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Re-Evaluation of Human Health Effects of Atrazine: 

Review of Cancer Epidemiology, Non-cancer Experimental Animal and 

In vitro Studies and Drinking Water Monitoring Frequency

Presented Jointly To The FIFRA Scientific Advisory Panel By:

U.S. Environmental Protection Agency

Office of Pesticide Programs

Health Effects Division

and

Environmental Fate and Effects Division

in collaboration with the

Office of Research and Development

Presented On:

July 26-29, 2011
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TABLE OF CONTENTS

1. INTRODUCTION...............................................................................................................7

2. MODE OF ACTION (MOA) & EXPERIMENTAL TOXICOLOGY....................................10

2.1 HISTORICAL BACKGROUND ............................................................................................................................................10

2.2 2010 SCIENTIFIC RE-EVALUATION ..................................................................................................................................11

2.3 MODE OF ACTION (MOA) AND ADVERSE HEALTH OUTCOMES.............................................................................................12

2.3.1 Background ....................................................................................................................................................12

2.3.2 Reproductive Senescence and Mammary tumors in rats: Well established MOA.........................................14

2.3.3 Adverse Health Outcomes – LH Changes as a Sentinel Effect .........................................................................16

2.3.3.1 Factors influencing GnRH Modulation of LH following atrazine exposure.................................................................. 17

2.3.3.1.2 GnRH in primates............................................................................................................................................. 17

2.3.3.2 Females of Reproductive Age: Role of LH, Progesterone and Corticosterone in Atrazine Induced Changes in 

Ovarian Cyclicity...................................................................................................................................................................... 18

2.3.3.2.1 Estrogen and progesterone regulation of LH secretion ..................................................................................... 19

2.3.3.2.2 Differential effects of single versus multiple atrazine exposures on the LH surge ........................................... 21

2.3.3.2.3 LH Attenuation in Humans – Pharmaceutical Experience.................................................................................. 24

2.3.3.3 Prepubertal Individuals: Atrazine-induced delays in sexual maturation.................................................................... 25

2.3.3.3.1 Delays in Female Sexual Maturation.................................................................................................................. 26

2.3.3.3.2 Delays in Male Sexual Maturation .................................................................................................................... 27

2.3.3.4 Atrazine-associated decrease in prolactin and prostatitis in young rats................................................................... 29

2.3.4 Mammary gland whole mounts.....................................................................................................................30

2.3.5 Summary ........................................................................................................................................................31

3. EPIDEMIOLOGIC EVIDENCE OF THE CARCINOGENIC POTENTIAL OF ATRAZINE

............................................................................................................................................33

3.1 INTRODUCTION...........................................................................................................................................................33

3.1.1 Background....................................................................................................................................................33

3.1.2 Identification of Epidemiology Studies included in Current Review...............................................................34

3.2 EPIDEMIOLOGY LITERATURE OF THE CARCINOGENIC POTENTIAL OF ATRAZINE .........................................................................35

3.2.1 Overview of Epidemiologic Database............................................................................................................36

3.2.2 Agricultural Health Study (AHS) Atrazine Cancer Point Estimates ................................................................37

3.2.2.1 Study Design and Methods....................................................................................................................................... 37

3.2.2.2 AHS Strengths and Weaknesses............................................................................................................................... 39

3.2.3 Reproductive and Endocrine System Cancers................................................................................................39

3.2.3.1 Prostate Cancer........................................................................................................................................................ 40

3.2.3.2 Breast Cancer ........................................................................................................................................................... 43

3.2.3.3 Ovarian Cancer......................................................................................................................................................... 48

3.2.3.4 Thyroid Cancer ......................................................................................................................................................... 52

3.2.4 Lymphohematopoietic Cancers .....................................................................................................................53

3.2.4.1 Population-Based Case-Control Studies: Midwestern U.S........................................................................................ 54

3.2.4.2 Hospital-Based Case-Control Studies (France) ......................................................................................................... 56

3.2.4.3 AHS Estimates of Atrazine and Lymphohematopoietic Cancers............................................................................... 58

3.2.4.4 Conclusions: Lymphohematopoietic Cancers........................................................................................................... 59

3.2.5 Other Cancer Sites.........................................................................................................................................60

3.2.5.1 Brain/Glioma ............................................................................................................................................................ 60
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3.2.5.2 Pediatric Cancers...................................................................................................................................................... 61

3.2.5.3 Colon Cancer ............................................................................................................................................................ 63

3.2.5.4 Other AHS................................................................................................................................................................. 64

3.3 SUMMARY OF EVIDENCE CONCERNING THE CARCINOGENIC POTENTIAL OF ATRAZINE ...............................................................65

3.3.1 Synthesis and Integration of Toxicology and Epidemiology Literature..........................................................65

3.3.2 EPA Conclusion .................................................................................................................................................71

4. PROPOSED UPDATES TO THE DOSE-RESPONSE ASSESSMENT........................72

4.1 BENCHMARK DOSE ANALYSIS ........................................................................................................................................72

4.1.1 Identification of Critical Studies......................................................................................................................73

4.1.2 Methods & Results .........................................................................................................................................74

4.1.3 Proposed Point of Departure .........................................................................................................................78

4.2 PHARMACOKINETIC ANALYSIS..........................................................................................................................................79

4.2.1 Background ......................................................................................................................................................79

4.2.2 Updates on the Pharmacokinetics of Atrazine and Its Metabolites in the Rat...............................................80

4.2.3 Oral Gavage Dosing of Sprague Dawley Rats with Atrazine ..........................................................................81

4.2.4 Dietary Exposures of Sprague Dawley Rats to Atrazine .................................................................................87

4.2.5 Plasma Clearance Discrepancy Based on Studies with Radiolabeled Atrazine...............................................88

4.2.6 Additional Rat Radiolabeled Atrazine Studies Evaluated for Plasma Clearance ............................................90

4.2.7 Pharmacokinetic Studies with Radiolabeled Atrazine in Monkeys.................................................................93

4.2.8 Pharmacokinetic Studies with Atrazine in Humans........................................................................................94

4.2.8.1 Human Blood Pharmacokinetic Information on Atrazine and its Chloro-s-triazine Metabolites .............................. 95

4.2.8.2 Human Urine Pharmacokinetic Information on Atrazine and its Chloro-s-triazine Metabolites.............................. 95

4.2.9 Updates on the Metabolism of Atrazine ........................................................................................................97

4.2.10 Summary of Pharmacokinetic Information ..................................................................................................98

4.2.11 Progress on Physiologically Based Pharmacokinetic Modeling Efforts ........................................................99

4.2.12 Proposed Interim Pharmacokinetic Modeling Approach............................................................................100

4.2.13 Implications for Water Monitoring Frequency ...........................................................................................103

4.2.13.1 Rat Forward Internal Dosimetry for LH Attenuation ............................................................................................. 104

4.2.13.2 Human Reverse Dosimetry from Rat Internal Dosimetry for LH Attenuation ....................................................... 105

4.2.14 Calculating Average Chlorotriazine Levels in Drinking Water for Human Plasma Triazine AUC Estimates107

5. SCIENTIFIC CONSIDERATIONS IN POTENTIAL SENSITIVITY OF INFANTS & 

CHILDREN........................................................................................................................109

5.1 BACKGROUND ..........................................................................................................................................................109

5.2 EXPERIMENTAL TOXICOLOGY .......................................................................................................................................110

5.2.1 Gestational Exposure...................................................................................................................................110

5.2.2 Multi-Lifestage Exposure..............................................................................................................................111

5.3. SUMMARY...............................................................................................................................................................112

6.EVALUATING ATRAZINE DRINKING WATER MONITORING DATA FOR USE IN 

HUMAN HEALTH ASSESSMENTS .................................................................................114

6.1 INTRODUCTION .........................................................................................................................................................114

6.2 KEY QUESTIONS TO ADDRESS IN ANALYZING MONITORING DATA........................................................................................115

6.3 SHORT-TERM TEMPORAL VARIABILITY: DAILY VARIATIONS WITHIN A GIVEN YEAR ..................................................................117

6.3.1 General Approach for Assessing Uncertainties in Monitoring Related to Short-term Temporal Variability 118

6.3.2 Comparative Analysis of Monitoring Frequency Performance.....................................................................120

6.3.3 Geostatistical Analysis and Stochastic Application ......................................................................................125
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6.3.4 Mechanistic Approach: PRZM Hybrid Model................................................................................................132

6.3.5 Watershed Atrazine Analysis Model.............................................................................................................133

6.3.6 Generating Atrazine Time Series from Weekly CWS Monitoring for Use in Dietary Exposure Assessments 134

6.4 YEAR-TO-YEAR TEMPORAL VARIABILITY .........................................................................................................................137

6.5 SPATIAL VARIABILITY ..................................................................................................................................................139

6.6 SUMMARY................................................................................................................................................................140

7. CASE STUDY: IMPLICATIONS OF MOA & TOXICITY PROFILE ON WATER 

MONITORING...................................................................................................................142

7.1 INTRODUCTION.........................................................................................................................................................142

7.2 ATRAZINE TIME SERIES ESTIMATES FOR USE IN DIETARY EXPOSURE ASSESSMENTS .................................................................143

7.3 ESTIMATING HUMAN PLASMA AVERAGE DAILY AUC FROM ROLLING AVERAGE ATRAZINE CONCENTRATIONS .................................145

8. REFERENCES............................................................................................................149

 

LIST OF FIGURES

FIGURE 1: LH SUPPRESSION AND ADVERSE OUTCOMES OBSERVED IN RATS .......................................................................................14

FIGURE 2: AUGMENTATION BY A SUBCUTANEOUS INJECTION OF PROGESTERONE ON AN LH SURGE IN OVARIECTOMIZED RATS IMPLANTED WITH 

AN ESTRADIOL CAPSULE AT THE TIME OF SURGERY. PROGESTERONE WAS ADMINISTERED JUST PRIOR TO THE SAMPLING OF SMALL 

ALIQUOTS OF BLOOD BY TAIL NICK OVER THE AFTERNOON OF THE THIRD DAY AFTER OVARIECTOMY. CONCENTRATIONS ARE INDICATED IN 

NG/ML ± SEM ..............................................................................................................................................................21

FIGURE 3: THE LH SURGE (NG/ML + SEM) AND AREAS UNDER THE CURVE IN RESPONSE TO 1 AND 4 DAYS OF ATRAZINE TREATMENT IN 

OVARIECTOMIZED / ESTRADIOL-PRIMED LONG-EVANS RATS. THE NUMBERS IN PARENTHESES INDICATE THE GROUP SIZES FOR EACH OF 

THE TREATMENTS. NUMBERS WITHIN THE 100 MG/KG COLUMNS AT THE RIGHT INDICATE THE PERCENT CHANGE FROM THE 0 MG/KG 

GROUP. *P<0.05..........................................................................................................................................................23

FIGURE 4: EPA BMDS EXPONENTIAL MODEL (CONSTANT VARIANCE; BMR = 1 SD FROM CONTROL MEAN) RESULTS FOR ATTENUATION OF 

LH SURGE IN FEMALE RATS ADMINISTERED ATRAZINE BY GAVAGE - NHEERL DATA..................................................................76

FIGURE 5: EPA BMDS HILL MODEL (CONSTANT VARIANCE; BMR = 1 SD FROM CONTROL MEAN) RESULTS FOR “AREA UNDER THE TRIAZINE 

EQUIVALENT PLASMA-CONCENTRATION TIME CURVE” AS THE DOSE METRIC - NHEERL DATA (ATTENUATION OF LH SURGE IN FEMALE 

RATS ADMINISTERED ATRAZINE BY GAVAGE).......................................................................................................................77

FIGURE 6: EPA BMDS HILL MODEL (CONSTANT VARIANCE; BMR = 1 SD FROM CONTROL MEAN) RESULTS FOR “DAILY STEADY STATE AUC

FOR TRIAZINE EQUIVALENTS” AS THE DOSE METRIC - NHEERL DATA (ATTENUATION OF LH SURGE IN FEMALE RATS ADMINISTERED 

ATRAZINE BY GAVAGE)....................................................................................................................................................78

FIGURE 7: METABOLIC SCHEME FOR ATRAZINE .............................................................................................................................80

FIGURE 8: PLASMA PROFILE OF ATRAZINE FOR 24 HOURS FOLLOWING ORAL GAVAGE DOSING OF RATS WITH 3, 10, OR 50 MG/KG BW ATRAZINE.

EACH TIME POINT REPRESENTS AVERAGE OF SIX MEASUREMENTS ± SD. DATA IS FROM CODER 2011. ............................................82

FIGURE 9: THE PARALLEL PLASMA PROFILES OF ATRAZINE VERSUS DEA OR DIA FOLLOWING ORAL GAVAGE DOSING OF RATS WITH 3 MG/KG BW 

ATRAZINE. DATA IS FROM CODER 2011..............................................................................................................................84

FIGURE 10: 24-H PLASMA PROFILE OF ATRAZINE AND ITS CHLORINATED METABOLITES DEA, DIA, AND DACT FOLLOWING ORAL GAVAGE 

DOSING OF RATS WITH 3 MG/KG BW ATRAZINE. EACH TIME POINT REPRESENTS AVERAGE OF SIX MEASUREMENTS ± SD. DATA IS FROM 

CODER 2011. ...............................................................................................................................................................85

FIGURE 11: PLASMA PROFILE OF ATRAZINE, DEA, DIA, AND DACT FROM REPEATED ONCE A DAY ORAL GAVAGE DOSING OF RATS WITH 3 MG/KG

BW ATRAZINE FOR 4 DAYS. VALUES REPRESENT THE MEAN OF SIX REPLICATES ± SD. DATA IS FROM CODER 2011. ............................86
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FIGURE 12: PLASMA PROFILE OF ATRAZINE, DEA, DIA, AND DACT FROM DIETARY DOSING OF RATS WITH ATRAZINE AT A NOMINAL DOSE OF 3

MG/KG BW/DAY ATRAZINE FOR FOUR DAYS. DATA AT EACH TIME POINT REPRESENTS MEASUREMENTS IN 6 ANIMALS. DATA POINTS ARE 

NOT CONNECTED TO HELP ELUCIDATE PATTERN. DATA IS FROM CODER 2011.............................................................................87

FIGURE 13: PLASMA PROFILES OF RADIOLABELED TRIAZINE EQUIVALENTS RESULTING FROM REPEATED ONCE DAILY DOSING OF RATS WITH 

RADIOLABELED ATRAZINE AT 1, 3, 7, 10, 50 AND 100 MG/KG BW ATRAZINE. DATA IS FROM THEDE 1987. ....................................90

FIGURE 14: PLASMA PROFILE AND ANALYSIS OF ELIMINATION OF A SINGLE ORAL GAVAGE DOSE OF 1 AND 100 MG/KG BW RADIOLABELED 

ATRAZINE IN RATS. DATA IS FROM PAUL ET AL. 1993. ...........................................................................................................92

FIGURE 15: PLASMA PROFILE AND ANALYSIS OF ELIMINATION OF REPEATED ORAL GAVAGE DOSING WITH EITHER 0.4 OR 4 MG/KG BW 14CATRAZINE IN RATS FOR SEVEN DAYS. DATA IS FROM SIMONEAUX 1985.....................................................................................93

FIGURE 16: UPDATED METABOLIC PROFILE FOR ATRAZINE. NEWLY IDENTIFIED METABOLITES ARE ABOVE THE DASHED LINE FOR COMPARISON TO 

FIGURE 7. NEW METABOLITES ARE FROM LEBLANC ET AL. 2011 AND JOO ET AL. 2011. ..............................................................98

FIGURE 17: PROPOSED INTERIM PHARMACOKINETIC MODEL FOR ATRAZINE AND ITS METABOLITES ........................................................101

FIGURE 18: PROPOSED LINKAGE BETWEEN ATRAZINE EXPOSURE AND OPTIMAL LH ATTENUATION IN RAT STUDIES.....................................104

FIGURE 19: PROPOSED LINKAGES BETWEEN ATRAZINE EXPOSURE AND LH ATTENUATION IN ADULT HUMANS. THE ASTERISKS INDICATE THE 

PARAMETERS THAT WERE ALLOMETRICALLY SCALED FROM ADULT RATS. ..................................................................................106

FIGURE 20: HYPOTHETICAL WATER CHEMOGRAPH SHOWING THE TYPICAL VARIATION IN CHLOROTRIAZINE LEVELS AS A FUNCTION OF TIME....106

FIGURE 21: THE TRAPEZOID RULE FOR ESTIMATING THE AREA UNDER A WATER CHEMOGRAPH FOR A GIVEN DURATION OF EXPOSURE...........107

FIGURE 22: ANALYSIS STRATEGY FOR MODELING PESTICIDE TIME SERIES CONCENTRATIONS FROM MONITORING DATA. ..........................126

FIGURE 23: VARIOGRAMS FOR 4-DAY AND 7-DAY SAMPLING FREQUENCY FOR THE MAUMEE AND MO-01 2007 DATA: A.) 4-DAY 1995

MAUMEE; B.) 7-DAY 1995 MAUMEE; C.) 4-DAY GRAB MO-01 2007; AND D.) 7-DAY GRAB MO-01 2007................................128

FIGURE 24: REPRESENTATIVE REALIZATIONS OF KRIGED ATRAZINE TIME SERIES FROM (A) 4-DAY, (B) 7-DAY, (C) 14-DAY, AND (D) 28-DAY 

SAMPLING OF THE NCWQR MAUMEE RIVER 1995 DATA....................................................................................................129

FIGURE 25: REPRESENTATIVE REALIZATIONS OF KRIGED ATRAZINE TIME SERIES FROM (A) 4-DAY, (B) 7-DAY, (C) 14-DAY, AND (D) 28-DAY 

SAMPLING OF THE MO-01 2007 DATA............................................................................................................................130

FIGURE 26: ESTIMATES OF DAILY AND ROLLING-AVERAGE ATRAZINE CONCENTRATIONS FROM THE ACTUAL TIME SERIES (BLACK LINE) AND 5TH 

AND 95TH PERCENTILES (DASHED RED LINES) OF REALIZATIONS OF KRIGED TIME SERIES FROM 7-DAY SAMPLING OF THE MAUMEE RIVER 

1995 DATA. ...............................................................................................................................................................135

FIGURE 27: ESTIMATES OF DAILY AND ROLLING-AVERAGE ATRAZINE CONCENTRATIONS FROM FROM THE ACTUAL TIME SERIES (BLACK LINE) AND 

5TH AND 95TH PERCENTILES (DASHED RED LINES) OF REALIZATIONS OF KRIGED TIME SERIES FROM 7-DAY SAMPLING OF THE MO-01

2007 DATA. ...............................................................................................................................................................136

FIGURE 28: LOCATION OF CWS IN THE AMP IN RELATION TO VULNERABLE WATERSHEDS IDENTIFIED BY WARP BASED ON ATRAZINE USE ON 

CORN AND SORGHUM....................................................................................................................................................140

FIGURE 29: ESTIMATES OF DAILY AND ROLLING-AVERAGE ATRAZINE CONCENTRATIONS FROM THE ACTUAL TIME SERIES (BLACK LINE) AND 5TH 

AND 95TH PERCENTILES (DASHED RED LINES) OF REALIZATIONS OF KRIGED TIME SERIES FROM 7-DAY SAMPLING OF THE MAUMEE RIVER 

1995 DATA. ...............................................................................................................................................................144

FIGURE 30: ESTIMATES OF DAILY AND ROLLING-AVERAGE ATRAZINE CONCENTRATIONS FROM THE ACTUAL TIME SERIES (BLACK LINE) AND 5TH 

AND 95TH PERCENTILES (DASHED RED LINES) OF REALIZATIONS OF KRIGED TIME SERIES FROM 7-DAY SAMPLING OF THE MO-01 2007

DATA. ........................................................................................................................................................................145

FIGURE 31: ESTIMATES OF ROLLING AVERAGE DAILY HUMAN PLASMA AUC VALUES CORRESPONDING TO THE ACTUAL TIME SERIES (BLACK LINE)

AND 5TH AND 95TH PERCENTILES (DASHED RED LINES) FOR THE MAUMEE RIVER 1995 DATASET ACCORDING TO FIGURE 29. THE DASHED 

LINE IS FOR COMPARISON TO THE RAT PLASMA POD AUC WITH A 300X APPLIED UNCERTAINTY FACTOR. ......................................147

FIGURE 32: ESTIMATES OF DAILY ROLLING-AVERAGE ATRAZINE CONCENTRATIONS FROM THE ACTUAL TIME SERIES (BLACK LINE) AND 5TH AND 

95TH PERCENTILES (DASHED RED LINES) OF REALIZATIONS OF KRIGED TIME SERIES FROM 7-DAY SAMPLING OF THE MO-01 2007 DATA.

................................................................................................................................................................................148
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LIST OF TABLES

TABLE 1: SUMMARY OF ADMINISTERED DOSE BMD ANALYSES........................................................................................................75

TABLE 2: BMD MODELING RESULTS WITH AVERAGE STEADY STATE PLASMA TRIAZINES AND AUC ESTIMATES............................................76

TABLE 3: PHARMACOKINETIC PARAMETERS ESTIMATED FOR ATRAZINE AND ITS CHLORINATED METABOLITES DEA, DIA, AND DACT FOLLOWING 

ORAL GAVAGE DOSING OF RATS WITH 3, 10, AND 50 MG/KG BW ATRAZINE. DATA ANALYZED IS FROM CODER 2011.........................83

TABLE 4: PHARMACOKINETIC ELIMINATION PARAMETERS ESTIMATED FOR ATRAZINE AND ITS CHLORINATED METABOLITES DEA, DIA, AND 

DACT FOLLOWING DOSING OF RATS WITH ATRAZINE THROUGH THE DIET AT NOMINAL DOSES OF 3, 10, AND 50 MG/KG BW. DATA IS 

FROM CODER 2011........................................................................................................................................................88

TABLE 5: FIRST ORDER ELIMINATION RATE CONSTANTS ESTIMATED FROM PLASMA DATA IN THE THEDE 1987 STUDY INVOLVING REPEATED ONCE 

DAILY DOSING OF RATS WITH RADIOLABELED ATRAZINE (THEDE 1987)......................................................................................90

TABLE 6: ESTIMATED PLASMA PHARMACOKINETIC PARAMETERS IN MONKEYS FOLLOWING ORAL GAVAGE DOSING WITH 1, 10, AND 100 MG 

[

14C]-ATRAZINE. DATA IS FROM HUI ET AL. 2011.................................................................................................................94

TABLE 7: ESTIMATED PHARMACOKINETIC PARAMETERS FROM HUMAN BLOOD AND URINE ....................................................................96

TABLE 8: EVALUATION OF THE ONE-COMPARTMENT LINEAR MODEL FOR RELATING AN ATRAZINE DOSE TO STEADY STATE PLASMA TRIAZINES..102

TABLE 9: SUMMARY OF AVAILABLE ATRAZINE EXPERIMENTAL TOXICOLOGY STUDIES FROM THE GESTATIONAL PERIOD................................111

TABLE 10: DOSING REGIMEN MULTI-LIFESTAGE STUDY................................................................................................................112

TABLE 11: SUMMARY OF 10,000 SIMULATIONS OF MONITORING ESTIMATES AT DIFFERENT SAMPLING INTERVALS FOR THE AEEMP MO-01

SITE IN 2007...............................................................................................................................................................122

TABLE 12: SUMMARY OF 10,000 SIMULATIONS OF MONITORING ESTIMATES AT DIFFERENT SAMPLING INTERVALS FOR THE NCWQR MAUMEE 

RIVER SITE IN 1995......................................................................................................................................................123

TABLE 13: VARIOGRAM MODELS USING THE NCWQR MAUMEE RIVER 1995 AND AEEMP MO-01 2007 TIME SERIES WITH DIFFERENT 

SAMPLING FREQUENCIES. ..............................................................................................................................................127

TABLE 14: COMPARISON OF LOWEST AND HIGHEST YEARLY MAXIMUM DETECTIONS FOR CWS IN THE AMP BASED ON NUMBER OF YEARS 

SAMPLED....................................................................................................................................................................138
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1. INTRODUCTION

Atrazine – a chlorotriazine herbicide – is currently one of the most widely used agricultural 

herbicides in the United States, with approximately 70 million pounds of active ingredient 

applied domestically per year. The Interim Reregistration Eligibility Document (IRED) for 

atrazine was finalized in January 2003 with an addendum added in October 2003 (US EPA 

2003). The Agency completed the reregistration eligibility document (RED) for atrazine in 

April 2006. In recent years, numerous experimental toxicology and epidemiologic studies 

evaluating the toxicity profile and/or mode of action (MOA) of atrazine have become 

available. To consider the extent to which these new studies may impact the Agency’s 

human health risk characterization for atrazine, the Office of Pesticide Programs (OPP) in 

collaboration with the Office of Research and Development (ORD) is re-evaluating atrazine 

and its chloro-s-triazines metabolites [deethyl-atrazine (DEA), deisopropyl-atrazine (DIA), 

and diamino-s-chlorotriazine (DACT)].1

 

As part of this re-evaluation, the Agency held three meetings of the FIFRA SAP in 2010 

which involved atrazine. The July 2011 meeting includes a variety of challenging scientific 

topics initially discussed at these three 2010 meetings; the July meeting will cover the most 

recent analyses on each. Specifically, the July SAP meeting will cover issues related to 

non-cancer effects, particularly related to effects on Luteinizing Hormone (LH) to several 

different lifestages and considering the temporal component of these health outcomes and 

how such temporality impacts the confidence/uncertainty in water monitoring data. In 

addition, the Agency will discuss cancer epidemiology studies and the integration of such 

cancer epidemiology studies with animal toxicology studies and mode of action information 

(MOA). 

The February, 2010 SAP meeting focused on a number of broad scientific issues related to 

the use of epidemiology and human incident data in risk assessment. One of the case 

studies presented at the February meeting involved a collaborative project between the 

Agency and investigators at the Agricultural Health Study (AHS) 

(http://aghealth.nci.nih.gov). The goal of this collaborative effort is to compare exposure 

assessment approaches used for pesticide applicator exposure assessment and is part of a 

broad goal of OPP to better use epidemiology data in risk assessment. In this broad 

context, the collaborative project is considered an on-going methods development effort; 

the Agency will not solicit comments from the Panel on the status of this effort. However, a 

short summary of the on-going analyses are provided simply as an update to the Panel and 

public on that effort. 

Two of the major goals of the atrazine human health re-evaluation are to consider the 

degree to which the current drinking water monitoring program conducted by Syngenta 

 1 Henceforth mention of atrazine in this document also infers its chloro-s-triazine metabolites (DEA, DIA, and 

DACT) unless otherwise specified.
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(and as required by EPA) is sufficient to protect human health and whether or not a new 

human health risk assessment needs to be developed. As part of the re-evaluation of the 

human health effects of atrazine, the Agency has evaluated the hazard potential of atrazine 

and the drinking water monitoring strategy together. Specifically, the frequency of drinking 

water monitoring is related to the temporal pattern of the toxicological endpoint of concern 

used for the risk assessment. Generally, longer durations of toxicological concern (e.g., a 

long-term chronic effect) require a less frequent drinking water sampling design to

approximate longer term exposures. However, as the duration of concern shortens, the 

frequency and timing of sampling become more important in determining how well the 

sample data capture short-duration exposures. The current drinking water monitoring 

frequency involves weekly sampling during the growing season and bi-weekly (i.e., every 

other week) sampling for the remainder of the year. This frequency is considered adequate 

for the 90-day rolling average duration used in the previous risk assessment. 

Relying on atrazine’s established neuroendocrine mode of action (MOA), this draft issue 

paper will discuss experimental toxicology findings in the context of adverse health 

outcomes and lifestages/populations that may be affected by atrazine exposure. 

Attenuation of the LH surge continues to be the most sensitive effect (i.e., occurs at the 

lowest dose) identified to date in the atrazine database. Perturbations of LH signal a 

disruption of the hormonal environment in the individual and serve as a sentinel effect used 

to establish a point of departure (PoD) for the risk assessment that would be health 

protective for the other effects noted in the database. These other effects occur at higher 

doses than the LH surge attenuation and include delays in puberty onset, disruption of 

estrous cycles, and and reduced prolactin from milk early in life and prostatitis in young 

adult rats; they provide insight into the temporal relationship between atrazine exposure 

and adverse health outcomes. At the September 2010 SAP, the Agency proposed a range 

of exposure durations relevant for the toxic effects of 4-28 days. A single value was not 

proposed due to uncertainties in the duration required to elicit toxic effects, particularly in 

humans. The SAP commented in the December 2010 report that “the imprecision in the 

Agency’s proposed sampling frequency seems justified. This may be about as precise an 

estimate as can be obtained when starting with the experimental animal data and the 

exposure requirements for LH surge suppression...” At the July 2011 meeting, the Agency 

will consider internal dosimetry of atrazine and its chlorinated metabolites in the context of 

potential adverse health outcomes relevant to specific lifestages and subpopulations, will 

update the benchmark dose analysis and internal dosimetry calculations based on new and 

additional pharmacokinetic data available for atrazine, and review the current status of 

efforts to develop a physiologically-based pharmacokinetic (PBPK) model. 

The Food Quality Protection Act (FQPA) requires the Agency to give special attention to 

the potential risk to infants and children. Specifically, FQPA instructs EPA, in making its 

“reasonable certainty of no harm” finding, that in “the case of threshold effects, an 

additional tenfold margin of safety for the pesticide chemical residue and other sources of 

exposure shall be applied for infants and children to take into account potential pre- and 
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post-natal toxicity and completeness of data with respect to exposure and toxicity to infants 

and children.” At the September 2010 SAP, the Agency considered the experimental 

toxicology and epidemiology studies considering the potential for increased susceptibility of 

infants and children; this literature review will be updated as part of the July 2011 SAP. 

Although the new science supports a duration of exposure shorter than 90-days like used in 

the previous risk assessment, this shortening of the duration of exposure may not 

necessarily translate to increased water monitoring. At the July 2011, the Agency has 

evaluated the confidence/uncertainty in existing water monitoring data; this analysis has 

been informed using advice from the Panel at the April and September 2010 meetings 

along with preliminary conclusions about the completeness of the data on exposure and 

toxicity to infants and children. 

The potential cancer effects of atrazine have been considered by the SAP on multiple 

occasions beginning in 1988 with an evaluation of rat mammary gland tumor response 

(FIFRA SAP, 1988). Subsequent to this meeting, substantial research was conducted on 

atrazine's hormonal or neuroendocrine mode of action (MOA) and the Agency returned to 

the SAP in 2000 (FIFRA SAP, 2000a) for advice on atrazine’s MOA leading to mammary 

gland tumors, reproductive and developmental effects in rats, as well as the human 

relevance of these findings. The SAP agreed with the Agency’s proposal for atrazine’s 

neuroendocrine mode of action, and they further concluded that it is unlikely that the 

mechanism by which atrazine induces mammary tumors in female Sprague Dawley rats 

could be operational in humans. At the April, 2010 SAP, the Agency reviewed new 

experimental toxicology studies on cancer published since the last atrazine risk 

assessment in 2003. The Agency concluded, and the SAP concurred, that the new 

experimental toxicology studies on cancer did not alter or contradict the major key events in 

the neuroendocrine MOA leading to mammary gland tumors in the rat or the conclusion 

that MOA leading to mammary gland tumors in the rat is not relevant to humans (USEPA, 

2010b; FIFRA SAP, 2010).

In 2003, the Agency presented its evaluation on prostate cancer and exposure to atrazine 

to the FIFRA SAP. At that meeting, the SAP evaluated the available epidemiology studies 

suggesting an association between atrazine and prostate cancer in workers at an atrazine 

manufacturing plant in St. Gabriel Louisiana. The SAP concurred with EPA’s conclusion 

that the data did not suggest an association between atrazine exposure and prostate 

cancer, but that the increase in incidence of prostate cancer in workers at the plant could 

likely be explained by the increase in Prostate Serum Antigen (PSA) screening at the plant 

(FIFRA SAP, 2003a). At the 2003 meeting, the SAP recommended a more thorough and 

systematic review of the epidemiologic literature on the topic of the potential or observed 

carcinogenic effects of atrazine exposure. The Agency developed such a comprehensive 

review of the cancer epidemiologic literature; this analysis will be reviewed as part of the 

July 2011 SAP. In addition, the Agency has developed an integrative analysis of cancer 

epidemiology studies and experimental toxicology studies to consider the cancer potential 

of atrazine. 
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As summarized above, this draft issue discussed a wide array of scientific issues related to 

atrazine. The document is organized by the following:

• Section 2 of this paper briefly describes the linkage between atrazine-induced 

hormonal perturbations and adverse health outcomes in females of reproductive 

age, prepubescent children, and newborns. 

• Section 3 discusses the Agency’s evaluation of the cancer epidemiology data (the 

non-cancer epidemiology data was reviewed during the September 2010 SAP 

meeting) and includes a brief summary of the Agency’s review of the pre-2003 data 

as well as a review of the epidemiology studies published since the 2003 IRED. 

• Section 4 describes the Agency’s proposed updates to the dose response 

assessment incorporating recommendations received during the September SAP 

meeting. 

• Section 5 discusses the scientific considerations in potential sensitivity of infants and 

children. 

• Section 6 discusses the Agency’s proposed approaches to evaluating water 

sampling strategies and frequency of monitoring. 

• Section 7 presents a case study integrating all the concepts discussed in the 

previous Sections and connects them to the water chemographs to get information

on lifestage/temporal/outcome specific risks using an internal dose metric. This 

Section will also illustrate how all these factors intersect to inform the FQPA Safety 

Factor determination.

2. MODE OF ACTION (MOA) & EXPERIMENTAL TOXICOLOGY

2.1 Historical Background

Over the past one and a half years, the Agency has conducted a comprehensive and 

extensive evaluation of atrazine. This review encompassing experimental toxicity data, 

pharmacokinetic data, epidemiological data, and water monitoring data was undertaken to 

determine if new data collected since the 2003 human health risk assessment needed to be 

updated. The 2003 human health risk assessment for atrazine is complex and 

incorporated significant data development, regulatory evaluation, and FIFRA SAP review. 

Prior to the 2010 re-evaluation, the Agency sought advice from the SAP on human health 

issues during three previous meetings (FIFRA SAP, 1988, 2000, 2003). Atrazine was first 

taken to the SAP for evaluation of rat mammary gland tumor response seen in adult cancer 

studies in 1988 (FIFRA SAP, 1988). At that time, the SAP noted that a “hormonal 

influence” might be an important consideration in the development of these mammary 

gland tumors in adult rats. The Agency returned to the SAP in 2000 (FIFRA SAP, 2000) for 

advice on atrazine’s MOA leading to mammary gland tumors, reproductive and 

developmental effects in rats, as well as the human relevance of these findings. 
Page 11 of 184

The SAP agreed with the Agency’s proposal for atrazine’s neuroendocrine MOA, and they 

further concluded that it is unlikely that the mechanism by which atrazine induces 

mammary tumors in adult female Sprague Dawley rats could be operational in humans. 

Based on the 2000 SAP guidance, EPA changed its position on atrazine and reclassified it 

from a “possible carcinogen” to “not likely to be carcinogenic in humans”. Nevertheless, the 

SAP further concluded that it is not unreasonable to expect that atrazine might cause 

adverse effects on hypothalamic-pituitary-gonadal (HPG) function in humans if exposures 

were sufficiently high and that the MOA was relevant for developmental and reproductive 

effects (FIFRA SAP, 2000). It is these developmental and reproductive effects of atrazine 

which have been the focus of significant research in the last few years. The Agency 

concluded, and the 2010 SAP concurred, that new research conducted in the last seven 

years supports the neuroendocrine MOA identified in the 2003 IRED and the conclusions 

regarding the human relevance of the mode of action for rat mammary gland tumors. As 

described in this draft issue paper, the critical and most sensitive adverse effects continue 

to be related to the reproductive/developmental consequences of perturbations to the HPG 

axis. 

In 2003, the Agency presented its evaluation on prostate cancer to the FIFRA SAP. At that 

meeting, the SAP evaluated the available epidemiology studies suggesting an association 

between atrazine and prostate cancer in workers at an atrazine manufacturing plant in St. 

Gabriel, Louisiana. The SAP concurred with EPA’s conclusion that the data did not 

suggest an association between atrazine exposure and prostate cancer, but that the 

increase in incidence of prostate cancer in workers at the plant could likely be explained by 

the increase in Prostate Specific Antigen (PSA) screening at the plant (FIFRA SAP, 2003). 

The SAP also stated that a more thorough and systematic review of the biologic and 

epidemiologic literature on the topic of the potential or observed carcinogenic effects of 

atrazine exposure was required before concluding that atrazine exposure is an unlikely 

explanation for at least part of the excess of prostate cancer in the St. Gabriel 

manufacturing plant. Review of the cancer epidemiologic studies with atrazine is now 

complete and the Agency is seeking the SAP’s comments on its evaluation and 

conclusions as part of this SAP meeting.

2.2 2010 Scientific Re-Evaluation

In preparation for the 2010 SAP meetings, the Agency reviewed more than 100 new 

experimental studies on a variety of topics including reproductive/developmental toxicity in 

males and females, neurotoxicity, immunotoxicity, mammary gland development, mixtures 

effects, andrology, aromatase and insulin resistance and mitochondrial function 

impairment. Additional studies have been reviewed since the September 2010 SAP. The 

Agency’s reviews on all these topics are provided in Appendix A. In addition summaries of 

the new literature are provided on each of the scientific areas in Appendix A. 
Page 12 of 184

In the 2003 IRED, the Agency identified perturbations of the neuroendocrine system 

(particularly LH regulation) leading to reproductive toxicity as the most biologically plausible 

and sensitive effects attributable to atrazine exposure. These adverse outcomes include 

disruption of estrous cyclicity and delays in puberty onset (males and females) occurring as 

a consequence of disruptions to the HPG axis in rats. An additional effect – not directly 

linked to LH disruption – is the finding of decreased suckling-induced prolactin release in 

milk early in life (perinatally) leads to increased incidence of prostatitis in young adult ratsis 

an atrazine-induced increase in prostatitis in animals exposed perinatally to atrazine. 

During the September 2010 SAP meeting, the Agency addressed the hypothesis that 

atrazine exposure during critical developmental stages may result in delays in mammary 

gland development (MGD). The SAP evaluated and agreed with the Agency’s review of 

the publicly available data that the significant limitations of these studies preclude definitive 

conclusions. The Panel, however, recommended that additional research be conducted 

including a comparison of the subjective scoring methodology used to assess MGD with an 

objective approach such as morphometric analysis. Agency scientists in the Office of 

Research and Development (ORD) have since conducted these studies and the results of 

this research are discussed in Appendix A.

2.3 Mode of Action (MOA) and Adverse Health Outcomes

2.3.1 Background

The Agency defines a MOA as “a sequence of key events and processes, starting with 

interaction of an agent with a cell, proceeding through operational and anatomical changes, 

and resulting in the adverse effect. The current evaluation of the post-2003 data supports 

the neuroendocrine MOA and key events originally identified in the 2003 IRED. In addition, 

new research has become available that extends our understanding of the neuroendocrine 

events that occur following atrazine exposure and that are germane to our understanding of 

the processes responsible for the adverse outcomes identified in different rodent models. 

Thus, this issue paper will briefly discuss atrazine’s established neuroendocrine MOA and 

then proceed to discuss how this MOA informs our understanding of the reproductive and 

developmental effects observed after atrazine exposure. 

 

For most pesticides, there is little information on the mode of action and even fewer with 

epidemiology studies that can be used in the risk assessment process. As such, the 

Agency makes assumptions about the relevance of animal findings to humans and 

quantitative animal to human extrapolation. In the case of atrazine, the wealth of data 

across many scientific disciplines allows for a highly refined assessment for atrazine using 

MOA understanding, human relevance of animal studies informed qualitatively by 

epidemiology studies, and refined analysis of critical durations of exposure. These 

refinements will continue to improve as the PBPK model currently being developed is 

refined.
Page 13 of 184

The 2003 IRED concluded that the most probable explanation for many of the cancer and 

non-cancer effects observed in laboratory species following atrazine exposure could be a 

disruption of LH release. In addition to the premature reproductive senescence and 

development of mammary gland tumors in the Sprague Dawley rat, the MOA also provides 

an explanation for the broader range of adverse outcomes attributed to atrazine exposure 

including altered ovarian function in the adult female, delays in the onset of puberty in both 

the male and female, impaired pregnancy maintenance (i.e., full litter resorptions) and 

premature reproductive senescence. Components of this MOA also provide an explanation 

for the decrease in serum testosterone observed at high experimental doses in studies of 

the male rat. In the sections below, the MOA for atrazine for the development of mammary 

tumors in the female rats is described followed by an evaluation of the role of LH level 

disruption in eliciting other adverse outcomes.

In the course of the current evaluation, the Agency identified an effect of atrazine on 

reproductive function in both male and female rats. These effects provide insight into 

evaluating the vulnerability of specific lifestages such as sexual development, puberty, and 

the perturbation of adult reproductive performance (including premature aging). These 

effects can be linked to the atrazine-induced changes in LH secretion. Consequently, the 

Agency will continue to use changes in LH secretion as the basis of the atrazine risk 

assessment. As such, any of the identified adverse outcomes would be protected since 

they occur at doses higher than those eliciting changes in LH (Figure 1).
Page 14 of 184

Figure 1: LH Suppression and Adverse Outcomes Observed in Rats

Atrazine-induced changes in the hormonal milieu lead to a cascade of effects on reproductive 

function in male and female rats. The decrease in LH is a precursor event to reproductive effects 

both on a quantitative (i.e., occurs at lower doses) and temporal basis (occurs after 4 days of 

exposure). An atrazine related suppression of suckling-induced prolactin release in the lactating 

dams, is another hormonal change leading to an adverse effect (prostatitis) in the rat animal model.

2.3.2 Reproductive Senescence and Mammary tumors in rats: Well 

established MOA

The key events initially postulated leading to the development of mammary gland tumors 

were described in the 2003 IRED. Briefly, this pathway begins with a perturbation of the 

brain’s ability to regulate LH secretion, a disruption of ovarian function and eventually the 

development of an endocrine environment that is favorable to the initiation of mammary 

gland tumors. This MOA is described below: 
Page 15 of 184

• Atrazine alters the secretion of LH through gonadotropin releasing hormone 

(GnRH) modulation: It is clear that atrazine impairs the secretion of LH from the 

pituitary. In the female rat, a number of reports describe the dose and time 

response for the suppression of the ovulatory surge of LH that typically occurs on 

the afternoon of vaginal proestrus (Cooper et al., 2007). It remains to be determined 

whether or not the changes in GnRH release are the result of a direct effect of 

atrazine (or its metabolites) on the GnRH neurons themselves, an indirect action of 

the chemical in other neurons that regulate GnRH activity, or both. Nevertheless, 

changes in GnRH neuronal activity represent a change in the key signal emanating 

from the central nervous system (CNS) and regulating gonadotropin (LH & FSH 

[follicular stimulating hormone]) secretion. This effect on the LH surge in the female 

rat is considered to be the primary reason that dietary atrazine leads to a premature 

reproductive senescence in this species.

• A loss of the LH surge is responsible for age-dependent disruption of ovarian 

cycles in the rat: The disruption of the positive feedback processes regulating the 

LH surge in the female rat is considered to be the primary cause of reproductive 

senescence in this species. In the young adult female, the LH surge is a key event 

in the ovarian cycle leading to ovulation. In the aging female (beginning 

approximately 1 year of age), the regular 4-5 day estrous cycles typical of the youngadult female gives way to a pattern of persistent or constant estrus. The transition 

from regular cycles to persistent estrus is the result of a loss of the ability of the 

female to sustain appropriate LH secretion. The preponderance of evidence 

indicates that the age-related impairment of LH secretion reflects the loss of the 

positive feedback mechanisms controlling the generation of the surge. This includes 

altered neurotransmitter and neuropeptide control of gonadotropin releasing 

hormone (GnRH) the neuropeptide that represents the final common signal from the 

brain regulating LH release. Thus, aging in the female rat is the result of an agerelated change in the hypothalamic (GnRH/LH) control of the reproductive cycle.

• Atrazine accelerates the rate of reproductive aging in rats: Similar to aging, the 

atrazine-induced changes in GnRH pulses lead to an attenuation of the proestrous, 

ovulatory LH surge (lower amplitude and reduced area under the curve). Therefore, 

it is not surprising that chronic exposure to dietary atrazine in young-adult females 

results in premature reproductive senescence. Similar to normal aging, these 

prematurely senescent females do not ovulate but develop persistent ovarian 

follicles which continue to secrete estradiol (i.e., polyfollicular ovaries). Since there 

is no ovulation, there are no corpora lutea in the polyfollicular ovary and thus 

progesterone secretion from the ovary is nil. At the same time there is increased 

prolactin secretion from the pituitary. It is this increase serum estradiol and prolactin 

and low serum progesterone concentrations that creates the endocrine milieu 

conducive to the development of mammary gland tumors.
Page 16 of 184

• Reproductive aging (menopause) in humans initiated differently than in rats. 

Unlike rats, reproductive senescence in humans (menopause) is caused by the 

depletion of follicles and a concomitant decrease in estrogen instead of changes in 

the LH surge (which remains normal during menopause). As described above, 

reproductive senescence in rodents is driven by changes in the regulation of LH at 

the level of the brain. Thus, the altered hormonal environment observed in the postreproductive female rat is not present in the human. In the absence of elevated 

estrogen levels in humans, prolactin levels remain constant or even decrease 

slightly. Consequently, there is no sustained prolactin stimulation of the proliferative 

processes in the mammary gland and thus no tumor development. Although there 

are similarities in hypothalamic/pituitary function, there is a fundamental difference 

between rats and humans, and thus, the key events in the MOA leading to 

mammary gland tumors in rats are not relevant for breast tumorigenesis in humans. 

2.3.3 Adverse Health Outcomes – LH Changes as a Sentinel Effect 

Although tumor development following chronic atrazine exposure is an adverse outcome 

mediated by a disruption of the neuroendocrine axis controlling reproduction in the female 

rat, this MOA for mammary tumor development is considered highly unlikely to occur in 

humans due to the different pattern of aging between the two species. However, it is 

plausible that the same initial atrazine-induced endocrine perturbations that induce tumors 

in rats (i.e., altered GnRH) could occur in humans. In fact, the Agency considered the 

atrazine-induced disruption of the LH surge, in rats, as the key event of the cascade of 

changes leading to the adverse reproductive outcomes following atrazine exposure. 

Relevant to this MOA, a number of recent studies have further characterized the cellular 

and neuroendocrine changes responsible for how atrazine interferes with the regulation of 

LH secretion. The preponderance of evidence provides added support for the hypothesis 

that atrazine modifies the hypothalamic (GnRH) control of pituitary function. The ovulatory 

surge of LH depends on the activity of GnRH neurons (Kalra and Kalra, 1983) with the 

afternoon rise in LH the result of modifications in GnRH pulsatile secretion into the 

hypothalamic portal system (Fox and Smith, 1985; Bergendahl et al., 1996; Veldhuis et al.,

2008). Research from two laboratories has shown that atrazine interferes with the pattern 

and amplitude of GnRH release by slowing the frequency of the pulses and lowering the 

overall amount of GnRH released into the portal system (Cooper et al ., 2007; Foradori et al

., 2009). It is important to note that the modulation of GnRH/LH during the peripubertal 

period is not limited to rodents but is seen across several species including primates, in 

which Terasawa and coworkers have demonstrated that all aspects of GnRH pulsatility 

(pulse amplitude, frequency, and mean GnRH release) are markedly increased during the 

peripubertal period (Terasawa et al., 1984).

Testing the hypothesis that atrazine-induced changes in the regulation of LH ultimately alter 

gonadal function in rodents, several studies reported adverse effects on reproductive 
Page 17 of 184

development and adult function including delayed puberty in both sexes (Stoker et al., 

2000; Laws et al ., 2000), disruption of regular ovarian cycles in the adult female (Cooper et 

al., 1996, 2000), inability to maintain pregnancy (Narotsky et al., 2001) and reduced 

testicular hormone secretion in the male (Stoker et al., 2000; Trentacoste et al., 2001; 

Rosenberg et al., 2007) after atrazine exposure. Of these potential adverse outcomes,

the two that appear to be the most sensitive (i.e. occurring at the lowest dose levels) and/or 

occurring after the shorter duration of exposure are the disruption of the ovarian cycles and 

the delays in puberty onset. 

2.3.3.1 Factors influencing GnRH Modulation of LH following atrazine 

exposure 

As indicated, the accepted mode of action (MOA) for the atrazine-induced impairment of LH 

secretion is a disruption in the central (CNS) mechanisms controlling LH release [(i.e., 

impaired gonadotropin releasing hormone (GnRH) release] (Cooper et al., 2007; Foradori 

et al., 2009). This MOA was reviewed and endorsed by the 2003 atrazine Scientific 

Advisory Panel (SAP). Since then, additional research from several laboratories (McMullen 

et al., 2004; Foradori et al., 2009) has provided further evidence supporting this MOA. Still, 

little is known about the exact molecular or neuronal targets responsible for an atrazineinduced change in GnRH/LH release. It is assumed that the changes in GnRH occur as a 

result of either (i) a direct effect of the atrazine and/or its metabolites on the GnRH neurons 

themselves, (ii) an effect on the neurons known to regulate GnRH (and thus affect GnRH 

cellular activity indirectly) or (iii) an effect on both the GnRH neurons and those neurons 

regulating GnRH activity. These three alternatives would imply that the CNS is the primary 

target for atrazine. Preliminary data suggest that atrazine exposure may lead to: (i) a 

decrease in c-fos in GnRH neurons (Foradori et al, 2009) or (ii) a disruption of 

catecholaminergic activity after in vitro exposure of undifferentiated PC12 cells [dopamine 

and norepinephrine have both been implicated in the generation of the LH surge] (Das et 

al., 2000). However, detailed studies examining any of the putative CNS mechanism 

controlling GnRH pulsatility are not available. 

2.3.3.1.2 GnRH in primates

The integral role of GnRH secretion in the function of the HPG axis is widely accepted in 

humans and non-human primates. Hypothalamic amenorrhea (abnormal absence of 

menstruation) is a prime example that impairment of pulsatile GnRH secretion can have an 

adverse functional impact on menstrual cycles. With our increasing understanding of the 

brain’s role in reproduction, the neuroendocrine similarities and disparities among 

mammalian species have become more apparent. Although there are anatomical 

differences in the distribution of the GnRH neurons, however the functional role of these 

neurons are similar in rodents and primates. In terms of intercellular connectivity, it 

appears that the regulatory input to human and nonhuman primate GnRH neurons is at 
Page 18 of 184

least comparable to those in rodents. With a few noted exceptions, the response of GnRH 

neuronal activity to the positive and negative feedback to endogenous and/or exogenous 

estrogen and progesterone is the same. It is noteworthy, that impairments in the activity of 

one or more of the factors regulating GnRH release (e.g., kisspeptin, galanin, and 

corticotrophin releasing hormone [CRH]) have been implicated with functional hypothalamic 

amenorrhea in women (Meczekalski et al., 2008). These observations support the use of 

the rat as a surrogate species for the evaluation of environmental toxicants on the 

regulation of the LH surge.

2.3.3.2 Females of Reproductive Age: Role of LH, Progesterone and 

Corticosterone in Atrazine Induced Changes in Ovarian 

Cyclicity 

In the previous sections, the evidence indicating that atrazine has a clear effect on the 

hypothalamic control of LH through regulation of the GnRH pulse generator was discussed. 

In this section these observations are discussed within the context of atrazine-induced 

changes in progesterone and corticosterone that adversely impact the steroid hormone 

feedback regulation of LH secretion. The significance of these atrazine-mediated changes 

stems from the observation that they occur both in vitro and in vivo through possibly 

different events in the toxicity pathway. In vitro work by Sanderson et al. (2000) and 

Hecker et al. using H295R cells demonstrated an atrazine-mediated increase in 

estradiol/estrone leading researchers to hypothesize that atrazine exerted its effect on 

steroidogenesis through alterations in aromatase activity. However, atrazine has also been 

shown to increase both progesterone (Tinfo et al., 2010) and testosterone (Pogrmic-Majkic 

et al., 2010) production in rat granulosa cells and primary rat Leydig cell cultures, 

respectively. In vivo, an immediate increase in the levels of circulating adrenocorticotropic 

hormone (ACTH) occurs in response to atrazine exposure. This in turn stimulates the 

production and release of progesterone and corticosterone from the adrenal cortex. 

Considered together, these data not only suggest that the effects of atrazine may involve 

several levels of biological organization beyond the HPG axis but that brief atrazine 

exposures (as brief as 4 days) elicit changes in the adrenal hormonal milieu that may have 

the potential to modify LH release. Whether these brief changes may result in adverse 

health outcomes, however, is not clear at this time. Nonetheless, it is important to note that 

the change in LH surge that serves as the basis for the atrazine risk assessment is 

considered a precursor event to the reproductive and developmental effects noted after 

atrazine exposure. 

A study recently submitted to the Agency was designed to evaluate the impact on fertility or 

reproductive performance following a brief (4-5 days) atrazine exposure at doses up to 100 

mg/kg/day (Coder 2011). In the study, Sprague Dawley rats were exposed either by 

gavage (≤ 100 mg/kg/day) or diet (≤51 mg/kg/day). The animals were then mated on the 

day of proestrous. No effects on reproductive performance (mating, fertility, or conception 
Page 19 of 184

indices) were observed at any dose level regardless of strain or method of administration 

(dietary or gavage). Moreover, no effects on pre-implantation loss, implantation sites, fetal 

development and/or offspring survival were reported. Based on these observations, the 

author concluded that “the magnitude of the atrazine-induced preovulatory surge 

suppression shown in previous studies was not sufficient to influence ovulation or 

reproductive performance of SD or LE female rats as evaluated in this study.” However, 

the interpretation of these data is confounded. LH measurements were not conducted as 

part of this study; although it is reasonable to assume the LH surge would be attenuated at 

the dose levels administered. The absence of these data, however, does introduce a 

confounding factor to the study’s interpretation since it is known that mating rats on the day 

of proestrous causes an LH surge due to coital stimulation causing the rats to become 

reflex ovulators and resulting in normal pregnancies. Furthermore, it has been previously 

shown with other chemicals affecting the LH surge (e.g. pentobarbital) that steroid hormone 

regulation of mating behavior in rats is not affected by this treatment. 

In a study intended to replicate the work by Cooper et al. (2007) and to assess the effects 

of brief atrazine exposure (4-14 days) on the LH surge, ovariectomized Sprague Dawley 

rats received atrazine via gavage at 0, 6.5, 50, and 100 mg/kg/day for 4, 8, or 14 days 

(Coder, 2011). A statistically significant decrease in LH was observed after 4 days of 

atrazine exposure at doses ≥ 50 mg/kg/day. Though a statistically significant decrease in 

LH was also noted at the 6.5 mg/kg/day dose level, the authors discounted it because there 

was no effect in the AUC for this measure. However, when a BMD analysis is conducted 

on these data, the BMDL is 3.4 mg/kg/day. Thus, the Agency considered the decrease in 

LH at 6.5 mg/kg/day to be treatment-related. A longer atrazine exposure (8 or 14 days) 

also resulted in a statistically significant decrease in LH. Furthermore, after 14 days of 

exposure the number of animals exhibiting a lengthened estrous cycle (6-7 days vs 4 days 

control) increased (16-20%) at doses ≥ 50 mg/kg/day. 

2.3.3.2.1 Estrogen and progesterone regulation of LH secretion 

Estradiol and progesterone have long been known to play coordinate roles in the 

generation of the LH surge (Mann & Barraclough, 1973). In a female rat exhibiting a 

normal 4 day estrous cycle, estradiol begins to rise on diestrus day 2, reaching a midday 

apex on the following day of proestrus. This increase serves to increase the 

responsiveness of the hypothalamic mechanisms that promote the secretion of GnRH into 

the portal vessels and the subsequent surge of LH on the afternoon of proestrus. One 

outcome of such an increase in estrogen is the increase in progesterone receptors within 

the positive feedback sites of the hypothalamus. Serum progesterone concentrations 

increase beginning early on the afternoon of vaginal proestrous within this sequence, a rise 

in estrogen followed by a rise in progesterone which act together to generate a surge 

(Brown-Grant and Naftolin, 1972). Importantly, it should be noted that in addition to ovarian 

derived progesterone, the adrenal cortex contributes a significant portion of the serum 

progesterone at this time (Feder and Ruf, 1968; Shaikh, 1974). As atrazine was found to 
Page 20 of 184

stimulate adrenal progesterone and corticosterone release, the possibility that these brief 

changes in the adrenal steroid milieu could be a part of the atrazine MOA for LH 

suppression was examined in a rat study conducted by EPA’s Office of Research and 

Development (ORD) . The background and rationale for evaluating the adrenal effects are 

presented below followed by a summary of the study. 

Estradiol alone can initiate a surge, as demonstrated in ovariectomized controls implanted 

with silastic capsules containing this steroid (see blue Figure 2). The time course for this 

induction is several days. Over this period, estradiol stimulates an increase in 

hypothalamic and pituitary progesterone receptors (Camp and Barraclough, 1985; Brown et 

al., 1987; Turgeon and Waring, 2000), which is essential for any subsequent effect of 

progesterone on the LH surge. In contrast to the effect of estradiol, progesterone alone is 

unable to induce a surge (e.g., Caligaris et al., 1971). However, when combined with 

estradiol, progesterone can either enhance or suppress LH secretion depending on the 

timing and sequence of exposure to these two hormones. The time course for an 

influence of progesterone under estrogen priming is different. In ovariectomized female

rats, primed with estradiol for 72 h, progesterone exposure around 10 AM in the morning of 

the surge, will act synergistically with the estrogen to cause a large augmentation in LH 

(see red line Figure 2). This synergistic effect of progesterone is dependent on the 

estrogen-induced increase in progesterone receptors mentioned previously. This effect is 

well-known in rodents (e.g., Caligaris et al., 1968; Brown-Grant and Naftolin, 1972; Banks 

and Freeman, 1978; 1980) and non-human primates (e.g., Terasawa et al., 1984) and has 

been identified in women. In fact, Odell and Swerdloff (1968) found that a progestin 

injection in post-menopausal women previously treated with ethinyl estradiol could induce a 

surge-like elevation in circulating LH in samples taken 24 hours later. 

In contrast to the augmentation in the LH surge by a progesterone exposure that 

immediately precedes an estradiol-induced rise in LH, longer exposure is essentially 

inhibitory to the surge (e.g., Caligaris et al., 1971; Banks & Freeman, 1978; 1980; Prilusky 

et al., 1984). Such extended treatment will cause a down-regulation by progesterone of its 

own receptors which appears to be a factor underlying the suppressive effect on LH 

(Turgeon and Waring, 2000). This influence of progesterone is the basis for the action of 

many oral contraceptives. Furthermore, increased levels of progesterone during a 

woman’s pregnancy, secreted first from the ovaries and subsequently from the placenta, 

prevent the appearance of a surge and ovulation over that time. Finally, it is noteworthy 

that treatment with progestins also lowers serum gonadotropins and testosterone 

production in men (e.g., Amory, 2004). 
Page 21 of 184

40

0

-6 -4 -2 Peak +2 +4 +6

Hours from Peak

30

20

10

OVX- Estradiol Implant / sc Progesterone

E only

E + P

Figure 2: Augmentation by a subcutaneous injection of progesterone on an LH surge in 

ovariectomized rats implanted with an estradiol capsule at the time of surgery. Progesterone was 

administered just prior to the sampling of small aliquots of blood by tail nick over the afternoon of the 

third day after ovariectomy. Concentrations are indicated in ng/ml ± SEM

2.3.3.2.2 Differential effects of single versus multiple atrazine 

exposures on the LH surge 

During the September 2010 SAP, the Panel raised the issue of single vs multiple atrazine 

exposure effects on the LH surge. In their report to the Agency the SAP commented:

“Data are clear in identifying that a greater-than-one pulse of exposure to atrazine is 

necessary for attenuation of the LH surge. For example, single high doses (over 100 

mg/kg) administered on the morning of proestrus did not alter characteristics of the 

LH surge occurring later the same day. Additional data clearly demonstrate a once 

daily dose for 4 days and beginning on estrus can induce significant inhibition of the 

LH surge peak. In this instance, a dose response is observed. However, what is not 

clear is if less than 4, but greater than 1 days’ exposure is sufficient to alter the LH 

surge. Further complicating the matter, it is not clear if a 4-day exposure, beginning 

on a different day of the cycle, will result in changes in the LH surge similar to those 

when dosing begins on the morning of proestrus. Understanding of the relationship 

between duration of exposure and phase of the cycle will be key in translating rodent 

data to humans for risk assessment purposes.”

In response to the Panel’s comments, EPA scientists in the Office of Research and 

Development have undertaken a series of experiments to try to elucidate the nexus 

between phase of the cycle and duration of exposure. This research, is in the early stages. 

However, initial results suggest that, unlike previous assumptions, a single high dose of 

atrazine (100 mg/kg bw) can affect the LH surge. Notably, the single atrazine exposure 

leads to an increase in LH instead of the decrease that is seen after 4 days of exposure.
Page 22 of 184

As noted above the biphasic effect of progesterone and the suppressive effect of 

corticosterone on the LH surge suggested that these two hormones may also play a role in 

the suppression of the LH surge following a 4-day exposure to atrazine. That is, the 

demonstrated atrazine-induced increase in these two adrenal hormones could modulate 

the secretion of LH by feeding back onto both the positive and negative control of the 

GnRH pulse generator. Indeed, if this were the case, it would be expected that a single 

dose of atrazine administered to the ovariectomized, estrogen-primed rat would first 

increase the LH surge because of the immediate increase in adrenal progesterone induced 

by the atrazine treatment. In contrast, repeated administration of atrazine (i.e., 4 days) to 

the same ovariectomized, estrogen primed female would result in a decrease in the surge 

because of the repeated increases in adrenal progesterone and corticosterone levels 

immediately following each exposure to this herbicide. Figure 3 demonstrates that this is 

what happens in the ovariectomized model. In response to a single exposure to atrazine 

the amplitude of the LH surge and the area under the curve were both significantly 

increased. It should be noted that these preliminary data are only available at high doses 

(100 mg/kg/day) far exceeding the dose levels currently being used for risk assessment 

purposes. Research to further elucidate the dose-response curve for this acute effect is 

ongoing.
Page 23 of 184

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4

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Ovariectomized / E2 prime - Atrazine 1 Day

Time Relative to Peak

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100

(11)

(10)

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*

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Atrazine (mg/kg)

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*

1 Day Dosing Atrz (1300h)

sampling 1400-1600-1800-2000h 

OVX / E2-implant

Serum LH (ng/ml)

Area Under the Curve

Serum LH (ng/ml)

181%

-4 -2 Peak +2 +4

0

1

2

3

4

5

6

7

8

9

10

Ovariectomized / E2 prime - Atrazine 4 Days

Time Relative to Peak

0

100

(10)

(10)

mg/kg

0

5

10

15

20

25

30

Atrazine (mg/kg)

0 100

*

35

40

Serum LH (ng/ml)

Area Under the Curve

Serum LH (ng/ml)

*

4 Days Dosing Atrz (1300h)

sampling 1400-1600-1800-2000h 

OVX / E2-implant

p= 0.06

54%

Figure 3: The LH surge (ng/ml + SEM) and areas under the curve in response to 1 and 4 days of 

atrazine treatment in ovariectomized / estradiol-primed Long-Evans rats. The numbers in 

parentheses indicate the group sizes for each of the treatments. Numbers within the 100 mg/kg 

columns at the right indicate the percent change from the 0 mg/kg group. *p<0.05.

Also, as predicted from our understanding of the continued exposure to progesterone and 

corticosterone, four daily doses of atrazine resulted in a significant decrease in LH 
Page 24 of 184

secretion. Again, this decrease was associated with an atrazine-induced increase in both 

progesterone and corticosterone. The details of this study are provided Goldman et al., 

2011 (Appendix A).

In summary, these data demonstrate two important factors pertaining to the temporal 

response to atrazine exposure. First, a single high dose of atrazine (100 mg/kg bw) affects

the HPG axis by increasing the amount of LH secreted during the afternoon LH surge. The 

fact that this same single dose of atrazine increases adrenal hormone secretion (primarily 

progesterone) would explain the enhancement of the surge. Second, multiple (4 daily) 

doses of atrazine resulted in an attenuation of the LH surge. Again, as atrazine was found 

to induce an immediate and significant increase in progesterone and corticosterone 

secretion following each dose (Fraites et al., 2009; Goldman, 2011), it is important to 

consider the implications of a brief LH surge attenuation.

2.3.3.2.3 LH Attenuation in Humans – Pharmaceutical Experience

Little is known about the direct effects of atrazine in humans and available epidemiology 

studies are not sufficiently robust for making strong conclusions. However, one can infer 

information about possible windows of susceptibility from what is known about human 

physiology and from the pharmaceutical literature. In considering the relevance and 

temporal concordance of a toxic effect in a test species to human health, a variety of 

factors need to be considered. Among them is the potential window of susceptibility to the 

toxic effect. For atrazine, the experimental data available indicate that, in rats, a 4-day 

exposure is sufficient to attenuate the LH surge – the most sensitive endpoint in the 

atrazine risk assessment. Since the estrous cycle in rats is 4 days long, this window of 

susceptibility is not unexpected. However, in humans, the menstrual cycle lasts – on 

average – 28 days. Thus, the question arises as to whether a brief exposure (e.g., a few 

days) in humans could lead to an attenuation of the LH surge. 

Evidence of chemically-induced decreases in GnRH or LH secretion is sparse in humans 

and non-human primates relative to rodents. The available evidence in humans comes 

primarily from the pharmaceutical arena. Nal-Glu, Cetrorelix®, and Ganirelix are three

GnRH antagonists used to block the LH surge and ovulation in women prior to in vitro 

fertilization (IVF) procedures. In a series of experiments, regularly ovulating women 

received two 5 mg injections of Nal-Glu on days 8 and 11 of the follicular phase of the 

natural cycle (Frydman et al. 1992). This treatment resulted in a block of the spontaneous 

LH surge. This work was further corroborated by Olivennes et al. who demonstrated that a 

single 3 mg administration of the GnRH antagonist Cetrorelix® on day 8 of the follicular 

phase was sufficient to block the LH surge. Ganirelix exposure during the late follicular 

phase of the menstrual cycle has also been demonstrated to inhibit the LH surge and 

ovulation by competing with the endogenous GnRH for receptor binding (Fauser et al.,

2002). One must consider these studies with caution with respect to how they relate to 

potential effects of atrazine since the potency and pharmacokinetics of these 
Page 25 of 184

pharmaceuticals relative to atrazine is unknown. However, these studies do help 

qualitatively inform a potential window of vulnerability to chemicals disrupting the HPG axis 

in women. Specifically, all of these pharmaceutical agents are administered during the late 

follicular phase of the menstrual cycle (days 8-12 of the follicular phase)2

. Thus, one can 

infer that the follicular phase (lasting ≈12 days) and possibly the late follicular phase (days 

8-12 of the follicular phase) of the menstrual cycle may be a possible window of 

susceptibility in humans. 

Given the differences between the rat and human ovarian cycles, as well as the differences 

in potency and likely MOA of pharmaceutical agents, these data are only considered in the 

context of helping to identify if there is a time period during the menstrual cycle that is 

particularly vulnerable to chemicals interfering with the LH surge. It is not intended to draw 

any correlations between the mode of action, dose levels, or frequency of dosing needed to 

elicit the LH suppression after exposure to a pharmaceutical agent or atrazine. Although 

theoretically possible, a 4 day exposure to atrazine leading to LH surge attenuation in 

humans and an adverse health outcome is unlikely, given the available dose response, 

pharmacokinetics, and exposure data. As discussed in Section 4 of this paper, the area 

under the plasma concentration-time curve (AUC) for atrazine and its chlorinated 

metabolites is the critical dose metric. Based on the available drinking water monitoring 

data, it is highly unlikely that the plasma AUC associated with the LH surge attenuation 

would be achieved in such a short period of time (i.e., a high concentration of atrazine 

and/or its metabolites would have to be present in the drinking water). Although a 4 day 

atrazine exposure resulting in LH suppression is a conservative assumption, the Agency is 

proposing to bound the critical window of exposure from 4 to 28 days as potentially relevant 

to potential human health outcomes. As the September SAP noted in their report, “this 

may be about as precise an estimate as can be obtained when starting with the 

experimental animal data and the exposure requirements for LH surge suppression as 

opposed to using outcomes that are more unequivocally adverse.”

2.3.3.3 Prepubertal Individuals: Atrazine-induced delays in sexual 

maturation

In addition to the disruption in ovarian cyclicity, atrazine exposure has also been implicated 

in delays in sexual maturation in both males and females following both perinatal and 

peripubertal exposure. Pubertal development is directly related to the progressive 

increases in the neurosecretory activity of GnRH neurons. In female rats, sheep, monkeys, 

and humans (Grumbach, 2002), detailed analyses of peripubertal LH secretory patterns 

have been conducted to provide surrogate measures of GnRH release throughout pubertal 

maturation. These studies have revealed that the initial stages of pubertal maturation are 

mediated by an acceleration of GnRH pulse generator activity (GnRH pulse frequency), an 

increase in the amplitude of GnRH pulses, or both of these alterations in GnRH 

 2 In humans, the follicular phase lasts approximately 12 days, assuming a 28-day menstrual cycle
Page 26 of 184

neurosecretion. Terasawa and colleagues have directly measured GnRH release patterns 

in female rhesus macaques and determined that all features of GnRH pulsatility (pulse 

amplitude, pulse frequency, and mean GnRH release) are significantly increased 

throughout pubertal maturation in this species. The work of Sisk et al. (2001) in the rat are 

consistent with the hypothesis that maturation of the female rodent’s reproductive axis is 

dependent upon a pubertal increase in GnRH pulse generator activity and a progressive 

increase in the ability of the hypothalamus to generate surge-like releases of GnRH. The 

cellular and molecular mechanisms mediating the pubertal acceleration of GnRH pulsatility 

and the stimulation of putative GnRH mini-surges remain to be resolved. 

2.3.3.3.1 Delays in Female Sexual Maturation

A number of studies have been conducted to evaluate the impact of atrazine and/or its 

metabolites on pubertal development and estrous cyclicity in female rats (Laws et al., 2000, 

2003; Ashby et al., 2002; Davis et al., 2011; Rayner et al., 2004). Collectively, these 

studies have shown that atrazine delays the onset of puberty, as measured by a delay in 

the age of vaginal opening (VO) and first estrus (Safranski et al., 1993). These findings are 

consistent with the postulated neuroendocrine MOA for atrazine. Female sexual 

maturation is the culmination of a complex cascade of cellular events at the hypothalamuspituitary-gonadal (HPG) levels that ultimately lead to the attainment of reproductive 

capacity. Activation of the HPG axis, resulting in the pulsatile secretion of GnRH that 

triggers a precisely regulated hormonal cascade of gonadotropins (LH and FSH) and 

ovarian steroids, is critical to puberty onset. Disruption of GnRH and LH release can lead 

to delays in pubertal development. 

Peripubertal exposure to atrazine and/or DACT for 19-23 days delays pubertal 

development in female rats at doses ≥ 34 mg/kg/day (Laws et al., 2000, Ashby et al. 2002, 

Laws et al. 2003). In some instances animals failed to attain VO by the time of their 

scheduled necropsy on PND 41. Interestingly, these delays in VO were not accompanied 

by lower body weights. Thus, the delays do not appear to be related to generalized growth 

delays. Gestational exposure to high doses of atrazine (100 mg/kg/day) during late 

gestation (GD 14-21) have been shown to delay sexual maturation of female offspring, 

however, exposures to lower doses (≤ 20 mg/kg/day) does not affect the age of pubertal 

onset. A recent study by Davis et al. (2011) evaluated the effects of prenatal exposure to 

atrazine on pubertal and postnatal reproductive indices in the female (Sprague Dawley) rat. 

Exposures from GD 14-21 at doses ranging from 1-20 mg/kg/day did not elicit a delay in 

VO or the timing of the first estrus. However, at 100 mg/kg/day atrazine exposure led to a 

significant decrease in pup weight (seen at birth but resolved by PND21) and most 

importantly a delay in VO. These results are consistent with the observations by Rayner 

and coworkers (2004) that atrazine exposure via the milk of dams exposed to 100 

mg/kg/day during GD15-19 led to a delay in VO without affecting estrous cyclicity once 

sexual maturation was reached. 
Page 27 of 184

While delays in female puberty onset – as determined by the time of VO – occur at doses ≈ 

20 times higher than the doses resulting in disruption of the LH surge, it is important to note 

that the duration of exposure sufficient to cause delays in VO ranges between 5 (prenatal 

exposure) and 23 days (peripubertal exposure). Thus, using the point of departure for the 

LH surge attenuation as the basis for the risk assessment is protective of this effect. These 

data also suggest that the critical duration of exposure that could lead to this effect –

although at high doses – can be as brief as 5 days. 

2.3.3.3.2 Delays in Male Sexual Maturation

Over the last decade a number of studies demonstrated that atrazine delays male puberty 

in different strains of rats following both peripubertal and perinatal exposure (Stoker et al., 

2000; Friedmann, 2002 ;Trentacoste et al., 2001; Rayner et al., 2006 and Rosenberg et 

al., 2008; Pogrimic et al., 2009). These studies support the hypothesis that impaired 

reproductive development is the result of an apparent delay in the maturation of the GnRH

pulse generating mechanism and lower LH concentrations leading to insufficient stimulation 

of the gonads during the period that puberty would normally occur. The low testosterone 

concentrations result in delayed maturation of the androgen dependent sex accessory 

tissues. 

A reduction in testosterone levels following atrazine exposure has been reported in a 

number of studies in mammals, as well as other species revealing a consistency in the 

effects of atrazine on androgens. It is well known that the development of the size of the 

penis and cornification of the epithelium of the prepuce and preputial separation in 

immature rats are regulated by androgens (Marshall, 1966). A decrease in testosterone 

secretion during the juvenile period can delay preputial separation (Lyons et al., 1942) and 

reduce the size of the androgen-dependent tissues, such as the ventral prostate and 

seminal vesicles. Normally, testosterone levels rise gradually from PND 20-40, and 

abruptly double by PND 50 (Matsumoto et al., 1986; Monosson et al., 1999). 

In the male rat, atrazine exposure resulted in delays in the onset of puberty, as determined 

by assessment of preputial separation (PPS) . Stoker et al. (2001) found that peri-pubertal 

exposure to atrazine delayed puberty for 1.5 to 2.5 days, and decreased the size of seminal 

vesicles and prostate. At PND 45, intratesticular testosterone was also decreased. In the 

studies examining the Sprague Dawley (SD) rat (Trentacoste et al., 2001), decreases in 

ventral prostate and seminal vesicle weights were observed along with 3-4 day delays in 

puberty onset. In addition, Friedmann (2002) found a decrease in intratesticular and serum 

testosterone following exposure to the peripubertal male SD rat. Thus, all three studies 

found decreases in either serum or intratesticular testosterone and Trentacoste et al. also 

found a decrease in LH. It should be noted that all these effects were observed at doses 

that were at least one order of magnitude higher than the NOAEL for LH surge attenuation 

currently used for risk assessment purposes.
Page 28 of 184

Ventral prostate, seminal vesicles, lateral prostates and epididymal weights are significantly 

reduced at doses just above the doses which delay preputial separation (Stoker et al., 

2001). These effects provide further evidence of a decrease in androgen stimulation. 

Decreases in androgen hormone concentrations also correspond with the effects observed 

on pubertal endpoints. For example, Stoker et al., 2001 demonstrated that intra-testicular 

testosterone was significantly decreased following atrazine exposure peripubertally. This 

has corresponded to the work of others showing that serum testosterone is decreased in 

SD rats when dosed during a similar period of PND 22 to 47 (Trentacoste et al., 2001; 

Friedmann, 2002).

The study by Stoker et al., 2002 also examined the effect of the atrazine metabolites, 

DACT, DIA and DEA on pubertal development in the rat. In this study, equimolar doses of 

the metabolite were compared to the effect seen with atrazine in the earlier study (Stoker et 

al., 2000). PPS was significantly delayed by DEA, DIA, and DACT. When the males were 

sacrificed on PND 53 treatments with DEA, DIA or DACT at atrazine equivalent dose (AED) 

≥50 mg/kg/day, caused a significant reduction in prostate weight. Seminal vesicle 

epididymal weights and serum testosterone levels were also reduced This study 

confirmed that the chlor-s-triazine metabolites of atrazine also result in similar effects on 

pubertal development. 

It should be mentioned that other factors, in addition to those changes within the HPG, can 

affect the timing of puberty in the male rat including nutritional status (leptin/body weight) 

and environmental (pineal gland/secretion of indole amines) influences (Ebling and Foster, 

1989).3

 Nutritional status and body weight are known to have effects on puberty, which are 

suggested to reflect the metabolic signals in the brain that serve as indices of the metabolic 

state. These metabolic alterations associated with weight loss or decrease in growth rate 

are inhibitory to the reproductive system and may be related to substances in the body that 

can alter the release of GnRH such as insulin, amino acids necessary for precursors of 

neurotransmitter synthesis and essential fatty acids (Ojeda and Urbanski, 1994).

The peripubertal exposure male study (Stoker et al., 2000) using Wistar rats showed 

decreased body weight at the 200 mg/kg dose, but a pair-fed group was included to discern 

the effects of changes in growth. There were changes in puberty at doses in which no 

body weight decreases were observed. However, in a study with Sprague Dawley rats 

Trentacoste et al., found that the food restriction group (receiving approximately 10% less 

food which was equivalent to the 100 mg/kg atrazine treatment food intake) showed the 

same effects on LH, testosterone and reproductive tract development. A recent paper 

examined the effects of food restriction on the endpoints in the pubertal male exposure 

 3 The direction of change in daylength provides the seasonal time cue for the timing of puberty in many

mammalian species. The pattern of melatonin secretion from the pineal gland transduces the environmental 

light/dark cycle into a signal influencing the neuroendocrine control of sexual maturation.
Page 29 of 184

period and found that reductions in body weight up to 10% did not influence pubertal onset 

or the growth of androgen dependent tissues in the rat (Laws et al., 2007). 

Perinatally a single 100 mg/kg atrazine dose to the dam resulted in an effect on pup growth 

that may involve changes in milk quality, as the offspring showed a decreased weight of 7% 

on PND 4 (Rayner et al., 2006). Work by Rosenberg and co-workers (2008) showed that 

pre-natal (GD14-parturition) exposure to atrazine at doses ≥10 mg/kg/day led to a 

significant delay in preputial separation (1-3 days) while anogenital distance on PND21 

albeit at significantly higher doses. Measuring testosterone levels in both the serum and 

intratesticular fluid of PND 60 rats revealed that testosterone levels were decreased at 

doses ≥ 50 mg/kg/day. It should be noted that all of the effects on PPS and AGD occurred 

in conjunction with nonsignifcant decreases in pup body weights (Rosenberg et al.,2008). 

These alterations of early postnatal body weight could play a role in the delay in puberty in 

these male offspring. 

In summary, the MOA of altered pubertal progression in the rat and decreased androgen 

production by the testes should be assumed to be relevant to humans, as GnRH and the 

regulation of pituitary hormones stimulate the gonadal secretion of steroids and is critical 

for the initiation of the set of events and time course of the HPG regulation of puberty in the 

human. All the effects on sexual maturation and altered androgen status reported after ≈ 

30 days of exposure occur at dose levels ≥ 5X higher than those leading to the LH surge 

disruption which serves as the basis of the Agency’s current risk assessment.

2.3.3.4 Atrazine-associated decrease in prolactin and prostatitis in 

young rats

In rodents, non-bacterial prostate inflammation is typically noted in older males (e.g. greater 

than one year of age) and can be induced with elevated prolactin concentrations 

(hyperprolactinemia) (Tangbanluekal and Robinette. 1994). In 1999, Stoker et al. reported 

an increase in prostatitis in the male offspring of mothers exposed orally to atrazine from 

PND 1 to 4. This effect was not linked to an alteration to LH, but rather the atrazine related 

suppression of suckling-induced prolactin release in the lactating dams. An increase in the 

incidence of prostatitis was observed in the 120 day old male offspring of dams treated with 

atrazine (≥ 12.5 mg/kg/day) from postnatal day 1-4. An increase in the incidence of 

prostatitis was also reported by Rayner et al. (2007) dams were exposed to 100 mg/kg/day 

atrazine during GD 15-19. The dose level eliciting the increase in the incidence in prostatitis 

in the offspring is > 10-fold higher than the dose leading to the LH surge attenuation used 

as the basis for the Agency’s risk assessment. 

In order to understand the significance of this observation, it is necessary to understand the 

development of the tuberoinfundibular dopaminergic (TIDA) neurons located within the 

hypothalamus and their role in regulating prolactin secretion in the adult. Prolactin plays a 

crucial role in the neonatal brain for normal TIDA neuron development. In the adult 
Page 30 of 184

offspring, the impaired TIDA regulation is reflected by elevated prolactin levels 

(hyperprolactinemia) (Shyr et al., 1986, Stoker et al., 1999; 2000). It is this elevated level 

of circulating prolactin in the adult males that has been linked to an increased incidence of 

prostatitis. Thus, an increased incidence of prostatitis in the offspring of dams exposed to 

atrazine during the critical time for TIDA neurons activation (first postnatal week) may be 

attributed to elevated blood prolactin concentrations due to impaired TIDA neuronal 

maturation (Stoker et al., 1999). In summary, the data indicate that atrazine induces 

prostatitis at doses ≥ 12.5 mg/kg/day and that – in rats – early postnatal exposure is a 

critical window of susceptibility to this effect. 

Stanko et al. (2010) evaluated reproductive development in male rats exposed prenatally to 

either atrazine or an atrazine metabolite mixture (AMM). The authors reported an increase 

incidence of prostatitis at doses ≥ 0.9 mg/kg AMM and at 100 mg/kg atrazine. This study is 

the companion study to the Enoch et al. (2007) study. While Enoch et al. assessed the 

female offspring of dams exposed during GD 15-19, Stanko and coworkers evaluated the 

male offspring. Therefore, the limitations identified by the Agency – and confirmed by the 

September 2010 SAP – in the Enoch et al. publication are also applicable to Stanko et al. 

The Agency concluded that the shortcomings in the study design, data reporting and 

analysis compromise the interpretation of the studies. A more detailed description of this 

study is available in Appendix A of this document.

In rats, the dorsal and lateral prostates are the most homologous to the human prostate 

(Price, 1963) and lateral prostate is most sensitive to prolactin, so the rat is a good model 

to study the effects of disrupted prolactin on the developing prostate. It is unknown when 

the TIDA neurons develop in the human fetus or whether this development is dependent on 

the maternal prolactin concentrations (Ben-Jonathan et al., 2007 review). As such, it is 

unclear what – if any – is the impact of atrazine exposure on developing prostatitis in 

humans.

2.3.4 Mammary gland whole mounts

Rodent mammary glands are widely used as experimental models for the human breast, 

and the pre- and postnatal developmental windows of the mammary glands lend several 

advantages for studying disruptions in normal development (Fenton 2006). The effects of 

prenatal atrazine exposure on mammary gland development in the rat have been 

investigated by several groups with conflicting results. Enoch et al. (2007) reported that 

Long Evans rats prenatally exposed to atrazine metabolite mixtures as low as 0.09 mg/kg/d 

during late gestation (GD 15-19) exhibited a persistent disruption in mammary gland 

development up until postnatal day (PND) 60. Similarly, Rayner et al. (2005) found that 

regardless of the length of gestational exposure (the longest being GD 13-19), 100 mg/kg 

atrazine stunted mammary gland growth up through PND 67. In contrast, Hovey et al. 

(2011) observed no differences in mammary gland morphology in Long Evans rats exposed 

to atrazine from GD 13-19. There were, however, differences in the methods of evaluation 
Page 31 of 184

employed in these studies. Enoch et al. (2007) and Rayner et al. (2005) using a subjective 

scoring method that relied on a holistic assessment of the mammary tissue, while Hovey et 

al. (2011) utilized a quantitative approach. A brief review of this study is available in 

Appendix A of this document.

In response to a recommendation during the September 2010 SAP meeting, the Agency 

has conducted a set of experiments investigating the potential impact of in utero atrazine 

exposure on mammary gland development in Sprague Dawley rats using both the 

subjective scoring methodology described by Enoch et al. and a computer-based 

quantitative methodology (morphometric analysis) (Davis et al. 2011). Mammary gland 

development was assessed using whole mounts of the 4th abdominal mammary gland of 

PND 45 old pups. PND 45 pups were selected based on consensus guidelines established 

following a recent 2009 workshop 

(http://www.silentspring.org/pdf/our_research/MammaryGlandWholeMountRoundRobinSu

mmary.pdf). No differences in the quantitative measures or in the subjective scores were 

found indicating that gestational atrazine exposure had no demonstrable effect on normal 

mammary gland development.

2.3.5 Summary

The neuroendocrine MOA of atrazine leads to a perturbation of the hormonal milieu in 

laboratory animals. This perturbation – in turn – leads to a series of adverse outcomes at 

different lifestages as observed in rats. Quantitatively, the most sensitive effect is the ≈ 

33% LH surge attenuation (BMDL = 2.56 mg/kg/day) observed after female rats of 

reproductive age are exposed to atrazine for 4 days. The Agency is using the 33% LH 

surge attenuation after a 4 day exposure as a precursor event to protect for other adverse 

outcomes including estrous cyclicity disruption, and delays in sexual maturation occurring 

at higher doses in laboratory animals. 

In the case of atrazine, it has been noted that in addition to dose, duration of exposure is an 

important parameter that must be considered in evaluating the relationship between dose 

and attenuation of the LH surge. Currently available data indicate that a 4-day exposure is 

sufficient to elicit a decrease of the LH surge in rats. Interestingly, this is the length of the 

estrous cycle in rats and also the exposure duration needed for atrazine to reach pseudosteady state – a topic that will be discussed in greater detail in Section 4 of this draft issue 

paper. Preliminary data, however, suggest that in rats even shorter atrazine exposures can 

result in LH changes, albeit at high doses (100 mg/kg/day). Other effects of concern, such 

as delays in puberty onset and decrease in suckling-induced prolactin release and 

eventually prostatitis in young rats, identified in the animal toxicity database occur at higher 

doses but have a different temporal profile compared to the LH surge attenuation. For 

instance, prostatitis can be seen in rats exposed to 12.5 mg/kg/day of atrazine for 3 days 

shortly after birth. Similarly, atrazine-induced delays in puberty onset have been reported 

in both peripubertal male and female rats after exposures to atrazine (≥12.5 mg/kg/day) for 
Page 32 of 184

approximately 20-30 days. Although drawing a direct temporal correlation between the 

effects seen in the rat animal model and potential human health outcomes is not feasible at 

this time, it is prudent to consider the possibility of a critical temporal window of < 90 days 

(the current rolling average drinking water concentration). This may have an impact on the 

frequency of drinking water monitoring that may be required. In Section 7 of this issue 

paper the Agency will present a case study integrating the animal toxicity, pharmacokinetic 

and water monitoring data to illustrate the relationships between critical windows of 

exposure and the sampling frequency needed to assess whether atrazine levels exceed the 

Agency’s level of concern for critical intervals.
Page 33 of 184

3. EPIDEMIOLOGIC EVIDENCE OF THE CARCINOGENIC POTENTIAL OF ATRAZINE 

3.1 Introduction

3.1.1 Background

This section provides EPA’s review of recently published (September 2003 – May 2011) 

epidemiologic evidence of the carcinogenic potential of the herbicide atrazine. This review 

updates EPA’s analysis of this observational database presented in two previously 

convened FIFRA science advisory panels (SAP): June 2000, and July 2003. Briefly for 

reference, the June 2000 FIFRA SAP considered studies identified by EPA that evaluated 

atrazine or triazine exposure in association with breast, ovarian, and prostate cancers as 

well as non-Hodgkin lymphoma (NHL) and other lymphohematopoietic cancer sub-types. 

The Final SAP report which provided recommendations to EPA concerning interpretation of 

these data is presented in Appendix A. The SAP recommended EPA consider the evidence 

in support of an association between atrazine and prostate and breast cancer to be 

inconclusive. With regards to ovarian cancers, the SAP commented that while one study 

was suggestive of an association, it required replication and improved study methods to 

better inform causal inference. In addition, the Panel urged EPA to re-evaluate the studies 

related to NHL, as the Panel believed there was some supportive evidence observed. 

Considering the SAP recommendation, EPA concluded that while the data were not of 

sufficient quality or quantity to conclude there was an association, EPA should continue to 

monitor the literature, and re-convene future SAPs when additional epidemiologic data 

were identified ((EPA), 2000). 

In 2003, the EPA presented its analysis of several epidemiology studies which evaluated 

the potential association between atrazine and prostate cancer. Mainly, this review 

consisted of studies which indicated a possible increased risk of prostate cancer among an 

occupational cohort of triazine manufacturing plant workers (i.e., the St. Gabriel 

manufacturing plant cohort). The Agency also considered a preliminary nested case control 

study from the Agricultural Health Study (AHS), and a correlation analysis of this 

association in this evaluation. The Final SAP report which provided recommendations to 

EPA concerning use of these data is presented in Appendix B. EPA concluded, in 

consultation with the FIFRA SAP, that the increased incidence of prostate cancer in this 

cohort was likely in part or in whole due to intensive prostate cancer screening program, 

the prostate specific antigen (PSA) test was available on site to employees and many 

employees participated in the program (EPA), 2003). Therefore, detection bias was a likely 

explanation for the apparent risk concerns presented in the occupational studies. 

Concerning the AHS study on prostate cancer, the Panel noted that although results of this 

initial, hypothesis-generating analysis did not provide evidence of a positive association 

between atrazine and prostate cancer, differences between agricultural and manufacturing 

exposure profiles, as well other demographic differences between the two populations, 

hinder a direct comparison of results. The Panel in its Final report stated the data were 
Page 34 of 184

insufficient to support a conclusion regarding the carcinogenic potential of atrazine in the 

human population. The Panel recommended EPA consider additional, follow-up studies 

from the AHS as well as other peer-review sources, to clarify the nature of the association 

with atrazine. As a result of this FIFRA SAP (2003) consultation, EPA concluded:

“The Agency does not find any results among the available studies that would lead us to 

conclude that potential cancer risk is likely from exposure to atrazine. EPA plans to revisit 

this conclusion upon receipt of new studies, especially those from [National Cancer 

Institute] NCI’s Agricultural Health Study on atrazine and all cancers, prostate cancer, and 

non-Hodgkin’s lymphoma, all of which are planned for completion in the next 1-2 years 

[sic].” (Blondell & Dellarco, October, 28, 2003 2003 #1026)

EPA believes it is appropriate to present this analysis at this time as several new 

evaluations on the topic have been published since 2003, including several evaluations by 

researchers with the Agricultural Health Study (AHS). 

3.1.2 Identification of Epidemiology Studies included in Current Review 

This review of the epidemiologic literature of an association between atrazine exposure and 

specific anatomical cancer sites includes studies previously reviewed by EPA and 

presented to the FIFRA SAP panels (June 2000 and July 2003), as well as more recent

peer-reviewed publications, including a new evaluation by AHS researchers published in 

May 2011. For the purpose of the current evaluation, emphasis is placed on epidemiologic 

studies published since the time of the last EPA review, i.e., September 2003 to May 2011. 

However, to provide a comprehensive evaluation, EPA presents epidemiology studies of 

atrazine or triazine exposure in association with any cancer outcome published between 

1985 and 2011 in this review. The current review is intended to update the Agency’s cancer 

epidemiology evaluation to determine whether new information alters the Agency’s 

previous conclusion regarding evidence of the carcinogenic potential of atrazine in the 

human population. 

EPA values and appreciates the counsel provided by previous FIFRA SAPs (February and 

September 2010) regarding the draft “Framework” for incorporating epidemiology data into 

the risk assessment process (U. EPA, 2010). Specifically, these previous Panels 

encouraged EPA to employ a clear and consistent methodology for identifying studies 

included in the review of epidemiologic literature, including a delineation of excluded 

studies; consistent application of guidelines for adjudicating the results of individual 

observational studies, e.g., consistent application of inclusion and exclusion criteria, 

potential for bias in the studies; and, a simple and understandable method to synthesize 

the evidence presented in the epidemiology database, as well as integrate with the 

experimental animal database. While EPA is in the process of refining the proposed 

Framework, EPA strives to adhere to the principles offered by previous Panels in this 

respect. However, EPA also acknowledges that the current literature review reflects a 

necessarily hybrid approach to performing a review of this literature, given the history of the 
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previous atrazine cancer scientific evaluations, and the decision to a priori include all 

previously reviewed materials as well as all AHS relevant atrazine cancer point estimates in 

the current EPA review.

Upon EPA’s request, previous FIFRA SAP panels (February and September 2010) 

provided several thoughtful suggestions to EPA regarding an epidemiological literature 

review methodology. Specifically, the Panel noted the importance of a clear and 

transparent literature search methodology, articulation of study inclusion and exclusion 

criteria, and the use of a scoring system with which multiple raters may adjudicate the 

quality of individual studies, among other suggestions. In the current review, inclusion 

criteria were purposely broad and reflected only that atrazine or triazine is measured in 

association with a cancer outcome. Upon title and abstract review, studies were excluded if 

they did not measure an association between atrazine or triazine exposure and a health 

outcome. Concerning the use of a scoring system, EPA considered this option but instead 

decided a broader and open evaluation of the epidemiologic literature was merited at this 

time while noting the strengths and limitations of each study. However, among the list of 

studies remaining after title and abstract review, a group of scientists jointly adjudicated the 

relevance of the study for inclusion, as well as “grouped” studies as generally “strongly” or 

“weakly” providing evidence to inform the discussion. A full description of the literature 

review methodology including a delineation of the search string utilized is presented in 

Appendix C. 

EPA considered several factors indicative of good quality epidemiologic analysis in 

performing this review. Among these factors were the ability to accurately measure the 

health effect (the outcome) as well as atrazine exposure; the identification and accurate 

measurement of potential confounding factors; the sample size and statistical power to 

measure the association; the appropriateness of the statistical analysis to the study design; 

and, the interpretation of the overall study results. Key among these factors is the method 

of exposure assessment. Specifically, whether authors were able to perform qualitative or 

quantitative exposure assessment; the ability to measure atrazine exposure during critical 

time window for the health effect; the range of the exposure captured in the study; and, the 

relevance of the biomarker (if measured) to actual environmental exposures. In combining 

evidence from individual studies and across disciplines, i.e., toxicology and epidemiology, 

EPA considered factors such as presence of an exposure-response relation, temporality of 

exposure-disease measurement, strength of individual risk point estimates, consistency of 

findings across studies, knowledge of biologically plausible hypotheses to support the 

observed findings, as well as professional judgment. 

3.2 Epidemiology Literature of the Carcinogenic Potential of Atrazine

The present review includes research articles presenting evidence concerning the 

carcinogenic potential of atrazine in the human population. However, the aspects of this 

database which have already been part of an external review, i.e., FIFRA SAP or other 
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external review processes, are not presented with the same level of detail in this chapter or 

in the study reviews. The relevant FIFRA SAP final reports which provide a thorough review 

of these studies as well as an overall synthesis of the epidemiologic evidence at prior 

points in time are included as appendices to this chapter for reference (Appendices A and 

B). Organized by anatomical cancer site, the chapter first briefly recollects studies 

previously reviewed, followed by a more detailed summary of the recent epidemiologic 

research. At the conclusion of each section, a synthesis of the epidemiologic data is 

proffered. EPA’s review of individual investigations are included in Appendix D (studies 

published prior to 2003), and Appendix E (studies published subsequent to 2003, including 

several from the AHS). In addition, a summary table of study characteristics is presented in 

Appendix F for the recently published epidemiology studies, the focus of the current EPA 

review. Ultimately, a synthesis and integration of the cancer epidemiology studies with the 

results of experimental toxicological investigations is presented in Section 3.3, using the 

modified Bradford Hill criteria as an organizing tool, in addition to scientific judgment. 

Importantly, EPA notes at this time that there was no evidence of tumors noted in any of 

the experimental toxicology studies performed, for any of the cancer sites evaluated in the 

epidemiology studies discussed below, with the exception of mammary gland tumors in rats 

which were later determined to have development through a biological mechanism not 

operational in humans (See full discussion in Section 3.3 below).

3.2.1 Overview of Epidemiologic Database 

The atrazine cancer epidemiology database includes 40 studies reflecting cohort, 

population-based case-control or nested case-control study designs. Among this number, 

several ecologic studies were identified, but were not weighed heavily in the weight of 

evidence evaluation, per the advice of previous Panels (U. EPA, 2010). Studies reflected 

general population exposure, occupational manufacturing as well as agricultural exposure. 

Various methods of atrazine and/or triazine exposure assessment were employed across 

these studies. In general, the studies varied in terms of strengths and weaknesses.

The observational studies included in EPA’s current review of the atrazine cancer 

epidemiology database mainly consider the herbicide’s potential carcinogenic effect upon 

the reproductive and endocrine systems as well as the lymphohematopoietic systems. 

Approximately one-third of the cancer studies published subsequent to 2003 relate to these 

types of cancer endpoints. Investigations of atrazine exposure in association with 

leukemias and lymphoma include Hodgkin’s disease, non-Hodgkin lymphoma (NHL), 

multiple myeloma, leukemia, among other specific sub-types. Two new evaluations of 

atrazine exposure in association with lymphomas and NHL specifically have been 

published since 2003, however, a majority of studies published in this database published 

prior to 2003, investigate atrazine’s link with leukemias and lymphomas. Other cancer sites 

considered in the studies reflected in the current review include brain/glioma and colorectal 

cancers, as well as two evaluations of atrazine exposure in association with pediatric 

cancers including childhood leukemia. Given the importance of the AHS investigations to 
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the question of atrazine carcinogenicity, as articulated by the FIFRA SAP (2003) (See 

Appendix B), all other atrazine-cancer point estimates measured in either nested casecontrol or cohort studies are also included for reference. For this reason, a brief review of 

AHS design and methods, and the strengths and limitations of this cohort study are 

presented below. Results of specific AHS evaluations are incorporated within respective 

cancer-specific discussion sections.

Following the discussion of the AHS, cancer-site specific studies are presented. Within 

each sub-section studies published prior to 2003 are briefly recalled, followed by a more 

expansive discussion of the recent epidemiologic data (2003-2011). EPA’s review of each 

individual study is presented in Appendix D (pre-2003) and E (post-2003). A synthesis of 

the observational studies within each cancer site-specific section as well as across all 

cancer endpoints researched is offered at the conclusion of each section. EPA presents its 

integrative discussion of both the toxicological and also the epidemiological evidence in 

Section 3.3.

3.2.2 Agricultural Health Study (AHS) Atrazine Cancer Point Estimates 

Since 2003 there have been six nested case-control analyses within the AHS which 

evaluated use of a number of agricultural pesticides, including atrazine, in association with 

specific anatomical cancer sites. These include investigations of prostate (Alavanja et al., 

2003), lung (Alavanja et al., 2004), colorectal (Lee et al., 2007), breast (Engel et al., 2005)

(among female spouses and female applicators in the AHS), pancreatic (Andreotti et al., 

2009), and melanoma (Dennis, Lynch, Sandler, & Alavanja, 2010). In addition, researchers 

also published two cohort analyses specifically examining the association between atrazine 

use and cancer outcomes; multiple cancer sites were evaluated in each study (Beane 

Freeman et al., 2011; J. A. Rusiecki et al., 2004). This section provides general information 

concerning the design and methods of the AHS. The results of these investigations are 

considered within respective cancer-site discussion sections presented in this section.

3.2.2.1 Study Design and Methods

The Agricultural Health Study (AHS) is a large, prospective cohort study comprised of 

private and commercial pesticide applicators in Iowa and North Carolina. The study 

includes 57,310 licensed pesticide applicators, comprising 52,394 private applicators and 

4,916 commercial applicators, as well as 32,346 spouses of pesticide applicators. 

Researchers successfully recruited 82% of the target population into the AHS cohort which 

represents a high recruitment rate for an epidemiologic study. While farmer and pesticide 

applicator participants in the AHS are generally healthier than the overall U.S. population, 

AHS participants have a higher rate of some cancers including prostate, lip, multiple 

myeloma and ovarian cancers (Alavanja et al., 2005; Koutros et al., 2010). Lower rates of 

smoking and alcohol use, and the physically demanding nature of the occupation are health 

protective factors in the AHS population, and pesticides, viruses (animals), bacteria, 
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sunlight, and other non-pesticide chemicals may be putative risk factors for carcinogenesis 

among these farmers. 

Pesticide applicators must obtain a license from the state to purchase and use restricteduse pesticides. Participants in the AHS were enrolled into the study at county pesticide 

licensing facilities and were asked to complete a 21-page written enrollment questionnaire. 

Enrollment occurred over a period of 4-years, December 13, 1993 to December 31, 1997 

(“Phase I”). The Phase 1 “enrollment” questionnaire solicited information concerning types 

of agricultural crops and livestock produced, types of pesticides used, pesticide application 

methods, use of personal protective equipment, and, lifestyle factors such as smoking and 

alcohol intake. A portion of the cohort also completed a “take-home” questionnaire (~40%) 

in which additional lifestyle (e.g., diet) and chemical exposure information (i.e., an 

additional 28 chemicals) was ascertained. 

To determine case- and vital-status, cohort members were linked to state cancer registries 

in Iowa and North Carolina and the state death registries and the National Death Index, 

respectively. Linking was accomplished through use of social security number, first and 

last name, date of birth, and gender. Study participants who were alive but no longer 

residing in either study states were identified through the Federal Internal Revenue Service 

address file, motor vehicle registration records, and the restricted-use pesticide license and 

certification records of the departments of agriculture in Iowa and North Carolina. At this 

time, less than 2% of the cohort has been “lost-to-follow-up,” i.e., can no longer be located 

for purposes of the study. This is a very low percentage lost to follow-up, reducing the 

chance of selection bias in this prospective cohort study.

To measure atrazine exposure, AHS researchers considered three metrics across the 

studies including an atrazine cancer point estimate: (i) whether participants ever used the 

insecticide; (ii) the cumulative exposure days (the total number of exposure days per year, 

and total number of years in lifetime); and, (iii) an intensity weighted cumulative exposure 

metric. Exposure information was collected via self-report questionnaire responses. The 

“ever used” metric dichotomized atrazine exposure into ever/never categories where “ever 

used” was considered as at least used one time in lifetime. The lifetime exposure- days 

metric was calculated by taking the product of the total number of years atrazine was 

applied and the number of days per year atrazine was applied. The intensity-weighted 

lifetime exposure day metric includes: the frequency and duration of application (lifetime 

exposure days), application methods, mixing and equipment repair status, and use of 

personal protective equipment. These factors were weighted to reflect the intensity of the 

exposure based on monitoring data available in the published literature. Methods of 

pesticide exposure assessment used in the AHS have been reviewed extensively and 

shown to have high reliability and validity (Blair et al., 2002; Hoppin, Yucel, Dosemeci, & 

Sandler, 2002). 
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There have been six nested case control studies published within the AHS since 2003: 

prostate, breast, pancreatic, lung, colorectal, and cutaneous melanoma. In addition, there 

have been two cohort analyses of the relation between atrazine and several different 

cancer sites. The results of these specific evaluations are integrated within the cancerspecific discussions presented below. 

3.2.2.2 AHS Strengths and Weaknesses

The AHS includes both strengths and weaknesses. The large sample size and sufficient 

statistical power to investigate the potential association between atrazine use and several 

cancer sites including prostate cancer is a strength of the study. In addition, given the 

prospective study design the potential for recall bias (differential exposure measurement 

error) is eliminated, as pesticide exposure information was collected prior to the 

identification of disease. Exposure measurement error exists in this study (Blair et al., 

2011), however it is likely non-differential in nature and would attenuate, and not falsely 

inflate, measured risk estimates. Selection bias is considered of low potential as very few 

participants have been lost to follow-up in this cohort (i.e., few participants dropped out of 

study or were unable to be followed through other means). In this cohort, cancer cases 

were ascertained through linkage with state cancer registries, and include histopathological 

confirmation as well as information as to stage and grade of cancer at diagnosis. High 

quality outcome ascertainment reduces the potential for outcome measurement error. The 

AHS collects a vast amount of information for use in statistical analysis including prostate 

cancer risk factors such as age, race and family history of prostate cancer; cancer risk 

factors such as smoking and alcohol use; and, use of other correlated pesticides. Lastly, 

the AHS exposure measurement tools have been illustrated to be valid (Hoppin et al., 

2002) and reliable (Blair et al., 2002), and provide a quantitative measure of pesticide 

exposure over the working lifetime.

In combination, these studies provide an important database of epidemiologic evidence 

pertaining to atrazine carcinogenicity. However, it is important to note that most of these 

studies are hypothesis-generating in nature and did not propose a specific biological 

mechanism as to the carcinogenic mode of action of atrazine per se. The recent evaluation 

(Beane Freeman et al., 2011) was able to test hypotheses related to prostate, breast, 

ovarian and NHL as these sites have been previously suggested to be related to atrazine 

use. These results are further discussed below. 

3.2.3 Reproductive and Endocrine System Cancers

Because atrazine has a known neuroendocrine mode of action several researchers have 

hypothesized a role for atrazine in the etiology of certain reproductive and endocrine 

system cancers. Investigations of a link between the herbicide and prostate, breast, 

ovarian, and thyroid cancer are available for consideration. Several studies have identified 

that farmers have a greater risk of prostate cancer, although overall cancer rates are less 
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than the rates in the general population (Alavanja et al., 2005; Koutros et al., 2010). 

Similarly, there is some indication for an increased risk of ovarian cancer among women 

who live near or work in agriculture (Alavanja et al., 2005). However, few analyses to date 

have hypothesized a role for atrazine specifically, i.e., hypothesis-testing in nature, and 

even fewer have investigated an association with female reproductive system cancers. In 

many of these investigations, the number of female cancer cases exposed to atrazine is 

small. 

3.2.3.1 Prostate Cancer

In 2003, EPA reviewed several studies that investigated the association between atrazine 

exposure and prostate cancer, and presented a review of these data to the FIFRA SAP 

(EPA, 2003b) [See Appendix]. Briefly, an increased risk of prostate cancer was observed 

among an occupational cohort of triazine manufacturing plant workers (P. A. MacLennan et 

al., 2002; Sathiakumar & Delzell, 1997; N. Sathiakumar, E. Delzell, & P. Cole, 1996), 

however further analyses by EPA and others, and supported by external peer review 

including the FIFRA SAP, concluded that the availability of an on-site prostate cancer 

screening program, i.e., the PSA test, likely explained some or all of the increased prostate 

cancer incidence (Blondell & Dellarco, October, 28, 2003 2003 #1026; EPA, 2003b). 

Authors hypothesized that the elevated cancer rates was due to detection bias, and that the 

increased prevalence of PSA screening could explain some or all of the increase in 

prostate cancer incidence observed in this occupational population. This is supported by 

the observation that the increase in cancer risk was only observed among active workers 

who had access to the screening, and a lack of exposure response gradient using a jobexposure matrix approach to measure exposure . As a result of several internal as well as 

external peer review processes, reviewers determined that given the limitations of the study 

design, small sample size and lack of refined exposure measurement, a possible role for 

atrazine could not be ruled out, however it did not appear likely. 

Subsequent nested case-control analyses of the triazine manufacturing plant workers using 

refined exposure measurements did not alter the conclusion (P. A. Hessel et al., 2004). 

However, the same limitations as noted above, primarily the small sample size and limited 

exposure information in this occupational study precluded a determination that an 

association between triazine exposure and prostate cancer was present or absent. Further, 

the results of a nested case control study within the AHS population indicated little 

evidence of an association with prostate cancer (OR 0.94, 95% CI 0.78, 1.14), and there 

was no observation of an exposure-response trend (p trend>0.10) (Alavanja et al., 2003). In 

another study, analyses using state cancer registry data (CA), and publicly available 

pesticide use data indicate some evidence of a statistical correlation, however limitations of 

the exposure analysis restricts use in causal inference (P. Mills, 1998; P. K. Mills & Yang, 

2003). However, recently published AHS cohort analysis results addressed this question in 

cohort analyses with sufficient statistical power to detect an association if one exists 
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(Beane Freeman et al., 2011; J. A. Rusiecki et al., 2004). These data are presented below, 

followed by a synthesis of the studies in this database.

In 2004, Rusiecki et al. published a cohort analysis among participants in the AHS of 

atrazine exposure in association with many different cancer endpoints, including prostate 

cancer. AHS study design and methods are noted in section 3.2.2.1. With reference to 

prostate cancer, authors reported no evidence of a statistical association between atrazine 

exposure and prostate cancer using either cumulative exposure or an intensity-weighted 

cumulative exposure metric. Specifically, across quintiles of atrazine lifetime exposure 

days, the following odds ratios were estimated in comparison to the lowest exposed group 

(1-20 days): 21-56 days, OR 0.89 (95% CI, 0.66 to 1.21); 57-178.5 days, OR 0.75 (95% CI, 

0.56 to 1.03); and greater than 178.5 days OR 0.88 (95% CI, 0.63 to 1.23) (J. A. Rusiecki 

et al., 2004). The test for linear trend was not significant, p=0.26. Results were similar using 

intensity-weighted lifetime exposure days. Authors state that in this study there was 

sufficient statistical power to detect an association of 1.3 in the highest quartile, assuming a 

linear trend across quintiles of atrazine exposure (1-β=0.89). 

A new evaluation within the AHS cohort study with over twice as many prostate cancer 

cases indicated similar results. Risk estimates in the upper quartiles of either lifetime 

exposure days or intensity-weighted lifetime days as compared to the lowest quartile of 

exposure were not significantly different from the null (Beane Freeman et al., 2011). 

Compared to the lowest exposure group, odds ratio of prostate cancer in the upper 

quartiles of atrazine exposure were 1.04, 1.06, 1.04, respectively. In addition, there was no 

visual or statistical evidence of any exposure-response trend (p for linear trend 0.81). The 

latter analysis reflected use of an updated exposure algorithm which reduced exposure 

measurement error in comparison to the original algorithm used in the earlier analysis. 

While reflecting two different study periods as well as two different case series (prostate 

cancer cases updated through 2007), these two investigations within the AHS are similar in 

design and methods. The initial investigation by Rusiecki et al. (2004) was hypothesis 

generating in nature, however previous research, as noted above, provided support for a 

possible association with prostate cancer. The follow-up investigation examined the same 

question, within the same cohort, at a different point in time, and in many ways confirms the 

original finding. Differences between the two studies include an increased number of 

prostate cancer cases in the latter investigation, and use of the updated exposure algorithm 

in the latter analysis. Both studies had sufficient statistical power to detect an association 

with atrazine, if an association exists. Notably, the analysis by Rusiecki (2004) and BeaneFreeman (2011) both adjusted for use of other pesticides including several that are typically 

highly correlated with atrazine use including alachlor and 2,4-D as well as other triazines 

including cyanazine and metribuzin. Overall, the results of these two cohort analysis in 

which atrazine use was compared between cases and non-cases of prostate cancer, 

among other cancer sites evaluated, did not suggest an association is present among 

pesticide applicators in the AHS, who demographically were primarily U.S. white men.
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Conclusions: Prostate Cancer - The results of several internal and external peer review 

processes concluded that the association between triazine exposure among an 

occupational cohort of manufacturing plant employees can likely be explained in part or in 

whole by the availability of intensive PSA screening, i.e., evidence of detection bias (EPA, 

2003b). Previously published analyses using California state cancer registry and pesticide 

use data (P. Mills, 1998; P. K. Mills & Yang, 2003) suggest a possibility of a correlation 

and/or an association with atrazine or triazine (simazine measure Mills et al. 2003), 

however other limitations of these studies reduce their use in a weight of evidence analysis, 

primarily the method of aggregate exposure assessment, and lack of an atrazine risk 

estimate. The results of two cohort analyses within the AHS with sufficient statistical power 

to detect an association if an association exists did not observe statistical evidence of a link 

between atrazine use and prostate cancer. The magnitude and direction of the association 

was not different from unity (i.e., a null association), and there was no evidence of an 

exposure response trend using either lifetime exposure days or intensity-weighted lifetime 

exposure days. The AHS study design assured the atrazine exposure preceded the 

disease outcome. Sensitivity analyses performed using both the low-exposed and nonexposed groups as referent did not differ. Notably both analyses simultaneously controlled 

for use of correlated pesticides in the statistical analyses, for which the analyses were 

adequately powered to investigate, and no associations were observed. 

As noted by previous SAPs (EPA, 2003b), the exposure profiles of the triazine 

manufacturing plant workers and the AHS private (farmers) and commercial applicators 

differ, and lack of association among farmers does not preclude the existence of a positive 

association with triazine manufacturing plant workers. The higher magnitude of the 

exposure, the different exposure profile, and the distinct population characteristics (age, 

race, decade of exposure, other pesticide/chemical exposures) may indeed result in two 

different measures of association. However, to date, the information is not sufficient to 

support this supposition, and the AHS population is likely among the most highly exposed 

occupational groups in the nation. 

Although the primary risk factors for prostate cancer are age and family history, the specific 

causes of prostate cancer remain unknown. In general, epidemiological evidence of a role 

of atrazine in prostate cancer is minimal. In spite of the herbicide’s neuroendocrine MOA, a 

clear role of atrazine in prostate carcinogenesis is not understood, and evidence from the

experimental toxicology literature would argue against a possible mode of action for 

atrazine in prostate tumor formation (See section 3.3 below). At this time the lack of 

evidence of a statistical association in the two cohort analyses available from the AHS 

which reflects a prospective design assuring temporality of exposure-disease 

measurement, consistency of results between manufacturing (P. Hessel et al., 2004; P. A. 

MacLennan et al., 2002) and agricultural (Alavanja et al., 2003; Beane Freeman et al., 

2011; J. A. Rusiecki et al., 2004) populations, and lack of compelling biological hypothesis 
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as to the mode of prostate carcinogenesis from the experimental studies, argue against a 

link between atrazine and prostate cancer.

3.2.3.2 Breast Cancer

Several authors have evaluated the association between atrazine/triazine exposure and 

breast cancer. The rationale for these investigations is the known neuroendocrine health 

effects of atrazine, and earlier experimental studies which reported mammary gland tumors 

in female rats and studies reporting an effect on mammary gland development. However, 

more recent toxicological data suggest that the mode of action for atrazine’s effect on 

mammary gland development in rats is not operational in humans (EPA, 2010; EPA, 2000)

(See Section 3.3). Nevertheless, there have been three ecologic studies evaluating this 

potential association in the human population, two were published prior to 2003 and 

previously reviewed by EPA (Bassil et al., 2007; Hopenhayn-Rich, Stump, & Browning, 

2002; M. A. Kettles, S. R. Browning, T. S. Prince, & S. W. Horstman, 1997), and one was 

more recent (Muir et al., 2004). The ecologic evaluations are collectively discussed below. 

In addition, a population-based case control study in Wisconsin (J. A. McElroy et al., 2007)

using atrazine well water concentrations to measure exposure, and two recent evaluations 

from the AHS (Beane Freeman et al., 2011; Engel et al., 2005) provide additional 

information to examine this question. 

There are three separate ecologic analyses of the association between 

groundwater/drinking water exposure to atrazine and breast cancer. Two were previously 

reviewed by EPA, and one is considered for the first time herein. Kettles et al. (1997) 

conducted an ecologic study to examine the association between breast cancer incidence 

and triazine herbicide exposure; in 2002 Hopenhayn-Rich updated the analysis. The 

Kettles et al. (1997) study calculated county breast cancer incidence rates using Kentucky’s 

statewide cancer registry for the years 1991-1994. Triazine herbicide exposure was 

assessed using data on ground and drinking water contamination, corn acreage, and 

pesticide usage by applicators. Researchers report a small, statistically significant 

association between breast cancer and water contamination. However, the investigators 

also indicate that the results were not consistent across study years. While the water 

contaminant associations were not consistent between time periods, the investigators did 

report that their composite exposure variable was associated with breast cancer during 

both 1991-92 and 1993-94 (i.e., Medium and High Exposed County ORs = 1.09 (1.04-

1.14), 1.07 (1.01-1.14) for 1991-92 and 1.14 (1.08-1.19), 1.20 (1.13-1.28) for 1993-94) (M. 

Kettles, S. Browning, T. Prince, & S. Horstman, 1997). This study is discussed in the final 

SAP report from the June 2000 meeting (Appendix A) and concluded these data were 

insufficient to make a determination as to the relation between atrazine and breast cancer. 

In 2002, Hopenhayn-Rich updated this analysis by refining exposure measurement and 

including three additional years of cancer registry data, but utilized similar methods in other 

ways. In this ecologic evaluation, authors reported that their study did not provide evidence 
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that supports an association between atrazine exposure and county level incidence of 

breast cancer(Hopenhayn-Rich et al., 2002).

Muir et al. (2004) performed an ecologic analysis of this association in two counties in 

England, Lincolnshire and Leichestershire. Researchers included all cases of breast cancer 

diagnosed among women, ages 45 years and greater in these two counties between the 

years 1988 to 1991. Cancer cases were ascertained through regional cancer registry (Trent 

Cancer Registry). Pesticide exposure was measured at the group level. A pesticide usage 

survey for 1991 was used to determine the total kilogram of active ingredient applied per 

kilometer by each ward within the two counties of England represented in the study. 

Investigators included atrazine, aldicarb, lindane and cyanazine in the study as these were 

identified as potentially estrogenic substances by the study authors and hypothesized to 

play an etiologic role in breast cancer development. Authors reported no significant 

correlations (“clusters”) of breast cancer by geographic area. Furthermore, authors 

observed no association between average pesticide usages by ward and breast cancer 

incidence rate ratio (IRR) in urban or rural areas of Lincolnshire, or urban areas of 

Leichershire. Authors did note a statistically significant (p<0.05) positive change in slope of 

breast cancer incidence rates and 1-unit change in kg of atrazine used per km2 (slope 0.17 

(0.03, 0.31)) (Muir et al., 2004). The researchers noted the inherent limitations of the study 

design, but suggest the results of this work supports excluding the possibility of a strong, 

positive association between atrazine usage and breast cancer among women diagnosed 

later in life (otherwise these studies would have detected greater associations).

Overall, these three ecologic studies provide limited, inconsistent evidence as to whether 

an association exists between atrazine exposure and breast cancer. The slight suggestion 

of an association in Kettles (1997) was not observed by Hopenhayn-Rich (2002) using 

similar methods and similar study population. The study by Muir et al. (2004) does not 

present internally consistent results, positive slope in only one of two study areas, and the 

weak magnitude and statistical significance of the slope (coefficient) does not strongly 

argue in favor of an association. Given the nature of the study design, these data offer 

weak, inconsistent evidence of a possible link between atrazine and breast cancer.

Mills et al. (2006) performed a hypothesis-testing, cohort study using ecologic exposure 

assessment methods to evaluate the association between atrazine exposure and breast 

cancer among Latinas diagnosed with breast cancer. Invasive breast cancer cases were 

identified from the California state cancer registry (CCR) between the years 1988 to 1999, 

and compared with county level pesticide exposure, and grouped demographic data. 

Pesticide use information from the years 1970 to 1988 was obtained from the California 

state pesticide use registry (PUR). Authors were interested in six pesticides, including 

atrazine and simazine. Authors calculated standardized incidence ratios (SIR) for 1988-

1993 and then for 1994-1999 to reflect secular changes in breast cancer trends (screening, 

diagnostic tools, knowledge in population), and to examine latency between exposure and 

diagnosis. Adjusting for age and other co-variables, authors reported no association 
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between atrazine exposure and breast cancer comparing the highest and lowest quartiles 

of exposure (1988-1993: OR 0.86 (95% CI 0.70, 1.05); 1994-1999 OR 0.87 (95% CI 0.73, 

1.04) 

Mills et al. (2006) performed a large, cohort study of atrazine exposure and breast cancer in 

a highly exposed group. Outcome classification was good, based upon high quality cancer 

registry, and using state pesticide use records is an innovative method to assess exposure. 

By design, authors assured pesticide exposure preceded disease diagnosis. Established 

breast cancer risk factors were indeed significantly associated with breast cancer in this 

study, which is reassuring of the study methods. The major limitation of this study is the 

ecologic nature of the exposure assessment. Authors assumed PUR reports for the county 

and group level confounder information were a good approximation of pesticide exposure 

of women diagnosed with breast cancer and co-variable information, which likely resulted in 

exposure misclassification. Because of the lack of individual level exposure and 

confounder information, an association cannot be ruled out based upon this study. 

However, in general, this study lacks evidence of an association. 

McElroy et al. (2007) performed a population-based case-control study of atrazine 

exposure and breast cancer among 22,495 women residing in the state of Wisconsin. 

However, given the timing of exposure and disease information collection, the study was 

essentially cross-sectional in nature. Cases included women who were diagnosed with 

breast cancer during the years 1988 through 2001, and reported to the state tumor registry. 

Controls were randomly selected from state DMV (20-65 years) and Health Care Finance 

Administration (65-79 years) records to roughly approximate the age distribution of cases 

(10-year age categories), and for whom no previous breast cancer diagnosis was made. 

Investigators were interested in atrazine exposure from untreated well water used for 

drinking water. Therefore, authors restricted the study population to those who resided in a 

rural area for whom well-water would likely supply local drinking water defined as a region 

with no public water system. Upon applications of the eligibility and restriction criteria, 3,275 

case and 3,669 controls were selected into this study. The concentration of atrazine and 

atrazine metabolites in well water which provided the sole residential drinking water source 

for participants in this study was measured in a state survey of pesticide contamination 

during three survey years: 1994, 1996, and 2001. To estimate atrazine exposure between 

sampling years of 1994, 1996, and 2001 to the entire study period of 1988-2001, authors 

utilized a “national neighborhood interpolation” method using ArcGIS. Authors considered 

atrazine well water concentration within the entire state and within the highest agricultural 

area of the state separately, the south central WI region. Overall, authors reported little 

evidence of an association between atrazine concentration in well water and breast cancer. 

Comparing those in the moderately high category to the lowest exposure category, minimal 

increased odds of disease were reported (OR 1.10, 95% CI 0.90, 1.40) (J. McElroy et al., 

2007). Among participants in the highest atrazine exposure area, odds of breast cancer 

was non-statistically significantly elevated to 1.3 (95% CI 0.30, 5.0), however precision was 
Page 46 of 184

low and the number of cases in the upper category was small (n<5) (J. McElroy et al., 

2007).

This study presents limited evidence of a role for atrazine drinking water exposure in 

relation to breast cancer. These study methods are an improvement over previously 

published population-based studies of atrazine and breast cancer; however there are likely 

significant information biases (resulting in exposure misclassification), and heterogeneity in 

outcome classification, in addition to low sample size in the upper-exposure categories that 

could explain the essentially null findings. There are several strengths to this method of 

exposure assessment including the fact that this is an improvement over previous ecologic 

studies, the interpolation across study years is supported by the fact that atrazine is 

relatively persistent in water, and the restriction to rural residents likely isolates the most 

highly exposed to atrazine in drinking water. Using these exposure methods, however, 

there are numerous possibilities for exposure measurement error in this study, several of 

which are acknowledged by the authors. Concerning residential history, the exposure 

measure in this study for a woman diagnosed at 60 years likely represents her adult life 

exposure, however a woman diagnosed at 25 years, the exposure may represents her 

early life exposure experience. Additionally, the outcome measurement may be 

heterogeneous: breast cancers diagnosed early in life (3rd decade) as opposed to later in 

life (7th decade of life) most likely reflect different etiologies, and known differences 

between estrogen receptor positive tumors in relation to possible influence by an endocrine 

disrupting pesticide were not evaluated. The effect of both exposure and outcome 

measurement error may have attenuated these results. However, as the authors state, 

there is no strong indication of an association, if there is a positive association in nature, it 

is likely modest. These data provide little evidence of a strong association between breast 

cancer and atrazine exposure, but this study cannot exclude the possibility of a poorly 

measured moderate association. 

Within the AHS cohort, two studies evaluated an association between atrazine use and 

breast cancer: a nested case-control analyses among female spouses of pesticide 

applicators (Engel et al. 2005), and a cohort analysis including female pesticide applicators 

(Beane-Freeman et al. 2011). AHS study design and methods are discussed above in 

section 3.2.2.1. Engel et al. (2005) compared ever-use of several pesticides, including 

atrazine, and breast cancer rates among female spouses of enrolled pesticide applicators. 

Authors were able to assess both direct, ever-use of pesticide in lifetime, and indirect 

pesticide exposure, reported use by the pesticide applicator husband. However, atrazinespecific exposure information was only available for the ever-use metric, i.e., detailed 

exposure information including duration and frequency of use was not available. The 

female spouses who responded to the supplemental questionnaire also provided 

information concerning other demographic data, reproductive health and known breast 

cancer risk factors. As noted above, cancer outcome information was ascertained through 

linkage with state cancer registries and is considered to be more than 95% complete 

(Alavanja et al., 1996). Adjusting for age, race and state, authors did not observe evidence 
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of an association between ever use of atrazine and incident breast cancer among female 

spouses of enrolled applicators when measuring direct exposure (OR ever use 0.7, 95% CI 

0.40, 1.2), or indirect exposure (husband’s ever use OR 1.1, 95% CI 0.7, 1.6) (Engel et al., 

2005). Slight differences in estimated risk likely due to increased stability of indirect 

measure (greater number indirectly exposed women). Considering use of any pesticides, 

authors observed no trend by duration of use (direct report of number years, days per 

year), or proximity to pesticide treated area. A slight trend by number of times per year wife 

washes clothes worn by husband when applying pesticide was observed, but confidence 

bounds overlapped across indirect exposure category. While exposure measurement is 

limited in this study, ever-use directly reported by only 11 atrazine exposed cases, the 

study lacks evidence of an association between atrazine use and breast cancer.

In the recent cohort analysis of atrazine use, authors estimated that association with breast 

cancer among female applicators enrolled in the AHS (Beane Freeman et al., 2011). In this 

study, authors used the ever-use metric only; there were too few breast cancer cases 

among female pesticide applicators to evaluate an exposure-response trend using quartiles 

of atrazine exposure. Authors elected to evaluate this cancer site due to a priori interest 

based upon previous epidemiologic research, and atrazine’s neuroendocrine health effects. 

Authors reported no significant association among nine atrazine exposed cases and 27 

controls (OR 1.14, 95% CI 0.47, 2.50) (Beane Freeman et al., 2011). Similar results were 

observed, according to these authors, when comparing breast cancer rates between 

women greater than and less than the median number of lifetime exposure days. Authors 

adjusted for age only in this study, as other breast cancer risk factors were not confounders 

in this sample and the use of fewer co-variables retains statistical power in a small sample. 

The strengths of the AHS in terms of design, exposure and outcome measurement, and 

confounder information have been noted above. In this analysis, the small number of 

female breast cancer cases and resultant inability to explore an exposure-response relation 

is a limitation. However, strengths of the study include temporal association, exposure 

precedes disease.

There have been seven evaluations of the association between atrazine or triazine 

exposure and breast cancer since 1997 identified in this review. Three of these studies 

utilized an ecologic study design, and offered limited, inconsistent evidence of a link in the 

human population, and are more supportive of the null hypothesis, no true association. 

Mills et al. (2006) evaluated the question among Latinas in the state of California using 

state cancer registry data, and aggregate level exposure analysis. These authors reported 

no evidence of an association in two time periods evaluated, however the measurement 

error in the method of exposure assessment, and uncertainties presented may have 

attenuated these results in this highly exposed population of Latina agricultural workers. 

AHS investigators evaluated the breast cancer-atrazine link among female spouses of 

licensed pesticide applicators (direct and indirect exposure) as well as female applicators 

themselves. Neither investigation observed evidence of an association. Although the AHS 

is generally a very strong study, estimating risks among women exposed to pesticides is 
Page 48 of 184

more limited than among men. Neither Engel et al. (2005) nor Beane-Freeman et al. (2011) 

observed strong statistical associations, nor were they able to explore exposure response 

relation due to the small number of atrazine exposed females, measuring only ever-use of 

atrazine during the lifetime. Both studies had wide non-statistically significant confidence 

intervals indicating lack of power to detect a difference if one truly was present. Research 

indicates chemical exposure during different points in the life cycle may critically impact 

cancer risks. None of these investigations were able to explore this aspect of the question. 

Considering this database in total, studies available reflect different designs; varied target 

populations including general population in high-use areas, agricultural workers, and 

occupational groups; evaluated both direct and indirect pesticide exposure; explored 

exposure through drinking water, or occupational (dermal/inhalation) pathways; ever-use of 

the herbicide as well as exposure-response, among other sources of variation. Overall, 

these studies lack strong evidence of an association between breast cancer and atrazine 

exposure among low general population levels, or presumably higher occupational 

exposure levels. Limitations across these studies include aggregate exposure assessment, 

exposure measurement issues in those studies that attempt to estimate exposure at the 

individual-level, and small number of atrazine exposed females in occupational settings 

(AHS). These studies are mainly initial, exploratory analyses, with the exception of BeaneFreeman et al. (2011) which estimated the association based upon a priori hypothesis. 

However, none of the investigations were able to explore critical windows of exposure, or 

exposure-response associations with sufficient number of exposed females in the higher 

exposure categories. At this time, the database lacks evidence of a relation between 

atrazine exposure and breast cancer.

3.2.3.3 Ovarian Cancer

Because of the neuroendocrine effects of atrazine exposure, some epidemiology studies 

have been performed evaluating the link between atrazine and ovarian cancer, however 

there are only a few. An elevated risk of ovarian cancer was reported comparing the AHS 

population to the general population of the respective study states (IA/NC) (Alavanja et al., 

2005; Waggoner et al., 2011).

Within the atrazine cancer epidemiology database, two evaluations included a point 

estimate for atrazine exposure in relation to ovarian cancer risk; one was previously 

reviewed by EPA (EPA, 2000) (See Appendix B-1). Briefly, for reference, Donna et al. 

(1989) performed a population-based case control study in Italy in the mid-1980s. Cases 

were ascertained through hospital registries, assumed to be complete among all cases in 

the region, and controls were selected from electoral roles of the same region. Among the 

65 cases and 126 controls selected, pesticide exposure was assessed through 

questionnaire and classified as ‘definite,’ ‘probable,’ or ‘non-exposed’ based upon 

questionnaire responses which were independently rated by two industrial hygienists. 

Relevant confounding variables were also collected and included in the analysis, e.g., 
Page 49 of 184

ovarian cancer risk factors. Authors observed two-fold elevated odds of ovarian cancer

among those who were classified as definitely exposed to triazines (OR 2.7, 95% CI 1.0, 

6.9) (Donna et al., 1984; Donna et al., 1989). In addition, authors observed evidence of 

exposure-response by duration of exposure and by probability of exposure (i.e., none, 

probable, definite). This study supported earlier findings from the same research group, 

however the earlier publication measured herbicide use only (Donna et al., 1984). 

A strength of the study was the inclusion of histopathologically confirmed cases which 

reduces outcome measurement error. In addition, authors were able to obtain relatively 

detailed exposure measures through interviewer administered questionnaire, however, 

exposure measurement error remains an uncertainty in this study. Case ascertainment was 

estimated to be 96%, and controls were similar to the source population indicating selection 

bias was unlikely. The relatively small sample size and the probabilistic assignment of 

exposure category are weaknesses. As noted in previous EPA reviews of this study, the 

inability to adjust for other pesticide exposure is an uncertainty. Factors in favor of a true 

association with ovarian cancer include the magnitude and statistical significance of the 

reported association in more than one study, evidence of an exposure-response trend, 

temporality of exposure-disease relation through study design decisions, and general 

plausibility of a role for triazine in endocrine cancers. Synthesis of these data with 

experimental studies is discussed in Section 3.3. 

In addition to this evaluations, Hopenhayn-Rich (2002) performed an ecologic analysis of 

triazine exposure, measured in state drinking water monitoring data, and ovarian and 

breast cancer. Regarding ovarian cancer and triazine/atrazine use authors observed no 

evidence of an association, and authors in fact reported an inverse association between the 

mid- and high-triazine exposure groups (ecologic assessment) (Hopenhayn-Rich et al., 

2002). A separate ecologic analysis did not observe a correlation between atrazine 

exposure and ovarian cancer in women in Ontario (J. A. Van Leeuwen, D. Waltner-Toews, 

T. Abernathy, B. Smit, & M. Shoukri, 1999). Under the conditions of these studies, the 

results do not lend evidence in support of an association with atrazine exposure. 

In a hypothesis-testing investigation, Young et al. (2005) performed a population-based 

case control study of ovarian cancer in association with triazine exposure in women who 

resided in the Central Valley, CA, an area of high agricultural land use and pesticide 

exposure. Histopathologically confirmed epithelial ovarian cancer cases were ascertained 

through two regional registries, and controls were selected through random digit dialing 

(RDD) of female residents in the study area who had a land-line telephone. Ovarian cancer 

cases diagnosed in 2000-01 were eligible; pesticide exposure information was obtained 

through a variety of sources and reflected the time period 1974-1999. Response rates were 

modest in this study: 40% for cases and 57% for controls. Researchers assessed 

occupational and residential history as well as other ovarian cancer risk factors through a 

self-administered questionnaire distributed to study participants. Occupational history 

included information such as location of agricultural job site, crops worked, job titles, years 
Page 50 of 184

of employment among other characteristics. However, participants were not directly queried 

as to specific pesticides used in the workplace. Authors estimated triazine exposure 

indirectly by linking California state pesticide use reports for the study period to participants’ 

reported job location, years of employment, and crops worked.. In addition, weighting 

factors were used to estimate intensity of exposure by job title, e.g., mixing and/loading 

pesticides versus harvesting. 

Only 8.8% of participants reported using triazines, and participants used simazine and 

cyanzine more than atrazine. Adjusting for several endocrine cancer risk factors, authors 

observed a non-statistically significantly elevated risk of ovarian cancer in relation to total 

triazines (OR 1.34 (95% confidence interval (95% CI) 0.77,2.33), but not in association with 

atrazine OR 0.76 (95% CI 0.16, 3.55), estimate based upon 2 and 12 exposed cases and 

controls (H. Young, P. Mills, D. Riordan, & R. Cress, 2005). Examining quintiles of total 

triazine exposure, a suggestion of an exposure-response trend upon visual inspection, 

however the test for linear trend was not significant at the p<0.10 significance level. Using 

an alternative statistical modeling approach, authors modeled triazine exposure as a 

continuous variable; no evidence of a significant slope was observed. Sensitivity analyses 

regarding weights used in the job exposure matrix, and the investigation of effect 

modification by reproductive cancer risk factors were not additionally informative.

Authors observed some evidence of a positive association between occupational and 

residential triazine exposure and ovarian cancer, however there was no statistical evidence 

observed for an association with ever-use of atrazine, and no statistical evidence of an 

exposure response trend for duration of exposure to triazines. The low prevalence of 

atrazine use among women in this cohort (triazine prevalence 8.8%, atrainze prevalence 

likely <5%), and the number of exposed cases and controls was small (2 and 12, 

respectively), likely explains the imprecise risk estimate for atrazine exposure. A study with 

a larger sample size would enhance the precision of the risk estimate, and clarify the true 

direction of the association, if any. In addition, the exposure assessment method likely led 

to non-differential exposure misclassification, attenuating the risk estimates. The low to 

moderate participation rates may indicate the possibility of non-response bias, i.e., those 

who refused to participate may have a different exposure profile than those who agreed. 

The reported comparability between included and excluded cases with respect to age and 

stage and grade of tumor at diagnosis somewhat reduces the potential for this type of 

systematic bias, however authors acknowledged this uncertainty in the study. Exposure 

information was collected to reflect the time period prior to ovarian cancer diagnosis, 

reducing the chance of temporal bias, and exposure was assessed similarly between cases 

and controls. Statistical analyses were clear and appropriate, including the selection of 

confounding variables in the analysis. However, triazine-specific models may have been 

too small to support adjustment for several of the ovarian cancer risk factors included in 

models, further reducing statistical power. Other specific strengths and weaknesses of this 

study are discussed in Appendix B-5. Overall, the authors conclude that this study cannot 
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support or refute the existence of a true association between atrazine/triazine exposure and 

ovarian cancer. 

A recent AHS investigation provided an estimate of the risk of ovarian cancer among 

female pesticide applicators in relation to ever use of atrazine during the working lifetime. 

There were too few atrazine exposed female applicators to explore an exposure-response 

relationship. Controlling for age only, as other ovarian cancer risk factors were not 

confounders in the analysis, authors observed a non-statistically significant elevated odds 

of ovarian cancer, OR 2.91 (95% CI 0.56, 13.60) in association with ever-use of atrazine 

(Beane Freeman et al., 2011). It should be noted that this analysis reflected only nine 

ovarian cancer cases, 4 of whom reported ever using atrazine. All of these cases reported 

lifetime number of days used below the median for all atrazine exposed cancer cases, i.e., 

the ovarian cancer cases were among the lower end of the exposure distribution. Authors 

state that the role of chance, or possibly another unknown source of unmeasured 

confounding may explain these results; however, there is some plausibility of the result 

given the neuroendocrine effects of atrazine. Strengths and weaknesses of the AHS are 

noted in section 3.2.2.1 above, however with reference to this evaluation, strengths of the 

study include hypothesis-testing nature of the estimation, the ability to investigate this 

question among a group of occupationally exposed women, and lack of confounding bias in 

the analysis. However, the lack of ability to explore an exposure-response association, and 

the imprecise nature of the risk estimate due to a small number of atrazine exposed cancer 

cases are limitations which must be addressed in further studies. 

There are four epidemiologic investigations which consider the relation between atrazine or 

triazine use and ovarian cancer: (1) an ecologic analysis which reported an inverse 

association; (2) a population-based case-control study in Italy which reported elevated odds 

of ovarian cancer for the most highly exposed group, but for which the study was limited in 

its ability to adjust for other pesticide and confounder exposures; (3) a population-based 

case-control study in California using publicly available data on outcome and exposure to 

estimate risks and, therefore, is reflective of a large sample size, but only a small number of 

atrazine exposed cases and controls (2 and 14 respectively); and, (4) the hypothesistesting risk estimate presented in a recent AHS cohort analysis that reported elevated odds 

of the cancer, but which only utilized an ever-use exposure metric. These data are 

supplemented by general information that the incidence and mortality of ovarian cancer are 

elevated among the AHS occupational cohort in comparison to the general population, but 

this information is clearly not specific to atrazine. Each study through design or methods 

reflects an appropriate temporal association (exposure precedes disease). However, given 

the typically late stage at ovarian cancer diagnosis, and long period of latency (Adami, 

2002), some uncertainty regarding the temporal association exists. Although elevated 

associations were observed in both Donna et al. (1989) and Beane-Freeman et al. (2011), 

estimates were somewhat imprecise most likely due to the small number of exposed and 

highly exposed ovarian cancer cases, and exposure-response relationships were not fully 

assessed in these studies. Overall, this database presents some indication of a possible 
Page 52 of 184

association between atrazine and/or triazine use and ovarian cancer; however, the small 

sample sizes, lack of ability to control for other pesticide use and possibility of unmeasured 

confounders in the association limit the ability to infer a causal association at this time. 

The key events in the neuroendocrine mode of action for atrazine in relation to ovarian 

cancer is discussed in Section 3.3 below. 

3.2.3.4 Thyroid Cancer 

EPA is aware of only one study of the possible association between atrazine use and 

thyroid cancer; a recently published atrazine cohort analysis through the AHS explored this 

association. However, EPA notes that the relation was only assessed in male pesticide 

applicators; there was only one atrazine exposed female applicator in the cohort. The 

etiology of thyroid cancer is not well understood, although family history of thyroid cancer 

and radiation exposure are known risk factors. Also, the etiology and epidemiology of 

thyroid cancer differs between men and women and differs among the types of thyroid 

tumors.

In the AHS, Beane-Freeman et al. (2011) compared lifetime atrazine exposure days among 

twenty-nine exposed thyroid cancer cases in comparison with non-cases of thyroid cancer. 

Author’s ascertained thyroid cancers among the cohort through linkage with state cancer 

registries, and self-administered questionnaires provided information on use of other 

pesticides, demographic characteristics and other cancer risk factors. Adjusting for several 

potential confounding factors, researchers observed a statistically significant, four-fold 

increased odds of thyroid cancer comparing the most highly exposed group to the referent 

(the lowest exposed) (OR intensity-weighted upper quartile 4.84 (95% CI 1.31, 17.93)) 

(Beane Freeman et al., 2011). The association remained when researchers controlled for 

body mass index, a known risk factor for thyroid cancer (OR 4.30 (95% CI 1.21, 15.28)). 

Non-significant, but positive 2- to 4-fold associations were consistently observed across 

quartiles of exposure using both the cumulative exposure metric and the intensity-weighted 

exposure metric. The test for linear trend was significant at p<0.10, but not at p<0.05, for 

intensity-weighted lifetime days. However, the lack of a clear exposure-response trend, and 

the small number of atrazine exposed cases in some categories, e.g., n>5, were limitations 

to this study and present some uncertainties in the interpretation of these results. The 

authors conclude that this investigation, given the strengths of the AHS in design and 

methods (mentioned above), provide some evidence of a positive link between atrazine 

and thyroid cancer in male pesticide applicators who reported using atrazine during their 

lifetime.

EPA identified no other evaluations of this relation in the literature. There is no known 

biological mechanism postulated for atrazine at this time (See Section 3.3). Authors noted 

that another AHS investigation examined pesticide use in association with hypothyroidism, 

a suspected risk factor for thyroid cancer, and observed no association with atrazine 
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(Goldner et al., 2010). Strengths of this one investigation of the topic include the design and 

method of the AHS, i.e., prospective, extensive pesticide exposure information that has 

been validated and reliable, appropriate temporal association to estimate risk, ability to 

examine an exposure-response relation, and general plausibility of result. However, the 

lack of an observed trend, paucity of other epidemiologic or experimental data providing 

any comparative information, low statistical power and ability to measure risk with less 

uncertainty due to small sample size, and lack of mechanistic information. 

3.2.4 Lymphohematopoietic Cancers 

In this section, cancer epidemiology studies of the association between atrazine and/or 

triazine use and lymphohematopoietic cancers are reviewed. Over the last twenty years, 

several studies have hypothesized a role for farming exposures and pesticides specifically 

in relation to leukemia and lymphomas. Specifically, a series of population-based case 

control studies located in the mid-western U.S. performed by the National Cancer Institute 

(NCI) estimated the risk between pesticide use including atrazine and several 

lymphohematopoietic outcomes (Brown et al., 1990; Brown, Burmeister, Everett, & Blair, 

1993; Burmeister, 1990; Cantor et al., 1992; Hoar et al., 1986; Hoar Zahm, Weisenburger, 

Cantor, Holmes, & Blair, 1993; Weisenburger, 1990). Among these studies, however, only 

two specifically tested the hypothesis of atrazine exposure in association with one of these 

cancer subtypes, non-Hodgkin lymphoma (NHL) (Hoar Zahm et al., 1993; Zahm et al., 

1993). In addition, cohort evaluations comparing cancer incidence (standardized incidence 

rates, SIR) and cancer mortality (standardized mortality rates, SMR) rates between an 

occupationally group exposed to triazines and the general population also estimated risk of 

NHL and other sub-types (P. MacLennan et al., 2002; P. A. MacLennan, E. Delzell, N. 

Sathiakumar, & S. L. Myers, 2003; Sathiakumar & Delzell, 1997; N Sathiakumar, E Delzell, 

& P Cole, 1996). Each of these evaluations has been previously reviewed by EPA (Blondell 

& Dellcarco, October, 28, 2003 2003 #1026; EPA, 2000, 2003a) [See Appendix B.3]. 

Since the time of the last EPA review, DeRoos et al. (2003) published a pooled analysis of 

the association between pesticide use and NHL pooling data from three population-based 

case control studies (IA/MN, KA, and NE) performed by the NCI in the 1980’s. These 

authors employed a hierarchical regression technique which allowed for co-adjustment for 

exposure to 47 different pesticides when measuring the specific putative pesticide-NHL 

association including atrazine (De Roos et al., 2003). EPA also identified two hospitalbased case-control studies performed in France investigating the possible link between 

atrazine and other pesticides with leukemias and lymphomas during two separate study 

periods (Clavel et al., 1996; Orsi et al., 2009). In addition, two cohort analyses in the AHS 

investigating the use of atrazine in relation to all cancer outcomes including leukemias and 

lymphomas have been published since EPA last reviewed this database (Beane Freeman 

et al., 2011; J. A. Rusiecki et al., 2004). Each of these studies is briefly discussed below. 
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3.2.4.1 Population-Based Case-Control Studies: Midwestern U.S.

In the early 1990’s, the NCI established three population-based case control study 

populations in the Midwestern U.S. to examine the association between several different 

lymphohematopoietic cancers and farm-related exposures including pesticides. Across 

these studies, cases of lymphohematopoietic (leukemias and lymphomas) were identified 

through state-based cancer registries (IA, KA), or state-wide cancer surveillance, and 

controls were identified from the general population. Demographic and pesticide exposure 

was obtained through in-person (IA, MN) or telephone-based (KS, NE), structured interview 

questionnaires administered by trained study staff members. Duration and intensity of 

pesticide exposure was available for a subset of participants, and a small pesticide 

validation study using sales records was performed (Hoar et al., 1986). Authors solicited 

information about ever use of specific pesticides, farm activities, residential history, nonfarm occupations, and first and last year of use of specific pesticides. Pesticide exposure 

information was obtained for 38 herbicides, 24 insecticides, 34 crop insecticides, and 16 

fungicides. Atrazine exposure information was collected in most of these studies; Hoar et 

al. (1986) however only measured triazine exposure. Across these evaluations, 

researchers were able to control for the influence of other pesticide exposure as well as 

cancer risk factors in the statistical analysis.

Brown et al. (1990) evaluated odds of leukemia in association with pesticide use, and 

reported no evidence of a statistical association: ever use of either triazine (OR 1.1 (95% CI 

0.8, 1.5), and atrazine use among 38 and 108 exposed cases and controls, respectively 

(OR 1.0 (95% CI 0.6, 1.5). In a separate evaluation, no evidence of an association was 

observed with multiple myeloma, although the number of atrazine exposure cases and 

controls were small: 12 and 74, respectively (Brown et al., 1993). 

Regarding an association with non-Hodgkin lymphoma (NHL), Hoar et al. (1986) reported 

that NHL risk, but not soft tissue sarcoma or Hodgkin’s disease, increases with duration 

(number of years) and frequency (days/year) of herbicide use (p for trend<0.05), and the 

association between triazine use (ever/never) and NHL are elevated 2-fold (OR 2.5 (95% 

CI 1.2, 5.4) (Hoar et al., 1986). When users of phenoxy or uracil herbicides, other highly 

prevalent herbicides, are excluded from the ever-triazine exposure group the association 

remained positively elevated, but uncertainty increased, (OR 2.2 (95% CI 0.4, 9.1)). The 

latter analysis is based on only 3 and 11 triazine exposed cases and controls. Cantor et al. 

(1992) reported some evidence of a link with farming exposures and NHL: corn growers 

(OR 1.4 (95% CI 0.9, 2.4), herbicide users (OR 1.3 (95% CI 1.0, 1.6), and triazine users 

(OR 1.6 (95% CI 1.0, 2.5) (Cantor et al., 1992). However, the risk of NHL in association 

with ever-use of atrazine is only weakly suggested 20% increased odds of NHL (95% CI 

0.9, 1.8), adjustment for use of other pesticides did not alter estimates.

Combining datasets from all three of the NCI sponsored, Hoar-Zahm 1993 reported a 

significant association between atrazine use and NHL (OR 1.4 (95% CI 1.1, 1.8); however, 
Page 55 of 184

in contrast to Cantor et al. (1992), adjustment for the use of other pesticides, 

organophosphates and 2,4-dichlorophenoxyacetic acid (2,4-D) in particular, diminished this 

estimate considerably (Hoar Zahm et al., 1993).Notably, there was no appreciable 

difference in the risk of NHL among ever-users of atrazine and those who reported 

personally handling atrazine in this analysis. When evaluation the same association among 

women only, authors observed little evidence of an association, although the number of 

atrazine exposed female NHL cases was limited (Zahm et al., 1993). Overall, these authors 

suggest their evaluation presented little evidence of an association between atrazine use 

and NHL. 

Strengths of the studies include use of low likelihood of selection bias, high quality outcome 

ascertainment including histopathological confirmation, performance of exposure validation 

study (using pesticide sales records), and ability to adjust for other pesticides in the 

atrazine-NHL association. Limitations include small numbers of exposed cases in some 

instances, the hypothesis-generating nature of these investigations, and probable 

exposure-measurement error each of which may lead to uncertainties in the risk estimates 

reported. EPA notes other details are noted in Appendix D.

The question as to the role of co-exposure to other pesticides when measuring the 

association between atrazine, or any other pesticide, and NHL was evaluated in a pooled 

analysis using these three population-based case control studies (IA/MN, NE, KS). DeRoos 

et al. (2003) estimated the association between atrazine exposure and NHL, as well as 

several other pesticides, in an analysis which concurrently controlled for the influence of all 

(n=47) other pesticide exposure using sophisticated analytic techniques. Hierarchical 

regression techniques use available information (pesticide class, functional group, 

carcinogenic classification) to estimate a “prior” distribution of the pesticide-NHL risk 

relations, and adjust observed associations (results of pooled analysis) toward the “prior” 

distribution (De Roos et al., 2003). In this way, authors used information external to the 

studies themselves to inform the shape of the exposure-response curve. Hierarchical 

regression can add precision and accuracy to risk estimates, however precision and 

accuracy are only enhanced relative to the quality of the “prior” distribution. 

Researchers reported statistically-significant 50% increased odds of NHL in association 

with exposure to atrazine (hierarchical regression adjusted odds ratio (OR) 1.5 (95% 

confidence interval (95% CI), 1.0 to 2.2)), adjusting for exposure to all (n=47) other 

pesticides (De Roos et al., 2003). It is notable that this result, which is essentially a 

weighted average of the three NCI case control studies discussed above (Hoar et al. 1986; 

Cantor et al. 1992; Zahm et al. 1993), is closer to 1.0 than the adjustments performed in the 

logistic model presented in the pooled analysis of the same question performed by Zahm et 

al. (1993). The analytic methods in DeRoos et al. (2003) including the ability to mutually 

adjust for many other pesticides may explain the different risk estimate. Overall, DeRoos et 

al. (2003) cautioned that these data only provide limited evidence of any specific pesticide-
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NHL associations as results of many different statistical tests, the role of chance may 

explain these (positive) findings.

EPA also identified epidemiology studies external to the NCI series of case-control studies, 

and reported its interpretation of these data in the earlier evaluation of this database. 

Burmeister et al. (1990) studied the relation between triazine exposure and multiple 

myeloma and reported a non-significant 30% increased odds of exposure among those 

with MM (Burmeister, 1990). In addition, in a correlation analysis using county level 

pesticide use data (CA DPR), i.e., ecologic exposure assessment, and state cancer registry 

statistics, Mills et al. (1998) reported a moderate correlation between leukemia and atrazine 

use (P. Mills, 1998) (See Appendix D). In addition, an ecologic study comparing all cancers 

reported to a regional registry and triazine usage, authors reported no evidence of a 

significant correlation with NHL (J. Van Leeuwen, D. Waltner-Toews, T. Abernathy, B. Smit, 

& M. Shoukri, 1999). Mills et al. (1998) in the same correlation analysis as noted above, 

reported no evidence of a correlation between pesticide use and NHL among most subpopulations examined, with the exception of Hispanic females. These authors reported a 

non-significant positive correlation with NHL among this group. It was also noted in 

previous EPA reviews that an occupational cohort study reported elevated standardized 

mortality rates (SMRs) for NHL (n=2 cases) among triazine manufacturing plant workers in 

comparison to the general population of the area (P. A. MacLennan et al., 2003; P. A. 

MacLennan et al., 2002). These analyses updated comparisons made between 

manufacturing workers occupationally exposed to triazines and the general public 

published in the mid-1990’s and also reported elevated rates of NHL among workers 

(Sathiakumar & Delzell, 1997; N. Sathiakumar et al., 1996). Overall, these studies offer 

weak evidence to our consideration of this question, and have been superseded by more

recent investigations.

3.2.4.2 Hospital-Based Case-Control Studies (France)

Clavel et al. (1996) performed a hospital-based case control study of hairy-cell leukemia 

(HCL) and pesticide use, including triazines. Authors identified cases through oncology 

departments of 18 regional hospitals, and control patients through other hospital 

departments. Participation rates were modest, however participants were blinded as to the 

study hypothesis, therefore it is unlikely participants declined to participate due to pesticide 

usage. Pesticide exposure was assessed via self-administered questionnaire distributed via 

mail, verified by an industrial hygiene expert in pesticide use who also classified exposure 

intensity based upon information provided in the questionnaire. Authors reported elevated 

odds of hairy cell leukemia for all farmers (OR 1.5, lower limit 1.0), corn growers (OR 3.2, 

95% CI 1.6, 6.2), ever use of herbicides (OR 1.8, 95% CI 1.0, 3.1), and ever use of 

triazines (OR 2.4, 95% CI 1.2, 3.5) (Clavel et al., 1996). However, when authors examined 

the association among never users of organophosphates only, the association with 

triazines was attenuated and becomes statistically non-significant OR 2.0 (95% CI 0.7, 5.6). 

The correlation between organohphosphate and triazines is high in this study population. 
Page 57 of 184

In a study with similar design and methods and study population, performed between 2000-

04, Orsi et al. (2009) evaluated the association between farming including specific 

pesticides, and lymphoid neoplasms. Outcomes examined included non-Hodgkin’s 

lymphoma (NHL), multiple myeloma (MM), Hodgkin’s lymphoma (HL), and lymphoid 

proliferative syndrome (LPS). Cancer cases were identified through medical record 

evaluation in oncology centers at six hospitals in France; and, controls were identified from 

the patients who attended different departments of the same hospitals (e.g., rheumatology, 

orthopedics) unrelated to cancer treatment. Pesticide exposure information was collected 

first by self-administered questionnaire, followed by a structured in-person questionnaire 

administered by trained staff. Industrial hygienists with expertise in pesticide use evaluated 

participant responses. Authors modeled both ever-use of pesticides, as well as total

number of years employed in agriculture as a measure of duration of exposure.

Authors reported associations between employment in farming, and corn farming 

specifically in addition to ever use of specific pesticides in association with NHL, HL, and 

MM. For reported triazine use specifically, associations were observed with HCL (OR 5.1, 

95% CI 1.4-19.3) (Orsi et al., 2009). In addition, triazine use was associated with NHL 

(diffuse large cell lymphoma 8/20 OR 2.1 [95% CI 0.8–5.0]) and follicular lymphoma (FL) 

4/20, OR 2.3 [95% CI, 0.7–7.7]), but not with chronic lymphocytic leukemia 4/17 OR 0.9 

[95% CI 0.3–3.0] (Orsi et al., 2009). Small sample size, particularly low numbers of triazine 

exposed cases of lymphoma sub-types resulted in imprecise measures of the association. 

The strong magnitude of the association suggests additional studies employing a larger 

sample may result in more stable, positive associations; however, the width of the 

confidence limits which include 1.0 in each instances suggests results could be due to 

chance. The statistically significant association with hairy-cell leukemia is noted; however, 

this confidence interval is also quite wide. 

Authors suggest an association between triazine use and lymphohematopoietic cancers 

may be present, specifically with HCL, NHL and FL. Important to this effort, EPA notes 

triazines and not atrazine per se was measured. Strengths of these studies include the 

performance of several sensitivity analyses to reduce some sources of uncertainty, and use 

of two levels of pesticide exposure data collection by trained, blinded interviewers. 

However, the relatively low statistical power and relatively wide confidence bounds suggest 

lack of precision in the estimates and an inability to exclude chance as an explanation for 

these findings. In addition, as acknowledged by the authors, systematic bias could not be 

entirely ruled out with respect to representativeness of the control series of the population 

from which cases arose. Exposure assessment reflected ever-use over a lifetime and 

disallowed more refined analysis for risk assessment. Overall, the potential for bias in 

terms of selection and exposure measurement error, the highly correlated nature of multiple 

pesticide use and the inability to control for this in the Orsi et al. (2009) study, among other 

critiques noted in the appendices weaken these results. 
Page 58 of 184

3.2.4.3 AHS Estimates of Atrazine and Lymphohematopoietic Cancers

Within the AHS, Rusiecki et al. (2004) performed a cohort analysis of cancer (all) in 

association with lifetime use of atrazine. While authors examined the link between atrazine 

use and several cancer outcomes, the results for NHL and MM are discussed here only. A 

brief discussion of AHS methods and design as well as strengths and weaknesses is 

presented above in Section 3.2.3.1. 

Using a measure of both cumulative exposure (total number of days per working lifetime, 

lifetime exposure days (LTED)) and also intensity-weighted cumulative exposure (intensityweighted LTED, IWED), exposure modified by use of personal protective equipment (PPE) 

and other use practices, researchers reported evidence of a positive, non-statistically 

significant associations with NHL (OR(IWED) 1.75 (95% CI 0.73 to 4.20, n=20), as well as 

with multiple myeloma (OR 2.17 (95% CI 0.45 to 10.32) (same comparison) comparing the 

upper-quartile of atrazine exposure (175 exposure days) with the lowest exposed group (1-

20 exposure days) (J. A. Rusiecki et al., 2004). A suggestion of a positive trend across 

quartiles of atrazine exposure was noted, albeit the test for trend was not significant 

(p>0.10). No evidence of a statistical association between atrazine exposure and leukemia 

was observed. In this analysis, authors controlled for age, sex, alcohol consumption, 

residence on a farm, smoking status, educational level, family history of cancer, state of 

residence, and use of 10 most highly correlated pesticides with atrazine (J. A. Rusiecki et 

al., 2004). Recently, Beane-Freeman et al. (2011) updated this analysis within the AHS 

with approximately twice as many cancer cases. Adjusting for standard confounding 

variables as well as other pesticide use authors observed no evidence of an association 

with any lymphohematopoietic cancer (Beane Freeman et al., 2011). Specifically, authors 

reported no association with NHL or any NHL sub-type, or with leukemia or multiple 

myeloma. Regarding NHL specifically, the updated analysis included 152 cases while the 

initial evaluation included 68 cases. The increased statistical stability produced by the 

larger sample size appears to have clarified the nature of the association between atrazine 

and NHL in the AHS cohort of private and commercial pesticide applicators. 

Therefore, while the result of an initial, hypothesis-generating study in the AHS reported 

suggestive, positive associations with NHL and MM, due to elevated risks in the upperquartile, and the suggestion, albeit not statistically significant, of an exposure-response 

trend, recent re-evaluation of the data did not replicate this initial finding. The recent 

evaluation with twice as many cases of non-solid tumors report no evidence of an 

association with atrazine use within the AHS cohort. The increased sample size may have 

reduced the role of random variability in the estimates (i.e., wide confidence intervals), and 

the use of an updated AHS exposure algorithm may have reduced exposure measurement 

error in such a way as to move the estimate toward the null values. Positive associations 

initially observed may also be the result of multiple statistical tests; however, earlier studies 

(discussed above) suggest a possible link with NHL. 
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3.2.4.4 Conclusions: Lymphohematopoietic Cancers

Several investigators have evaluated the association between pesticide exposure including 

atrazine/triazine exposure specifically and lymphohematopoietic cancers since the early 

1990’s. Studies published prior to the 2003 EPA review provided little evidence of a link 

between leukemia or multiple myeloma and atrazine use (Brown et al., 1990; Brown et al., 

1993; Burmeister, 1990), however the small sample size and role of random variability in 

addition to non-differential exposure measurement error may have influenced these results. 

The recent AHS study with enhanced design, lifetime exposure measurement, outcome 

ascertainment, and increased sample size, reported risk estimates for leukemia and 

multiple myeloma that did not differ from the null (Beane Freeman et al., 2011). The latter 

study reduces uncertainty around this association, and is supportive of the conclusion of no 

association among occupationally exposed applicators. 

The results of both individual case-control studies and pooled analyses based upon these 

studies provided some evidence of an association between atrazine and NHL (Cantor et 

al., 1992; De Roos et al., 2003; Hoar Zahm et al., 1993; Zahm et al., 1993), however the 

authors themselves cautioned against considering specific pesticide-NHL associations 

reported as evidence of a causal link. For example, NHL risk was estimated in association 

with numerous pesticides, including 33 herbicides, in these studies; no adjustment for 

multiple statistical tests was performed. However, these studies provided adequate control 

for systematic biases such as selection bias (comparability of controls to population from 

which cases arose), recall bias (participants blinded to study hypothesis), and some control 

for confounding due to use of other pesticides. The hospital-based case control studies 

performed in France during two separate study periods present some evidence of a 

positive link, however the potential for systematic error in the conduct of these studies 

including selection bias due to method of control identification, weakens the strength of this 

evidence in the overall assessment of the epidemiologic database (Clavel et al., 1996; Orsi 

et al., 2009). 

In 2004, the initial AHS evaluation (Rusiecki et al. 2004) suggested a positive, elevated risk 

of multiple myeloma, but neither the risk estimates nor the test for trend was statistically 

significant although a positive exposure response trend was suggested in the data (J. A. 

Rusiecki et al., 2004). However, this finding was not replicated in subsequent AHS cohort 

analysis (Beane Freeman et al., 2011). These authors observed no evidence of a statistical 

association between atrazine use and leukemia, multiple myeloma, NHL and NHL subtypes, although there were fewer than 5 exposed cases per exposure category in some of 

the analyses. Differences in study design, i.e., population-based case-control and cohort, 

and exposure assessment methods between the earlier studies and the recent AHS study, 

as well as difference in sample size and the number of exposed cases between the earlier 

and later AHS cohort analysis, may explain these findings. Overall, the database lacks 

evidence of an association between atrazine and triazine exposure and these lymphoma 

and leukemia sub-types, including NHL.
Page 60 of 184

3.2.5 Other Cancer Sites

In addition to studies of atrazine’s potential influence on lymphohematopoietic cancers or 

reproductive and endocrine system tumors noted above, EPA has identified additional 

investigations of a potential link between atrazine with other cancer sites in both adults and 

children. These studies reflect atrazine’s potential influence upon tumors in the brain 

(glioma) and digestive tract including colon. Other cancer endpoints evaluated within AHS 

studies, but not yet discussed in the chapter, are also summarized herein to aid 

completeness of the data presentation. These studies are briefly mentioned herein.

3.2.5.1 Brain/Glioma

EPA identified a hypothesis-generating evaluation of the relation between farm exposures, 

including atrazine, and brain cancer. Results were reported separately for men (Ruder et 

al., 2004) and women (Carreon et al., 2005). The study was performed in the upper 

Midwest U.S. by a National Institute of Occupational Safety and Health (NIOSH) research 

team. Authors performed a population-based case control investigation of the association 

between pesticide exposure and gliomas, a type of brain cancers, among persons living in 

the upper mid-western states of Iowa, Michigan, Minnesota, and Wisconsin. In the study 

among women, cases were ascertained (1995-97) through interactions with medical offices 

or neurosurgeons, and were histopathologically confirmed. Proxy respondents were used in 

instances in which the case was deceased. Controls were non-cases of glioma. Controls 

may have had other cancers, and were identified through either state DMV (ages 18-65) or 

state health care files (ages 65-80). Pesticide exposure as well as other demographic 

information was obtained through self-reported questionnaire; trained industrial hygienists 

reviewed written responses and compared reported pesticide trade names with database to 

determine active ingredients used. Data collection included comparability of methods 

between cases and controls including a standardized interview, intermittent re-training of 

interviewers within the study period, and blinding of interviewers to the study hypothesis. 

Among 341 female cases and 527 controls, authors reported little evidence of an 

association between triazine or atrazine use and gliomas. The results include: ever-use of 

an herbicide, OR 1.0 (95% CI 0.6, 1.5); triazine OR 1.0 (95% CI 0.6, 1.7); and atrazine OR 

1.1 (95% CI 0.7, 1.8) (Carreon et al., 2005). Results for men were similarly unremarkable. 

Authors observed no association between glioma and triazine use (OR 0.9, 95% CI 0.6, 

1.2), and they did not separately estimate glioma odds in relation to atrazine use (Ruder et 

al., 2004). Although both evaluations were derived from the same study, Carreon et al. 

(2005) provided more extensive detail concerning study design and methods. For example, 

details concerning case and control participation rates were not provided in the study by 

Ruder et al. (2004). However, in both evaluations the risk estimate for atrazine excluding 

proxy respondents was similar. 
Page 61 of 184

Authors performed a hypothesis-generating evaluation of pesticide exposure in relation to 

glioma diagnosis. There are many strengths to this study including obtaining information 

from those who refused to participate in the study to compare characteristics and assess 

potential systematic bias; use of a 2-year lag in exposure through design to assure 

exposure among cases is not over-estimated; moderately high recruitment rate; and, use of 

several quality assurance and control methods including standardized interview techniques, 

as well as interviewer training and blinding to study hypothesis to reduce observer bias. 

However, there are some limitations as well including the method of selection of cases and 

controls and the possibly under-ascertainment of cases, possible heterogeneity in outcome 

classification, and quality of exposure measurement. Overall, the uncertainties presented in 

this study make it difficult to discern whether the lack of an observed association reflects 

the true population risk of glioma in relation to pesticide exposure, or reflects errors in study 

design, i.e., selection bias or exposure measurement error as noted by the study authors.

Other than results from the NIOSH studies, EPA identified two ecologic evaluations of the 

possible link between atrazine exposure and brain cancer. Mills et al. (1998) suggested a 

moderate correlation between this exposure and disease, while Van Leeuwen et al. (1999) 

reported no significant association (P. Mills, 1998; J. A. Van Leeuwen et al., 1999). Overall, 

the NIOSH studies provide insufficient evidence to determine whether an association 

between atrazine or pesticide exposure and glioma exists. EPA notes that the result 

evaluation within the AHS does not report a significant, positive association with any brain 

cancer (Beane Freeman et al., 2011). 

3.2.5.2 Pediatric Cancers

In an ecologic analysis, Thorpe et al. (2005) compared concentration of atrazine and other 

environmental contaminants in residential drinking water, in Maryland with state cancer 

registry data on childhood cancers to estimate risk of childhood cancers in association with 

contaminants. Several pediatric cancer sites were evaluated. Authors reported nonsignificantly elevated odds of bone cancer and leukemia among 689 pediatric cancers 

reported during the study period. There are few studies of pediatric cancers in association 

with pesticide use, and fewer in relation to atrazine use (Thorpe & Shirmohammadi, 2005). 

Therefore this study contributes to our understanding of the potential relation. Moderately 

elevated risk estimates were observed in this ecologic analysis, albeit non-statistically 

significant. However, in addition to the standard limitations of ecologic studies for use in 

causal inference, this study assumed all participants used well water for drinking water, did 

not address the uncertainty presented by residential mobility during pregnancy which may 

have also contributed to childhood cancers, and could not provide much information as to 

whether prenatal or postnatal exposures may be more strongly associated with pediatric 

cancers. Significant uncertainties are present in this study, which limits its use in 

determining whether a true link with atrazine exists.
Page 62 of 184

Rull et al. (2009) performed a population-based case control study of pediatric acute 

lymphocytic leukemia (ALL) within the Northern California Childhood Leukemia Study 

(1995-2002). Maternal residence at birth was compared with state pesticide use records 

(PUR) to estimate pesticide exposure. Authors considered exposure to 118 active 

ingredients for which there was some evidence of cancer, developmental or reproductive 

health effects, or evidence of endocrine disruption (adjudicated by authors); or, frequency

of use was high. Using proximity of maternal home as a measure of exposure, authors 

evaluated pesticide exposure over the child’s lifetime as well as during the first year of life 

only.

Authors reported borderline significant associations between those in the moderately 

exposed category of triazines (OR 1.9 (95% CI 1.0, 3.7), but not among those in the most 

highly exposed category (R. P. Rull et al., 2009). Adjusting for exposure to other classes of 

pesticides, the magnitude of the association in the moderately exposed category increased, 

but the precision decreased (OR 4.1, (95% CI 1.5, 11.1)) (R. Rull et al., 2009). Associations 

were also observed for herbicide categories, but results were not statistically significant 

(moderately exposed: OR 1.2 (95% CI 0.8, 1.9), and highly exposed: OR 0.9 (95% CI 0.5, 

1.5)) for lifetime exposure to herbicides. There was no association between pediatric 

triazine exposure during the first year of life and ALL risk in this study. Triazines to which 

this study population was exposed included atrazine, cyanazine, prometryn, pymetrozine, 

and simazine.

Authors performed a hypothesis-generating, population based case-control study of 

pediatric ALL in relation to pesticide exposure. Strengths of the study include method of 

case ascertainment, within 72 hours of diagnosis; the recruitment rate was high (84%); and 

the cases and controls were similar on several (matching) factors, reducing the potential for 

selection bias and residual confounding. The major limitation of this study is the method of 

exposure assessment, i.e., using maternal proximity to farm-site as a proxy for exposure, 

and the lack of information on critical windows of exposure for development of a pediatric 

cancer, and lack of precision of estimation of triazine with acute lymphocytic leukemia. 

Overall, given both the noted limitations of this study as well as the lack of statistical 

evidence of an association, the study is not strongly supportive of a link with atrazine.

Although not triazine or atrazine specific, within the AHS, authors evaluated childhood risk 

of cancer among children of parents enrolled in the AHS and reported an overall slight 

excess risk (OR 1.36, 95% CI 1.03, 1.79) (Flower et al., 2004). When examining specific 

cancers, risk of lymphoma was overall elevated among children of AHS families. Authors 

observed few pesticide-specific associations; but statistical power may have limited the 

ability to detect associations. Authors suggested non-pesticide farm exposures may explain 

these overall findings, such as Epstein-Barr virus. 

At this time the database is insufficient to determine whether an association exists between 

atrazine or triazine exposure and pediatric cancers. Importantly for pediatric cancers, 
Page 63 of 184

neither of these initial evaluations measured pesticide exposure during critical windows of 

development, including maternal exposures. The ecologic study is not of sufficient quality to 

support the hypothesis of a relation between atrazine and pediatric cancers. The lack of an 

exposure-response trend by proximity to field, and imprecision of risk estimate due to small 

number of cases in upper exposure category as well as some degree of non-differential 

exposure misclassification, among other factors, weakens the ability to draw a conclusion 

based upon these data.

3.2.5.3 Colon Cancer

Hoar et al. (1985) published a brief report of a case control analysis of pesticide use and 

colon cancer. Authors selected cases diagnosed with colon cancer in Kansas during 1976-

82, and population-based controls. As a brief report, limited detail concerning data 

collection procedures or statistical methods used to evaluate the association between colon 

cancer and exposure to herbicides was provided. Hoar et al. (1985) reported that 

employment on a farm was associated with a non-significant increase in colon cancer risk 

(OR = 1.6, 95% CI: 0.8 – 3.5), which was similar to the risk in farmers who did not report 

using herbicides, and reported use of triazines was not associated with colon cancer (OR 

1.4, 95% CI 0.2, 7.9) (Hoar, Blair, Holmes, Boysen, & Robel, 1985). With regard to selfreported use of herbicides, the investigators reported that the risk of colon cancer among 

farmers who reported mixing herbicides was similar to the risk in farmers who never 

reported using herbicides and there was a lack of dose-response relationship between 

years of herbicide use and colon cancer risk. Based on these reported findings, the 

investigators suggest that the study results do not support an association between colon 

cancer and herbicide exposure including triazines. In addition to this estimate, previously 

mentioned analyses in which cancer rates were compared between a cohort of triazine 

manufacturing plant workers and the general population did not report an elevated rate of 

either colon or rectal cancer incidence or mortality (P. MacLennan, E. Delzell, N. 

Sathiakumar, & S. Myers, 2003; P. MacLennan et al., 2002), and an ecologic analysis did 

not observe a link between atrazine and colon cancer (J. A. Van Leeuwen et al., 1999). 

Lee et al. (2007) reported colorectal cancer risk estimates for several pesticides including 

atrazine among 305 incident cases of colorectal cancer and 56, 813 non-colorectal cancer 

cases. Overall, authors observed no evidence of an association between atrazine use and 

colorectal cancer (OR 0.9 (95% CI 0.7, 1.2)), or colon cancer (OR 0.8 ( 95% CI 0.6, 1.1)), 

or cancer of the rectum (OR 1.2 (95% CI 0.7, 2.0)) (Lee et al., 2007). In this investigation, 

authors only reported the atrazine ever-use exposure metric, as no evidence of an atrazine 

exposure-response trend was observed. Recent cohort analyses within the AHS did not 

observe evidence of an association between lifetime atrazine use and either colon or rectal 

cancer (Beane Freeman et al., 2011; J. A. Rusiecki et al., 2004). 

The magnitude and direction of the risk estimates were suggestive of no association, and 

among the studies with sufficient sample size to evaluate an exposure-response trend, 
Page 64 of 184

none was observed. While the database is a small one, the more recent evaluations within 

the AHS have several strengths over previously published studies include the prospective 

design, larger sample size, and measurement of other potentially confounding variables 

including use of other pesticides. Among a nested case-control study and two cohort 

analyses which estimated colorectal cancer risk in association with atrazine, no evidence of 

an association was observed. Authors did not propose a mechanism through which 

atrazine could influence colorectal cancer risk; animal studies did not observed tumors in 

these sites (See Section 3.3). Overall, this database lacks evidence of an association.

3.2.5.4 Other AHS

In the interest of providing a complete delineation of the atrazine or triazine and cancer 

point estimates reported in the AHS, EPA notes the following additional results which have 

not yet been mentioned in cancer specific sections above. Neither the earlier nor the 

updated cohort analysis noted an association with all cancers combined, nor with several 

additionally evaluated cancer sites including: oral, esophagus, pancreas, melanoma, 

kidney, larnynx, brain or liver. Suggestive findings regarding other anatomical cancer sites 

noted by Rusiecki et al. (2004) in the earlier cohort analysis were not replicated in the 

updated study (Beane Freeman et al., 2011). Specifically, the odds of either lung or bladder 

cancer were not significantly different by atrazine exposure category in the more recent 

AHS study (Beane Freeman et al., 2011). Other anatomical cancer sites for which some 

statistical evidence of an association was noted, but for which no exposure-response trend 

was observed, or lacked an a priori hypotheses, were not considered to provide strong 

biological evidence of an association (esophageal and oral cancers, and inverse 

associations with pancreatic and laryngeal cancers).

As noted above, AHS researchers also performed several nested case-control analyses 

since the last EPA evaluation. Among these, neither the study of lung cancer nor 

cutaneous melanoma included specific risk estimates for atrazine use, due to lack of an 

observed exposure-response association, however EPA notes that lack of an observed 

association is important evidence to consider in the totality of the atrazine cancer 

epidemiology database (Alavanja et al., 2004; Dennis et al., 2010). Andreotti et al. (2009), 

compared pesticide use, including atrazine, among 93 pancreatic cancer cases and 82,503 

cancer-free applicator and applicator spouse controls, and reported no evidence of an 

association with atrazine use (OR 0.70 (95% CI 0.4-1.2)) (Andreotti et al., 2009). Due to the 

low numbers of atrazine-exposure pancreatic cancer cases, no exposure-response 

analysis was attempted. 

Across these AHS studies, evidence suggesting a statistical association is lacking, results 

did not significantly differ from unity. 
Page 65 of 184

3.3 Summary of Evidence Concerning the Carcinogenic Potential of Atrazine 

3.3.1 Synthesis and Integration of Toxicology and Epidemiology Literature

Over the past several decades, there have been numerous experimental toxicological as 

well as epidemiologic evaluations of the carcinogenic potential of atrazine. EPA has 

presented its evaluation of earlier iterations of this combined database in both 2000 and 

2003 as noted in section 3.1 (Blondell & Dellarco, October, 28, 2003 2003 #1026; EPA, 

2000, 2003a). In April 2010, EPA presented the most current assessment of the atrazine 

cancer toxicological database (EPA, 2010). In this section, EPA will consider the most 

current assessment of the atrazine cancer epidemiology database in the context of the 

available experimental data, combining the evidence from these two streams of 

information. 

EPA has extensively considered the experimental studies of the carcinogenic potential of 

atrazine. Briefly, in the 1986 review, EPA considered atrazine a “Group C” carcinogen, i.e.,

a possibly human carcinogen, based upon increased incidence of mammary gland tumors 

in Sprage-Dawley rats, and lack of mechanistic data to evaluate the mode of action and the 

relevance in the human population. Subsequently, mechanistic data were produced 

concerning the mode of action of atrazine which indicated that the mode of mammary 

tumor development in the rat was not operational in the human (EPA, 2000; SAP 2000). 

There were no other tumor sites noted in the animal bioassays (mice and rats) other than 

the mammary tumors in female rats. Additional mechanistic experimental data included 

laboratory-based genotoxicity and mutagenicity studies, as well as studies of atrazine’s 

tumor promoting potential. Study results indicated that atrazine was neither mutagenic or 

genotoxic, nor was there evidence of tumor promotion in the thyroid, pituitary, ovarian or 

breast cancer experimental systems evaluated (EPA, 2010; EPA, 2000). Studies of the 

tumor promotion potential of atrazine included both whole body and human cancer cell line 

systems. Furthermore, experimental studies published or submitted to EPA since the 

previous EPA review (i.e., the 2003 IRED) did not reveal additional evidence of tumor 

formation or promotion in animals (EPA, 2010). Based on the weight of evidence (including 

both empirical and mechanistic studies), atrazine exacerbates a condition to which the SD 

rat is normally predisposed due to an endocrine environment that is the normal cause of 

reproductive senescence in this strain for which there is no human counterpart. Thus, the 

experimental evidence does not indicate a role for atrazine in the carcinogenic process in 

humans.

In its review of earlier iterations of the atrazine cancer epidemiology database, studies 

published prior to the 2003 IRED, EPA stated that the data were insufficient to conclude 

whether an association between atrazine and cancer was present, and that the database 

lacked adequate information upon which EPA could perform a cancer risk assessment 

(EPA, 2000, 2003a, 2003b). In this chapter, EPA has considered the results of atrazine 
Page 66 of 184

cancer epidemiologic evidence published between the mid-1980’s to the present, inclusive 

of epidemiologic data previously reviewed as well as newly published studies. 

Epidemiologic studies have mainly focused upon cancers of the lymphohematopoietic 

system (leukemia and lymphoma including NHL), the reproductive and endocrine systems 

(prostate, breast, ovarian and thyroid cancer), and a small number of studies considering 

other cancer endpoints. 

While there were earlier suggestions of a possible link between atrazine and 

lymphohematopoietic cancers in a few studies published in the early 1990’s, overall, the 

evidence was weak. Even among the studies which reported a suggestive, positive relation 

between atrazine and NHL, authors cautioned against the ability to link any one pesticide 

with NHL in these studies due to the number of statistical comparisons made, the modest 

magnitude and marginal statistical significance of the associations estimated, the 

attenuation of the association upon mutual adjustment for other pesticide exposure, and 

lack of an exposure-response trends, among other factors (De Roos et al., 2003; Hoar 

Zahm et al., 1993; Zahm et al., 1993). Two hospital-based case-control studies in France 

also offered some additional information to consider the question; however concern 

regarding possible systematic error in the selection of the control series, role of 

confounding bias, lack of an exposure-response trend and often small sample size in the 

evaluation of specific leukemia and lymphoma sub-types, limits use of these data (Clavel et 

al., 1996; Orsi et al., 2009). More recently, within the AHS, although initial cohort analysis 

results reported some evidence of a non-statistically significant, positive relation between 

lifetime atrazine use and NHL and multiple myeloma based upon suggestion of an 

exposure-response trend (J. A. Rusiecki et al., 2004), a follow-up study with twice the 

number of cancer cases did not observe an association between atrazine and any leukemia 

or lymphoma sub-type (Beane Freeman et al., 2011). In addition, within the experimental 

database, there is no evidence of an increase in lymphoma non-solid tumor formation, 

including NHL, when test animals are dosed with atrazine, simazine or propazine. In 

addition, there is no evidence in the cancer epidemiology literature that hormones or other 

reproductive factors are related to the development of leukemias or lymphomas (Adami, 

2002). Therefore, at this time, there is no experimental evidence to suggest a role for the 

herbicide in the etiology of these tumors, nor do the epidemiology studies performed within 

agricultural, occupational populations, and demographically consisted primarily of white, 

males, suggest evidence of an association. 

Given the known neuroendocrine effects of atrazine, several investigators evaluated a 

potential link between atrazine exposure and cancers of the reproductive and endocrine 

systems including prostate, breast, ovarian as well as thyroid cancer. In 2003, EPA 

concluded, in consultation with the FIFRA SAP, that results of studies of triazine 

manufacturing plant workers which suggested an association with prostate cancer were 

likely due to the presence of an on-site prostate cancer screening program, the PSA test, in 

which most of the male employees participated during the time period of this study (EPA, 

2003a, 2003b). Follow-up studies within this occupational cohort reflecting more refined 
Page 67 of 184

triazine exposure information did not alter this conclusion (P. A. Hessel et al., 2004). Two 

studies within the California cancer registry suggested a possible correlation and/or 

association with atrazine (P. Mills, 1998) or simazine (P. K. Mills & Yang, 2003), however 

the aggregate level exposure measurement and lack of risk estimate for atrazine per se in 

the latter study, presents uncertainty in the use of these data. 

An initial nested case-control studies within the AHS (Alavanja et al., 2003) observed no 

evidence of a relation between ever-use of atrazine and prostate cancer, and, more 

recently, two cohort evaluations with sufficient statistical power to detect an association 

between atrazine use and prostate cancer, if an association exists, did not report evidence 

of a link with prostate cancer, i.e., risk estimates did not differ from the null (Beane 

Freeman et al., 2011; J. A. Rusiecki et al., 2004). During the 2003 FIFRA SAP evaluation, 

the Panel suggested that differences in exposure profile, including the periodicity and 

intensity of exposure, between triazine manufacturing plant workers and corn and soybean 

pesticide applicators, precluded consideration of these data as part of a whole database 

(EPA, 2003b). However, EPA believes that the AHS cohort represents one of the most 

highly exposed occupational cohorts available for study, and lack of evidence of an 

association in this cohort which has adequate statistical power to detect an association, is 

strongly suggestive of no evidence of an association among occupationally exposed 

primarily white, males in the U.S. population. Furthermore, the results of chronic toxicology 

studies in two species (rat and dog) indicated no evidence of increased incidence of 

prostate tumors. As noted briefly above, there is no evidence of a role for atrazine in 

prostate tumor formation in either the animal bioassays, or within in vitro studies of tumor 

promotion, genotoxicity or mutagenicity with atrazine. There is no known mechanism for 

atrazine’s possible role in prostate tumor formation in humans. Considering both the 

epidemiologic and toxicologic studies, the current database lacks evidence of an 

association between atrazine and prostate cancer.

The results of three ecologic epidemiology studies provided inconsistent and generally 

weak evidence concerning a possible association between atrazine and breast cancer 

(Hopenhayn-Rich et al., 2002; M. A. Kettles et al., 1997; Muir et al., 2004). One ecologic 

study indicated a small, but significant 20% increased odds of the disease among the 

exposed (Kettles et al. 1997), however a follow-up analysis using the same methods in the 

same study population failed to replicate the finding (Hopenhayn-Rich et al. 2002). Muir et 

al. (2004) found evidence of a positive association with breast cancer, but in only one of the 

two study sites, thereby inconsistent evidence. A study among Latinas who reported 

employment in agricultural work residing in California, using state cancer registry data and 

reported pesticide use and other self-reported data, lacked evidence in support of an 

association with breast cancer, however, the ecologic nature of the exposure assessment 

again precludes making any strong conclusions from this study (P. Mills & Yang, 2006). In 

a population-based case control study in a high-use area of the upper-midwest (WI), 

authors reported no evidence of an association with breast cancer, however the upperexposure quartiles included fewer than 5 exposed cases (J. A. McElroy et al., 2007). Within 
Page 68 of 184

the AHS, considering either female spouses of pesticide applicators (Engel et al., 2005) or 

female applicators themselves (Beane Freeman et al., 2011), no association between everuse of atrazine and breast cancer was observed. However, these authors were not able to 

address exposure-response trends given the relatively small number of cases included in 

the analysis. Strengths of this database include the reflection of several different target 

populations across the studies including women in the general population, although 

residing in high-use areas, female agricultural workers, female spouses residing on a farm 

or residing with a commercial pesticide applicator, and female professional applicators, as 

well as study designs. In addition, investigations measured both indirect, i.e., exposure 

through laundering atrazine contaminated clothing, as well as direct use of the herbicide. 

Limitations include use of aggregate exposure measurement techniques (ecologic) as well 

as lack of ability to investigate exposure response association, and relatively small sample 

of atrazine exposed females in many of the studies. The observational data are insufficient 

to determine whether or not a link between atrazine and breast cancer may exist, and at 

this time, are suggestive of no true association. Further, the initial observation of mammary 

tumors in early experimental animal studies (female Sprague-Dawley rats), were 

determined through follow-up mechanistic studies to be due to a mode of action that is not 

operative in humans, i.e., reproductive senescence due to high estrogen production 

resulting in constant estrogen stimulation of the mammary tissue. Therefore, at this time, 

considering the current toxicological and experimental evidence, the database lacks 

evidence of an association between atrazine and breast cancer. 

The epidemiologic database for the relation between atrazine and ovarian cancer is small, 

and weakly suggestive of a possible association across three studies, however the possible 

role of random variability, bias or confounding in the risk estimates observed cannot be 

excluded. A recent ecologic analysis suggested an inverse (protective) association 

between atrazine exposure and ovarian cancer in an area of agricultural use in the U.S. 

(Hopenhayn-Rich et al., 2002), and an earlier ecologic study reported no evidence of an 

association (J. A. Van Leeuwen et al., 1999). However, an earlier case-control study in Italy 

suggested a potential 1.5- to 2-fold increased odds of ovarian cancer among those 

“possibly” and “definitely” exposed to triazines (Donna et al., 1989). Earlier evaluation of 

this study by both EPA and the FIFRA SAP suggested uncontrolled confounding by other 

pesticide exposure may explain results. 

Using state cancer registry data and pesticide reporting survey information, Young et al. 

(2005) reported slight increased risk for total triazine users, but not for atrazine exposed 

women (H. Young et al., 2005). However, the prevalence of atrazine exposure was less 

than eight percent in this study, limiting the ability to detect an association. Recently, AHS 

researchers reported an elevated odds of ovarian cancer among ever-users of atrazine in a 

population of female pesticide applicators. Investigators were not able to evaluate an 

exposure-response relation, and only reflected nine ovarian cancer cases, four of whom 

reported atrazine use. Overall, there are weak, but somewhat suggestive observations of 

an association between ovarian cancer and atrazine use, however significant uncertainties 
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in the observational database exist. At this time, data are insufficient to inform whether an 

association may exist. 

The experimental animal studies do not strongly suggest a role for atrazine in ovarian 

cancer etiology, however uncertainties in both databases must be considered. Animal 

bioassays, as noted above, do not show increased incidence of ovarian tumor formation 

upon administration of atrazine, simazine or propazine (common mechanism group). The 

mode of action through which atrazine influences neuro-endocrine effects, i.e., suppression 

of the lutenizing hormone (LH) surge and reduced number of ovulatory cycles, the 

“incessant ovulation” hypothesis, would likely lead to a decreased, and not an increased 

risk of this cancer in women. Suppression of the LH surge would result in altered pituitary 

and steroid hormone concentration in the ovary, and reduce the mitogenic potential of 

estrogens in the target tissue as well as the trauma of ovulatory cycles suspected to be 

linked to ovarian cancer etiology. However, epidemiologic evidence against the “incessant 

ovulation” hypothesis exists, including the observation that both pregnancy and lactation 

result in increased, and not decreased, levels of estradiol and follicle-stimulating hormone 

(FSH), respectively, and also appear to be protective against ovarian cancer (Henderson, 

Ponder, & Ross, 2003). Furthermore, recent in vitro studies performed by EPA indicate that 

while it does not appear that atrazine is an aromatase inhibitor, atrazine does appear to 

increase CYP19 (the aromatase gene) messenger RNA (mRNA) synthesis, and may 

increase concentration of aromatase. The aromatase enzyme converts testosterone to 

estradiol; increasing aromatase could lead to increased concentrations of estradiol. 

However, an increase in aromatase has not been detected in vivo. Although there are 

some indications of an association with ovarian cancer and a biologically plausible role for 

the herbicide, although no known alternative mode of action at this time, overall considering 

the strengths, weaknesses and uncertainties of the limited number of studies available, the 

current database lacks strong evidence of a true association.

Among all the epidemiology studies included in this evaluation, only one study offers a risk 

estimate for the association between thyroid cancer and atrazine exposure: the recent AHS 

cohort analysis (Beane Freeman et al., 2011). In this evaluation, authors report evidence of 

a statistically significant four-fold increased odds of thyroid cancer among male pesticide 

applicators; the estimate remains elevated and significant when adjusting for body mass 

index, a known risk factor for thyroid cancer. However, authors note the lack of a clear 

exposure-response trend, and the small number of atrazine exposed cases in some 

categories, e.g., n<5. These and other limitations to this study present some uncertainties 

in the interpretation of these results. There is no evidence of an increased incidence of 

thyroid tumors in experimental animal bioassays, nor does atrazine act as a tumor 

promoter in experimental systems, or illustrate the ability to alter thyroid hormone levels or 

thyroid-stimulating hormone (EPA, 2010). In the AHS, atrazine was not associated with 

thyroid disease (Goldner et al., 2010). Epidemiologic evidence indicates a likely link 

between reproductive factors including steroid and peptide hormones in the etiology of this 

cancer; the striking difference in the trends and pattern of thyroid cancer between men and 
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women support this hypothesis (Adami, 2002). However, a clear mode of action is lacking, 

and no toxicological evidence exists as to a mode of action for atrazine in thyroid tumor 

development. Thus, at this time, evidence is insufficient to determine the nature of the 

association between thyroid cancer and atrazine.

In addition to these more commonly assessed atrazine-cancer associations, EPA identified 

a few other studies. Considering pediatric cancers, the evidence is currently insufficient to 

make a determination as to whether a relation exists. One ecologic study of the association 

between atrazine concentration in drinking water and all pediatric cancers reported to the 

Maryland state cancer registry, only provided unadjusted risk estimates as the result of 

simple contingency table analysis. Other methodological limitations noted by EPA in 

Appendix E render this study of low quality. In the AHS, parental atrazine use is weakly, 

non-significantly associated with childhood cancers (combined) (OR 1.27 95% CI 0.70, 

2.30), but multiple statistical tests were performed (Flower et al., 2004). An evaluation of 

acute lymphocytic leukemia (ALL) among children in the Northern California childhood 

cancer cohort (R. P. Rull et al., 2009) suggested a non-significantly elevated risk of ALL 

with triazine exposure, however lack of exposure refinement including information on 

critical windows of effect in development result in uncertainties that preclude the Agency 

from reaching any definitive conclusions based upon this study. Maternal hormone levels 

have been associated with childhood leukemias in some studies (Adami, 2002); however, a 

potential role for atrazine in the alteration of maternal hormone and subsequent childhood 

leukemia risk is not available. Overall, this database is insufficient to inform whether a 

causal association may exist.

Studies of a link between atrazine or triazine with colorectal cancer do not present 

compelling evidence of an association at this time, overall this database is weak. The 

magnitude and direction of the measured associations were not strong, exposure-response 

trends were lacking, and the number of cases was often small (Beane Freeman et al., 

2011; Hoar et al., 1985; Lee et al., 2007; P. K. Mills & Yang, 2007; J. A. Van Leeuwen et 

al., 1999). Concerning glioma (brain cancer), a population-based case-control study in the 

upper Midwest of the U.S., suggest no association between atrazine exposure and glioma 

in men (Ruder et al., 2004) or women (Carreon et al., 2005), however under-ascertainment 

among the elderly given difficulty in diagnosing this cancer in the elderly population, and 

other sources of information bias may have influenced this result. EPA notes the recent 

AHS cohort study observed no link between atrazine use and brain cancer among 

occupationally exposed men and women (Beane Freeman et al., 2011). Overall, the 

studies of an association between either digestive system tumors, or brain gliomas are 

limited in number, and reflect uncertainties that hinder use in making causal inference. At 

this time, the database lacks evidence of a link between atrazine and these types of 

cancers.

To be exhaustive in the consideration of all the available evidence, EPA considered all 

available evidence presented in other AHS studies which have estimated the association 
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between atrazine exposure and several other anatomical cancer sites not yet discussed. 

Overall, no additional evidence of an association was identified across these atrazinecancer risk estimates. Specifically, these include: lung, pancreatic, cutaneous melanoma, 

oral, esophageal, kidney, larynx, and liver cancer (Beane Freeman et al., 2011; J. Rusiecki 

et al., 2004). As already stated, results of animal bioassays did not indicate increased 

incidence of any tumors, other than mammary which is discussed above. 

3.3.2 EPA Conclusion

As a result of extensive investigations within both experimental and observational studies, 

the cancer database does not strongly suggest a link between atrazine exposure and 

increased cancer incidences in the human population. Recent cohort analyses among an 

occupationally exposed population of pesticide applicators (AHS) indicated no evidence of 

an association with leukemias, or lymphomas including several sub-types of lymphoma. 

Researchers observed no increased incidence in these tumors in experimental studies. The 

AHS reports were also strongly supportive of the conclusion of no association between 

atrazine and prostate cancer, as a result of studies with sufficient statistical power (1-

β>0.85) to detect an association, no other peer-reviewed papers were identified since the 

last EPA evaluation. The database for breast cancer is small, and, at this time, is not 

strongly supportive of a link with atrazine given limitations of study design, sample size, and 

lack of exposure-response information. EPA determined that the mode of action through 

with atrazine influences an increased incidence of mammary gland tumors in animals is not 

operative in humans. Indications of a role of atrazine in ovarian cancer in epidemiology 

evaluations is based on a small number of studies, some of which reported positive 

association (Beane Freeman et al., 2011; Donna et al., 1984; Donna et al., 1989; H. A. 

Young, P. K. Mills, D. G. Riordan, & R. D. Cress, 2005). However, available information 

concerning atrazine’s known mode of action, decrease LH surge and exposure to 

estrogen/ovulation, suggest a relation is not likely. There is only one study available which 

estimated an association between atrazine and thyroid cancer. While statistical results are 

somewhat suggestive of an association, the exposure-response trend was not monotonic, 

based on a small number of exposed cases, and not yet replicated in other populations. 

Other atrazine cancer epidemiology studies were not suggestive of an association including 

cancers of the digestive tract (colorectal, pancreas), brain, and lung, pancreatic, cutaneous 

melanoma, oral, esophageal, kidney, larynx, and liver cancer. The data regarding pediatric 

cancers are insufficient to draw a conclusion. As noted several time above, there were no 

other animal tumors identified in the atrazine bioassays submitted to EPA. 

While some epidemiology studies are weakly suggestive of an association between 

atrazine exposure and cancer incidence in the human population, the preponderance of the 

different lines of evidence – including both experimental and epidemiology studies – does 

not substantiate a role of atrazine on human carcinogenesis. Thus, the weight of the 

evidence supports that atrazine is not likely to be carcinogenic in the human population.
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4. PROPOSED UPDATES TO THE DOSE-RESPONSE ASSESSMENT

4.1 Benchmark Dose Analysis

Detailed information about the BMD analysis summarized below can be found in Appendix 

C.

Numerous scientific peer review panels over the last decade have supported the Agency’s 

application of the BMD approach as a scientifically supportable method for deriving points 

of departures (PoDs) in human health risk assessment, and as an improvement over the 

historically applied approach of using no-observed-adverse-effect levels (NOAELs) or 

lowest-observed-adverse-effect-levels (LOAELs). The NOAEL/LOAEL approach does not 

account for the variability and uncertainty in the experimental results, which are due to 

characteristics of the study design, such as dose selection, dose spacing, and sample size. 

With the BMD approach, all the dose response data are used to derive a PoD. Moreover, 

the response level used for setting regulatory limits can vary based on the chemical and/or 

type of toxic effect and for a given chemical, all available studies can be evaluated using a 

common response level which aids interpretation (USEPA, 2000). 

In recent months, the Agency pursued performing BMD analysis on a variety of endpoints 

(attenuation of LH, prostatitis, delayed PPS, delayed vaginal opening). However, after an 

initial scoping exercise, it became clear that the LH data were the only datasets with 

sufficient dose-response data to support empirical modeling. For the other endpoints, 

toxicological effects have only been observed at 1) only at very high doses; 2) only at single 

dose levels; or 3) exhibit flat dose-response curves. As such, the only BMD analyses 

provided here and in Appendix C relate to LH attenuation. Although, it would be preferred 

to model additional endpoints the other outcomes reported following atrazine exposure are 

less sensitive than the LH data. As such, the Agency is focusing on the data which are 

most sensitive and thus most protective of human health. 

Generally, EPA calculates both the BMD (central estimate) and the BMDL (the BMDL 

corresponds to the 95% lower bound on dose). As a matter of science policy, EPA uses 

the BMDL as the point of departure. A key consideration when performing BMD modeling 

is determining the response level—known as the benchmark response (BMR). In the case 

of continuous endpoints, like LH attenuation, the BMR most often represents an X% 

change from background levels (or untreated controls). Typically, the BMR is selected on 

the basis of a combination of biological (mode of action, quantitative link between key 

events, historical/concurrent controls) and statistical considerations (sample size, 

variability, etc). However, in the absence of information concerning the level of response 

(or % change) associated with an adverse effect, the Agency’s draft BMD guidance 

suggests that the BMD and BMDL corresponding to a change in the mean response equal 

to one standard deviation from the control mean be used as the BMR. In the case of 

atrazine, the level of attenuation of the LH surge considered to be adverse is a function of 
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several factors including but not limited to the life-stage and functional outcomes under 

consideration (e.g., estrous cyclicity disruptions in rats). Moreover, the differences in 

reproductive cycles/aging between rodents and humans add an additional level of 

complexity to establishing a specific BMR value. These differences notwithstanding, in 

general, the reproductive physiology among mammals is remarkably similar making the use 

of the LH surge as a critical effect of concern appropriate. This perturbation of the LH 

surge is the cornerstone of the cascade of events leading to the adverse reproductive 

outcomes attributed to atrazine exposure. Activation of the HPG axis resulting in the 

pulsatile secretion of GnRH and LH is critical to puberty onset. For instance, decreased LH 

during puberty would lead to insufficient stimulation of the gonads to reduce the circulating 

hormone levels needed for development of sex accessory tissues in males and females. 

Moreover, researchers have found that disruption of GnRH release and the ensuing 

dampening of the LH surge can lead to delays in vaginal opening and preputial separation. 

Based on the available data, attenuation of the LH surge continues to be the most sensitive 

endpoint in the developmental/reproductive toxicity profile for atrazine. Attenuation of the 

LH surge occurs at doses that are either comparable to or ≈ 4- to 30-fold lower than those 

eliciting other developmental/reproductive effects (e.g., delayed ossification, estrous 

cyclicity disruptions, delays in sexual maturation, and prostatitis). Taking into account these 

challenges in establishing a response level, a BMR of one standard deviation from the 

control mean was chosen as the suggested choice by the EPA BMDS 2000 guidance 

whenever the biologically significant degree of change in a response is not clearly defined.

 

There are various options for selection of the appropriate dose metric for atrazine including 

administered dose and average daily steady state AUC for plasma triazines (i.e., atrazine 

and its metabolites). The average daily steady state AUC dose metric may be an 

appropriate internal dose metric to better characterize the link between atrazine exposure 

and attenuation of the pre-ovulatory LH surge since it considers the contributions of all the 

relevant triazine species. BMD modeling was conducted using the administered atrazine 

dose for each of the studies identified below and using the corresponding AUC metric for 

the study selected as the most robust for deriving a point of departure (4-day exposure data 

from Cooper et al., 2010). AUC estimates were derived as indicated below using the

corresponding linear expression that relates this dose metric to administered doses of 

atrazine.

Y = 13.0x -8.57

Where Y is the daily steady state estimate and x is the administered dose of atrazine

4.1.1 Identification of Critical Studies 

Several oral toxicity studies in the rat provided LH data that were considered for doseresponse modeling. These include Morseth et. al. (1996a & b), Minnema (2001; 2002) and 

Cooper et al. (2010). Recent data from the NHEERL provided the most robust LH data in 

terms of dose selection (number of dose levels - particularly low dose range - and the 
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spacing between dose levels) and variability of the data. The study design addressed the 

low-dose region of the dose-response curve and exhibited less data variability (i.e., smaller 

standard deviations).

 

A one month oral study by Morseth et al . (1996a) established a LOAEL of 40 mg/kg/day 

and a NOAEL of 5 mg/kg/day for an effect on LH surge. A limitation of the study is the large 

variability of the data (as reflected by standard deviations). A subsequent 6 month study 

(Morseth et al . 1996b) established a LOAEL of 3.65 mg/kg/day for attenuation of LH surge; 

the NOAEL was 1.8 mg/kg/day. The data from this study also indicated large variability. 

The NOAEL from this study was the basis for the point of departure used to establish the 

chronic reference dose (cRfD) in the EPA’s 2003 human health risk assessment for 

atrazine. 

Minnema (2001) established a LOAEL of 40 mg/kg/day for attenuation of LH surge in rats 

treated with atrazine for 1 month. The NOAEL was 5 mg/kg/day. These data also 

displayed variability. In a follow-up 6 month dietary study (Minnema 2002), atrazine (1.8, 

3.4, 4.9, and 29.1 mg/kg/day) was without effect on LH surge in ovariectomized female SD 

rats. Confidence in this study is low; BMD/BMDL values were not calculated for Minnena 

(2002). Based on historical control data, the induction of LH surge in control animals 

seems to be suboptimal. Rats exhibited an infection that may have impacted LH surge 

response. In addition, estradiol data were not submitted to evaluate LH induction 

response.

In a recent study by Coder (2011), a decreased estradiol induced LH surge was observed 

in female SD rats following oral gavage administration of atrazine (0. 6.5, 50, or 100 

mg/kgday) for 4 days. However, no statistically acceptable BMD/BMDL values were 

obtained with the LH surge data from this study. The Coder (2011) study is limited by the 

poor selection of doses in the low dose region of the response curve. 

 

4.1.2 Methods & Results 

EPA’s Benchmark Dose Software (BMDS) version 2.1.2 was utilized to estimate BMD 

estimates from the available LH studies. All models pertinent for continuous data were 

evaluated including exponential, Hill, power, polynomial and linear. Criteria used to assess 

the best fit included statistical (goodness-of-fit) values, model criteria (Akaike Information 

Criteria; AIC), BMD/BMDL ratios, and visual inspection.

 

The most reliable model fits and BMD and BMDL estimates were obtained from the 4-day 

data from Cooper et al. (2010). In contrast to the NHEERL dataset, less reliable model fits 

and failure of some models to compute BMD/BMDL values were encountered with the 

other available studies that had more data variability and less well-defined dose selection. 

There is uncertainty in the BMD/BMDL estimates from these other studies. The table below 
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summarizes the results of BMD analyses of the available LH studies and the level of 

confidence in the estimates. Detailed results are provided in Appendix C.

Table 1: Summary of Administered Dose BMD analyses

LH Studies BMD BMDL Confidence

NHEERL 4 day 

(oral gavage) 4.92 2.42

High – robust dataset (less 

data variability; well-defined 

dose selection); describes LH 

response very well.

Minnema 1 Month 

(oral gavage) 653.3 245.1

Low – data variability; both 

BMD and BMDL estimates 

outside experimental range; 

large BMD/BMDL ratio; failure 

of some exponential models to 

compute estimates.

Morseth 1 Month 

(oral gavage) 523.7 174.7

Low – data variability; BMD 

estimate outside experimental 

range; large BMD/BMDL ratio; 

failure of some exponential 

models to compute estimates.

Morseth 6 Month 

(dietary) 36.2 17.1

Low – data variability;BMD 

estimate outside experimental 

range; failure of some 

exponential models to 

compute estimates.

Coder 4 day

(oral gavage) 4.30 3.40

Low – No statistically 

acceptable BMD/BMDL 

values

For the Cooper et al. (2010) 4-day data, EPA calculated the BMD/BMDL for the 

administered dose and internal dose metrics. When administered dose was used as the 

dose metric, the exponential model (subset model 4) provided the best fit to the data, and 

based on statistical (goodness-of-fit values), modeling criteria (Akaike Information Criteria; 

AIC values), and visual inspection, BMD and BMDL estimates were 4.92 and 2.42 

mg/kg/day, respectively (see Cooper et al. 2010) BMD output and summaries provided in 

Appendix C). The BMD and BMDL estimates described the LH surge data very well. 

 

BMD analyses were also carried out with the corresponding average steady state plasma 

levels and average daily steady state AUC. The Hill model provided the best fits for both 
Page 76 of 184

dose metrics. The following table summarizes BMD and BMDL estimates corresponding to 

administered dose and AUC as the dose metrics. 

Table 2: BMD Modeling Results with average steady state plasma triazines and AUC estimates

BMD and BMDL 

estimates (BMR = 

1SD from control 

mean)

SS levels (mg/L) of 

Total Triazine 

Equivalents

Daily AUCss 

(mg/L-hr)

BMD 2.41 57.9

BMDL 1.03 24.7

BMDL values of 1.03 mg/L and 24.7 (mg/L-hr) were estimated for steady state levels of 

plasma triazines and daily AUCss, respectively. These BMDL estimates would be 

particularly useful for comparison to drinking water monitoring estimates.

Figure 4: EPA BMDS Exponential Model (Constant Variance; BMR = 1 SD from control mean) 

Results for Attenuation of LH Surge in Female Rats Administered Atrazine by Gavage - NHEERL 

Data. 

 5

 10

 15

 20

 25

 0 10 20 30 40 50 60 70

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

12:24 05/04 2011

BMDL BMD

 

Exponential
Page 77 of 184

Figure 5: EPA BMDS Hill Model (Constant Variance; BMR = 1 SD from control mean) Results for 

“Area Under the Triazine Equivalent Plasma-Concentration Time Curve” as the Dose Metric - 

NHEERL Data (Attenuation of LH Surge in Female Rats Administered Atrazine by Gavage). 

 5

 10

 15

 20

 25

 0 2 4 6 8 10 12

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

08:46 05/09 2011

BMDL BMD

 

Exponential
Page 78 of 184

Figure 6: EPA BMDS Hill Model (Constant Variance; BMR = 1 SD from control mean) Results for 

“Daily Steady State AUC for Triazine Equivalents” as the Dose Metric - NHEERL Data (Attenuation 

of LH Surge in Female Rats Administered Atrazine by Gavage). 

4.1.3 Proposed Point of Departure

Plasma levels of atrazine and its metabolites appear to reach pseudo steady state at or 

near four days of exposure in the adult rat. Specifically, after four days of exposure, the 

plasma levels of total triazine equivalents plateau during periods of repeated dosing. 

Similarly, data from multiple laboratories ranging in duration from four days up to six 

months of exposure show that attenuation of LH is fairly constant at a given dose and 

suggest PD steady state. As such, given both PK and PD steady state, it is prudent to 

select the most robust study from those studies where steady state has been reached (i.e., 

any study four days or longer). Data from Cooper et al. (2010) contain multiple doses 

across a broad range and provide comparatively low variability. As such, the data from 

Cooper et al. (2010) are proposed as the most appropriate for deriving a PoD. 

Specifically, the BMDL of 2.56 mg/kg/day (based on AUC dose metrics) is proposed as a 

point of departure (PoD) for atrazine risk assessment for repeated exposure scenarios. 

 5

 10

 15

 20

 25

 0 50 100 150 200 250 300

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

09:12 05/09 2011

BMDL BMD

 

Exponential
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Possible approaches for extrapolating this animal PoD to humans is discussed in Section 8 

below.

4.2 Pharmacokinetic Analysis

4.2.1 Background

Characterization of the pharmacokinetics and internal dosimetry of atrazine and its

metabolites represents a critical step for elucidating the link between exposure and 

attenuation of the pre-ovulatory LH-surge for the application of a mode of action approach 

to risk assessment. LH attenuation resulting from atrazine exposure has been identified as 

an early key event in the progression towards apical toxicities (for example, disruption of 

ovarian function including estrous cyclicity and delays in puberty onset), However, the 

temporal relationship between atrazine exposure and LH attenuation has not been fully 

characterized.. One of the information gaps noted at the September 2010 SAP meeting 

was the lack of detailed pharmacokinetic information for atrazine and its individual 

metabolites (FIFRA 2010c). Pharmacokinetic information is particularly important since 

atrazine as a parent chemical is short-lived and its chloro-s-triazine metabolites are 

presumed to be responsible for most, if not all, of the neuroendocrine mode of action 

effects including LH attenuation.

The current understanding of atrazine metabolism is that upon entering the body via the 

oral route of administration, it is quickly and extensively metabolized to the monodealkylated chloro-s-triazine metabolites deethylatrazine (DEA) and deisopropylatrazine 

(DIA). A second round of CYP-450 mediated dealkylation results in diaminochlorotriazine 

(DACT), the most abundant metabolite of atrazine frequently observed in animal toxicity 

studies and human biomonitoring (depicted in Figure 7). Atrazine can also undergo 

dechlorination to form glutathione conjugates. This metabolic pathway, however, is 

assumed to be more of a detoxification pathway since the chlorinated triazine ring is 

considered to be the toxicologically important moiety. It has been postulated, based on 

urine radioactivity percentages, that glutathione conjugation may represent as much as 

30% of atrazine metabolism (Timchalk et al. 1990).
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Figure 7: Metabolic Scheme for Atrazine

The following section is an update to the pharmacokinetic information evaluated by the 

Agency for atrazine and its metabolites that now includes different species including rats, 

monkeys, and humans. This information is being used to propose an interim 

pharmacokinetic modeling approach that relates orally administered doses of atrazine to 

plasma triazine levels given that a thorough review and evaluation of a recently submitted 

PBPK model by Syngenta has not been completed.

4.2.2 Updates on the Pharmacokinetics of Atrazine and Its Metabolites in the 

Rat

Additional details of the pharmacokinetics of atrazine and its metabolites are emerging 

based on the need to characterize the temporal relationship between atrazine exposure 

and attenuation of the LH surge. In a recent effort aimed at characterizing the 

pharmacokinetic behavior of atrazine and its metabolites, Syngenta in collaboration with 

Glutathione conjugation

Deisopropylatrazine (DIA) Deethylatrazine (DEA)

Atrazine

Diaminochlorotriazine (DACT)

CYP450

CYP450

Dealkylation Dealkylation

Dealkylation Dealkylation

Glutathione conjugation

Deisopropylatrazine (DIA) Deethylatrazine (DEA)

Atrazine

Diaminochlorotriazine (DACT)

CYP450

CYP450

Dealkylation Dealkylation

Dealkylation Dealkylation
Page 81 of 184

WIL Research Laboratories, LLC, embarked on a time-intensive in vivo pharmacokinetic 

study with female Sprague Dawley rats following oral administration of atrazine (Coder 

2011). The study consisted of two modes of oral administration: once daily bolus dosing via 

oral gavage for 4 days or distributed dosing for 4 days through the diet. Particular attention 

was given to capturing the elusive short in vivo plasma half-life of atrazine following oral 

administration since previous studies have not sampled plasma early enough post-dosing 

to properly characterize it. In the new study, plasma was sampled and analyzed for 

atrazine, DEA, DIA, and DACT as early as 5 minutes following oral gavage dosing. The 

treatment duration of 4 days was selected based on the critical study that established the 

LOAEL for LH attenuation at 3.12 mg/kg bw/day over a 4 day period (Cooper et al. 2010). 

Also included in the pharmacokinetic study was a 4-day washout period during which 

dosing with atrazine did not occur in order to characterize the clearance of all 4 plasma 

chloro-s-triazines (i.e., atrazine, DEA, DIA, and DACT) resulting from once daily dosing for 

4 days. It should be noted that a mass balance analysis of the administered atrazine dose 

was not performed in the study at any of the levels tested.

4.2.3 Oral Gavage Dosing of Sprague Dawley Rats with Atrazine

The results from oral gavage dosing indicate that atrazine is rapidly absorbed and cleared 

from rat plasma at all doses tested. Figure 8 shows the 24-h plasma profile of atrazine 

following oral gavage dosing at 3, 10, and 50 mg/kg bw atrazine. Maximum plasma levels 

(Cmax) were reached as early as 20 minutes following dosing at 3 and 10 mg/kg bw 

atrazine and much earlier at 10 minutes following dosing with the higher dose of 50 mg/kg 

bw atrazine. The increase in Cmax was less than proportional with respect to dose, 

increasing by only 2.8- and 1.7-fold as compared to 3.3-(3 to 10 mg/kg bw) and 5-fold (10 

to 50 mg/kg bw) increases in dose levels, respectively. In contrast, the area under the 

plasma concentration-time curve (AUC) estimated from zero to infinity increased more 

directly in proportion with dose (Table 3). When plasma levels during the elimination phase 

were plotted on a semi-logarithmic scale (i.e., lnCp) versus time, a significant linear 

correlation was observed for all doses of atrazine tested. A plasma elimination half-life as 

short as 44 minutes was estimated for the low dose of 3 mg/kg bw but was noted to 

increase with dose to 1.2 and 2.9 hours at 10 and 50 mg/kg bw atrazine, respectively 

(Table 3). 
Page 82 of 184

Figure 8: Plasma profile of atrazine for 24 hours following oral gavage dosing of rats with 3, 10, or 50 

mg/kg bw atrazine. Each time point represents average of six measurements ± SD. Data is from 

Coder 2011.

The plasma profiles of the mono-dealkylated metabolites DEA and DIA were found to 

parallel that of atrazine. Figure 9 shows the individual plasma profile of DEA or DIA in 

comparison to that of atrazine at the low dose of 3 mg/kg bw atrazine. Several features can 

be noted from Figure 9. First, the same time to maximum plasma levels (i.e., tmax) of 20 

minutes as atrazine is observed for DEA and DIA. Such parallel absorption profiles into 

plasma for these two metabolites is indicative of a first pass effect, that is, a fraction of 

atrazine is metabolized to DEA and DIA in either the GI tract or liver prior to reaching the 

systemic circulation. Second, although plasma levels differ in magnitude, the same generic 

elimination profile as atrazine was observed for DEA, and DIA (see elimination decay after 

tmax in Figure 9).Therefore, the plasma clearance of atrazine does not appear to be due to 

metabolism to either DEA or DIA since its plasma profile does not decrease in 

correspondence to the appearance of either of these two species. Since atrazine is known 

to be short-lived as a parent chemical and barely detected in urine, if at all, (Ross et al. 

2009; Catenacci et al. 1990; Ikonen et al., 1988), its fast disappearance from plasma may 

be due to metabolism to species other than DEA and DIA.

0 1 2 3 4 5 6 7 8

0

5

10

15

20

25

30

35

40

10 12 14 16 18 20 22 24

3 mg/kg bw/day ATRA

10 mg/kg bw/day ATRA

50 mg/kg bw/day ATRA

time (hr)

Plasma levels(

μg/L)
Page 83 of 184

Table 3: Pharmacokinetic parameters estimated for atrazine and its chlorinated metabolites DEA, 

DIA, and DACT following oral gavage dosing of rats with 3, 10, and 50 mg/kg bw atrazine. Data 

analyzed is from Coder 2011.

ORAL GAVAGE DOSING

ATRAZINE

Atrazine dose

(mg/kg bw/day)

tmax Cmax

(μg/L)

AUC 

(μg*hr/L)

0→∞

kel

(/hr)

Elimination

Half-life

3.0 20 min 6.43 ± 1.42 6.41 0.941 ± 0.069 44.2 min

10.0 20 min 18.04 ± 6.38 24.87 0.569 ± 0.047 1.22 hr

50.0 10 min 31.07 ± 10.31 109.6 0.238 ± 0.013 2.91 hr

DEA

3.0 20 min 58.17 ± 7.40 153.5 0.240 ± 0.009 3.40 hr

10.0 1 hr 141.00 ± 22.61 465.3 0.0928 ± 0.0034 7.50 hr

50.0 45 min 219.67 ± 71.50 2277 0.0678 ± 0.0035 10.22 hr

DIA

3.0 20 min 220.67 ± 43.03 370.3 0.564 ± 0.030 1.20 hr

10.0 1 hr 614.17 ± 93.20 1336.0 0.2425 ± 0.0146 2.86 hr

50.0 45 min 1066.0 ± 419.0 7255.0 0.176 ± 0.0065 3.95 hr

DACT

3.0 2 hr 682.00 ± 95.01 8909.2 0.0873 ± 0.006 7.90 hr

10.0 6 hr 2125.00 ± 398.00 29492.0 0.0845 ± 0.0070 8.20 hr

50.0 8 hr 8321.67 ± 1630.57 149124.9 0.0752 ± 0.0102 9.22 hr
Page 84 of 184

Figure 9: The parallel plasma profiles of atrazine versus DEA or DIA following oral gavage dosing of 

rats with 3 mg/kg bw atrazine. Data is from Coder 2011.

Oral gavage dosing with the higher doses of 10 and 50 mg/kg bw atrazine resulted in much 

longer tmax values of 60 and 45 minutes, respectively, for both DEA and DIA while that of 

atrazine remained at 20 minutes for 10 mg/kg bw and even decreased to 10 minutes at 50 

mg/kg bw (Table 3). This observation suggests that the increases in tmax for DEA and DIA 

are likely due to saturation of first pass metabolism resulting in prolonged absorption of 

these two species into plasma. Limited solubility of DEA and DIA produced in the GI tract at 

the dose levels tested is a less likely possibility since a biphasic absorption phase in the 

plasma profile would be observed in such case as previously reported by McMullin and coworkers (McMullin et al. 2007).

Although comparable tmax values were exhibited by DEA and DIA, plasma levels (Cmax 

and AUC) were several folds higher for DIA (Table 3). As with atrazine, both DEA and DIA 

0 1 2 3 4 5 6 7 8

0.0

2.5

5.0

7.5

10.0

20

40

60 Atrazine

DEA

time (hr)

Plasma Levels (

μg/L)

0 1 2 3 4 5 6 7 8

0

2

4

6

8

10

100

200

300

Atrazine

DIA

time (hr)

Plasma Levels (

μg/L)
Page 85 of 184

exhibited fast linear elimination kinetics. At the low dose of 3 mg/kg bw atrazine, plasma 

elimination half-life estimates were 3.4 and 1.2 hours for DEA and DIA, respectively. Dose 

dependent decreases in plasma elimination were also noted for DEA and DIA. Plasma halflife estimates for DEA increased to 7.5 hr (~2.2 fold decrease) and 10.2 hr (~ 3-fold 

decrease) at 10 and 50 mg/kg bw atrazine, respectively. Comparable decreases in plasma 

clearance were seen for DIA with plasma half-life increasing to 2.9 (~2.4-fold clearance 

decrease) and 3.9 hrs (~3.2-fold clearance decrease) at 10 mg/kg and 50 mg/kg bw 

atrazine, respectively. The only metabolite that did not exhibit dose-dependent decreases 

in plasma clearance was DACT for which a plasma elimination half-life was consistently 

estimated at 8-9 hours (Table 3). DACT was also the predominant species detected in rat 

plasma with estimated AUC values 20-24 fold higher than those estimated for DIA and 

even higher for DEA. A much longer tmax of 2 hr was seen for DACT at 3 mg/kg bw 

atrazine while plasma levels of DIA and DEA (primarily DIA) were seen decreasing (Figure 

10). Peak plasma levels of DACT did not decrease much until about 14 hours post-dosing 

when plasma levels of DEA and DIA had been significantly depleted. Such observation is 

consistent with DACT being formed from the precursor metabolites DEA and DIA after 

these species have entered the systemic circulation. The increases in plasma half-life at 10 

and 50 mg/kg bw atrazine observed with DEA and DIA (Table 3) are likely due to saturation 

of the metabolic systems responsible for their metabolism to DACT. The lack of dosedependent changes in the clearance of DACT is consistent with this species being a 

terminal metabolite and/or the lack of saturation for further metabolism at the dose levels 

tested.

Figure 10: 24-h plasma profile of atrazine and its chlorinated metabolites DEA, DIA, and DACT 

following oral gavage dosing of rats with 3 mg/kg bw atrazine. Each time point represents average of 

six measurements ± SD. Data is from Coder 2011.

0.00 0.25 0.50 0.75 1.00

0

50

100

150

200

250

400

600

800

2 4 6 8 10 12 14 16 18 20 22 24

Atrazine

DEA

DIA

DACT

time (hr)

Plasma Levels (

μg/L)
Page 86 of 184

Repeated daily dosing with atrazine did not change the pharmacokinetic behavior of any of 

the species analyzed (i.e., atrazine, DEA, DIA, or DACT) as compared to single dose 

behavior. The entire 4-day treatment/4-day washout plasma profile at 3 mg/kg bw atrazine 

is shown in Figure 11. The most abundant species was DACT followed by DIA, DEA, and 

atrazine as the least abundant. DACT was also the species with the slowest clearance, 

followed by DEA, DIA, and atrazine. With the exception of DACT for which a bit of 

accumulation is observed due to sustained peak plasma levels, all 4 species analyzed can 

be expected to be cleared from plasma by 24 hours, constituting a repeated episodic 

pharmacokinetic profile upon repeated (once per day) daily dosing.

In summary, atrazine as a parent chemical exhibited very fast pharmacokinetics with a 

tmax as short as 20 minutes and a plasma half-life of 44 minutes at the low dose of 3 

mg/kg bw. The tmax information for atrazine in comparison to those for DEA and DIA is 

consistent with the formation of these two metabolites being due to first pass metabolism. 

The parallel plasma elimination profiles of DEA and DIA relative to that of atrazine do not 

support the fast plasma clearance of atrazine being due to metabolism to DEA and DIA. 

Without mass balance in a pharmacokinetic study, it is difficult to disregard other 

metabolites as a possibility to explain the fast plasma clearance of atrazine since urinary 

excretion of intact atrazine is likely to be minimal.

Figure 11: Plasma profile of atrazine, DEA, DIA, and DACT from repeated once a day oral gavage 

dosing of rats with 3 mg/kg bw atrazine for 4 days. Values represent the mean of six replicates ± SD. 

Data is from Coder 2011.

3 mg/kg bw/day atrazine: oral gavage dosing

0 20 40 60 80 100 120

0

200

400

600

800

140 160 180

Atrazine

DEA

DIA

DACT

time (hr)

Plasma Levels (

μg/L)
Page 87 of 184

4.2.4 Dietary Exposures of Sprague Dawley Rats to Atrazine

Dosing with atrazine through the diet resulted in plasma levels for all 4 monitored 

chlorotriazines (i.e., atrazine, DEA, DIA, and DACT) being more variable and overall much 

lower as compared to oral gavage dosing at comparable dose levels. Figure 12 shows the 

plasma profile of all 4 species at the equivalent dietary dose level of 3 mg/kg/day. 

Maximum plasma levels of all species analyzed were reached much later at between 12.5 

and 20 hours, reflecting the consumption of diet by rodents during the night. Only the 

plasma profile of DACT can be deciphered with the high level of variation observed. 

Plasma levels of DACT seem to reach a more defined pseudo steady state level by the 

fourth day of daily dosing, probably reflecting the more frequent dosing with atrazine 

through the diet and the longer half-life of this compound as compared to the other 

metabolites. A similar behavior was observed at the higher dose levels of 10 and 50 mg/kg 

bw atrazine (data not shown).

Figure 12: Plasma profile of atrazine, DEA, DIA, and DACT from dietary dosing of rats with atrazine at 

a nominal dose of 3 mg/kg bw/day atrazine for four days. Data at each time point represents 

measurements in 6 animals. Data points are not connected to help elucidate pattern. Data is from 

Coder 2011.

The fluctuating behavior of plasma levels resulting from dietary dosing made analysis 

difficult and thus only the elimination phase of each triazine species was analyzed for 

comparison to oral gavage dosing. In the case of atrazine, no analysis was performed due 

to plasma levels being too low and highly variable for any informative analysis. The results 

for DEA, DIA, and DACT indicate, as with oral gavage dosing, that these species exhibit 

3 mg/kg bw/day atrazine: dietary dosing

0 24 48 72 96 120

0

100

200

300

400

500

144 168 192

ATRA

DEA

DIA

DACT

time (hr)

Plasma Levels (

μg/L)
Page 88 of 184

linear elimination kinetics (assessed by plotting lnCp vs. time). As listed in Table 4, the 

estimated first order elimination rate constants are very comparable to those estimated via 

oral gavage dosing. The estimated plasma half-life of DEA of 3.7 hr is very comparable to 

that of 3.4 hr estimated via oral gavage. Greater differences were observed at 10 and 50 

mg/kg bw/day, but with no clear pattern since a 1.2-fold slower clearance was observed at 

10 mg/kg bw but an increase of about the same magnitude was observed at 50 mg/kg bw 

(Table 4). In the case of DIA, with the exception of 3 mg/kg bw atrazine for which an 

approximately 2-fold decrease in clearance was observed (plasma half-life of 3 hr versus 

1.2 hr via oral gavage dosing), the results with 10 and 50 mg/kg bw atrazine were 

comparable to those estimated via oral gavage dosing. Plasma clearance estimates were 

particularly consistent for DACT as compared to oral gavage dosing.

Table 4: Pharmacokinetic Elimination Parameters estimated for atrazine and its chlorinated 

metabolites DEA, DIA, and DACT following dosing of rats with atrazine through the diet at nominal 

doses of 3, 10, and 50 mg/kg bw. Data is from Coder 2011.

Dietary Dosing with Atrazine

Dose

(mg/kg bw/day)

Elimination Rate 

Constant (hr-1)

Elimination Plasma 

Half-life (hr)

DEA

3.0 0.187 ± 0.012 3.70

10.0 0.0761 ± 0.004 9.10

50.0 0.104 ± 0.005 6.65

DIA

3.0 0.231 ± 0.016 3.00

10.0 0.248 ± 0.015 2.80

50.0 0.243 ± 0.013 2.85

DACT

3.0 0.0897 ± 0.0019 7.73

10.0 0.0729 ± 0.0021 9.51

50.0 0.0734 ± 0.0018 9.44

4.2.5 Plasma Clearance Discrepancy Based on Studies with Radiolabeled

Atrazine 

Analysis of the new pharmacokinetic dataset submitted to the Agency in which atrazine, 

DEA, DIA, and DACT are monitored in rat plasma following once daily oral dosing with 

atrazine indicates a plasma clearance discrepancy as compared to studies performed with 

radiolabeled atrazine. None of the 4 species monitored in the new study exhibits a plasma 

clearance that is comparable to that estimated based on radiolabeled atrazine. All four 

species (i.e., atrazine, DEA, DIA, and DACT) can be expected based on their estimated 

plasma half-lives to be cleared from plasma by 24 hours, constituting an episodic 

pharmacokinetic profile that contrasts the bioaccumulative profile observed based on 
Page 89 of 184

radiolabeled atrazine (Thede 1987). Pharmacokinetic studies with radiolabeled atrazine 

have been carried out with a metabolically stable [14C]-radiolabeled triazine ring that allows 

for the study of the overall disposition of all species associated with the radiolabel (Thede 

1987; Paul et al 1993). In general, radiolabel disposition studies achieve a high degree of 

mass balance (some very close to 100%) due to minimal sample preparation being 

required for quantification of radioactivity.

At the September 2010 SAP meeting, the Thede 1987 study was presented as a key 

pharmacokinetic study for estimating plasma AUC based on radiolabeled triazine 

equivalents. The study consisted of repeated once daily dosing of young female Sprague 

Dawley rats with a wide range of radiolabeled atrazine doses (1-100 mg/kg bw/day). The 

resulting plasma profile (shown in Figure 13) clearly shows accumulation of the radiolabel 

continuing until the fourth day of daily dosing when pseudo steady state plasma levels 

seem to be reached (Thede 1987). Given that the LOAEL for LH attenuation was also 

observed in rats after 4 days of once daily dosing with atrazine (Cooper et al. 2010), the 

bioaccumulative pharmacokinetic profile resulting from daily dosing with radiolabeled 

atrazine, not the episodic profile observed with atrazine, DEA, DIA, or DACT, is consistent 

with the temporality of the LH attenuation endpoint.

The plasma elimination from the Thede 1987 study was noted to be linear with a consistent 

elimination rate constant of 0.010-0.015 hr-1 (plasma half-life of 53.3 - 69.3 hrs) for the 1, 3, 

7, and 10 mg/kg bw/day dose groups (Table 5) that indicates accumulation upon repeated 

once daily dosing with radiolabeled atrazine. At the higher doses of 50 and 100 mg/kg 

bw/day atrazine, the elimination rate constant was substantially higher (>20-fold) compared 

to the other dose groups at about 0.3 hr-1. The nature for the difference is not known, but it 

may be reflective of the limited time points examined, particularly at these high dose levels. 
Page 90 of 184

Figure 13: Plasma profiles of radiolabeled triazine equivalents resulting from repeated once daily 

dosing of rats with radiolabeled atrazine at 1, 3, 7, 10, 50 and 100 mg/kg bw atrazine. Data is from 

Thede 1987.

Table 5: First order elimination rate constants estimated from plasma data in the Thede 1987 study 

involving repeated once daily dosing of rats with radiolabeled atrazine (Thede 1987).

4.2.6 Additional Rat Radiolabeled Atrazine Studies Evaluated for Plasma

Clearance

One of the limitations of the Thede 1987 study noted at the September 2010 SAP meeting 

was the limited number of plasma data points for characterizing the elimination phase of 

the radiolabel (FIFRA SAP 2010b). The elimination phase consisted of plasma data at only 

3 time points corresponding to one animal of each dose group (i.e., 1, 3 ,7 , and 10 mg/kg 

bw). In essence, the elimination rate constant of 0.010-0.015 hr-1 was estimated with 12 

ATRA dose (mg/kg/day) Elimination rate constant (hr-1)

1 0.010

3 0.015

7 0.013

10 0.014

50 0.376

100 0.308
Page 91 of 184

data points from 4 individual animals. Recognizing that radiolabeled atrazine studies offer 

at this time the best information to inform internal dosimetry and risk assessment, the 

Agency embarked on an effort to gather more evidence for the plasma clearance obtained 

from analyzing the plasma data from the Thede 1987 study. Thus, other rat radiolabeled 

atrazine pharmacokinetic studies regardless of dose, sex, or dosing regimen (single or 

repeated) were gathered for evaluation. 

Paul et al (1993)

In the first study by Paul et al. 1993, single oral gavage doses of 1 or 100 mg/kg bw 

radiolabeled atrazine were administered to groups of three male and female SD rats and 

the resulting levels of radioactivity in plasma and other tissues were measured at different 

times post-dosing (Paul et al. 1993). At both administered dose levels, plasma 

measurements were limited to male rats and at the following time points: 2, 48, 168, and 

336 hrs for 1 mg/kg bw and 24, 72, 168, and 336 hrs for 100 mg/kg bw. As seen in Figure 

14, the plasma elimination phase at both dose levels exhibited linear behavior when plotted 

on a semi-logarithmic plot versus time. The resultant elimination rate constants were 0.013 

and 0.014 hr-1 which are consistent with those estimated for the 1-10 mg/kg bw/day dose 

groups from Thede 1987 study (Thede 1987). It should be noted that the plasma analysis 

of the 100 mg/kg bw/day dose group in the Thede study resulted in a much higher 

clearance rate as compared to those obtained from Paul et al. 1993. The nature for 

discrepancy is unknown.

Other tissues were also examined for radioactivity clearance in the Paul et al. 1993 study 

including the brain, lungs, liver, kidneys, and others. Reported clearance values for all 

tissues examined were much greater than 24 hours, estimated at as a range of 59-300 

hours (Paul et al. 1993) (data not shown).
Page 92 of 184

Figure 14: Plasma profile and analysis of elimination of a single oral gavage dose of 1 and 100 mg/kg 

bw radiolabeled atrazine in rats. Data is from Paul et al. 1993.

Simoneaux 1985 

In another study with radiolabeled atrazine by Simoneaux 1985, thirty six male rats (3 per 

time point) were given oral doses of either 0.4 or 4.0 mg/kg bw 14C-atrazine for seven days 

and sacrificed at 5, 7, 9, 10, 14, and 18 days post-dosing at which point plasma was 

sampled for radioactivity (Simoneaux 1985). The plasma profiles are given in Figure 15. 

Similar to the other two studies already discussed, analysis of the elimination phase at both 

dose levels revealed linear behavior when plotted on a semi-logarithmic scale versus time. 

When the resultant rate constants (estimated as per day from the reported plasma data) 

are converted to per hr, the resultant values of 0.013 and 0.014 hr-1 are remarkably 

consistent with those estimated from the Thede 1987 and Paul et al. 1993 studies.

In summary, one of the caveats of the plasma clearance analysis from the Thede 1987 

study has been addressed by the evaluation of two additional rat pharmacokinetic studies 

with radiolabeled atrazine. The two additional studies were carried out in different 

laboratories with different sexes and dosing regimens (single and repeated dosing) and 

along with the Thede 1987 study support an elimination half-life in the order of 53.3 - 69.3 

Single dose of 1 mg/kg bw 14C- atrazine

0 48 96 144 192 240 288 336

0.0

0.2

0.4

0.6

Paul et al. 1993

time (hr)

Plasma radiolabeled Equivalents (ppm)

Single dose of 100 mg/kg bw 14C-atrazine

0 24 48 72 96 120 144 168 192 216 240 264 288 312 336

0

5

10

15

20

25

Paul et al. 1993

time (hr)

Plasma radiolabeled equivalents (ppm)

Single dose of 1 mg/kg bw 14C-atrazine

0 48 96 144 192 240 288 336

-8

-6

-4

-2

0

Paul et al. 1993

Slope = - 0.013 ± 0.002

R2

 = 0.74

time (hr)

ln (Plasma radiolabeled equivalents) (ppm)

Single dose of 100 mg/kg bw 14C-atrazine

48 96 144 192 240 288 336 384

-4

-2

0

2

4 Slope = - 0.014 ± 0.001

R2

 = 0.94

Paul et al. 1993

time (hr)

ln (Plasma radiolabeled equivalents) (ppm)
Page 93 of 184

hrs (kel of 0.013-0.015 hr-1), indicative of bioaccumulation upon repeated daily dosing with 

atrazine.

Figure 15: Plasma profile and analysis of elimination of repeated oral gavage dosing with either 0.4 or 

4 mg/kg bw 14C- atrazine in rats for seven days. Data is from Simoneaux 1985.

4.2.7 Pharmacokinetic Studies with Radiolabeled Atrazine in Monkeys

All of the pharmacokinetic studies discussed thus far were perfomed in rats. In an effort to 

investigate the pharmacokinetics of radiolabeled atrazine in non-human primates, Hui et 

al., 2011 dosed adult rhesus monkeys with either 1, 10, and 100 mg of [14C]- radiolabeled 

atrazine via oral gavage and sampled plasma for radioactivity at various time points postdosing (Hui et al. 2011). An intraveneously administered dose of 0.25 mg of [14C]-atrazine 

was also included in the study to estimate the oral bioavailability of atrazine. As with other 

radiolabel studies already discussed, a high degree of mass balance of over 95% of the 

administered dose was achieved. As with other species, urinary excretion was the primary 

route of body excretion followed by fecal excretion. An oral bioavailability greater than 60% 

was estimated for atrazine. Analysis of plasma radioactivity levels indicates that there were 

dose-dependent changes in absorption across the different doses of atrazine tested. 

Repeated dosing with 0.4 mg/kg bw 14C-Atrazine

0 2 4 6 8 10 12 14 16 18

0.00

0.02

0.04

0.06

0.08

0.10

Simoneaux 1985

time (days)

Plasma radiolabeled equivalents (ppm)

Repeated dosing with 4.0 mg/kg bw 14C-Atrazine

0 2 4 6 8 10 12 14 16 18

0.0

0.5

1.0

1.5

Simoneaux 1985

time (days)

Plasma radiolabeled equivalents (ppm)

Elimination Phase Analysis: 0.4 mg/kg bw/day 14C-atrazine

10 12 14 16 18

-6

-5

-4

-3

-2

-1

0

1

Simoneaux 1985

slope = -0.236 ± 0.031 day-1

= -0.010 ± 0.001 hr-1

 time (days)

Ln (Plasma radiolabeled equivalents) (ppm)

Elimination Phase Analysis: 4.0 mg/kg bw/day 14C-atrazine

10 12 14 16 18

-4

-3

-2

-1

0

1

slope = -0.258 ± 0.019 day-1

 = -0.011 ± 0.008 hr-1

Simoneaux 1985

 time (days)

Ln (Plasma radiolabeled equivalents) (ppm)
Page 94 of 184

However, as was the case in rats, plasma clearance was linear. In fact, the plasma profiles 

for the three different doses could be reproduced with a one comparment linear model with 

optimized parameters (i.e., volume of distribution and elimination rate constant). Estimates 

of the steady state volume of distribution via non-compartmental analysis were about the 

same for the three dose groups given the variability reported around the mean values. An 

average value of about 3.4 L/kg bw was estimated for the three doses tested. Moreover, as 

similarly discussed for rat studies, both plasma Cmax and AUC increased with dose, but 

AUC notably scaled more directly with dose (Hui et al. 2011). The estimated plasma 

elimination rate constants along with other pharmacokinetic parameters are listed in Table 

6 below. It should be noted that the estimated monkey plasma elimination rate constant of 

0.03-0.06 hr-1 is only 2-4 fold greater than the estimated rat value of about 0.013 hr-1 with 

considerable variability of as much as 50% (Table 6). The corresponding plasma half-life in 

monkeys is estimated to be on average about 20 hours. Thus, bioaccumulation can also be 

expected to occur in monkeys upon repeated daily dosing with atrazine.Thus, there is a 

remarkable consistency in the clearance across different doses and species (rats and 

monkeys) resulting from the linear pharmacokinetic behavior exhibited by radiolabeled 

atrazine. 

Table 6: Estimated plasma pharmacokinetic parameters in monkeys following oral gavage dosing 

with 1, 10, and 100 mg [14C]-atrazine. Data is from Hui et al. 2011.

14C-Atrazine Dose

(mg)

Cmax

(mg/L)

AUC

0→∞

((mg/L)*hr)

Vdss

(L/kg bw)

kel

(hr-1

)

t1/2

(hr)

1 0.05

± 0.02

1.61

± 0.35

2.427

± 0.397

0.04

± 0.03 20 ± 8

10 0.32

± 0.02

13.42

± 3.58

4.413

± 0.781

0.06

± 0.03 16 ± 9

100 3.04

± 0.71

151

± 24

3.255

± 0.597

0.03

± 0.00 24 ± 1

4.2.8 Pharmacokinetic Studies with Atrazine in Humans 

As with the majority of environmental chemicals, human information on the 

pharmacokinetics of atrazine is very limited. In one of the few controlled human studies 

available, atrazine was administered orally to six volunteers (bw range of 74 to 117 kg) via 

a single oral dose of 0.1 mg/kg bw (Davidson 1985). Urine was collected for each subject 

for up to 168 hours post-dosing while blood samples were obtained from a single subject at 

various time points starting at 2 hrs post-dosing. Atrazine, DEA, DIA and DACT were 

measured in blood and urine samples using analytical methods with 0.005 ppm minimum 

detection levels. 
Page 95 of 184

4.2.8.1 Human Blood Pharmacokinetic Information on Atrazine and its 

Chloro-s-triazine Metabolites

It should be noted that the information reported for blood samples refers to whole blood 

rather than plasma. This is particularly important in comparing results to rat findings since 

accumulation of up to 1.5% of administered radiolabel has been reported in rat red blood 

cells (Thede 1987; McMullin 2003). Although the human blood information reported may be 

limited (from a single subject), at minimum, pharmacokinetic behavior can still be compared 

for consistency across different species. From the blood information reported, the most 

important finding from a species extrapolation perspective is the linear first order behavior 

exhibited by DIA and DACT which has been consistently observed in all animal species 

discussed thus far. Peak blood levels of DIA were reported to be reached within 2 hours 

and decreased rapidly thereafter with an apparent linear elimination rate constant of 0.25 

hr-1 (half-life of 2.8 hr) (Table 7) which is only about 2.2-fold lower than that estimated for 

rats at 3.0 mg/kg bw atrazine (Table 3). The rate of appearance of DACT in human blood 

was noted to be slower than DIA with a tmax of about 5 hr, consistent with oxidative 

metabolism from the precursor metabolites DEA and DIA. A first order elimination rate 

constant of 0.039 hr-1 (half-life of 17.5 hr) was estimated for DACT which is about 2-fold 

lower than the rat value of about 0.08 hr-1 (half-life of 8-9 hours) estimated for rats (Table 

3). Atrazine and DEA were detected in human blood at levels below quantitation at the 

administered dose level of 0.1 mg/kg bw atrazine. Such finding is consistent with rat 

findings in that these two plasma chlorotriazines represent the least abundant out of the 4 

chlorotriazines monitored in plasma. Moreover, atrazine would be barely detected at 2 hr 

(the first time point analyzed) if the rat plasma half-life of about 44 minutes at 3 mg/kg bw 

happens to be similar or even shorter in humans. Thus, the human blood information 

provided in the Davidson 1987 report is at minimum qualitatively similar to the 

pharmacokinetics of atrazine and DEA observed in rats and other species. From a species 

extrapolation perspective, it should be underscored that DIA and DACT exhibited linear 

elimination kinetics that is about 2-3 fold lower than that observed in rats (Table 3). When 

the elimination rate constant of about 0.013 hr-1 estimated from the Thede 1987 study and 

supported by other rat radiolabeled atrazine studies is allometrically scaled for a human 

female with a bw of 60 kg (for example), the resulting kel of 0.0033 hr-1 is less than 4-fold 

lower. Thus, allometric scaling of adult rat plasma triazine linear elimination kinetics 

represents a reasonable approach for estimating the elimination kinetics in adult humans.

4.2.8.2 Human Urine Pharmacokinetic Information on Atrazine and its 

Chloro-s-triazine Metabolites

Most of the information reported in the Davidson 1985 report is based on urinary levels of 

DEA, DIA, and DACT; atrazine was not detected in urine (Davidson 1987). The urinary 

excretion information for these three chloro-s-triazine metabolites were pooled and 

analyzed as atrazine equivalents for pharmacokinetic behavior. The kinetics of each 
Page 96 of 184

subject was found to be adequately described by a one-compartment linear model of 

urinary excretion. An estimated first order elimination rate constant of 0.065 hr-1 was 

estimated based on renal excretion as compared to 0.039 hr-1 estimated for DACT from 

blood information from a single subject (Table 7). Given the limited information and 

experimental variability, the blood and urinary clearance results were deemed by the study 

author to be comparable (Davidson 1987). By far, however, the most important result 

reported from the urinary excretion information was a very significant mass balance 

discrepancy when only atrazine, DEA, DIA, and DACT were monitored. Only a total of 

14.5% of the atrazine dose administered was accounted for when only these four 

metabolites were monitored. It should be noted that urinary excretion is well-established as 

the primary route of excretion of atrazine (i.e., if present at all) and its metabolites (DEA, 

DIA, and DACT) and as also supported by the rapid absorption from the GI tract and peak 

plasma levels being reached as early as 44 minutes post-dose. Thus, biliary/fecal excretion 

is unlikely to explain the magnitude of the mass balance discrepancy reported in the study. 

The most likely possibility as reported by the study author was that a large portion of 

atrazine metabolites have not been identified. Several metabolites including conjugates of 

glutathione, glucoronide and sulfate were reported as possibilities (Davidson 1987). Such 

conclusion is consistent with the notion that the metabolism of atrazine has not been 

completely characterized.

Table 7: Estimated Pharmacokinetic Parameters from Human blood and urine

Whole Blood a

Chlorotriazine tmax (hr) Elimination rate constant 

(hr-1

)

Elimination Half-life 

(hr)

Atrazine - b - -

DEA - b - -

DIA ≤ 2 0.25 2.8

DACT ~ 5 0.039 17.8

Urine 

Atrazine - b

DEA - 0.29 2.4 ± 1.0 c

DIA - 0.30 2.3 ± 0.5 c

DACT - 0.060 11.5 ± 0.4

a information was from only one human subject

b Below quantitation levels

c Only the α phase is reported since the β phase was considered minor.
Page 97 of 184

4.2.9 Updates on the Metabolism of Atrazine

Focusing on DEA, DIA, and DACT as the major toxicologically active metabolites of 

atrazine’s neuroendocrine mode of action is a reasonable approach given the evidence to 

date, assuming that the remaining metabolites are toxicologically inactive dechlorinated 

glutathione conjugates. Recent reports have identified novel reactive metabolites of 

atrazine from in vitro human liver microsomal preparations. In one of the most recent 

studies by Leblanc et al. 2011, a new untargeted sensitive analytical chemistry method was 

used to identify novel metabolites of atrazine (Leblanc et al. 2011). A new phase I 

metabolic scheme for atrazine was proposed based on the formation of a newly identified 

N-oxide metabolite which undergoes hydrolysis to form a reactive electrophilic imine 

species which can conjugate with cellular nucleophiles like glutathione (GSH). As indicated 

in the updated metabolic scheme in Figure 16, nitrogen hydroxylation and subsequent 

imine formation takes place at the ethyl side chain on the triazine ring. In another recent 

study by Joo et al. 2011, 1-hydroxyisopropyl atrazine and 2-hydroxyethylatrazine were 

identified along with DEA and DIA as the major metabolites from human liver microsomal 

incubations (Joo et al. 2011). These metabolites are also included in the updated metabolic 

scheme in Figure 16. 

Although the N-oxide, imine, and chlorinated glutathione conjugate metabolites as well as 

1-hydroxyisopropylatrazine and 2-hydroxyethylatrazine depicted in Figure 16 were 

identified using in vitro human liver microsomal preparations, at minimum, these in vitro 

studies raise questions about the potential formation for these additional metabolites in vivo

which may explain the plasma clearance of atrazine which according to the plasma profiles 

in Figure 9 does not seem to be directly due to either DEA or DIA. It should be noted that 

the new metabolites in Figure 16 remain chlorinated and thus may be assumed to be 

relevant in LH attenuation if the chlorinated triazine ring is indeed the important moiety for 

such effect.
Page 98 of 184

Figure 16: Updated metabolic profile for atrazine. Newly identified metabolites are above the dashed 

line for comparison to Figure 7. New metabolites are from Leblanc et al. 2011 and Joo et al. 2011.

4.2.10 Summary of Pharmacokinetic Information

Atrazine and its metabolites exhibit linear kinetic behavior when monitored in plasma 

individually (atrazine, DEA, DIA, and DACT) or as a whole through the use of radiolabeled 

triazine ring across a wide range of orally administered doses and different species 

including rats, monkeys, and humans. When only DEA, DIA, and DACT are individually 

monitored in rats, the species with the slowest clearance is DACT with an estimated 

plasma elimination half-life of 8-9 hr. With a plasma half-life of 8-9 hrs, a female rat being 

dosed daily can be expected to clear most, if not all, of the chemical by 24 hours, resulting

in a repeated episodic plasma profile such as that described for DACT in Figure 11

following daily gavage dosing with atrazine. Thus, if only atrazine, DEA, and DIA, and 

DACT are monitored, little or no plasma bioaccumulation can be expected to occur upon 

repeated daily dosing. Moreover, if the pharmacokinetics of these 4 species are pooled and 

described as molar chlorotriazine equivalents in a one-compartment linear model, 

GSH

Diaminochlorotriazine (DACT)

Deisopropylatrazine (DIA)

Deethylatrazine (DEA)

Atrazine

GSH

N-oxide

Imine

2-hydroxyethylatrazine

1-hydroxyisopropylatrazine

Chlorinated conjugate

Non-chlorinated

Conjugate
Page 99 of 184

clearance would be dominated by DACT as the most abundant and slowly cleared 

metabolite. Indeed, McMullin et al. 2003 reported a one-compartment linear model based 

on atrazine, DEA, DIA, and DACT as chlorotriazine equivalents with an estimated plasma 

elimination rate constant of 0.08 hr-1 (t1/2 =8.8 hr) which is in excellent agreement with the 

values estimated for DACT at the three atrazine dose levels of 3, 10, and 50 mg/kg bw in 

Table 3 (McMullin 2003). In contrast, when the pharmacokinetics of atrazine are monitored 

in rats via a radiolabeled triazine ring, a much longer plasma half-life in the range of 53-69 

hours is observed across different studies and doses, suggestive of bioaccumulation upon 

repeated daily dosing. Bioaccumulation of plasma radiolabeled triazine equivalents is clear 

from available repeated daily dosing pharmacokinetic studies including Thede 1987 (Figure 

13) and Simoneaux 1985 (Figure 15) . Thus, a plasma clearance discrepancy exists 

between radiolabeled triazine studies and those that only monitor atrazine, DEA, DIA, and 

DACT. The discrepancy may be due to unidentified/unmonitored metabolites of atrazine in 

vivo such as those reported from recent in vitro metabolism studies (Figure 16) (Leblanc et 

al. 2011 and Joo et al. 2011). It should be noted that glutathione conjugates formed via 

substitution of the chlorine atom on the triazine ring are unlikely to explain the clearance 

discrepancy since these are presumed to be cleared rapidly, a typical consequence of 

phase II metabolism, and have been estimated to represent about 30% of the administered 

atrazine dose (Timchalk et al., 1990 and McMullin et al. 2007). Because of this plasma 

clearance discrepancy, the Agency is relying on studies based on radiolabeled atriazine on 

the basis that such studies account for known and unknown metabolites of atrazine and 

achieve a high degree of mass balance, although the toxicological relevance of these 

metabolites related to atrazine’s neuroendocrine mode of action is unclear. 

4.2.11 Progress on Physiologically Based Pharmacokinetic Modeling Efforts

In the case of chemicals like atrazine with a very short in vivo plasma half-life and 

toxicologically active metabolites, pharmacokinetic modeling is necessary for the 

application of a mode of action approach to risk assessment. Some of the current 

pharmacokinetic modeling needs for atrazine include the estimation of relevant internal 

measures of parent chemical and toxicologically active metabolites (related to the 

endocrine mode of action), reliable means for extrapolating such internal measures across 

different species and life-stages, routes, and conditions of exposure, and most importantly 

for exposure assessment, means for reconstructing the human ingested dose through 

drinking water. The preferred modeling approach for doing the aforementioned 

extrapolations and dose reconstructions involves a properly calibrated and evaluated PBPK 

model. 

Previous efforts to develop a PBPK model for atrazine and its metabolites have been 

hampered in part by sub-optimal pharmacokinetic data for model parameterization. The 

integrated composite PBPK model for atrazine and its chloro-s-triazine metabolites 

developed by McMullin and co-workers was parameterized with plasma data obtained from 

oral gavage dosing of rats with atrazine, DEA, DIA, or DACT at molar equivalent levels of 
Page 100 of 184

150 mg/kg bw atrazine (McMullin et al. 2007). At such high dose levels, limited solubility in 

the gastrointestinal tract and continuing absorption of more slowly dissolving chemical 

resulted in complex plasma profiles difficult to interpret for model parameterization, 

particularly if oral uptake is not adequately described in the model. The final composite 

model could not simulate the pharmacokinetics of atrazine or its metabolites, resulting in 

overprediction of plasma levels of atrazine while underpredicting plasma levels of its 

metabolites. In addition to inadequate data for model parameterization, the model lacked 

postulated target tissues such as the brain and was limited to liver and rest of body 

compartments (McMullin et al. 2007)

Some of the shortcomings of the McMullin 2007 PBPK model have been recently 

addressed in a PBPK model recently submitted to the Agency by Syngenta in collaboration 

with the Hamner Institutes (Campbell et al. 2011). Although the Agency has not performed 

a thorough review and evaluation of the model, a preliminary review indicates that the 

original McMullin 2007 model (McMullin et al. 2007) has been expanded to include 

compartments of suspected target tissues including mammary gland, hypothalamus, 

pituitary, testes/ovaries, and adrenals. The oral uptake of atrazine has also been refined by 

removing a saturable uptake description and replacing it with separate compartments for 

insoluble/bound and free fractions of atrazine as well as describing the potential slow 

release of bound atrazine. New empirically determined tissue:blood partition coefficients 

and hepatic in vitro metabolic rates were also incorporated. Most importantly, however, was 

the model calibration performed with the new in vivo time-intensive rat pharmacokinetic 

study described above where the chloro-s-triazine metabolites DEA, DIA, and DACT were 

individually monitored in female SD rats following dosing with atrazine via the oral route of 

administration (Coder 2011). The Agency is still evaluating the newly improved PBPK and 

will consider using it after it has undergone a thorough review and evaluation process. In 

the meantime, for purpose of this integrated toxicity-exposure assessment, an interim 

pharmacokinetic modeling approach is presented below.

4.2.12 Proposed Interim Pharmacokinetic Modeling Approach

Non-compartmental pharmacokinetic analysis was introduced at the September 2010 

meeting as a reliable way of estimating plasma AUC of radiolabeled triazine equivalents as 

measure of internal exposure (FIFRA SAP 2010c). Daily pseudo steady state AUC 

estimates were obtained from the Thede 1987 study for internal dose response analysis 

associated with LH attenuation. Non-compartmental analysis works best when linear 

elimination kinetics is observed in the plasma profile; that is, when elimination proceeds at 

a rate that is proportional to the remaining plasma concentration. From a modeling 

perspective, the most attractive feature of linear pharmacokinetics is its predictability based 

on a constant plasma half-life. In the case of atrazine and its metabolites, linear elimination 

kinetics is the most consistent feature observed when monitored individually (i.e., atrazine, 

DEA, DIA, and DACT) or as a whole in radiolabel studies. Thus, the behavior of plasma 

triazines may be described by a series of linear processes collapsed into a simplified 
Page 101 of 184

deterministic one-compartment model as indicated in Figure 13. The oral uptake of atrazine 

administered orally can be reasonably assumed to be fast and complete given its estimated 

short tmax of 20 minutes or less (Table 3). The first order elimination rate constant (kel) 

indicated in the model is representative of all clearance processes for plasma triazines that 

consists primarily of urinary and biliary/fecal excretion. Metabolism is not reflected in kel 

since the radiolabeled triazine ring is metabolically stable and thus, although metabolism is 

important in the clearance of individual species including atrazine, DEA, DIA, and DACT, it 

does not affect the clearance of the triazine ring. 

Deterministic models such as the one compartment linear model proposed for plasma 

triazines differ from statistical/probabilistic modeling approaches in that their parameters 

are independently estimated rather than fitted to a particular dataset. In the case of kel in 

Figure 17, an average value of 0.013hr-1 estimated for rats across different studies and 

doses of atrazine is most appropriate for relating pharmacokinetic behavior to LH 

attenuation in rats. 

Figure 17: Proposed Interim pharmacokinetic model for atrazine and its metabolites

Another feature of the behavior of plasma triazines as it relates to the toxicological endpoint 

of concern (i.e., LH attenuation) is that pseudo steady state is reached (or nearly reached) 

at the time when LH attenuation has also been observed. The condition of steady state 

plasma levels for LH attenuation further supports the use of a one-compartment model 

which assumes by definition that plasma and tissues are at least at equilibrium. Thus, 

plasma triazines and the two parameters (Vd and kel) that govern their behavior in the one 

compartment linear model are presumed to represent steady state values. In the case of 

kel, it was already demonstrated from analyzing plasma data from the Paul et al. 1993 

study that its values does not change in single dose versus repeated dosing studies.

There are several ways of estimating Vdss based on the available information. First, noncompartmental analysis offers a way of estimating Vdss through an area analysis as 

depicted by equation 1. AUCM refers to the area under the first moment curve and is 

calculated in a similar way as AUC except that it is based on the product of plasma 

concentration and time (Benet 1979). Using the plasma data from Paul et al. 1993 study at 

100 mg/kg bw atrazine (Paul et al. 1993), a Vdss estimate of 6.55 L/kg bw was obtained. 

Plasma Triazines

(Cp, mg/L) kel (hr-1) Triazines eliminated

Oral dose of 

Atrazine

(mg)

Fast/complete

absorption

Vdss(L)
Page 102 of 184

Detailed calculations are given in Appendix A.9. The high dose of 100 mg/kg bw atrazine 

was selected because plasma levels are well-defined at the later time points at this dose 

level as compared to 1 mg/kg bw (Paul et al. 1993). 

In an attempt to evaluate the one-compartment linear model in Figure 17, the 

corresponding equation that relates atrazine dose rate to steady state plasma triazine 

levels (i.e., equation 2) was used to predict the average pseudo steady state plasma 

radiolabeled equivalents reported in the Thede 1987 study. Using a Vdss of 6.55 L/kg bw 

and kel of 0.013 hr-1, both of which were estimated independently from the single dose 

plasma data from the Paul et al. 1993 study using non-compartmental pharmacokinetic 

approaches, equation 2 yielded predictions of steady state plasma triazines that are very 

consistent with those from the Thede 1987 study (Table 8).

Table 8: Evaluation of the one-compartment linear model for relating an atrazine dose to steady state 

plasma triazines

Equation (2) can also be rearranged to estimate Vdss based on the plasma levels from the 

Thede 1987 study (Table 5). An average Vdss value of 5.71 ± 0.65 L/kg bw for atrazine 

doses of 1-10 mg/kg bw/day was obtained. Thus, this second approach gave a Vdss 

estimate that is consistent with that obtained through non-compartmental analysis. 

However, the Vdss estimate of 6.55 L/kg bw estimated from the Paul et al. 1993 study was 

selected since it is based on an independent dataset that reasonably reproduces the 

average pseudo steady plasma triazine levels (Css) at all doses tested in the Thede 1987 

study.

(1) Vdss (L/kg bw) = Dose (mg/kg bw) *

(AUC mg*hr/L)2

0 ∞

AUCM (mg*hr2/L)

0 ∞

animal 11 animal 21

Atrazine dose rate (mg/kg/day) ave. steady state plasma levels (mg/L)2 ave. steady state plasma levels (mg/L)2 Predicted value 3

1 0.56 0.62 0.49

3 1.88 2.16 1.47

7 3.84 3.45 3.43

10 5.11 4.83 4.89

50 22.80 25.61 24.47

100 53.10 56.84 48.93 1

 Data is from Thede 1987

2

 average steady state plasma levels were estimated by taking the mean value of plasma levels from day 4 through 8, i.e., when pseudo steady state was reached and maintained.

2 Using the one-compartment linear model expression that relates dose rate to steady state plasma levels (Equation 2)
Page 103 of 184

It should be noted that the estimated value of 6.55 L/kg bw for Vdss is comparable to the 

range estimated for the monkey of 2.427 - 4.413 L/kg bw (Table 6). Thus, there is 

reasonable consistency across different rat studies (Paul et al. 1993 and Thede 1987) and 

species (rats and monkeys) for which there is plasma triazine information from dosing orally 

with radiolabeled atriazine.

The one compartment linear model depicted in Figure 17 represents a reasonable 

approach at this time for informing the pharmacokinetic behavior and internal dosimetry of 

atrazine and its metabolites given that the recent PBPK model submitted by Syngenta is 

still being reviewed. However, the Agency is cognizant of the emerging science with the 

ultimate goal of having a well-calibrated and evaluated PBPK model for atrazines and its 

metabolites.

4.2.13 Implications for Water Monitoring Frequency

The ongoing re-evaluation of the health effects associated with atrazine includes the 

drinking water monitoring frequency currently based on the 2003 risk assessment involving 

90-day rolling averages of atrazine and its metabolites DEA, DIA, and DACT. Ideally the 

frequency of drinking water monitoring should be reflective of the temporal pattern of the 

toxicological endpoint of concern used in risk assessment, i.e., the duration of exposure 

needed to elicit the toxicological endpoint. Given that the state of science has changed for 

atrazine, the Agency is in position to re-evaluate current risk estimates based on the 

current water monitoring frequency. The temporal pattern of LH attenuation is related to the 

pharmacokinetic behavior and internal dosimetry of all relevant plasma triazines. There are 

two features in the pharmacokinetic behavior of triazines that can be used to inform the 

frequency for water monitoring frequency. First, the linear behavior that can be reasonably 

described by the simplified one compartment linear model in Figure 17. The analytical 

solution for a one-compartment linear model relates plasma levels of triazines to 

administered doses of atrazine (Equation 3). The linear behavior of plasma triazines can be 

reasonably expected to remain linear at lower doses of atrazines due to an even lower 

probability of saturating ADME processes. Second, upon repeated dosing with atrazine, 

plasma triazines reach (or nearly reach) pseudo steady state when LH attenuation is 

observed. The steady state condition indicates that absorption and distribution processes 

are offset by clearance processes and that while brain (a postulated site of action for LH 

attenuation) and plasma triazine levels may be not equal, they will be at equilibrium, 

making plasma triazine levels a reasonable surrogate for internal dose response analysis.

Dose rate (mg/kg bw/day)

Vdss(L/kg bw) * kel (hr-1) * 24hr/day (2) Css(mg/L) =
Page 104 of 184

4.2.13.1 Rat Forward Internal Dosimetry for LH Attenuation

The current understanding of the linkage between pharmacokinetics and LH attenuation 

following atrazine exposure in rat studies is depicted in Figure 18. An oral gavage dose of 

atrazine reasonably assumed to be completely absorbed from the GI tract (step 1) will be 

distributed in a body volume of distribution to give plasma levels of triazines (step 2) which 

can be cleared by the body with an overall first order rate constant (kel) that accounts for all 

plasma clearance processes (primarily urinary and biliary/fecal excretion). Upon continued 

exposure to atrazine (step 3), plasma levels of triazines will accumulate until steady state is 

reached (or nearly reached) when optimal LH attenuation is observed. The proposed 

critical duration is the time to reach steady state plasma triazines. It should be noted that 

other metrics such as the time above a critical threshold of plasma levels have also been 

considered. Based on the currently available information where 4 days is the shortest 

exposure duration evaluated (Cooper et al. 2010) and the remarkably similar LOAELs 

reported for studies of different durations (as short as 4 days and as long as 6 months), the 

Agency is currently presenting steady state plasma levels in its derivation of an internal 

dose metric associated with attenuation of the LH surge. As additional information becomes 

available and as the Agency completes its review of the recently submitted Syngenta’s 

PBPK model , the Agency will consider other internal dose metrics and adapt appropriately.

Figure 18: Proposed linkage between atrazine exposure and optimal LH attenuation in rat studies

Atrazine

Oral gavage dose (mg/day)

Absorbed Dose (mg/day) 

100% absorption

Body volume of distribution (Vd, L)

Plasma triazines(Cp, mg/L)

Optimal 

LH attenuation

clearance

OUTPUT

RAT: FORWARD DOSIMETRY FOR LH ATTENUATION

Elimination kel (hr-1)

Continued exposure

Steady state 

(Vdss, L)

INPUT

Steady state plasma triazines(Cpss, mg/L)

bioaccumulation

(1)

(2)

(3)

Daily steady state AUC ((mg/L)*hr)

(4) Duration
Page 105 of 184

4.2.13.2 Human Reverse Dosimetry from Rat Internal Dosimetry for LH 

Attenuation 

The starting point for the human situation is the assumption of pharmacokinetic 

equivalency. In other words, if the same plasma triazine AUC as the rat is ever reached in 

humans, an equivalent LH attenuating effect can be reasonably expected (step 1 in Figure 

19) (US EPA 2006). Notably, AUC is the product of levels times duration (i.e., “how much 

and for how long”). Therefore, a given plasma AUC may be “matched” by high levels and 

short duration or alternatively by moderate levels for a longer duration. The same one 

compartment linear model expression but with allometrically scaled human parameters 

(equation 4) can be used to match the rat AUC PoD associated with LH attenuation. These 

types of forward and reverse calculations provide the basis for species extrapolation in risk 

assessment particularly when a pharmacokinetic model is available. In the case of atrazine, 

these calculations can be performed using simplified empirical calculations based on the 

consistent linear pharmacokinetic behavior of triazines observed across different studies, 

doses of atrazine, and species including humans.

In contrast to animal toxicity studies where the frequency and level of dosing are controlled, 

the dose rate in humans is determined primarily by the water consumption rate and the 

chlorotriazine content of the water (step 3 in Figure 19), both of which are variable. In 

particular, water levels of chlorotriazines can be highly variable (See hypothetical water 

chemograph in Figure 20) due to being dependent on numerous factors including 

application rate, proximity to growing field, and time of season. Nonetheless, an estimate of 

the equivalent human time to reach steady state can be obtained via allometric scaling of 

the rat elimination rate constant and assuming that it would take between 3-5 plasma halflives to reach steady state. A rat kel of 0.010 -0.013 hr-1 allometrically scaled for a human 

body weight of 60 kg results in kel of approximately 0.00343 -0.00471 hr-1and a plasma 

half-life estimate of about 6 - 8 days. Thus, an equivalent human time to reach steady state 

plasma triazine levels based on a constant exposure level and frequency can be estimated 

to be between 21-30 days. 

The highly variable levels of atrazine and its metabolites in drinking water can be analyzed 

through the use of an integrated average daily level of exposure given the predicted 

duration of concern of 21-30 days. The observation that LH attenuation is driven by 

sustained rather than acute exposures to atrazine further supports the use of an integrated 

average daily dose metric. Rolling averages can be used to “carry over” exposures from 

previous days and avoid gaps in the exposure estimates.

(Dose rate) rat

Vdssrat * kel rat

=

PoDDaily Plasma AUC

Associated with 

LH attenuation

= (Dose rate) human

Vdss human * kel human

(3)
Page 106 of 184

Figure 19: Proposed linkages between atrazine exposure and LH attenuation in adult humans. The 

asterisks indicate the parameters that were allometrically scaled from adult rats.

Figure 20: Hypothetical water chemograph showing the typical variation in chlorotriazine levels as a 

function of time.

Rat Daily Steady State AUC (mg/L*hr)

≅

Human average daily AUC ((mg/L)* hr)

Average daily plasma concentration (Cpave, mg/L))

Plasma triazine concentration (Cp, mg/L) Elimination

Body volume of distribution (Vd, L) *

Absorbed dose (mg)

Water Consumption 

(L/day)

Chlorotriazinesin water 

(μg/L)

LH attenuation

duration

HUMANS: PROPOSED REVERSE DOSIMETRY FOR LH ATTENUATION

Continued exposure

Bioaccumulation

Equivalent time to steady state

(Vdss, L)

clearance

kel (hr-1) *

100% absorption

(1)

(2)

(3)

Hypothetical Water Chemograph

Critical 

duration

0

10

20

30

40

50

 Time

Triazine Levels
Page 107 of 184

4.2.14 Calculating Average Chlorotriazine Levels in Drinking Water for

Human Plasma Triazine AUC Estimates

At the April 2010 Science Advisory Panel (SAP) meeting, the panel recommended that the 

Agency consider a drinking water monitoring approach based on the area under the 

concentration-time curve (AUC) for atrazine and its chlorinated metabolites that can be 

used in conjunction with an integrated internal dose response assessment (FIFRA SAPa). 

Based on the currently available data, the Agency is presenting the use of pseudo steady 

state plasma triazine levels to derive a plasma PoD AUC estimate associated with LH 

attenuation. This value can be compared to a daily average AUC for drinking water 

(AUCwater) for a given duration of concern on the basis that humans are unlikely to reach 

steady state plasma levels of triazines as in rat studies, (Step 2 in Figure 19). AUCwater 

can be estimated using the trapezoid rule whereby the area for a given exposure duration 

would be divided into individual trapezoids as dictated by the water sampling frequency. 

The sum of the individual trapezoids would be AUCwater. In the case the water sampling 

frequency allows the estimation of a single trapezoid, AUCwater may be under- or overestimated depending on the shape of the individual trapezoid as depicted in Figure 21A. In 

essence, the more frequent the water sampling, the better the estimation of AUCwater 

(Figure 21B).

Time-weighted average levels of chlorotriazines in water can be estimated from AUCwater 

according to equation 4. Rolling averages can be computed to “carry over” exposures from 

previous days and avoid gaps in the estimates. In combination, equations 5 and 6 offers an 

approach for estimating a human average daily AUC for plasma triazines that accounts for 

an integrated dose rate for a duration of concern, body distribution and plasma clearance 

information. The resulting human plasma AUC can be compared to the rat PoD AUC 

estimate for a margin of exposure (MOE) calculation that is based on internal rather than 

external measures of exposure (Equation 6). 

Figure 21: The trapezoid rule for estimating the area under a water chemograph for a given duration 

of exposure.

time

Chlorotriazine Levels

Chlorotriazine Levels

time

A B 
Page 108 of 184

[atrazine]ave (mg/L) = AUCwater ((mg/L)*hr) for given duration

# days * 24 hrs

(4)

[triazine water levels]ave (μg/L) * water consumption (L/day)

kel (hr-1) * Vdss (L)

=

From water chemograph From human consumption information

Allometrically scaled from rat values

Human average 

daily plasma AUC (5)

Rat PoD BMDL plasma AUC

Human plasma AU C

(6) MOE =
Page 109 of 184

5. SCIENTIFIC CONSIDERATIONS IN POTENTIAL SENSITIVITY OF INFANTS & 

CHILDREN 

5.1 Background 

The FFDCA, as amended by the FQPA (1996), requires the Agency to give special 

attention to the potential risk to infants and children. Specifically, FQPA instructs EPA, in 

making its “reasonable certainty of no harm” finding, that in “the case of threshold effects, 

an additional tenfold margin of safety for the pesticide chemical residue and other 

sources of exposure shall be applied for infants and children to take into account potential 

pre- and post-natal toxicity and completeness of data with respect to exposure and 

toxicity to infants and children.” Section 408 (b)(2)(C) further states that “the 

Administrator may use a different margin of safety for the pesticide chemical residue only if, 

on the basis of reliable data, such margin will be safe for infants and children.” This 

additional margin of safety is referred to as the “FQPA Safety Factor.” 

In this section, the Agency describes the state of the science with respect to the potential 

for pre- and post-natal toxicity and the completeness of data with respect to toxicity to 

infants and children. This discussion focuses on experimental toxicology relevant for 

considering pre- and post-natal toxicity. Although exposure considerations are a 

component of the FQPA Safety Factor analysis, this section of the draft issue paper does 

not include drinking water exposure. Drinking water exposure and statistical approaches 

for assessing monitoring frequency are covered in Section 6. 

Several key experimental toxicology studies important for the FQPA analysis have been 

conducted since the September 2010 SAP. These include studies evaluating 1) the 

potential hormonal changes and outcomes from gestational exposure to male and female 

rats; 2) behavioral changes in male rats (e.g., rough and tumble play) following gestational 

exposure (GD 14-21) and 3) other possible latent effects manifested in adults from 

gestational and lactational exposure. Although additional experimental toxicology studies 

are still on-going to better characterize the potential adverse health outcomes resulting 

from atrazine exposure (including the duration of exposure that may lead to an adverse 

health outcome), the available data do not indicate that pre- and/or post-natal exposure 

leads to increased sensitivity in the young relative to the attenuation of the LH surge that 

serves as the basis for the atrazine risk assessment. The Agency is evaluating potential 

approaches for assessing drinking water monitoring strategies. The uncertainties and 

confidence in the performance of water monitoring sampling strategies to potentially 

capture atrazine levels exceeding the Agency’s level of concern are an important 

consideration in the determination of the FQPA Safety Factor. Section 6 of this issue paper 

will provide examples of how these uncertainties might be addressed by evaluating the 

performance of different sampling strategies and some accompanying models for their 

interpretation. As such, this draft issue paper does not propose a new FQPA Safety 

Factor. Instead, the Agency will solicit comment from the SAP on 1) the current state of the 
Page 110 of 184

science with respect to assessing potential sensitivity to the young and 2) the overall 

approach for determining the FQPA Safety Factor. 

5.2 Experimental Toxicology 

5.2.1 Gestational Exposure 

Recently, EPA’s ORD conducted a series of experiments to further evaluate the impact of 

gestational exposure on reproductive development in both males and females. These 

studies in conjunction with those evaluated by the Agency in preparation for the April and 

September 2010 SAPs provide insight into the overall pattern of toxicity associated with 

atrazine exposure during this lifestage not only in terms of the dose levels but also the 

critical durations of exposure capable of eliciting an adverse effect. 

With respect to toxicity outcomes following gestational exposure, Fraites et al. (2011) did 

not observe effects on male reproductive development or the androgen-dependent 

endpoints measured in the study after in utero exposure during gestation (GD 14-21)

including (i) testosterone production at birth and on PND 59, (ii) rough and tumble play 

behavior, (iii) AGD and PPS, and (iv) androgen-dependent organ weights at doses as high 

as 100 mg/kg/day. This is consistent with the findings reported by Rayner et al. (2007) who 

observed no change in the timing of male puberty, but did report a higher incidence in 

prostatitis at 100 mg/kg/day. In contrast, Rosenberg et al. (2008) reported delays in PPS at 

50 mg/kg/day. Another high dose effect reported after gestational exposure to atrazine is a 

delay in mammary gland development of female offspring (Rayner et al., 2005, 2007). This 

effect, however, was not replicated by Davis et al. (2011) at doses as high as 100 

mg/kg/day when evaluated either using a subjective scoring approach (as described by 

Rayner and coworkers) or a morphometric analysis. Table 9 summarizes the toxicological 

data available on atrazine after gestational exposure. Detailed reviews for all the studies in 

the table are part of Appendix A.
Page 111 of 184

Table 9: Summary of available atrazine experimental toxicology studies from the gestational period

AUTHOR 

(YR) EXPOSURE NOAEL/LOAEL mg/kg/day EFFECT

Gestation

Rosenberg 

(2008)

GD14-

parturition 10/50 Delayed PPS

Rayner et al.

(2005) GD 13-19 NA/100 Delay in mammary gland development

Rayner et al.

(2005) GD 17-19 NA/100 Delay in mammary gland development

Rayner et al.

(2007) GD 15-19 NA/100 Prostatitis

Fraites et al.

(2011) GD 14-21 20/100

Decreased pup weight 

and pup viability (PND 1-

4)†

Davis et al.

(2011) GD 14-21 20/100

Decreased pup weight 

and pup viability (PND 0-

4). Delay in VO

†

Decreased maternal body weight was also observed at the 100 mg/kg/day dose level

5.2.2 Multi-Lifestage Exposure

A multifaceted study evaluating the impact of atrazine exposure across several lifestages 

has been submitted by the atrazine registrant – Syngenta. The purpose of this study was 

to evaluate the effects of atrazine on sexual maturation, estrous cyclicity, and the LH surge 

in Sprague-Dawley [Crl:CD(SD)] rats following at atrazine doses of 0, 6.5, 25 or 50 

mg/kg/day administered via gavage. Animals were divided into two cohorts. Cohort I 

animals were exposed from the time of conception through sexual maturation (VO) or PND 

133. Half of the animals dosed until PND 133 were evaluated after a 20 days recovery 

period (PND 134-154) while the remainder of the animals were evaluated on PND 133. 

Cohort II animals were exposed only post-natally from PND 21 through sexual maturation 

(VO + 5 days) or for 14 days as adults of reproductive age. Table 10 (excerpted from

MRID 48381001) describes the dosing regimen for these animals.
Page 112 of 184

Table 10: Dosing regimen Multi-Lifestage Study

Cohort I animals (all subsets) exposed to 50 mg/kg/day atrazine exhibited a 1.4-2.3 day 

delay in VO (mean = 1.6 day delay). Unlike the findings reported by several investigators 

(Foradori et al., 2009; Cooper et al., 2007; Morseth et al., 1996, Davis et al., 2011)), no LH 

surge attenuation was observed at any dose level. Corticosterone and estrous cyclicity 

also appeared to be unaffected by atrazine exposure. The study author, however, 

acknowledged that the corticosterone measures were not obtained at the optimal time and 

this may account for the discrepancy between the observations in this study and findings 

previously reported by other researchers including Laws et al. (2009), Fraites et al. (2009), 

Morseth et al. (1996), and Cooper et al.(1996).

In the case of Cohort II animals, no effect on VO was noted in animals whose exposure 

began on PND21 (prior to the typical age of VO) at any dose level. LH surge and 

corticosterone levels also appeared to be unaffected in this cohort (all subsets).

A unique feature of this study is that it is the only one available in the atrazine database 

that evaluates the impact of atrazine exposure through multiple lifestages. The 

preponderance of the data available to date supports the conclusion that LH surge 

attenuation occurs at doses lower than those used in this study. The reason for the 

inconsistency between this study and the body of peer reviewed literature evaluating the 

impact of atrazine exposure across different life stages over the last 15 years is unclear. A 

detailed review of this study is included in Appendix A of this document. 

5.3. Summary

During the September 2010 SAP meeting, the Panel discussed several studies evaluating 

the impact of atrazine exposure during development. At that time, the Agency concluded –

and the SAP concurred – that the available data did not identify a unique quantitative 

susceptibility in the developing organism. None of the available studies with atrazine 

evaluating the rats exposed during gestation, lactation or the peri-pubertal periods have 
Page 113 of 184

shown effects at doses lower than those eliciting the LH surge attenuation in adult female 

rats after 4 days of exposure. Three additional studies evaluating the effect of atrazine 

exposure across lifestages have become available within the last few months. These 

studies reinforce the conclusions reached during the September SAP meeting since all of 

the effects observed in the young in these set of studies occurred at doses ≈ 25 times 

higher than the dose EPA is proposing to use (BMDL of 2.56 mg/kg/day) as the PoD for 

human health risk assessment (derived from LH data collected after four days of exposure 

to adult females).

In making determinations regarding the FQPA Safety Factor, not only should potential lifestage sensitivity be considered but the need to ensure the exposure assessment will not 

underestimate risks during development. During the September 2010 SAP meeting the 

Agency proposed a water monitoring frequency between a few days and four weeks based 

on durations of exposure that may result in adverse human health outcomes. In its 

December 2010 report, the SAP commented that based on the assumptions and 

extrapolations to the proposed critical window of exposure and the overall uncertainties 

these assumptions introduce, “the imprecision in the Agency’s proposed sampling 

frequency seems justified. This may be about as precise an estimate as can be obtained 

when starting with the experimental animal data and the exposure requirements for LH 

surge suppression...” Based on the SAP’s feedback, the Agency will continue to use 

attenuation of the LH surge as the critical effect for the atrazine risk assessment and will 

conduct an analysis of the water monitoring frequency ranging from a few days to 4 weeks. 

Section 7 of this draft issue paper will present various approaches to assess the inherent 

uncertainties associated with various drinking water monitoring strategies.

 
Page 114 of 184

6.EVALUATING ATRAZINE DRINKING WATER MONITORING DATA FOR USE IN 

HUMAN HEALTH ASSESSMENTS 

6.1 Introduction

The 2003 risk assessment for atrazine (USEPA, 2003) determined that LH surge 

attenuation was the most sensitive toxicological endpoint for the human health risk 

assessment associated with exposure to atrazine and other chloro-triazines. Based on the 

best available information at the time, the USEPA identified a 90-day critical duration of 

exposure, based on results showing LH surge attenuation following dietary dosing in a 6 

month toxicity study and corresponding to the period within the year during which most 

elevated concentrations of atrazine are likely to occur in drinking water. As a condition of 

reregistration, the USEPA required the registrant, Syngenta, to monitor vulnerable 

community water systems (CWS) to ensure that 90-day average concentrations did not 

exceed the toxicological level of concern (37.5 μg/L total chloro-triazines) for that duration. 

Syngenta proposed monitoring weekly during the use/ runoff season and bi-weekly during 

the rest of the year to estimate total chloro-triazine (TCT) exposure over a 90-day time 

frame for both source (raw) and treated (finished) waters at vulnerable CWS in the atrazine 

use area. Any CWS that exceeded annual mean concentrations of 1.6 μg/L atrazine based 

on quarterly Safe Drinking Water Act (SDWA) sampling was included in this more 

intensively sampled Atrazine Monitoring Program (AMP). Since 2004, the AMP has 

monitored TCTs in water at up to 166 CWS. No 90-day average concentration has 

exceeded the toxicological level of concern in that time. 

New data related to the hazard potential and dose-response for atrazine resulted in this reevaluation of human health effects for atrazine. Those studies suggest a shorter duration of 

exposure may be appropriate for the toxicological endpoint of concern. With a shorter 

duration of concern, the USEPA needs to consider how well the existing AMP monitoring 

design characterizes shorter durations of exposure and whether the monitoring program 

needs to include more frequent sampling or other means to augment existing data. 

In the April and September 2010 FIFRA SAP consultations on atrazine, the USEPA 

reviewed methods for designing monitoring studies to capture exposures of concern and 

approaches for analyzing and interpreting existing monitoring data and characterizing the 

uncertainties in those estimates for use in human health risk assessments (USEPA, 2010a 

and 2010b). The SAP commented that the toxicological exposure timeframe of interest will 

define the importance of peaks and determine the most useful approaches both for 

designing a monitoring study and for evaluating the utility of existing monitoring studies 

(FIFRA SAP, 2010a). However, the SAP noted that, in light of uncertainty over a critical 

exposure period ranging from a few days to a few weeks, the best course of action may be 

attempting “to capture the pattern of atrazine concentrations in the source water of each 

CWS based on the characteristics of that particular water system, as opposed to a onesize-fits-all approach” (FIFRA SAP, 2010b). 
Page 115 of 184

Identifying monitoring sites and determining the sampling frequency require careful 

consideration to avoid underestimating true atrazine concentrations (FIFRA SAP, 2010b). A 

predictive model can be used to target sites that merit the most detailed monitoring and 

target the time periods when atrazine is most likely to be present (FIFRA SAP, 2010a, 

2010b). The Panel recommended that the USEPA give thought to “using the simulation 

models and CWS characterizations as part of the monitoring process. In particular, it is 

feasible that models will eventually be accurate enough to provide predictions of atrazine 

concentrations in source waters to a CWS for the coming crop season. Instead of requiring 

a CWS to collect and analyze water samples in their output stream (drinking water) at some 

predefined frequency (e.g., daily or weekly in the case of some sites), it should be possible 

to use the models to facilitate targeting sampling to periods of time most likely to 

experience an exceedance.” (FIFRA SAP, 2010b)

To estimate exposures from monitoring data sampled at intervals, particularly where peak 

concentrations over shorter durations are important, the USEPA needs methods that can 

predict values that may be greater than those sampled (FIFRA SAP, 2010a). The SAP 

recommended combining regression-based models, such as the United States Geological 

Survey’s (USGS) Watershed Regression on Pesticides (WARP), with either a deterministic 

model or with a statistical approach using kriging or random-function models (FIFRA SAP, 

2010b). 

In preparation for the upcoming registration review for atrazine scheduled to begin in 2013, 

the USEPA has focused on methods for characterizing the uncertainties resulting from the 

existing AMP monitoring study design. Such methods include providing the uncertainty 

bounds in estimated exposures based on how well the sampling design captures the 

temporal variability of pesticide concentrations in water at locations where concentrations 

are expected to be greatest. Additional uncertainties related to the number of years of 

monitoring and site locations are also considered. While the discussion in this section 

focuses on estimating atrazine concentrations, the human health assessment will be based 

on TCTs. In most surface water sources, the elevated exposures will be dominated by 

atrazine; in some instances, simazine or chloro-triazine degradates of atrazine may also 

occur. 

6.2 Key Questions to Address in Analyzing Monitoring Data

Pesticide occurrence in water is the result of a complex interaction of pesticide properties, 

use patterns, application methods and timing, weather, watershed characteristics (e.g., soil, 

slope, land cover type), and hydrology (Gilliom et al., 2006). While pesticide concentration 

patterns resulting from this interaction are difficult to adequately characterize both 

geographically and temporally with monitoring (ILSI, 1999; USEPA, 2000; Stone and 

Gilliom, 2009), an understanding of the factors affecting pesticide distributions in water can 

be used to predict pesticide occurrence in place and time and to characterize the degree of 

uncertainty resulting from a monitoring study design for use in exposure estimates. 
Page 116 of 184

For a pesticide with an established use pattern, such as atrazine, the variability of pesticide 

occurrence in surface waters can be described by:

• The spatial pattern related to pesticide use, crop and management practices, soil 

and hydrologic vulnerabilities, and rainfall distribution;

• Seasonal and daily temporal patterns at any given location related to intensity and 

timing of pesticide applications and coincidence of rainfall events;

• A temporal correlation in which pesticide concentrations found on one day are 

related to concentrations occurring on the previous and following days;

• Year-to-year temporal patterns at any given location reflecting changes in cropping 

and pesticide use as well as variations in rainfall from year to year.

A well-designed surface water monitoring study takes into account both spatial and 

temporal patterns of exposure, focusing sampling on when and where the pesticide is used. 

Considerations for sampling frequency include not only the duration of toxicological 

concern, but also the pesticide properties and use patterns, expected interactions of the 

pesticide with the environment, and the nature of the water body and watershed. The 

USEPA considers the following questions in determining the extent to which monitoring 

data can be incorporated in exposure assessments:

• Given monitoring of a specified sampling frequency, what can we say about the 

actual structure (magnitude, frequency, duration, time series distribution) of the 

concentrations at that site in a given year? This relates to the short-term temporal 

variability and associated temporal correlations in pesticide occurrence in water. 

• What can we say about the nature of pesticide occurrence at the given site in other 

years? How much variability in magnitude and duration of exposures can we expect 

from year to year? This relates to longer-term temporal variability.

• What can we say about pesticide exposures at other sites based on the results at 

one or a few sites? This relates to the spatial variability of pesticide concentrations in 

water.

• Given the spatial variability in pesticide occurrence, how do we link monitoring data 

with sound statistical designs? How do we deal with non-random monitoring 

designs, such as the current atrazine CWS monitoring program? 

• How do we combine monitoring results across datasets, taking into account such 

factors as watershed sizes, water body types, climate/weather differences, and 

different study purposes and designs?
Page 117 of 184

• How do we best analyze datasets with censored data (i.e., below the limit of 

quantification)? This question spans a number of issues, including timing (were 

samples taken when the pesticide was most likely to occur in water?), spatial 

distribution (were samples taken from areas where the pesticide was used?), and 

analytical methods (are the analytical methods sufficient to detect the pesticide at 

concentrations of concern?). Determining the reason for non-detects can be useful 

in interpreting the extent of risk both spatially and temporally and can aid in the 

development of risk mitigation practices, if necessary.

• How do we best integrate modeling and monitoring data to provide water exposure 

estimates?

Each question addresses aspects of the uncertainties in monitoring analyses that need to 

be considered in the context of the risk assessment. The recent series of SAP 

consultations on human health and drinking water for atrazine have focused on addressing 

the short-term temporal variability based on monitoring sampling frequency. Section 6.3 

evaluates a cross-section of approaches for evaluating monitoring data and uncertainties, 

ranging from conventional statistical analyses to mechanistic models and combinations in 

between. However, in determining the uncertainty in monitoring data, the USEPA must also 

consider year-to-year variability. How many years of monitoring are sufficient to 

characterize the likely range in exposures resulting from year-to-year variability? Section 

6.4 briefly explores ways of using monitoring and modeling together to estimate the 

potential long-term range pesticide concentrations at a given location. 

The USEPA typically addresses the spatial variability in pesticide concentrations in water 

by focusing on vulnerable water bodies that are expected to have the greatest exposures. 

For the atrazine monitoring program, CWS were considered vulnerable based on quarterly 

compliance monitoring data. Section 6.5 will provide a brief discussion on spatial variability 

and sound statistical designs, as well as potential applications of lessons learned regarding 

watershed characteristics that contribute to higher atrazine concentrations from a separate 

assessment of atrazine monitoring for ecological exposures (USEPA, 2007, 2009a, and 

2009b). 

6.3 Short-term Temporal Variability: Daily Variations within a Given Year

In a given year, pesticide concentrations in surface water bodies generally occur at low or 

undetectable levels except during the active use period, where short-term seasonal pulses 

of higher concentrations are related to the timing of pesticide use and rainfall patterns. 

Such short-term pulses in pesticide concentrations range from less than a day to several 

weeks, depending on the interaction of pesticide properties, use practices and patterns, 

watershed characteristics, weather, and management practices. Atrazine, with relatively 

widespread use and properties favorable to pesticide transport (mobility and persistence), 
Page 118 of 184

may be detected year-round, with periods of high concentrations ranging from days to 

weeks during the spring and summer. Consequently, monitoring data for atrazine provides 

a good test for evaluating alternative methods for data analysis, as trends in time and 

space can be observed above the level of quantification.

Within this seasonal pattern, pesticide concentrations in water show levels of temporal 

autocorrelation (serial correlation) that are not well accounted for with traditional statistical 

methods (Crawford, 2004). Pesticides such as atrazine show a serial correlation in which 

the probability of detecting the pesticide in water on a particular day is greater when the 

pesticide was detected on the previous day. Sampling designs and methods of interpreting 

monitoring data need to account for the temporal co-dependence of pesticide 

concentrations in water: the exposures measured on one day are related to the exposures 

measured on preceding and subsequent days.

The extent to which monitoring studies capture this short-term variability depends on how 

frequently and how targeted to the use season the sampling events occur. The AMP study 

focused weekly samples during the anticipated high use/runoff period to estimate 90-day 

average concentrations of concern. To estimate shorter duration exposures from 

noncontinuously-sampled monitoring data, particularly where peak concentrations may 

become more important, the USEPA needs methods that can predict values that may be 

greater than those sampled (FIFRA SAP, 2010a). 

The Agency considered several approaches for characterizing time series distributions and 

uncertainties based on sampling design, beginning with statistical simulations and moving 

on to geostatistical approaches, combinations of WARP with statistical models, and 

deterministic models. While the emphasis is on analyzing the CWS monitoring data 

collected for atrazine, such approaches could be applied to other monitoring datasets and 

other pesticides. 

The USEPA used a limited number of intensive monitoring datasets to provide a proof-ofconcept for the approaches presented below. A more complete analysis, involving the full 

set of intensively sampled datasets, grouped by watershed area, water body type, and 

geography, could be used for estimating uncertainty factors and time series for the weekly 

CWS monitoring data collected for the AMP. 

6.3.1 General Approach for Assessing Uncertainties in Monitoring Related 

to Short-term Temporal Variability

To assess how well monitoring at a specified sampling frequency estimates the actual 

magnitude, frequency, and duration of the concentrations at that site, we must consider 

daily variations in pesticide concentrations and the temporal auto-correlation in those 

concentrations. The USEPA is considering whether shorter durations of exposure (ranging 

from 4 to 28 days) are more appropriate for assessing atrazine effects on humans. The 
Page 119 of 184

question applied to atrazine is how well exposure estimates based on 7-day sampling 

intervals during the high use season reflect actual exposures for 4-, 14-, or 28-day toxicity 

durations of concern, as well as for the current 90-day duration. In this analysis, the USEPA 

also evaluated uncertainty in estimates based on different sampling intervals (ranging from 

4 to 28 days). 

The SAP emphasized that evaluations of different exposure estimation methods need to be 

benchmarked against intensive, preferably daily, monitoring data for an adequately 

representative range of sites (FIFRA SAP, 2010a and 2010b). While the AMP covers the 

geographic range of intensive atrazine use, the weekly sampling interval is not sufficient 

because it provides a “biased representation” of actual concentration profiles (FIFRA SAP, 

2010a). The USEPA proposed using the more intensively sampled monitoring data from 

Heidelberg University’s National Center for Water Quality Research (NCWQR) and the 

more frequently-sampled headwater streams from Syngenta’s Atrazine Ecological 

Exposure Monitoring Program (AEEMP) to evaluate sampling frequency and exposure 

estimation methods (USEPA, 2010a and 2010b).

The NCWQR (formerly known as the Heidelberg College Water Quality Laboratory) has 

intensively sampled a number of pesticides, including atrazine, on selected Ohio rivers 

since the 1980s (NCWQR, 2010). The Heidelberg dataset reflects frequent sampling over 

many years in a geographically limited area. Syngenta’s AEEMP includes a subset of sites 

with intensive atrazine measurements using an autosampler for a number of headwater 

watersheds across the atrazine use area. While this dataset adds to the geographic 

diversity in monitoring, it represents watersheds that are smaller than most watersheds that 

supply CWS. Thus, generalizations from these datasets need to be done in the context of 

both geographic distribution and representative watershed sizes. With this context in mind, 

these two datasets do provide a basis for evaluating the ability of data analysis methods to 

evaluate the uncertainty in monitoring datasets and their ability to predict the likelihood of 

pesticide concentration excursions outside the range of the measured data in between 

sampling events. In keeping with SAP recommendations, the USEPA will attempt to match 

the intensively-sampled sites to CWS sites based on water body and watershed 

characteristics to the extent possible (FIFRA SAP, 2010b).

Syngenta submitted a report summarizing a plan to monitor daily during the use season at 

six CWS that are included in the AMP (Merritt, 2011). The six CWS – three in Kansas, two 

in Indiana, and one in Ohio – draw water from single sources of flowing water. This 

monitoring data will provide more intensive sampling of source and treated water samples 

at CWS drawing from flowing waters that can be used to further evaluate the approaches 

proposed in this section. Analogous daily monitoring data do not exist for reservoirs or 

other static water bodies. A different temporal exposure profile would be expected from 

reservoirs, which integrate pesticide loading from their watersheds and have slower flowthrough, resulting in a greater residence time and smaller temporal variations than flowing 

water bodies (Blomquist et al., 2001). The SAP suggested collecting more intensively-
Page 120 of 184

sampled data for other representative water bodies, such as reservoirs (FIFRA SAP, 

2010b). An effort to target CWS drawing from a single reservoir water source similar to that 

described in Merritt (2011) could be completed in time for the 2013 registration review, 

providing additional data to analyze uncertainties in sampling frequencies for static water 

bodies. 

Given the uncertainty in defining the duration of toxicological concern, the USEPA plans to 

use a multi-step approach for analyzing the uncertainty in exposure estimates from 

monitoring data. The first step is a statistical analysis of monitoring frequency by simulating 

different sampling frequencies using the intensively sampled datasets described above. 

This approach, described in Section 6.3.2, addresses the question: 

Given a sampling frequency of X (samples per month or average interval between 

samples) and an exposure duration of concern of Y (4 to 28) days, what is the relative 

performance of each sampling strategy (X), based on measures representing the 

differences of its estimates (Y) from the known true values? These differences may be 

used to quantify the uncertainty from sampling frequency.

A bias or uncertainty factor generated from the analysis could be applied to bracket the 

exposure estimates from the weekly CWS samples for comparison against the 

concentration and duration of concern. If the bracketed exposure estimates exceed the 

concentration of toxicological concern for the specific duration of concern, further analysis 

using geostatistical methods, hybrid geostatistical-WARP, or deterministic modeling 

approaches described in Sections 6.3.3 – 6.3.5 may be needed. The case study (Section 7) 

illustrates how a time series estimated from weekly monitoring using geostatistical models 

could be incorporated into the dietary exposure assessment.

6.3.2 Comparative Analysis of Monitoring Frequency Performance

To evaluate the relative performance of different monitoring frequencies for estimating 

exposure windows of specific durations, the USEPA performed a simulation analysis on 

two example sites representing two different watershed areas for which daily or near daily 

concentration measurements are available. The AEEMP MO-01 data from 2007 and the 

NCWQR Maumee River data from 1995 will be used in example analyses (see Appendix 

D.1 for the measured atrazine concentrations for each of these data sets). 

From the daily to near daily concentrations measured at from April through August/ 

September in each year, the USEPA divided the data into sampling intervals of 4, 7, 14, or 

28 days and sampled at random from each interval 10,000 times. The resulting compiled 

chemographs, derived from the known true profile, represented trials that reproduced 

sampling within 4-day, 7-day, 14-day (biweekly), or 28-day (monthly) windows. Intervening 

values were estimated for each resulting chemograph by linear interpolation, with daily, 4-

day, 14-day, 28-day, and 90-day running averages calculated for each sampling. The 
Page 121 of 184

maximum value for each of these running averages was compiled into a distribution, 

resulting in five distributions of 10,000 maxima for each estimated average. Percentiles of 

those distributions of maximum values (Tables 11 and 12) are compared against the site’s 

true maximum value for each measurement. 

Tables 11 and 12 provide the results for each example site, along with a simple ‘bias’ factor 

for assessing the varying average duration estimates (peak, 4-, 14-, 28-, and 90-day 

averaging periods) based on the 4-, 7-, 14-, and 28-day sampling intervals. The 

multiplicative ‘bias’ factor in the tables is simply the ratio of the true maximum to the 5th

percentile maximum for each estimate derived from the 10,000 runs. For example, with 4-

day sampling intervals, estimates of the 1-day peak can be nearly 4 times lower than the 

true peak concentration. Sampling within a 2-week window can return estimates of the 4-

day average that are over 10 times lower than the true 4-day average. (Minus signs on the 

factors simply note an underestimation of true values.) 

The bias factors in Tables 11 and 12 assume that the sampled concentrations reflect an 

average across the day. This assumption only holds true for autosampler data collected for 

MO-01, which integrated time sips across a 24-hour period. Grab samples collected during 

a single time period within a day have additional uncertainty related to intra-day variability 

in pesticide concentrations in the water body. This intra-day variability is not reflected in the 

analysis below. 

The bias factors for estimating daily concentrations run from about 1.5X lower to more than 

50X lower for the 2007 MO-01 data, increasing as the sampling interval increases. 

Sampling within every 4 or 7 day period underestimates the true average, but not by as 

large a factor as with biweekly or monthly sampling intervals. 
Page 122 of 184

Table 11: Summary of 10,000 simulations of monitoring estimates at different sampling intervals for 

the AEEMP MO-01 site in 2007. 

Averag

-ing 

Period

Sampling 

Interval

True 

Max 

(ppb)

Lowest 

Max 

(ppb)

5th%ile 

Max 

(ppb)

10th%ile 

Max 

(ppb)

Bias 

Factor 

(5th%ile)

50th%ile 

Max 

(ppb)

95th%ile 

Max 

(ppb)

Daily 

Peak

4 days 91.6 23.2 23.4 26.2 -3.9 71.8 >91

7 days 91.6 17.2 19.0 23.2 -4.8 33.3 >91

14 days 91.6 2.6 6.2 8.1 -14.8 23.2 >91

28 days 91.6 0.97 1.57 2.58 -58 14.26 71.8

4-day 

avg.

4 days 59.7 18.9 22.8 23.1 -2.6 57.5 76.4

7 days 59.7 15.6 17.4 20.3 -3.4 29.4 82.2

14 days 59.7 2.5 5.9 7.6 -10.1 21.7 85.0

28days 59.7 1.0 1.6 2.5 >-37 14.0 69.4

14-

day 

avg

4 days 27.0 11.2 15.3 16.6 -1.8 29.6 41.9

7 days 27.0 11.4 12.4 13.1 -2.2 20.7 56.0

14 days 27.0 2.2 4.8 6.4 -5.6 17.8 69.3

28days 27.0 1.0 1.5 2.4 -18.0 13.2 63.0

28-

day 

avg.

4 days 15.1 6.7 8.9 9.8 -1.7 16.8 23.1

7 days 15.1 7.2 7.7 8.2 -2.0 13.2 34.3

14 days 15.1 1.7 3.5 4.8 -4.3 12.6 46.8

28 days 15.1 1.0 1.4 2.2 -10.8 11.9 54.3

90-

day 

avg.

4 days 8.1 4.3 5.5 5.8 -1.5 7.9 10.8

7 days 8.1 4.0 4.9 5.1 -1.7 7.2 14.4

14 days 8.1 1.3 2.1 2.8 -3.9 7.7 17.9

28 days 8.1 0.8 1.2 1.5 -6.7 5.5 23.7

In contrast, data for the Maumee River generally show much lower bias factors compared

with those for MO-01 (Table 12). 
Page 123 of 184

Table 12: Summary of 10,000 simulations of monitoring estimates at different sampling intervals for 

the NCWQR Maumee River site in 1995.

Averag

-ing 

Period

Sampling 

Interval

True 

Max 

(ppb)

Lowest 

Max 

(ppb)

5th%ile 

Max 

(ppb)

10th%ile 

Max 

(ppb)

Bias 

Factor 

(5th%ile)

50th%ile 

Max 

(ppb)

95th%ile 

Max 

(ppb)

Daily 

Peak

4 days 14.1 8.9 8.9 8.9 -1.6 10.6 >14.03

7 days 14.1 5.7 6.2 7.4 -2.3 9.9 >14.03

14 days 14.1 4.6 4.7 5.1 -3.0 7.6 >14.0

28 days 14.1 3.4 4.4 4.5 -3.2 5.7 12.3

4-day 

avg.

4 days 11.7 7.9 8.1 8.3 -1.4 9.8 >13.3

7 days 11.7 5.3 6.0 7.0 -2.0 9.0 13.2

14 days 11.7 4.5 4.7 5.0 -2.5 7.4 13.3

28days 11.7 3.3 4.4 4.5 -2.7 5.6 12.0

14-

day 

avg

4 days 7.8 5.7 6.3 6.4 -1.2 7.3 9.5

7 days 7.8 4.4 5.4 5.9 -1.4 7.3 10.8

14 days 7.8 4.3 4.7 4.9 -1.7 6.9 11.3

28days 7.8 3.1 4.3 4.4 -1.8 5.4 11.1

28-

day 

avg.

4 days 6.3 5.3 5.7 5.8 -1.1 6.2 7.1

7 days 6.3 4.4 5.1 5.3 -1.2 6.3 7.9

14 days 6.3 3.9 4.4 4.6 -1.4 6.1 9.1

28 days 6.3 2.8 4.1 4.2 -1.5 5.1 9.9

90-

day 

avg.

4 days 4.4 3.7 4.0 4.1 -1.1 4.4 4.7

7 days 4.4 3.1 3.7 3.8 -1.2 4.3 5.1

14 days 4.4 2.5 3.3 3.4 -1.3 4.2 5.7

28 days 4.4 1.8 3.0 3.1 -1.5 3.9 6.4

In these examples, as in all sites, the differences are directly related to the nature of the 

data and the magnitude of difference between what may be a short-lived peak and the 

concentrations preceding and following it. Concentrations at Maumee are lower overall, 

thus making the magnitude of potential bias smaller with regard to missed peaks. In 

addition, if the duration of peak values extends over the course of a week instead of for a 

day or two, then sampling within a 4-day window is more likely to ‘pick up’ some portion of 

the elevated concentration profile. For averages of longer duration, potentially missed 

peaks obviously have less impact on the estimates. These differences in the nature and 

magnitude of the profiles can be attributed, at least in part, to differences in watershed size 

and hydrology but may also reflect differences in atrazine use intensity in the watersheds.

Based on these two examples only, an uncertainty factor for a 7-day sampling frequency, 

as used for the CWS in the AMP, might be in the range of 2-4X for estimating a 4-day 

average concentration and 2X for estimating a 14- or 28-day average concentration. Using 

these examples for the 90-day average period from the 2003 assessment (USEPA, 2003), 
Page 124 of 184

the bias factor would be less than a factor of 2 for 7-day sampling intervals. Differences are 

expected to exist for sampling from larger watersheds and from reservoirs, and for other 

conditions that a larger, representative sample of watersheds might show. Bias factors 

may tend to be slightly lower for reservoirs, for example, based on both differences in size 

and on the ‘smoothing out’ we would expect to see as water from streams/rivers reach the 

larger static water body. They may be higher where sampling is primarily from small rivers. 

The USEPA’s planned analysis of intensive monitoring representative of a range of 

watersheds and water bodies will likely result in a smaller uncertainty factor for CWS that 

are supplied by reservoirs with a large watershed and a larger uncertainty factor for CWS 

supplied by flowing waters with smaller watersheds. 

Syngenta has submitted a report using this basic methodology, but covering a larger 

sample of sites and years. In that report, Mosquin et al ( 2011b) examined data from three 

different sources: (1) one Community Water System (St. Louis/St. Charles, 1993-98 and 

2000) with results for treated (“finished”) water; (2) four rivers/streams from the NCWQR 

data set (Maumee River, Sandusky River, Honey Creek, and Rock Creek) for the years 

1993-2008; and (3) sites from the Atrazine Ecological Exposure Monitoring Program 

(AEEMP) with autosampler data (11 sites from 2009, and 23 sites from 2010).

Simulating sampling frequencies from 2 days to 7 days on the near-daily data, Mosquin et 

al (2011b) calculated 1-day (peak), 2-day, 3-day, 4-day, 7-day, and 28-day average 

concentrations. Performance was measured by examining % error, % bias, and % relative 

mean squared error (RMSE). The authors calculated exact measures of performance for 

systematic sampling, in addition to performing the stratified random sampling as described 

above, running 1,000 simulations of each combination of sampling interval and average 

duration.

For 7-day sampling intervals, Mosquin et al (2011b) found up to -77% error for estimating 

daily peaks and -67% error for estimating 4-day averages. They did not investigate 

sampling periods of longer than 7 days, where performance would be expected to decline. 

It is not always clear how the percentiles were compiled, however, and results for the 

AEEMP sites have been presented as a group. In the AEEMP, particular sites and years 

can have fairly high values that would be missed in certain instances by 7-day grab 

samples. Bias factors corresponding to these error estimates, as a result, are expected to 

be much greater when individual sites and years are examined. 

The USEPA has presented results for two example sites to demonstrate both some general 

concepts as well as the difference between results for two particular site/types, and it is 

clear that the discussion can be greatly enhanced by the addition of more data. 

The Agency believes, however, that a more helpful approach would be to present summary 

statistics on the low end percentiles of the simulated runs by site and by year, rather than to 

present percentiles of the grouped data. Important differences exist among sites and from 
Page 125 of 184

one year to the next at the same site (see Section 6.4). A protective approach will highlight 

these differences where they exist. 

 

6.3.3 Geostatistical Analysis and Stochastic Application

The September 2010 SAP noted that combining a deterministic model, such as PRZM or 

USGS’s SEAWAVE-Q (used in Sullivan et al, 2009), with a regression-based model, such 

as WARP, offers “the most promising approach to deal with sparse data in flowing water” 

(FIFRA SAP, 2010b). However, because of the difficulty in implementing these methods, 

the SAP suggested that it may be easier to use WARP in combination with a statistical 

approach such as kriging (FIFRA SAP, 2010b). This section describes two approaches that 

build on geostatistical analysis and stochastic simulations: (1) conditional stochastic 

simulations from variography with kriging and (2) using soft (indirect) data when monitoring 

data are sparse. This second approach involves regressions with WARP combined with 

variography and stochastic simulations. The full analysis can be found in Appendix D.2 

(Geostatistical Analysis and Conditional Stochastic Simulation Application).

The geostatistical modeling approach selects a stationary random function model to 

describe the relationship of variance as a function of distance or time (Isaaks and 

Srivastava, 1989). This relationship can be quantified using a variogram, correlogram, or 

covariance function. For a pesticide time series, this nearest-neighbor modeling approach 

assumes that concentrations in close proximity in time are more similar than concentrations 

farther apart in time. Applying a random function model to pesticide time series generally 

requires a condition of stationarity: that is, an estimate has an equal probability of occurring 

regardless of location in space and time (Isaaks and Srivastava, 1989). This may not hold 

for an entire year of pesticide concentrations due to seasonal trends, but may be applicable 

to specific times during the pesticide use season. To address stationarity concerns 

associated with seasonality of pesticide concentrations in water, the USEPA targeted the 

monitoring analysis from the earliest planting date to approximately 100 days post-planting. 

This represents the time with the highest magnitude and frequency of atrazine detections. 

The USEPA chose two intensively sampled monitoring datasets – NCWQR Maumee River 

monitoring for 1995 and AEEMP monitoring for MO-01 in 2007 – to evaluate the approach 

for different years and hydrologic characteristics (watershed size and stream flow 

characteristics). We simulated 4-, 7-, 14-, and 28-day sampling intervals with the monitored 

time series. Sample sets with less than 15 points – the 14- and 28-day sampling intervals –

provided the poorest variograms for predicting daily pesticide concentrations from kriging. 

In those instances, we first used WARP to estimate the percentiles in the time series and 

evaluated options for fitting the merged WARP and sampled data in a new time series. 

Figure 22 illustrates the analysis strategy used for time series modeling from the sampled 

monitoring data.
Page 126 of 184

Figure 22: Analysis Strategy for Modeling Pesticide Time Series Concentrations from Monitoring 

Data.

In the initial variogram analysis, spherical or gaussian models fit the 1995 Maumee River 

and MO-01 2007 atrazine data simulated with 4-, 7-, and 14-day sampling intervals (Table 

13). Although the extent of autocorrelation, as depicted by the range, was highly variable 

among the case study data sets, longer ranges (52-80 days) were found in the 1995 

Maumee River data. This phenomenon may be associated with the larger watershed size 

and daily flow characteristics. While kriged time series provided reasonable prediction of 

MERGE 

MONITORING 

DATA

ARRANGE 

PERCENTILES 

OF FLOW 

DATA WITH 

MERGE DATA 

PERCENTILES

DETERMINE APPROPRIATE 

POINT ESTIMATION 

PROCEDURE FOR TIME 

SERIES

ARE THERE MORE THAN 15 

DATA POINTS IN TIME 

SERIES?

PROCEED WITH 1-

DIMENSIONAL 

ORDINARY KRIGING

INFILL TIME SERIES 

WITH MODEL 

ESTIMATED VALUES

YES

NO

USE WARP 

MODEL TO 

ESTIMATE 

PERCENTILES 

IN TIME SERIES

PROCEED WITH 

VARIOGRAM ANALYSIS 

USE COVARIATE 

VARIABLE TO 

ESTIMATE 

CONCENTRATION

SEQUENTIAL STOCHASTIC 

SIMULATION- GENERATE 

EQUALLY PROBABLE 

REALIZATIONS OF TIME 

SERIES
Page 127 of 184

missing data for the 4- and 7-day sample intervals (Figure 23), reliable variograms could 

not be estimated for the 14- and 28-day sample intervals from either monitoring dataset. 

Table 13: Variogram Models Using the NCWQR Maumee River 1995 and AEEMP MO-01 2007 Time 

Series with Different Sampling Frequencies.

Data Sampling 

Interval

Number 

of 

samples

Nugget1

(μg/L2)

Model2 Sill3

(μg/L2)

Range4

(Days)

Maumee 

River, 1995 

(days 100-

200) 

Actual (daily) 100 0.10 Spherical 0.97 16

4-day 25 0.00 Spherical 3.70 52

7-day 15 0.00 Spherical 3.20 61

14-day 8 0.30 Gaussian 3.20 80

28-day 4 2.2 No temporal correlation

MO-01, 2007 

(days 91-

190), 

complete 

time series5

4-day6 27 0.00 Spherical 3.60 27

7-day6 15 0.00 Spherical 3.80 27

14-day6 8 0.50 Spherical 3.80 32

28-day6 4 3.80 No temporal correlation

1 The nugget describes the amount of variance resulting from random processes without temporal 

correlation; it is associated with sampling error, sampling technique, or inherent variation in daily 

pesticide concentrations.

2 Variogram models provide a description of variance as a function of lag distance or lag time 

intervals. The Spherical model is defined as the variance at lag(h)=C*(1.5*h/a-0.58(h/a)3

)+nugget. 

The Gaussian model is defined as the variance at lag(h)=C*(1-exp(-(3h)2

/a2)+nugget. This model 

does not represent the Gaussian distribution model. Parameters in the variogram model:

h = lag time or lag distance

C = sill, the variance for random process (ug/L2

)

a = range in days or distance for correlation of variance 

3 The sill is the variance value at which the variogram levels off (see Figure 23 for an illustration).

4 The range is the lag time at which the variogram reaches the sill. It represents the period in which 

monitoring values are autocorrelated. 

5 Grab samples collected every 4 days from 4/01/07 to 7/19/07. Complete time series includes 

autosampler data collected on most days in between the grab sample dates. Appendix 7.A also 

includes an analysis of the MO-01 2007 monitoring using only the 4-day grab samples. The models 

based on the daily sampling series had greater means, maximums, and variances than the models 

based on the 4-day grab samples, likely because the 4-day samples missed the maximum 

concentration detected in the daily samples.

6 Sampling intervals estimated from kriged samples. 
Page 128 of 184

Figure 23: Variograms for 4-day and 7-day Sampling Frequency for the Maumee and MO-01 2007 

data: a.) 4-day 1995 Maumee; b.) 7-day 1995 Maumee; c.) 4-day grab MO-01 2007; and d.) 7-day 

grab MO-01 2007.

We used conditional stochastic simulations to assess the uncertainty associated with 

estimated concentrations by running 1,000 equally-probable realizations of time series with 

the same temporal autocorrelation and descriptive statistics as the kriged time series. A 

subsampling of realizations for the 4-day and 7-day sampling derived time series provide a 

reasonable description of temporal behavior and descriptive statistics of atrazine 

concentration for the Maumee 1995 site from Julian day 100 to 200 (Figure 24a and b). 

Although a variogram was modeled for the 14-day sample data (Figure 24c), insufficient 

data precluded providing a reliable estimate of the temporal structure and descriptive 

statistic of atrazine at the Maumee River site. Because a variogram could not be 

constructed from 4 data points for the 28 day sample data, we tried to use daily flow to 

approximate the temporal structure of atrazine concentrations (Figure 24d). As expected, 

the estimate of peak concentrations decreased and the uncertainty around the time series 

estimations increased as the sampling interval increased; beyond 14-day intervals, the 

number of data points was insufficient for reliable estimates.
Page 129 of 184

Figure 24: Representative realizations of kriged atrazine time series from (a) 4-day, (b) 7-day, (c) 14-

day, and (d) 28-day sampling of the NCWQR Maumee River 1995 data.

Similar patterns are evident in the conditional simulations for the MO-01 2007 monitoring 

data, although the realizations underestimated the actual peak concentrations in the 4- and 

7-day sampling simulations (Figure 25). Although the temporal pattern for the 14-day 

sampling interval for MO-01-2007 did not exactly match the actual data, the conditional 

simulations were capable of identifying the temporal occurrence of the peaks (Figure 25c), 

in contrast to the 14-day simulations for the Maumee River data (Figure 24c).

The simulated sampling intervals for the 2007 MO-01 data set did not capture the highest 

atrazine peak that occurred on Julian days 152-154 (Figure 25). This results in an 

underestimate of the true maximum concentrations for the subsequent conditional 

simulations. While a different sampling of the daily concentrations from MO-01 might 

capture the true peak, this particular sampling was used in Section 6.3.6 as an evaluation 

of how much of an impact missing the true peak concentration might have on estimating 

longer duration rolling average concentrations with conditional simulations of the variogram 

models. 

Julian Day

100 120 140 160 180 200 220

Atrazine Conc (ug/L)

0

2

4

6

8

10

12

14

16

Estimate Mean (SD) 

 Actual Data 

Julian Day

100 120 140 160 180 200 220

Atrazine Conc (ug/L)

0

2

4

6

8

10

12

14

16

Estimate Mean (SD) 

 Actual Data 

Julian Day

100 120 140 160 180 200 220

Atrazine Conc (ug/L)

0

2

4

6

8

10

12

14

16

Estimate Mean (SD) 

 Actual Data 

Julian Day

100 120 140 160 180 200 220

Atrazine Conc (ug/L)

0

2

4

6

8

10

12

14

16

Estimate Mean (SD) 

 Actual Data 

(a) 4-day intervals (b) 7-day intervals

(c) 14-day intervals (d) 28-day intervals
Page 130 of 184

Figure 25: Representative realizations of kriged atrazine time series from (a) 4-day, (b) 7-day, (c) 14-

day, and (d) 28-day sampling of the MO-01 2007 data.

With fewer data points resulting from less frequent monitoring samples, the uncertainty in 

predicting daily atrazine concentrations increases. In the absence of data, the incorporation 

of additional information may assist in predicting a daily time series of pesticide 

concentrations. One approach involves the use of a covariate, such as average daily flow, 

to estimate atrazine concentrations between monitoring points. Another approach is to 

merge monitoring data with WARP estimates of the annual distribution of pesticide 

concentrations. This approach requires additional steps to derive a time series from the 

distribution. 

Appendix D.2 analyzes two covariate approaches – one using a co-occurring pesticide and 

one using stream flow data. Covariate approaches using other pesticides will suffer from 

the same sampling frequency issues as atrazine monitoring. One option may be to explore 

the use of more frequently monitored nutrient concentrations, such as nitrate as a 

covariate. While average daily stream flow data may be a useful covariate for estimating 
Page 131 of 184

atrazine concentrations in flowing water bodies, the relationship between flow and pesticide 

concentrations is also dependent on the timing of application as well as the watershed size. 

Analyses of the NCWQR 1995 Maumee River and the AEEMP 2007 MO-O1 monitoring 

data sets found significant regressions between atrazine concentrations and average daily 

flow rate within certain time segments of the data sets. The timing of atrazine application is 

a critical piece of information in determining the correct time interval. The highest 

concentrations in a time series are generally found in the first runoff events after pesticide 

application (USEPA, 2007, 2009a). Additionally, high average daily flow events in the 

absence of atrazine prior to atrazine application or after extensive degradation of atrazine 

are expected to impact the slope of the regression equation. Exploiting any relationship of 

flow and atrazine concentration requires knowledge of the atrazine application dates within 

a watershed. Unfortunately, data on the date of pesticide applications in a watershed are 

not readily available. However, since atrazine application dates will likely parallel corn 

planting dates in most areas, any variable influencing corn planting dates, such as air 

temperature, soil temperature, or soil moisture conditions, may be an appropriate predictor 

for estimating atrazine application timing. 

Appendix D.2 presents a proof-of-concept approach for integrating sparse monitoring data 

with WARP predictions of distribution percentiles into a reliable concentration time series. 

WARP provides estimates of the 5th, 10th, 15th, 25th, 50th, 75th, 85th, 90th and 95th

percentiles and maximum concentration across the entire year (Stone and GIlliom, 2009). 

The WARP percentile estimates and the available monitoring data are merged into a single 

dataset. 

The newly generated distribution still needs to be sequenced in a time series. One possible 

procedure for sequencing is relating atrazine concentrations to flow. As previously 

discussed, a significant regression equation exists for atrazine and flow in the 120 -200 day 

samples in the Maumee River data. The flow was weighted to ensure that flow events 

immediately after pesticide application have more influence on atrazine runoff. The 

weighting factors for flow were derived using the first-order degradation model: 

A=A0e-k*time

where 

A0 = 1 lb/A

k = degradation rate (0.01152 days-1)

time = days 

The actual daily flow data from Day 120 to Day 200 were multiplied by the daily flow 

weighting factor. 

The flow and atrazine data were separately ranked according to percentiles in the 

distribution and then matched to create a time series. The newly sequenced time series 

were used for variogram analysis and kriging. In the absence of watershed- and year-
Page 132 of 184

specific WARP parameters for the 1995 Maumee River monitoring data, the USEPA used 

surrogate inputs from two Ohio sites (OH-01-2004 and OH-03-2005) included in the 

AEEMP study to evaluate how well the approach could identify the times of peak 

concentrations. The merged WARP data predicted for OH-01-2004 had a much higher 

predicted maximum atrazine concentration than OH-03-2005. This difference is mainly 

attributed to the difference in May and June precipitation. However, the two simulated time 

series illustrate that the temporal occurrence of the atrazine peak mimics the actual time 

series. This situation suggests that weighted flow may be a reasonable predictor of 

temporal occurrence daily atrazine concentrations for location where flow data is available.

6.3.4 Mechanistic Approach: PRZM Hybrid Model

Syngenta submitted a proposal for developing a PRZM Hybrid model to augment measured 

monitoring data by estimating possible runoff events that may result in increased 

concentrations on unsampled days (Miller et al, 2011). The model would use local rainfall 

and crop planting data for the year of monitoring and watershed-specific soil, cropping, and 

hydrologic properties to simulate the environmental fate and transport of atrazine at the 

watershed scale. For each watershed, daily atrazine concentrations will be estimated from 

PRZM-generated atrazine flux and runoff values for each soil map unit that are then 

aggregated to the watershed based on area-weighted map unit concentrations and 

adjusted by the percent of corn and sorghum in the watershed (Miller et al, 2011). 

Miller et al (2011) propose combining the resulting PRZM time series with monitoring data 

to estimate potential concentrations on unsampled dates between measured values. This 

approach appears to be similar to a method Syngenta explored to estimate atrazine 

concentrations between 4-day sampling events for the ecological exposure monitoring 

program (Snyder et al, 2007). The estimation approach may not be suitable for larger 

watersheds where the time of travel for pesticide loads is greater than one day without 

accounting for the lag time. 

Although no results were available in time to be reviewed for this document, this approach 

may provide an option for estimating pesticide time series where available monitoring data 

are too sparse to estimate daily concentrations with other statistical methods. Miller et al

(2011) plan to explore distribution matching to calibrate PRZM concentrations to measured 

data. However, in instances where the monitoring data are sparse, distributions based on 

measured data are likely to underestimate the true distribution of pesticide concentrations 

in water. An alternate approach may be to match PRZM model distributions with percentile 

distributions estimated by WARP. 
Page 133 of 184

6.3.5 Watershed Atrazine Analysis Model 

In addition to the hybrid PRZM modeling approach, the USEPA briefly evaluated a simple 

watershed-scale mass balance model (see Appendix D.3). This mass balance model uses 

an iterative fitting method to optimize the parameter estimates of a simplified watershed 

mass balance model for watersheds currently monitored for atrazine. Potentially, spatial 

variation in watershed parameters from sampled watersheds could be used to predict 

parameters for non-sampled watersheds located between the sampled watersheds, which 

in turn could be used to predict daily concentrations for those non-sampled watersheds.

If a simple model could be found that fits the data well, it would provide a method based on 

mass balance principles for predictions of “non-sampled” dates (i.e., the concentration of 

atrazine rises based on the amount of atrazine applied to the watershed and falls based on 

the amount degraded and discharged from the watershed). However, the more complex 

such a model needs to be, the more data would be needed to provide assurance that 

proper calibration has been achieved. Because larger watersheds are generally more 

complex than smaller watersheds (greater variation in application dates, rainfall events 

differ across a large watershed, greater variation in travel times to deliver atrazine to the 

monitoring point), a simpler model may not be possible to provide enough assurance of 

proper calibration at CWS with larger catchment areas that have a limited numbers of 

samples. 

The USEPA conducted a preliminary evaluation of the mass balance approach on selected 

AEEMP monitoring sites (Appendix D.3). We used simple mass balance models to 

estimate the contribution of atrazine to the watershed from each individual storm event 

greater than 0.4 cm rainfall based on the load of atrazine discharged from the watershed. 

Discharge was calculated by iteratively fitting daily predictions of atrazine concentration to 

the less-frequently measured (monitoring) atrazine concentrations. While the simple 

watershed mass balance models were often able to fit the variation in atrazine 

concentrations (r2

s often exceeding 0.8), the mass of atrazine discharged often differed by 

an order of magnitude in either direction from estimates of atrazine applied to the

watershed based on estimates of the watershed area planted in corn and sorghum and 

local estimates of atrazine use rates (documented in USEPA, 2009a and 2009b). 

The “imbalance” issues may result in part from apparent anomalies or errors in the flow 

records. However, the imbalance problem is likely due primarily to the coarseness of the 

model temporal resolution; i.e., the time-step (see Appendix D.3 for a discussion on this). 

The discrepancy between expected maximum mass applied and expected loads may be

because the watershed effluent flow and atrazine concentrations are quite dynamic in these 

small watersheds. Therefore a grab sample with a high concentration in a relatively low 

flow condition can be collected early on a day with rising flow conditions and falling 

concentrations. Because flow appears in the data set as average daily flow, the high 
Page 134 of 184

concentration paired with a large average daily flow results in a load for a particular day 

that exceeds maximum expected mass of atrazine applied to the watershed.

For this method to be useful, the model would need a better method of deriving a flowweighted atrazine concentration. One method would be to tie atrazine concentrations 

measured in a particular day to the flow data at the sub-daily intervals. Typically, USGS 

collects flow data at automated gages at 15 minute intervals and then calculates an 

average daily flow from the 15 minute interval data. 

6.3.6 Generating Atrazine Time Series from Weekly CWS Monitoring for Use 

in Dietary Exposure Assessments

Sections 6.3.2 – 6.3.5 evaluated approaches that could be applied to weekly monitoring 

from the CWS to generate estimates of TCT exposures for comparison to a human health 

endpoint of concern with a duration that may range from 4 to 28 days. The analysis of 

monitoring frequencies in Section 6.3.2 indicates that the bias factors from 7-day sampling 

intervals would likely range from 2 to 4 for a 4-day average duration to 2 for a 28-day 

duration. 

While the September 2010 SAP recommended using WARP in combination with 

geostatistical or deterministic models to estimate atrazine concentrations for drinking water 

exposures, the analysis in Section 6.3.3 suggests that a reasonable time series can be 

approximated from the 7-day sampling frequencies using only kriging and stochastic 

simulations. 

We evaluated this for the case study (Section 7), using the variogram models for the 1995 

Maumee River and 2007 MO-01 data to generate 1,000 daily time series realizations from 

conditional stochastic simulations (Figures 26 and 27). The actual daily time series for each 

data set in black. The 95th and 5th percentiles of the stochastic simulations are shown as 

red dashes, with the shaded area representing a 90 percent probability range for the time 

series concentrations based on the simulations. In the absence of a full daily time series, 

the stochastic simulations of weekly monitoring would characterize the likely range in 

exposures over time (shown as the grade shading between the red-dashed lines in Figures 

26 and 27). 

The simulations of the daily time series from the variogram models underestimated the 

actual peak concentrations (Figures 26a and 27a), suggesting a bias factor may still be 

needed for estimates that require a peak exposure. However, the 95th percentile of the 

rolling average concentrations from the simulations provided a reasonable approximation of 

the actual rolling-average concentrations (Figures 26b-d and 27b-d). 
Page 135 of 184

Figure 26: Estimates of daily and rolling-average atrazine concentrations from the actual time series 

(black line) and 5th and 95th percentiles (dashed red lines) of realizations of kriged time series from 

7-day sampling of the Maumee River 1995 data.

0

2

4

6

8

10

12

14

16

91 98 105 112 119 126 133 140 147 154 161 168 175 182 189

Atrazine Concentration, ug/L

Julian Day

Daily Time Series Sim 95th %ile

Sim 5th %ile

Actual

0

2

4

6

8

10

12

14

16

91 98 105 112 119 126 133 140 147 154 161 168 175 182 189

Atrazine Concentration, ug/L

Julian Day

Sim 95th %ile 4-Day Averages

Sim 5th %ile

Actual

0

2

4

6

8

10

12

14

16

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Atrazine Concentration, ug/L

Julian Day

Sim 95th %ile 14-Day Averages

Sim 5th %ile

Actual

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Atrazine Concentration, ug/L

Julian Day

Sim 95th %ile 28-Day Averages

Sim 5th %ile

Actual
Page 136 of 184

Figure 27: Estimates of daily and rolling-average atrazine concentrations from from the actual time 

series (black line) and 5th and 95th percentiles (dashed red lines) of realizations of kriged time series 

from 7-day sampling of the MO-01 2007 data.

While the CWS monitoring data for the AMP are sampled at 7-day intervals, other 

monitoring data for atrazine and for other pesticides are often sampled less frequently. For 

those monitoring data sets with sampling intervals of longer than 7 days, time series and 

rolling-average concentration estimations will require a more complicated approach, 

combining monitoring data with WARP percentile estimates and using kriging and 

conditional simulations to generate a time series for exposure estimates:

• Generate a WARP-predicted distribution for the site, using watershed data specific 

for the CWS or monitoring location

• Merge monitoring data with WARP distributions by ranking flow within a determined 

time frame in which flow and concentrations are correlated and matching predicted 

concentration percentiles with flow percentiles

• Generate a variogram of simulated time series with nugget, range, and sill

• Use kriging to interpolate the time series

0

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Sim 5th %ile

Actual

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Page 137 of 184

• Generate stochastic simulations of the time series for uncertainty analysis

• Generate realizations of time series and bracket the estimated time series based on 

an upper and lower percentile of realizations. 

These steps are illustrated in Appendix D.2.

6.4 Year-to-Year Temporal Variability

Uncertainty factor considerations based on monitoring design must account not only for 

uncertainty in sampling frequencies in capturing short-term temporal variability, but also for 

the year-to-year variability at the same site. During registration review, the USEPA will look 

at the multi-year history of CWS monitoring for atrazine to characterize the expected range 

in concentrations from year to year. 

In any given location, the magnitude and duration of pesticide concentrations in water vary 

from year to year, depending on such factors as the magnitude, timing, and frequency of 

pesticide application, the timing and intensity of rainfall events post-application, soil 

moisture conditions at the time of application and rainfall, and the type, timing, and extent 

of land management practices in place (Gilliom et al., 2006). Atrazine occurrence in water 

follows a seasonal pattern that corresponds to seasonal usage and weather patterns, with 

larger spikes in concentration related to pre-emergent spring applications that are followed 

by periods of runoff-producing rainfall. 

For the most part, atrazine has had fairly consistent usage (average application rate, 

percent of crop treated) over time. Changes in the relative proportion of the watershed in 

corn and sorghum as a result of crop rotations and commodity demands may occur, 

particularly in smaller watersheds, but can be accounted for with yearly cropping data. 

However, much of the year-to-year variations in atrazine concentrations will likely result 

from yearly differences in rainfall during the atrazine use season. An analysis of the 

influence of rainfall on atrazine concentrations in the AEEMP study suggests that the 

relationship between atrazine concentrations in streams and precipitation is a function of 

the distribution and timing of the rainfall in relation to application rather than to the amount 

of rainfall in a given month or season (USEPA, 2007 and 2009a). 

In evaluating a monitoring study, the USEPA has to take into consideration where the 

exposure estimates from the time period of sampling fall into the extended period of 

pesticide use and how protective the exposure estimates from a monitoring study are in 

relation to that longer use period. For atrazine, the key issue in characterizing the potential 

year-to-year variability in atrazine concentrations in water relates to how long sampling 

should continue at individual CWSs before concluding, with a reasonable certainty of no 

harm, that TCT concentrations in drinking water do not exceed the level of toxicological 

concern. 
Page 138 of 184

An analysis of the year-to-year variations in TCT concentrations for the CWS included in 

the AMP indicates that the ratio of lowest to highest maximum yearly TCT detections 

increases with increasing number of years of monitoring (Table 14). This would be 

expected since longer study durations increase the likelihood of capturing wider ranges in 

likely exposures. 

Table 14: Comparison of lowest and highest yearly maximum detections for CWS in the AMP based 

on number of years sampled.

No. of Yrs. 

Sampled

No. of 

CWS 

Sampled

Ratio of the Lowest to Highest Annual Maximum 

Detections

Minimum Median 90th

Percentile

Maximum

Total Chloro-Triazines Measured in Source Waters

2-3 years 30 1.1 2.9 5.6 11.3

4-5 years 13 1.7 3.0 11.8 25.4

6 years 102 1.6 4.7 19.9 57.4

Total Chloro-Triazines Measured in Treated Waters

2-3 years 30 1.3 2.9 6.4 15.8

4-5 years 13 2.1 6.1 13.2 15.7

6 years 102 1.5 4.2 13.2 49.0

Mosquin et al (2011a) collected atrazine monitoring data from different datasets at across a 

range in years (1993-2009). The authors considered quarterly compliance monitoring at 

CWS for the Safe Drinking Water Act (SDWA) as well as more intensive monitoring with up 

to weekly sampling for the AMP and earlier atrazine Voluntary Monitoring Program (VMP). 

The more intensively sampled AMP and VMP datasets offer the potential to evaluate the 

range in maximum yearly atrazine detections over time for those CWS that were included in 

both studies. Even with the intensity of sampling in the VMP and AMP datasets (4 per 

month), a number of actual peak concentrations are expected to be underestimated for 

each CWS sampling year by a factor that depends on the sampling frequency (see Section 

6.3.2). 

The PRZM Hybrid model described by Miller et al (2011), or a variation of the modeling 

approach, may provide an alternative approach to estimating the potential year-to-year 

variability in atrazine concentrations in water as a result of variations in rainfall. The 

USEPA has used PRZM to generate multiple years of exposure estimates using daily 

rainfall and temperature data collected from meteorological stations across the country. A 

hybrid modeling approach would entail using site- and year-specific weather, cropping, soil, 

hydrology, and pesticide usage information to calibrate the estimated daily PRZM 

concentrations with measured monitoring data. Subsequent years of weather data could 

then be modeled to characterize the range in potential exposures as a result of variability in 

rainfall patterns from year to year. Such an approach would involve substantial data 
Page 139 of 184

collection and model calibration against available monitoring in order to characterize the 

year-to-year variability in atrazine concentrations in water. 

The USGS WARP model could also be used to characterize the range in year-to-year 

variability in atrazine/TCT concentrations. This approach would involve varying the 

temporal parameters – May-June precipitation, atrazine use intensity – within expected 

ranges to characterize the extent of year-to-year variations. Because atrazine use intensity, 

which reflects both application rates and area grown in treated crops in the watershed, is a 

major driver in the WARP model (Stone and Gilliom, 2009), the error in estimating this 

parameter may be the dominant component in the variability in the WARP output. 

6.5 Spatial Variability

The applicability of any pesticide monitoring data beyond the sites for which they are 

collected depends on the extent to which we can place the monitoring data in a broader 

spatial context. Spatial statistical designs that are tiered by relative watershed vulnerability 

assist in placing monitoring results in context with other sites in the pesticide use area (see 

USEPA, 2007, for a spatially-balanced design used to select monitoring sites representing 

vulnerable watersheds for the AEEMP). In the case of the AMP monitoring study, CWS 

were not selected randomly or with a statistical design, but were identified as vulnerable 

based on results of quarterly compliance monitoring. Thus, the results are applicable to this 

population of CWS. To the extent that we can relate the CWS sites to vulnerable tiers (such 

as those estimated with WARP), we may be able to make inferences about other CWS or 

about the population of CWS. 

While the geographic distribution of pesticide use is the major predictive variable for 

pesticide occurrence in surface water on a broad geographic scale (Gilliom et al., 2006; 

Stone and Gilliom, 2009), watershed characteristics such as soil susceptibility to runoff 

and/or erosion, weather patterns, land management practices, and hydrology can be 

important factors driving pesticide occurrence on a local scale (USEPA, 2009a and 2009b; 

FIFRA SAP, 2009). 

The CWS included in the AMP occur in the more vulnerable watershed tiers identified by 

WARP (Figure 28; USEPA, 2010b). While this provides confirmation that the CWS included 

in the AMP are located in the most vulnerable atrazine use areas, it does not necessarily 

provide a basis from which to draw inferences regarding other CWS within the same 

vulnerable areas that did not meet the monitoring threshold. As a part of registration review, 

the USEPA will evaluate the entire population of CWS within the most vulnerable atrazine 

use area to determine whether additional CWS should be included in the AMP. The USEPA 

(2009a and 2009b) watershed-scale vulnerability assessment for atrazine in the AEEMP

focused on headwater watersheds, but may have potential applications for better identifying 

vulnerable CWS that should be included for more intensive monitoring. 
Page 140 of 184

Figure 28: Location of CWS in the AMP in relation to vulnerable watersheds identified by WARP 

based on atrazine use on corn and sorghum.

6.6 Summary 

This section focused on methods for evaluating how well the existing AMP drinking water 

monitoring study characterizes the uncertainties in temporal and spatial variability of TCT 

concentrations in water. The analysis needs to address uncertainties resulting from (a) how 

well the monitoring sampling frequency captures daily and seasonal variations in pesticide 

concentrations in surface water, (b) year-to-year variations in magnitude and duration of 

concentrations, and (c) spatial patterns of pesticide occurrence in surface water. 

Estimating atrazine/TCT concentrations for various durations (ranging from 4 to 28 days) in 

a given year at a given CWS involves reconstructing the exposure pattern from weekly 

snapshots taken of the actual time series. The degree of uncertainty in those estimates 

depends upon the frequency of sampling (uncertainty or bias factors increase with 

increasing intervals), the exposure duration of concern (factors decreasing as the 
Page 141 of 184

timeframe of concern increases), the size of the watersheds, and the magnitude of 

concentration (Section 6.3.2). An analysis of two intensively sampled monitoring data sets 

– those with daily or near-daily sampling during the high use/runoff season – to represent 

larger (NCWQR monitoring for Maumee River in OH) and smaller (AEEMP monitoring in 

MO-01) suggests that a bias factor of at least 2 to 4X may be needed to estimate maximum 

exposures from 7-day sampling intervals. The actual bias factors are likely to vary 

depending not only on sampling intervals and exposure durations of concern but also on 

watershed size and water body type. 

As noted in the previous SAPs on atrazine (USEPA, 2010a and 2010b), a combination of 

monitoring and statistical/modeling approaches can provide an approximation of time series 

distributions for use in drinking water exposure assessments. While the SAP recommended 

a combination of WARP with deterministic models or kriging, the USEPA’s analysis

described in Section 6.3.3 found that a reasonable estimate of 4- to 28-day rolling average 

concentrations can be approximated from 7-day sampling frequencies using kriging and 

stochastic simulations, with upper and lower percentiles from the conditional simulations 

providing a characterization of uncertainty. The USEPA used the 95th and 5th percentiles for 

uncertainty bounds in the case study described in Section 7. 

For less-frequent sampling (in most instances, 14-day sampling intervals is a more 

common lower bound of sampling intervals), the bias factors will be much greater. Where 

estimated exposures from monitoring adjusted to account for the bias factor overlap with 

the toxicity concentration of concern, exposure approximations become more complicated, 

involving not only geostatistical simulations but WARP and/or deterministic modeling. 

In determining the uncertainty in monitoring data, the USEPA must also consider year-toyear variability. Approaches described in section 6.4 include extending the analysis of yearto-year variations within the CWS monitoring time frame using a PRZM hybrid approach or 

WARP. 

The applicability of the monitoring results in the AMP beyond the CWS included in the 

study depends on the extent to which we can place the data in a broader spatial context. 

For the AMP, CWS were considered vulnerable based on quarterly compliance monitoring 

data. The USEPA (2009a and 2009b) watershed-scale vulnerability assessment for 

atrazine in the AEEMP may provide additional context in better interpreting the CWS 

results and identifying vulnerable CWS that should be included for more intensive 

monitoring. 
Page 142 of 184

7. CASE STUDY: IMPLICATIONS OF MOA & TOXICITY PROFILE ON WATER 

MONITORING

7.1 Introduction

In the 2003 human health risk assessment, a duration of 90 days was used for doing 

exposure assessment of trichlorotriazines (i.e., atrazine and its metabolites) for comparison 

to an administered dose no-observed adverse effect level (NOAEL) for attenuation of the 

LH surge in a 6-month rat study (IRED 2003). Syngenta proposed a weekly sampling 

frequency of selected CWS to characterize TCT concentrations over the critical exposure 

duration of 90 days. The current scientific re-evaluation of the health effects of atrazine has 

identified shorter atrazine exposure durations that can potentially result in LH attenuation in 

rats. According to the critical study by Cooper et al. 2010, 4 days of repeated once daily 

dosing with atrazine (the minimum duration evaluated thus far) beginning on the day of 

proestrous is enough to induce a statistically significant attenuation of the LH surge in rats 

(Cooper et al. 2010). Since the frequency of drinking water monitoring should be informed 

by the temporal relationship between exposure and the toxicological endpoint of concern 

(i.e., LH attenuation), shorter durations than 90 days are being considered in the current reevaluation of the health effects associated with atrazine exposure.

Plasma triazines in the rat build up upon repeated once daily dosing with atrazine to reach 

pseudo steady state by the fourth day when attenuation of the LH surge is observed. In 

essence, based on the information available, LH attenuation is observed when plasma 

levels are sustained at a specific range. The time of 4 days to reach steady state in the rat 

is in the range of predictions based on linear elimination pharmacokinetics that such time 

should take roughly 3-5 half-lives. When the estimated rat plasma elimination rate constant 

for triazines is allometrically scaled for a human female body weight of 60 kg, the estimated 

time to reach steady state is 21-30 days (plasma half-life of 6-8 days). Similar to rat studies, 

this time estimate would be based on a constant dose level and frequency of water 

consumption, both of which are known to be variable in the human situation. Thus given the 

lack of human specific information that would allow a more precise estimate of the critical 

duration of exposure, the Agency proposes a range of exposure durations of 4 - 28 days 

relevant to LH attenuation. More specifically, three exposure durations consisting of 4, 14, 

and 28 days are being proposed to bracket the most likely human exposure durations that 

may result in LH attenuation based on the exposure requirements for LH attenuation in rat 

toxicity studies. The 28-day duration is within the range predicted by allometric scaling of 

rat plasma elimination pharmacokinetics for humans and is also the average duration of the 

human menstrual cycle. Notably, in both rats and humans, the time estimated to reach 

steady state coincides with the respective length of their ovarian cycles of 4 and 28 days, 

respectively. 
Page 143 of 184

The duration of 14 days is being proposed as a midpoint between the other two durations 

that would allow better characterization of an appropriate water monitoring frequency.

Finally, the duration of 4 days was selected on the basis that the limited human information 

available from the Davidson 1987 study suggests that the pharmacokinetics of atrazine in 

humans may be somewhat similar to that observed in rats. For instance, similar to rat 

findings, atrazine and DEA appeared to be short-lived in human blood, not having been 

detected at the first time point of 2 hours (Table 7). The estimated human whole blood halflife of 2.8 hours for DIA is in the range of estimated rat plasma half-life values of 1.2 - 3.9 

hours at 3, 10, and 50 mg/kg bw atrazine (Table 3). The estimated human half-life for 

DACT of about 11-18 hours (Table 7) is also very comparable to the rat plasma value of 8-

9 hours (Table 3). Thus, human pharmacokinetics may not be that much different as 

compared to rats and justifies including 4 days as an exposure duration that should be 

evaluated. 

7.2 Atrazine Time Series Estimates for Use in Dietary Exposure Assessments

In Section 6, the USEPA evaluated methods for characterizing the uncertainty in estimating 

4- to 28-day rolling average TCT concentrations in drinking water from weekly samples 

from CWS included in the AMP. The Agency determined that a reasonable time series can 

be approximated from the 7-day sampling frequencies using geostatistical methods (kriging 

and variogram models) and stochastic simulations (see Sections 6.3.3 and 6.3.6). For the 

case study, we used the NCWQR 1995 Maumee River and AEEMP 2007 MO-01 data sets 

to represent a range in watershed sizes for flowing water bodies and a range in atrazine 

concentrations. These data sets do not represent actual CWS but have been used for the 

monitoring analysis because they include daily or near-daily sampling during the high 

use/runoff season and allow for evaluation of sampling frequencies. The results presented 

below are for illustrative purposes only. 

Figures 29 and 30 (also shown as Figures 27 and 28 in Section 6) show the actual daily 

time series for each data set in black for comparison to the stochastic simulations of the 

variogram models for 7-day sampling intervals. The 95th and 5th percentiles of the 

stochastic simulations are shown as red dashes, with the shaded area representing a 90 

percent probability range for the time series concentrations based on the simulations. The 

simulations of variogram models for the daily time series underestimate the actual peak 

concentrations (Figures 29a and 30a); for acute toxicity endpoints of concern, estimates of 

peak, single-day exposures might require an additional bias factor for characterization. 

However, the 95th percentile of the rolling average concentrations from the simulations 

provided a reasonable approximation of the 4- to 28-day rolling-average concentrations 

(Figures 29b-d and 30b-d). 
Page 144 of 184

Figure 29: Estimates of daily and rolling-average atrazine concentrations from the actual time series 

(black line) and 5th and 95th percentiles (dashed red lines) of realizations of kriged time series from 

7-day sampling of the Maumee River 1995 data.

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8

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Atrazine Concentration, ug/L

Julian Day

Daily Time Series Sim 95th %ile

Sim 5th %ile

Actual

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Sim 5th %ile

Actual

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Atrazine Concentration, ug/L

Julian Day

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Sim 5th %ile

Actual
Page 145 of 184

Figure 30: Estimates of daily and rolling-average atrazine concentrations from the actual time series 

(black line) and 5th and 95th percentiles (dashed red lines) of realizations of kriged time series from 

7-day sampling of the MO-01 2007 data.

7.3 Estimating human plasma average daily AUC from rolling average 

atrazine concentrations

The short in vivo half-life of atrazine, primarily due to metabolism and the intrinsic activity of 

metabolites, suggest that the link between atrazine exposure and attenuation of the LHsurge can be better characterized through the use of an internal dose metric. At the 

September 2010 SAP meeting, the Agency presented AUC estimates of plasma triazines 

as the basis for doing an internal dose-response assessment associated with LH 

attenuation in the rat. In this report, methodologies based on the pharmacokinetic behavior 

of plasma triazines are being proposed by the Agency for estimating the corresponding 

human plasma average daily AUC from water exposure estimates that can then be 

compared to the PoD plasma AUC for LH attenuation in the rat. The proposed approach 

incorporates the best information available with respect to LH attenuation and 

pharmacokinetics of plasma triazines. As compared to the approach used in the 2003 

human health risk assessment, the proposed water monitoring approach identifies not only 

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Sim 5th %ile

Actual

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Sim 95th %ile 14-Day Averages

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Atrazine Concentration, ug/L

Julian Day

Sim 95th %ile 28-Day Averages

Sim 5th %ile

Actual
Page 146 of 184

the magnitude of exceedance but also the time regions in the water chemograph where 

those exceedances are more likely to occur. It incorporates an integrated measure of the 

dose intake and information on body distribution and plasma clearance.

The process begins by estimating AUCwater for a given duration of concern. As previously 

discussed, durations of 4, 14 and 28 days are being proposed based on LH attenuation 

studies in rats and human information. AUCwater is calculated based on the trapezoid rule 

(see Figure 21). The more frequent the water sampling frequency, the better the estimate 

of AUCwater. The resulting AUCwater is used in the estimation of a time-weighted average 

daily intake of atrazine through drinking water according to Equation 4 in Section 4. The 

predicted AUCplasma can then be calculated through the use of equation 5 in Section 4 that 

accounts for dose intake, body distribution, and plasma clearance information. For the 1995 

Maumee River and 2007 M0-01 case studies, the rat plasma elimination rate constant (kel) 

and volume of distribution (Vdss) were allometrically scaled for a human female body 

weight of 60 kg and a water consumption rate of 2 L/day was assumed. A body weight of

60 kg was selected for this comparative analysis on the basis that young females may be a 

particular susceptible population to disruptions of LH. However, the proposed approach is 

readily amenable to evaluations of distributions of body weights and water consumption 

rates.

The results corresponding to the exposure estimates of the 1995 Maumee River and 2007 

MO-01 monitoring data sets, sampled at weekly intervals, (Figures 29 and 30) are shown in 

Figures 31 and 32, respectively. For comparison purposes, the PoD BMDL rat plasma AUC 

with an applied 300X combined uncertainty factor (UF) is depicted in the graphs. The 

combined UF is hypothetical at this time but would presumably be reflective of inter- and 

intra-species extrapolation factors as well as any contribution for FQPA. As can be seen in 

Figure 31, the predicted human AUCplasma profile for the 1995 Maumee River dataset is 

well-below the BMDL PoD rat plasma AUC at all time points. The MOEs based on the 95th

percentiles and the rat PoD plasma AUC range from 1424 at the low end to over 64,000 at 

the high endpoint. Thus, the exposure levels of the 1995 Maumee River dataset seem to be 

below the level of concern based on a 300X combined uncertainty factor.
Page 147 of 184

Figure 31: Estimates of rolling average daily human plasma AUC values corresponding to the actual 

time series (black line) and 5th and 95th percentiles (dashed red lines) for the Maumee River 1995 

dataset according to Figure 29. The dashed line is for comparison to the rat plasma PoD AUC with a 

300X applied uncertainty factor.

Evaluation of the 2007 MO-01 dataset suggests, unlike the Maumee River 1995 dataset, 

that there are time periods of exceedance for a 4-day duration of exposure based on a level 

of concern of 300. Figure 32 shows that the rat plasma PoD AUC is exceeded for ≈ 5 days 

based on the 95th percentile. The time periods, but not necessarily the magnitudes of 

exceedance, seem to be adequately captured by 4- but not 14- or 28-day rolling averages. 

A 100X combined UF is also included in Figure 32 to show that the resulting human 

AUCplasma values would be below the level of concern in such case. 

In comparison to the constant dose level and dosing frequency in rat studies, TCT 

exposures resulting from consuming drinking water will be variable in nature (see, for 

example, Figures 29 and 30). While such variability would likely preclude human plasma 

levels from reaching steady state, the equations described in Section provide an average 

time-weighted estimate of TCT levels in human plasma representative of sustained levels 

of exposure for a given duration that can then be compared to the PoD BMDL rat plasma 

AUC. The key comparison, thus, would be TCT plasma levels greater than the PoD BMDL 

for extended periods of time, related to a given duration of potential concern.

Maumee 95: Daily Sampling

100 110 120 130 140 150 160 170 180 190 200

0.00

0.02

0.04

0.06

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Sim 95%ile

Sim 5%ile

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Julian Day

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Maumee 95: 4-day rolling averages

100 110 120 130 140 150 160 170 180 190 200

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Maumee 95: 14-d rolling averages

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 Sim95%ile

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Maumee 95: 28-d rolling averages

100 110 120 130 140 150 160 170 180 190 200

0.00

0.02

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 Sim95%ile

 Sim5%ile

actual (28-d rolling ave) BMDL AUC/300x

Jilian Days

Predicted human plasma AUC ((mg/L)*h)
Page 148 of 184

The proposed approach that estimates internal measures of exposures in the form of 

human plasma AUC can provide valuable perspective for evaluating water monitoring 

frequencies in the characterization of exposure to pesticides like atrazine. 

Figure 32: Estimates of daily rolling-average atrazine concentrations from the actual time series 

(black line) and 5th and 95th percentiles (dashed red lines) of realizations of kriged time series from 

7-day sampling of the MO-01 2007 data.

MO-01: daily sampling

100 110 120 130 140 150 160 170 180 190 200

0.00

0.05

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0.15

0.20

0.25

actual (daily sampling)

Sim 95%ile

Sim 5%ile

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BMDL AUC/300x

Julian Day

Predicted human plasma AUC ((mg/L)*h)

MO-01: 4-d rollin averages

100 110 120 130 140 150 160 170 180 190 200

0.00

0.05

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actual (4-d rolling ave)

Sim 95%ile

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BMDL AUC/300x

Julian Days

Predicted human plasma AUC ((mg/L)*h)

MO-01: 14-d rolling averages

100 110 120 130 140 150 160 170 180 190 200

0.00

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Julian Days

Predicted human plasma AUC ((mg/L)*h)

MO-01: 28-d rolling averages

90 100 110 120 130 140 150 160 170 180 190 200

0.00

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actual (28-d rolling ave)

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Julian Days

Predicted human plasma AUC ((mg/L)*h)
Page 149 of 184

8. REFERENCES

Adami, H. e. (2002). Textbook of Cancer Epidemiology. New York NY: Oxford University Press.

Alavanja, M. C., Dosemeci, M., Samanic, C., Lubin, J., Lynch, C. F., Knott, C., Blair, A. (2004). 

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