geological Survey developed procedures 

to assess the susceptibility of public water-supply source waters 

in Texas to the occurrence of 227 contaminants. One component of the assessments is the determination of susceptibility of 

surface-water sources to nonpoint-source contamination. To 

accomplish this, water-quality data at 323 monitoring sites were 

matched with geographic information system-derived watershed-characteristic data for the watersheds upstream from the 

sites. Logistic regression models then were developed to estimate the probability that a particular contaminant will exceed a 

threshold concentration specified by the Texas Commission on 

Environmental Quality. Logistic regression models were developed for 63 of the 227 contaminants. Of the remaining contaminants, 106 were not modeled because monitoring data were 

available at less than 10 percent of the monitoring sites; 29 were 

not modeled because there were less than 15 percent detections 

of the contaminant in the monitoring data; 27 were not modeled 

because of the lack of any monitoring data; and 2 were not modeled because threshold values were not specified.

Introduction

The 1996 Amendment to the Safe Drinking Water Act 

(U.S. Environmental Protection Agency, 2003) requires that 

each State prepare a source-water assessment for all public 

water supplies (PWS). For the assessment, States are required 

to identify PWS sources, which contaminants are monitored, 

which contaminants should be monitored, and the intrinsic susceptibility of the source water to contamination. The susceptibility of PWS source water is defined as the potential for a PWS 

to withdraw water containing a listed contaminant at a concentration that could pose a public health concern. Surface-water 

sources of public water supply (streams, canals, and reservoirs) 

are susceptible to contamination from point (discrete) and nonpoint (spatially diffuse) sources. 

Texas Source Water Assessment Project

The Texas Commission on Environmental Quality 

(TCEQ) and the U.S. Geological Survey (USGS) developed 

procedures to assess the susceptibility of PWS source waters to 

selected drinking-water contaminants. The procedures as 

implemented in automated software will better enable TCEQ 

staff and others to (1) identify the most susceptible PWS’s on 

which to focus source-water protection efforts, (2) reduce monitoring costs associated with ensuring safe drinking water, 

(3) enhance public understanding of source-water protection, 

and (4) identify land-management practices needed to protect 

source waters (Ulery, 2000; http://www.tnrcc.state.tx.us/

permitting/waterperm/pdw/swap/, accessed 8/1/2003). 

A surface-water susceptibility assessment for a PWS has 

eight components. Several of the components result in relative 

susceptibility ratings of high, medium, or low. These ratings are 

designed to support an overall susceptibility rating for the PWS 

sources and system. The components are: 

1. Identification Component—determination of hydrologic 

attributes (for example, topography and drainage network) used to delineate the area(s) that potentially contribute source water for a PWS. 

2. Delineation Component—delineation of PWS source 

area(s) based on attributes determined by the 

identification component.

3. Intrinsic Susceptibility Component—determination of 

the intrinsic attributes (for example, soil erodibility or 

mean annual runoff) within PWS source areas and 

evaluation of their potential to make the PWS more or 

less susceptible to contamination. This component 

produces a relative susceptibility rating for all 

contaminants. 
2 Estimating the Susceptibility of Surface Water in Texas to Nonpoint-Source Contamination by Use of Logistic Regression 

Modeling

4. Nonpoint-Source Susceptibility Component—

determination of statistical relations between measured 

contaminants in surface water and watershed 

characteristics (for example, land use or pesticide use) 

within PWS source areas. Statistical model results will 

determine the probability that a drinking-water 

contaminant will exceed a TCEQ-specified threshold 

concentration. In cases of few or no data, a relative 

susceptibility rating is assigned and is based on the 

frequency of contaminant detection or level of land-use 

activities within the PWS source area associated with 

contaminant production or use.

5. Point-Source Susceptibility Component—

determination of potential point sources of contaminants 

to the PWS. A relative susceptibility rating is produced 

for each contaminant and is based on the permitted 

discharges from known point-source locations (for 

example, oil refineries). Estimates of contaminant 

concentrations at low streamflow will be calculated from 

permitted releases from point sources within the PWS 

source area. A relative susceptibility rating is assigned to 

each contaminant and is based on the ratio of total 

permitted releases to mean annual streamflow. 

6. Area of Primary Influence Component—determination 

of the density and proximity of threatening human 

activities (for example, pipeline or railroad) and 

associated contaminants within a limited area (2-hour 

travel or 1,000-foot buffer) around the PWS surfacewater intake (or reservoir). A relative susceptibility 

rating is produced for each contaminant and is based on 

number, type, size, and location of threatening activities 

in the PWS source area. 

7. Contaminant Occurrence Component—determination 

of contaminant detections (at or greater than TCEQspecified threshold concentrations) at water-quality 

monitoring sites or other nearby PWS. A relative 

susceptibility rating of “high” is assigned to the 

contaminant if it has been detected greater than the 

threshold concentration at a nearby site. No relative 

susceptibility rating is assigned to the contaminant if it 

has not been detected greater than the contaminant 

threshold concentration.

8. Susceptibility Summary Component—combination of 

the seven components into a summary relative 

susceptibility rating for each PWS source and a single 

summary rating for the PWS.

Purpose and Scope

The purpose of this report is to describe the data, process, 

and criteria used to estimate the susceptibility of surface water 

in Texas to nonpoint-source contamination by use of logistic 

regression models. The nonpoint-source susceptibility component (component number 4 above) of PWS susceptibility 

assessments in Texas is addressed by estimating the probability 

that specified drinking-water contaminants will occur in surface 

water at or greater than a threshold concentrations specified by 

the TCEQ. This report focuses on the contaminants for which 

models are developed and does not discuss in detail how relative susceptibility ratings are assigned to those contaminants 

that are not modeled. The models developed are specific to 

Texas, but similar methods could be applied in other locations 

if sufficient monitoring data are available.

Surface-Water Nonpoint-Source 

Component

The nonpoint-source susceptibility component (component 4) is addressed statistically by investigating the relations 

between known contaminant occurrences in surface water and 

watershed characteristics, either natural or anthropogenic, 

quantified for the PWS contributing source areas (watersheds). 

Where monitoring data are sufficient in the State, logistic 

regression models quantify the relations and provide a means to 

transfer results statistically from monitored sites to unmonitored PWS intakes. For this analysis, “sufficient” is defined as 

50 or more contaminant analyses from 10 percent or more of the 

monitoring sites with at least 15 percent of sites having contaminant detections at or greater than the TCEQ-specified threshold 

concentration. The statistical analysis produces logistic regression models that estimate the probability (p) of contaminant 

detection at or greater than the TCEQ-specified threshold concentration. These probability values are used to assign a relative 

susceptibility rating as follows:

Susceptibility rating is high if p Contaminant > 0.67

Susceptibility rating is medium if 0.33 < p Contaminant < 0.67

Susceptibility rating is low if p Contaminant < 0.33

In instances of few or no monitoring data, the nonpointsource relative susceptibility rating is assigned on the basis of 

either the frequency of contaminant detections or the level of 

land-use activities associated with contaminant production or 

use. When there are less than 50 samples with analyses for the 

contaminant or those analyses are from less than 10 percent of 

sites, then a relative susceptibility rating is assigned on the basis 

of the detection frequency of the contaminant in those samples. 

When there are few or no monitoring data available for a particular contaminant, a relative susceptibility rating is assigned on 

the basis of the prevalence of land uses or practices that are 

likely to be associated with the use or occurrence of the contaminant. This report does not discuss in detail how relative susceptibility ratings are assigned to those contaminants that are not 

modeled with logistic regression.
Surface-Water Nonpoint-Source Component 3

Surface-Water Sites

The USGS maintains a surface-water data-collection network in Texas that has more than 500 sites on rivers and reservoirs (Gandara and Barbie, 2001). The majority of data collected at these sites are related to streamflow, but some waterquality data are available at many sites. For the purposes of this 

report, a set of 323 sites was selected to represent surface-water 

quality in Texas. Most sites are at USGS gages, but some are 

PWS reservoirs that were sampled for selected contaminants in 

1999 and 2000, and some are sites sampled by the TCEQ. The 

selected sites are required to have some water-quality data associated with them, and then they are selected to maximize spatial 

coverage of Texas.

Water-Quality Data

A total of 227 drinking-water contaminants (for example, 

lead) and water-quality properties (for example, pH) are identified by the TCEQ as the subjects for this report (table 1). Most 

of the 227 contaminants are not naturally occurring (for example, pesticides like atrazine) but some (for example, iron) are, 

and some (for example, pH) are really water-quality properties 

and not contaminants. The contaminants or properties can be 

grouped into nine broad classes: inorganics, major ions, metals, 

microbiologicals, nutrients, organics (other than pesticides), 

pesticides, physical properties, and radionuclides (table 1). 

All water-quality data collected in the past 20 years associated with the 323 surface-water sites from the USGS and 

TCEQ databases are aggregated into a single database of 

approximately 2 million records. Duplicate data records and 

records reported with a remark code that questioned data reliability are eliminated. The data then are used to assign a contaminant occurrence code to each site for each contaminant. The 

codes are assigned as follows:

–1 = the contaminant was never analyzed for at the site;

 0 = the contaminant was analyzed for, but never detected 

at the site;

 1 = the contaminant was analyzed for and detected, but 

always at a concentration less than the TCEQ-specified 

threshold; and 

 2 = the contaminant was analyzed for and detected one or 

more times at a concentration equal to or greater than the threshold. 

This coding process results in a binary response (yes = 2 or 

no =1 , 0, or –1) to the question, “Has contaminant A occurred 

at or greater than concentration B at site C?” This binary 

response is the dependent variable in the logistic regression 

models. Initial contaminant threshold concentrations were provided by the TCEQ. These initial thresholds occasionally are 

modified to provide a more suitable data set for logistic 

regression.

Watershed-Characteristics Data

Data on land use, soil characteristics, population, agricultural chemical use, manure production, and land and stream 

physiography (Brown and others, 2000) are quantified within 

the watersheds of the 323 surface-water sites by using a geographic information system (GIS). Most of the resulting watershed characteristics are expressed as either a percentage of the 

basin areas (for example, percent land use 41 – deciduous forest) or as a basin-area averaged input (for example, atrazine use 

rate in kilograms per square kilometer). Land-cover characteristics are derived from the 1992 Multi-Resolution Landscape 

Characterization (MRLC) data set, classified in accordance 

with the 1992 National Land Cover Dataset (NLCD) land-cover 

definitions (U.S. Geological Survey, 2000). Average soil characteristics are derived by area weighting soils data (vertically 

and horizontally) from the U.S. Department of Agriculture 

(USDA), Natural Resources Conservation Service State Soil 

Geographic Data Base (STATSGO) (U.S. Department of Agriculture, 1995). Population density estimates are computed from 

1990 Census Blocks (U.S. Department of Commerce, 1990), 

and oil- and gas-well density estimates are obtained from the 

TCEQ Potential Sources of Contamination (PSOC) database. 

Agricultural chemical use, fertilizer use, and manure-production estimates are calculated from various sources (Battaglin 

and Goolsby, 1994; Goolsby and others, 1999). Land and 

stream physiography are derived from the National Elevation 

Dataset (NED) digital elevation model (DEM) data (Brown and 

others, 2000). Watershed characteristics are computed for individual basins by overlaying drainage basin boundaries on all 

nonpoint-source data sets and computing the spatially averaged 

values with the GIS.

Subsequently, all GIS-derived watershed characteristics 

are statistically tested for their ability to predict the occurrence 

of the 227 contaminants using logistic regression. A total of 

25 land use, 6 soils, 2 population, 44 agricultural chemical use, 

2 manure production, and 5 land and stream physiography variables are tested for potential use in the logistic regression models. All of the watershed characteristics have approximately 

normal distributions, so the values are not transformed to 

improve their statistical properties. The 39 characteristics listed 

in table 2 are those used in one or more of the logistic regression 

models developed.

Many of the watershed characteristics are related to or correlated (collinear) with each other. For example, the percentage 

of high-intensity residential land in the 323 watersheds is correlated with population density in the watersheds, having a Pearson product-moment correlation (Helsel and Hirsch, 1992) of 

0.86. Including strongly correlated explanatory variables in 

multivariate statistical models can have undesirable consequences in regression equations such as coefficients with unrealistic signs or unstable values (Helsel and Hirsch, 1992). For 

the results presented in this report, if the correlation between 

two watershed characteristics was 0.7 or greater, those two 

characteristics were not included in the same regression model.
4 Estimating the Susceptibility of Surface Water in Texas to Nonpoint-Source Contamination by Use of Logistic Regression 

Modeling

Table 1. Chemical class, threshold concentration, and model status for 

227 surface-water contaminants and water-quality properties in Texas.

[ND, not modeled - no data; LA, not modeled ñ limited analyses; MD, logistic models 

developed; LD, not modeled ñ limited detections; NT, not modeled ñ no threshold specified; 

μg/L, micrograms per liter; pCi/L, picocuries per liter; mg/L, milligrams per liter; μS/cm, 

microsiemens per centimeter at 25 degrees Celsius; mL, milliliter]

Contaminant or

property

Threshold

concentration

Model

status

Inorganics

Asbestos 1.0 μg/L ND

Hydrogen sulfide 1.0 mg/L ND

RDX 0.01 μg/L ND

Major Ions

Alkalinity 250 mg/L MD

Bicarbonate 200 mg/L MD

Bromide 1.0 μg/L LA

Calcium 100 mg/L MD

Carbonate 1.0 mg/L MD

Chloride 125 mg/L MD

Cyanide 0.01 μg/L LA

Fluoride 1.0 mg/L MD

Hardness 300 mg/L MD

P-alkalinity 1.0 mg/L ND

Sodium 75 mg/L MD

Sulfate 100 mg/L MD

Metals

Aluminum 100 μg/L MD

Antimony 0.01 μg/L MD

Arsenic 5.0 μg/L MD

Barium 200 μg/L MD

Beryllium 1.0 μg/L MD

Boron 200 μg/L MD

Cadmium 5.0 μg/L MD

Chromium 15 μg/L MD

Copper 20 μg/L MD

Iron 150 μg/L MD

Lead 25 μg/L MD

Magnesium 20 mg/L MD

Manganese 75 μg/L MD

Mercury 0.5 μg/L MD

Nickel 10 μg/L MD

Selenium 2.0 μg/L MD

Silver 2.0 μg/L MD

Thallium 0.1 μg/L LA

Zinc 50 μg/L MD

Microbiologicals

Cryptosporidium parvum None ND

Escherichia Coli None ND

Fecal virus None ND
Surface-Water Nonpoint-Source Component 5

Microbiologicals—Continued

Giardia lamblia None ND

Total coliform 20,000/100 mL MD

Nutrients

Nitrate 2.0 mg/L MD

Nitrate + nitrite 2.0 mg/L MD

Nitrite 0.1 mg/L MD

Organics

1,1,1,2-tetrachloroethane 0.1 μg/L ND

1,1,1-trichloroethane 0.1 μg/L LA

1,1,2,2-tetrachloroethane 0.1 μg/L LA

1,1,2-trichloroethane 0.1 μg/L LA

1,1-dichloroethane 0.1 μg/L LA

1,1-dichloroethylene 0.1 μg/L LA

1,1-dichloropropene 0.1 μg/L LA

1,2,3-trichlorobenzene 0.1 μg/L LA

1,2,3-trichloropropane 0.1 μg/L LA

1,2,4-trichlorobenzene 0.1 μg/L LA

1,2,4-trimethylbenzene 0.1 μg/L LA

1,2-dichloroethane 0.1 μg/L LA

1,2-dichloropropane 0.1 μg/L LA

1,2-diphenylhydrazine 0.1 μg/L LA

1,3,5-trimethylbenzene 0.1 μg/L LA

1,3- dichlorobenzene 0.1 μg/L LA

1,3- dichloropropane 0.1 μg/L LA

2,2- dichloropropane 0.1 μg/L LA

2,3,7,8-TCDD (dioxin) 0.1 μg/L ND

2,4,6- trichlorophenol 0.1 μg/L LA

2,4-dichlorophenol 0.1 μg/L LA

2,4-dinitrophenol 0.1 μg/L LA

2,4-dinitrotoluene 0.1 μg/L LA

2,6-dinitrotoluene 0.1 μg/L LA

2-chlorotoluene 0.1 μg/L LA

2-hexanone 0.1 μg/L LA

2-methylphenol 0.1 μg/L ND

4-chlorotoluene 0.1 μg/L LA

4-isopropyltoluene 0.1 μg/L LA

4-methyl-2-pentanone 0.1 μg/L LA

Acenaphthene 0.1 μg/L LA

Table 1. Chemical class, threshold concentration, and model status for 

227 surface-water contaminants and water-quality properties in Texas.—Continued

[ND, not modeled - no data; LA, not modeled ñ limited analyses; MD, logistic models 

developed; LD, not modeled ñ limited detections; NT, not modeled ñ no threshold specified; 

μg/L, micrograms per liter; pCi/L, picocuries per liter; mg/L, milligrams per liter; μS/cm, 

microsiemens per centimeter at 25 degrees Celsius; mL, milliliter]

Contaminant or

property

Threshold

concentration

Model

status
6 Estimating the Susceptibility of Surface Water in Texas to Nonpoint-Source Contamination by Use of Logistic Regression 

Modeling

Organics—Continued

Acenaphthylene 0.1 μg/L LA

Acetone 0.01 μg/L LA

Acrylonitrile 0.01 μg/L LA

Anthracene 0.1 μg/L LA

Aroclor (pcb) 0.1 μg/L ND

Benzene 0.01 μg/L MD

Benzo(A)pyrene 0.01 μg/L LA

Benzo(A)anthracene 0.01 μg/L LA

Benzo(B)fluoranthene 0.01 μg/L LA

Benzo(G,H,I)perylene 0.01 μg/L LA

Benzo(K)fluoranthene 0.01 μg/L LA

Bromobenzene 0.01 μg/L LA

Bromochloromethane 0.01 μg/L LA

Bromodichloro-methane 0.01 μg/L LA

Bromoform 0.01 μg/L LA

Butyl benzyl phthalate 0.01 μg/L LA

Carbon tetrachloride 0.01 μg/L LA

Chlorobenzene 0.01 μg/L LA

Chloroethane 0.01 μg/L LA

Chloroform 0.1 μg/L LA

Chloromethane (methyl chloride) 0.01 μg/L LA

Chrysene 0.1 μg/L LA

Cis-1,2-dichloroethylene 0.1 μg/L LA

Cis-1,3-dichloropropene 0.1 μg/L LA

Di-(2-ethylhexyl) adipate 0.01 μg/L ND

Di-(2-ethylhexyl) phthalate 0.01 μg/L ND

Dibenz(A,H)anthracene 0.1 μg/L LA

Dibromochloro-methane 0.1 μg/L LA

Dibromochloro-propane 0.1 μg/L LA

Dibromomethane 0.1 μg/L LA

Dichlorodifluoro-methane 0.1 μg/L LA

Diethyl phthalate 0.1 μg/L LA

Dimethyl phthalate 0.1 μg/L LA

Di-N-butyl phthalate 0.1 μg/L LA

Ethyl methacrylate 0.1 μg/L LA

Ethylbenzene 0.1 μg/L LD

Ethylene dibromide 0.1 μg/L LA

Fluorine 0.1 μg/L LA

Hexachlorobenzene 0.1 μg/L LA

Table 1. Chemical class, threshold concentration, and model status for 

227 surface-water contaminants and water-quality properties in Texas.—Continued

[ND, not modeled - no data; LA, not modeled ñ limited analyses; MD, logistic models 

developed; LD, not modeled ñ limited detections; NT, not modeled ñ no threshold specified; 

μg/L, micrograms per liter; pCi/L, picocuries per liter; mg/L, milligrams per liter; μS/cm, 

microsiemens per centimeter at 25 degrees Celsius; mL, milliliter]

Contaminant or

property

Threshold

concentration

Model

status
Surface-Water Nonpoint-Source Component 7

Organics—Continued

Hexachlorobutadiene 0.1 μg/L LA

Hexachlorocyclo-pentadiene 0.1 μg/L LA

Indeno(1,2,3,CD)pyrene 0.1 μg/L LA

Iodomethane (methyl iodide) 0.1 μg/L LA

Isopropylbenzene 0.1 μg/L LA

M+P xylene 0.1 μg/L MD

Methyl ethyl ketone 0.1 μg/L LA

Methyl methacrylate 0.1 μg/L LA

Methyl-tert-butyl ether (MTBE) 0.2 μg/L MD

Monochlorobenzene 0.01 μg/L LA

M-xylene 0.1 μg/L MD

Naphthalene 0.1 μg/L LA

N-butylbenzene 0.1 μg/L LA

Nitrobenzene 0.1 μg/L LA

N-propylbenzene 0.1 μg/L LA

Orth-1,2-dichlorobenzene 0.1 μg/L LA

O-xylene 0.01 μg/L MD

Para-1,4-dichlorobenzene 0.1 μg/L LA

PCBs 0.01 μg/L MD

Pentachlorophenol 0.1 μg/L LA

Perchlorate 0.01 μg/L ND

Phenanthrene 0.01 μg/L LA

P-xylene 0.1 μg/L MD

Pyrene 0.1 μg/L LA

S-butylbenzene 0.01 μg/L LA

Styrene 0.01 μg/L LA

T-butylbenzene 0.1 μg/L LA

Tetrachloro-ethylene 0.1 μg/L LA

Tetrahydrofuran 0.1 μg/L LA

Toluene 0.01 μg/L MD

Total trihalomethane 0.01 μg/L ND

Trans-1,2-dichloroethylene 0.1 μg/L LA

Trans-1,3-dichloropropene 0.1 μg/L LA

Trichloroethylene 0.1 μg/L LA

Trichlorofluoro-methane 0.1 μg/L LA

Vinyl acetate 0.1 μg/L LA

Vinyl chloride 0.1 μg/L LA

Xylenes (total) 0.1 μg/L MD

Table 1. Chemical class, threshold concentration, and model status for 

227 surface-water contaminants and water-quality properties in Texas.—Continued

[ND, not modeled - no data; LA, not modeled ñ limited analyses; MD, logistic models 

developed; LD, not modeled ñ limited detections; NT, not modeled ñ no threshold specified; 

μg/L, micrograms per liter; pCi/L, picocuries per liter; mg/L, milligrams per liter; μS/cm, 

microsiemens per centimeter at 25 degrees Celsius; mL, milliliter]

Contaminant or

property

Threshold

concentration

Model

status
8 Estimating the Susceptibility of Surface Water in Texas to Nonpoint-Source Contamination by Use of Logistic Regression 

Modeling

Pesticides

1,3- dichloropropene 0.1 μg/L LA

2,4,5-T 0.01 μg/L MD

2,4,5-TP (silvex) 0.01 μg/L MD

2,4-D 0.01 μg/L MD

3-hydroxycarbofuran 0.1 μg/L LD

Acetochlor 0.01 μg/L LD

Alachlor 0.01 μg/L MD

Aldicarb 0.01 μg/L LD

Aldicarb sulfone 0.01 μg/L LD

Aldicarb sulfoxide 0.01 μg/L LD

Aldrin 0.01 μg/L MD

Atrazine 0.1 μg/L MD

Bentazon 0.01 μg/L LD

Bromacil 0.01 μg/L LD

Bromomethane (methyl bromide) 0.01 μg/L LA

Butachlor 0.01 μg/L LA

Carbaryl 0.01 μg/L MD

Carbofuran 0.01 μg/L LD

Carbon disulfide 0.01 μg/L LA

Chlordane 0.01 μg/L MD

Cis-chlordane (alpha-chlordane) 0.01 μg/L LA

Trans-chlordane (gamma-chlordane) 0.01 μg/L LA

Chlordane (trans-nonachlor) 0.01 μg/L ND

Cyanazine 0.01 μg/L LD

Dalapon 0.01 μg/L ND

DCPA di-acid degradate 0.001 μg/L MD

DCPA mono-acid degradate 0.01 μg/L LD

DDE 0.001 μg/L MD

Diazinon 0.01 μg/L MD

Dicamba 0.01 μg/L LD

Dichloromethane (methylene chloride) 0.1 μg/L LA

Dieldrin 0.01 μg/L MD

Dinoseb 0.01 μg/L LD

Diquat 0.01 μg/L ND

Disulfoton 0.01 μg/L LD

Diuron 0.01 μg/L MD

Endothall 0.01 μg/L ND

Endrin 0.01 μg/L LD

EPTC 0.01 μg/L LD

Table 1. Chemical class, threshold concentration, and model status for 

227 surface-water contaminants and water-quality properties in Texas.—Continued

[ND, not modeled - no data; LA, not modeled ñ limited analyses; MD, logistic models 

developed; LD, not modeled ñ limited detections; NT, not modeled ñ no threshold specified; 

μg/L, micrograms per liter; pCi/L, picocuries per liter; mg/L, milligrams per liter; μS/cm, 

microsiemens per centimeter at 25 degrees Celsius; mL, milliliter]

Contaminant or

property

Threshold

concentration

Model

status
Surface-Water Nonpoint-Source Component 9

Pesticides—Continued

Fonofos 0.01 μg/L LD

Glyphosate 0.01 μg/L LA

Heptachlor 0.01 μg/L MD

Heptachlor epoxide 0.01 μg/L MD

Lambast 0.01 μg/L LA

Lindane 0.01 μg/L MD

Linuron 0.01 μg/L LD

Methiocarb 0.01 μg/L LD

Methomyl 0.01 μg/L LD

Methoxychlor 0.01 μg/L LD

Metolachlor 0.01 μg/L MD

Metribuzin 0.01 μg/L MD

Molinate 0.01 μg/L LD

Organotins 0.1 μg/L ND

Oxamyl 0.1 μg/L LD

Picloram 0.01 μg/L LD

Prometon 0.01 μg/L MD

Propachlor 0.01 μg/L LA

Propazine 0.01 μg/L LD

Simazine 0.01 μg/L MD

Terbacil 0.01 μg/L LD

Terbufos 0.01 μg/L LD

Toxaphene 0.01 μg/L LD

Triazines None NT

Trifluralin 0.001 μg/L LD

Physical Properties

pH None NT

Specific conductance 1,500 μS/cm at 25oC MD

Total dissolved solids 500 mg/L MD

Radionuclides

Gross Alpha 1.0 pCi/L LA

Gross Beta 1.0 pCi/L LA

Radium-226 0.1 pCi/L LA

Radium-228 0.1 pCi/L ND

Radon 0.1 pCi/L ND

Strontium-89 1.0 pCi/L ND

Strontium-90 1.0 pCi/L ND

Total alpha-emitting radium 1.0 pCi/L ND

Tritium 1.0 pCi/L ND

Uranium 2.0 μg/L MD

Table 1. Chemical class, threshold concentration, and model status for 

227 surface-water contaminants and water-quality properties in Texas.—Continued

[ND, not modeled - no data; LA, not modeled ñ limited analyses; MD, logistic models 

developed; LD, not modeled ñ limited detections; NT, not modeled ñ no threshold specified; 

μg/L, micrograms per liter; pCi/L, picocuries per liter; mg/L, milligrams per liter; μS/cm, 

microsiemens per centimeter at 25 degrees Celsius; mL, milliliter]

Contaminant or

property

Threshold

concentration

Model

status
10 Estimating the Susceptibility of Surface Water in Texas to Nonpoint-Source Contamination by Use of Logistic Regression 

Modeling

Table 2. Watershed characteristics used in surface-water contaminant occurrence logistic regression 

models.

Variable Definition

p21 Percent land use 21 – low intensity residential

p22 Percent land use 22 – high intensity residential

p23 Percent land use 23 – commercial/industrial/transportation

p41 Percent land use 41 – deciduous forest

p42 Percent land use 42 – evergreen forest

p43 Percent land use 43 – mixed forest

p51 Percent land use 51 – shrubland

p61 Percent land use 61 – orchards/vineyards

p71 Percent land use 71 – grasslands/herbaceous

p81 Percent land use 81 – pasture/hay

p82 Percent land use 82 – row crops

p83 Percent land use 83 – small grains

p85 Percent land use 85 – urban/recreational grasses

p91 Percent land use 91 – woody wetlands

p92 Percent land use 92 – emergent herbaceous wetlands

p_ag Percent agricultural land – sum of p81, p82, p83, p84, p85, and p61

p_clay Total clay content of soil expressed as percent clay in material less than 2 millimeters in size.

p_drain Percent artificially drained agricultural land

p_forest Percent forest land – sum of p41, p42, and p43

p_org Total soil organic matter in percent by weight.

p_urban Percent urban land – sum of p21, p22, and p23

p_wet Percent wetlands – sum of p91 and p92

ala_use Alachlor use rate in kilograms per square kilometer

atr_use Atrazine use rate in kilograms per square kilometer

BS Average basin slope in feet per mile

bulk_d Average soil bulk density in grams per cubic centimeter 

DCPA_use DCPA use rate in kilograms per square kilometer

diuron_use Diuron use rate in kilograms per square kilometer

fert_n Fertilizer nitrogen use in metric tons per square kilometer

hydgrp Average soil hydrologic group 

kfact Average soil total K factor 

manure_n Manure N production in metric tons per square kilometer

manure_p Manure P production in metric tons per square kilometer

MCS Main channel slope in feet per mile

perm Average soil permeability in inches per hour 

popdn Basin population density in people per square mile

sod-chl_use Sodium-chloride use rate in kilograms per square kilometer

TDA Total drainage area in square miles

welldn Density of oil- and gas-wells in number per square kilometer
Surface-Water Nonpoint-Source Component 11

Logistic Regression Models

Logistic regression is a statistical technique that uses one 

or more independent explanatory variables to predict the probability of a categorical response. Logistic regression is well 

suited for modeling the probability or likelihood of the occurrence or nonoccurrence of some event (Hosmer and Lemeshow, 

2000). Used primarily in medical research, logistic regression is 

gaining popularity in environmental investigations (Battaglin 

and Goolsby, 1998; Davidson and others, 2001; Nolan, 2001; 

Rupert, 2003; Smith and others, 2001; Mineau, 2002).

The response or dependent variable in logistic regression 

is the logarithm of the odds ratio p/(1-p), where p is the probability of a data value being in one of the categories (Helsel and 

Hirsch, 1992). The logit transformation converts a variable constrained between zero and one into a continuous variable that is 

linear with respect to the vector of the explanatory variables, X:

Y = Log [p/(1-p)] = b0 + b1X1 + ...bkXk (1) 

where 

Y is the response variable; [p/(1-p)] is the odds ratio; 

b0 is the intercept; 

b1 is the slope coefficient for the first explanatory variable 

X1; and 

bk is the slope coefficient for the kth explanatory variable 

Xk. 

Slope coefficients for logistic regression models are fit to 

the categorical response variable by using the method of maximum likelihood, which optimizes the probability that the 

observed data will be estimated from the set of slope coefficients. To calculate the predicted value of the response variable 

(a probability), the logistic transformation (the inverse of the 

logit transformation) is used. The resulting equation has the 

form:

p = (exp(b0 +b1X1 + ..bkXk))

(1+(exp(b0 +b1X1 + ..bkXk))) 

The SAS statistical software system (SAS Institute, 1990) 

is used to develop the logistic regression models. The “Best” 

and the “Stepwise” model-selection procedures are used to 

screen potential models. The “Best” procedure tests all possible 

combinations of model variables and lists the 10 best 1 through 

5 variable models. The “Stepwise” procedure starts with one 

variable and then adds in the next most significant variable. 

After each “Step,” the remaining potential variables are tested 

for their ability to explain the remaining model error. The variable that explains the most of the remaining model error is the 

next to be added. Neither the “Best” nor the “Stepwise” modelselection procedures will prevent correlated explanatory variables from entering the models, so supervision of the process is 

required.

Logistic regression models for individual contaminants are 

compared on the basis of model fit statistics such as the Akaike 

information criterion (AIC) and the Schwartz criterion (SAS 

Institute, 1990). The AIC is analogous to Mallow’s Cp and 

includes a measure of model error and a penalty for extra variables (Helsel and Hirsch, 1992). The better models have lower 

AIC values, but AIC values for models of different contaminants cannot be compared unless they have identical source 

data. The predictive abilities of the models are compared on the 

basis of the Somers’ D (SAS Institute, 1990) measure of rank 

correlation between the observed and predicted responses, and 

by contingency table analysis of predicted compared to 

observed responses. Somers’ D values range from –1 to 1; the 

higher values indicate better model predictive abilities. Model 

fit and predictive ability are used to select the best two models 

for each contaminant. The final logistic regression models 

should not be affected by collinearity (correlation) among the 

explanatory variables because variables that are highly correlated (Pearson’s correlation coefficient of 0.7 or greater) are not 

included in the same model.

Optimal performance of logistic regression models is 

obtained when there are nearly equal numbers of responses for 

the dichotomous response variable. For instances when there 

are either few detections greater than the threshold concentration or few nondetections, the resulting logistic regression models are subject to fitting problems, and the likelihood of developing spurious models increases (Hosmer and Lemeshow, 

2000). 

Logistic regression models are developed that predict the 

probability of occurrence at or greater than the TCEQ-specified 

threshold concentration for 63 of the 227 contaminants. For the 

remaining 164 contaminants, logistic regression models for susceptibility of occurrence in surface water from nonpoint sources 

cannot be produced. These contaminants are not modeled for 

one of the following mutually exclusive reasons:

1. There are fewer than 50 sample analyses total at the 

323 surface-water sites; hence, there is not enough 

analytical data to justify model development.

2. There are less than 10 percent (~32) of the 323 surfacewater sites with analytical data; hence, there is not 

enough spatial coverage of data to justify model 

development.

3. There are more than 50 samples total, at more than 

32 sites, but the target contaminant is detected at or 

greater than the threshold concentration at less than 

15 percent of those sites; hence, there is insufficient 

variability in the contaminant occurrence to produce a 

model.

4. Threshold concentrations are not specified. Two 

contaminants, pH and total triazines, are not modeled for 

this reason.

(2)
12 Estimating the Susceptibility of Surface Water in Texas to Nonpoint-Source Contamination by Use of Logistic Regression 

Modeling

Estimating the Susceptibility of Surface 

Water to Nonpoint-Source Contamination

The result of the nonpoint-source susceptibility component 

for each of the 227 contaminants is either two logistic regression 

models that estimate the probability of contaminant occurrence 

at or greater than a threshold concentration at surface-water PWS 

intakes in Texas, or an assignment of a relative susceptibility rating based on frequency of contaminant detection or level of landuse activities associated with the contaminant. 

Summary of Modeled Contaminants

The modeling status of each of the 227 contaminants is 

listed in table 1. Models were developed for 63 contaminants. No 

analytical data were available for 27 contaminants. All other 

contaminants had at least 50 sample analyses total, but 106 contaminants were analyzed at fewer than 32 sites. Monitoring data 

were sufficient for 29 contaminants but they were detected 

greater than the threshold concentration too infrequently to 

develop a model. Threshold concentrations were not specified 

for two contaminants. 

The percentage of contaminants that were modeled varies 

considerably by contaminant type. Logistic regression models 

are developed for 100 percent (3 of 3) of nutrients, 95 percent (18 

of 19) of metals, 75 percent (9 of 12) of major ions, 67 percent 

(2 of 3) of physical properties, 32 percent (20 of 63) of pesticides, 20 percent (1 of 5) of microbiologicals, 10 percent (1 of 

10) of radionuclides, 8 percent (9 of 109) of organics, and 0 percent (0 of 3) of inorganics (table 1). Pesticide occurrence is not 

modeled primarily because of a lack of detections in the monitoring data (28 of 43 that were not modeled); organics are not modeled primarily because of limitations in the extent of monitoring 

data (91 of 100 not modeled); and microbiologicals, inorganics, 

and radionuclides are all not modeled primarily because of the 

complete absence of monitoring data.

Examples of Modeling Results

Examples of the monitoring data and modeling results are 

shown in figures 1 and 2. Figures 1(A) and 2(A) show the monitoring data that are available for model development, whereas 

figures 1(B) and 2(B) show model results. In both figures, areas 

shaded as “not in study area” are the parts of Texas that are not 

covered by the watershed of 1 or more of the 323 surface-water 

sites. The 323 watersheds are shaded according to the results of 

the monitoring data analysis or logistic regression model results. 

Figure 1(A) shows where dissolved manganese occurred at least 

once at a concentration of 75 μg/L (the TCEQ-specified threshold concentration) or greater, occurred but always at a concentration less than 75 μg/L, was analyzed for but not detected, and 

was not analyzed for. In figure 1(B) the modeled probabilities 

that manganese will occur at a concentration of 75 μg/L or 

greater are shown as high (probability of 0.67 or greater), 

medium (probability of 0.34 to 0.66), or low (probability of less 

than 0.34). The monitoring data for manganese were available at 

232 of the 323 sites with about 14,000 analyses. Equation 3 is the 

model used to predict the probability of manganese occurrence 

in figure 1. This model indicates that the presence of evergreen 

and mixed forestland and shrubland is inversely related to manganese occurrence. 

p = (exp (5.35 – 0.069*p42 + 0.058*p43 – 0.020*p51 – 11.3*kfact))

(1 + (exp (5.35 – 0.069*p42 + 0.058*p43 – 0.020*p51 – 11.3*kfact)))

Figure 2(A) shows where dissolved simazine occurred at 

least once at a concentration of 0.01 μg/L (the TCEQ-specified 

threshold concentration) or greater, occurred but always at a concentration less than 0.01 μg/L, was analyzed for but not detected, 

and was not analyzed for. In figure 1(B) the modeled probabilities that simazine will occur at a concentration of 0.01 μg/L or 

greater are shown as high, medium, or low. The monitoring data 

for simazine were available at 129 of the 323 sites with about 

1,300 analyses. Equation 4 is the model used to predict the probability of simazine occurrence in figure 2. This model indicates 

that the presence of low-intensity residential land and pasture/

hay is positively related to simazine occurrence, and evergreen 

forestland and woody wetlands are inversely related to simazine 

occurrence. 

p = (exp (-0.719 + 0.098*p21 + 0.052*p81 – 0.028*p42 – 0.463*p91)) 

(1 + (exp (-0.719 + 0.098*p21 + 0.052*p81 – 0.028*p42 – 0.463*p91))) 

The logistic regression models developed for the 63 contaminants and selected model fit statistics are listed in table 3. 

This table and figures similar to 1 and 2 for the other 61 contaminants for which logistic regression models were developed are 

available on the Internet at http://co.water.usgs.gov/midconherb/

html/texas.html.

(3)

(4)
Estimating the Susceptibility of Surface Water to Nonpoint-Source Contamination 13

105°30' 104°

36°

35°

34°

31°

30° 30°

31°

29° 29° 29°

28° 28°

27° 27°

102°30' 101°

102°30'

99°30'

98° 97°

98° 97°

96° 95°

94°

105°30' 104°

36°

35°

34°

31° 31°

30° 30°

29° 29° 29°

28° 28°

27° 27°

102°30'

102°30'

101°

99°30'

99°30'

98°

98°

97°

97°

96° 95°

94°

33° 33°

32°

33° 33°

32°

0 100 200 MILES

0 100 200 KILOMETERS

EXPLANATION

Not in study area

0 to 0.33

0.34 to 0.66

0.67 to 1

Monitoring site

EXPLANATION

Not in study area

Not analyzed

Not detected

Detected below

75 μg/L

Detected at or

above 75 μg/L

Monitoring site

OBSERVED MANGANESE

MONITORING DATA

(B)

(A)

Base from U.S. Geological Survey digital data, 2002, 1:2,000,000 

Albers Equal-Area Conic projection

Standard papallels 34°55'N and 27°25'N, central meridian 31°10'W

Datum of 1983 (NAD 83)

Figure 1. Summary of (A) dissolved manganese monitoring data and (B) the results of a logistic regression model preducting the probability of manganese occurrence at or greater than 75 micrograms per liter in Texas surface water.
14 Estimating the Susceptibility of Surface Water in Texas to Nonpoint-Source Contamination by Use of Logistic Regression 

Modeling

105°30' 104°

36°

35°

34°

31°

30°

29°

28°

27°

102°30' 101°

99°30'

98° 97° 96° 95°

94°

33°

105°30' 104°

36°

35°

34°

31°

30°

29° 29°

28°

27°

102°30' 101°

99°30'

98° 97° 96° 95°

94°

33°

0 100 200 MILES

0 100 200 KILOMETERS

EXPLANATION

Not in study area

0 to 0.33

0.34 to 0.66

0.67 to 1

Monitoring site

EXPLANATION

Not in study area

Not analyzed

Not detected

Detected below

0.01 μg/L

Detected at or

above 0.01 μg/L

Monitoring site

OBSERVED SIMAZINE

MONITORING DATA

PROBABILITY OF SIMAZINE

OCCURENCE AT OR ABOVE 

0.01 μg/L

(B)

(A)

Base from U.S. Geological Survey digital data, 2002, 1:2,000,000 

Albers Equal-Area Conic projection

Standard papallels 34°55'N and 27°25'N, central meridian 31°10'W

Datum of 1983 (NAD 83)

Figure 2. Summary of (A) dissolved simazine monitoring data and (B) the results of a logistic regression model predicting the probability of simazine occurrence at or greater than 0.01 microgram per liter in Texas surface water.
Estimating the Susceptibility of Surface Water to Nonpoint-Source Contamination 15

Table 3. Logistic regression-model equations and model-fit statistics for selected surface-water nonpoint-source 

contaminants in Texas.

[AIC, Akaike information criterion; TCEQ, Texas Commission on Environmental Quality; see table 2 for abbreviation definitions of 

model variables]

Contaminant and

(TCEQ contaminant number)

Water-quality

parameter code(s)

Threshold

concentration

Model

comments

First logistic regression model AIC Somers’ D

Second logistic regression model AIC Somers’ D

2,4,5-T (21) 39742, 39740 0.01 μg/L

p=(exp(-6.44 + 0.085*p22 + 0.0001*TDA - 0.034*p51 + 1.77*hydgrp))/

(1+(exp(-6.44 + 0.085*p22 + 0.0001*TDA - 0.034*p51 + 1.77*hydgrp)))

178.5 0.618

p=(exp(-6.28 + 0.031*p_urb + 0.0001*TDA - 0.033*p51 + 1.71*hydgrp))/

(1+(exp(-6.28 + 0.031*p_urb + 0.0001*TDA - 0.033*p51 + 1.71*hydgrp)))

180.5 0.613

2,4,5-TP (SILVEX)(22) 39762, 39760 0.01 μg/L

p=(exp(-2.38 + 0.056*p_urb + 118.5*perm - 0.066*MCS + 0.122*p_wet))/

(1+(exp(-2.38 + 0.056*p_urb + 118.5*perm - 0.066*MCS + 0.122*p_wet)))

148.6 0.631

p=(exp(-2.94 + 0.849*p85 + 74.0*perm + 0.000035*TDA + 0.388*p92))/

(1+(exp(-2.94 + 0.849*p85 + 74.0*perm + 0.000035*TDA + 0.388*p92)))

145.3 0.607

2,4-D (24) 39732, 39730 0.01 μg/L

p=(exp(-3.42 + 0.151*p21 + 0.889*hydgrp + 0.00005*TDA + 0.325*p91))/

(1+(exp(-3.42 + 0.151*p21 + 0.889*hydgrp + 0.00005*TDA + 0.325*p91)))

194.9 0.531

p=(exp(-3.35 + 0.055*p_urb + 0.852*hydgrp + 0.00006*TDA + 0.241*p_wet))/

(1+(exp(-3.35 + 0.055*p_urb + 0.852*hydgrp + 0.00006*TDA + 0.241*p_wet)))

193.3 0.551

Alachlor (41) 46342, 77825 0.01 μg/L

p=(exp(-12.9 + 6.66*ala_use + 0.146*p22 + 0.059*p81 + 2.32*hydgrp + 0.066*p71))/ 

(1+(exp(-12.9 + 6.66*ala_use + 0.146*p22 + 0.059*p81 + 2.32*hydgrp + 0.066*p71)))

88.2 0.754

p=(exp(-9.09 + 3.97*ala_use + 0.032*p_urban + 0.026*p_ag + 1.79*hydgrp))/ 

(1+(exp(-9.09 + 3.97*ala_use + 0.032*p_urban + 0.026*p_ag + 1.79*hydgrp)))

91.9 0.705

Aldrin (45) 39330, 39331, 39332 0.01 μg/L

p=(exp(-3.52 + 0.166*P23 + 0.042*P81 + 0.491*welldn))/ 

(1+(exp(-3.52 + 0.166*P23 + 0.042*P81 + 0.491*welldn)))

81.8 0.646

p=(exp(-3.51 + 0.043*p_urb + 0.025*p_ag + 0.487*welldn))/ 

(1+(exp(-3.51 + 0.043*p_urb + 0.025*p_ag + 0.487*welldn)))

85.7 0.624

Alkalinity (46) 39036, 39086, 39087 250 mg/L Poor models

p=(exp(-0.169 + 0.058*p41 - 0.224*p43 - 0.025*p81 ñ 0.0003*popdn ñ 0.0001*TDA))/ 

(1+(exp(-0.169 + 0.058*p41 - 0.224*p43 - 0.025*p81 ñ 0.0003*popdn ñ 

0.0001*TDA)))

247.0 0.515

p=(exp(0.811 - 0.045*p22 - 0.190*p43 - 0.026*p81 ñ 0.002*BS – 0.0001*TDA))/ 

(1+(exp(0.811 - 0.045*p22 - 0.190*p43 - 0.026*p81 – 0.002*BS – 0.0001*TDA)))

247.9 0.491
16 Estimating the Susceptibility of Surface Water in Texas to Nonpoint-Source Contamination by Use of Logistic Regression 

Modeling

Aluminum (47) 01106 100 μg/L Poor models

p=(exp(-1.12 + 0.549*fert_n + 0.046*MCS + 0.304*p91 + 0.269*p85))/ 

(1+(exp(-1.12 + 0.549*fert_n + 0.046*MCS + 0.304*p91 + 0.269*p85)))

154.4 0.427

p=(exp(-1.23 + 0.445*fert_n + 0.053*MCS + 0.199*p_wet + 0.513*welldn))/ 

(1+(exp(-1.23 + 0.445*fert_n + 0.053*MCS + 0.199*p_wet + 0.513*welldn)))

155.6 0.435

Antimony (49) 01095, 01097, 99897 0.01 μg/L

p=(exp(-2.16 - 0.349*P41 + 0.376*P81 + 0.00006*TDA))/ 

(1+(exp(-2.16 - 0.349*P41 + 0.376*P81 + 0.00006*TDA)))

43.8 0.879

p=(exp(3.40 - 0.172*P22 - 0.123*P71 - 0.079*p42))/ 

(1+(exp(3.40 - 0.172*P22 - 0.123*P71 - 0.079*p42)))

50.5 0.812

Arsenic (51) 01000 5.0 μg/L

p=(exp(1.40 - 0.048*p_forest + 0.111*p_urb + 0.105*p_drain ñ 0.032*MCS))/ 

(1+(exp(1.40 - 0.048*p_forest + 0.111*p_urb + 0.105*p_drain ñ 0.032*MCS)))

202.9 0.634

p=(exp(-0.193 - 0.049*p_forest + 0.098*p_urb + 0.624*hydgrp ñ 0.038*MCS))/ 

(1+(exp(-0.193 - 0.049*p_forest + 0.098*p_urb + 0.624*hydgrp ñ 0.038*MCS)))

209.3 0.599

Atrazine (53) 39632, 39630 0.1 μg/L

p=(exp(-5.02 + 0.135*atr_use + 0.250*P21 + 0.193*p_clay))/ 

(1+(exp(-5.02 + 0.135*atr_use + 0.250*P21 + 0.193*p_clay)))

132.9 0.746

p=(exp(-8.77 + 0.057*p_ag + 0.119*p_urban + 0.035*p71 + 1.75*hydgrp))/

(1+(exp(-8.77 + 0.057*p_ag + 0.119*p_urban + 0.035*p71 + 1.75*hydgrp)))

132.6 0.740

Barium (54) 01005 200 μg/L

p=(exp(6.76 + 0.244*P22 + 0.921*P92 - 0.061*p43 – 2.30*hydgrp))/ 

(1+(exp(6.76 + 0.244*P22 + 0.921*P92 - 0.061*P43 – 2.30*hydgrp)))

219.7 0.596

p=(exp(3.33 + 0.269*P22 + 1.03*P92 + 0.040*p71 – 1.53*hydgrp))/ 

(1+(exp(3.33 + 0.269*P22 + 1.03*P92 + 0.040*P71 – 1.53*hydgrp)))

220.9 0.579

Benzene (56) 34030 0.01 μg/L

p=(exp(-4.33 + 0.125*p21 – 3.18*fert_n + 0.282*p_clay + 184*p61))/ 

(1+(exp(-4.33 + 0.125*p21 – 3.18*fert_n + 0.282*p_clay + 184*p61)))

48.6 0.657

p=(exp(-3.14 + 0.027*p_urban – 1.86*fert_n + 0.178*p_clay))/ 

(1+(exp(-3.14 + 0.027*p_urban – 1.86*fert_n + 0.178*p_clay)))

51.0 0.585

Beryllium (62) 01010 1.0 μg/L

p=(exp(-2.61 - 0.209*p22 + 5.05*bulk_d + 0.650*p85 - 0.096*p_clay))/

(1+(exp(-2.61 - 0.209*p22 + 5.05*bulk_d + 0.650*p85 - 0.096*p_clay)))

165.2 0.505

p=(exp(-3.23 - 0.061*p22 + 6.20*bulk_d - 0.430*fert_n - 0.078*p_clay))/

(1+(exp(-3.23 - 0.061*p22 + 6.20*bulk_d - 0.430*fert_n - 0.078*p_clay)))

167.3 0.505

Table 3. Logistic regression-model equations and model-fit statistics for selected surface-water nonpoint-source 

contaminants in Texas.—Continued

[AIC, Akaike information criterion; TCEQ, Texas Commission on Environmental Quality; see table 2 for abbreviation definitions of 

model variables]

Contaminant and

(TCEQ contaminant number)

Water-quality

parameter code(s)

Threshold

concentration

Model

comments
Estimating the Susceptibility of Surface Water to Nonpoint-Source Contamination 17

Bicarbonate (63) 00453 200 mg/L

p=(exp(-4.39 + 0.038*p_forest ñ 2.11*p_wet + 0.053*p51 + 0.186*p_clay))/

(1+(exp(-4.39 + 0.038*p_forest ñ 2.11*p_wet + 0.053*p51 + 0.186*p_clay)))

96.3 0.635

p=(exp(-2.22 + 0.127*p41 ñ 3.22*p92 + 0.057*p51 + 0.872*fert_n))/

(1+(exp(-2.22 + 0.127*p41 – 3.22*p92 + 0.057*p51 + 0.872*fert_n)))

100.8 0.632

Boron (64) 01020 200 μg/L

p=(exp(2.61 - 0.056*p_forest - 0.045*MCS - 0.253*welldn))/ 

(1+(exp(2.61 - 0.056*p_forest - 0.045*MCS - 0.253*welldn)))

95.2 0.675

p=(exp(1.84 - 0.021*p42 - 0.437*p91 - 0.005*BS))/ 

(1+(exp(1.84 - 0.021*p42 - 0.437*p91 - 0.005*BS)))

100.4 0.591

Cadmium (74) 01025 5.0 μg/L Poor models

p=(exp(-0.424 - 0.025*p51 + 0.027*p71 + 0.455*fert_n + 0.002*BS))/

(1+(exp(-0.424 - 0.025*p51 + 0.027*p71 + 0.455*fert_n + 0.002*BS)))

245.3 0.371

p=(exp(2.03 - 0.052*p21 - 0.053*p41 - 0.034*p43 - 0.043*p51))/

(1+(exp(2.03 - 0.052*p21 - 0.053*p41 - 0.034*p43 - 0.043*p51)))

244.1 0.360

Calcium (75) 00915 100 mg/L

p=(exp(0.642 - 0.055*p42 + 0.059*p82 - 0.032*p81 ñ 0.401*p91))/

(1+(exp(0.642 - 0.055*p42 + 0.059*p82 - 0.032*p81 ñ 0.401*p91)))

299.7 0.650

p=(exp(-1.33 - 0.040*p_forest - 0.431*p_wet + 2.71*bulk_d))/

(1+(exp(-1.33 - 0.040*p_forest - 0.431*p_wet + 2.71*bulk_d)))

312.3 0.627

Carbaryl (76) 39750, 49310, 82680 0.01 μg/L

p=(exp(-1.18 + 0.118*p23 + 21.6*p61 - 0.061*MCS))/

(1+(exp(-1.18 + 0.118*p23 + 21.6*p61 - 0.061*MCS)))

119.4 0.600

p=(exp(-1.46 + 0.032*p_urban + 0.030*p51 - 0.065*MCS))/

(1+(exp(-1.46 + 0.032*p_urban + 0.030*p51 - 0.065*MCS)))

120.8 0.570

Carbonate (80) 00452 1.0 mg/L

p=(exp(1.16 ñ 0.140*p41 + 0.078*p51 + 0.072*p21 ñ 0.064*MCS))/

(1+(exp(1.16 - 0.140*p41 + 0.078*p51 + 0.072*p21 ñ 0.064*MCS)))

92.9 0.714

p=(exp(6.00 ñ 0.089*p_forest ñ 0.060*p_ag - 0.070*MCS ñ 1.16*welldn))/

(1+(exp(6.00 - 0.089*p_forest ñ 0.060*p_ag - 0.070*MCS ñ 1.16*welldn)))

92.3 0.699

Chlordane (81) 39350, 39352 0.01 μg/L

p=(exp(12.3 + 0.49*p21 – 49.7*Kfact + 0.00004*TDA))/

(1+(exp(12.3 + 0.49*p21 – 49.7*Kfact + 0.00004*TDA)))

85.7 0.848

p=(exp(9.19 + 0.148*p_urban – 38.1*Kfact + 0.00003*TDA))/

(1+(exp(9.19 + 0.148*p_urban – 38.1*Kfact + 0.00003*TDA)))

88.8 0.820

Table 3. Logistic regression-model equations and model-fit statistics for selected surface-water nonpoint-source 

contaminants in Texas.—Continued

[AIC, Akaike information criterion; TCEQ, Texas Commission on Environmental Quality; see table 2 for abbreviation definitions of 

model variables]

Contaminant and

(TCEQ contaminant number)

Water-quality

parameter code(s)

Threshold

concentration

Model

comments
18 Estimating the Susceptibility of Surface Water in Texas to Nonpoint-Source Contamination by Use of Logistic Regression 

Modeling

Chloride (85) 00940 125 mg/L

p=(exp(-6.42 + 0.066*p51 - 0.034*p_forest + 6.63*bulk_d + 0.646*welldn))/

(1+(exp(-6.42 + 0.066*p51 - 0.034*p_forest + 6.63*bulk_d + 0.646*welldn)))

262.8 0.766

p=(exp(-7.85 + 0.067*p51 + 0.034*p71 + 6.75*bulk_d + 0.048*p82))/

(1+(exp(-7.85 + 0.067*p51 + 0.034*p71 + 6.75*bulk_d + 0.048*p82)))

273.1 0.728

Chromium (90) 01030, 01032 15.0 μg/L Poor models

p=(exp(-0.979 - 0.062*p51 - 0.042*p81 + 0.845*hydgrp + 0.00007*TDA))/

(1+(exp(-0.979 - 0.062*p51 - 0.042*p81 + 0.845*hydgrp + 0.00007*TDA)))

240.0 0.514

p=(exp(-2.01 + 0.038*p_urban + 0.032*p_forest + 0.693*fert_n + 0.00006*TDA))/

(1+(exp(-2.01 + 0.038*p_urban + 0.032*p_forest + 0.693*fert_n + 0.00006*TDA)))

246.0 0.456

Copper (94) 01040 20 μg/L Poor models

p=(exp(-1.71 - 0.028*p_forest + 0.160*p_wet + 0.020*p51 + 2.20*bulk_d))/

(1+(exp(-1.71 - 0.028*p_forest + 0.160*p_wet - 0.020*p51 + 2.20*bulk_d)))

220.7 0.461

p=(exp(-1.18 - 0.041*p42 - 0.302*p85 + 0.021*p82 + 0.019*p71))/

(1+(exp(-1.18 - 0.041*p42 - 0.302*p85 + 0.021*p82 + 0.019*p71)))

219.0 0.468

DCPA di-acid degradate (99) 82682 0.001 μg/L

p=(exp(-4.03 + 0.197*p21 + 0.043*p71 + 2.48*DCPA_use + 0.004*BS + 

0.0001*TDA))/

(1+(exp(-4.03 + 0.197*p21 + 0.043*p71 + 2.48*DCPA_use + 0.004*BS + 

0.0001*TDA)))

91.5 0.713

p=(exp(-0.837 + 0.040*p_urban ñ 1.10*p_wet - 0.070*MCS + 0.0001*TDA))/

(1+(exp(-0.837 + 0.040*p_urban ñ 1.10*p_wet - 0.070*MCS + 0.0001*TDA)))

87.0 0.666

DDE (101) 346853, 39366 0.001 μg/L

p=(exp(-4.30 + 0.035*p_urban + 0.055*p_drain + 0.00008*TDA + 0.008*BS))/

(1+(exp(-4.30 + 0.035*p_urban + 0.055*p_drain + 0.00008*TDA + 0.008*BS)))

75.6 0.663

p=(exp(-12.0 + 0.954*p85 + 0.008*BS + 25.1*Kfact + 0.00008*TDA))/

(1+(exp(-12.0 + 0.954*p85 + 0.008*BS + 25.1*Kfact + 0.00008*TDA)))

71.4 0.665

Diazinon (104) 39570, 39572, 39573 0.01 μg/L

p=(exp(-1.24 + 0.402*p21 + 0.00007*TDA + 0.027*p81))/

(1+(exp(-1.24 + 0.402*p21 + 0.00007*TDA + 0.027*p81)))

188.3 0.664

p=(exp(-0.866 + 0.078*p_urban + 0.00008*TDA + 1.60*p_org – 0.027*p51))/

(1+(exp(-0.866 + 0.078*p_urban + 0.00008*TDA + 1.60*p_org – 0.027*p51)))

202.5 0.599

Dieldrin (112) 39380, 39381 0.01 μg/L

p=(exp(-8.02 + 0.048*p_urban - 0.080*p51 + 2.02*hydgrp + 0.0001*TDA))/

(1+(exp(-8.02 + 0.048*p_urban - 0.080*p51 + 2.02*hydgrp + 0.0001*TDA)))

141.0 0.745

p=(exp(-7.59 + 0.313*p23 + 0.499*p92 + 1.52*hydgrp + 0.00005*TDA))/

(1+(exp(-7.59 + 0.313*p23 + 0.499*p92 + 1.52*hydgrp + 0.00005*TDA)))

140.5 0.705

Diuron (119) 49300 0.01 μg/L

Table 3. Logistic regression-model equations and model-fit statistics for selected surface-water nonpoint-source 

contaminants in Texas.—Continued

[AIC, Akaike information criterion; TCEQ, Texas Commission on Environmental Quality; see table 2 for abbreviation definitions of 

model variables]

Contaminant and

(TCEQ contaminant number)

Water-quality

parameter code(s)

Threshold

concentration

Model

comments
Estimating the Susceptibility of Surface Water to Nonpoint-Source Contamination 19

p=(exp(-1.81 + 0.980*diuron_use + 0.127*p21 + 0.544*welldn))/

(1+(exp(-1.81 + 0.980*diuron_use + 0.127*p21 + 0.544*welldn)))

95.3 0.543

p=(exp(-1.75 + 0.868*diuron_use + 0.051*p_urban + 0.542*welldn))/

(1+(exp(-1.75 + 0.868*diuron_use + 0.051*p_urban + 0.542*welldn)))

96.8 0.536

Fluoride (129) 00950, 00951 1.0 mg/L

p=(exp(-1.68 - 0.018*p_ag - 0.044*p_forest + 80.3*perm + 0.096*p_clay))/

(1+(exp(-1.68 - 0.018*p_ag - 0.044*p_forest + 80.3*perm + 0.096*p_clay)))

335.5 0.507

p=(exp(-1.10 - 0.024*p81 - 0.040*p42 ñ 0.079*p43 + 1.86*bulk_d))/

(1+(exp(-1.10 - 0.024*p81 - 0.040*p42 ñ 0.079*p43 + 1.86*bulk_d)))

335.3 0.506

Hardness (135) 00900 300 mg/L

p=(exp(-4.39 + 0.111*p22 + 0.046*p51 + 0.034*p71 + 0.073*p82 + 2.66*bulk_d))/

(1+(exp(-4.39 - 0.111*p22 ñ 0.046*p51 + 0.034*p71 + 0.073*p82 + 2.66*bulk_d)))

305.7 0.642

p=(exp(-3.75 - 0.014*p_ag ñ 0.055*p_forest + 206*perm + 1.23*hydgrp + 

0.139*welldn))/

(1+(exp(-3.75 - 0.014*p_ag ñ 0.055*p_forest + 206*perm + 1.23*hydgrp + 

1.39*welldn)))

323.9 0.575

Heptachlor (136) 39410, 39411 0.01 μg/L

p=(exp(-4.01 + 0.031*p_ag + 0.058*p_urban + 0.045*p_drain))/

(1+(exp(-4.01 + 0.031*p_ag + 0.058*p_urban + 0.045*p_drain)))

69.6 0.772

p=(exp(-3.87 + 0.029*p_ag + 0.0012*popdn + 0.043*p_drain))/

(1+(exp(-3.87 + 0.029*p_ag + 0.0012*popdn + 0.043*p_drain)))

68.9 0.769

Heptachlor epoxide(137) 39420, 39421 0.01 μg/L

p=(exp(-3.75 + 0.027*p_ag + 0.071*p_urban + 0.375*welldn))/

(1+(exp(-3.75 + 0.028*p_ag + 0.071*p_urban + 0.375*welldn)))

77.1 0.820

p=(exp(-3.39 + 0.088*p81 + 0.198*p21 – 1.39*p91))/

(1+(exp(-3.39 + 0.088*p81 + 0.198*p21 – 1.39*p91)))

71.2 0.818

Iron (144) 01046 150 μg/L Poor models

p=(exp(2.42 – 0.048*p42 - 0.031*p51 – 0.020*p71 - 0.030*P82))/

(1+(exp(2.42 – 0.048*p42 - 0.031*p51 – 0.020*p71 – 0.030*p82)))

269.3 0.504

p=(exp(-1.54 - 0.019*p51 + 2.31*bulk_d + 0.509*manure_n))/

(1+(exp(-1.54 - 0.019*p51 + 2.31*bulk_d + 0.509*manure_n)))

275.0 0.472

Lead (147) 01049 25 μg/L Poor models

p=(exp(0.185 – 0.050*p43 – 0.026*p51 + 0.155*p85 + 0.002*BS))/

(1+(exp(0.185 – 0.050*p43 - 0.026*p51 + 0.155*p85 + 0.002*BS)))

260.7 0.318

p=(exp(-0.315 + 0.029*p22 – 0.028*p51 + 0.027*p71 + 0.002*BS))/

(1+(exp(-0.315 + 0.029*p22 - 0.028*p51 + 0.021*p71 + 0.002*BS)))

266.2 0.282

Lindane (148) 39340, 39341 0.01 μg/L

Table 3. Logistic regression-model equations and model-fit statistics for selected surface-water nonpoint-source 

contaminants in Texas.—Continued

[AIC, Akaike information criterion; TCEQ, Texas Commission on Environmental Quality; see table 2 for abbreviation definitions of 

model variables]

Contaminant and

(TCEQ contaminant number)

Water-quality

parameter code(s)

Threshold

concentration

Model

comments
20 Estimating the Susceptibility of Surface Water in Texas to Nonpoint-Source Contamination by Use of Logistic Regression 

Modeling

p=(exp(-5.03 + 0.199*p23 – 0.040*p51 + 1.11*hydgrp + 0.0001*TDA))/

(1+(exp(-5.03 + 0.199*p23 - 0.040*p51 + 1.11*hydgrp + 0.0001*TDA)))

175.4 0.645

p=(exp(-4.80 + 0.043*p_urban ñ 0.039*p51 + 1.06*hydgrp + 0.0001*TDA))/

(1+(exp(-4.80 + 0.043*p_urban - 0.039*p51 + 1.06*hydgrp + 0.0001*TDA)))

179.1 0.641

M+P xylene (150) 85795 0.1 μg/L Poor models

p=(exp(-3.79 + 0.047*p_urban + 0.005*BS + 171.5*perm))/

(1+(exp(-3.79 + 0.047*p_urban + 0.005*BS + 171.5*perm)))

47.3 0.475

p=(exp(-3.19 + 0.100*p22 + 0.052*p42 + 157.2*perm))/

(1+(exp(-3.19 + 0.100*p22 + 0.052*p42 + 157.2*perm)))

46.7 0.496

Magnesium (151) 00925 20.0 mg/L

p=(exp(-3.04 - 0.068*p22 - 0.132*p43 - 0.089*p81 + 6.49*bulk_d))/

(1+(exp(-3.04 - 0.068*p22 - 0.132*p43 - 0.089*p81 + 6.49*bulk_d)))

267.8 0.747

p=(exp(-2.23 + 0.067*p51 + 0.040*p71 + 0.068*p82 + 0.0006*popdn))/

(1+(exp(-2.23 + 0.067*p51 + 0.040*p71 + 0.068*p82 + 0.0006*popdn)))

284.0 0.680

Manganese (152) 01056 75 μg/L

p=(exp(5.35 - 0.069*p42 + 0.058*p43 - 0.020*p51 – 11.3*kfact))/

(1+(exp(5.35 - 0.069*p42 + 0.058*p43 - 0.020*p51 – 11.3*kfact)))

225.1 0.613

p=(exp(7.59 + 0.013*p_ag – 1.81*hydgrp - 0.007*BS – 0.00004*TDA))/

(1+(exp(7.59 + 0.013*p_ag – 1.81*hydgrp - 0.007*BS – 0.00004*TDA)))

233.8 0.559

Mercury (153) 71890 0.5 μg/L Poor models

p=(exp(-4.11 + 0.121*p23 + 0.026*p82 + 1.29*hydgrp + 0.00004*TDA))/

(1+(exp(-4.11 + 0.121*p23 + 0.026*p82 + 1.29*hydgrp + 0.00004*TDA)))

230.3 0.391

p=(exp(-3.20 + 0.017*p_urban + 0.225*atr_use + 0.967*hydgrp + 0.00004*TDA))/

(1+(exp(-3.20 + 0.017*p_urban + 0.225*atr_use + 0.967*hydgrp + 0.00004*TDA)))

228.6 0.418

Methyl-t-butyle ether 

(MTBE)(159)

78032

0.2 μg/L Poor models

p=(exp(-1.48 + 0.041*p81 – 2.01*p91 + 0.106*p43 + 1.69*manure_n))/

(1+(exp(-1.48 + 0.041*p81 ñ 2.01*p91 + 0.106*p43 + 1.69*manure_n)))

62.2 0.568

p=(exp(2.75 + 0.037*p_ag ñ 5.77*bulk_d + 1.06*manure_n))/

(1+(exp(2.75 + 0.037*p_ag ñ 5.77*bulk_d + 1.06*manure_n)))

66.3 0.421

Metolachlor (160) 39415, 82612 0.01 μg/L

p=(exp(-4.17 + 0.048*p82 + 0.032*p81 + 6.20*p_org + 70.0*p61))/

(1+(exp(-4.17 + 0.048*p82 + 0.032*p81 + 6.20*p_org + 70.0*p61)))

122.6 0.660

p=(exp(-2.58 + 0.028*p_ag - 0.066*MCS + 5.29*p_org + 0.00007*TDA))/

(1+(exp(-2.58 + 0.028*p_ag - 0.066*MCS + 5.29*p_org + 0.00007*TDA)))

119.7 0.655

Metribuzin (161) 82611, 82612 0.01 μg/L

Table 3. Logistic regression-model equations and model-fit statistics for selected surface-water nonpoint-source 

contaminants in Texas.—Continued

[AIC, Akaike information criterion; TCEQ, Texas Commission on Environmental Quality; see table 2 for abbreviation definitions of 

model variables]

Contaminant and

(TCEQ contaminant number)

Water-quality

parameter code(s)

Threshold

concentration

Model

comments
Estimating the Susceptibility of Surface Water to Nonpoint-Source Contamination 21

p=(exp(-1.61 + 0.040*p21 - 0.057*p42 + 12.4*p61))/

(1+(exp(-1.61 + 0.040*p21 - 0.057*p42 + 12.4*p61)))

87.2 0.533

p=(exp(-0.541 + 0.013*p_urban - 0.033*p_forest – 0.075*MCS))/

(1+(exp(-0.541 + 0.013*p_urban - 0.033*p_forest – 0.075*MCS)))

87.8 0.474

M-xylene (164) No codes

 0.1 μg/L Same models as for M+P 

xylene

p=(exp(-3.79 + 0.047*p_urban + 0.005*BS + 171.5*perm))/

(1+(exp(-3.79 + 0.047*p_urban + 0.005*BS + 171.5*perm)))

47.3 0.475

p=(exp(-3.19 + 0.100*p22 + 0.052*p42 + 157.2*perm))/

(1+(exp(-3.19 + 0.100*p22 + 0.052*p42 + 157.2*perm)))

46.7 0.496

Nickel (167) 01065 10 μg/L Poor models

p=(exp(-4.17 + 0.077*p22 + 0.023*p71 + 0.238*p83 + 0.539*p91 + 1.00*hydgrp))/

(1+(exp(-4.17 + 0.077*p22 + 0.023*p71 + 0.238*p83 + 0.539*p91 + 1.00*hydgrp)))

205.5 0.434

p=(exp(-0.185 + 0.056*p22 - 0.022*p82 + 0.253*p83 + 0.377*p91))/

(1+(exp(-0.185 + 0.056*p22 - 0.022*p82 + 0.253*p83 + 0.377*p91)))

206.0 0.414

Nitrate (168) and nitrate + 

nitrite (169)

00630, 00631, 

00618, 00620

2.0 mg/L Poor models

p=(exp(-6.10 + 0.078*p82 + 0.244*p85 + 1.75*hydgrp ñ 0.018*MCS)))/

(1+(exp(-6.10 + 0.078*p82 + 0.244*p85 + 1.75*hydgrp ñ 0.018*MCS)))

336.3 0.494

p=(exp(-6.02 + 0.079*p82 + 0.046*p22 + 1.74*hydgrp ñ 0.021*MCS))/

(1+(exp(-6.02 + 0.079*p82 + 0.046*p22 + 1.74*hydgrp ñ 0.021*MCS)))

336.8 0.496

Nitrite (170) 00613, 00615 0.1 mg/L Poor models

p=(exp(-2.56 + 0.020*p_ag + 0.019*p_urban + 0.608*hydgrp ñ 0.036*p_drain))/

(1+(exp(-2.56 + 0.020*p_ag + 0.019*p_urban + 0.608*hydgrp ñ 0.036*p_drain)))

316.1 0.394

p=(exp(-2.51 + 0.692*fert_n + 0.077*p22 + 0.487*hydgrp ñ 0.039*p_drain))/

(1+(exp(-2.51 + 0.692*fert_n + 0.077*p22 + 0.487*hydgrp ñ 0.039*p_drain)))

306.4 0.424

O-xylene (176) 77135 0.01 μg/L

p=(exp(-6.24 + 0.492*p23 + 0.051*p71 + 0.014*BS))/

(1+(exp(-6.24 + 0.492*p23 + 0.051*p71 + 0.014*BS)))

29.4 0.764

p=(exp(-4.49 + 0.104*p_urban + 0.012*BS))/

(1+(exp(-4.49 + 0.104*p_urban + 0.012*BS)))

29.3 0.694

PCBs (179) 39516, 39517 0.01 μg/L

p=(exp(-1.14 + 0.059*p_urban - 0.088*MCS + 0.393*welldn))/

(1+(exp(-1.14 + 0.059*p_urban - 0.088*MCS + 0.393*welldn)))

85.8 0.653

p=(exp(-0.900 + 0.178*p22 - 0.128*MCS + 0.034*p82))/

(1+(exp(-0.900 + 0.178*p22 - 0.128*MCS + 0.034*p82)))

82.7 0.638

Prometon (185) 04037, 39056 0.01 μg/L

Table 3. Logistic regression-model equations and model-fit statistics for selected surface-water nonpoint-source 

contaminants in Texas.—Continued

[AIC, Akaike information criterion; TCEQ, Texas Commission on Environmental Quality; see table 2 for abbreviation definitions of 

model variables]

Contaminant and

(TCEQ contaminant number)

Water-quality

parameter code(s)

Threshold

concentration

Model

comments
22 Estimating the Susceptibility of Surface Water in Texas to Nonpoint-Source Contamination by Use of Logistic Regression 

Modeling

p=(exp(0.870 + 0.080*p_urban - 0.043*p_forest))/

(1+(exp(0.870 + 0.080*p_urban - 0.043*p_forest)))

139.1 0.611

p=(exp(0.762 + 0.139*p22 - 0.075*p43 - 0.003*BS))/

(1+(exp(0.762 + 0.139*p22 - 0.075*p43 - 0.003*BS)))

150.1 0.509

P-xylene (188) No codes or data 0.1 μg/L Same models as for M+P 

xylene

p=(exp(-3.79 + 0.047*p_urban + 0.005*BS + 171.5*perm))/

(1+(exp(-3.79 + 0.047*p_urban + 0.005*BS + 171.5*perm)))

47.3 0.475

p=(exp(-3.19 + 0.100*p22 + 0.052*p42 + 157.2*perm))/

(1+(exp(-3.19 + 0.100*p22 + 0.052*p42 + 157.2*perm)))

46.7 0.496

Selenium (195) 01145 2.0 μg/L

p=(exp(-1.58 - 0.061*p43 + 0.028*p82 + 0.259*p85 + 146*perm))/

(1+(exp(-1.58 - 0.061*p43 + 0.028*p82 + 0.259*p85 + 146*perm)))

245.9 0.426

p=(exp(-2.49 - 0.076*p43 - 0.027*p81 + 3.58*bulk_d))/

(1+(exp(-2.49 - 0.076*p43 - 0.027*p81 + 3.58*bulk_d)))

247.1 0.411

Silver (196) 01075 2.0 μg/L Poor models

p=(exp(0.703 + 0.107*p83 + 0.230*p91 + 0.338*welldn - 122*perm))/

(1+(exp(0.703 + 0.107*p83 + 0.230*p91 + 0.338*welldn – 122*perm)))

258.6 0.298

p=(exp(1.67 + 0.016*p_ag + 0.342*welldn – 2.06*p_org - 130*perm))/

(1+(exp(1.67 + 0.016*p_ag + 0.342*welldn – 2.06*p_org - 130*perm)))

260.7 0.259

Simazine (197) 04035, 39055 0.01 μg/L

p=(exp(-0.719 + 0.098*p21 + 0.052*p81 – 0.028*p42 - 0.463*p91))/

(1+(exp(-0.719 + 0.098*p21 + 0.052*p81 ñ 0.028*p42 - 0.463*p91)))

146.8 0.640

p=(exp(-2.97 + 0.045*p_urban + 0.038*p_ag + 2.61*p_org + 0.00004*TDA))/

(1+(exp(-2.97 + 0.045*p_urban + 0.038*p_ag + 2.61*p_org + 0.00004*TDA)))

147.8 0.615

Sodium (198) 00930 75 mg/L

p=(exp(-3.65 - 0.043*p_forest + 0.037*p51 + 4.76*bulk_d + 0.821*welldn))/

(1+(exp(-3.65 - 0.043*p_forest + 0.037*p51 + 4.76*bulk_d + 0.821*welldn)))

254.4 0.773

p=(exp(-6.13 - 0.074*p43 + 0.041*p51 + 6.48*bulk_d + 0.790*welldn + 0.872*sodchl_use))/

(1+(exp(-6.13 - 0.074*p43 + 0.041*p51 + 6.48*bulk_d + 0.790*welldn + 0.872*sodchl_use)))

253.1 0.771

Specific conductance (199) 00095 1,500 μS/cm

p=(exp(-2.02 – 0.066*p42 + 0.090*p82 + 0.048*p51 + 0.208*welldn))/

(1+(exp(-2.02 - 0.066*p42 + 0.090*p82 + 0.048*p51 + 0.208*welldn)))

253.2 0.757

p=(exp(-0.871 ñ 0.067*p_forest ñ 0.018*p_urban + 173*perm + 0.187*welldn))/

(1+(exp(-0.871 - 0.067*p_forest ñ 0.018*p_urban + 173*perm + 0.187*welldn)))

266.6 0.720

Sulfate (203) 00945 100 mg/L

Table 3. Logistic regression-model equations and model-fit statistics for selected surface-water nonpoint-source 

contaminants in Texas.—Continued

[AIC, Akaike information criterion; TCEQ, Texas Commission on Environmental Quality; see table 2 for abbreviation definitions of 

model variables]

Contaminant and

(TCEQ contaminant number)

Water-quality

parameter code(s)

Threshold

concentration

Model

comments
Estimating the Susceptibility of Surface Water to Nonpoint-Source Contamination 23

p=(exp(-3.47 - 0.089*p41 + 5.76*bulk_d ñ 0.182*p43 - 0.053*p_drain))/

(1+(exp(-3.47 - 0.089*p41 + 5.76*bulk_d ñ 0.182*p43 - 0.053*p_drain)))

293.4 0.676

p=(exp(0.575 ñ 0.065*p_forest + 124*perm + 0.015*p_urban ñ 0.035*p_drain))/

(1+(exp(0.575 ñ 0.065*p_forest + 124*perm + 0.015*p_urban ñ 0.035*p_drain)))

292.9 0.681

Total dissolved solids 

(TDS)(205)

70301 500 mg/L

p=(exp(1.11 - 0.061*p42 ñ 0.051*p43 - 0.033*p81 + 0.048*p82 + 0.75*welldn))/

(1+(exp(1.11 - 0.061*p42 ñ 0.051*p43 - 0.033*p81 + 0.048*p82 + 0.75*welldn)))

273.1 0.717

p=(exp(-0.532 - 0.063*p_forest - 0.024*p_ag + 3.28*bulk_d + 0.653*welldn))/

(1+(exp(-0.532 - 0.063*p_forest - 0.024*p_ag + 3.28*bulk_d + 0.653*welldn)))

273.8 0.710

Toluene (211) 34010 0.01 μg/L

p=(exp(-1.58 + 0.120*p21 – 0.222*p43 - 0.079*MCS + 301*perm))/

(1+(exp(-1.58 + 0.120*p21 – 0.222*p43 - 0.079*MCS + 301*perm)))

66.5 0.615

p=(exp(9.69 - 0.269*p43 – 0.050*p71 – 2.73*hydgrp – 0.048*MCS))/

(1+(exp(9.69 - 0.269*p43 – 0.050*p71 – 2.73*hydgrp ñ 0.048*MCS)))

69.0 0.607

Total coliform (213) 31501 20,000 counts/100 mL

p=(exp(-0.266 + 0.085*p_urban ñ 0.028*p_forest + 4.09*manure_p))/

(1+(exp(-0.266 + 0.085*p_urban ñ 0.028*p_forest + 4.09*manure_p)))

54.5 0.801

p=(exp(-1.77 + 1.09*p21 ñ 0.057*p42 + 7.54*p_org))/

(1+(exp(-1.77 + 1.09*p21 ñ 0.057*p42 + 7.54*p_org)))

52.1 0.786

Uranium (223) 22703, 75990, 80020 2.0 μg/L

p=(exp(-8.84 + 0.117*p81 + 0.122*p82 + 1.11*p85 + 0.138*p51))/

(1+(exp(-8.84 + 0.117*p81 + 0.122*p82 + 1.11*p85 + 0.138*p51)))

44.1 0.856

p=(exp(-13.1 + 0.157*p_ag + 0.114*p_urban + 185*perm + 0.162*p51))/

(1+(exp(-13.1 + 0.157*p_ag + 0.114*p_urban + 185*perm + 0.162*p51)))

45.0 0.877

Xylenes (total) (226) 81551 0.1 μg/L

p=(exp(-2.43 + 0.00013*TDA – 1.88*welldn + 0.625*p92 + 127*perm))/

(1+(exp(-2.43 + 0.00013*TDA – 1.88*welldn + 0.625*p92 + 127*perm)))

51.6 0.533

p=(exp(1.56 + 0.00011*TDA – 1.34*welldn - 0.934*hydgrp))/

(1+(exp(1.56 + 0.00011*TDA – 1.34*welldn - 0.934*hydgrp)))

52.1 0.563

Zinc (227) 01090 50 μg/L

p=(exp(-0.401 + 0.0005*popdn + 0.146*p83 + 0.384*p92))/

(1+(exp(-0.401 + 0.0005*popdn + 0.146*p83 + 0.384*p92)))

262.4 0.342

p=(exp(-0.446 + 0.075*p22 + 0.152*p83 + 0.410*p92))/

(1+(exp(-0.446 + 0.075*p22 + 0.152*p83 + 0.410*p92)))

259.7 0.373

Table 3. Logistic regression-model equations and model-fit statistics for selected surface-water nonpoint-source 

contaminants in Texas.—Continued

[AIC, Akaike information criterion; TCEQ, Texas Commission on Environmental Quality; see table 2 for abbreviation definitions of 

model variables]

Contaminant and

(TCEQ contaminant number)

Water-quality

parameter code(s)

Threshold

concentration

Model

comments
24 Estimating the Susceptibility of Surface Water in Texas to Nonpoint-Source Contamination by Use of Logistic Regression 

Modeling

Summary

The Texas Commission on Environmental Quality 

(TCEQ) and the U.S. Geological Survey (USGS) developed 

procedures for assessing the susceptibility of public water-supply (PWS) source waters to the occurrence of 227 drinkingwater contaminants identified by the TCEQ. These procedures 

are being used by TCEQ to protect sources of drinking water 

and to develop protective and cost-effective water-supply monitoring and management strategies. To assess the susceptibility 

of surface waters used as PWS to contamination from nonpoint 

sources, water-quality data at 323 USGS and TCEQ sampling 

sites were matched with geographic information systemderived watershed characteristics and contaminant source information for the drainage basins associated with these sites. 

Logistic regression models were developed to estimate the 

probability that a contaminant will occur at or greater than a 

TCEQ-specified threshold. Statistically significant regression 

models were developed for 63 of the 227 contaminants. Of the 

remaining 164 contaminants, 106 were not modeled because 

monitoring data were available at less than 10 percent of the 

323 sites; 29 were not modeled because of few detections of the 

contaminant in the otherwise adequate monitoring data; 27 

were not modeled because of the absence of monitoring data; 

and 2 were not modeled because thresholds were not specified. 

The logistic regression models in this report were developed 

using the best available data. Many of the models could be 

improved with additional data, in particular from locations that 

were lacking data. Likewise, many of the 164 unmodeled contaminants could potentially be modeled if additional monitoring 

data are collected that target gaps in the current information.

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