Water-Resources Investigations Report 01-4178

NATIONAL WATER-QUALITY ASSESSMENT PROGRAM

Variability of Pesticide Detections and

Concentrations in Field Replicate Water

Samples Collected for the National

Water-Quality Assessment Program,

1992-97

U.S. Department of the Interior

U.S. Geological Survey
Variability of Pesticide Detections and

Concentrations in Field Replicate Water

Samples Collected for the National

Water-Quality Assessment Program,

1992-97

U.S. Department of the Interior

U.S. Geological Survey

By Jeffrey D. Martin

Indianapolis, Indiana

2002

NATIONAL WATER-QUALITY ASSESSMENT PROGRAM

Water-Resources Investigations Report 01-4178
U.S. Department of the Interior

Gale A. Norton, Secretary

U.S. Geological Survey

Charles G. Groat, Director

The use of firm, trade, and brand names in this report is for identification purposes

For additional information write to:

Chief, Pesticides National Synthesis Project, NAWQA

U.S. Geological Survey

Placer Hall, Suite 2012

6000 J Street

Sacramento, CA 95819-6129

U.S. Geological Survey

Branch of Information Services

Box 25286

Federal Center

Denver, CO 80225

Copies of this report can be purchased from:

only and does not constitute endorsement by the U.S. Government.

Information regarding the National Water-Quality Assessment (NAWQA) Program is available on

the Internet via the World Wide Web. You can connect to the NAWQA Home Page at:

http://water.usgs.gov/nawqa/
Foreword

The U.S. Geological Survey (USGS) is committed

to serve the Nation with accurate and timely scientific information that helps enhance and protect

the overall quality of life, and facilitates effective

management of water, biological, energy, and

mineral resources. Information on the quality of

the Nation’s water resources is of critical interest

to the USGS because it is so integrally linked to

the long-term availability of water that is clean

and safe for drinking and recreation and that is

suitable for industry, irrigation, and habitat for

fish and wildlife. Escalating population growth

and increasing demands for the multiple water uses

make water availability, now measured in terms

of quantity and quality, even more critical to the

long-term sustainability of our communities and

ecosystems.

The USGS implemented the National WaterQuality Assessment (NAWQA) Program to

support national, regional, and local information

needs and decisions related to water-quality management and policy. Shaped by and coordinated

with ongoing efforts of other Federal, State, and

local agencies, the NAWQA Program is designed

to answer: What is the condition of our Nation’s

streams and ground water? How are the conditions

changing over time? How do natural features

and human activities affect the quality of streams

and ground water, and where are those effects most

pronounced? By combining information on water

chemistry, physical characteristics, stream habitat,

and aquatic life, the NAWQA Program aims to

provide science-based insights for current and

emerging water issues. NAWQA results can contribute to informed decisions that result in practical

and effective water-resource management and

strategies that protect and restore water quality.

Since 1991, the NAWQA Program has implemented interdisciplinary assessments in more than

50 of the Nation’s most important river basins and

aquifers, referred to as Study Units. Collectively,

these Study Units account for more than 60 percent

of the overall water use and population served by

public water supply, and are representative of

the Nation’s major hydrologic landscapes, priority

ecological resources, and agricultural, urban, and

natural sources of contamination.

Each assessment is guided by a nationally consistent study design and methods of sampling and

analysis. The assessments thereby build local

knowledge about water-quality issues and trends

in a particular stream or aquifer while providing

an understanding of how and why water quality

varies regionally and nationally. The consistent,

multi-scale approach helps to determine if certain types of water-quality issues are isolated

or pervasive, and allows direct comparisons of

how human activities and natural processes affect

water quality and ecological health in the Nation’s

diverse geographic and environmental settings.

Comprehensive assessments on pesticides, nutrients, volatile organic compounds, trace metals,

and aquatic ecology are developed at the national

scale through comparative analysis of the StudyUnit findings.

The USGS places high value on the communication and dissemination of credible, timely, and

relevant science so that the most recent and available knowledge about water resources can be

applied in management and policy decisions. We

hope this NAWQA publication will provide you

the needed insights and information to meet your

needs, and thereby foster increased awareness

and involvement in the protection and restoration

of our Nation’s waters.

The NAWQA Program recognizes that a national

assessment by a single program cannot address all

water-resource issues of interest. External coordination at all levels is critical for a fully integrated

understanding of watersheds and for cost-effective

management, regulation, and conservation of our

Nation’s water resources. The Program, therefore,

depends extensively on the advice, cooperation,

and information from other Federal, State, interstate, Tribal, and local agencies, non-government

organizations, industry, academia, and other stakeholder groups. The assistance and suggestions of

all are greatly appreciated.

Robert M. Hirsch

Chief Hydrologist
Contents V

CONTENTS

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

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

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

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

Objectives and Methods for Collection and Analysis of Field Replicates.................................................................. 4

Objectives and Use............................................................................................................................................ 4

Types of Field Replicates .................................................................................................................................. 5

Collection Guidelines........................................................................................................................................ 5

Analytical Methods for Pesticides .................................................................................................................... 6

Data Compilation and Characteristics............................................................................................................... 7

Statistical Methods, Calculations, and Analytical Approach............................................................................ 10

Variability of Pesticide Detections .............................................................................................................................. 18

Mean Detection Rate......................................................................................................................................... 31

Replicate Sets with Inconsistent Detections ..................................................................................................... 31

Variability of Pesticide Concentrations....................................................................................................................... 35

General Patterns of Variability .......................................................................................................................... 35

Pooled Estimates of Variability......................................................................................................................... 35

Presentation and Rounding of Estimates of Variability .................................................................................... 47

Use of Estimates of Variability of Concentrations in Water-Quality Assessments..................................................... 49

Example 1: Confidence Limits for a Single Water-Quality Measurement....................................................... 50

Example 2: Confidence Limits for a Single Water-Quality Measurement, Corrected for Recovery ............... 51

Example 3: The Concentration Needed to be Assured of Exceeding a Water-Quality Standard .................... 53

Example 4: Are Two Water-Quality Measurements Different? ....................................................................... 55

Summary ..................................................................................................................................................................... 55

References Cited ......................................................................................................................................................... 57

Appendixes

1. Pesticide registry numbers, analytical methods, and parameter codes ......................................................... 60

2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets

with inconsistent detections of pesticides in field replicates......................................................................... 64

FIGURES

1. Map showing the locations of U.S. Geological Survey National Water-Quality Assessment Program

Study Units, 1991............................................................................................................................................. 3

2–6. Graphs showing:

2. Temporal distribution of field replicates ................................................................................................... 9

3. Variability of atrazine in field replicates ................................................................................................... 17

4. Three approaches for the analysis of variability of concentrations for replicate sets with

inconsistent detections ............................................................................................................................. 19

5. Variability of detection of pesticides in field replicates ............................................................................ 32

6. Comparison of estimates of variability of concentrations in field replicates ............................................ 48

TABLES

1. Distribution of field replicates among type of site, analytical method, and Study Units of the

National Water-Quality Assessment Program............................................................................................... 8

2. Number of replicate sets and consistency of pesticide detection or nondetection......................................... 11

3. Comparison of variability of pesticide concentrations in surface-water and ground-water field

replicates........................................................................................................................................................ 16
VI Contents

4. Variability of pesticide detections in field replicates where mean concentration of the replicate

sets was less than the minimum reporting level ............................................................................................ 22

5. Variability of pesticide detections in field replicates where mean concentration of the replicate

sets was greater than or equal to the minimum reporting level and was less than or equal to

10 times the minimum reporting level........................................................................................................... 25

6. Variability of pesticide detections in field replicates where mean concentration of the replicate

sets was more than 10 times the minimum reporting level ........................................................................... 28

7. Variability of pesticide concentrations in field replicates.............................................................................. 36

8. Typical variability of pesticide concentrations in field replicates.................................................................. 45

9. Assessment of constant variance in a concentration range ........................................................................... 46

10. Pooled estimate of bias in the analytical method for a measurement of alachlor near

0.009 micrograms per liter ............................................................................................................................ 52

WATER-QUALITY UNITS AND ABBREVIATIONS

Water-quality units used in this report: Chemical concentration is given in micrograms per liter (μg/L).

Micrograms per liter is a unit expressing the concentration of chemical constituents in solution as weight

(micrograms) of solute per unit volume (liter) of water. For pesticide concentrations in the range commonly

found in environmental water samples, the numerical value is the same as for concentrations in parts per

billion.

The following abbreviations are used in this report:

Abbreviation Description

α Probability of a Type I error

CV Coefficient of variation

E Remark indicating that concentration is estimated

Factor for calculating a one-sided tolerance bound

GCMS Gas chromatography/mass spectrometry

HPLC High-performance liquid chromatography

IRS Inconsistent replicate sets

LOWESS Locally weighted scatterplot smooths

LT-MDL Long-term method detection limit

MDL Method detection limit

MRL Minimum reporting level

μ Population mean

μg/L Microgram per liter

n Sample size

NAWQA National Water-Quality Assessment

NWQL National Water Quality Laboratory

OBSP Organic Blind Sample Program

p Probability of obtaining the result by chance

p Proportion of the normal population

QC Quality control

RSD Relative standard deviation

SD Standard deviation

Tp Upper tolerance bound

t Value of the t-distribution

USGS U.S. Geological Survey

X Sample mean or a single measurement

g'
Abstract 1

Variability of Pesticide Detections and

Concentrations in Field Replicate Water Samples

Collected for the National Water-Quality

Assessment Program, 1992–97

By Jeffrey D. Martin

Abstract

Field replicate water samples (“field

replicates”) collected for the U.S. Geological

Survey National Water-Quality Assessment

(NAWQA) Program during 1992 to 1997 were

used to assess the variability of pesticide detections and concentrations in environmental

water samples collected from the surfaceand ground-water-quality networks of the

NAWQA Program. Field replicates are two or

more identically collected, processed, and analyzed environmental water samples that are

used to assess the overall variability of field

and laboratory procedures. Variability is the

degree of random error in independent measurements of the same quantity and is the opposite of precision—the degree of mutual

agreement. Information on variability can be

used to estimate the reproducibility of individual measurements, the concentration needed to

be assured of exceeding a water-quality standard, and the likelihood that two measurements of water quality are different.

Variability of pesticide detections was

assessed by calculating the mean percentage

detection of a pesticide and the percentage of

inconsistent replicate sets. Variability of pesticide concentrations was assessed by pooling

estimates of the standard deviation and relative

standard deviation in replicate sets. Variability

of pesticide detections and concentrations was

a function of concentration, and estimates of

variability were developed for discrete ranges

of concentration. Reliability of estimates of

variability was assessed by calculating

90-percent upper confidence bounds for the

percentage of inconsistent replicate sets and

for the pooled estimates.

The variability of detection for most

pesticides is high at concentrations less than

the minimum reporting level, but the variability of detection decreases dramatically at higher

concentrations. In view of the highly diverse

sources of water submitted as field replicates

for the NAWQA Program and the generally

low concentrations (concentrations in

79 percent of replicate sets were less than

0.1 microgram per liter) of pesticides in most

replicates, inconsistent detections in replicate

sets likely were caused by variability in the analytical method and by water-matrix interferences (or other loss processes) that result in

false-negative errors. Consequently, estimates

of the frequency of detection of pesticides in

environmental water samples collected for the

NAWQA Program probably are biased low because of false-negative errors at concentrations

near the minimum reporting level.

Correlation analysis indicates that for

most pesticides and concentrations, pooled estimates of relative standard deviation rather

than pooled estimates of standard deviation

should be used to estimate variability because

pooled estimates of relative standard deviation

are less affected by heteroscedasticity. The
2 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

median pooled relative standard deviation was

calculated for all pesticides to summarize the

typical variability for pesticide data collected

for the NAWQA Program. The median pooled

relative standard deviation was 15 percent at

concentrations less than 0.01 micrograms per

liter (μg/L), 13 percent at concentrations near

0.01 μg/L, 12 percent at concentrations near

0.1 μg/L, 7.9 percent at concentrations near

1 μg/L, and 2.7 percent at concentrations

greater than 5 μg/L. Pooled estimates of standard deviation or relative standard deviation

presented in this report are larger than estimates based on averages, medians, smooths, or

regression of the individual measurements of

standard deviation or relative standard deviation from field replicates. Pooled estimates,

however, are the preferred method for characterizing variability because they provide unbiased estimates of the variability of the

population. Assessments of variability based

on standard deviation (rather than variance)

underestimate the true variability of the population. Because pooled estimates of variability

are larger than estimates based on other approaches, users of estimates of variability must

be cognizant of the approach used to obtain the

estimate and must use caution in the comparison of estimates based on different approaches.

INTRODUCTION

The U.S. Geological Survey (USGS) began

implementing the National Water-Quality Assessment (NAWQA) Program in 1991. The goals of

the NAWQA Program are to describe current water-quality conditions and trends in the Nation’s

streams and ground water and to understand the

natural characteristics and human influences that

affect water quality (Hirsch and others, 1988, p. 1).

The NAWQA Program is assessing the water

quality in more than 50 of the Nation’s largest river

basins and aquifers. These river basins and aquifers, known as NAWQA Study Units, account for

about half the land area of the conterminous United

States and approximately 60 to 70 percent of the

Nation’s water use and population served by public

water supplies (Leahy and Wilber, 1991, p. 1).

The Study-Unit investigations are divided into

three groups that assess water quality on a rotational schedule. Investigations of water quality in 20

Study Units began in 1991 (fig. 1). Study-Unit investigations and national synthesis are the major

design features of the NAWQA Program that allow

water-quality information collected and interpreted

locally to be integrated into a national description

of water quality (Gilliom and others, 1995, p. 2–3).

One of the major tasks of the NAWQA Program is to assess the occurrence and distribution of

pesticides in surface and ground water. The goal

for Study-Unit investigations is to identify pesticides in the water resources of the Study Unit and

to characterize and explain the geographic and seasonal distributions of pesticides (Gilliom and others, 1995, p. 4–6). The goal for national synthesis

is to characterize, compare, and explain the geographic and seasonal distributions of pesticides

among the broad range of land-use and hydrologic

settings in the United States.

Purpose and Scope

The purpose of this report is to assess the

variability of pesticide detections and concentrations in field replicate water samples and, from the

data for the field replicate samples, estimate the

variability of pesticide detections and concentrations in environmental water samples collected

from the surface- and ground-water-quality networks of the NAWQA Program. This report

summarizes concentrations of 86 pesticides and

pesticide degradates (hereafter referred to as “pesticides”) in field replicate water samples collected

by the first 20 Study Units of the NAWQA Program during 1992 to 1997 and provides examples

of the use of estimates of variability in waterquality assessments.

Field replicate water samples (hereafter referred to as “field replicates”) were collected in

sets—either duplicates (sets consisting of two replicates) or triplicates (sets consisting of three replicates) routinely throughout the period of collection

of environmental water samples. Analytical data

for 241 sets of surface-water field replicates and

95 sets of ground-water field replicates for pesticides analyzed by gas chromatography/mass
INTRODUCTION 3

Other Study Units

EXPLANATION

First 20 Study Units

ALBE

ACFB

CCPT

CNBR

CONN

GAFL

HDSN

LSUS

NVBR

OZRK

POTO

REDN

RIOG

SANJ

SPLT

TRIN

USNK

WMIC

WHIT

WILL

Albemarle-Pamlico Drainage

Apalachicola-Chattahoochee-Flint River Basin

Central Columbia Plateau

Central Nebraska Basin

Connecticut, Housatonic, and Thames River Basins

Georgia-Florida Coastal Plain

Hudson River Basin

Lower Susquehanna River Basin

Nevada Basin and Range

Ozark Plateaus

Potomac River Basin

Red River of the North

Rio Grande Valley

San Joaquin-Tulare Basins

South Platte River Basin

Trinity River Basin

Upper Snake River Basin

Western Lake Michigan Drainage

White River Basin

Willamette Basin

Map Acronym NAWQA Study Units

CCPT

WILL

SANJ

NVBR

USNK

RIOG

SPLT

CNBR

REDN

TRIN

OZRK

WHIT

WMIC

ACFB

GAFL

ALBE

POTO

LSUS

HDSN

CONN

Figure 1. Locations of U.S. Geological Survey National Water-Quality Assessment Program Study Units, 1991.
4 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

spectrometry (GCMS) and data for 161 sets of surface-water field replicates and 92 sets of groundwater field replicates for pesticides analyzed by

high-performance liquid chromatography (HPLC)

were pooled for analysis and are presented in tables

and selected figures that provide national summaries of the variability of pesticide detections and

concentrations.

The variability of pesticide detections

was assessed by calculating the mean percentage

detection of a pesticide and the percentage of inconsistent field replicates. The mean percentage

detection and the percentage of inconsistent replicate sets were calculated separately for three

ranges of concentration that are a function of the

minimum reporting level (MRL): (1) less than the

MRL, (2) the MRL to 10 times the MRL, and

(3) greater than 10 times the MRL. The reliability

of the estimates of variability of detection was assessed by calculation of the 90-percent upper confidence bound for the percentage of inconsistent

field replicates.

The variability of pesticide concentrations

was assessed by calculating standard deviation and

relative standard deviation of replicates in a set and

examining these statistics as a function of the mean

concentration of the replicate set. Replicate sets

consisting of all nondetections were excluded from

the analysis of variability of pesticide concentrations. Pooled estimates of the standard deviation

and relative standard deviation are reported for

eight overlapping ranges of concentration. The reliability of the pooled estimates of variability was

assessed by calculation of the 90-percent upper

confidence bound.

Acknowledgments

I thank the NAWQA field teams that

collected, reviewed, compiled, and answered questions about the analytical data for the field replicates summarized in this report. I thank the

following U.S. Geological Survey employees:

David K. Mueller, Charles G. Crawford,

Jonathon C. Scott, Mark E. Brigham,

Timothy C. Willoughby, and Robert J. Gilliom for

invaluable discussions on analytical approaches;

Jonathon C. Scott for assistance in data compilation and programming; Charles G. Crawford,

Robert J. Gilliom, David K. Mueller, and

Peter F. Rogerson for technical review of

early drafts of the report; Michael Eberle for

editorial review; and Barbara A. Korzendorfer,

Patricia H. Long, and Ann Marie Squillacci for

assistance in production of the text.

OBJECTIVES AND METHODS FOR

COLLECTION AND ANALYSIS OF

FIELD REPLICATES

Replicates are environmental water samples

that are used to assess variability. Replicates are

two or more environmental water samples that are

collected or processed such that they are thought to

be identical in composition (Mueller and others,

1997, p. 2) and are analyzed by identical laboratory

methods. Variability is the degree of random error

in independent measurements of the same quantity

(Mueller, 1998, p. vii) and is the opposite of precision—the degree of mutual agreement in independent measurements of the same quantity (Taylor,

1987, p. 7). High-quality data are characterized

by low variability (high precision), whereas lowquality data are characterized by high variability

(low precision). Replicates measure different

sources of variability depending on the point in the

sampling process that replication is done and the

specific procedures, equipment, and personnel

used to collect, process, or analyze the replicates.

Objectives and Use

Field replicates are a particular type of replicate that allow assessment of all or nearly all of the

sources of variability that affect environmental water samples. Field replicates are identically collected, processed, and analyzed environmental water

samples that provide information on the overall

variability of field and laboratory procedures

(termed “sampling variability” by Mueller, 1998,

p. vii). Because field replicates are collected, processed, and analyzed identically to environmental

water samples (or as much so as practicable), the

variability of pesticide detections or concentrations

in field replicates is used to estimate the variability

of pesticide detections or concentrations in environmental water samples.
OBJECTIVES AND METHODS FOR COLLECTION AND ANALYSIS OF FIELD REPLICATES 5

Information on variability is used to

(1) document the quality of the environmental data; (2) decide whether data quality is sufficient to

meet the study objectives or whether changes to

the data program or objectives are needed; and

(3) qualify, where needed, interpretation of waterquality data. Data-quality goals for the NAWQA

Program are (1) use of documented data-collection

methods, (2) measurement and assessment of the

quality of the data, and (3) water-quality assessments done with data of appropriate quality. Specifically, information on variability can be used to

estimate the precision or reproducibility of individual measurements, the concentration needed to be

assured of exceeding a water-quality standard, and

the likelihood that two measurements of water

quality are different.

Types of Field Replicates

The terminology concerning replicates

is confusing. Field replicates are collected in

sets—either duplicates (sets consisting of two replicates) or triplicates (sets consisting of three replicates). The term “replicates” refers to all similarly

collected and analyzed samples in a replicate set.

For the purposes of providing instructions for collecting and processing field replicates and for data

management, the terms “primary environmental

sample,” “duplicate environmental sample,” and

“triplicate environmental sample” are used to refer

to particular samples in the replicate set (Mueller

and others, 1997, p. 2).

Several types of field replicates were collected or processed to assess variability. The types of

field replicates differ in the sources of variability

assessed. Split replicates are processed by dividing a single sample of water into multiple samples.

Split replicates are used to assess variability associated with sample processing in the field (division

into subsamples, filtration of subsamples, field extraction, and transport) and laboratory analysis.

Split replicates cannot be used to assess variability

associated with sample collection. Concurrent

replicates are multiple samples collected from an

environmental matrix as closely as possible to the

same location and at the same time. Concurrent

replicates are used to assess variability associated

with sample collection, processing, and analysis.

Depending on the specific sampling procedures,

concurrent replicates also may include an unknown

amount of temporal or spatial environmental variability (true differences in concentrations over

short time intervals or small distances). Sequential

replicates are multiple samples collected from an

environmental matrix as closely as possible to the

same location but at different times (usually one

right after the other). Sequential replicates are used

to assess the same sources of variability as concurrent replicates but include a larger amount of temporal environmental variability because the time

between collection of the replicates is longer.

Field replicates were collected or processed

by use of similar procedures as for environmental

water samples. Field procedures were similar—but

not exactly the same—because the collection and

processing of field replicates might have required

larger volumes of water, larger or more numerous

containers, or longer holding times for sample processing. Procedures for the collection and processing of environmental water samples for the

NAWQA Program are described by Shelton (1994)

for surface water and by Koterba and others (1995)

for ground water.

Collection Guidelines

Guidelines for the collection of qualitycontrol (QC) samples for the 20 NAWQA StudyUnit investigations that began in 1991 recommended that approximately 15 percent of the Study-Unit

analytical budget be allocated for the analysis of

QC samples collected by NAWQA field teams.

Field blanks (for estimating bias), field replicates

(for estimating variability), and replicate field matrix spikes (for estimating bias and variability)

were the recommended types of QC samples, but

NAWQA field teams had the flexibility to collect

the types of QC samples that addressed individual

Study-Unit conditions and the concerns of field

teams (P.P. Leahy, U.S. Geological Survey, written

commun., December 21, 1992, and June 9, 1993).

The guidelines recommended that field

replicates be (1) collected routinely during the

collection period of environmental water samples;

(2) collected during periods when concentrations

are expected to be greater than the MRL; and

(3) distributed among sites and times to assess a
6 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

broad range of locations, hydrologic conditions,

concentrations, water types, field personnel, and

field equipment. Field replicates for pesticides in

ground water were not emphasized to the same degree as field replicates for surface water because

pesticide concentrations were expected to be less

than the MRL at many ground-water sites. Guidelines for the collection of QC samples for NAWQA

Study-Unit investigations that began in 1994 or

1997 have been revised and published (Koterba

and others, 1995; Mueller and others, 1997).

Analytical Methods for Pesticides

Environmental water samples and field replicates were analyzed for pesticides at the National

Water Quality Laboratory (NWQL) of the USGS in

Arvada, Colo. The NWQL developed two analytical methods for identification and quantitation of

various pesticides at concentrations as low as

0.001 μg/L. NAWQA field teams select these analytical methods by requesting NWQL laboratory

schedules, which are specific lists of pesticides that

are analyzed by particular types of laboratory instrumentation and procedures (Timme, 1995,

p. 22). NWQL schedules are identified for the

benefit of USGS readers of this report. Chemical

Abstract Service registry numbers, analytical

methods, and USGS National Water Information

System and U.S. Environmental Protection Agency

Data Storage and Retrieval System parameter

codes are presented in appendix 1.

NWQL schedules 2001 and 2010

(Timme, 1995, pp. 60, 80) request analyses for

47 pesticides that are isolated from filtered water

by C-18 solid-phase extraction and identified and

quantitated by capillary column GCMS with selected-ion monitoring (Zaugg and others, 1995).

The pesticide acetochlor was added to the GCMS

method in June 1994 (Lindley and others, 1996).

NWQL schedules 2050 and 2051 (Timme, 1995,

pp. 61, 80) request analyses for 39 pesticides that

are isolated from filtered water by Carbopak-B

solid-phase extraction and identified and quantitated by HPLC with a photodiode-array detector

(Werner and others, 1996). The pesticides carbaryl,

carbofuran, and linuron are analyzed by both analytical methods. Both methods have optional procedures for the onsite extraction of water samples

by field personnel. Schedules 2010 and 2051

request analyses for pesticides that were extracted

from filtered water samples onsite, whereas schedules 2001 and 2050 request analyses for pesticides

that were extracted from filtered water samples at

the NWQL. For the purposes of this report, the location of sample extraction is not considered in the

analysis of field replicates (that is, a valid replicate

set may consist of field-extracted and laboratoryextracted samples).

The NWQL has historically used the minimum reporting level (MRL) for reporting analytical data (Oblinger Childress and others, 1999,

p. 2). The MRL is the “less-than” concentration

used for reporting nondetections of an analyte. The

MRL is defined as

the smallest measured concentration of a

constituent that may be reliably reported using a

given analytical method (Timme, 1995, p. 92).

The definition of the MRL is not quantitatively specific, and various approaches have been

used by NWQL to set the concentration of the

MRL. For the analytical methods used in this

report, the MRL initially was set equal to the

method detection limit (MDL) but was subsequently revised for 14 pesticides on the basis of

laboratory QC information. An in-depth discussion

of the various reporting levels used by the NWQL

and considerations for their use and interpretation

is presented in Oblinger Childress and others

(1999).

Statistically determined method detection

limits were calculated for all pesticides in both

methods. The MDL is defined as

the minimum concentration of a substance

that can be identified, measured, and reported with

99 percent confidence that the analyte concentration is greater than zero; determined from analysis

of a sample in a given matrix containing [the] analyte (Wershaw and others, 1987, p. 4)

and was determined by the procedure described by

the U.S. Environmental Protection Agency (1992).

The calculated MDL controls the rate of falsepositive errors (determining that a pesticide is

present in a sample when, in truth, it is absent)

primarily on the basis of quantitation variability at

concentrations near the MDL. The MDLs

determined in a matrix of pesticide-grade water

ranged from 0.001 to 0.032 μg/L (Zaugg and

others, 1995, pp. 32–33; Werner and others, 1996,

p. 18).
OBJECTIVES AND METHODS FOR COLLECTION AND ANALYSIS OF FIELD REPLICATES 7

The MDL does not control the rate of falsenegative errors (determining that a pesticide is absent in a sample when, in truth, it is present). If a

pesticide is present in a sample at the concentration

of the MDL, the probability is 50 percent that the

measured concentration will be less than the MDL

(U.S. Geological Survey National Water Quality

Laboratory Technical Memorandum 94–12, 1994).

If detections are censored at the MDL, then

50 percent of the samples with pesticides present at

the concentration of the MDL will be reported as

nondetections. In the above discussion, no bias in

the analytical method is assumed. If the analytical

method is negatively biased (recovery is less than

100 percent), the frequency of false-negative errors

may be increased (P.F. Rogerson, U.S. Geological

Survey, written commun., March 2, 2001).

Low-level detections of pesticides, however,

are not censored at the MRL/MDL for the analytical methods used in this report.

With clean environmental samples, analysts

are able to detect analytes in concentrations less

than the MDL; while conversely, with complex samples, analysts may be unable to detect analytes in

concentrations greater than the MDL (U.S. Geological Survey National Water Quality Laboratory

Technical Memorandum 94–12, 1994).

All detections (pesticides conclusively identified by retention time and spectral characteristics)

are quantitated, and concentrations less than the

MRL/MDL are reported by the NWQL with an “E”

remark (for example, E0.004 μg/L) to indicate that

the concentration (but not the presence) of the pesticide is estimated. Although detections of pesticides by the analytical methods used for this report

are not censored at the MRL, the probability of detection decreases as concentration decreases. The

word “minimum” in MRL, in conjunction with analytical methods that report detections at estimated

concentrations that are less than the MRL, has created confusion for some users and is one of the reasons why new data-reporting conventions and

terminology developed by NWQL were needed

(Oblinger Childress and others, 1999, pp. 6–10).

 Any detections of five pesticides analyzed

by GCMS (azinphos-methyl, carbaryl, carbofuran,

desethylatrazine, and terbacil) and six pesticides

analyzed by HPLC (aldicarb, aldicarb sulfone, aldicarb sulfoxide, chlorothalonil, dichlobenil, and

DNOC) also are reported by the NWQL with an

“E” remark, regardless of concentration. These

pesticides have lower or more variable recovery in

laboratory QC spikes than the other pesticides analyzed by the method (Zaugg and others, 1995,

p. 35; Werner and others, 1996, pp. 27, 34; U.S.

Geological Survey National Water Quality Laboratory Technical Memorandum 98–03A, 1998).

Nondetections (pesticides that could not be

conclusively identified by retention time and spectral characteristics) are reported by the NWQL as

less than the MRL. Before December 15, 1997, the

MRL was set equal to the MDL. On December 15,

1997, the MRLs for 14 of the 39 pesticides analyzed by HPLC were raised (U.S. Geological Survey National Water Quality Laboratory Technical

Memorandum 98–03A, 1998). Justification of an

MRL that was greater than the MDL was based on

internal NWQL spiking programs, which showed

that the rate of false-negative errors was unacceptably high at concentrations near the MDL. Detections of these 14 pesticides before December 15,

1997, are valid; only the numerical threshold used

to indicate nondetections increased.

Data Compilation and Characteristics

Water-quality data for field replicates and

other types of QC samples were reviewed by

NAWQA field teams and submitted for aggregation into a national QC data base for the NAWQA

Program consistent with guidance provided by the

NAWQA Data and Software Integration Group

(written commun., October 23, 1997). Most teams

submitted QC data in December 1997 or January

1998.

The data set of field replicates used for this

report is a subset of the NAWQA national QC data

base obtained by retrieving samples that were

(1) analyzed for pesticides by GCMS or HPLC

(2) coded as environmental samples or QCreplicate environmental samples, (3) collected at

the same field site and on the same date, and

(4) collected by the first 20 Study Units. Replicate

sets having replicates with times of sample collection more than 2 hours apart (seven sets) were examined carefully to ensure that the samples were

truly field replicates. Four sets of replicates were

deleted from the data set as a result of this check.

The frequency of inconsistently detected pesticide
8 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

in replicates was calculated for all replicate sets

and pesticides. Replicate sets with an unusually

large number (more than five or six) of inconsistently detected pesticides were examined carefully,

and seven of these replicate sets were referred to

Study-Unit teams for further review. Errors in two

replicate sets were caused by the switching of duplicate environmental samples among sites. Errors

in five replicate sets were caused by inclusion of

field spiked environmental samples as replicates,

either because of miscoding or by inadvertent

switching of sample bottles. Either the errors were

resolved or the samples were deleted from the data

set.

The data set used for this report consisted of

241 sets of surface-water field replicates and

95 sets of ground-water field replicates for pesticides analyzed by GCMS and 161 sets of surfacewater field replicates and 92 sets of ground-water

field replicates for pesticides analyzed by HPLC.

Field replicates were fairly well distributed among

the first 20 Study Units (table 1). Differences in the

number of replicates among Study Units can partly

be explained by differences in the number of environmental water samples collected. Of 402 sets of

surface-water replicates, 63 (16 percent) were triplicates (45 sets analyzed by GCMS and 18 sets analyzed by HPLC). Of 187 sets of ground-water

replicates, 7 (4 percent) were triplicates (7 sets analyzed by GCMS). Of the surface-water replicates,

49 percent were split replicates, 45 percent were

sequential replicates, and 6 percent were concurrent replicates. Of the ground-water replicates,

96 percent were sequential replicates and 4 percent

were concurrent replicates. Approximately

3 percent of the replicate sets consisted of both

field-extracted and laboratory-extracted samples.

Most of the surface-water replicates

(93 percent) and all of the ground-water replicates

used for this report were collected during 1993–95

(fig. 2), the 3-year intensive data-collection phase

for the first 20 Study Units (Gilliom and others,

1995, pp. 2–4). A much smaller number of replicates were collected during 1992 (a prototype

study of surface-water sampling by three Study

Units) and during 1996–97 (low-intensity monitoring at selected sites). Most replicates (74 percent)

were collected during April-August (fig. 2), a period that corresponds to the pesticide-application

season in much of the United States and to the period of high-frequency pesticide sampling at surfacewater sites for most Study Units.

Table 1. Distribution of field replicates among type of

site, analytical method, and Study Units of the National

Water-Quality Assessment Program

[Study-Unit abbreviations are explained in figure 1. GCMS, gas

chromatography/mass spectrometry; HPLC, high-performance liquid

chromatography]

Study

unit

Number of replicate sets

Surface-water sites Ground-water sites

GCMS HPLC GCMS HPLC

ACFB 18 19 6 7

ALBE 3 1 7 4

CCPT 8 8 5 3

CNBR 5 3 0 0

CONN 3 2 3 3

GAFL 13 10 8 9

HDSN 17 10 5 6

LSUS 15 7 0 6

NVBR 10 10 4 5

OZRK 4 2 8 9

POTO 24 7 10 3

REDN 13 10 11 10

RIOG 8 7 0 0

SANJ 25 11 3 2

SPLT 9 7 3 3

TRIN 19 12 11 11

USNK 12 7 0 0

WHIT 20 14 6 6

WILL 5 4 3 3

WMIC 10 10 2 2

Total 241 161 95 92
OBJECTIVES AND METHODS FOR COLLECTION AND ANALYSIS OF FIELD REPLICATES 9

Figure 2. Temporal distribution of field replicates (GCMS, gas chromatography/mass spectrometry; HPLC, highperformance liquid chromatography).
10 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

Statistical Methods, Calculations, and

Analytical Approach

The UNIVARIATE procedure of SAS

(SAS Institute, Inc., 1990, pp. 617–634) was used

to calculate mean concentration, variance of concentration, standard deviation of concentration

(SD), range of concentration, number of replicates,

detection rate, and other common statistics for the

replicates in each replicate set. The coefficient of

variation (CV) of replicates in a set was calculated

as the standard deviation divided by the mean

(CV is expressed as a proportion), and the relative

standard deviation (RSD) was calculated as CV

multiplied by 100 percent (RSD is expressed as a

percentage, Taylor, 1987, p. 20). Kendall’s tau, a

nonparametric measure of the correlation between

two variables (Conover, 1980, p. 256), and the approximate significance probability of the correlation were calculated using the KENDALL option

of the CORR procedure (SAS Institute, Inc., 1990,

pp. 209–224) and were used to test SD and RSD of

replicate sets for heteroscedasticity (increasing or

decreasing variability) over selected ranges of concentration. An approximate significance probability of less than 0.05 for Kendall’s tau was used to

indicate heteroscedasticity. Tests for heteroscedasticity were done only for pesticides with three or

more replicate sets in a concentration range. Locally weighted scatterplot smooths, termed LOWESS

smooths (Cleveland, 1979), were used to show the

relation of variability and concentration. A

smoothing factor of 0.25 was used for all smooths.

The UNIVARIATE procedure also was used

to calculate statistics that summarized variability of

detection and concentration over the appropriate

number of replicate sets. Other than for counts of

the number of replicates sets collected (table 2),

replicate sets that contained only nondetections of

a pesticide were excluded from statistical analysis

because they provide little information on the variability of detections or concentrations. The mean

detection rate of a pesticide is a measure of the

consistency of detection and was calculated as the

average of the percentage detections in each replicate set. The percentage detections in a replicate

set was 100 or 50 percent for duplicates or was

100, 66.7, or 33.3 percent for triplicates (replicate

sets with all nondetections were excluded from

analysis). Mean detection rate was weighted by the

number of replicates in the set (either 2 or 3). Confidence limits were not calculated for the mean

detection rate because, for most pesticides, sample

size was insufficient (less than 30) and the distribution of percent detection in replicate sets was too

highly skewed to assume a normal distribution of

means using the central limit theorem (Helsel and

Hirsch, 1992, p. 74).

Replicate sets with inconsistent detections

are those where a pesticide was detected in at least

one, but not all, replicates in the set (table 2). The

percentage of replicate sets with inconsistent detections (replicate sets that contain both detections

and nondetections of a pesticide) is a measure of

the variability of detection and was calculated as

the number of replicate sets with inconsistent detections divided by the sum of the number of replicate sets with consistent detections plus the number

of replicate sets with inconsistent detections. Replicate sets with consistent nondetections were excluded from the calculation because the objective

of the analysis was to evaluate the variability of detection rather than the variability of nondetection.

For brevity, “replicate sets with inconsistent detections” is sometimes referred to as “inconsistent

replicate sets” (IRS) in the text.

One-sided, 90-percent upper confidence

bounds were calculated for the percentage of nonconforming units following the method of Hahn

and Meeker (1991, pp. 104–105). Nonconforming

units in the context of this report are replicate sets

with inconsistent detection. Conforming units are

replicate sets that contain only detections of a pesticide (consistent detection). One-sided, upper confidence bounds were calculated to estimate an

upper limit of uncertainty in the measured rate of

inconsistency of detection. An upper confidence

bound was used because the objective of the analysis was to obtain a pessimistic estimate of detection

variability; in other words, “how bad might things

be?” (Hahn and Meeker, 1991, p. 30). A 90-percent

confidence level was selected for calculation of

the upper confidence bound, primarily because

higher levels of confidence have extremely wide

confidence limits (large confidence bounds). The

90-percent confidence level is a compromise between a reasonable level of confidence and the size

of the confidence interval. The 90-percent confidence bound is conservative because calculated

confidence bounds typically are greater than

90 percent (Hahn and Meeker, 1991, p. 101).
OBJECTIVES AND METHODS FOR COLLECTION AND ANALYSIS OF FIELD REPLICATES 11

Table 2. Number of replicate sets and consistency of pesticide detection or nondetection

Parameter

code

Pesticide

Analytical

method

MRL

(μg/L)

Number of

replicate

sets

Number of replicate sets

where

replicates in the set have

Percentage of

replicate sets where

replicates in the set

have Median

detected

concentration4

(μg/L)

Consistent

nondetections1

Consistent

detections2

Inconsistent

detections3

Consistent

detections

or consistent

nondetections

At least

one

detection

82671 Molinate GCMS 0.004 336 325 11 0 100.0 3.3 0.140

82665 Terbacil GCMS .007 330 324 6 0 100.0 1.8 .017

82672 Ethoprop GCMS .003 336 332 4 0 100.0 1.2 .009

49293 Norflurazon HPLC .024 253 250 3 0 100.0 1.2 .090

04024 Propachlor GCMS .007 336 332 4 0 100.0 1.2 .031

49299 DNOC HPLC .420 248 247 1 0 100.0 .4 .505

49297 Fenuron HPLC .013 252 251 1 0 100.0 .4 .140

49292 Oryzalin HPLC .310 253 252 1 0 100.0 .4 .515

49291 Picloram HPLC .050 245 244 1 0 100.0 .4 .110

49312 Aldicarb HPLC .550 253 253 0 0 100.0 .0 nc

49313 Aldicarb sulfone HPLC .100 250 250 0 0 100.0 .0 nc

49307 Chloramben HPLC .420 253 253 0 0 100.0 .0 nc

49306 Chlorothalonil HPLC .480 248 248 0 0 100.0 .0 nc

49305 Clopyralid HPLC .230 246 246 0 0 100.0 .0 nc

49304 Dacthal monoacid HPLC .017 248 248 0 0 100.0 .0 nc

38746 2,4-DB HPLC .240 249 249 0 0 100.0 .0 nc

38442 Dicamba HPLC .035 248 248 0 0 100.0 .0 nc

49302 Dichlorprop HPLC .032 249 249 0 0 100.0 .0 nc

49308 3-Hydroxycarbofuran HPLC .014 252 252 0 0 100.0 .0 nc

38487 MCPB HPLC .140 249 249 0 0 100.0 .0 nc

38501 Methiocarb HPLC .026 253 253 0 0 100.0 .0 nc

49294 Neburon HPLC .015 253 253 0 0 100.0 .0 nc

38866 Oxamyl HPLC .018 249 249 0 0 100.0 .0 nc

39542 Parathion GCMS .004 336 336 0 0 100.0 .0 nc

82664 Phorate GCMS .002 336 336 0 0 100.0 .0 nc

49236 Propham HPLC .035 253 253 0 0 100.0 .0 nc

39762 Silvex HPLC .021 248 248 0 0 100.0 .0 nc

39742 2,4,5-T HPLC .035 248 248 0 0 100.0 .0 nc

82684 Napropamide GCMS .003 336 320 15 1 99.7 4.8 .012

82666 Linuron GCMS .002 336 326 9 1 99.7 3.0 .022

Table 2. Number of replicate sets and consistency of pesticide detection or nondetection

[Pesticides are sorted by the percentage of sets that have consistent detections or nondetections, the percentage of sets that have at least one

detection, and pesticide name; parameter code, the number used to identify a pesticide in the U.S. Geological Survey National Water Information

System; MRL, minimum reporting level; μg/L, microgram per liter; GCMS, gas chromatography/mass spectrometry; HPLC, high-performance

liquid chromatography; nc, not calculated]
12 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

82681 Thiobencarb GCMS 0.002 336 330 5 1 99.7 1.8 0.011

82667 Methyl parathion GCMS .006 336 331 4 1 99.7 1.5 .018

82669 Pebulate GCMS .004 336 331 4 1 99.7 1.5 .024

34253 alpha-HCH GCMS .002 336 334 1 1 99.7 .6 .019

82677 Disulfoton GCMS .017 336 335 0 1 99.7 .3 .003

82675 Terbufos GCMS .013 336 335 0 1 99.7 .3 .005

38811 Fluometuron HPLC .035 253 246 6 1 99.6 2.8 .115

38478 Linuron HPLC .018 253 250 2 1 99.6 1.2 .057

49311 Bromoxynil HPLC .035 248 246 1 1 99.6 .8 .093

49235 Triclopyr HPLC .250 249 247 1 1 99.6 .8 .141

49314 Aldicarb sulfoxide HPLC .021 250 249 0 1 99.6 .4 .900

49303 Dichlobenil HPLC 1.200 253 252 0 1 99.6 .4 .020

49301 Dinoseb HPLC .035 249 248 0 1 99.6 .4 .025

49296 Methomyl HPLC .017 250 249 0 1 99.6 .4 .050

04095 Fonofos GCMS .003 336 313 21 2 99.4 6.8 .005

04028 Butylate GCMS .002 336 320 14 2 99.4 4.8 .006

82686 Azinphos-methyl GCMS .001 333 320 11 2 99.4 3.9 .074

82685 Propargite GCMS .013 336 324 10 2 99.4 3.6 .033

82663 Ethalfluralin GCMS .004 336 329 5 2 99.4 2.1 .023

82676 Pronamide GCMS .003 336 329 5 2 99.4 2.1 .009

82679 Propanil GCMS .004 336 332 2 2 99.4 1.2 .007

82687 cis-Permethrin GCMS .005 336 334 0 2 99.4 .6 .002

49260 Acetochlor GCMS .002 122 110 11 1 99.2 9.8 .045

04029 Bromacil HPLC .035 252 246 4 2 99.2 2.4 .100

49310 Carbaryl HPLC .008 253 247 4 2 99.2 2.4 .060

49315 Acifluorfen HPLC .035 249 245 2 2 99.2 1.6 .105

49309 Carbofuran HPLC .120 253 250 1 2 99.2 1.2 .080

38482 MCPA HPLC .170 248 245 1 2 99.2 1.2 .005

38538 Propoxur HPLC .035 242 240 0 2 99.2 .8 .075

82678 Triallate GCMS .001 336 320 13 3 99.1 4.8 .005

39341 gamma-HCH GCMS .004 336 327 6 3 99.1 2.7 .010

82673 Benfluralin GCMS .002 336 332 1 3 99.1 1.2 .004

82668 EPTC GCMS .002 336 286 46 4 98.8 14.9 .016

82660 2,6-Diethylaniline GCMS .003 336 323 8 5 98.5 3.9 .001

38711 Bentazon HPLC .014 248 236 8 4 98.4 4.8 .153

Table 2. Number of replicate sets and consistency of pesticide detection or nondetection—Continued

Parameter

code

Pesticide

Analytical

method

MRL

(μg/L)

Number of

replicate

sets

Number of replicate sets

where

replicates in the set have

Percentage of

replicate sets where

replicates in the set

have Median

detected

concentration4

(μg/L)

Consistent

nondetections1

Consistent

detections2

Inconsistent

detections3

Consistent

detections

or consistent

nondetections

At least

one

detection
OBJECTIVES AND METHODS FOR COLLECTION AND ANALYSIS OF FIELD REPLICATES 13

39532 Malathion GCMS 0.005 336 313 17 6 98.2 6.8 0.010

39381 Dieldrin GCMS .001 336 318 12 6 98.2 5.4 .008

49300 Diuron HPLC .020 252 227 20 5 98.0 9.9 .110

82683 Pendimethalin GCMS .004 336 310 18 8 97.6 7.7 .010

82674 Carbofuran GCMS .003 336 314 14 8 97.6 6.5 .018

04041 Cyanazine GCMS .004 336 258 69 9 97.3 23.2 .045

46342 Alachlor GCMS .002 336 261 65 10 97.0 22.3 .015

82682 Dacthal GCMS .002 336 274 50 12 96.4 18.5 .003

82680 Carbaryl GCMS .003 336 279 45 12 96.4 17.0 .019

82661 Trifluralin GCMS .002 336 294 30 12 96.4 12.5 .007

39732 2,4-D HPLC .150 247 228 10 9 96.4 7.7 .105

04037 Prometon GCMS .018 336 192 131 13 96.1 42.9 .020

39632 Atrazine GCMS .001 336 116 206 14 95.8 65.5 .039

39415 Metolachlor GCMS .002 336 168 154 14 95.8 50.0 .027

82630 Metribuzin GCMS .004 336 298 24 14 95.8 11.3 .012

38933 Chlorpyrifos GCMS .004 336 264 57 15 95.5 21.4 .010

39572 Diazinon GCMS .002 334 216 102 16 95.2 35.3 .018

04040 Desethylatrazine GCMS .002 336 161 158 17 94.9 52.1 .016

04035 Simazine GCMS .005 336 149 168 19 94.3 55.7 .028

82670 Tebuthiuron GCMS .010 336 263 54 19 94.3 21.7 .010

34653 p,p’-DDE GCMS .006 336 302 12 22 93.5 10.1 .001

1Replicate sets that have consistent nondetections are those where the pesticide was not detected in any replicate in the set.

2

Replicate sets that have consistent detections are those where the pesticide was detected in all replicates in the set.

3

Replicate sets that have inconsistent detections are those where the pesticide was detected in at least one, but not all, replicates in the set. 4

Median detected concentration of all replicates where the pesticide was detected.

Table 2. Number of replicate sets and consistency of pesticide detection or nondetection—Continued

Parameter

code

Pesticide

Analytical

method

MRL

(μg/L)

Number of

replicate

sets

Number of replicate sets

where

replicates in the set have

Percentage of

replicate sets where

replicates in the set

have Median

detected

concentration4

(μg/L)

Consistent

nondetections1

Consistent

detections2

Inconsistent

detections3

Consistent

detections

or consistent

nondetections

At least

one

detection
14 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

The variances of individual replicate sets

were pooled by use of the procedure given in

Anderson (1987, pp. 44–45). Pooling the variances

provides a better estimate of variability than do individual estimates because the pooled estimate is

based on a larger number of degrees of freedom

(Taylor, 1987, p. 24). The variance is a squared

term. The positive square root of the pooled

variance yields the pooled standard deviation

(a statistic more commonly used to describe variability because the units of measurement are the

same as those for individual measurements).

Pooled estimates were weighted by the number of

replicates in the set. Pooled estimates of the RSD

were computed by use of the same procedure

(Anderson, 1987, pp. 44–45).

Pooled estimates of variance were tested for

equality of variance between surface-water and

ground-water field replicates by use of a twotailed F-test, as shown in Sokal and Rohlf (1969,

pp. 185–186). The PROBF function of SAS

(SAS Institute, Inc., 1982, p. 178) was used to calculate probabilities and significance levels of the

F-distribution for tests of equality of variance.

Analysis of the variability of pesticide

detections and concentrations was complicated by

(1) nondetections of pesticides in many replicate

sets, (2) collection of different types of field replicates, (3) different numbers of replicates in replicate sets, (4) variability that is a strong function

of concentration, (5) excessively rounded analytical data for pesticide concentrations, and

(6) inconsistent detection of pesticides in a single

replicate set. These difficulties were addressed by

the following analytical approaches.

Replicate sets that contain only nondetections provide information on the variability of nondetection but provided little useful information on

the variability of detection or concentration. Replicate sets that contained only nondetections of a

pesticide were excluded from statistical analysis.

Of 86 pesticides analyzed for in this report, 19

were not detected in any field replicate (table 2).

Laboratory QC samples provide information on

some aspects of variability for these pesticides.

Some of the most useful information is obtained

from laboratory control (analytical set) spikes done

by NWQL and summarized by Martin (1999,

table 4), blind spikes done by the Organic Blind

Sample Program (OBSP) (http://btdqs.usgs.gov/

OBSP/index.html), and low-concentration longterm method detection limit (LT-MDL) spikes done

by NWQL (http://wwwnwql.cr.usgs.gov/Public/

ltmdl/ltmdlsplash.html).

Split, concurrent, and sequential field replicates measure different sources of variability but

were combined for analysis. Different types of replicates were combined because (1) laboratory processing and analysis are expected to be the main

sources of variability, (2) the low number of replicates with detections for most pesticides requires

combining the replicates to increase sample size

and improve reliability of the estimated variability,

and (3) the lack of a nested experimental design

(split replicates nested within concurrent or sequential replicates) prevented a rigorous evaluation

of the importance of variability contributed by

sample collection. If sample collection adds an important component of variability, then estimates of

variability given in this report could be biased low

because split replicates do not measure the variability of sample collection. Mueller (1998, pp. 11–

12) assessed the standard deviation of concentrations of nitrogen and phosphorus among split and

other types of field replicates. His evaluation did

not find differences in variability that could be attributed to the type of replicate and, subsequently,

the various types of field replicates were combined

for further analysis. The use of pessimistic estimates of uncertainty (upper confidence bounds) for

the estimated variability of pesticide detections and

concentrations provided in this report may compensate for a potentially low bias in variability

caused by the use of split replicates. The NAWQA

Study Units that began investigations in 1994 were

directed to collect a limited number of surfacewater field replicates by use of a nested experimental design so that the importance of variability of

sample collection could be evaluated (T.L. Miller,

U.S. Geological Survey, written commun.,

July 17, 1996).

Surface-water and ground-water field replicates also were combined for analysis. Replicates

from these two sources were combined because

(1) laboratory processing and analysis (rather than

water matrix or sampling procedures) are expected

to be the main sources of variability, (2) the low

number of replicates with detections (particularly

for ground water) for most pesticides requires

combining surface-water and ground-water field
OBJECTIVES AND METHODS FOR COLLECTION AND ANALYSIS OF FIELD REPLICATES 15

replicates to increase sample size and improve reliability of the estimated variability, and (3) statistically significant differences in variability are either

generally lacking or inconsistent between surfacewater and ground-water field replicates. Variability

of surface-water and ground-water replicates was

compared by use of an F-test of the pooled variances of replicates sets with consistent detections in

eight ranges of concentration. Taylor (1987, p. 38)

recommends that the F-test be based on at least

14 degrees of freedom. Although none of the pesticides met this criterion, the F-test was performed

for six pesticides that had at least 3 degrees of freedom for ground-water field replicates (table 3). No

statistically significant (p >=0.05) differences in

variability between surface-water and groundwater replicates were identified for alachlor, desethylatrazine, or metolachlor. Statistically significant differences in variability were identified for

atrazine, simazine, or prometon in some ranges of

concentration (table 3). In other ranges of concentration, however, differences in variability were not

statistically significant or, in the case of prometon,

were inconsistent as to which type of replicate (surface water or ground water) was more variable.

Comparison of variability between surface- and

ground-water replicates where nondetections were

set to zero for inconsistent replicate sets yielded

similar results to those discussed here. In view of

the few ground-water replicates with detections of

pesticides, the lack of a consistent pattern of variability between surface- and ground-water replicates indicates that differences in variability are not

a major function of differences in the source of the

water, sampling protocols, or sampling equipment,

and that surface- and ground-water replicates may

be combined for analysis.

Replicate sets of duplicates and triplicates

were combined for analysis. Different numbers of

replicates in a replicate set complicated analysis of

variability by restricting analytical approaches, requiring multiple analytical approaches for the variability of detection, and introducing bias in some

measures of variability. Triplicates prevented the

calculation of percent difference or the use of log

percent difference (Tornqvist and others, 1985), a

simple but useful, intuitive, and nonparametric

measure of variability of concentration. Mean detection rate was calculated solely to account for

differences in the number of replicates in a set.

The percentage of inconsistent replicates is the preferred measure of variability of detection because

estimates of uncertainty can be made. Combined

analysis of duplicates and triplicates required that

measures of variability be weighted by the number

of replicates in a set; however, not all measures

could be weighted. LOWESS smooths, the percentage of inconsistent replicate sets, and correlations (Kendall’s tau) were not weighted; thus,

inferences based on these measures may be biased.

Variability of pesticide detections and concentrations usually is a strong function of concentration (for purposes of this report, “strong” means

that the measure of variability increases or decreases markedly as concentration increases). Not only

is the magnitude of the variability a function of

concentration (particularly the standard deviation),

but the variance or scatter of the individual measurements of variability also may be a function of

concentration. This condition is known as “heteroscedasticity” or nonconstant variance. For example, even though the general relation (as shown

by the smooth) of the magnitude of the RSD and

concentration is relatively constant over the range

of concentration (fig. 3), the scatter of the individual measurements of RSD is much greater at low

concentrations than at high concentrations. Pooling

the individual measurements of RSD for the entire

range of concentration would serve to overestimate

variability at high concentrations and underestimate variability at low concentrations. Consequently, estimates of the variability of pesticide

detections or concentrations were pooled separately for selected ranges of concentration where the

magnitude of the variability (and the scatter of the

individual measurements) is constant or relatively

constant over the range of concentration.

Regression equations were not used to model

variability of concentrations because regression

models did not adequately describe the relation between variability and concentration. Even the nonlinear least-squares regression model used by

Mueller (1998, p. 6) provided poor fit, perhaps because standard deviation increased with concentration over the entire range of concentration and did

not exhibit regions of constant standard deviation

at very high or very low concentrations as did replicates for nutrients. In addition, estimates of uncertainty (confidence limits) for variability were

desired but could not be calculated because the
16 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

Table 3. Comparison of variability of pesticide concentrations in surface-water and ground-water field replicates

[Replicate sets with no detections or inconsistent detections were excluded from analysis. μg/L, microgram per liter; p, the probability of obtaining an

F ratio greater than or equal to that shown by chance; parameter code, the number used to identify a pesticide in the U.S. Geological Survey National

Water Information System; MRL, minimum reporting level; GW, ground water; SW, surface water; ns, not significant at p <= 0.05; *, significant at

p <= 0.05; **, significant at p <= 0.01; ***, significant at p <= 0.001]

Concentration

range

(μg/L)

Surface-water

replicates Field

replicates

with

greater

variance

Ground-water

replicates

F ratio p Statistical

significance Degreesof

freedom

Pooled

standard

deviation

(μg/L)

Degreesof

freedom

Pooled

standard

deviation

(μg/L)

Alachlor, parameter code 46342, analysis by GCMS, MRL 0.002 μg/L

< 0.01 18 0.00081 SW 3 0.00041 3.9 0.2928 ns

Atrazine, parameter code 39632, analysis by GCMS, MRL 0.001 μg/L

< 0.01 50 .0012 SW 8 .00068 3.3 .0786 ns

0.005 to < 0.05 99 .0014 GW 6 .0021 2.2 .0898 ns

0.01 to < 0.1 86 .0039 GW 6 .0043 1.3 .5736 ns

0.05 to < 0.5 78 .0130 SW 12 .0120 1.2 .8278 ns

0.1 to < 1 64 .0271 SW 9 .0135 4.0 .0298 *

Desethylatrazine, parameter code 04040, analysis by GCMS, MRL 0.002 μg/L

< 0.01 47 .0010 SW 9 .00064 2.4 .1516 ns

0.005 to < 0.05 78 .0045 GW 8 .0057 1.6 .2521 ns

0.01 to < 0.1 83 .0061 GW 9 .0065 1.2 .6606 ns

0.05 to < 0.5 42 .0150 GW 9 .0155 1.1 .8098 ns

0.1 to < 1 24 .0273 SW 6 .0184 2.2 .3308 ns

Metolachlor, parameter code 39415, analysis by GCMS, MRL 0.002 μg/L

0.005 to < 0.05 73 .0014 SW 4 .0010 2.0 .5320 ns

0.01 to < 0.1 74 .0023 SW 3 .0010 5.1 .2004 ns

Prometon, parameter code 04037, analysis by GCMS, MRL 0.018 μg/L

0.005 to < 0.05 102 .0034 SW 7 .0011 9.8 .0038 **

0.01 to < 0.1 100 .0052 SW 8 .0016 10.5 .0014 **

0.05 to < 0.5 37 .0085 GW 3 .0187 4.9 .0118 *

Simazine, parameter code 04035, analysis by GCMS, MRL 0.005 μg/L

< 0.01 33 .0011 SW 4 .00041 7.1 .0689 ns

0.005 to < 0.05 102 .0018 GW 9 .0038 4.5 .0001 ***

0.01 to < 0.1 103 .0025 GW 8 .0049 3.8 .0011 **

0.05 to < 0.5 59 .0139 SW 3 .0061 5.3 .1941 ns
OBJECTIVES AND METHODS FOR COLLECTION AND ANALYSIS OF FIELD REPLICATES 17

STANDARD DEVIATION OF 

ATRAZINE IN REPLICATE SETS, 

IN MICROGRAMS PER LITER

0

1

2

3

4

5

STANDARD DEVIATION OF 

ATRAZINE IN REPLICATE SETS, 

IN MICROGRAMS PER LITER

0.0001

0.001

0.01

0.1

1

10

RELATIVE 

STANDARD DEVIATION OF 

ATRAZINE IN REPLICATE SETS, 

IN PERCENT

MEAN CONCENTRATION OF ATRAZINE IN REPLICATE SETS, 

IN MICROGRAMS PER LITER

0

10

20

30

40

50

60

0.0001 0.001 0.01 0.1 1 10 100

Figure 3. Variability of atrazine in field replicates. Standard deviation of 0 μg/L is plotted as 0.0001 μg/L. The

solid line in the scatterplot is a smooth that shows the general relation of variability and concentration. The vertical

dashed line is the minimum reporting level.
18 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

residuals from regression models were not normally distributed nor were they of constant variance

over the entire range of concentration (Helsel and

Hirsch, 1992, pp. 224–225). Logarithmic transformations of the concentrations only marginally improved problems of heteroscedasticity and lack of

normality. Rounding of analytical data resulted in

many replicate sets where the concentrations of all

replicates in the set were the same and, therefore,

the estimated variability was zero. Estimates of zero variability contributed greatly to lack of normality and to heteroscedasticity (fig. 3).

Replicate sets with inconsistent detections

(replicate sets that contain both detections and nondetections of a pesticide) typically are deleted from

assessments of the variability of concentrations because of the difficulty in assigning a concentration

to a nondetection. Three approaches were used in

this report for the analysis of variability of concentrations for replicate sets with inconsistent detections: (1) nondetections were deleted as is typically

done, (2) nondetections were set to zero concentration, and (3) nondetections were set to the concentration of the MRL (fig. 4). The intent of setting

nondetections to zero and to the MRL is an attempt

to bound the probable concentration of the nondetections. For most pesticides, setting nondetections

to zero probably provides a worst-case estimate of

variability (estimated variability is largest), whereas deleting nondetections provides a much better

case estimate of variability (estimated variability is

much smaller) (fig. 4). A best-case estimate of

variability could have been obtained by setting the

nondetections equal to the concentration of the other replicate(s) in the set (estimated variability is the

smallest). This approach, however, was not pursued because an optimistic estimate of data quality

was not desired.

Estimates of the variability of concentrations

using approach 1 generally are the most useful

(a) for assessments of variability, (b) for comparison with other studies, (c) when assumptions about

nondetections are not desired, or (d) for estimating

variability in water samples where matrix interference is low. Estimates of the variability of concentrations using approaches 2 or 3 usually provide

different, higher estimates of variability (generally

at low concentrations) that may be appropriate for

some special types of assessments including (e) estimating variability in water samples where matrix

interference is high, or (f) estimating a detection

limit that is more conservative (higher) than the

MDL (by use of estimates of variability that incorporate the variability of detecting pesticides at low

concentrations in a wide variety of natural water

matrices). In essence, approach 1 estimates variability of concentration in the quantitation step of

the analysis, whereas approaches 2 and 3 estimate

variability of concentration in the detection and

quantitation steps combined.

VARIABILITY OF PESTICIDE

DETECTIONS

Variability of pesticide detections was estimated for each pesticide by calculating the mean

detection rate of a pesticide in replicate sets and the

percentage of replicate sets with inconsistent pesticide detections (the percentage of inconsistent replicate sets). These measures provide information

on the consistency of detection. Given that a pesticide was detected in at least one replicate of a set,

these measures indicate the likelihood that the pesticide also would be detected in other replicates of

the set. Uncertainty in the estimates of the variability of detection was evaluated by calculating the

90-percent upper confidence bound for the percentage of inconsistent replicate sets.

The mean detection rate and the percentage

of inconsistent replicate sets are closely related

measures of the variability of detection. Mean detection rates that are high correspond to percentages of inconsistent replicate sets that are low, and

the converse also is true. Both measures are provided because both have limitations related to either the goal of the analysis or to the characteristics

of the data set. The mean detection rate is used as a

measure of the variability of detection because the

replicate sets in this report are a combination of duplicates and triplicates and the mean detection rate

can be weighted by the number of replicates in the

set, thus giving more emphasis to sets with triplicates. The major shortcoming of the mean

detection rate is that an estimate of uncertainty

(confidence limits) cannot be calculated for this

measure of variability. The percentage of inconsistent replicate sets is the preferred measure of the

variability of detection for this assessment because

uncertainty in this measure of variability can be
VARIABILITY OF PESTICIDE DETECTIONS 19

0.0001

0.001

0.01

0.1

1

10

Approach 1 

Nondetections deleted

0.0001

0.001

0.01

0.1

1

10

Approach 2 

Nondetections set to zero

STANDARD DEVIATION OF ATRAZINE IN REPLICATE SETS, IN PERCENT

0.0001

0.001

0.01

0.1

1

10

Approach 3 

Nondetections set to the 

minimum reporting level

MEAN CONCENTRATION OF ATRAZINE IN REPLICATE SETS, 

IN MICROGRAMS PER LITER

0.0001

0.001

0.01

0.1

1

10

0.0001 0.001 0.01 0.1 1 10 100

Comparison of smooths

Approach 1

Approach 2

Approach 3

Figure 4. Three approaches for the analysis of variability of concentrations for replicate sets with inconsistent detections. Standard deviation of 0 μg/L is plotted as 0.0001 μg/L. The vertical dashed line is the

minimum reporting level.
20 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

0

50

100

150

200

Approach 1 

Nondetections deleted

0

50

100

150

200

Approach 2 

Nondetections set to zero

RELATIVE STANDARD DEVIATION OF ATRAZINE IN REPLICATE SETS, IN PERCENT

0

50

100

150

200

Approach 3 

Nondetections set to the 

minimum reporting level

MEAN CONCENTRATION OF ATRAZINE IN REPLICATE SETS, 

IN MICROGRAMS PER LITER

0

2

4

6

8

10

0.0001 0.001 0.01 0.1 1 10 100

Comparison of smooths

Approach 1

Approach 2

Approach 3

Figure 4. Three approaches for the analysis of variability of concentrations for replicate sets with

inconsistent detections—Continued.
VARIABILITY OF PESTICIDE DETECTIONS 21

estimated by calculating confidence limits. The

percentage of inconsistent replicate sets could not

be weighted by the number of replicates in a set,

and the shortcoming of this measure is that duplicates and triplicates are given equal weight. Detections in a single replicate set either are consistent or

inconsistent (only two possible outcomes) regardless of the number of replicates in the set. Measures that have only two possible outcomes (and

the percentages of these outcomes) are suitable for

the calculation of confidence limits by use of the

binomial distribution (Hahn and Meeker, 1991,

pp. 100–108).

Although the percentage of inconsistent

replicate sets is not weighted by the number of replicates in a set, this measure of variability is sensitive to the number of replicates. The likelihood of

an inconsistent detection increases with the number

of replicates in the set; therefore, inconsistent replicate sets are more likely for triplicates than for duplicates. The inclusion of triplicates in the analysis

probably increases the percentage of inconsistent

replicate sets for some pesticides over that which

could be calculated on the basis of duplicates

alone. Triplicates were included in the analysis because (1) sometimes they are the only replicate sets

with detections; (2) a larger number of replicate

sets increases the precision of the estimate of uncertainty; and (3) inclusion of triplicates increases

the variability of detection, consistent with the objective of obtaining a pessimistic estimate of data

quality (in estimating how high variability might

be).

Variability of pesticide detection is a strong

function of concentration, and mean concentrations

of replicate sets for some pesticides span five orders of magnitude. Therefore, mean detection rates

and the percentages of replicate sets with inconsistent detections were calculated separately for three

ranges of mean concentration in replicate sets: less

than the MRL (table 4), the MRL to 10 times the

MRL (table 5), and more than 10 times the MRL

(table 6). For convenience in the text and for relative comparisons, the three ranges of concentration

are referred to as “low,” “medium,” and “high,” respectively. In an absolute sense, however, nearly

all of the concentrations of the replicates are very

low (less than a few tenths of a microgram per liter).

Nondetections in a replicate set were set to

zero for calculating the mean concentration of the

replicate set. Although replicate sets were assigned

to the low, medium, and high ranges of concentration on the basis of the mean concentration of the

replicate set, the median of the individual means is

reported in the tables to characterize the typical

concentration in the range. Data on the variability

of pesticide detections presented in tables 4–6 are

sorted by the mean detection rate, the percentage of

sets with inconsistent detections, the number of

sets with at least one detection, and pesticide name.

In this presentation, pesticides with low variability

and estimates based on large sample sizes are

ranked above pesticides with high variability and

estimates based on small sample sizes.

Twenty-two percent (19 of 86) of the pesticides analyzed for were not detected in any field

replicates: aldicarb, aldicarb sulfone, chloramben,

chlorothalonil, clopyralid, dacthal monoacid,

2,4-DB, dicamba, dichlorprop, 3-hydroxycarbofuran, MCPB, methiocarb, neburon, oxamyl, parathion, phorate, propham, silvex, and 2,4,5-T.

Evaluation of variability of detection or concentration cannot be done for these pesticides. The

number of pesticides with no detections in field

replicates was 36 of 86 (42 percent) in the low

range of concentration (table 4), 30 of 86

(35 percent) in the medium range of concentration

(table 5), and 40 of 86 (47 percent) in the high

range of concentration (table 6).
22 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

Table 4. Variability of pesticide detections in field replicates where mean concentration of the replicate sets was less

than the minimum reporting level

Parameter

code Pesticide

Analytical

method

MRL

(μg/L)

Number of replicate sets

where replicates in the

set have Mean

detection rate

(percent)

Median

concentration

(μg/L)

Replicate sets

withinconsistent

detections

(percent)

At

least

one

detection

Consis

tent

detections

Inconsistent

detections

Measured

90-

percent

upper

confidence

bound

04024 Propachlor GCMS 0.007 1 1 0 100.0 0.006 0.0 90.0

82665 Terbacil GCMS .007 1 1 0 100.0 .007 .0 90.0

04037 Prometon GCMS .018 69 58 11 92.1 .009 15.9 23.1

82685 Propargite GCMS .013 5 3 2 81.8 .010 40.0 75.3

04029 Bromacil HPLC .035 2 1 1 80.0 .013 50.0 94.9

49235 Triclopyr HPLC .250 2 1 1 80.0 .141 50.0 94.9

38811 Fluometuron HPLC .035 2 1 1 75.0 .006 50.0 94.9

04095 Fonofos GCMS .003 2 1 1 75.0 .002 50.0 94.9

82684 Napropamide GCMS .003 2 1 1 75.0 .003 50.0 94.9

82670 Tebuthiuron GCMS .010 35 17 18 72.2 .005 51.4 63.4

82660 2,6-Diethylaniline GCMS .003 11 6 5 70.8 .001 45.5 68.2

39732 2,4-D HPLC .150 13 5 8 69.2 .045 61.5 79.9

82682 Dacthal GCMS .002 18 7 11 68.4 .001 61.1 76.9

38482 MCPA HPLC .170 3 1 2 66.7 .005 66.7 96.5

82677 Disulfoton GCMS .017 1 0 1 66.7 .003 100.0 100.0

04035 Simazine GCMS .005 24 6 18 63.2 .003 75.0 86.3

04040 Desethylatrazine GCMS .002 13 3 10 61.3 .001 76.9 91.2

04028 Butylate GCMS .002 2 0 2 60.0 .001 100.0 100.0

82663 Ethalfluralin GCMS .004 2 0 2 60.0 .003 100.0 100.0

82687 cis-Permethrin GCMS .005 2 0 2 60.0 .002 100.0 100.0

34653 p,p’-DDE GCMS .006 28 7 21 59.7 .001 75.0 85.5

39415 Metolachlor GCMS .002 9 1 8 57.9 .002 88.9 98.8

38933 Chlorpyrifos GCMS .004 10 2 8 56.5 .003 80.0 94.5

39532 Malathion GCMS .005 6 1 5 50.0 .003 83.3 98.3

39572 Diazinon GCMS .002 4 0 4 50.0 .002 100.0 100.0

82683 Pendimethalin GCMS .004 4 0 4 50.0 .003 100.0 100.0

82668 EPTC GCMS .002 3 0 3 50.0 .001 100.0 100.0

49309 Carbofuran HPLC .120 2 0 2 50.0 .063 100.0 100.0

49315 Acifluorfen HPLC .035 1 0 1 50.0 .010 100.0 100.0

82674 Carbofuran GCMS .003 1 0 1 50.0 .003 100.0 100.0

Table 4. Variability of pesticide detections in field replicates where mean concentration of the replicate sets was less

than the minimum reporting level

[Pesticides are sorted by the mean detection rate, the percentage of sets with inconsistent detections, the number of sets with at least one detection,

and pesticide name. Concentration of nondetections was set to zero for calculations. Replicate sets with no detections were excluded from analysis.

Median concentration may not be less than the MRL because of rounding. Parameter code, the number used to identify a pesticide in the U.S.

Geological Survey National Water Information System; MRL, minimum reporting level; μg/L, microgram per liter; GCMS, gas chromatography/mass spectrometry; HPLC, high-performance liquid chromatography; nc, not calculated]
VARIABILITY OF PESTICIDE DETECTIONS 23

04041 Cyanazine GCMS 0.004 1 0 1 50.0 0.003 100.0 100.0

49303 Dichlobenil HPLC 1.200 1 0 1 50.0 .020 100.0 100.0

49301 Dinoseb HPLC .035 1 0 1 50.0 .025 100.0 100.0

34253 alpha-HCH GCMS .002 1 0 1 50.0 .001 100.0 100.0

39341 gamma-HCH GCMS .004 1 0 1 50.0 .001 100.0 100.0

38478 Linuron HPLC .018 1 0 1 50.0 .009 100.0 100.0

82669 Pebulate GCMS .004 1 0 1 50.0 .003 100.0 100.0

82676 Pronamide GCMS .003 1 0 1 50.0 .002 100.0 100.0

38538 Propoxur HPLC .035 1 0 1 50.0 .020 100.0 100.0

82675 Terbufos GCMS .013 1 0 1 50.0 .005 100.0 100.0

39632 Atrazine GCMS .001 4 0 4 44.4 .001 100.0 100.0

46342 Alachlor GCMS .002 3 0 3 42.9 .001 100.0 100.0

82630 Metribuzin GCMS .004 3 0 3 42.9 .004 100.0 100.0

82661 Trifluralin GCMS .002 6 0 6 40.0 .001 100.0 100.0

38711 Bentazon HPLC .014 1 0 1 33.3 .013 100.0 100.0

82680 Carbaryl GCMS .003 1 0 1 33.3 .002 100.0 100.0

49300 Diuron HPLC .020 1 0 1 33.3 .010 100.0 100.0

82667 Methyl parathion GCMS .006 1 0 1 33.3 .003 100.0 100.0

82679 Propanil GCMS .004 1 0 1 33.3 .002 100.0 100.0

82678 Triallate GCMS .001 1 0 1 33.3 .001 100.0 100.0

49260 Acetochlor GCMS .002 0 0 0 nc nc nc nc

49312 Aldicarb HPLC .550 0 0 0 nc nc nc nc

49313 Aldicarb sulfone HPLC .100 0 0 0 nc nc nc nc

49314 Aldicarb sulfoxide HPLC .021 0 0 0 nc nc nc nc

82686 Azinphos-methyl GCMS .001 0 0 0 nc nc nc nc

82673 Benfluralin GCMS .002 0 0 0 nc nc nc nc

49311 Bromoxynil HPLC .035 0 0 0 nc nc nc nc

49310 Carbaryl HPLC .008 0 0 0 nc nc nc nc

49307 Chloramben HPLC .420 0 0 0 nc nc nc nc

49306 Chlorothalonil HPLC .480 0 0 0 nc nc nc nc

49305 Clopyralid HPLC .230 0 0 0 nc nc nc nc

49304 Dacthal monoacid HPLC .017 0 0 0 nc nc nc nc

38746 2,4-DB HPLC .240 0 0 0 nc nc nc nc

38442 Dicamba HPLC .035 0 0 0 nc nc nc nc

49302 Dichlorprop HPLC .032 0 0 0 nc nc nc nc

Table 4. Variability of pesticide detections in field replicates where mean concentration of the replicate sets was less

than the minimum reporting level—Continued

Parameter

code Pesticide

Analytical

method

MRL

(μg/L)

Number of replicate sets

where replicates in the

set have Mean

detection rate

(percent)

Median

concentration

(μg/L)

Replicate sets

withinconsistent

detections

(percent)

At

least

one

detection

Consis

tent

detections

Inconsistent

detections

Measured

90-

percent

upper

confidence

bound
24 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

39381 Dieldrin GCMS 0.001 0 0 0 nc nc nc nc

49299 DNOC HPLC .420 0 0 0 nc nc nc nc

82672 Ethoprop GCMS .003 0 0 0 nc nc nc nc

49297 Fenuron HPLC .013 0 0 0 nc nc nc nc

49308 3-Hydroxycarbofuran HPLC .014 0 0 0 nc nc nc nc

82666 Linuron GCMS .002 0 0 0 nc nc nc nc

38487 MCPB HPLC .140 0 0 0 nc nc nc nc

38501 Methiocarb HPLC .026 0 0 0 nc nc nc nc

49296 Methomyl HPLC .017 0 0 0 nc nc nc nc

82671 Molinate GCMS .004 0 0 0 nc nc nc nc

49294 Neburon HPLC .015 0 0 0 nc nc nc nc

49293 Norflurazon HPLC .024 0 0 0 nc nc nc nc

49292 Oryzalin HPLC .310 0 0 0 nc nc nc nc

38866 Oxamyl HPLC .018 0 0 0 nc nc nc nc

39542 Parathion GCMS .004 0 0 0 nc nc nc nc

82664 Phorate GCMS .002 0 0 0 nc nc nc nc

49291 Picloram HPLC .050 0 0 0 nc nc nc nc

49236 Propham HPLC .035 0 0 0 nc nc nc nc

39762 Silvex HPLC .021 0 0 0 nc nc nc nc

39742 2,4,5-T HPLC .035 0 0 0 nc nc nc nc

82681 Thiobencarb GCMS .002 0 0 0 nc nc nc nc

Total 310 124 186 nc nc 60.0 nc

Table 4. Variability of pesticide detections in field replicates where mean concentration of the replicate sets was less

than the minimum reporting level—Continued

Parameter

code Pesticide

Analytical

method

MRL

(μg/L)

Number of replicate sets

where replicates in the

set have Mean

detection rate

(percent)

Median

concentration

(μg/L)

Replicate sets

withinconsistent

detections

(percent)

At

least

one

detection

Consis

tent

detections

Inconsistent

detections

Measured

90-

percent

upper

confidence

bound
VARIABILITY OF PESTICIDE DETECTIONS 25

Table 5. Variability of pesticide detections in field replicates where mean concentration of the replicate sets was

greater than or equal to the minimum reporting level and was less than or equal to 10 times the minimum reporting level

Parameter

code

Pesticide

Analytical

method

MRL

(μg/L)

Number of replicate sets

where replicates in the set

have

Mean

detection rate

(percent)

Median

concentration

(μg/L)

Replicate sets

with inconsistent

detections

(percent)

Atleast

one

detection

Consistent

detections

Inconsistentdetections

Measured

90-

percent

upper

confidence

bound

04028 Butylate GCMS 0.002 12 12 0 100.0 0.006 0.0 17.5

82684 Napropamide GCMS .003 10 10 0 100.0 .010 .0 20.6

82667 Methyl parathion GCMS .006 4 4 0 100.0 .020 .0 43.8

82685 Propargite GCMS .013 4 4 0 100.0 .065 .0 43.8

82665 Terbacil GCMS .007 4 4 0 100.0 .017 .0 43.8

82663 Ethalfluralin GCMS .004 3 3 0 100.0 .023 .0 53.6

82672 Ethoprop GCMS .003 3 3 0 100.0 .004 .0 53.6

38811 Fluometuron HPLC .035 3 3 0 100.0 .115 .0 53.6

82671 Molinate GCMS .004 3 3 0 100.0 .011 .0 53.6

82669 Pebulate GCMS .004 3 3 0 100.0 .024 .0 53.6

82660 2,6-Diethylaniline GCMS .003 2 2 0 100.0 .007 .0 68.4

38478 Linuron HPLC .018 2 2 0 100.0 .071 .0 68.4

49293 Norflurazon HPLC .024 2 2 0 100.0 .088 .0 68.4

04024 Propachlor GCMS .007 2 2 0 100.0 .031 .0 68.4

82686 Azinphos-methyl GCMS .001 1 1 0 100.0 .006 .0 90.0

49309 Carbofuran HPLC .120 1 1 0 100.0 .790 .0 90.0

49299 DNOC HPLC .420 1 1 0 100.0 .505 .0 90.0

49292 Oryzalin HPLC .310 1 1 0 100.0 .515 .0 90.0

49291 Picloram HPLC .050 1 1 0 100.0 .110 .0 90.0

04035 Simazine GCMS .005 99 98 1 99.5 .017 1.0 3.9

04037 Prometon GCMS .018 73 71 2 98.8 .043 2.7 7.1

82682 Dacthal GCMS .002 36 35 1 98.7 .004 2.8 10.4

82670 Tebuthiuron GCMS .010 34 33 1 98.6 .020 2.9 11.0

82668 EPTC GCMS .002 28 27 1 98.3 .007 3.6 13.2

04095 Fonofos GCMS .003 18 17 1 97.4 .005 5.6 19.9

39532 Malathion GCMS .005 12 11 1 96.2 .010 8.3 28.7

39415 Metolachlor GCMS .002 70 64 6 96.0 .007 8.6 14.6

04040 Desethylatrazine GCMS .002 80 73 7 95.9 .005 8.8 14.3

38933 Chlorpyrifos GCMS .004 52 45 7 92.8 .010 13.5 21.6

39732 2,4-D HPLC .150 6 5 1 92.3 .397 16.7 51.0

Table 5. Variability of pesticide detections in field replicates where mean concentration of the replicate sets was greater

than or equal to the minimum reporting level and was less than or equal to 10 times the minimum reporting level

[Pesticides are sorted by the mean detection rate, the percentage of sets with inconsistent detections, the number of sets with at least one detection,

and pesticide name. Concentration of nondetections was set to zero for calculations. Replicate sets with no detections were excluded from analysis.

Parameter code, the number used to identify a pesticide in the U.S. Geological Survey National Water Information System; MRL, minimum

reporting level; μg/L, microgram per liter; GCMS, gas chromatography/mass spectrometry; HPLC, high-performance liquid chromatography;

nc, not calculated]
26 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

34653 p,p’-DDE GCMS 0.006 6 5 1 92.3 0.011 16.7 51.0

82676 Pronamide GCMS .003 6 5 1 91.7 .010 16.7 51.0

46342 Alachlor GCMS .002 44 37 7 91.5 .007 15.9 25.3

82678 Triallate GCMS .001 11 9 2 91.3 .004 18.2 41.5

39572 Diazinon GCMS .002 59 48 11 91.1 .008 18.6 26.8

82666 Linuron GCMS .002 5 4 1 90.9 .016 20.0 58.4

39632 Atrazine GCMS .001 60 50 10 90.2 .006 16.7 24.5

82661 Trifluralin GCMS .002 29 23 6 90.2 .007 20.7 33.5

49300 Diuron HPLC .020 14 11 3 89.7 .067 21.4 41.7

04041 Cyanazine GCMS .004 35 27 8 89.2 .012 22.9 34.5

82681 Thiobencarb GCMS .002 4 3 1 87.5 .009 25.0 68.0

82683 Pendimethalin GCMS .004 15 11 4 86.7 .010 26.7 46.4

82680 Carbaryl GCMS .003 34 24 10 86.3 .011 29.4 41.6

49310 Carbaryl HPLC .008 3 2 1 83.3 .033 33.3 80.4

82630 Metribuzin GCMS .004 26 16 10 81.5 .008 38.5 52.9

49260 Acetochlor GCMS .002 4 3 1 80.0 .006 25.0 68.0

39341 gamma-HCH GCMS .004 5 3 2 80.0 .009 40.0 75.3

82674 Carbofuran GCMS .003 13 6 7 75.9 .011 53.8 73.6

49315 Acifluorfen HPLC .035 2 1 1 75.0 .105 50.0 94.9

04029 Bromacil HPLC .035 2 1 1 75.0 .100 50.0 94.9

49311 Bromoxynil HPLC .035 2 1 1 75.0 .093 50.0 94.9

39381 Dieldrin GCMS .001 12 6 6 74.1 .004 50.0 71.2

38711 Bentazon HPLC .014 5 2 3 70.0 .110 60.0 88.8

82673 Benfluralin GCMS .002 4 1 3 70.0 .004 75.0 97.4

82679 Propanil GCMS .004 2 1 1 66.7 .007 50.0 94.9

49296 Methomyl HPLC .017 1 0 1 50.0 .050 100.0 100.0

38538 Propoxur HPLC .035 1 0 1 50.0 .130 100.0 100.0

49312 Aldicarb HPLC .550 0 0 0 nc nc nc nc

49313 Aldicarb sulfone HPLC .100 0 0 0 nc nc nc nc

49314 Aldicarb sulfoxide HPLC .021 0 0 0 nc nc nc nc

49307 Chloramben HPLC .420 0 0 0 nc nc nc nc

49306 Chlorothalonil HPLC .480 0 0 0 nc nc nc nc

49305 Clopyralid HPLC .230 0 0 0 nc nc nc nc

49304 Dacthal monoacid HPLC .017 0 0 0 nc nc nc nc

38746 2,4-DB HPLC .240 0 0 0 nc nc nc nc

Table 5. Variability of pesticide detections in field replicates where mean concentration of the replicate sets was

greater than or equal to the minimum reporting level and was less than or equal to 10 times the minimum reporting level

—Continued

Parameter

code

Pesticide

Analytical

method

MRL

(μg/L)

Number of replicate sets

where replicates in the set

have

Mean

detection rate

(percent)

Median

concentration

(μg/L)

Replicate sets

with inconsistent

detections

(percent)

Atleast

one

detection

Consistent

detections

Inconsistentdetections

Measured

90-

percent

upper

confidence

bound
VARIABILITY OF PESTICIDE DETECTIONS 27

38442 Dicamba HPLC 0.035 0 0 0 nc nc nc nc

49303 Dichlobenil HPLC 1.200 0 0 0 nc nc nc nc

49302 Dichlorprop HPLC .032 0 0 0 nc nc nc nc

49301 Dinoseb HPLC .035 0 0 0 nc nc nc nc

82677 Disulfoton GCMS .017 0 0 0 nc nc nc nc

49297 Fenuron HPLC .013 0 0 0 nc nc nc nc

34253 alpha-HCH GCMS .002 0 0 0 nc nc nc nc

49308 3-Hydroxycarbofuran HPLC .014 0 0 0 nc nc nc nc

38482 MCPA HPLC .170 0 0 0 nc nc nc nc

38487 MCPB HPLC .140 0 0 0 nc nc nc nc

38501 Methiocarb HPLC .026 0 0 0 nc nc nc nc

49294 Neburon HPLC .015 0 0 0 nc nc nc nc

38866 Oxamyl HPLC .018 0 0 0 nc nc nc nc

39542 Parathion GCMS .004 0 0 0 nc nc nc nc

82687 cis-Permethrin GCMS .005 0 0 0 nc nc nc nc

82664 Phorate GCMS .002 0 0 0 nc nc nc nc

49236 Propham HPLC .035 0 0 0 nc nc nc nc

39762 Silvex HPLC .021 0 0 0 nc nc nc nc

39742 2,4,5-T HPLC .035 0 0 0 nc nc nc nc

82675 Terbufos GCMS .013 0 0 0 nc nc nc nc

49235 Triclopyr HPLC .250 0 0 0 nc nc nc nc

Total 940 841 133 nc nc 13.7 nc

Table 5. Variability of pesticide detections in field replicates where mean concentration of the replicate sets was

greater than or equal to the minimum reporting level and was less than or equal to 10 times the minimum reporting level

—Continued

Parameter

code

Pesticide

Analytical

method

MRL

(μg/L)

Number of replicate sets

where replicates in the set

have

Mean

detection rate

(percent)

Median

concentration

(μg/L)

Replicate sets

with inconsistent

detections

(percent)

Atleast

one

detection

Consistent

detections

Inconsistentdetections

Measured

90-

percent

upper

confidence

bound
28 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

Table 6. Variability of pesticide detections in field replicates where mean concentration of the replicate sets was more

than 10 times the minimum reporting level

Parameter

code

Pesticide

Analytical

method

MRL

(μg/L)

Number of replicate sets

where replicates in the set

have

Mean

detection rate

(percent)

Median

concentration

(μg/L)

Replicate sets

with inconsistent

detections

(percent)

At

least

one

detection

Consis

tent

detections

Inconsistent

detections

Measured

90-

percent

upper

confidence

bound

39632 Atrazine GCMS 0.001 156 156 0 100.0 0.095 0.0 1.5

39415 Metolachlor GCMS .002 89 89 0 100.0 .113 .0 2.6

04040 Desethylatrazine GCMS .002 82 82 0 100.0 .052 .0 2.8

04035 Simazine GCMS .005 64 64 0 100.0 .145 .0 3.5

04041 Cyanazine GCMS .004 42 42 0 100.0 .178 .0 5.3

46342 Alachlor GCMS .002 28 28 0 100.0 .065 .0 7.9

82668 EPTC GCMS .002 19 19 0 100.0 .058 .0 11.4

38933 Chlorpyrifos GCMS .004 10 10 0 100.0 .128 .0 20.6

49260 Acetochlor GCMS .002 8 8 0 100.0 .196 .0 25.0

82674 Carbofuran GCMS .003 8 8 0 100.0 .120 .0 25.0

82682 Dacthal GCMS .002 8 8 0 100.0 .051 .0 25.0

82671 Molinate GCMS .004 8 8 0 100.0 1.975 .0 25.0

82683 Pendimethalin GCMS .004 7 7 0 100.0 .060 .0 28.0

82661 Trifluralin GCMS .002 7 7 0 100.0 .062 .0 28.0

38711 Bentazon HPLC .014 6 6 0 100.0 .193 .0 31.9

39381 Dieldrin GCMS .001 6 6 0 100.0 .015 .0 31.9

82666 Linuron GCMS .002 5 5 0 100.0 .125 .0 36.9

39532 Malathion GCMS .005 5 5 0 100.0 .063 .0 36.9

82684 Napropamide GCMS .003 4 4 0 100.0 .064 .0 43.8

82670 Tebuthiuron GCMS .010 4 4 0 100.0 .203 .0 43.8

82678 Triallate GCMS .001 4 4 0 100.0 .054 .0 43.8

04095 Fonofos GCMS .003 3 3 0 100.0 .059 .0 53.6

39341 gamma-HCH GCMS .004 3 3 0 100.0 .086 .0 53.6

82685 Propargite GCMS .013 3 3 0 100.0 .460 .0 53.6

04029 Bromacil HPLC .035 2 2 0 100.0 .722 .0 68.4

04028 Butylate GCMS .002 2 2 0 100.0 .028 .0 68.4

82663 Ethalfluralin GCMS .004 2 2 0 100.0 .076 .0 68.4

38811 Fluometuron HPLC .035 2 2 0 100.0 3.323 .0 68.4

04037 Prometon GCMS .018 2 2 0 100.0 .628 .0 68.4

82681 Thiobencarb GCMS .002 2 2 0 100.0 .027 .0 68.4

Table 6. Variability of pesticide detections in field replicates where mean concentration of the replicate sets was more

than 10 times the minimum reporting level

[Pesticides are sorted by the mean detection rate, the percentage of sets with inconsistent detections, the number of sets with at least one detection,

and pesticide name. Concentration of nondetections was set to zero for calculations. Replicate sets with no detections were excluded from analysis.

Parameter code, the number used to identify a pesticide in the U.S. Geological Survey National Water Information System; MRL, minimum

reporting level; μg/L, microgram per liter; GCMS, gas chromatography/mass spectrometry; HPLC, high-performance liquid chromatography;

nc, not calculated]
VARIABILITY OF PESTICIDE DETECTIONS 29

49315 Acifluorfen HPLC 0.035 1 1 0 100.0 0.745 0.0 90.0

82672 Ethoprop GCMS .003 1 1 0 100.0 .043 .0 90.0

49297 Fenuron HPLC .013 1 1 0 100.0 .140 .0 90.0

34253 alpha-HCH GCMS .002 1 1 0 100.0 .038 .0 90.0

49293 Norflurazon HPLC .024 1 1 0 100.0 .575 .0 90.0

82669 Pebulate GCMS .004 1 1 0 100.0 .195 .0 90.0

04024 Propachlor GCMS .007 1 1 0 100.0 .085 .0 90.0

82679 Propanil GCMS .004 1 1 0 100.0 .051 .0 90.0

82665 Terbacil GCMS .007 1 1 0 100.0 .540 .0 90.0

39572 Diazinon GCMS .002 55 54 1 99.2 .051 1.8 6.9

82680 Carbaryl GCMS .003 22 21 1 98.0 .092 4.5 16.6

49300 Diuron HPLC .020 10 9 1 95.2 .795 10.0 33.7

82630 Metribuzin GCMS .004 9 8 1 94.7 .090 11.1 36.8

82686 Azinphos-methyl GCMS .001 12 10 2 92.6 .078 16.7 38.6

49310 Carbaryl HPLC .008 3 2 1 85.7 .495 33.3 80.4

49314 Aldicarb sulfoxide HPLC .021 1 0 1 50.0 .900 100.0 100.0

49312 Aldicarb HPLC .550 0 0 0 nc nc nc nc

49313 Aldicarb sulfone HPLC .100 0 0 0 nc nc nc nc

82673 Benfluralin GCMS .002 0 0 0 nc nc nc nc

49311 Bromoxynil HPLC .035 0 0 0 nc nc nc nc

49309 Carbofuran HPLC .120 0 0 0 nc nc nc nc

49307 Chloramben HPLC .420 0 0 0 nc nc nc nc

49306 Chlorothalonil HPLC .480 0 0 0 nc nc nc nc

49305 Clopyralid HPLC .230 0 0 0 nc nc nc nc

39732 2,4-D HPLC .150 0 0 0 nc nc nc nc

49304 Dacthal monoacid HPLC .017 0 0 0 nc nc nc nc

38746 2,4-DB HPLC .240 0 0 0 nc nc nc nc

34653 p,p’-DDE GCMS .006 0 0 0 nc nc nc nc

38442 Dicamba HPLC .035 0 0 0 nc nc nc nc

49303 Dichlobenil HPLC 1.200 0 0 0 nc nc nc nc

49302 Dichlorprop HPLC .032 0 0 0 nc nc nc nc

82660 2,6-Diethylaniline GCMS .003 0 0 0 nc nc nc nc

49301 Dinoseb HPLC .035 0 0 0 nc nc nc nc

82677 Disulfoton GCMS .017 0 0 0 nc nc nc nc

Table 6. Variability of pesticide detections in field replicates where mean concentration of the replicate sets was more

than 10 times the minimum reporting level—Continued

Parameter

code

Pesticide

Analytical

method

MRL

(μg/L)

Number of replicate sets

where replicates in the set

have

Mean

detection rate

(percent)

Median

concentration

(μg/L)

Replicate sets

with inconsistent

detections

(percent)

At

least

one

detection

Consis

tent

detections

Inconsistent

detections

Measured

90-

percent

upper

confidence

bound
30 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

49299 DNOC HPLC 0.420 0 0 0 nc nc nc nc

49308 3-Hydroxycarbofuran HPLC .014 0 0 0 nc nc nc nc

38478 Linuron HPLC .018 0 0 0 nc nc nc nc

38482 MCPA HPLC .170 0 0 0 nc nc nc nc

38487 MCPB HPLC .140 0 0 0 nc nc nc nc

38501 Methiocarb HPLC .026 0 0 0 nc nc nc nc

49296 Methomyl HPLC .017 0 0 0 nc nc nc nc

82667 Methyl parathion GCMS .006 0 0 0 nc nc nc nc

49294 Neburon HPLC .015 0 0 0 nc nc nc nc

49292 Oryzalin HPLC .310 0 0 0 nc nc nc nc

38866 Oxamyl HPLC .018 0 0 0 nc nc nc nc

39542 Parathion GCMS .004 0 0 0 nc nc nc nc

82687 cis-Permethrin GCMS .005 0 0 0 nc nc nc nc

82664 Phorate GCMS .002 0 0 0 nc nc nc nc

49291 Picloram HPLC .050 0 0 0 nc nc nc nc

82676 Pronamide GCMS .003 0 0 0 nc nc nc nc

49236 Propham HPLC .035 0 0 0 nc nc nc nc

38538 Propoxur HPLC .035 0 0 0 nc nc nc nc

39762 Silvex HPLC .021 0 0 0 nc nc nc nc

39742 2,4,5-T HPLC .035 0 0 0 nc nc nc nc

82675 Terbufos GCMS .013 0 0 0 nc nc nc nc

49235 Triclopyr HPLC .250 0 0 0 nc nc nc nc

Total 712 704 8 nc nc 1.1 nc

Table 6. Variability of pesticide detections in field replicates where mean concentration of the replicate sets was more

than 10 times the minimum reporting level—Continued

Parameter

code

Pesticide

Analytical

method

MRL

(μg/L)

Number of replicate sets

where replicates in the set

have

Mean

detection rate

(percent)

Median

concentration

(μg/L)

Replicate sets

with inconsistent

detections

(percent)

At

least

one

detection

Consis

tent

detections

Inconsistent

detections

Measured

90-

percent

upper

confidence

bound
VARIABILITY OF PESTICIDE DETECTIONS 31

Mean Detection Rate

The mean detection rate is a measure of the

variability of pesticide detection and shows the

overall rate of detection of a pesticide in field

replicates for a given range of concentration. For

example, in the medium range of concentration,

simazine was detected in 99 replicate sets that had

mean concentrations greater than or equal to

0.005 μg/L (the MRL for simazine) and less than

or equal to 0.050 μg/L (10 times the MRL for simazine). Simazine was detected in all replicates in

98 of the 99 sets, but in 1 of the 99 replicate sets

simazine was inconsistently detected (table 5). The

mean detection rate for simazine is 99.5 percent,

which indicates very low variability in the detection of simazine in the medium range of concentration. On the basis of the high mean detection rate

and the large number of replicate sets with at least

one detection, data users are assured that detections

of simazine at concentrations between 0.005 μg/L

and 0.050 μg/L are reproducible.

In the low range of concentration (less than

0.005 μg/L, the MRL for simazine), simazine was

detected in 24 replicates (table 4). Simazine was

detected in all replicates in 6 of the 24 sets but

was inconsistently detected in 18 of the 24 replicate sets. The mean detection rate for simazine is

63.2 percent, which indicates high variability in the

detection of simazine at concentrations less than

the MRL. On the basis of the low mean detection

rate and the relatively large number of replicate

sets with at least one detection, data users are assured that detections of simazine at concentrations

less than 0.005 μg/L are not reproducible.

The variability of detection for most pesticides is high at concentrations less than the MRL

but the variability of detection decreases dramatically at higher concentrations (fig. 5). A mean detection rate of 75 percent or less is used in this

assessment to indicate high variability of detection,

whereas a mean detection rate of 90 percent or

more is used to indicate low variability of detection. The number of pesticides where the mean detection rate indicates high variability of detection is

44 of 50 (88 percent) in the low range, 9 of 57

(16 percent) in the medium range, and 1 of 46

(2 percent) in the high range. The number of pesticides where the mean detection rate indicates low

variability of detection is 3 of 50 (6 percent) in the

low range, 38 of 57 (67 percent) in the medium

range, and 44 of 46 (96 percent) in the high range.

Prometon is a notable counterexample—a pesticide

with low variability of detection at concentrations

less than the MRL (table 4).

Replicate Sets with Inconsistent

Detections

The percentage of replicate sets with inconsistent detections also is a measure of the variability of detection. The percentage of replicate sets

with inconsistent detections measures the frequency that a pesticide was detected in at least one, but

not all, replicates in a set. In the context of the variability of detection in environmental samples, this

measure estimates the likelihood that a pesticide

that is detected in an environmental sample would

not have been detected in a duplicate sample. Alternately, the likelihood that a pesticide would have

been detected in a duplicate sample (an estimate of

the consistency of detection) is 100 percent minus

the percentage of replicate sets with inconsistent

detections. Although duplicates and triplicates

were included in this assessment, most replicate

sets were duplicates (88 percent), and restricting

the inference to the likelihood of not detecting (or

detecting) a pesticide in a duplicate sample helps

clarify the application of this measure of data quality.

For example, diazinon was detected in 59

replicate sets that had mean concentrations greater

than or equal to 0.002 μg/L (the MRL for diazinon)

and less than or equal to 0.020 μg/L (10 times the

MRL for diazinon). Diazinon was detected in all

replicates in 48 of the 59 sets but was inconsistently detected in 11 of the 59 replicate sets (table 5).

The percentage of replicate sets with inconsistent

detections for diazinon, 18.6 percent, indicates low

variability in the detection of diazinon in the medium concentration range. Alternately stated, the percentage of replicate sets with consistent detections

for diazinon is 81.4 percent (100 percent minus

18.6 percent). On the basis of the low percentage of

replicate sets with inconsistent detections and the

large number of replicate sets with at least one detection, data users are assured that detections of

diazinon at concentrations between 0.002 and

0.020 μg/L are reproducible.
32 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97 NUMBER OF PESTICIDES WHERE MEAN DETECTION RATE IS LESS THAN 75 PERCENT

0

10

20

30

40

50

HIGH VARIABILITY 

OF DETECTION

NUMBER OF PESTICIDES 

WHERE MEAN DETECTION RATE 

IS MORE THAN 90 PERCENT

0

10

20

30

40

50

LOW VARIABILITY 

OF DETECTION

NUMBER OF PESTICIDES 

WHERE THE RATE OF 

INCONSISTENT REPLICATE SETS 

IS MORE THAN 50 PERCENT

0

10

20

30

40

50

NUMBER OF PESTICIDES 

WHERE THE RATE OF 

INCONSISTENT REPLICATE SETS 

IS LESS THAN 25 PERCENT

0

10

20

30

40

50

NUMBER OF PESTICIDES 

WHERE THE 90-PERCENT UPPER 

CONFIDENCE BOUND FOR THE RATE 

OF INCONSISTENT REPLICATE SETS 

IS MORE THAN 50 PERCENT

0

10

20

30

40

50

LOW MEDIUM HIGH

RANGES OF CONCENTRATION

NUMBER OF PESTICIDES 

WHERE THE 90-PERCENT UPPER 

CONFIDENCE BOUND FOR THE RATE 

OF INCONSISTENT REPLICATE SETS 

IS LESS THAN 25 PERCENT

0

10

20

30

40

50

LOW MEDIUM HIGH

RANGES OF CONCENTRATION

Figure 5. Variability of detection of pesticides in field replicates. Ranges of concentration are a function of the

minimum reporting level (MRL) for a pesticide (Low, less than the MRL; Medium, the MRL to 10 times the MRL; High,

more than 10 times the MRL).
VARIABILITY OF PESTICIDE DETECTIONS 33

As with the mean detection rate, variability

of detection measured by the percentage of inconsistent replicate sets is high at concentrations less

than the MRL but decreases with increasing concentrations (fig. 5). A percentage of inconsistent

replicate sets of 25 percent or less is used in this assessment to indicate that variability of detection is

low, whereas a percentage of 50 percent or more is

used to indicate that variability of detection is high.

The number of pesticides where the percentage of

inconsistent replicate sets indicates low variability

of detection is 3 of 50 (6 percent) in the low range

(table 4), 42 of 57 (74 percent) in the medium

range (table 5), and 44 of 46 (96 percent) in the

high range (table 6). The number of pesticides

where the percentage of inconsistent replicate sets

indicate high variability of detection is 45 of 50

(90 percent) in the low range, 10 of 57 (18 percent)

in the medium range, and 1 of 46 (2 percent) in the

high range. The numbers of replicate sets were

summed within concentration range for all pesticides (tables 4–6). The overall rate of inconsistent

replicate sets is 60.0 percent in the low range,

13.7 percent in the medium range, and 1.1 percent

in the high range.

Inconsistent detections are caused by either

false-positive or false-negative errors. (See section

“Analytical Methods for Pesticides.”) Because

field replicates, rather than reference materials of

known composition, were used to assess inconsistent detections, one cannot determine with certainty the cause of an inconsistent detection for a

particular replicate set. False-positive errors usually are caused by sample contamination, whereas

false-negative errors usually are caused by watermatrix interference, pesticide degradation, or other

chemical-loss processes. Both types of errors may

be caused by variability inherent in the analytical

method but, as discussed previously, calculation

and use of MDLs are intended to protect against

false-positive errors.

In an assessment of sample contamination

for the NAWQA Program, 63 of the pesticides

analyzed for were not detected in any field blank

(Martin and others, 1999, p. 24). Of those pesticides that were detected in field blanks, only atrazine, simazine, metolachlor, and p,p’-DDE were

detected in more than 3 percent of the field blanks

(Martin and others, 1999, tables 1–4). On the basis

of the low frequency of detection in field blanks,

sample contamination is an unlikely cause of inconsistent detections in replicate sets. In view of

the highly diverse sources of water submitted as

field replicates for the NAWQA Program and the

generally low concentrations (concentrations in

79 percent of replicate sets were less than

0.1 μg/L) of pesticides in most replicates, inconsistent detections in replicate sets likely were caused

by variability in the analytical method and by water-matrix interferences (or other loss processes)

that result in false-negative errors. Additional support for this hypothesis is found in histograms of

the distribution of pesticide concentrations in environmental surface-water samples of the NAWQA

Program. Most pesticides are detected much more

frequently at low concentrations than at high concentrations, and many histograms of pesticide

concentrations show a gradual increase in the frequency of detection as concentration decreases. At

concentrations at and near the MRL, however, the

frequency of detection for many pesticides changes

and decreases markedly (S.J. Larson, U.S. Geological Survey, written commun., July, 14, 1997). The

decreased frequency of pesticide detections at concentrations near and below the MRL probably can

be attributed to false-negative errors rather than a

true decrease in environmental concentrations.

Both lines of evidence indicate that estimates of the

frequency of detection of pesticides in environmental water samples collected for the NAWQA

Program probably are biased low because of falsenegative errors at concentrations near the MRL.

The measured percentage of inconsistent

replicate sets in tables 4–6 only is an estimate of

the unknown, true percentage of inconsistent replicate sets in the population of all possible replicate

sets that could have been collected for the

NAWQA Program. Confidence limits quantify

knowledge about the true percentage of inconsistent replicate sets in the population by providing a

probability-based estimate of the uncertainty in the

measured percentage. A one-sided, upper confidence limit (termed an upper confidence “bound”)

was calculated to estimate an upper limit of the

percentage of inconsistent replicate sets in the

population of all possible replicates sets at the

90-percent confidence level. An upper confidence

bound is used because the objective of the analysis

is to make a pessimistic estimate of detection variability; that is, how high might the variability of
34 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

detection truly be? The precision of the estimate of

uncertainty in the measured rate of detection variability (the length of the upper confidence bound)

primarily is a function of sample size (Hahn, 1979,

pp. 294–295), which is the number of replicate sets

with at least one detection.

The upper confidence bound for the percentage of replicate sets with inconsistent detections is

an estimate of the uncertainty in the measured rate

of detection variability and provides an upper limit

of the likelihood that a pesticide detected in an environmental sample would fail to be detected in a

duplicate sample. Alternatively, a lower limit of the

likelihood that a pesticide detected in an environmental sample also would be detected in a duplicate sample (also a pessimistic estimate of

detection variability) can be approximated as

100 percent minus the upper confidence bound.

For example, diazinon was detected in

55 replicate sets that had mean concentrations

greater than 0.020 μg/L (10 times the MRL for

diazinon). The median concentration of diazinon

in the 55 replicate sets is 0.051 μg/L (table 6).

Diazinon was detected in all replicates in 54 of the

55 sets but was inconsistently detected in 1 of the

55 replicate sets. The percentage of replicate sets

with inconsistent detections for diazinon, 1.8 percent (1 divided by 55 multiplied by 100 percent),

indicates very low variability in the detection of

diazinon in the high range of concentration. The

90-percent upper confidence bound for the measured percentage of replicate sets with inconsistent

detections of diazinon is 6.9 percent (table 6).

Therefore, the probability is less than 10 percent

that the true percentage of inconsistent replicate

sets for diazinon is greater than 6.9 percent. Data

users are 90 percent confident that the true percentage of inconsistent replicate sets for diazinon is

less than or equal to 6.9 percent. In the context of

variability of detection of diazinon in environmental samples, data users are 90 percent confident

that, when detected in environmental samples, diazinon would fail to be detected in only 6.9 percent

or less of duplicate samples.

Alternatively, the percentage of replicate

sets with consistent detections of diazinon is

98.2 percent (100 percent minus 1.6 percent). The

approximate 90-percent lower confidence bound

for the measured percentage of replicate sets with

consistent detections of diazinon is 93.1 percent

(100 percent minus 6.9 percent). Therefore, the

probability is less than 10 percent that the true

percentage of consistent replicate sets is less than

93.1 percent. Data users are 90 percent confident

that the true percentage of consistent replicate sets

in the high range of concentration is more than or

equal to 93.1 percent. In the context of variability

of detection of diazinon in environmental samples,

data users are 90 percent confident that, when

detected in environmental samples, diazinon also

would be detected in 93.1 percent or more of

duplicate samples. Data users have a high degree

of confidence that detections of diazinon at concentrations greater than 0.020 μg/L are reproducible.

As expected, the pessimistic estimate of detection variability for all three ranges of concentration indicates many pesticides where detection

variability is or might be high and fewer pesticides

where data users are confident that detection variability is low (fig. 5). The number of pesticides

where the upper confidence bound for the percentage of inconsistent replicate sets indicates high

variability of detection is 49 of 50 (98 percent) in

the low range, 33 of 57 (58 percent) in the medium

range, and 20 of 46 (43 percent) in the high range.

The number of pesticides where the upper confidence bound for the percentage of inconsistent

replicate sets indicates low variability of detection

is 1 of 50 (2 percent) in the low range, 12 of 57

(21 percent) in the medium range, and 14 of 46

(30 percent) in the high range. For many pesticides

in the medium or high ranges of concentration

(propachlor, for example), the measured percentage of inconsistent replicate sets is very low or zero, yet the upper confidence bound indicates that

the variability of detection could be high. Data users lack confidence that variability of detection for

these pesticides truly is low because the measured

percentage is based only on a small number of replicate sets with at least one detection. Future compilations of field replicates for the NAWQA

Program will increase the number of replicate sets

with at least one detection and will thus improve

the reliability of the estimates of variability of detection.
VARIABILITY OF PESTICIDE CONCENTRATIONS 35

VARIABILITY OF PESTICIDE

CONCENTRATIONS

Variability of pesticide concentrations was

estimated for each pesticide by calculating the

pooled SD and pooled RSD of pesticide concentrations in replicate sets. Uncertainty in the estimates

of variability of concentrations was evaluated by

calculating the 90-percent upper confidence

bounds for the pooled estimates of variability. Because variability is a strong function of concentration, variability of pesticide concentrations was

estimated separately for eight overlapping ranges

of concentration: less than 0.01 μg/L, 0.005 to less

than 0.05 μg/L, 0.01 to less than 0.1 μg/L, 0.05 to

less than 0.5 μg/L, 0.1 to less than 1 μg/L, 0.5 to

less than 5 μg/L, 1 to less than 10 μg/L, and greater

than or equal to 5 μg/L (table 7). Overlapping concentration ranges were used to improve estimates

for concentrations that otherwise would be at the

extremes of a range. In addition to the pooled estimates of variability and upper confidence bounds,

selected summary statistics for replicate sets (including the median SD and the median RSD) also

are provided. The median SD and RSD are useful

statistics for comparisons of variability in studies

where variability was modeled by regression or

smoothing or determined by methods other than

pooling.

Some ranges of concentration had no detections for some pesticides; consequently, estimates

and statistics for these ranges of concentration are

not shown in table 7. Only estimates of variability

on the basis of analytical approach 1 (nondetections in inconsistent replicate sets deleted—see

section “Statistical Methods, Calculations, and Analytical Approach”) are provided in table 7 because

they are the most generally useful and to simplify

the table. Estimates of variability using all three

approaches are provided in appendix 2. Ranges of

concentration with no inconsistent replicates sets

are shown by a single entry (no IRS) and indicate

that analyses by all three approaches are identical.

General Patterns of Variability

Median values of selected statistics presented in appendix 2 were calculated for each range of

concentration for all pesticides combined (table 8).

The medians are based solely on the statistics

published in appendix 2 and are not weighted by

the number of replicate sets for each combination

of pesticide and concentration range. The purpose

of the medians in table 8 is to summarize the typical variability of pesticide concentrations so that

(1) the variability for an individual pesticide could

be compared to a benchmark for typical variability

and (2) general patterns of variability among concentration ranges and analytical approaches could

be investigated.

The median pooled SD increases markedly

with increasing concentration (0.00083 μg/L to

0.42 μg/L, table 8). Scatterplots and smooths of the

SD of replicate sets for most pesticides are similar

to those for atrazine (figs. 3 and 4) and show that

the SD increases by several orders of magnitude as

mean concentration increases by several orders of

magnitude. The pooled RSD, however, is much less

a function of concentration than the pooled SD

(particularly when nondetections in IRS are deleted) and decreases over the range of concentration

(100 percent to 2.7 percent, table 8).

The three analytical approaches for IRS produced different estimates of variability, particularly

at low concentrations where the frequency of IRS

is most common (table 8). The lowest estimates of

variability for every range of concentration are obtained by deleting nondetections in IRS (approach

1). The highest estimates of variability at ranges of

concentrations less than 0.1 μg/L are obtained by

setting nondetections in IRS to zero (approach 3).

Estimates of variability obtained by deleting nondetections in IRS probably are most useful for water-quality assessments because this approach is

widely used in assessments of variability and requires no assumptions about nondetections in IRS.

Pooled Estimates of Variability

Pooling individual measurements of variability is appropriate if the individual measurements estimate the same variance. Both SD and

RSD are functions of concentration over the entire

range of concentration (several orders of magnitude). The validity of pooling individual estimates

of variability over limited ranges of concentration

depends upon on the distribution of the individual

measurements of variability in the concentration

range. If the individual measurements show
36 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

Table 7. Variability of pesticide concentrations in field replicates

Concentration range

(μg/L)

N df

Pooled

standard

deviation

(μg/L)

90-

percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-

percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

Mean concentration of

replicate sets

Minimum

(μg/L)

Median

(μg/L)

Maximum

(μg/L)

Acetochlor, parameter code 49260, analysis by GCMS, MRL 0.002 μg/L

< 0.01 3 4 0.00079 0.0015 0.00071 12.4 24.0 7.4 0.003 0.006 0.010

0.005 to < 0.05 5 5 .0016 .0029 .0014 11.7 20.6 6.7 .006 .031 .048

0.01 to < 0.1 4 4 .0018 .0035 .0014 4.3 8.3 3.2 .031 .045 .087

0.05 to < 0.5 2 2 .0051 .0157 .0042 2.0 6.2 2.0 .087 .196 .305

0.1 to < 1 1 1 .0071 .0563 .0071 2.3 18.4 2.3 nc .305 nc

0.5 to < 5 2 2 .0583 .1796 .0566 2.5 7.8 2.5 1.43 2.47 3.51

1 to < 10 3 3 .0981 .2222 .0707 2.6 5.9 2.7 1.43 3.51 5.40

>= 5 1 1 .1485 1.182 .1485 2.7 21.9 2.7 nc 5.40 nc

Acifluorfen, parameter code 49315, analysis by HPLC, MRL 0.035 μg/L

0.05 to < 0.5 1 1 .0778 .6190 .0778 67.6 538.2 67.6 nc .115 nc

0.1 to < 1 2 2 .0930 .2865 .0919 48.9 150.6 40.9 .115 .430 .745

0.5 to < 5 1 1 .1061 .8441 .1061 14.2 113.3 14.2 nc .745 nc

Alachlor, parameter code 46342, analysis by GCMS, MRL 0.002 μg/L

< 0.01 20 21 .00076 .00095 .00046 18.3 23.0 7.4 .003 .005 .010

0.005 to < 0.05 39 44 .0018 .0021 .00071 9.7 11.3 5.7 .005 .016 .036

0.01 to < 0.1 33 38 .0041 .0048 .00071 10.0 11.8 4.6 .010 .020 .073

0.05 to < 0.5 10 10 .0174 .0249 .0106 11.0 15.8 4.8 .059 .203 .460

0.1 to < 1 10 10 .0300 .0430 .0141 6.6 9.5 4.8 .155 .383 .863

0.5 to < 5 6 6 .0445 .0735 .0177 6.4 10.5 2.1 .515 .766 3.75

1 to < 10 2 2 .0522 .1608 .0460 2.0 6.1 2.0 1.04 2.39 3.75

Atrazine, parameter code 39632, analysis by GCMS, MRL 0.001 μg/L

< 0.01 49 58 .0012 .0013 .00058 16.3 18.5 8.5 .002 .006 .010

0.005 to < 0.05 90 105 .0014 .0016 .00071 11.8 13.0 6.1 .005 .013 .049

0.01 to < 0.1 80 92 .0039 .0043 .0014 7.6 8.4 3.8 .010 .030 .095

0.05 to < 0.5 78 90 .0128 .0142 .0058 7.5 8.3 4.0 .050 .135 .497

0.1 to < 1 62 73 .0258 .0289 .0071 6.9 7.8 4.4 .110 .208 .970

0.5 to < 5 18 20 .1396 .1770 .0389 7.1 9.0 3.9 .535 1.04 4.35

1 to < 10 12 12 .1732 .2390 .0707 5.8 8.0 2.2 1.10 2.95 7.55

>= 5 6 6 1.377 2.271 .2475 2.5 4.1 1.4 5.10 10.6 69.4

Azinphos-methyl, parameter code 82686, analysis by GCMS, MRL 0.001 μg/L

< 0.01 1 1 .0014 .0113 .0014 23.6 187.6 23.6 nc .006 nc

0.005 to < 0.05 4 5 .0030 .0053 .0025 19.9 35.1 20.1 .006 .020 .027

0.01 to < 0.1 6 9 .0116 .0171 .0039 20.0 29.4 12.7 .015 .050 .085

0.05 to < 0.5 7 9 .0431 .0633 .0141 21.7 31.9 8.7 .073 .125 .465

0.1 to < 1 4 4 .0623 .1209 .0389 22.8 44.2 14.4 .125 .203 .465

Table 7. Variability of pesticide concentrations in field replicates

[All estimates of variability use analytical approach 1: Nondetections in inconsistent replicate sets deleted. Estimates based on measurements that

showed increasing or decreasing variability in the range of concentration are shown in bold italic type. μg/L, microgram per liter; N, number of

replicate sets; df, degrees of freedom; GCMS, gas chromatography/mass spectrometry; HPLC, high-performance liquid chromatography; <, less

than; >=, greater than or equal to; parameter code, the number used to identify a pesticide in the U.S. Geological Survey National Water Information System; MRL, minimum reporting level; nc, not calculated]
VARIABILITY OF PESTICIDE CONCENTRATIONS 37

Benfluralin, parameter code 82673, analysis by GCMS, MRL 0.002 μg/L

< 0.01 1 1 0.0 nc 0.0 0.0 nc 0.0 nc 0.003 nc

Bentazon, parameter code 38711, analysis by HPLC, MRL 0.014 μg/L

0.05 to < 0.5 7 7 .0440 0.0692 .0283 19.5 30.6 15.7 0.120 .165 0.380

0.1 to < 1 8 8 .0610 .0923 .0318 19.7 29.8 17.2 .120 .173 .600

0.5 to < 5 1 1 .1273 1.013 .1273 21.2 168.8 21.2 nc .600 nc

Bromacil, parameter code 04029, analysis by HPLC, MRL 0.035 μg/L

< 0.01 1 2 .0035 .0107 .0035 43.3 133.4 43.3 nc .008 nc

0.005 to < 0.05 1 2 .0035 .0107 .0035 43.3 133.4 43.3 nc .008 nc

0.01 to < 0.1 1 1 .0 nc .0 .0 nc .0 nc .090 nc

0.05 to < 0.5 1 1 .0 nc .0 .0 nc .0 nc .090 nc

0.1 to < 1 2 2 .0814 .2508 .0813 11.6 35.7 11.5 .640 .722 .805

0.5 to < 5 2 2 .0814 .2508 .0813 11.6 35.7 11.5 .640 .722 .805

Bromoxynil, parameter code 49311, analysis by HPLC, MRL 0.035 μg/L

0.05 to < 0.5 1 1 .0071 .0563 .0071 5.2 41.7 5.2 nc .135 nc

0.1 to < 1 1 1 .0071 .0563 .0071 5.2 41.7 5.2 nc .135 nc

Butylate, parameter code 04028, analysis by GCMS, MRL 0.002 μg/L

< 0.01 9 10 .00087 .0012 .00071 26.5 38.0 20.2 .002 .004 .009

0.005 to < 0.05 9 10 .0011 .0016 .00064 9.7 14.0 2.0 .005 .012 .031

0.01 to < 0.1 5 5 .0013 .0024 .00064 5.4 9.5 2.0 .012 .020 .031

Carbaryl, parameter code 82680, analysis by GCMS, MRL 0.003 μg/L

< 0.01 8 9 .00088 .0013 .00071 12.3 18.1 8.3 .005 .007 .010

0.005 to < 0.05 28 33 .0034 .0040 .0014 14.3 17.1 8.8 .005 .017 .050

0.01 to < 0.1 26 32 .0067 .0080 .0017 16.0 19.2 9.9 .010 .025 .073

0.05 to < 0.5 13 17 .0268 .0347 .0199 16.2 21.0 12.3 .051 .123 .460

0.1 to < 1 11 14 .0775 .1040 .0212 16.3 21.8 12.3 .110 .385 .810

0.5 to < 5 3 4 .1352 .2621 .1838 21.0 40.8 22.7 .503 .560 .810

Carbaryl, parameter code 49310, analysis by HPLC, MRL 0.008 μg/L

0.005 to < 0.05 2 2 .0280 .0861 .0230 85.8 264.4 69.9 .033 .034 .035

0.01 to < 0.1 3 3 .0306 .0694 .0354 74.1 167.8 41.6 .033 .035 .085

0.05 to < 0.5 2 2 .0354 .1089 .0354 29.8 91.9 24.4 .085 .290 .495

0.1 to < 1 1 1 .0354 .2814 .0354 7.1 56.8 7.1 nc .495 nc

Carbofuran, parameter code 82674, analysis by GCMS, MRL 0.003 μg/L

0.005 to < 0.05 7 8 .0032 .0049 .0021 16.4 24.8 9.4 .011 .023 .035

0.01 to < 0.1 8 9 .0140 .0206 .0021 28.9 42.4 14.8 .011 .024 .056

0.05 to < 0.5 6 6 .0249 .0411 .0039 34.3 56.6 1.2 .056 .120 .336

0.1 to < 1 6 6 .0189 .0312 .0039 16.8 27.7 .5 .109 .155 .975

0.5 to < 5 1 1 .0099 .0788 .0099 1.0 8.1 1.0 nc .975 nc

Table 7. Variability of pesticide concentrations in field replicates—Continued

Concentration range

(μg/L)

N df

Pooled

standard

deviation

(μg/L)

90-

percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-

percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

Mean concentration of

replicate sets

Minimum

(μg/L)

Median

(μg/L)

Maximum

(μg/L)
38 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

Carbofuran, parameter code 49309, analysis by HPLC, MRL 0.120 μg/L

0.1 to < 1 1 1 0.0 nc 0.0 0.0 nc 0.0 nc 0.790 nc

0.5 to < 5 1 1 .0 nc .0 .0 nc .0 nc .790 nc

Chlorpyrifos, parameter code 38933, analysis by GCMS, MRL 0.004 μg/L

< 0.01 22 24 .0018 0.0022 .00071 23.9 29.6 12.7 0.003 .007 0.010

0.005 to < 0.05 46 52 .0024 .0027 .0013 18.5 21.2 8.7 .005 .011 .041

0.01 to < 0.1 29 34 .0030 .0035 .0014 12.0 14.3 8.3 .010 .021 .081

0.05 to < 0.5 8 9 .0157 .0231 .0141 9.9 14.6 9.1 .057 .140 .320

0.1 to < 1 6 6 .0189 .0312 .0177 10.5 17.3 9.1 .125 .168 .320

Cyanazine, parameter code 04041, analysis by GCMS, MRL 0.004 μg/L

< 0.01 6 6 .0012 .0019 .00084 13.3 22.0 10.4 .008 .008 .010

0.005 to < 0.05 33 37 .0023 .0027 .0014 10.1 11.9 8.8 .008 .016 .048

0.01 to < 0.1 38 45 .0040 .0047 .0019 9.8 11.4 7.8 .010 .033 .098

0.05 to < 0.5 25 29 .0314 .0380 .0057 14.8 17.9 5.7 .050 .102 .330

0.1 to < 1 19 20 .0759 .0962 .0071 19.1 24.3 3.8 .100 .247 .620

0.5 to < 5 11 11 .2940 .4128 .0424 16.8 23.7 5.8 .530 1.07 4.67

1 to < 10 6 6 .3794 .6260 .2581 9.1 15.0 7.2 1.07 3.44 4.67

2,4-D, parameter code 39732, analysis by HPLC, MRL 0.150 μg/L

0.005 to < 0.05 2 2 .0071 .0218 .0071 22.9 70.5 22.0 .025 .035 .045

0.01 to < 0.1 3 3 .0058 .0131 .0071 18.7 42.3 15.7 .025 .045 .070

0.05 to < 0.5 6 6 .0327 .0539 .0071 10.6 17.5 6.2 .070 .180 .370

0.1 to < 1 7 7 .0434 .0683 .0354 11.0 17.3 6.7 .105 .265 .740

0.5 to < 5 2 2 .0583 .1796 .0566 9.3 28.6 8.8 .600 .670 .740

Dacthal, parameter code 82682, analysis by GCMS, MRL 0.002 μg/L

< 0.01 34 39 .00036 .00043 .0 11.0 13.0 .0 .001 .003 .008

0.005 to < 0.05 20 25 .0058 .0071 .00071 26.8 33.0 5.9 .005 .012 .041

0.01 to < 0.1 14 16 .0078 .0103 .0011 32.9 43.1 6.3 .011 .017 .081

0.05 to < 0.5 4 4 .0159 .0308 .0095 11.3 22.0 6.7 .061 .118 .320

0.1 to < 1 2 2 .0206 .0635 .0177 7.0 21.7 6.7 .155 .238 .320

p,p’-DDE, parameter code 34653, analysis by GCMS, MRL 0.006 μg/L

< 0.01 9 10 .00073 .0010 .00071 31.6 45.3 23.2 .001 .002 .009

0.005 to < 0.05 5 5 .0025 .0044 .0028 15.2 26.9 16.4 .008 .014 .028

0.01 to < 0.1 3 3 .0031 .0070 .0028 16.1 36.5 16.4 .014 .022 .028

Desethylatrazine, parameter code 04040, analysis by GCMS, MRL 0.002 μg/L

< 0.01 50 56 .00095 .0011 .00064 18.2 20.8 10.9 .001 .004 .010

0.005 to < 0.05 79 86 .0046 .0051 .0014 20.4 22.6 10.9 .005 .020 .049

0.01 to < 0.1 82 92 .0061 .0068 .0026 18.5 20.5 8.8 .010 .030 .093

0.05 to < 0.5 42 51 .0151 .0173 .0088 12.0 13.8 6.6 .050 .109 .370

0.1 to < 1 25 30 .0258 .0311 .0141 10.8 13.1 6.1 .103 .200 .874

Table 7. Variability of pesticide concentrations in field replicates—Continued

Concentration range

(μg/L)

N df

Pooled

standard

deviation

(μg/L)

90-

percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-

percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

Mean concentration of

replicate sets

Minimum

(μg/L)

Median

(μg/L)

Maximum

(μg/L)
VARIABILITY OF PESTICIDE CONCENTRATIONS 39

Desethylatrazine, parameter code 04040, analysis by GCMS, MRL 0.002 μg/L—Continued

0.5 to < 5 3 3 0.0784 0.1777 0.0919 8.0 18.2 7.6 0.510 0.874 1.22

1 to < 10 1 1 .0919 .7315 .0919 7.6 60.2 7.6 nc 1.22 nc

Diazinon, parameter code 39572, analysis by GCMS, MRL 0.002 μg/L

< 0.01 32 34 .0014 .0016 .00071 20.6 24.6 9.4 .003 .007 0.010

0.005 to < 0.05 69 77 .0023 .0026 .0010 16.5 18.4 7.4 .005 .017 .046

0.01 to < 0.1 63 75 .0040 .0045 .0014 10.7 12.0 4.3 .011 .038 .100

0.05 to < 0.5 25 31 .0061 .0073 .0021 6.9 8.3 2.9 .051 .076 .410

0.1 to < 1 6 7 .0282 .0443 .0071 5.8 9.2 4.2 .115 .165 .567

0.5 to < 5 2 3 .0918 .2079 .0964 7.9 18.0 7.1 .567 1.683 2.800

1 to < 10 1 1 .1414 1.125 .1414 5.1 40.2 5.1 nc 2.800 nc

Dieldrin, parameter code 39381, analysis by GCMS, MRL 0.001 μg/L

< 0.01 6 7 .0025 .0040 .00071 28.8 45.2 13.8 .004 .008 .010

0.005 to < 0.05 11 12 .0033 .0046 .0035 28.2 38.9 13.3 .006 .011 .027

0.01 to < 0.1 6 6 .0039 .0064 .0039 26.2 43.3 20.8 .011 .015 .027

2,6-Diethylaniline, parameter code 82660, analysis by GCMS, MRL 0.003 μg/L

< 0.01 7 7 .00038 .00059 .0 25.2 39.6 .0 .001 .001 .003

0.005 to < 0.05 1 1 .0014 .0113 .0014 12.9 102.3 12.9 nc .011 nc

0.01 to < 0.1 1 1 .0014 .0113 .0014 12.9 102.3 12.9 nc .011 nc

Diuron, parameter code 49300, analysis by HPLC, MRL 0.020 μg/L

0.005 to < 0.05 4 4 .0132 .0257 .0106 40.9 79.3 37.7 .025 .030 .035

0.01 to < 0.1 8 9 .0150 .0220 .0071 33.2 48.8 18.9 .025 .043 .080

0.05 to < 0.5 8 9 .0202 .0297 .0177 21.3 31.4 10.4 .050 .093 .235

0.1 to < 1 9 10 .2314 .3318 .0354 32.0 45.9 15.0 .105 .620 .937

0.5 to < 5 8 9 .4031 .5923 .2440 39.4 57.9 18.4 .620 .888 4.30

1 to < 10 3 3 .5565 1.261 .4243 39.1 88.6 9.9 1.29 1.75 4.30

DNOC, parameter code 49299, analysis by HPLC, MRL 0.420 μg/L

0.1 to < 1 1 1 .0495 .3939 .0495 9.8 78.0 9.8 nc .505 nc

0.5 to < 5 1 1 .0495 .3939 .0495 9.8 78.0 9.8 nc .505 nc

EPTC, parameter code 82668, analysis by GCMS, MRL 0.002 μg/L

< 0.01 16 19 .0018 .0023 .00044 30.6 39.0 6.2 .002 .005 .008

0.005 to < 0.05 27 29 .0050 .0061 .0014 29.0 35.1 8.7 .005 .017 .048

0.01 to < 0.1 26 27 .0052 .0064 .0019 18.9 23.0 5.8 .012 .023 .083

0.05 to < 0.5 10 11 .0166 .0233 .0032 6.3 8.9 3.9 .051 .082 .345

0.1 to < 1 4 4 .0281 .0544 .0177 8.8 17.0 6.6 .145 .300 .500

0.5 to < 5 1 1 .0141 .1125 .0141 2.8 22.5 2.8 nc .500 nc

Table 7. Variability of pesticide concentrations in field replicates—Continued

Concentration range

(μg/L)

N df

Pooled

standard

deviation

(μg/L)

90-

percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-

percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

Mean concentration of

replicate sets

Minimum

(μg/L)

Median

(μg/L)

Maximum

(μg/L)
40 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

Ethalfluralin, parameter code 82663, analysis by GCMS, MRL 0.004 μg/L

0.005 to < 0.05 4 4 0.00094 0.0018 0.00071 4.9 9.5 4.6 0.011 0.023 0.045

0.01 to < 0.1 4 4 .00094 .0018 .00071 4.9 9.5 4.6 .011 .023 .045

0.05 to < 0.5 1 1 .0318 .2532 .0318 29.6 235.6 29.6 nc .108 nc

0.1 to < 1 1 1 .0318 .2532 .0318 29.6 235.6 29.6 nc .108 nc

Ethoprop, parameter code 82672, analysis by GCMS, MRL 0.003 μg/L

< 0.01 2 2 .00040 .0012 .00028 9.1 28.0 6.4 .003 .004 .004

0.005 to < 0.05 2 2 .00050 .0015 .00035 1.2 3.6 .8 .014 .028 .043

0.01 to < 0.1 2 2 .00050 .0015 .00035 1.2 3.6 .8 .014 .028 .043

Fenuron, parameter code 49297, analysis by HPLC, MRL 0.013 μg/L

0.05 to < 0.5 1 1 .0 nc .0 .0 nc .0 nc .140 nc

0.1 to < 1 1 1 .0 nc .0 .0 nc .0 nc .140 nc

Fluometuron, parameter code 38811, analysis by HPLC, MRL 0.035 μg/L

< 0.01 1 1 .0049 .0394 .0049 76.1 606.0 76.1 nc .007 nc

0.005 to < 0.05 1 1 .0049 .0394 .0049 76.1 606.0 76.1 nc .007 nc

0.01 to < 0.1 1 1 .0212 .1688 .0212 28.3 225.1 28.3 nc .075 nc

0.05 to < 0.5 4 4 .0892 .1729 .0141 24.7 47.8 17.2 .075 .145 .445

0.1 to < 1 3 3 .1022 .2316 .0071 23.3 52.8 6.1 .115 .175 .445

1 to < 10 1 1 .4243 3.376 .4243 6.8 54.5 6.8 nc 6.20 nc

>= 5 1 1 .4243 3.376 .4243 6.8 54.5 6.8 nc 6.20 nc

Fonofos, parameter code 04095, analysis by GCMS, MRL 0.003 μg/L

< 0.01 14 14 .00072 .00096 .00015 15.4 20.6 2.6 .002 .004 .009

0.005 to < 0.05 9 11 .0012 .0018 .00040 6.6 9.2 4.3 .006 .012 .034

0.01 to < 0.1 7 9 .0022 .0033 .0010 4.9 7.2 4.3 .012 .021 .096

0.05 to < 0.5 2 2 .0039 .0120 .0036 4.5 14.0 4.5 .059 .077 .096

alpha-HCH, parameter code 34253, analysis by GCMS, MRL 0.002 μg/L

0.005 to < 0.05 1 1 .0035 .0281 .0035 9.4 75.0 9.4 nc .038 nc

0.01 to < 0.1 1 1 .0035 .0281 .0035 9.4 75.0 9.4 nc .038 nc

gamma-HCH, parameter code 39341, analysis by GCMS, MRL 0.004 μg/L

< 0.01 2 2 .0016 .0049 .0014 18.2 56.1 17.6 .006 .008 .010

0.005 to < 0.05 4 4 .0027 .0053 .0014 13.9 27.0 11.4 .006 .016 .050

0.01 to < 0.1 4 4 .0034 .0065 .0032 5.9 11.4 3.7 .022 .068 .092

0.05 to < 0.5 2 2 .0032 .0099 .0032 3.6 11.2 3.6 .086 .089 .092

Linuron, parameter code 82666, analysis by GCMS, MRL 0.002 μg/L

0.005 to < 0.05 5 6 .00090 .0015 .00049 6.4 10.6 3.1 .011 .019 .024

0.01 to < 0.1 6 7 .0020 .0032 .00082 6.6 10.3 4.5 .011 .019 .067

0.05 to < 0.5 4 5 .0916 .1614 .0060 33.3 58.7 6.6 .067 .141 .277

0.1 to < 1 3 4 .1024 .1985 .0071 37.1 71.9 5.7 .125 .157 .277

Table 7. Variability of pesticide concentrations in field replicates—Continued

Concentration range

(μg/L)

N df

Pooled

standard

deviation

(μg/L)

90-

percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-

percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

Mean concentration of

replicate sets

Minimum

(μg/L)

Median

(μg/L)

Maximum

(μg/L)
VARIABILITY OF PESTICIDE CONCENTRATIONS 41

Linuron, parameter code 38478, analysis by HPLC, MRL 0.018 μg/L

0.01 to < 0.1 2 3 0.0312 0.0706 0.0225 54.8 124.1 37.6 0.057 0.071 0.085

0.05 to < 0.5 2 3 .0312 .0706 .0225 54.8 124.1 37.6 .057 .071 .085

MCPA, parameter code 38482, analysis by HPLC, MRL 0.170 μg/L

0.01 to < 0.1 1 1 .0495 .3939 .0495 58.2 463.4 58.2 nc .085 nc

0.05 to < 0.5 1 1 .0495 .3939 .0495 58.2 463.4 58.2 nc .085 nc

Malathion, parameter code 39532, analysis by GCMS, MRL 0.005 μg/L

< 0.01 6 6 .0012 .0020 .00035 13.5 22.3 3.7 .004 .008 .010

0.005 to < 0.05 11 13 .0019 .0026 .00071 13.1 17.8 6.7 .006 .011 .044

0.01 to < 0.1 11 13 .0079 .0107 .0021 15.0 20.3 6.7 .011 .044 .090

0.05 to < 0.5 5 5 .0124 .0218 .0078 18.9 33.2 15.1 .052 .063 .090

Methyl parathion, parameter code 82667, analysis by GCMS, MRL 0.006 μg/L

0.005 to < 0.05 4 4 .00053 .0010 .00035 3.8 7.4 1.8 .011 .020 .044

0.01 to < 0.1 4 4 .00053 .0010 .00035 3.8 7.4 1.8 .011 .020 .044

Metolachlor, parameter code 39415, analysis by GCMS, MRL 0.002 μg/L

< 0.01 37 43 .00065 .00076 .0 16.7 19.5 .0 .002 .005 .010

0.005 to < 0.05 70 77 .0013 .0015 .00071 6.8 7.6 2.8 .005 .015 .050

0.01 to < 0.1 68 77 .0023 .0026 .00071 5.8 6.5 3.3 .010 .028 .097

0.05 to < 0.5 47 56 .0236 .0270 .0042 11.2 12.8 3.6 .052 .125 .450

0.1 to < 1 36 42 .0554 .0648 .0141 13.5 15.8 3.5 .107 .235 .985

0.5 to < 5 16 18 .1569 .2020 .0707 10.7 13.8 4.7 .560 1.42 4.25

1 to < 10 12 13 .1707 .2319 .0707 9.0 12.2 4.7 1.15 1.78 9.12

>= 5 3 3 .7829 1.774 .1768 6.4 14.5 3.2 5.56 9.12 12.6

Metribuzin, parameter code 82630, analysis by GCMS, MRL 0.004 μg/L

< 0.01 7 9 .00071 .0010 .0 10.6 15.6 .0 .004 .007 .010

0.005 to < 0.05 17 19 .0027 .0034 .00071 11.4 14.6 4.7 .005 .018 .042

0.01 to < 0.1 13 13 .0034 .0046 .00071 10.9 14.8 4.7 .011 .026 .090

0.05 to < 0.5 5 5 .0060 .0106 .0 4.7 8.2 .0 .050 .130 .211

0.1 to < 1 4 4 .0149 .0288 .0064 3.5 6.9 1.9 .130 .183 .719

0.5 to < 5 1 1 .0269 .2138 .0269 3.7 29.7 3.7 nc .719 nc

Molinate, parameter code 82671, analysis by GCMS, MRL 0.004 μg/L

< 0.01 1 1 .0 nc .0 .0 nc .0 nc .007 nc

0.005 to < 0.05 3 3 .0016 .0037 .0 14.8 33.6 .0 .007 .011 .036

0.01 to < 0.1 3 3 .0023 .0052 .0028 15.0 33.9 3.5 .011 .036 .081

0.05 to < 0.5 4 4 .0107 .0208 .0106 7.7 14.9 7.5 .081 .133 .150

0.1 to < 1 3 3 .0122 .0277 .0141 8.6 19.5 9.4 .125 .140 .150

0.5 to < 5 1 1 .0 nc .0 .0 nc .0 nc 3.80 nc

1 to < 10 3 3 .0 nc .0 .0 nc .0 3.80 5.00 9.70

>= 5 3 3 .0 nc .0 .0 nc .0 5.00 9.70 20.0

Table 7. Variability of pesticide concentrations in field replicates—Continued

Concentration range

(μg/L)

N df

Pooled

standard

deviation

(μg/L)

90-

percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-

percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

Mean concentration of

replicate sets

Minimum

(μg/L)

Median

(μg/L)

Maximum

(μg/L)
42 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

Napropamide, parameter code 82684, analysis by GCMS, MRL 0.003 μg/L

< 0.01 6 6 0.00058 0.00095 0.00071 13.3 22.0 8.4 0.003 0.008 0.010

0.005 to < 0.05 10 11 .0015 .0021 .00071 11.2 15.8 8.4 .007 .010 .019

0.01 to < 0.1 9 10 .0020 .0028 .0014 10.8 15.5 6.4 .011 .019 .070

0.05 to < 0.5 4 4 .0019 .0038 .0011 3.4 6.6 1.6 .056 .064 .070

Norflurazon, parameter code 49293, analysis by HPLC, MRL 0.024 μg/L

0.01 to < 0.1 2 2 .0112 .0344 .0106 12.6 38.7 12.0 .085 .088 .090

0.05 to < 0.5 2 2 .0112 .0344 .0106 12.6 38.7 12.0 .085 .088 .090

0.1 to < 1 1 1 .0919 .7315 .0919 16.0 127.2 16.0 nc .575 nc

0.5 to < 5 1 1 .0919 .7315 .0919 16.0 127.2 16.0 nc .575 nc

Oryzalin, parameter code 49292, analysis by HPLC, MRL 0.310 μg/L

0.1 to < 1 1 1 .2758 2.195 .2758 53.5 426.1 53.5 nc .515 nc

0.5 to < 5 1 1 .2758 2.195 .2758 53.5 426.1 53.5 nc .515 nc

Pebulate, parameter code 82669, analysis by GCMS, MRL 0.004 μg/L

0.005 to < 0.05 3 3 .0042 .0094 .0014 17.2 38.9 3.8 .013 .024 .037

0.01 to < 0.1 3 3 .0042 .0094 .0014 17.2 38.9 3.8 .013 .024 .037

0.05 to < 0.5 1 1 .0071 .0563 .0071 3.6 28.9 3.6 nc .195 nc

0.1 to < 1 1 1 .0071 .0563 .0071 3.6 28.9 3.6 nc .195 nc

Pendimethalin, parameter code 82683, analysis by GCMS, MRL 0.004 μg/L

< 0.01 6 6 .00076 .0013 .00071 12.5 20.7 10.1 .006 .007 .010

0.005 to < 0.05 11 11 .0021 .0030 .00071 12.7 17.8 7.4 .006 .010 .030

0.01 to < 0.1 10 11 .0058 .0082 .0028 13.1 18.5 9.5 .011 .040 .063

0.05 to < 0.5 7 9 .0428 .0629 .0087 21.7 32.0 16.1 .050 .060 .305

0.1 to < 1 2 3 .0734 .1664 .0748 32.6 73.9 34.0 .103 .204 .305

Picloram, parameter code 49291, analysis by HPLC, MRL 0.050 μg/L

0.05 to < 0.5 1 1 .0141 .1125 .0141 12.9 102.3 12.9 nc .110 nc

0.1 to < 1 1 1 .0141 .1125 .0141 12.9 102.3 12.9 nc .110 nc

Prometon, parameter code 04037, analysis by GCMS, MRL 0.018 μg/L

< 0.01 32 38 .00089 .0010 .00064 12.3 14.5 8.1 .003 .008 .010

0.005 to < 0.05 90 109 .0033 .0036 .00071 12.6 13.8 5.8 .005 .016 .050

0.01 to < 0.1 89 108 .0050 .0055 .0014 12.3 13.5 4.6 .010 .029 .097

0.05 to < 0.5 34 40 .0096 .0113 .0048 11.9 14.0 4.6 .054 .075 .225

0.1 to < 1 9 10 .0146 .0209 .0071 12.3 17.6 6.1 .103 .121 .225

0.5 to < 5 1 1 .0141 .1125 .0141 1.4 10.9 1.4 nc 1.03 nc

1 to < 10 1 1 .0141 .1125 .0141 1.4 10.9 1.4 nc 1.03 nc

Table 7. Variability of pesticide concentrations in field replicates—Continued

Concentration range

(μg/L)

N df

Pooled

standard

deviation

(μg/L)

90-

percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-

percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

Mean concentration of

replicate sets

Minimum

(μg/L)

Median

(μg/L)

Maximum

(μg/L)
VARIABILITY OF PESTICIDE CONCENTRATIONS 43

Pronamide, parameter code 82676, analysis by GCMS, MRL 0.003 μg/L

< 0.01 3 3 0.00041 0.00092 0.0 6.3 14.2 0.0 0.007 0.009 0.009

0.005 to < 0.05 5 5 .00055 .00097 .00071 6.3 11.2 6.1 .007 .009 .012

0.01 to < 0.1 2 2 .00071 .0022 .00071 6.4 19.9 6.4 .011 .011 .012

Propachlor, parameter code 04024, analysis by GCMS, MRL 0.007 μg/L

< 0.01 1 1 .0 nc .0 .0 nc .0 nc .006 nc

0.005 to < 0.05 3 3 .0021 .0047 .00071 5.2 11.8 4.6 .006 .016 .046

0.01 to < 0.1 3 3 .0053 .0121 .0035 7.8 17.6 7.8 .016 .046 .085

0.05 to < 0.5 1 1 .0085 .0675 .0085 10.0 79.4 10.0 nc .085 nc

Propanil, parameter code 82679, analysis by GCMS, MRL 0.004 μg/L

< 0.01 1 2 .00058 .0018 .00058 6.0 18.4 6.0 nc .010 nc

0.005 to < 0.05 1 2 .00058 .0018 .00058 6.0 18.4 6.0 nc .010 nc

0.01 to < 0.1 1 1 .0021 .0169 .0021 4.2 33.4 4.2 nc .051 nc

0.05 to < 0.5 1 1 .0021 .0169 .0021 4.2 33.4 4.2 nc .051 nc

Propargite, parameter code 82685, analysis by GCMS, MRL 0.013 μg/L

< 0.01 1 1 .00071 .0056 .00071 7.4 59.2 7.4 nc .010 nc

0.005 to < 0.05 5 6 .0016 .0027 .0011 9.2 15.2 8.9 .010 .012 .039

0.01 to < 0.1 6 7 .0109 .0171 .0025 14.2 22.4 13.7 .010 .033 .092

0.05 to < 0.5 4 4 .0469 .0910 .0269 19.9 38.5 17.5 .091 .131 .460

0.1 to < 1 3 4 .0510 .0989 .0346 12.8 24.8 16.6 .170 .460 .780

0.5 to < 5 1 2 .0346 .1067 .0346 4.4 13.7 4.4 nc .780 nc

Simazine, parameter code 04035, analysis by GCMS, MRL 0.005 μg/L

< 0.01 28 37 .0010 .0012 .00058 14.8 17.5 8.9 .002 .007 .010

0.005 to < 0.05 98 111 .0020 .0022 .00074 11.1 12.2 5.8 .005 .017 .050

0.01 to < 0.1 97 111 .0027 .0030 .0014 8.4 9.2 4.3 .010 .028 .099

0.05 to < 0.5 52 62 .0137 .0155 .0047 7.9 8.9 4.0 .051 .118 .425

0.1 to < 1 36 41 .0197 .0231 .0071 8.8 10.3 4.2 .105 .175 .843

0.5 to < 5 12 13 .1472 .2001 .0332 7.0 9.6 4.0 .500 1.18 4.25

1 to < 10 7 7 .1989 .3127 .1485 9.1 14.2 6.7 1.05 1.40 4.25

Tebuthiuron, parameter code 82670, analysis by GCMS, MRL 0.010 μg/L

< 0.01 17 21 .0010 .0013 .00071 15.8 19.9 8.7 .003 .007 .010

0.005 to < 0.05 46 54 .0042 .0048 .00071 16.1 18.5 6.5 .007 .014 .045

0.01 to < 0.1 33 37 .0052 .0061 .0011 16.2 19.1 4.6 .010 .021 .078

0.05 to < 0.5 6 6 .0188 .0310 .0110 8.8 14.5 8.4 .075 .119 .312

0.1 to < 1 4 4 .0227 .0440 .0177 9.6 18.6 9.3 .108 .203 .312

Table 7. Variability of pesticide concentrations in field replicates—Continued

Concentration range

(μg/L)

N df

Pooled

standard

deviation

(μg/L)

90-

percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-

percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

Mean concentration of

replicate sets

Minimum

(μg/L)

Median

(μg/L)

Maximum

(μg/L)
44 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

Terbacil, parameter code 82665, analysis by GCMS, MRL 0.007 μg/L

< 0.01 2 2 0.00071 0.0022 0.00071 10.2 31.4 10.2 0.007 0.007 0.008

0.005 to < 0.05 4 5 .0021 .0037 .00071 11.9 21.0 10.2 .007 .010 .020

0.01 to < 0.1 3 4 .0042 .0082 .0032 13.1 25.4 13.6 .013 .020 .052

0.05 to < 0.5 1 1 .0071 .0563 .0071 13.6 108.2 13.6 nc .052 nc

0.1 to < 1 1 1 .0 nc .0 .0 nc .0 nc .540 nc

0.5 to < 5 1 1 .0 nc .0 .0 nc .0 nc .540 nc

Thiobencarb, parameter code 82681, analysis by GCMS, MRL 0.002 μg/L

< 0.01 1 1 .0 nc .0 .0 nc .0 nc .008 nc

0.005 to < 0.05 5 5 .00077 .0014 .00071 6.9 12.1 2.1 .008 .013 .034

0.01 to < 0.1 4 4 .00087 .0017 .00071 7.7 14.9 3.9 .010 .017 .034

Triallate, parameter code 82678, analysis by GCMS, MRL 0.001 μg/L

< 0.01 9 9 .0032 .0046 .00071 39.3 57.8 12.9 .003 .004 .009

0.005 to < 0.05 6 6 .0039 .0065 .0011 45.3 74.7 9.3 .006 .008 .037

0.01 to < 0.1 3 3 .0039 .0088 .0021 6.4 14.5 5.8 .024 .037 .072

0.05 to < 0.5 2 2 .0157 .0482 .0138 12.1 37.3 11.8 .072 .108 .145

0.1 to < 1 1 1 .0212 .1688 .0212 14.6 116.4 14.6 nc .145 nc

Triclopyr, parameter code 49235, analysis by HPLC, MRL 0.250 μg/L

0.05 to < 0.5 1 2 .0306 .0941 .0306 14.1 43.4 14.1 nc .217 nc

0.1 to < 1 1 2 .0306 .0941 .0306 14.1 43.4 14.1 nc .217 nc

Trifluralin, parameter code 82661, analysis by GCMS, MRL 0.002 μg/L

< 0.01 12 14 .0010 .0014 .00071 20.5 27.5 10.2 .002 .006 .008

0.005 to < 0.05 21 22 .0012 .0015 .00071 15.4 19.3 1.6 .005 .010 .047

0.01 to < 0.1 17 17 .0059 .0077 .00071 11.3 14.7 1.6 .010 .016 .091

0.05 to < 0.5 5 5 .0144 .0253 .0071 13.6 23.9 7.0 .061 .084 .495

0.1 to < 1 1 1 .0212 .1688 .0212 4.3 34.1 4.3 nc .495 nc

Table 7. Variability of pesticide concentrations in field replicates—Continued

Concentration range

(μg/L)

N df

Pooled

standard

deviation

(μg/L)

90-

percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-

percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

Mean concentration of

replicate sets

Minimum

(μg/L)

Median

(μg/L)

Maximum

(μg/L)
VARIABILITY OF PESTICIDE CONCENTRATIONS 45

constant variance (no pattern of increase or decrease with concentration), then it is appropriate to

pool them. If the individual measurements increase

or decrease in the range of concentration, then the

pooled estimates of variability are biased.

The assumption that variability was constant

(homoscedastic) in a concentration range was examined by calculating the significance level of the

correlation between the individual estimate of variability and the mean concentration of the replicate

set for all replicate sets in a concentration range.

Statistically significant correlations (p <0.05 or,

equivalently, α = 0.05) indicate increasing or decreasing variability in a concentration range.

Results of the correlation analysis show that

for most pesticides and concentrations, pooled estimates of RSD should be used to estimate variability

because RSD is a more robust estimate of variability (less affected by heteroscedasticity) than is SD.

In a correlation analysis of 170 combinations of

pesticide and concentration range (approach 1,

nondetections in IRS deleted), 43 combinations

(25.3 percent) showed a statistically significant

correlation between SD and concentration, whereas

only 11 (6.5 percent) showed a statistically significant correlation between RSD and concentration

(table 9). The Type I error rate selected for the

correlation analysis (α = 0.05) predicts that

Table 8. Typical variability of pesticide concentrations in field replicates

[Data in this table are the median values of the statistics published in appendix 2. μg/L, microgram per liter; IRS, inconsistent replicate sets; <, less

than; >=, greater than or equal to; deleted, nondetections in IRS deleted; zero, nondetections in IRS set to zero; mrl, nondetections in IRS set to the

minimum reporting level]

Concentration

range

(μg/L)

Analytical

approach

for IRS

Number of

pesticides

Median

pooled

standard

deviation

(μg/L)

Median

standard

deviation

(μg/L)

Median

pooled

relative

standard

deviation

(percent)

Median

relative

standard

deviation

(percent)

Median

concentration of

replicate

sets

(μg/L)

< 0.01 deleted 38 0.00083 0.00068 15 8.4 0.007

zero 50 .0034 .0016 100 71 .005

mrl 46 .0018 .00071 33 20 .006

0.005 to < 0.05 deleted 46 .0022 .00072 13 7.1 .016

zero 54 .0047 .0014 40 9.4 .013

mrl 55 .0036 .0014 27 9.4 .014

0.01 to < 0.1 deleted 49 .0040 .0017 12 6.3 .028

zero 58 .0060 .0023 20 8.3 .028

mrl 56 .0053 .0021 18 7.3 .028

0.05 to < 0.5 deleted 46 .016 .0082 12 6.9 .117

zero 49 .019 .011 15 10 .108

mrl 49 .019 .011 15 10 .110

0.1 to < 1 deleted 41 .028 .018 11 6.7 .208

zero 43 .030 .018 13 7.1 .208

mrl 44 .031 .018 13 9.2 .203

0.5 to < 5 deleted 25 .078 .050 7.9 4.7 .780

zero 27 .081 .057 8.0 5.8 .790

mrl 28 .087 .057 8.7 6.5 .785

1 to < 10 deleted 12 .16 .081 6.3 4.9 2.60

zero 13 .17 .092 6.8 5.1 2.39

mrl 13 .17 .092 6.8 5.1 2.39

>= 5 no IRS 5 .42 .18 2.7 2.7 9.12
46 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

8.5 combinations (170 x 0.05) would show a significant correlation by error, a number very near

that shown (11) for the relation between RSD and

concentration. In general, the statistically significant correlations between SD and concentration

had a much lower probability of occurring by

chance (a lower value of p) than the correlations

between RSD and concentration.

SD increased with concentration for all significant correlations, whereas RSD decreased with

concentration for 8 of the 11 significant correlations (table 9). RSD increased with concentration

for atrazine in the >= 5 μg/L concentration range

and for bentazon in the 0.05 to <0.5 μg/L and the

0.1 to <1 μg/L concentration ranges. Most (33) of

the significant correlations between the SD and

concentration occurred in the 0.005 to <0.05 μg/L,

the 0.01 to <0.1 μg/L, and the 0.05 to <0.05 μg/L

concentrations ranges, whereas most (5) of the

significant correlations between RSD and concentration occurred in the 0.05 to <0.5 μg/L concentration range (table 9). Atrazine, desethylatrazine,

p,p’-DDE, and prometon exhibited increasing SD

in the lowest ranges of concentration, near the

MDL—a finding contrary to the assumption of

constant variance at low concentrations needed for

the MDL process (Oblinger Childress and others,

1999, p. 4). Estimates of variability that are biased

(based on individual measurements of variability

that increased or decreased in the concentration

range) are shown in table 7 and appendix 2 in bold

italic type.

Estimates of variability were developed for

eight overlapping ranges of concentrations. As a

consequence, two different estimates of variability

often can be made for a particular concentration.

An estimate of variability at a concentration of

0.15 μg/L, for example, can be obtained from use

of the information presented for the concentration

range 0.05 to < 0.5 μg/L or from the concentration

range 0.1 to < 1 μg/L. In general, data users should

select the appropriate concentration range on the

basis of the median (and perhaps the minimum and

maximum) concentration of individual replicate

sets used to develop the pooled estimates of variability for the concentration range (table 7). The

number of replicates in the concentration range and

the reliability of the pooled estimate are additional

considerations. An estimate of the variability of

atrazine at 0.15 μg/L, for example, should be based

on the information provided for the concentration

range 0.05 to < 0.5 μg/L because the median

concentration of the field replicates in this range

(0.135 μg/L) is much nearer to 0.15 μg/L than is

the median concentration of field replicates for the

concentration range 0.1 to <1 μg/L (0.208 μg/L).

Table 9. Assessment of constant variance in a concentration range

[All estimates of variability use analytical approach 1: Nondetections in inconsistent replicate sets deleted. μg/L, microgram per liter;

<, less than; >=, greater than or equal to]

Concentration

range

(μg/L)

Pesticides

with three

or more

replicates

sets

Pesticides with statistically

significant1 nonconstant

variance

(heteroscedasticity)

1

The probability of obtaining a statistically significant relation between variance and concentration by chance is less than 0.05 (p < 0.05).

Slope of the statistically

significant1 relations

between standard

deviation and

concentration

Slope of the statistically

significant1 relations

between relative standard

deviation and

concentration

Standard

deviation

Relative

standard

deviation

Positive Negative Positive Negative

< 0.01 26 4 0 4 0 0 0

0.005 to < 0.05 38 10 5 10 0 0 5

0.01 to < 0.1 39 10 2 10 0 0 2

0.05 to < 0.5 27 13 1 13 0 1 0

0.1 to < 1 22 4 1 4 0 1 0

0.5 to < 5 8 1 1 1 0 0 1

1 to < 10 7 0 0 0 0 0 0

>= 5 3 1 1 1 0 1 0

Total 170 43 11 43 0 3 8
VARIABILITY OF PESTICIDE CONCENTRATIONS 47

The pooled estimates of variability presented

in table 7 are estimates of the unknown, true variability of pesticide concentrations in the population

of all possible replicate sets (and environmental

samples) that could have been collected for the

NAWQA Program. An upper confidence bound

was calculated to estimate an upper limit of the

true variability of pesticide concentrations at the

90-percent confidence level. The upper confidence

bound is a pessimistic estimate of variability that

can be used (1) in assessing the reliability of the

pooled estimates of variability given in table 7 or

(2) in place of the pooled estimates of variability in

situations where it is important not to underestimate the magnitude of pesticide variability. The reliability of the pooled estimate of variability (how

close the upper confidence bound is to the pooled

estimate) is a function of the magnitude of the

pooled estimate of variability and the number of

replicate sets (degrees of freedom) used for the

pooled estimate.

Pooled estimates of SD or RSD presented in

this report are larger than estimates based upon averages, medians, smooths, or regression of the individual measurements of SD or RSD from field

replicates (fig. 6). The reason the pooled estimates

of variability are larger is that the squares of the SD

(the variance) or the squares of the RSD are averaged then the square root is taken to obtain the

pooled estimate. Because the squares of the SD or

the RSD are used, the effect of field replicates that

have large estimates of variability is enhanced in

comparison to estimates that are not based on

squares. For example, assume that three measurements of SD from field replicates are 1, 3, and 8.

The average of the three measurements is 4, the

median is 3, but the pooled estimate of the SD is

4.97.

Pooled estimates are the preferred method

for characterizing variability because they provide

unbiased estimates of the variability of the population. Assessments of variability based on SD (rather than variance) underestimate the true variability

of the population. The degree of underestimation is

a function of number of replicates in a replicate set

and is most pronounced for duplicates. The mean

SD calculated from duplicates is 80 percent of the

true population SD, whereas the mean SD calculated from triplicates is 89 percent of the true population SD (Natrella, 1963, pp. 1–10). Because pooled

estimates of variability are larger (but less biased)

than estimates based on other approaches, users of

estimates of variability must be cognizant of the

approach used to obtain the estimate and must use

caution in the comparison of estimates based on

different approaches. A future area of research

would be to compare pooled estimates of variability as was done in this report with those obtained by

(1) smooths or regression of the variances of replicate sets (rather than the SD) followed by (2) a

square-root transformation of the smooth or regression line to obtain an estimate of the SD.

Presentation and Rounding of

Estimates of Variability

The presentation and rounding of data and

of statistics derived from data is a topic of considerable interest to the scientific community. Agreement has not been reached on appropriate rules for

rounding and, as a consequence, diverse rules

have been proposed (Eisenhart, 1968, p. 1,203;

Sokal and Rohlf, 1969, p. 148; Anderson, 1987,

pp. 11–12; Taylor, 1987, p. 202; American Public

Health Association and others, 1998, pp. 1–26;

American Society for Testing and Materials, 1998,

pp. 75–76). Nearly all authorities agree that several

“extra” digits should be carried and that rounding

should be done only after all calculations have

been completed and the statistical characteristics of

the data have been evaluated.

This report follows the recommendations of

Eisenhart (1968, p. 1,203) that systematic or random errors should be stated to no more than two

significant figures and that a reported result

should be stated at most to the last place affected by the finer of the two qualifying statements

(unless it is desired to indicate and preserve such

relative accuracy or precision of a higher order that

it may possess for certain particular uses).

The practice of rounding SD or other estimates of uncertainty to two significant figures for

presentation in reports is followed by Croarkin

(1984, p. 33), Mandel and Nanni (1986, p. 35),

Taylor (1987, p. 202), Taylor and Kuyatt (1994,

section 7.3), and the American Society for Testing

and Materials (1998, p. 76). Nearly all authorities

note that additional digits should be provided if the

SD will be used for further calculations (such as the

calculation of confidence intervals).
48 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97 STANDARD DEVIATION OF ATRAZINE IN REPLICATE SETS, IN MICROGRAMS PER LITER

0.0001

0.001

0.01

0.1

1

10

Pooled estimate

Smoothed estimate

Median estimate

RELATIVE STANDARD DEVIATION OF 

ATRAZINE IN REPLICATE SETS, 

IN PERCENT

MEAN CONCENTRATION OF ATRAZINE IN REPLICATE SETS, 

IN MICROGRAMS PER LITER

0

10

20

30

40

50

60

0.0001 0.001 0.01 0.1 1 10 100

Pooled estimate

Smoothed estimate

Median estimate

Figure 6. Comparison of estimates of variability of concentrations in field replicates. Standard deviation of 0 μg/L

is plotted as 0.0001 μg/L. The vertical dashed line is the minimum reporting level.
USE OF ESTIMATES OF VARIABILITY OF CONCENTRATIONS IN WATER-QUALITY ASSESSMENTS 49

Pooled estimates of SD and RSD and their

respective upper confidence bounds have been provided with at least two and as many more digits as

is practicable within the limitations of the space

available in the tables and the desire for legibility.

Users are encouraged to follow the rounding recommendations of Eisenhart (1968, p. 1,203)

in reporting these estimates of variability or confidence intervals based on these estimates. For example, the estimates of variability for acetochlor at

concentrations greater than or equal to 5 μg/L in

table 7 should be reported as 0.15 μg/L for SD

(1.2 μg/L for the 90-percent upper confidence

bound) and 2.7 percent for RSD (22 percent for the

upper confidence bound). Individual measurements

of acetochlor (and confidence limits for individual

measurements) in this range of concentration

should be reported to the hundreths place (for example, 7.32 μg/L). Note that the estimates of

typical variability in table 8 are rounded to two significant figures because calculations based on these

estimates are inappropriate.

USE OF ESTIMATES OF VARIABILITY

OF CONCENTRATIONS IN WATERQUALITY ASSESSMENTS

 Estimates of the variability of pesticide concentrations can be used to answer various questions

relevant to water-quality assessments. Examples of

such questions and methods of addressing them are

provided in the sections that follow. The reader is

assumed to have a basic knowledge of statistics, including calculation of confidence intervals. In all

of the examples, the distribution of analytical measurements of a pesticide at a particular concentration are assumed to be normally distributed. A

normal distribution of repeated measurements of

the same quantity is a common assumption for

chemical measurement systems (Taylor, 1987,

p. 18; American Public Health Association and

others, 1998, p. 1-1) and is a reasonable assumption for most, but not all, of the pesticides in this

report. Visual analysis of histograms of the recovery of pesticides in approximately 1,000 GCMS

and 700 HPLC laboratory control spikes showed

that measurements of the following pesticides were

not approximately normally distributed: azinphosmethyl, carbaryl, cis-permethrin, and prometon

determined by GCMS and chlorothalonil, clopyralid, 2,4-DB, dichlobenil, DNOC, and MCPB determined by HPLC. Application of techniques that

assume a normal distribution to these pesticides

may result in large errors. The distribution of recovery of pesticides in laboratory control spikes is

summarized in Martin (1999).

It is beyond the scope of this report to explain in detail the various approaches and statistical

basis for expressing uncertainties in measurement

processes. Most authorities agree that separate narrative statements of random error (variability) and

systematic error (bias) are required and that a probability interpretation (such as a level of confidence)

is desirable (Eisenhart, 1968, p. 1,202; Taylor and

Kuyatt, 1994, sec. 7.1; American Public Health

Association and others, 1998, pp. 1-13 to 1-16;

American Society for Testing and Materials, 2000,

p. 222). Estimates of variability are given in this

report that can be used to describe random errors

(and the uncertainty of these estimates) in the

NAWQA pesticide data. Various estimates of bias

have been provided previously (Martin and others,

1999; Martin, 1999) that can be used to describe

systematic errors (and the uncertainty of these estimates) in the NAWQA pesticide data.

Various approaches are available for combining estimates of bias and variability into a single, overall estimate of uncertainty. The most

conservative approach is to sum the random and

systematic errors (Taylor, 1987, p. 200), and several authorities advocate this approach (Eisenhart,

1968, p. 1,203–1,204; Croarkin, 1984, pp. 29–30;

Taylor and Kuyatt, 1994, sec. 5.1–5.2; Gookins,

1999, pp. 23.35–23.36). These authorities, however, assume that systematic errors have been identified and estimated and that corrections for

systematic error (bias) have been applied to the

measurement result. Uncertainty from systematic

error, therefore, is not the bias itself but uncertainty

about the true value of the correction applied to the

measurement (Taylor and Kuyatt, 1994, sec. 5.2,

note 1). Corrections for systematic error (bias in

the analytical method) typically are not done for

chemical measurements (Taylor, 1987, p. 200;

Keith, 1991, p. 116) and are not done by NWQL

for the pesticide data for the NAWQA Program.

Consequently, corrections for systematic error

must be done by data users if a combined estimate

of measurement uncertainty is desired. Likewise,
50 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

corrections for bias related to field activities

(contamination, degradation, matrix effects, or

sampling technique) are not known or applied by

the analyzing laboratory. It is the responsibility of

the data user to consider the various sources of bias

and variability (and uncertainty in these estimates)

in the chemical measurements used for waterquality assessments. It is the purpose of this report

to provide estimates of variability and to provide

approaches for using information on variability in

water-quality assessments.

The examples that follow investigate various

uses of variability as estimated from field replicates. The effect of bias from the analytical method

(recovery) is considered in some of the examples.

The effects of bias from contamination, degradation, matrix effects or sampling technique, if any,

are not considered in these examples. Some approaches for considering these sources of bias in

water-quality assessments have been presented

previously (Martin and others, 1999; Martin,

1999). Additional examples of the use of variability in water-quality assessments are presented in

Mueller (1998, pp. 8, 22–24). Estimates of variability of concentrations in the following examples

are based on approach 1 (nondetections in inconsistent replicate sets were deleted) and use the estimates of variability presented in table 7. Finally,

the reader should note that the estimates of variability and the intervals and probabilities presented

as examples for the use of variability are approximations that, for a variety of practical reasons

(some of which relate to representativeness and

random sampling), generally provide only a lower

bound on the true uncertainty (Hahn and Meeker,

1991, pp. 5–8).

Example 1: Confidence Limits for a

Single Water-Quality Measurement

A pressing need in many water-quality assessments is to determine the variability of a single

measurement of a water-quality sample. Ideally,

the data user wants to know how different the single measurement is from the mean that would have

been calculated if the sample had been analyzed a

large number of times (and thus was believed to be

an accurate estimate of the true mean). Croarkin

(1984, p. 25) describes this need as determining the

limits to random error for a single measurement,

whereas Taylor (1987, p. 28) describes this need as

determining a confidence interval for a mean of a

single measurement. Calculations to address both

needs are identical. In essence, a confidence interval is calculated for a mean by use of the t-distribution. In this case, the estimated value of the mean is

the value of the single measurement but the degrees of freedom used in the calculation are based

on QC information (estimates of variability given

in this report). The formula for the confidence interval for a mean is

, (1)

where

is the sample mean (in this case, the single

measurement, in micrograms per liter),

μ is the population mean (the mean of an

infinite number of measurements of the

water sample, in micrograms per liter),

n is the sample size used to calculate the

sample mean (in this case, n = 1),

SD is the standard deviation, in micrograms

per liter,

t is the value of the t-distribution with v

degrees of freedom and 1-α confidence,

and

α is the probability of a Type I error (the

probability that the confidence interval

does not include the population mean).

An example follows.

A data user wishes to determine the

variability of a single measurement of alachlor

of 0.009 μg/L. Proceed as follows:

Step 1. Calculate SD for an alachlor concentration of 0.009 μg/L, using an appropriate estimate of variability from table 7. The most

applicable concentration range is <0.01 μg/L rather

than 0.005 to <0.05 μg/L because estimates of variability in the <0.01 μg/L range are based on sets of

replicates that have a median concentration of

0.005 μg/L (table 7) and are closer to the desired

concentration (0.009 μg/L) than the median for the

higher range (0.016 μg/L). Note that SD is not determined directly from the tabled value of the

pooled SD but is calculated from the pooled RSD

(because pooled RSD is a more robust estimate of

variability):

X t

SD

n

------- μ X t SD

n – × < < + × -------

X
USE OF ESTIMATES OF VARIABILITY OF CONCENTRATIONS IN WATER-QUALITY ASSESSMENTS 51

, (2)

where

RSD is the pooled relative standard deviation,

in percent, and

SD and are as previously defined (in this case,

 = 0.009 μg/L).

The pooled RSD for concentrations of

alachlor less than 0.01 μg/L is 18.3 percent

(table 7); therefore, the SD of alachlor at a concentration of 0.009 μg/L is 0.0016 μg/L (0.009 μg/L x

18.3 percent / 100 percent).

Step 2. Determine the appropriate degrees of

freedom for the SD estimated in Step 1. Estimates

of variability for alachlor measurements less than

0.01 μg/L are based on 21 degrees of freedom

(table 7).

Step 3. Select a level of confidence for the

confidence interval. The data user chooses to calculate a 95-percent confidence interval. This level

of confidence is equivalent to selecting α = 0.05.

Step 4. Determine a value for the t-distribution that has 21 degrees of freedom and α/2 of

the error in each tail of the distribution. Values of

the t-distribution are tabulated in various statistical

text books, including Rohlf and Sokal (1969,

pp. 159–161) or Walpole and Myers (1978, p. 514)

and can be obtained from various statistical software packages. The value of the t-distribution with

21 degrees of freedom and 0.025 α in each tail is

2.080.

Step 5. Calculate the confidence interval

(eq. 1):

0.009 μg/L - 2.080 x 0.0016 μg/L / 11/2 < μ <

0.009 μg/L + 2.080 x 0.0016 μg/L / 11/2,

0.009 μg/L - 0.0033 μg/L < μ < 0.009 μg/L +

0.0033 μg/L,

0.0057 μg/L < μ < 0.0123 μg/L.

Step 6. Interpret the confidence interval. The

data user is 95 percent confident that the true mean

concentration that would be determined by the analytical method for this water sample is between

0.0057 μg/L and 0.0123 μg/L. If the analytical

method is unbiased (100 percent recovery) and

other biases are negligible (contamination, degradation, matrix effects, or sampling technique), the

data user also is 95 percent confident that the true

concentration of the water body is between

0.0057 μg/L and 0.0123 μg/L.

Example 2: Confidence Limits for a

Single Water-Quality Measurement,

Corrected for Recovery

This example presents an approach for correcting the confidence limits presented in example

1 for bias in the analytical method. Web-based resources are available that characterize bias in the

analytical method for the pesticides presented in

this report. The most useful information is obtained

from laboratory control (analytical set) spikes done

by NWQL and summarized by Martin (1999,

table 4), blind spikes done by the Organic Blind

Sample Program (OBSP) (http://btdqs.usgs.gov/

OBSP/index.html), and low-concentration longterm method detection limit (LT-MDL) spikes done

by NWQL (http://wwwnwql.cr.usgs.gov/Public/

ltmdl/ltmdlsplash.html). All of these spikes are

done in pesticide-grade blank water and, consequently, do not provide information on matrix effects (if any) of environmental water samples.

An important assumption in this approach is

that the bias in recovery at the concentration of interest to the data user is the same as that at the concentration of the QC spikes used to characterize the

bias in recovery. The concentrations of laboratory

control spikes are 0.1 μg/L for pesticides analyzed

by GCMS and 0.5 μg/L for pesticides analyzed by

HPLC. These concentrations represent the

midrange of the calibration curves and probably

are concentrations where bias is minimized for

many pesticides. The concentrations of the OBSP

blind spikes are done at several concentrations in

the calibration range of the analytical method and,

for selected pesticides, at concentrations greater

than the calibration range of the method. The concentrations of the LT-MDL spikes are at low concentrations near the method detection limit.

Three data sets were identified with the most

value for determining the bias in the analytical

method for alachlor at concentrations near

0.009 μg/L. The information was pooled to obtain

an estimate that characterizes bias in the analytical

method over 6 years (table 10).

SD X RSD

100 percent = × --------------------------------

X

X
52 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

The mean recovery of alachlor at concentration near 0.009 μg/L is 133.3 percent (table 10) and

indicates a positive bias in the analytical method.

The calculated mean recovery of alachlor is only

an estimate of the true recovery of alachlor in this

range of concentration. Calculation of a confidence

interval quantifies the uncertainty in the value of

the recovery correction factor to be applied to the

range of measurements determined in example 1.

Calculation of a 95-percent confidence interval for

the mean percent recovery of alachlor in this low

range of concentration is done similarly to that in

example 1, except that the mean percent recovery

is based on a sample size of 80 (n = 80) and the

value of the t-distribution is based on 77 degrees of

freedom (t = 1.991). The 95-percent confidence interval for the mean recovery of 133.3 percent is:

133.3 percent - 1.991 x 22.3 percent / 801/2 < μ

< 133.3 percent + 1.991 x 22.3 percent / 801/2

133.3 percent - 4.96 percent < μ < 133.3 percent

+ 4.96 percent,

128.3 percent < μ < 138.3 percent.

On the basis of the laboratory QC information presented in table 10, the data user is

95 percent confident that the mean recovery of

alachlor at concentrations near 0.009 μg/L is between 128.3 and 138.3 percent (the upper and lower confidence limits are not rounded to the unit’s

place because they are used in further calculations). Correct the range of measurements determined in example 1 for bias in the analytical

method as follows:

Step 1. Calculate and apply a correction factor for bias in recovery for a single measurement of

alachlor of 0.009 μg/L. The mean recovery of

alachlor at concentrations near 0.009 μg/L was estimated to be 133.3 percent (table 10) and indicates

a positive bias. In order to estimate the true concentration in a water sample, a correction factor

less than 1 is needed to reduce the value of the

measurement to account for positive bias from the

analytical method. The correction factor is 0.7502

(100 percent / 133.3 percent). The value of the

alachlor measurement, corrected for recovery, is

0.0068 μg/L (0.009 μg/L x 0.7502).

Step 2. Calculate and apply correction factors for bias in recovery to the confidence interval

for the mean of a single measurement that was calculated in example 1. The correction factors for bias in recovery includes the uncertainty about the

true value of the correction to be applied. This step

combines the random and systematic errors of the

measurement process (and combines the uncertainties in these estimates of error). The correction factors are 0.7794 (100 percent / 128.3 percent) and

0.7231 (100 percent / 138.3 percent). Apply the

correction factors to each confidence limit and select the corrected values that maximize the length

of the combined confidence interval:

0.0057 μg/L x 0.7794 = 0.0044 μg/L,

0.0057 μg/L x 0.7231 = 0.0041 μg/L,

0.0123 μg/L x 0.7794 = 0.0096 μg/L,

0.0123 μg/L x 0.7231 = 0.0089 μg/L.

Step 3. Determine the combined 95-percent

confidence limits for a single water-quality measurement of 0.009 μg/L, corrected for recovery:

0.0041 μg/L < μ < 0.0096 μg/L.

Table 10. Pooled estimate of bias in the analytical method for a measurement of alachlor near 0.009 micrograms per

liter

[μg/L, microgram per liter; OBSP, Organic Blind Sample Program; NWQL, National Water Quality Laboratory; LT-MDL, long-term method

detection limit]

Spike source and type

Spiked

concentration

(μg/L)

Time period Number of

spikes

Degrees of

freedom

Mean percent

recovery

(percent)

Standard

deviation of

percent recovery

(percent)

OBSP Blind spikes 0.005–0.040 1996–1999 31 30 115.8 26.2

NWQL LT-MDL spikes 0.004 2000 24 23 133.3 16.7

NWQL LT-MDL spikes 0.004 2001 25 24 155.1 21.7

Pooled estimate 0.004–0.040 1996–2001 80 77 133.3 22.3
USE OF ESTIMATES OF VARIABILITY OF CONCENTRATIONS IN WATER-QUALITY ASSESSMENTS 53

Step 4. Interpret the combined 95-percent

confidence limits for a single water-quality measurement of 0.009 μg/L, corrected for recovery.

The best estimate of the mean concentration

of the water sample, corrected for recovery is

0.0068 μg/L. The data user is 95 percent confident

that the true mean concentration that would be determined by the analytical method for this water

sample, corrected for recovery, is between

0.0041 μg/L and 0.0096 μg/L. If other biases are

negligible (contamination, degradation, matrix

effects, or sampling technique), the best estimate

of the true concentration of the water body is

0.0068 μg/L, and the data user also is 95 percent

confident that the true concentration of the water

body is between 0.0041 μg/L and 0.0096 μg/L.

Example 3: The Concentration Needed

to be Assured of Exceeding a WaterQuality Standard

Water-quality measurements often are compared to a water-quality standard to determine

whether the water body is in compliance with the

standard. The objective for this example is to determine, in view of variability, how much greater than

the standard an individual measurement must be in

order to be assured that the water body has exceeded the standard. The approach is to estimate an upper limit to random error at the concentration of the

standard. If a measurement exceeds the upper limit

to random error at the concentration of the standard, then it is likely that the concentration of the

water sample exceeds the standard.

The upper limit to random error is determined by calculation of a one-sided tolerance

bound for a normal distribution (Hahn and Meeker,

1991, pp. 34–36, pp. 58–61). A tolerance bound is

used to enclose a proportion of the population

(whereas a confidence bound is used to enclose a

population parameter—mean, standard deviation,

percentile, and so on). The formula for a one-sided

upper tolerance bound for a sample from a normal

distribution is

, (3)

where

Tp is the upper tolerance bound to contain at

least p proportion of the population with

1-α confidence (in micrograms per

liter),

p is the proportion of the normal population

of measurements contained in the

tolerance bound (this is the upper limit

to random error selected by the user),

n is the number of samples used to estimate

SD,

g'

(1-α, p, n)

is a factor for calculating one-sided

tolerance bounds with 1-α confidence,

p proportion of the population, and

n samples (in this application, n should

be set equal to 1 plus the number of

degrees of freedom used to estimate SD),

and

, SD, and α

are as previously defined (in this application, is the concentration of the waterquality standard).

Assume, for example, that 0.009 μg/L is a

water-quality standard for alachlor. Calculate the

upper limit of random error at the standard as follows:

Step 1. Calculate SD for an alachlor concentration of 0.009 μg/L, using an appropriate estimate of variability from table 7. This calculation

was done in example 1, and the SD is 0.0016 μg/L.

Step 2. Determine the appropriate degrees of

freedom for the SD estimated in Step 1. This determination was done in example 1, and the estimate

of SD is based on 21 degrees of freedom.

Step 3. Select the proportion of measurements to be contained in the tolerance bound (the

upper limit to random error selected by the user).

The data user chooses to bound 95 percent of the

measurements (p = 0.95).

Step 4. Select a level of confidence for the

upper tolerance bound. The data user chooses to

calculate a 95-percent tolerance bound. This level

of confidence is equivalent to selecting α = 0.05.

Step 5. Determine a value for g'

(1-α, p, n).

Values of the factor g' are based on the noncentral

t-distribution and are summarized in table A12 of

Hahn and Meeker (1991, pp. 312–315) from the

original work presented in Odeh and Owen (1980).

In this application, n should be set equal to 1 plus T p X g'

( ) 1 – α, , p n = + × SD

X

X
54 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

the number of degrees of freedom used to estimate

SD in step 2 (n = 22 = 1 + 21). The value of g' is

2.349 (based on an estimate of SD with 21 degrees

of freedom and the desire to bound 95 percent of

the normal distribution with 95 percent confidence).1

Step 6. Calculate the upper tolerance bound,

using equation 3:

T0.95 < 0.009 μg/L + 2.349 x 0.0016 μg/L,

T0.95 < 0.009 μg/L + 0.0038 μg/L,

T0.95 < 0.0128 μg/L.

Step 7. Interpret the upper tolerance bound

in terms of a upper limit to random error. If the analytical method is unbiased (100 percent recovery)

and other biases are negligible (contamination,

degradation, matrix effects, or sampling technique), the data user is 95 percent confident that

95 percent of the measurements of alachlor at the

standard (a true concentration of 0.009 μg/L) are

less than 0.0128 μg/L. Consequently, the data user

is confident that a measurement of alachlor greater

than 0.0128 μg/L indicates that a water body has

exceeded the water-quality standard.

If the analytical method is biased, however,

the upper tolerance bound is less useful for determining whether or not water quality has exceeded a

standard. For biased analytical methods, the upper

tolerance bound only provides an upper limit to

random error for the mean response of the (biased)

measurement system. The data user needs an estimate of the upper limit to random error for an unbiased measurement system to assess whether or not

a water-quality standard has been exceeded. The

upper limit to random error can be corrected for

bias in the analytical method by the same approach

that was used in example 2.

The mean recovery of alachlor at concentrations near 0.009 μg/L was estimated to be

133.3 percent (table 10) and indicates a positive

bias. On the basis of the calculations in example 2,

the data user is 95 percent confident that the mean

recovery of alachlor at concentrations near

0.009 μg/L is between 128.3 and 138.3 percent.

Correct the upper limit of random error for bias in

the analytical method as follows:

Step 8. Calculate and apply a correction factor for bias in recovery for the upper limit of random error for a water-quality standard for alachlor

of 0.009 μg/L. Because the analytical method is

positively biased, a correction factor greater than 1

is needed to increase the upper limit of random error in order to be assured that a positively biased

measurement exceeds the standard. (If the method

was negatively biased, a correction factor less than

1 would be needed to reduce the upper limit.) The

correction factors for bias in recovery includes the

uncertainty about the true value of the correction to

be applied. This step combines the random and

systematic errors of the measurement process (and

combines the uncertainties in these estimates of

error). The correction factors for bias are

1.283 (128.3 percent / 100 percent) and 1.383

(138.3 percent / 100 percent). Apply the correction

factors to the upper limit of random error and select

the corrected value that maximizes the length of

the combined tolerance bound:

0.0128 μg/L x 1.283 = 0.0164 μg/L,

0.0128 μg/L x 1.383 = 0.0177 μg/L.

Step 9. Determine the combined 95-percent

tolerance bound for the upper limit of random error

for a water-quality standard for alachlor of

0.009 μg/L, corrected for recovery:

T0.95 < 0.0177 μg/L.

Step 10. Interpret the combined 95-percent

tolerance bound for the upper limit of random

error for a water-quality standard for alachlor of

0.009 μg/L, corrected for recovery. If other biases

are negligible (contamination, degradation, matrix

effects, or sampling technique), the data user is

95 percent confident that 95 percent of the measurements of alachlor at the standard (a true concentration of 0.009 μg/L) would be less than

0.0177 μg/L. Consequently, the data user is confident that a measurement of alachlor greater than

0.0177 μg/L indicates that a water body has exceeded the water-quality standard.

Note that the concentration needed to be assured of not exceeding a water-quality standard

could have been determined by a similar approach.

The data user could have calculated a one-sided

lower tolerance bound to determine the lower limit

to random error.

1

Note that the value of g' (2.349) is substantially larger

than a comparable value of the t-distribution (1.721) and

shows that efforts to bound a percentage of measurements by

using the t-distribution (incorrectly) will bound a smaller

percentage of measurements than that desired.
SUMMARY 55

Example 4: Are Two Water-Quality

Measurements Different?

Another need in water-quality assessments is

to determine whether two water-quality measurements are different. The objective for this example

is to determine whether the difference in two individual measurements indicates a true difference in

water quality or could be attributable solely to variability. The approach is to calculate confidence intervals for the mean (as was done in example 1) for

each measurement and to compare the intervals. If

the intervals do not overlap, a difference in water

quality is indicated at the selected level of confidence. If the intervals overlap, the difference in

measurements can be attributable to variability. In

the following example, measurements of alachlor

in two water samples yield values of 0.009 μg/L

and 0.020 μg/L. A data user is interested in determining whether the measurements indicate that

water quality differs. A 95-percent confidence interval is needed for the mean concentration for

each measurement. One was calculated for the

sample of 0.009 μg/L in example 1, and the interval is 0.0057 μg/L to 0.0123 μg/L. For the sample

of 0.020 μg/L, proceed as follows:

Step 1. Determine SD for an alachlor concentration of 0.020 μg/L, using an appropriate

estimate of variability from table 7. The most applicable concentration range is 0.01 to < 0.1 μg/L

and the pooled RSD is 10.0 percent. SD is calculated from the pooled RSD by use of equation 2.

SD is 0.0020 μg/L (0.020 μg/L x 10.0 percent /

100 percent).

Step 2. Determine the appropriate degrees of

freedom for the SD estimated in Step 1. Estimates

of variability for alachlor measurements in concentration range 0.01 to < 0.1 μg/L are based on

38 degrees of freedom (table 7).

Step 3. Select a level of confidence for the

confidence interval. The data user chooses to calculate a 95-percent confidence interval. This is

equivalent to selecting α = 0.05. (Select the same

level of confidence for both intervals).

Step 4. Determine a value for the t-distribution that has 38 degrees of freedom and α/2 of the

error in each tail of the distribution. The value of

the t-distribution with 38 degrees of freedom and

0.025α in each tail is 2.024.

Step 5. Calculate the confidence interval

(eq. 1) for a mean concentration of a single measurement of 0.020 μg/L:

0.020 μg/L - 2.024 x 0.0020 μg/L / 11/2 < μ <

0.020 μg/L + 2.024 x 0.0020 μg/L / 11/2,

0.020 μg/L - 0.0040 μg/L < μ < 0.020 μg/L +

0.0040 μg/L,

0.0160 μg/L < μ < 0.0240 μg/L.

Step 6. Compare the confidence intervals.

The 95-percent confidence intervals for the mean

are 0.0057 μg/L to 0.0123 μg/L for a measurement

of 0.009 μg/L and are 0.0160 μg/L to 0.0240 μg/L

for a measurement of 0.020 μg/L. The intervals do

not overlap.

Step 7. Interpret the confidence intervals.

The data user is 95 percent confident that the mean

concentrations of alachlor in the water samples are

different. If biases in the analytical method, contamination, degradation, matrix effects, or sampling technique are negligible or affect each

sample similarly, the data user is 95 percent confident that the true concentrations of alachlor in the

water bodies are different. Because bias in the analytical method should be similar over narrow ranges of concentration, a correction for recovery is not

needed to determine whether concentrations differ.

SUMMARY

Field replicates collected for the

U.S. Geological Survey National Water-Quality

Assessment Program during 1992 to 1997 were

used to assess the variability of pesticide detections

and concentrations in environmental water samples

collected from the surface- and ground-waterquality networks of the NAWQA Program. Field

replicates are two or more identically collected,

processed, and analyzed environmental water samples that are used to assess the overall variability of

field and laboratory procedures.Variability is the

degree of random error in independent measurements of the same quantity and is the opposite of

precision—the degree of mutual agreement. Information on variability can be used to estimate the

reproducibility of individual measurements, the

concentration needed to be assured of exceeding a

water-quality standard, and the likelihood that

two measurements of water quality are different.
56 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

Variability of pesticide detections was

assessed by calculating the mean percentage

detection of a pesticide and the percentage of inconsistent replicate sets. Variability of pesticide

concentrations was assessed by pooling estimates

of the SD and RSD in replicate sets. Variability of

pesticide detections and concentrations was a function of concentration and estimates of variability

were developed for discrete, overlapping ranges of

concentration. Reliability of estimates of variability was assessed by calculating 90-percent upper

confidence bounds for the percentage of inconsistent replicate sets and for the pooled estimates of

SD and RSD.

Twenty-two percent (19 of 86) of the pesticides analyzed for were not detected in any field

replicates: aldicarb, aldicarb sulfone, chloramben,

chlorothalonil, clopyralid, dacthal monoacid,

2,4-DB, dicamba, dichlorprop, 3-hydroxycarbofuran, MCPB, methiocarb, neburon, oxamyl, parathion, phorate, propham, silvex, and 2,4,5-T.

Evaluation of variability of detection or concentration could not be done for these pesticides.

The mean detection rate shows the overall

rate of detection of a pesticide in field replicates.

The variability of detection for most pesticides is

high at concentrations less than the MRL, but the

variability of detection decreases dramatically at

higher concentrations. The percentage of replicate

sets with inconsistent detections measures the frequency that a pesticide was not detected in all replicates in a set. In the context of the variability of

detection in environmental samples, the percentage

of replicate sets with inconsistent detections estimates the likelihood that a pesticide that is detected

in a single environmental sample would not be detected in a duplicate environmental sample. As

with the mean detection rate, variability of detection measured by the percentage of inconsistent

replicate sets is high at concentrations less than the

MRL but decreases with increasing concentrations.

The overall rate of inconsistent replicate sets is

60.0 percent in the low range of concentration,

13.7 percent in the medium range, and 1.1 percent

in the high range.

Inconsistent detections are caused by falsepositive or false-negative errors. False-positive errors usually are caused by sample contamination,

whereas false-negative errors usually are caused by

water-matrix interference, pesticide degradation, or

other chemical-loss processes. Both types of errors

may be caused by variability inherent in the analytical method, but calculation and use of MDLs are

intended to protect against false-positive errors. On

the basis of the low frequency of detection in field

blanks, sample contamination is an unlikely cause

of inconsistent detections in replicate sets. In view

of the highly diverse sources of water submitted as

field replicates for the NAWQA Program and

the generally low concentrations (concentrations

in 79 percent of replicate sets were less than

0.1 μg/L) of pesticides in most replicates, inconsistent detections in replicate sets likely were caused

by variability in the analytical method and by

water-matrix interferences (or other loss processes)

that cause false-negative errors. Consequently, estimates of the frequency of detection of pesticides in

environmental water samples collected for the

NAWQA Program probably are biased low because of false-negative errors at concentrations

near the minimum reporting level.

Pooled estimates of SD and RSD were used

to assess the variability of concentrations. The

pooled SD increases markedly with increasing concentration, whereas the pooled RSD decreases with

increasing concentration but is much less a function of concentration than is the pooled SD. Results

of correlation analyses indicate that for most pesticides and concentrations, pooled estimates of RSD

rather than pooled estimates of SD should be used

to estimate variability because pooled estimates of

RSD are less affected by heteroscedasticity. The

median pooled RSD was calculated for all pesticides to summarize the typical variability for pesticide data collected for the NAWQA Program. The

median pooled RSD was 15 percent at concentrations less than 0.01 μg/L, 13 percent at concentrations near 0.01 μg/L, 12 percent at concentrations

near 0.1 μg/L, 7.9 percent at concentrations near

1 μg/L, and 2.7 percent at concentrations greater

than 5 μg/L.

Pooled estimates of SD or RSD presented in

this report are larger than estimates based on averages, medians, smooths, or regression of the individual measurements of SD or RSD from field

replicates. Pooled estimates, however, are the

preferred method for characterizing variability

because they provide unbiased estimates of the

variability of the population. Assessments of

variability based on SD (rather than variance)
REFERENCES CITED 57

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Because pooled estimates of variability are larger

than estimates based on other approaches, users of

estimates of variability must be cognizant of the

approach used to obtain the estimate and must use

caution in the comparison of estimates based on

different approaches.

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APPENDIX 1
60 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

Appendix 1. Pesticide registry numbers, analytical methods, and parameter codes

Parameter

code

Analytical

method Pesticide Othernames Use Class

Chemical Abstract

Service registry

number

 49260 GCMS Acetochlor Harness Plus, Acenit H AMID 34256-82-1

 49315 HPLC Acifluorfen Blazer, Tackle 2S H ACID 50594-66-6

 46342 GCMS Alachlor Lasso, Bullet, Alagan H AMID 15972-60-8

 49312 HPLC Aldicarb Temik, Sanacarb I CB 116-06-3

 49313 HPLC Aldicarb sulfone Aldicarb metabolite M CB 1646-88-4

 49314 HPLC Aldicarb sulfoxide Aldicarb metabolite M CB 1646-87-3

 39632 GCMS Atrazine AAtrex, Gesaprim H TRI 1912-24-9

 82686 GCMS Azinphos-methyl Guthion, Carfene I OP 86-50-0

 82673 GCMS Benfluralin Benefin, Balan, Bonalan H DNA 1861-40-1

 38711 HPLC Bentazon Bentazone, Basagran H MISC 25057-89-0

 04029 HPLC Bromacil Bromax, Hyvar X, Urox B H UR 314-40-9

 49311 HPLC Bromoxynil Torch, Buctril, Brominal H ACID 1689-84-5

 04028 GCMS Butylate Genate Plus, Sutan + H CB 2008-41-5

 82680 GCMS Carbaryl Sevin, Savit I CB 63-25-2

 49310 HPLC Carbaryl Sevin, Savit I CB 63-25-2

 82674 GCMS Carbofuran Furadan, Carbodan I CB 1563-66-2

 49309 HPLC Carbofuran Furadan, Carbodan I CB 1563-66-2

 49307 HPLC Chloramben Methyl amiben H ACID 133-90-4

 49306 HPLC Chlorothalonil Bravo, Echo F OC 1897-45-6

 38933 GCMS Chlorpyrifos Dursban, Lorsban I OP 2921-88-2

 49305 HPLC Clopyralid Stinger, Lontrel, Reclaim H ACID 1702-17-6

 04041 GCMS Cyanazine Bladex, Fortrol H TRI 21725-46-2

 39732 HPLC 2,4-D 2,4-PA; Ded-Weed SULV H CPA 94-75-7

 82682 GCMS Dacthal DCPA, Chlorthal-dimethyl H OC 1861-32-1

 49304 HPLC Dacthal monoacid Dacthal metabolite M OC 887-54-7

 38746 HPLC 2,4-DB Butyrac, Embutox H CP 94-82-6

 34653 GCMS p,p’-DDE DDT metabolite M OC 72-55-9

 04040 GCMS Desethylatrazine Atrazine metabolite M TRI 6190-65-4

 39572 GCMS Diazinon Diazol, Basudin, Neocidol I OP 333-41-5

 38442 HPLC Dicamba Banval, Mediben, Dianat H ACID 1918-00-9

 49303 HPLC Dichlobenil Barrier, Casoron H OC 1194-65-6

 49302 HPLC Dichlorprop 2,4-DP; Seritox 50; Kildip H CPA 120-36-5

 39381 GCMS Dieldrin Panoram D-31, Octalox I OC 60-57-1

 82660 GCMS 2,6-Diethylaniline Alachlor metabolite M AMID 579-66-8

 49301 HPLC Dinoseb DNPB, Dinosebe H ACID 88-85-7

Appendix 1. Pesticide registry numbers, analytical methods, and parameter codes

[Parameter code, the number used to identify a pesticide in the U.S. Geological Survey National Water Information System and the U.S. Environmental Protection Agency Data Storage and Retrieval System. Analytical method: GCMS, gas chromatography/mass spectrometry; HPLC, highperformance liquid chromatography. Use: F, fungicide; H, herbicide; I, insecticide; M, metabolite. Class: ACID, miscellaneous acids; AMID,

amides; CB, carbamates; CPA, chlorophenoxy acids; DNA, dinitroanilines; MISC, miscellaneous; OC, organochlorines; OP, organophosphates;

PY, pyrethroids; TRI, triazines; UR, uracils; UREA, ureas]
Pesticide registry numbers, analytical methods, and parameter codes 61

 82677 GCMS Disulfoton Disyston, Dithiosystox I OP 298-04-4

 49300 HPLC Diuron DCMU, Karmex, Direx H UREA 330-54-1

 49299 HPLC DNOC Sinox, Trifocide H ACID 534-52-1

 82668 GCMS EPTC Eptam, Alirox, Niptan H CB 759-94-4

 82663 GCMS Ethalfluralin Sonalan, Sonalen H DNA 55283-68-6

 82672 GCMS Ethoprop Ethoprophos, Mocap I OP 13194-48-4

 49297 HPLC Fenuron Beet-Klean, Dybar, Urab H UREA 101-42-8

 38811 HPLC Fluometuron Flo-Met, Cotoran, Cottonex H UREA 2164-17-2

 04095 GCMS Fonofos Dyfonate, Capfos I OP 944-22-9

 34253 GCMS alpha-HCH Lindane metabolite M OC 319-84-6

 39341 GCMS gamma-HCH Lindane, Lintox I OC 58-89-9

 49308 HPLC 3-Hydroxycarbofuran Carbofuran metabolite M CB 16655-82-6

 82666 GCMS Linuron Lorox, Linex, Linurex H UREA 330-55-2

 38478 HPLC Linuron Lorox, Linex, Linurex H UREA 330-55-2

 39532 GCMS Malathion Cythion, Fyfanon I OP 121-75-5

 38482 HPLC MCPA Metaxon, Agritox H CPA 94-74-6

 38487 HPLC MCPB Tropotox, Thistrol H CPA 94-81-5

 38501 HPLC Methiocarb Mesurol, Draza I CB 2032-65-7

 49296 HPLC Methomyl Lannate, Nudrin I CB 16752-77-5

 82667 GCMS Methyl parathion Penncap-M, Romethyl-P I OP 298-00-0

 39415 GCMS Metolachlor Dual, Pennant H AMID 51218-45-2

 82630 GCMS Metribuzin Lexone, Sencor H TRI 21087-64-9

 82671 GCMS Molinate Ordram, Sakkimol H CB 2212-67-1

 82684 GCMS Napropamide Devrinol, Naproquard H AMID 15299-99-7

 49294 HPLC Neburon Neberex, Neburea, Neburyl H UREA 555-37-3

 49293 HPLC Norflurazon Telok, Evital, Solicam H MISC 27314-13-2

 49292 HPLC Oryzalin Surflan, Dirimal, Ryzelan H DNA 19044-88-3

 38866 HPLC Oxamyl Vydate L, Pratt I CB 23135-22-0

 39542 GCMS Parathion Thiophos, Bladan, Folidol I OP 56-38-2

 82669 GCMS Pebulate Tillam, PEBC H CB 1114-71-2

 82683 GCMS Pendimethalin Prowl, Stomp H DNA 40487-42-1

 82687 GCMS cis-Permethrin Ambush, Pounce I PY 54774-45-7

 82664 GCMS Phorate Thimet, Rampart I OP 298-02-2

 49291 HPLC Picloram Amdon, Grazon, Tordon H ACID 1918-02-1

 04037 GCMS Prometon Prometone, Gesagran H TRI 1610-18-0

 82676 GCMS Pronamide Kerb, Propyzamid H AMID 23950-58-5

 04024 GCMS Propachlor Propachlore, Ramrod H AMID 1918-16-7

 82679 GCMS Propanil Stampede, Surcopur H AMID 709-98-8

 82685 GCMS Propargite Omite, Comite, BPPS I ACID 2312-35-8

 49236 HPLC Propham IPC, Tuberite H CB 122-42-9

Appendix 1. Pesticide registry numbers, analytical methods, and parameter codes—Continued

Parameter

code

Analytical

method Pesticide Othernames Use Class

Chemical Abstract

Service registry

number
62 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97

 38538 HPLC Propoxur Baygon, Blattanex, Unden I CB 114-26-1

 39762 HPLC Silvex 2,4,5-TP; Fenoprop H CPA 93-72-1

 04035 GCMS Simazine Aquazine, Princep, GEsatop H TRI 122-34-9

 39742 HPLC 2,4,5-T Brush Killer, Esterone H CPA 93-76-5

 82670 GCMS Tebuthiuron Spike, Perflan H UREA 34014-18-1

 82665 GCMS Terbacil Sinbar, Geonter H UR 5902-51-2

 82675 GCMS Terbufos Counter, Contraven I OP 13071-79-9

 82681 GCMS Thiobencarb Benthiocarb, Bolero, Saturn H CB 28249-77-6

 82678 GCMS Triallate Avadex BW, Far-Go H CB 2303-17-5

 49235 HPLC Triclopyr Crossbow, Garlon, Grazon H ACID 55335-06-3

 82661 GCMS Trifluralin Treflan, Elancolan, Trinin H DNA 1582-09-8

Appendix 1. Pesticide registry numbers, analytical methods, and parameter codes—Continued

Parameter

code

Analytical

method Pesticide Othernames Use Class

Chemical Abstract

Service registry

number
APPENDIX 2
64 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97 Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates Concentration range

(μg/L)

Analytical

approach

for IRS

N df

Pooled

standard

deviation

(μg/L)

90-percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

M

Acetochlor, parameter code 49260, analysis by GCMS, MRL 0.002 μg/L

< 0.01 deleted 3 4 0.00079 0.0015 0.00071 12.4 24.0 7.4 

zero 4 6 .0064 .0105 .0011 100.5 165.8 15.5 mrl 4 6 .0057 .0094 .0011 74.6 123.1 15.5 

0.005 to < 0.05 deleted 5 5 .0016 .0029 .0014 11.7 20.6 6.7 

zero 6 7 .0060 .0095 .0014 93.1 146.4 7.1 mrl 6 7 .0054 .0085 .0014 69.1 108.7 7.1 

0.01 to < 0.1 no IRS 4 4 .0018 .0035 .0014 4.3 8.3 3.2 0.05 to < 0.5 no IRS 2 2 .0051 .0157 .0042 2.0 6.2 2.0 0.1 to < 1 no IRS 1 1 .0071 .0563 .0071 2.3 18.4 2.3 0.5 to < 5 no IRS 2 2 .0583 .1796 .0566 2.5 7.8 2.5 1 to < 10 no IRS 3 3 .0981 .2222 .0707 2.6 5.9 2.7 >= 5 no IRS 1 1 .1485 1.182 .1485 2.7 21.9 2.7 

Acifluorfen, parameter code 49315, analysis by HPLC, MRL 0.035 μg/L

0.005 to < 0.05 zero 1 1 .0141 .1125 .0141 141.4 1125. 141.4 

mrl 1 1 .0106 .0844 .0106 38.6 306.9 38.6 

0.01 to < 0.1 zero 2 2 .0955 .2943 .0742 141.4 435.7 141.4 

mrl 1 1 .0106 .0844 .0106 38.6 306.9 38.6 

0.05 to < 0.5 deleted 1 1 .0778 .6190 .0778 67.6 538.2 67.6 

zero 2 2 .1098 .3382 .1061 110.8 341.5 104.5 mrl 2 2 .0950 .2928 .0937 83.9 258.4 82.5 

0.1 to < 1 deleted 2 2 .0930 .2865 .0919 48.9 150.6 40.9 

zero 2 2 .0930 .2865 .0919 48.9 150.6 40.9 mrl 3 3 .0988 .2240 .1061 69.0 156.3 67.6 

0.5 to < 5 no IRS 1 1 .1061 .8441 .1061 14.2 113.3 14.2 

Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates

[Estimates based on measurements that showed increasing or decreasing variability in the range of concentration are shown in bold italic type. μg/L, microgram per l

N, number of replicate sets; df, degrees of freedom; GCMS, gas chromatography/mass spectrometry; HPLC, high-performance liquid chromatography; MRL, minim>=, greater than or equal to; parameter code, the number used to identify a pesticide in the U.S. Geological Survey National Water Information System; deleted, nondtions in IRS set to zero; mrl, nondetections in IRS set to the MRL; nc, not calculated]
Comparison of three approaches for the analysis of variability of concentrations 65

Alachlor, parameter code 46342, analysis by GCMS, MRL 0.002 μg/L

< 0.01 deleted 20 21 0.00076 0.00095 0.00046 18.3 23.0 7.4 

zero 30 34 .0043 .0052 .00071 91.2 108.6 14.3 mrl 29 32 .0013 .0015 .00071 32.4 38.8 11.1 

0.005 to < 0.05 deleted 39 44 .0018 .0021 .00071 9.7 11.3 5.7 

zero 40 46 .0038 .0044 .00071 37.3 43.3 5.8 mrl 41 47 .0036 .0042 .00071 32.9 38.1 5.9 

0.01 to < 0.1 deleted 33 38 .0041 .0048 .00071 10.0 11.8 4.6 

zero 33 38 .0041 .0048 .00071 10.0 11.8 4.6 mrl 34 40 .0052 .0061 .00071 32.9 38.7 4.6 

0.05 to < 0.5 no IRS 10 10 .0174 .0249 .0106 11.0 15.8 4.8 0.1 to < 1 no IRS 10 10 .0300 .0430 .0141 6.6 9.5 4.8 0.5 to < 5 no IRS 6 6 .0445 .0735 .0177 6.4 10.5 2.1 

1 to < 10 no IRS 2 2 .0522 .1608 .0460 2.0 6.1 2.0 

Aldicarb sulfoxide, parameter code 49314, analysis by HPLC, MRL 0.021 μg/L

0.1 to < 1 zero 1 1 1.273 10.13 1.273 141.4 1125. 141.4 

mrl 1 1 1.258 10.01 1.258 138.2 1100. 138.2 

0.5 to < 5 zero 1 1 1.273 10.13 1.273 141.4 1125. 141.4 

mrl 1 1 1.258 10.01 1.258 138.2 1100. 138.2 

Atrazine, parameter code 39632, analysis by GCMS, MRL 0.001 μg/L

< 0.01 deleted 49 58 .0012 .0013 .00058 16.3 18.5 8.5 

zero 63 76 .0019 .0021 .00071 77.4 86.6 9.4 

mrl 63 76 .0017 .0019 .00071 38.6 43.2 9.4 

0.005 to < 0.05 deleted 90 105 .0014 .0016 .00071 11.8 13.0 6.1 

zero 91 106 .0017 .0019 .00071 18.1 19.9 6.1 

mrl 92 107 .0018 .0019 .00071 19.9 21.9 6.2 

0.01 to < 0.1 no IRS 80 92 .0039 .0043 .0014 7.6 8.4 3.8 

0.05 to < 0.5 no IRS 78 90 .0128 .0142 .0058 7.5 8.3 4.0 

0.1 to < 1 no IRS 62 73 .0258 .0289 .0071 6.9 7.8 4.4 

Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates

—Continued

Concentration

range

(μg/L)

Analytical

approach

for IRS

N df

Pooled

standard

deviation

(μg/L)

90-percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

M
66 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97 Atrazine, parameter code 39632, analysis by GCMS, MRL 0.001 μg/L—Continued 0.5 to < 5 no IRS 18 20 0.1396 0.1770 0.0389 7.1 9.0 3.9 

1 to < 10 no IRS 12 12 .1732 .2390 .0707 5.8 8.0 2.2 >= 5 no IRS 6 6 1.377 2.271 .2475 2.5 4.1 1.4 

Azinphos-methyl, parameter code 82686, analysis by GCMS, MRL 0.001 μg/L < 0.01 no IRS 1 1 .0014 .0113 .0014 23.6 187.6 23.6 

0.005 to < 0.05 deleted 4 5 .0030 .0053 .0025 19.9 35.1 20.1 

zero 5 6 .0150 .0247 .0036 60.5 99.9 23.6 mrl 5 6 .0147 .0242 .0036 58.4 96.4 23.6 

0.01 to < 0.1 deleted 6 9 .0116 .0171 .0039 20.0 29.4 12.7 

zero 8 11 .0385 .0541 .0057 63.0 88.4 20.4 mrl 8 11 .0382 .0537 .0057 61.5 86.4 20.4 

0.05 to < 0.5 deleted 7 9 .0431 .0633 .0141 21.7 31.9 8.7 

zero 8 10 .0552 .0792 .0186 49.2 70.6 15.8 mrl 8 10 .0551 .0790 .0186 48.8 69.9 15.8 

0.1 to < 1 no IRS 4 4 .0623 .1209 .0389 22.8 44.2 14.4 

Benfluralin, parameter code 82673, analysis by GCMS, MRL 0.002 μg/L < 0.01 deleted 1 1 .0 nc .0 .0 nc .0 

zero 4 6 .0035 .0058 .0028 92.1 152.0 87.9 mrl 4 6 .0025 .0041 .0017 50.4 83.1 41.4 

0.005 to < 0.05 mrl 1 1 .0049 .0394 .0049 90.0 716.2 90.0 

Bentazon, parameter code 38711, analysis by HPLC, MRL 0.014 μg/L 0.005 to < 0.05 zero 3 4 .0303 .0587 .0283 158.1 306.6 141.4 

mrl 3 4 .0215 .0417 .0184 72.7 141.0 68.1 

0.01 to < 0.1 zero 3 4 .0303 .0587 .0283 158.1 306.6 141.4 

mrl 3 4 .0215 .0417 .0184 72.7 141.0 68.1 

0.05 to < 0.5 deleted 7 7 .0440 .0692 .0283 19.5 30.6 15.7 

zero 8 8 .0687 .1040 .0318 53.2 80.6 17.2 mrl 8 8 .0659 .0998 .0318 47.6 72.1 17.2 

Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates

—Continued

Concentration

range

(μg/L)

Analytical

approach

for IRS

N df

Pooled

standard

deviation

(μg/L)

90-percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

M
Comparison of three approaches for the analysis of variability of concentrations 67

Bentazon, parameter code 38711, analysis by HPLC, MRL 0.014 μg/L—Continued

0.1 to < 1 deleted 8 8 0.0610 0.0923 0.0318 19.7 29.8 17.2 

zero 9 9 .0774 .1138 .0354 50.7 74.5 18.6 mrl 9 9 .0753 .1106 .0354 45.5 66.8 18.6 

0.5 to < 5 no IRS 1 1 .1273 1.013 .1273 21.2 168.8 21.2 

Bromacil, parameter code 04029, analysis by HPLC, MRL 0.035 μg/L

< 0.01 no IRS 1 2 .0035 .0107 .0035 43.3 133.4 43.3 0.005 to < 0.05 deleted 1 2 .0035 .0107 .0035 43.3 133.4 43.3 

zero 2 3 .0146 .0330 .0141 89.0 201.6 92.4 mrl 2 3 .0028 .0064 .0017 35.4 80.1 21.7 

0.01 to < 0.1 deleted 1 1 .0 nc .0 .0 nc .0 

zero 2 2 .0175 .0539 .0124 100.0 308.1 70.7 mrl 2 2 .0 nc .0 .0 nc .0 

0.05 to < 0.5 deleted 1 1 .0 nc .0 .0 nc .0 

zero 2 2 .1100 .3389 .0778 100.0 308.1 70.7 mrl 2 2 .0925 .2850 .0654 72.5 223.5 51.3 

0.1 to < 1 deleted 2 2 .0814 .2508 .0813 11.6 35.7 11.5 

zero 3 3 .1117 .2532 .0849 82.2 186.2 13.3 mrl 3 3 .1006 .2279 .0849 60.0 135.9 13.3 

0.5 to < 5 no IRS 2 2 .0814 .2508 .0813 11.6 35.7 11.5 

Bromoxynil, parameter code 49311, analysis by HPLC, MRL 0.035 μg/L

0.01 to < 0.1 zero 1 1 .0707 .5627 .0707 141.4 1125. 141.4 

mrl 1 1 .0460 .3658 .0460 68.1 541.9 68.1 

0.05 to < 0.5 deleted 1 1 .0071 .0563 .0071 5.2 41.7 5.2 

zero 2 2 .0502 .1548 .0389 100.1 308.3 73.3 mrl 2 2 .0329 .1013 .0265 48.3 148.8 36.7 

0.1 to < 1 no IRS 1 1 .0071 .0563 .0071 5.2 41.7 5.2 

Butylate, parameter code 04028, analysis by GCMS, MRL 0.002 μg/L

< 0.01 deleted 9 10 .00087 .0012 .00071 26.5 38.0 20.2 

zero 11 13 .00094 .0013 .00071 60.1 81.7 20.2 mrl 11 13 .00079 .0011 .00071 26.9 36.6 20.2 

Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates

—Continued

Concentration

range

(μg/L)

Analytical

approach

for IRS

N df

Pooled

standard

deviation

(μg/L)

90-percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

M
68 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97 Butylate, parameter code 04028, analysis by GCMS, MRL 0.002 μg/L—Continued 0.005 to < 0.05 no IRS 9 10 0.0011 0.0016 0.00064 9.7 14.0 2.0 

0.01 to < 0.1 no IRS 5 5 .0013 .0024 .00064 5.4 9.5 2.0 

Carbaryl, parameter code 82680, analysis by GCMS, MRL 0.003 μg/L < 0.01 deleted 8 9 .00088 .0013 .00071 12.3 18.1 8.3 

zero 17 19 .0056 .0071 .0035 107.9 137.8 141.4 mrl 16 18 .0036 .0046 .0019 49.5 63.7 35.8 

0.005 to < 0.05 deleted 28 33 .0034 .0040 .0014 14.3 17.1 8.8 

zero 35 40 .0097 .0114 .0014 60.6 71.1 10.1 mrl 38 43 .0088 .0102 .0020 46.9 54.7 10.5 

0.01 to < 0.1 deleted 26 32 .0067 .0080 .0017 16.0 19.2 9.9 

zero 29 35 .0112 .0133 .0021 44.1 52.4 10.1 mrl 30 36 .0108 .0127 .0022 40.6 48.1 10.1 

0.05 to < 0.5 no IRS 13 17 .0268 .0347 .0199 16.2 21.0 12.3 

0.1 to < 1 no IRS 11 14 .0775 .1040 .0212 16.3 21.8 12.3 0.5 to < 5 no IRS 3 4 .1352 .2621 .1838 21.0 40.8 22.7 

Carbaryl, parameter code 49310, analysis by HPLC, MRL 0.008 μg/L 0.005 to < 0.05 deleted 2 2 .0280 .0861 .0230 85.8 264.4 69.9 

zero 3 3 .0242 .0549 .0141 107.6 243.8 119.7 mrl 3 3 .0233 .0529 .0085 78.3 177.5 60.6 

0.01 to < 0.1 deleted 3 3 .0306 .0694 .0354 74.1 167.8 41.6 

zero 4 4 .0274 .0532 .0247 95.5 185.1 80.6 mrl 4 4 .0269 .0521 .0219 70.9 137.6 51.1 

0.05 to < 0.5 no IRS 2 2 .0354 .1089 .0354 29.8 91.9 24.4 0.1 to < 1 no IRS 1 1 .0354 .2814 .0354 7.1 56.8 7.1 0.5 to < 5 zero 1 2 .9866 3.039 .9866 87.1 268.2 87.1 

mrl 1 2 .9820 3.025 .9820 86.4 266.3 86.4 

1 to < 10 zero 1 2 .9866 3.039 .9866 87.1 268.2 87.1 

mrl 1 2 .9820 3.025 .9820 86.4 266.3 86.4 

Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates

—Continued

Concentration

range

(μg/L)

Analytical

approach

for IRS

N df

Pooled

standard

deviation

(μg/L)

90-percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

M
Comparison of three approaches for the analysis of variability of concentrations 69

Carbofuran, parameter code 82674, analysis by GCMS, MRL 0.003 μg/L

< 0.01 zero 6 8 0.0070 0.0106 0.0069 117.6 178.0 141.4 

mrl 5 6 .0048 .0080 .0035 62.5 103.1 64.3 

0.005 to < 0.05 deleted 7 8 .0032 .0049 .0021 16.4 24.8 9.4 

zero 12 14 .0088 .0118 .0043 83.3 111.7 23.9 

mrl 13 15 .0074 .0098 .0035 61.0 80.8 25.7 

0.01 to < 0.1 deleted 8 9 .0140 .0206 .0021 28.9 42.4 14.8 

zero 10 11 .0149 .0209 .0025 65.7 92.3 21.2 mrl 11 13 .0135 .0184 .0028 55.6 75.6 22.2 

0.05 to < 0.5 no IRS 6 6 .0249 .0411 .0039 34.3 56.6 1.2 0.1 to < 1 no IRS 6 6 .0189 .0312 .0039 16.8 27.7 .5 0.5 to < 5 no IRS 1 1 .0099 .0788 .0099 1.0 8.1 1.0 

Carbofuran, parameter code 49309, analysis by HPLC, MRL 0.120 μg/L

0.005 to < 0.05 zero 1 1 .0636 .5064 .0636 141.4 1125. 141.4 0.01 to < 0.1 zero 2 2 .0918 .2828 .0884 141.4 435.7 141.4 0.05 to < 0.5 zero 1 1 .1131 .9003 .1131 141.4 1125. 141.4 

mrl 2 2 .0250 .0770 .0247 20.2 62.2 20.2 

0.1 to < 1 deleted 1 1 .0 nc .0 .0 nc .0 

zero 1 1 .0 nc .0 .0 nc .0 mrl 3 3 .0204 .0462 .0212 16.5 37.4 20.2 

0.5 to < 5 no IRS 1 1 .0 nc .0 .0 nc .0 

Chlorpyrifos, parameter code 38933, analysis by GCMS, MRL 0.004 μg/L

< 0.01 deleted 22 24 .0018 .0022 .00071 23.9 29.6 12.7 

zero 36 41 .0043 .0050 .0027 95.2 111.5 37.9 mrl 34 39 .0022 .0026 .0011 32.1 37.7 22.2 

0.005 to < 0.05 deleted 46 52 .0024 .0027 .0013 18.5 21.2 8.7 

zero 52 59 .0052 .0059 .0014 54.9 62.4 11.1 

mrl 57 65 .0043 .0049 .0014 36.7 41.5 12.9 

0.01 to < 0.1 deleted 29 34 .0030 .0035 .0014 12.0 14.3 8.3 

zero 30 36 .0056 .0066 .0014 42.5 50.3 8.5 mrl 32 38 .0054 .0064 .0014 36.8 43.4 8.7 

Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates

—Continued

Concentration

range

(μg/L)

Analytical

approach

for IRS

N df

Pooled

standard

deviation

(μg/L)

90-percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

M
70 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97 Chlorpyrifos, parameter code 38933, analysis by GCMS, MRL 0.004 μg/L—Continued 0.05 to < 0.5 no IRS 8 9 0.0157 0.0231 0.0141 9.9 14.6 9.1 

0.1 to < 1 no IRS 6 6 .0189 .0312 .0177 10.5 17.3 9.1 

Cyanazine, parameter code 04041, analysis by GCMS, MRL 0.004 μg/L < 0.01 deleted 6 6 .0012 .0019 .00084 13.3 22.0 10.4 

zero 14 14 .0056 .0075 .0049 107.3 143.8 141.4 mrl 14 14 .0036 .0049 .0025 45.9 61.6 37.7 

0.005 to < 0.05 deleted 33 37 .0023 .0027 .0014 10.1 11.9 8.8 

zero 38 43 .0050 .0058 .0014 50.6 59.0 9.6 mrl 42 47 .0042 .0048 .0015 32.7 37.8 10.9 

0.01 to < 0.1 deleted 38 45 .0040 .0047 .0019 9.8 11.4 7.8 

zero 39 47 .0052 .0061 .0020 25.5 29.5 8.0 

mrl 39 47 .0050 .0058 .0020 21.7 25.2 8.0 

0.05 to < 0.5 no IRS 25 29 .0314 .0380 .0057 14.8 17.9 5.7 0.1 to < 1 no IRS 19 20 .0759 .0962 .0071 19.1 24.3 3.8 0.5 to < 5 no IRS 11 11 .2940 .4128 .0424 16.8 23.7 5.8 1 to < 10 no IRS 6 6 .3794 .6260 .2581 9.1 15.0 7.2 

2,4-D, parameter code 39732, analysis by HPLC, MRL 0.150 μg/L < 0.01 zero 2 2 .0071 .0218 .0071 141.4 435.7 141.4 

0.005 to < 0.05 deleted 2 2 .0071 .0218 .0071 22.9 70.5 22.0 

zero 7 7 .0256 .0403 .0071 120.1 188.9 141.4 mrl 2 2 .0071 .0218 .0071 22.9 70.5 22.0 

0.01 to < 0.1 deleted 3 3 .0058 .0131 .0071 18.7 42.3 15.7 

zero 7 7 .0568 .0892 .0283 107.6 169.1 141.4 mrl 6 6 .0655 .1081 .0424 80.0 131.9 55.1 

0.05 to < 0.5 deleted 6 6 .0327 .0539 .0071 10.6 17.5 6.2 

zero 10 11 .1795 .2521 .0530 83.1 116.7 17.1 mrl 15 16 .1149 .1506 .0636 58.5 76.7 29.8 

0.1 to < 1 deleted 7 7 .0434 .0683 .0354 11.0 17.3 6.7 

zero 10 11 .1767 .2481 .0566 71.5 100.4 13.4 mrl 13 14 .1172 .1571 .0566 35.5 47.7 16.6 

0.5 to < 5 no IRS 2 2 .0583 .1796 .0566 9.3 28.6 8.8 

Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates

—Continued

Concentration

range

(μg/L)

Analytical

approach

for IRS

N df

Pooled

standard

deviation

(μg/L)

90-percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

M
Comparison of three approaches for the analysis of variability of concentrations 71

Dacthal, parameter code 82682, analysis by GCMS, MRL 0.002 μg/L

< 0.01 deleted 34 39 0.00036 0.00043 0.0 11.0 13.0 0.0 

zero 46 53 .00082 .00094 .0 73.3 84.0 .0 mrl 46 53 .00057 .00066 .0 23.1 26.5 .0 

0.005 to < 0.05 no IRS 20 25 .0058 .0071 .00071 26.8 33.0 5.9 0.01 to < 0.1 no IRS 14 16 .0078 .0103 .0011 32.9 43.1 6.3 0.05 to < 0.5 no IRS 4 4 .0159 .0308 .0095 11.3 22.0 6.7 0.1 to < 1 no IRS 2 2 .0206 .0635 .0177 7.0 21.7 6.7 

p,p’-DDE, parameter code 34653, analysis by GCMS, MRL 0.006 μg/L

< 0.01 deleted 9 10 .00073 .0010 .00071 31.6 45.3 23.2 

zero 30 37 .0020 .0023 .00097 125.2 148.0 141.4 

mrl 31 38 .0024 .0028 .0026 63.6 74.9 49.5 

0.005 to < 0.05 deleted 5 5 .0025 .0044 .0028 15.2 26.9 16.4 

zero 6 7 .0039 .0061 .0028 48.2 75.8 17.1 mrl 8 10 .0022 .0031 .0020 19.6 28.1 17.1 

0.01 to < 0.1 no IRS 3 3 .0031 .0070 .0028 16.1 36.5 16.4 

Desethylatrazine, parameter code 04040, analysis by GCMS, MRL 0.002 μg/L

< 0.01 deleted 50 56 .00095 .0011 .00064 18.2 20.8 10.9 

zero 67 78 .0016 .0017 .00071 74.1 82.8 15.7 

mrl 67 78 .0011 .0012 .00071 26.6 29.7 12.4 

0.005 to < 0.05 no IRS 79 86 .0046 .0051 .0014 20.4 22.6 10.9 

0.01 to < 0.1 no IRS 82 92 .0061 .0068 .0026 18.5 20.5 8.8 

0.05 to < 0.5 no IRS 42 51 .0151 .0173 .0088 12.0 13.8 6.6 

0.1 to < 1 no IRS 25 30 .0258 .0311 .0141 10.8 13.1 6.1 0.5 to < 5 no IRS 3 3 .0784 .1777 .0919 8.0 18.2 7.6 1 to < 10 no IRS 1 1 .0919 .7315 .0919 7.6 60.2 7.6 

Diazinon, parameter code 39572, analysis by GCMS, MRL 0.002 μg/L

< 0.01 deleted 32 34 .0014 .0016 .00071 20.6 24.6 9.4 

zero 45 47 .0035 .0040 .00077 76.4 88.5 20.2 mrl 45 47 .0029 .0034 .00071 43.6 50.5 20.2 

Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates

—Continued

Concentration

range

(μg/L)

Analytical

approach

for IRS

N df

Pooled

standard

deviation

(μg/L)

90-percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

M
72 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97 Diazinon, parameter code 39572, analysis by GCMS, MRL 0.002 μg/L—Continued 0.005 to < 0.05 deleted 69 77 0.0023 0.0026 0.0010 16.5 18.4 7.4 

zero 75 84 .0039 .0043 .0014 40.3 44.8 8.3 mrl 76 85 .0036 .0040 .0014 34.1 37.9 8.5 

0.01 to < 0.1 deleted 63 75 .0040 .0045 .0014 10.7 12.0 4.3 

zero 66 80 .0100 .0112 .0015 27.2 30.3 4.4 mrl 66 80 .0098 .0109 .0015 24.4 27.2 4.4 

0.05 to < 0.5 deleted 25 31 .0061 .0073 .0021 6.9 8.3 2.9 

zero 26 33 .0151 .0180 .0021 22.4 26.7 2.9 mrl 26 33 .0148 .0177 .0021 21.8 26.0 2.9 

0.1 to < 1 no IRS 6 7 .0282 .0443 .0071 5.8 9.2 4.2 0.5 to < 5 no IRS 2 3 .0918 .2079 .0964 7.9 18.0 7.1 1 to < 10 no IRS 1 1 .1414 1.125 .1414 5.1 40.2 5.1 

Dichlobenil, parameter code 49303, analysis by HPLC, MRL 1.200 μg/L 0.005 to < 0.05 zero 1 1 .0283 .2251 .0283 141.4 1125. 141.4 

0.01 to < 0.1 zero 1 1 .0283 .2251 .0283 141.4 1125. 141.4 0.1 to < 1 mrl 1 1 .8202 6.527 .8202 132.3 1053. 132.3 0.5 to < 5 mrl 1 1 .8202 6.527 .8202 132.3 1053. 132.3 

Dieldrin, parameter code 39381, analysis by GCMS, MRL 0.001 μg/L < 0.01 deleted 6 7 .0025 .0040 .00071 28.8 45.2 13.8 

zero 12 15 .0037 .0048 .0027 103.7 137.3 75.3 mrl 12 15 .0033 .0044 .0020 73.6 97.5 64.5 

0.005 to < 0.05 deleted 11 12 .0033 .0046 .0035 28.2 38.9 13.3 

zero 11 12 .0033 .0046 .0035 28.2 38.9 13.3 mrl 12 13 .0036 .0049 .0035 41.4 56.3 20.8 

0.01 to < 0.1 no IRS 6 6 .0039 .0064 .0039 26.2 43.3 20.8 

2,6-Diethylaniline, parameter code 82660, analysis by GCMS, MRL 0.003 μg/L

< 0.01 deleted 7 7 .00038 .00059 .0 25.2 39.6 .0 

zero 12 14 .00062 .00083 .00058 114.8 153.9 47.1 

mrl 12 14 .00088 .0012 .00071 42.3 56.7 37.7 

Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates

—Continued

Concentration

range

(μg/L)

Analytical

approach

for IRS

N df

Pooled

standard

deviation

(μg/L)

90-percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

M
Comparison of three approaches for the analysis of variability of concentrations 73

2,6-Diethylaniline, parameter code 82660, analysis by GCMS, MRL 0.003 μg/L—Continued

0.005 to < 0.05 no IRS 1 1 0.0014 0.0113 0.0014 12.9 102.3 12.9 0.01 to < 0.1 no IRS 1 1 .0014 .0113 .0014 12.9 102.3 12.9 

Dinoseb, parameter code 49301, analysis by HPLC, MRL 0.035 μg/L

0.005 to < 0.05 zero 1 1 .0354 .2814 .0354 141.4 1125. 141.4 

mrl 1 1 .0106 .0844 .0106 25.0 198.6 25.0 

0.01 to < 0.1 zero 1 1 .0354 .2814 .0354 141.4 1125. 141.4 

mrl 1 1 .0106 .0844 .0106 25.0 198.6 25.0 

Disulfoton, parameter code 82677, analysis by GCMS, MRL 0.017 μg/L

< 0.01 zero 1 2 .0029 .0089 .0029 86.6 266.8 86.6 

mrl 1 2 .0069 .0213 .0069 77.0 237.2 77.0 

0.005 to < 0.05 mrl 1 2 .0069 .0213 .0069 77.0 237.2 77.0 

Diuron, parameter code 49300, analysis by HPLC, MRL 0.020 μg/L

0.005 to < 0.05 deleted 4 4 .0132 .0257 .0106 40.9 79.3 37.7 

zero 6 7 .0210 .0331 .0157 111.3 174.9 53.9 mrl 6 7 .0150 .0235 .0106 43.0 67.5 37.7 

0.01 to < 0.1 deleted 8 9 .0150 .0220 .0071 33.2 48.8 18.9 

zero 11 13 .0331 .0449 .0141 92.0 125.0 39.7 mrl 11 13 .0279 .0378 .0071 45.9 62.4 28.3 

0.05 to < 0.5 deleted 8 9 .0202 .0297 .0177 21.3 31.4 10.4 

zero 11 12 .1129 .1557 .0212 73.1 100.8 15.0 mrl 11 12 .1067 .1472 .0212 62.0 85.6 15.0 

0.1 to < 1 deleted 9 10 .2314 .3318 .0354 32.0 45.9 15.0 

zero 11 12 .2371 .3272 .0651 64.7 89.3 20.2 mrl 11 12 .2347 .3239 .0651 58.5 80.7 20.2 

0.5 to < 5 no IRS 8 9 .4031 .5923 .2440 39.4 57.9 18.4 1 to < 10 no IRS 3 3 .5565 1.261 .4243 39.1 88.6 9.9 

DNOC, parameter code 49299, analysis by HPLC, MRL 0.420 μg/L

0.1 to < 1 no IRS 1 1 .0495 .3939 .0495 9.8 78.0 9.8 0.5 to < 5 no IRS 1 1 .0495 .3939 .0495 9.8 78.0 9.8 

Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates

—Continued

Concentration

range

(μg/L)

Analytical

approach

for IRS

N df

Pooled

standard

deviation

(μg/L)

90-percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

M
74 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97 PTC, parameter code 82668, analysis by GCMS, MRL 0.002 μg/L < 0.01 deleted 16 19 0.0018 0.0023 0.00044 30.6 39.0 6.2 

zero 20 23 .0019 .0023 .00058 65.2 81.1 10.9 mrl 20 23 .0017 .0021 .00058 32.2 40.0 9.7 

0.005 to < 0.05 no IRS 27 29 .0050 .0061 .0014 29.0 35.1 8.7 0.01 to < 0.1 no IRS 26 27 .0052 .0064 .0019 18.9 23.0 5.8 

0.05 to < 0.5 no IRS 10 11 .0166 .0233 .0032 6.3 8.9 3.9 

0.1 to < 1 no IRS 4 4 .0281 .0544 .0177 8.8 17.0 6.6 0.5 to < 5 no IRS 1 1 .0141 .1125 .0141 2.8 22.5 2.8 

Ethalfluralin, parameter code 82663, analysis by GCMS, MRL 0.004 μg/L < 0.01 zero 2 3 .0028 .0063 .0029 108.0 244.7 114.0 

mrl 2 3 .00041 .00092 .00035 9.1 20.6 7.9 

0.005 to < 0.05 no IRS 4 4 .00094 .0018 .00071 4.9 9.5 4.6 0.01 to < 0.1 no IRS 4 4 .00094 .0018 .00071 4.9 9.5 4.6 0.05 to < 0.5 no IRS 1 1 .0318 .2532 .0318 29.6 235.6 29.6 0.1 to < 1 no IRS 1 1 .0318 .2532 .0318 29.6 235.6 29.6 

Ethoprop, parameter code 82672, analysis by GCMS, MRL 0.003 μg/L < 0.01 no IRS 2 2 .00040 .0012 .00028 9.1 28.0 6.4 

0.005 to < 0.05 no IRS 2 2 .00050 .0015 .00035 1.2 3.6 .8 0.01 to < 0.1 no IRS 2 2 .00050 .0015 .00035 1.2 3.6 .8 

Fenuron, parameter code 49297, analysis by HPLC, MRL 0.013 μg/L 0.05 to < 0.5 no IRS 1 1 .0 nc .0 .0 nc .0 

0.1 to < 1 no IRS 1 1 .0 nc .0 .0 nc .0 

Fluometuron, parameter code 38811, analysis by HPLC, MRL 0.035 μg/L

< 0.01 deleted 1 1 .0049 .0394 .0049 76.1 606.0 76.1 

zero 2 2 .0061 .0188 .0060 113.6 349.9 108.8 mrl 1 1 .0049 .0394 .0049 76.1 606.0 76.1 

0.005 to < 0.05 deleted 1 1 .0049 .0394 .0049 76.1 606.0 76.1 

zero 2 2 .0061 .0188 .0060 113.6 349.9 108.8 mrl 2 2 .0130 .0400 .0113 77.4 238.4 77.4 

Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates

—Continued

Concentration

range

(μg/L)

Analytical

approach

for IRS

N df

Pooled

standard

deviation

(μg/L)

90-percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

M
Comparison of three approaches for the analysis of variability of concentrations 75

Fluometuron, parameter code 38811, analysis by HPLC, MRL 0.035 μg/L—Continued

0.01 to < 0.1 deleted 1 1 0.0212 0.1688 0.0212 28.3 225.1 28.3 

zero 1 1 .0212 .1688 .0212 28.3 225.1 28.3 mrl 2 2 .0195 .0602 .0194 59.0 181.9 53.4 

0.05 to < 0.5 no IRS 4 4 .0892 .1729 .0141 24.7 47.8 17.2 0.1 to < 1 no IRS 3 3 .1022 .2316 .0071 23.3 52.8 6.1 1 to < 10 no IRS 1 1 .4243 3.376 .4243 6.8 54.5 6.8 >= 5 no IRS 1 1 .4243 3.376 .4243 6.8 54.5 6.8 

Fonofos, parameter code 04095, analysis by GCMS, MRL 0.003 μg/L

< 0.01 deleted 14 14 .00072 .00096 .00015 15.4 20.6 2.6 

zero 16 16 .0019 .0024 .00028 52.0 68.2 5.3 mrl 16 16 .0013 .0017 .00028 23.3 30.6 5.3 

0.005 to < 0.05 deleted 9 11 .0012 .0018 .00040 6.6 9.2 4.3 

zero 9 11 .0012 .0018 .00040 6.6 9.2 4.3 mrl 10 12 .0017 .0024 .00056 21.4 29.5 4.5 

0.01 to < 0.1 no IRS 7 9 .0022 .0033 .0010 4.9 7.2 4.3 

0.05 to < 0.5 no IRS 2 2 .0039 .0120 .0036 4.5 14.0 4.5 

alpha-HCH, parameter code 34253, analysis by GCMS, MRL 0.002 μg/L

< 0.01 zero 1 1 .0014 .0113 .0014 141.4 1125. 141.4 

mrl 1 1 .0 nc .0 .0 nc .0 

0.005 to < 0.05 no IRS 1 1 .0035 .0281 .0035 9.4 75.0 9.4 0.01 to < 0.1 no IRS 1 1 .0035 .0281 .0035 9.4 75.0 9.4 

gamma-HCH, parameter code 39341, analysis by GCMS, MRL 0.004 μg/L

< 0.01 deleted 2 2 .0016 .0049 .0014 18.2 56.1 17.6 

zero 5 5 .0068 .0119 .0021 110.1 194.1 141.4 mrl 4 4 .0028 .0054 .0018 42.5 82.5 34.7 

0.005 to < 0.05 deleted 4 4 .0027 .0053 .0014 13.9 27.0 11.4 

zero 6 6 .0065 .0107 .0035 82.4 136.0 17.6 mrl 6 6 .0050 .0083 .0035 47.0 77.5 17.6 

Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates

—Continued

Concentration

range

(μg/L)

Analytical

approach

for IRS

N df

Pooled

standard

deviation

(μg/L)

90-percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

M
76 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97 gamma-HCH, parameter code 39341, analysis by GCMS, MRL 0.004 μg/L—Continued 0.01 to < 0.1 deleted 4 4 0.0034 0.0065 0.0032 5.9 11.4 3.7 

zero 4 4 .0034 .0065 .0032 5.9 11.4 3.7 mrl 5 5 .0054 .0094 .0035 40.6 71.5 4.1 

0.05 to < 0.5 no IRS 2 2 .0032 .0099 .0032 3.6 11.2 3.6 

Linuron, parameter code 82666, analysis by GCMS, MRL 0.002 μg/L < 0.01 zero 1 1 .0042 .0338 .0042 141.4 1125. 141.4 

mrl 1 1 .0028 .0225 .0028 70.7 562.7 70.7 

0.005 to < 0.05 no IRS 5 6 .00090 .0015 .00049 6.4 10.6 3.1 0.01 to < 0.1 no IRS 6 7 .0020 .0032 .00082 6.6 10.3 4.5 0.05 to < 0.5 no IRS 4 5 .0916 .1614 .0060 33.3 58.7 6.6 0.1 to < 1 no IRS 3 4 .1024 .1985 .0071 37.1 71.9 5.7 

Linuron, parameter code 38478, analysis by HPLC, MRL 0.018 μg/L < 0.01 zero 1 1 .0127 .1013 .0127 141.4 1125. 141.4 

0.005 to < 0.05 zero 1 1 .0127 .1013 .0127 141.4 1125. 141.4 

mrl 1 1 .0 nc .0 .0 nc .0 

0.01 to < 0.1 deleted 2 3 .0312 .0706 .0225 54.8 124.1 37.6 

zero 2 3 .0312 .0706 .0225 54.8 124.1 37.6 mrl 3 4 .0270 .0524 .0071 47.4 92.0 8.3 

0.05 to < 0.5 no IRS 2 3 .0312 .0706 .0225 54.8 124.1 37.6 

MCPA, parameter code 38482, analysis by HPLC, MRL 0.170 μg/L < 0.01 zero 2 2 .0071 .0218 .0071 141.4 435.7 141.4 

0.005 to < 0.05 zero 2 2 .0071 .0218 .0071 141.4 435.7 141.4 0.01 to < 0.1 deleted 1 1 .0495 .3939 .0495 58.2 463.4 58.2 

zero 1 1 .0495 .3939 .0495 58.2 463.4 58.2 mrl 3 3 .0967 .2191 .1131 108.0 244.7 125.7 

0.05 to < 0.5 deleted 1 1 .0495 .3939 .0495 58.2 463.4 58.2 

zero 1 1 .0495 .3939 .0495 58.2 463.4 58.2 mrl 3 3 .0967 .2191 .1131 108.0 244.7 125.7 

Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates

—Continued

Concentration

range

(μg/L)

Analytical

approach

for IRS

N df

Pooled

standard

deviation

(μg/L)

90-percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

M
Comparison of three approaches for the analysis of variability of concentrations 77

Malathion, parameter code 39532, analysis by GCMS, MRL 0.005 μg/L

< 0.01 deleted 6 6 0.0012 0.0020 0.00035 13.5 22.3 3.7 

zero 12 16 .0036 .0047 .0012 121.5 159.2 59.0 mrl 12 16 .0021 .0028 .00095 33.2 43.6 13.9 

0.005 to < 0.05 deleted 11 13 .0019 .0026 .00071 13.1 17.8 6.7 

zero 12 14 .0027 .0036 .00085 39.8 53.4 7.1 mrl 15 19 .0023 .0029 .0010 24.4 31.1 7.4 

0.01 to < 0.1 no IRS 11 13 .0079 .0107 .0021 15.0 20.3 6.7 0.05 to < 0.5 no IRS 5 5 .0124 .0218 .0078 18.9 33.2 15.1 

Methomyl, parameter code 49296, analysis by HPLC, MRL 0.017 μg/L

0.01 to < 0.1 zero 1 1 .0707 .5627 .0707 141.4 1125. 141.4 

mrl 1 1 .0587 .4670 .0587 100.3 798.4 100.3 

0.05 to < 0.5 zero 1 1 .0707 .5627 .0707 141.4 1125. 141.4 

mrl 1 1 .0587 .4670 .0587 100.3 798.4 100.3 

Methyl parathion, parameter code 82667, analysis by GCMS, MRL 0.006 μg/L

< 0.01 zero 1 2 .0052 .0160 .0052 173.2 533.6 173.2 

mrl 1 2 .0017 .0053 .0017 24.7 76.2 24.7 

0.005 to < 0.05 deleted 4 4 .00053 .0010 .00035 3.8 7.4 1.8 

zero 4 4 .00053 .0010 .00035 3.8 7.4 1.8 mrl 5 6 .0011 .0018 .00071 14.6 24.1 3.7 

0.01 to < 0.1 no IRS 4 4 .00053 .0010 .00035 3.8 7.4 1.8 

Metolachlor, parameter code 39415, analysis by GCMS, MRL 0.002 μg/L

< 0.01 deleted 37 43 .00065 .00076 .0 16.7 19.5 .0 

zero 51 60 .0020 .0022 .00071 68.0 77.3 8.3 mrl 51 60 .0015 .0017 .00058 30.1 34.2 7.4 

0.005 to < 0.05 deleted 70 77 .0013 .0015 .00071 6.8 7.6 2.8 

zero 71 78 .0017 .0019 .00071 17.4 19.4 3.0 

mrl 72 79 .0017 .0019 .00071 16.8 18.8 3.1 

0.01 to < 0.1 no IRS 68 77 .0023 .0026 .00071 5.8 6.5 3.3 

0.05 to < 0.5 no IRS 47 56 .0236 .0270 .0042 11.2 12.8 3.6 

Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates

—Continued

Concentration

range

(μg/L)

Analytical

approach

for IRS

N df

Pooled

standard

deviation

(μg/L)

90-percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

M
78 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97 Metolachlor, parameter code 39415, analysis by GCMS, MRL 0.002 μg/L—Continued 0.1 to < 1 no IRS 36 42 0.0554 0.0648 0.0141 13.5 15.8 3.5 

0.5 to < 5 no IRS 16 18 .1569 .2020 .0707 10.7 13.8 4.7 1 to < 10 no IRS 12 13 .1707 .2319 .0707 9.0 12.2 4.7 >= 5 no IRS 3 3 .7829 1.774 .1768 6.4 14.5 3.2 

Metribuzin, parameter code 82630, analysis by GCMS, MRL 0.004 μg/L < 0.01 deleted 7 9 .00071 .0010 .0 10.6 15.6 .0 

zero 18 21 .0059 .0074 .0053 111.5 140.4 141.4 mrl 17 20 .0034 .0044 .0021 45.1 57.2 38.6 

0.005 to < 0.05 deleted 17 19 .0027 .0034 .00071 11.4 14.6 4.7 

zero 25 27 .0082 .0100 .0014 77.6 94.7 9.0 mrl 29 32 .0065 .0078 .0021 48.1 57.7 14.3 

0.01 to < 0.1 deleted 13 13 .0034 .0046 .00071 10.9 14.8 4.7 

zero 16 17 .0208 .0270 .0018 57.7 74.9 5.0 mrl 17 18 .0193 .0248 .0021 51.2 65.9 5.1 

0.05 to < 0.5 deleted 5 5 .0060 .0106 .0 4.7 8.2 .0 

zero 6 7 .0299 .0470 .0021 46.5 73.1 3.0 mrl 6 7 .0287 .0451 .0021 43.6 68.6 3.0 

0.1 to < 1 no IRS 4 4 .0149 .0288 .0064 3.5 6.9 1.9 0.5 to < 5 no IRS 1 1 .0269 .2138 .0269 3.7 29.7 3.7 

Molinate, parameter code 82671, analysis by GCMS, MRL 0.004 μg/L < 0.01 no IRS 1 1 .0 nc .0 .0 nc .0 

0.005 to < 0.05 no IRS 3 3 .0016 .0037 .0 14.8 33.6 .0 0.01 to < 0.1 no IRS 3 3 .0023 .0052 .0028 15.0 33.9 3.5 0.05 to < 0.5 no IRS 4 4 .0107 .0208 .0106 7.7 14.9 7.5 

0.1 to < 1 no IRS 3 3 .0122 .0277 .0141 8.6 19.5 9.4 0.5 to < 5 no IRS 1 1 .0 nc .0 .0 nc .0 1 to < 10 no IRS 3 3 .0 nc .0 .0 nc .0 >= 5 no IRS 3 3 .0 nc .0 .0 nc .0 

Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates

—Continued

Concentration

range

(μg/L)

Analytical

approach

for IRS

N df

Pooled

standard

deviation

(μg/L)

90-percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

M
Comparison of three approaches for the analysis of variability of concentrations 79

Napropamide, parameter code 82684, analysis by GCMS, MRL 0.003 μg/L

< 0.01 deleted 6 6 0.00058 0.00095 0.00071 13.3 22.0 8.4 

zero 7 7 .0014 .0023 .00071 54.9 86.2 9.4 mrl 7 7 .00076 .0012 .00071 18.2 28.6 9.4 

0.005 to < 0.05 no IRS 10 11 .0015 .0021 .00071 11.2 15.8 8.4 0.01 to < 0.1 no IRS 9 10 .0020 .0028 .0014 10.8 15.5 6.4 0.05 to < 0.5 no IRS 4 4 .0019 .0038 .0011 3.4 6.6 1.6 

Norflurazon, parameter code 49293, analysis by HPLC, MRL 0.024 μg/L

0.01 to < 0.1 no IRS 2 2 .0112 .0344 .0106 12.6 38.7 12.0 0.05 to < 0.5 no IRS 2 2 .0112 .0344 .0106 12.6 38.7 12.0 0.1 to < 1 no IRS 1 1 .0919 .7315 .0919 16.0 127.2 16.0 0.5 to < 5 no IRS 1 1 .0919 .7315 .0919 16.0 127.2 16.0 

Oryzalin, parameter code 49292, analysis by HPLC, MRL 0.310 μg/L

0.1 to < 1 no IRS 1 1 .2758 2.195 .2758 53.5 426.1 53.5 0.5 to < 5 no IRS 1 1 .2758 2.195 .2758 53.5 426.1 53.5 

Pebulate, parameter code 82669, analysis by GCMS, MRL 0.004 μg/L

< 0.01 zero 1 1 .0035 .0281 .0035 141.4 1125. 141.4 

mrl 1 1 .00071 .0056 .00071 15.7 125.0 15.7 

0.005 to < 0.05 deleted 3 3 .0042 .0094 .0014 17.2 38.9 3.8 0.01 to < 0.1 deleted 3 3 .0042 .0094 .0014 17.2 38.9 3.8 0.05 to < 0.5 deleted 1 1 .0071 .0563 .0071 3.6 28.9 3.6 0.1 to < 1 deleted 1 1 .0071 .0563 .0071 3.6 28.9 3.6 

Pendimethalin, parameter code 82683, analysis by GCMS, MRL 0.004 μg/L

< 0.01 deleted 6 6 .00076 .0013 .00071 12.5 20.7 10.1 

zero 13 15 .0052 .0069 .0028 108.4 143.6 87.7 mrl 12 14 .0023 .0031 .00085 34.1 45.7 16.4 

0.005 to < 0.05 deleted 11 11 .0021 .0030 .00071 12.7 17.8 7.4 

zero 14 14 .0109 .0146 .00074 66.4 89.1 12.4 mrl 18 20 .0083 .0106 .0012 44.9 56.9 16.4 

Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates

—Continued

Concentration

range

(μg/L)

Analytical

approach

for IRS

N df

Pooled

standard

deviation

(μg/L)

90-percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

M
80 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97 Pendimethalin, parameter code 82683, analysis by GCMS, MRL 0.004 μg/L—Continued 0.01 to < 0.1 deleted 10 11 0.0058 0.0082 0.0028 13.1 18.5 9.5 

zero 11 12 .0120 .0165 .0035 42.7 58.9 12.0 mrl 12 13 .0112 .0152 .0046 43.9 59.7 13.7 

0.05 to < 0.5 no IRS 7 9 .0428 .0629 .0087 21.7 32.0 16.1 

0.1 to < 1 no IRS 2 3 .0734 .1664 .0748 32.6 73.9 34.0 

cis-Permethrin, parameter code 82687, analysis by GCMS, MRL 0.005 μg/L < 0.01 zero 2 3 .0033 .0075 .0024 115.5 261.6 120.7 

mrl 2 3 .0024 .0053 .0025 62.1 140.8 65.5 

0.005 to < 0.05 mrl 1 2 .0021 .0064 .0021 36.7 113.2 36.7 

Picloram, parameter code 49291, analysis by HPLC, MRL 0.050 μg/L 0.05 to < 0.5 no IRS 1 1 .0141 .1125 .0141 12.9 102.3 12.9 

0.1 to < 1 no IRS 1 1 .0141 .1125 .0141 12.9 102.3 12.9 

Prometon, parameter code 04037, analysis by GCMS, MRL 0.018 μg/L < 0.01 deleted 32 38 .00089 .0010 .00064 12.3 14.5 8.1 

zero 43 52 .0024 .0027 .00071 70.2 80.6 14.4 

mrl 33 39 .0021 .0025 .00071 23.6 27.8 8.3 

0.005 to < 0.05 deleted 90 109 .0033 .0036 .00071 12.6 13.8 5.8 

zero 93 113 .0056 .0062 .00071 25.3 27.7 6.1 

mrl 103 126 .0048 .0053 .0010 27.7 30.2 6.9 

0.01 to < 0.1 deleted 89 108 .0050 .0055 .0014 12.3 13.5 4.6 

zero 91 111 .0068 .0074 .0014 21.5 23.6 4.7 

mrl 101 124 .0059 .0065 .0014 25.4 27.7 5.7 

0.05 to < 0.5 no IRS 34 40 .0096 .0113 .0048 11.9 14.0 4.6 

0.1 to < 1 no IRS 9 10 .0146 .0209 .0071 12.3 17.6 6.1 0.5 to < 5 no IRS 1 1 .0141 .1125 .0141 1.4 10.9 1.4 1 to < 10 no IRS 1 1 .0141 .1125 .0141 1.4 10.9 1.4 

Pronamide, parameter code 82676, analysis by GCMS, MRL 0.003 μg/L

< 0.01 deleted 3 3 .00041 .00092 .0 6.3 14.2 .0 

zero 4 4 .0015 .0028 .00035 70.9 137.5 5.4 mrl 4 4 .00050 .00097 .00035 11.5 22.2 5.4 

Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates

—Continued

Concentration

range

(μg/L)

Analytical

approach

for IRS

N df

Pooled

standard

deviation

(μg/L)

90-percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

M
Comparison of three approaches for the analysis of variability of concentrations 81

Pronamide, parameter code 82676, analysis by GCMS, MRL 0.003 μg/L—Continued

0.005 to < 0.05 deleted 5 5 0.00055 0.00097 0.00071 6.3 11.2 6.1 

zero 6 6 .0150 .0248 .00071 58.0 95.7 6.4 mrl 6 6 .0142 .0234 .00071 51.8 85.4 6.4 

0.01 to < 0.1 deleted 2 2 .00071 .0022 .00071 6.4 19.9 6.4 

zero 3 3 .0212 .0481 .00071 81.8 185.4 6.7 mrl 3 3 .0200 .0453 .00071 72.9 165.2 6.7 

Propachlor, parameter code 04024, analysis by GCMS, MRL 0.007 μg/L

< 0.01 no IRS 1 1 .0 nc .0 .0 nc .0 0.005 to < 0.05 no IRS 3 3 .0021 .0047 .00071 5.2 11.8 4.6 0.01 to < 0.1 no IRS 3 3 .0053 .0121 .0035 7.8 17.6 7.8 0.05 to < 0.5 no IRS 1 1 .0085 .0675 .0085 10.0 79.4 10.0 

Propanil, parameter code 82679, analysis by GCMS, MRL 0.004 μg/L

< 0.01 deleted 1 2 .00058 .0018 .00058 6.0 18.4 6.0 

zero 3 6 .0045 .0074 .0035 141.5 233.4 173.2 mrl 3 6 .0028 .0046 .0012 42.6 70.3 24.7 

0.005 to < 0.05 deleted 1 2 .00058 .0018 .00058 6.0 18.4 6.0 

zero 1 2 .00058 .0018 .00058 6.0 18.4 6.0 mrl 2 4 .0033 .0064 .0026 49.2 95.4 37.6 

0.01 to < 0.1 no IRS 1 1 .0021 .0169 .0021 4.2 33.4 4.2 0.05 to < 0.5 no IRS 1 1 .0021 .0169 .0021 4.2 33.4 4.2 

Propargite, parameter code 82685, analysis by GCMS, MRL 0.013 μg/L

< 0.01 deleted 1 1 .00071 .0056 .00071 7.4 59.2 7.4 

zero 3 4 .0058 .0112 .0058 94.0 182.4 87.5 mrl 1 1 .00071 .0056 .00071 7.4 59.2 7.4 

0.005 to < 0.05 deleted 5 6 .0016 .0027 .0011 9.2 15.2 8.9 

zero 6 8 .0038 .0057 .0012 44.5 67.3 11.1 mrl 7 9 .0018 .0027 .0012 13.6 20.1 9.4 

0.01 to < 0.1 deleted 6 7 .0109 .0171 .0025 14.2 22.4 13.7 

zero 6 7 .0109 .0171 .0025 14.2 22.4 13.7 mrl 8 10 .0092 .0131 .0024 16.1 23.0 13.7 

Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates

—Continued

Concentration

range

(μg/L)

Analytical

approach

for IRS

N df

Pooled

standard

deviation

(μg/L)

90-percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

M
82 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97 Propargite, parameter code 82685, analysis by GCMS, MRL 0.013 μg/L—Continued 0.05 to < 0.5 no IRS 4 4 0.0469 0.0910 0.0269 19.9 38.5 17.5 

0.1 to < 1 no IRS 3 4 .0510 .0989 .0346 12.8 24.8 16.6 0.5 to < 5 no IRS 1 2 .0346 .1067 .0346 4.4 13.7 4.4 

Propoxur, parameter code 38538, analysis by HPLC, MRL 0.035 μg/L 0.005 to < 0.05 zero 1 1 .0283 .2251 .0283 141.4 1125. 141.4 

mrl 1 1 .0035 .0281 .0035 9.4 75.0 9.4 

0.01 to < 0.1 zero 1 1 .0283 .2251 .0283 141.4 1125.4 141.4 

mrl 1 1 .0035 .0281 .0035 9.4 75.0 9.4 

0.05 to < 0.5 zero 1 1 .1838 1.463 .1838 141.4 1125. 141.4 

mrl 1 1 .1591 1.266 .1591 107.9 858.4 107.9 

0.1 to < 1 zero 1 1 .1838 1.463 .1838 141.4 1125. 141.4 

mrl 1 1 .1591 1.266 .1591 107.9 858.4 107.9 

Simazine, parameter code 04035, analysis by GCMS, MRL 0.005 μg/L < 0.01 deleted 28 37 .0010 .0012 .00058 14.8 17.5 8.9 

zero 46 60 .0026 .0029 .0011 89.4 101.6 16.5 

mrl 46 60 .0015 .0017 .00071 29.6 33.6 13.3 

0.005 to < 0.05 deleted 98 111 .0020 .0022 .00074 11.1 12.2 5.8 

zero 99 112 .0025 .0027 .00078 17.3 19.0 5.9 

mrl 109 125 .0023 .0025 .00092 16.8 18.3 6.1 

0.01 to < 0.1 deleted 97 111 .0027 .0030 .0014 8.4 9.2 4.3 

zero 98 112 .0031 .0034 .0014 15.7 17.2 4.3 

mrl 98 112 .0029 .0032 .0014 11.8 13.0 4.3 

0.05 to < 0.5 no IRS 52 62 .0137 .0155 .0047 7.9 8.9 4.0 

0.1 to < 1 no IRS 36 41 .0197 .0231 .0071 8.8 10.3 4.2 

0.5 to < 5 no IRS 12 13 .1472 .2001 .0332 7.0 9.6 4.0 

1 to < 10 no IRS 7 7 .1989 .3127 .1485 9.1 14.2 6.7 

Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates

—Continued

Concentration

range

(μg/L)

Analytical

approach

for IRS

N df

Pooled

standard

deviation

(μg/L)

90-percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

M
Comparison of three approaches for the analysis of variability of concentrations 83

Tebuthiuron, parameter code 82670, analysis by GCMS, MRL 0.010 μg/L

< 0.01 deleted 17 21 0.0010 0.0013 0.00071 15.8 19.9 8.7 

zero 35 44 .0035 .0041 .0021 108.7 126.5 86.6 mrl 31 38 .0024 .0028 .0014 32.1 37.9 25.0 

0.005 to < 0.05 deleted 46 54 .0042 .0048 .00071 16.1 18.5 6.5 

zero 50 59 .0048 .0055 .00088 38.8 44.2 7.4 mrl 65 78 .0039 .0044 .0012 25.5 28.5 8.7 

0.01 to < 0.1 deleted 33 37 .0052 .0061 .0011 16.2 19.1 4.6 

zero 34 38 .0056 .0066 .0011 27.9 32.9 4.7 mrl 38 44 .0049 .0057 .0011 16.7 19.4 4.7 

0.05 to < 0.5 no IRS 6 6 .0188 .0310 .0110 8.8 14.5 8.4 

0.1 to < 1 no IRS 4 4 .0227 .0440 .0177 9.6 18.6 9.3 

Terbacil, parameter code 82665, analysis by GCMS, MRL 0.007 μg/L

< 0.01 no IRS 2 2 .00071 .0022 .00071 10.2 31.4 10.2 0.005 to < 0.05 no IRS 4 5 .0021 .0037 .00071 11.9 21.0 10.2 0.01 to < 0.1 no IRS 3 4 .0042 .0082 .0032 13.1 25.4 13.6 0.05 to < 0.5 no IRS 1 1 .0071 .0563 .0071 13.6 108.2 13.6 0.1 to < 1 no IRS 1 1 .0 nc .0 .0 nc .0 0.5 to < 5 no IRS 1 1 .0 nc .0 .0 nc .0 

Terbufos, parameter code 82675, analysis by GCMS, MRL 0.013 μg/L

< 0.01 zero 1 1 .0064 .0506 .0064 141.4 1125. 141.4 0.005 to < 0.05 mrl 1 1 .0028 .0225 .0028 25.7 204.6 25.7 0.01 to < 0.1 mrl 1 1 .0028 .0225 .0028 25.7 204.6 25.7 

Thiobencarb, parameter code 82681, analysis by GCMS, MRL 0.002 μg/L

< 0.01 deleted 1 1 .0 nc .0 .0 nc .0 

zero 2 2 .0025 .0077 .0018 100.0 308.1 70.7 mrl 2 2 .0015 .0046 .0011 42.9 132.0 30.3 

0.005 to < 0.05 no IRS 5 5 .00077 .0014 .00071 6.9 12.1 2.1 0.01 to < 0.1 no IRS 4 4 .00087 .0017 .00071 7.7 14.9 3.9 

Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates

—Continued

Concentration

range

(μg/L)

Analytical

approach

for IRS

N df

Pooled

standard

deviation

(μg/L)

90-percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

M
84 Variability of Pesticide Detections and Concentrations in Field Replicate Water Samples, 1992–97 Triallate, parameter code 82678, analysis by GCMS, MRL 0.001 μg/L < 0.01 deleted 9 9 0.0032 0.0046 0.00071 39.3 57.8 12.9 

zero 12 14 .0026 .0035 .00093 88.2 118.3 20.2 mrl 12 14 .0026 .0034 .00064 39.9 53.5 20.2 

0.005 to < 0.05 no IRS 6 6 .0039 .0065 .0011 45.3 74.7 9.3 0.01 to < 0.1 no IRS 3 3 .0039 .0088 .0021 6.4 14.5 5.8 0.05 to < 0.5 no IRS 2 2 .0157 .0482 .0138 12.1 37.3 11.8 0.1 to < 1 no IRS 1 1 .0212 .1688 .0212 14.6 116.4 14.6 

Triclopyr, parameter code 49235, analysis by HPLC, MRL 0.250 μg/L 0.01 to < 0.1 zero 1 1 .0919 .7315 .0919 141.4 1125. 141.4 

0.05 to < 0.5 deleted 1 2 .0306 .0941 .0306 14.1 43.4 14.1 

zero 2 3 .0586 .1329 .0612 82.5 186.8 77.8 mrl 2 3 .0550 .1246 .0577 28.2 64.0 29.4 

0.1 to < 1 deleted 1 2 .0306 .0941 .0306 14.1 43.4 14.1 

zero 1 2 .0306 .0941 .0306 14.1 43.4 14.1 mrl 2 3 .0550 .1246 .0577 28.2 64.0 29.4 

Trifluralin, parameter code 82661, analysis by GCMS, MRL 0.002 μg/L < 0.01 deleted 12 14 .0010 .0014 .00071 20.5 27.5 10.2 

zero 24 30 .0026 .0031 .0014 109.7 132.4 71.6 

mrl 24 30 .0018 .0022 .00071 39.2 47.3 24.2 

0.005 to < 0.05 deleted 21 22 .0012 .0015 .00071 15.4 19.3 1.6 

zero 22 23 .0019 .0023 .00071 33.1 41.2 1.7 mrl 23 24 .0019 .0024 .00071 30.4 37.7 1.9 

0.01 to < 0.1 no IRS 17 17 .0059 .0077 .00071 11.3 14.7 1.6 

0.05 to < 0.5 no IRS 5 5 .0144 .0253 .0071 13.6 23.9 7.0 0.1 to < 1 no IRS 1 1 .0212 .1688 .0212 4.3 34.1 4.3 

Appendix 2. Comparison of three approaches for the analysis of variability of concentrations for replicate sets with inconsistent dete

replicates

—Continued

Concentration

range

(μg/L)

Analytical

approach

for IRS

N df

Pooled

standard

deviation

(μg/L)

90-percent

upper

confidence

bound

(μg/L)

Median

standard

deviation

(μg/L)

Pooled

relative

standard

deviation

(percent)

90-percent

upper

confidence

bound

(percent)

Median

relative

standard

deviation

(percent)

M