UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

WASHINGTON, D.C. 20460 

OFFICE OF CHEM ICAL SAFETY AND 

POLLUTION PREVENTION 

MEMORANDUM 

Date: December 29,2014 

SUB,JECT: Chlorpyrifos: Revised Human Health Risk Assessment for Registration Review 

PC Code: 0591 0 I 

Decision No.: NA 

Petition No.: NA 

DP Barcode: D424485 

Registration No.: NA 

Risk Assessment Type: 

Regulatory Action: Registration Review 

Single Chemical Aggregate Case No.: NA 

TXRNo.: NA 

MRIDNo.: NA 

FROM: 

And 

And 

THROUGH: 

And 

TO: 

CAS No.: 2921-88-2 

40 CFR: 40 CFR§ 180.342 

Danette 

Wade Britton

Drew

, 

, 

MPH, 

Chemist

Indu

, RAB 

strial 

VII 

Hygienist

v« , ~ RAB ~ VII 1/UMll /1 ~ 7 t:J(J -

Caroi

Anna 

William 

.

Lowit

Christensen

Irwin, 

, Ph.D.

Ph.D., 

, 

, 

Ph

Seni

.D., 

DABT

or 

MPH

Scientist

, Toxicologist

, Epid

, 1

emiolog

0 

, 

ist, 

B 

TEB

VII 

~ 

-Po { ~ Health Effects Division (7509P) · 

r 

'( 

;Rochelle 

Environmental 

Bohaty, 

Fate 

Ph.D., 

and Effects 

Chemi ~~ Division (75 · 

Office of Pesticide Programs 

Cecilia Tan, Ph.D. ~ ,f:v Cj 

National 

Virginia Moser, 

Exposure 

Ph.D. 

~aboratory (NERL) 

tA 

~ ~ 71 ~ 1 V M. 

National Health and Environmental Effects Research Lab rato HEERL') 

Office of Research and Development E-205-0 I, B-1 05-04 

Research Triangle Park. NC 

Michael Metzger, Branch Chief 

Risk Assessment Branch V/Vll 

Health Effects Division (7509P) 

Dana Vogel, Acting Division Director 

Health Effects Division (7509P) 

Tom Myers, Chemical Review Manager 

Joel Wolf, Chemical Review Manager 

Risk Management and Implementation Branch II 

Pesticide Re-evaluation Division (7508P) 
Chlorpyrifos Human Health Risk Assessment D424485 

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Chlorpyrifos Human Health Risk Assessment D424485 

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Table of Contents 

1.0 Executive Summary ........................................................................................................... 5

2.0 HED Recommendations .................................................................................................. 13

2.1 Data Deficiencies .............................................................................................................. 13

2.1 Tolerance Considerations ................................................................................................ 14

2.2.1 Enforcement Analytical Method .................................................................................... 14

2.2.2 International Harmonization .......................................................................................... 14

2.2.3 Recommended/Reassessed Tolerances ........................................................................... 15

3.0 Introduction ...................................................................................................................... 15

3.1 Chemical Identity ............................................................................................................. 16

3.2 Physical/Chemical Characteristics ................................................................................. 16

3.3 Anticipated Exposure Pathways ..................................................................................... 17

3.4 Consideration of Environmental Justice ....................................................................... 17

4.0 Hazard Characterization and Dose-Response Assessment .......................................... 18

4.1 Introduction & Background ........................................................................................... 18

4.2 Types of Studies Available for Analysis ......................................................................... 20

4.3 Absorption, Distribution, Metabolism, & Excretion (ADME) .................................... 21

4.4 Summary of Experimental Toxicology Studies ............................................................. 24

4.4.1 AChE Inhibition in Experimental Laboratory Animal Studies .................................. 24

4.4.2 Neurodevelopmental Outcomes in Laboratory Animals ............................................. 25

4.4.3 Plausible hypotheses on MOA/AOP for neurodevelopmental outcomes .................... 27

4.5 Epidemiology Studies in Mothers and Children ........................................................... 32

4.5.1 Review of Children’s Environmental Health Epidemiology Studies ............................ 32

4.5.2 Review of Study Design and Research Methods ............................................................. 33

4.5.3 Neurological and Neurodevelopmental Health Effects in Children .............................. 35

4.5.3.1 The CCCEH Mothers and Newborn Study .................................................................. 35

4.5.3.2 Supporting Epidemiological Evidence: The Mt. Sinai and CHAMACOS Cohorts . 38

4.5.4 Dose Reconstruction ........................................................................................................ 40

4.5.4. Summary of EPA’s Conclusions Regarding Children’s Environmental Health Cohort 

Studies .......................................................................................................................................... 42

4.6 Weight of Evidence Analysis ........................................................................................... 43

4.6.1 Summary of the draft “Framework for Incorporating Human Epidemiologic & 

Incident Data in Health Risk Assessment” ............................................................................... 43

4.6.2 Integration Across Multiple Lines of Evidence ............................................................. 44

4.7 Safety Factor for Infants and Children (FQPA Safety Factor) ................................... 48

4.8 Dose-Response Assessment ............................................................................................. 49

4.8.1 Durations of Exposure, Critical Windows of Exposure, & Temporality of Effects .. 49

4.8.2. Introduction to the PBPK-PD Model ............................................................................. 50

4.8.3 Description & Structure of the PBPK-PD Model ........................................................... 51

4.8.4 Use of the PBPK-PD Model .............................................................................................. 61

4.8.4.1 Derivation of Human Equivalent Doses/Concentrations ............................................ 62

4.8.4.2 Intra-species extrapolation ............................................................................................. 66

5.0 Dietary Exposure and Risk Assessment ......................................................................... 71

5.1 Residues of Concern Summary and Rationale .............................................................. 71
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5.2 Food (Residue Chemistry) Profile .................................................................................. 72

5.3 Water Residue Profile...................................................................................................... 73

5.4 Dietary (Food Only) Risk Assessment............................................................................ 73

5.4.1 Description of Residue Data Used in Dietary (Food Only) Assessment ........................ 74

5.4.2 Percent Crop Treated Used in Dietary Assessment ...................................................... 74

5.4.3 Acute Dietary (Food Only) Risk Assessment ................................................................. 74

5.4.4 Steady State Dietary (Food Only) Risk Assessment ..................................................... 75

6.0 Residential (Non-Occupational) Exposure/Risk Characterization ............................. 76

6.1 Residential Handler Exposure ........................................................................................ 77

6.2 Residential Post-Application Exposure ......................................................................... 77

6.3 Residential Bystander Post-application Inhalation Exposure ..................................... 82

6.3.1 Spray Drift ........................................................................................................................ 82

6.3.2 Volatilization ...................................................................................................................... 83

7.0 Aggregate Exposure/Risk Characterization .................................................................. 84

7.1 Acute Aggregate Risk ...................................................................................................... 85

7.2 Steady State Aggregate Risk .......................................................................................... 86

7.3 Aggregate Risk Estimates ............................................................................................... 87

8.0 Cumulative Exposure/Risk Characterization ............................................................... 96

9.0 Occupational Exposure/Risk Characterization ............................................................ 97

9.1 Steady State Occupational Handler Risk ...................................................................... 97

9.2 Steady State Occupational Post-Application Risk ...................................................... 103

9.2.1 Dermal Post-Application Risk ...................................................................................... 104

9.2.2 Occupational Post-application Dermal Exposure/Risk Estimates: Chlorpyrifos Oxon

 106

9.2.3 Inhalation Post-application Risk .................................................................................. 107

10.0 References ....................................................................................................................... 108

11. List of Appendices ............................................................................................................... 125

Appendix 1. Evaluation of Experimental Toxicology Data .................................................. 126

Appendix 2. Detailed Review and Synthesis of Three Children’s Environmental Health 

Cohort Studies ........................................................................................................................... 221

Appendix 3. Epidemiology Study Specific Evaluations ......................................................... 266

Appendix 4. Detailed Summary Tables of Children’s Environmental Health Epidemiology 

Studies ........................................................................................................................................ 370

Appendix 5. Summary of OPP’s ChE Policy & Use of BMD Modeling ............................. 383

Appendix 6. Columbia Center for Children’s Environmental Health (CCCEH) 

Epidemiology Data Acquisition “Raw Data” Request ......................................................... 384

Appendix 7. Physical/Chemical Properties............................................................................ 397

Appendix 8. Current U.S. Tolerances and International Residue Limits ........................... 398

Appendix 9. Master Use Summary Document ....................................................................... 402

Appendix 10. Dose Reconstruction Analysis ......................................................................... 448

Appendix 11. New Literature on Chlorpyrifos since the 2012 FIFRA SAP Meeting ........ 460
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1.0 Executive Summary 

Background 

This document presents the revised human health risk assessment for the organophosphate 

insecticide chlorpyrifos. Chlorpyrifos is currently being evaluated under the FIFRA section 3(g) 

registration review program. The preliminary human health risk assessment (HHRA) for 

chlorpyrifos registration review was completed on June 30, 2011 (USEPA, 2011). This revised 

assessment addresses issues raised in the comments received on the preliminary HHRA, as well 

as incorporates new information and new approaches that have also become available since the 

June 2011 risk assessment. The most significant changes include the following: 

 Hazard 

o Findings from multiple lines of evidence from experimental toxicology and 

epidemiology with respect to acetylcholinesterase/cholinesterase (AChE/ChE) 

inhibition and neurodevelopmental outcomes are used in the weight-of-evidence 

determination supporting the retention of the 10X FQPA Safety Factor (SF). 

o Utilization of a physiologically-based, pharmacokinetic-pharmacodynamic 

(PBPK-PD) model for deriving toxicological points of departure (PoDs) and for 

determining the appropriate intra-species and inter-species uncertainty factors. 

o Findings from two vapor phase, inhalation toxicity studies indicating no adverse 

effects even at the saturation concentration for chlorpyrifos and chlorpyrifos oxon 

in vapor form. 

 Dietary Exposure 

o Updated food monitoring data and percent crop treated information. 

 Residential and Bystander Exposure 

o Use of the 2012 Standard Operating Procedures for Residential Pesticide 

Exposure Assessment1 which prescribe updates to several elements of the 

assessment. 

o Use of the AgDISP model for estimation spray drift (deposition and airborne 

concentrations) following aerial mosquito adulticide application. 

o Updated methodology for determining the airborne concentration of chlorpyrifos 

following ground-based mosquito adulticide applications. 

o Completion of a dose reconstruction evaluation to further evaluate the 

epidemiological findings. 

 Occupational Exposure 

o Inclusion of use patterns based on a Master Use Summary Document developed 

for chlorpyrifos. 

o Use of revised guidance for all of the currently recommended unit exposures (and 

body weights) for standard EPA occupational handler exposure scenarios2, 

Occupational Pesticide Handler Unit Exposure Surrogate Reference Table – 

Revised March 2013. 

 

1 http://www.epa.gov/pesticides/science/USEPA-OPP-HED_Residential%20SOPs_Oct2012.pdf 2 http://www.epa.gov/opp00001/science/handler-exposure-data.html
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o Use of the revised inputs for seed treatment use patterns. 

 Aggregate Assessment 

o Use of a calculated drinking water level of comparison (DWLOC) approach for 

comparison to model estimated drinking water concentrations (EDWCs). 

Use Profile 

Chlorpyrifos (0,0-diethyl-0-3,5,6-trichloro -2-pyridyl phosphorothioate) is a broad-spectrum, 

chlorinated organophosphate (OP) insecticide. Registered use sites include a large variety of 

food crops (including fruit and nut trees, many types of fruits and vegetables, and grain crops), 

and non-food use settings (e.g., golf course turf, industrial sites, greenhouse and nursery 

production, sod farms, and wood products). Public health uses include aerial and ground-based 

fogger adulticide treatments to control mosquitoes. There are also residential uses of roach bait 

products and ant mound treatments. Permanent tolerances are established (40 CFR§180.342) for 

the residues of chlorpyrifos in/on a variety of agricultural commodities (including meat, milk, 

poultry and eggs). There are also tolerances for use in food handling establishments (FHE). 

Chlorpyrifos is manufactured as granular, microencapsulated liquid, soluble concentrate liquid, 

water dispersible granular in water soluble packets (WSP), wettable powders in WSPs, 

impregnated paints, cattle ear tags, insect bait stations and total release foggers. There is a wide 

range of application rates and methods. The residues of concern for risk assessment purposes are 

chlorpyrifos and chlorpyrifos oxon under some circumstances. 

Hazard Characterization 

The hazard characterization for chlorpyrifos and its oxon is based on adverse health effects in 

animals and humans related to two different endpoints - acetylcholinesterase (AChE) inhibition 

and potential for neurodevelopmental effects. A weight-of-the-evidence (WOE) analysis has 

been completed using the draft “Framework for Incorporating Human Epidemiologic & Incident 

Data in Health Risk Assessment.” The WOE analysis integrated quantitative and qualitative 

findings from experimental toxicology studies, epidemiology studies and physiologically-based 

pharmacokinetic-pharmacodynamic (PBPK-PD) modeling does not support reduction of the 10X 

FQPA Safety Factor. In addition, the Agency is explicitly using the robust PBPK-PD model to 

estimate human equivalent doses/concentrations which are used as the toxicological points of 

departure (PoDs) and also to define inter- and intra-species factors for chlorpyrifos and/or its 

oxon from multiple exposure pathways (e.g. food, water, occupational, residential). 

The key issues considered in the WOE are 1) whether chlorpyrifos causes long-term effects from 

prenatal and/or early lifestage exposure and 2) whether adverse effects can be attributed to doses 

lower than those which elicit 10% inhibition of RBC AChE. When taken together, the evidence 

from 1) the experimental toxicology studies evaluating outcomes such as behavior and cognitive 

function; 2) mechanistic data on possible /modes of action/ adverse outcome pathways 

(MOA/AOP); and 3) epidemiologic and biomonitoring studies, indicate that chlorpyrifos likely 

played a role in the neurodevelopmental outcomes reported by the epidemiologic study 

(Columbia University) investigators. However, uncertainties such as the lack of an established 

MOA/AOP for neurodevelopmental effects and the potential exposure to multiple AChEinhibiting pesticides preclude definitive causal inference. However, there is sufficient 
Chlorpyrifos Human Health Risk Assessment D424485 

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uncertainty in the human dose-response relationship for neurodevelopmental effects to prevent 

the Agency from reducing or removing the statutory 10X FQPA Safety Factor. The FQPA 10X 

Safety Factor will be retained for infants, children, youths, and women of childbearing age for all 

exposure scenarios. 

EPA has applied the Data-Derived Extrapolation Factor (DDEF) guidance (USEPA, 2014), in its 

use of the PBPK-PD model; the model that was used to calculate a DDEF intra-species 

extrapolation for some populations. The PBPK-PD model estimates human RBC AChE 

inhibition from exposures via oral, dermal, and inhalation routes and thus obviates the need for 

the typical inter-species factor. Administered doses leading to the response level of interest 

(10% change in RBC AChE inhibition) are compared between a measure of average response 

and response at the tail of the distribution representing sensitive individuals in order to calculate 

the intra-species factor. Based on the 99th percentile of the distributions, the DDEF for intraspecies extrapolation is 4X for chlorpyrifos and 5X for the oxon (Dow, 2014b). For this revised 

risk assessment, the 99th percentile is being used to account for variation of sensitivity (i.e., the 

intra-species factor used in the risk assessment is 4X for chlorpyrifos and 5X for the oxon for all 

groups except women who are pregnant or may become pregnant for whom the 10X intraspecies factor was retained). 

While the current PBPK-PD model accounts for age-related growth from infancy to adulthood 

by using polynomial equations to describe tissue volumes and blood flows as a function of age, 

the model does not include any descriptions on physiological, anatomical and biochemical 

changes associated with pregnancy. Due to the uncertainty in extrapolating the current model 

predictions among women of child bearing age, the Agency is applying the standard 10X intraspecies extrapolation factor for women of childbearing age. For all other relevant populations, 

the Agency has reliable data to reduce the standard 10x intra-species factor.

The PBPK-PD model has been used to estimate exposure levels resulting in 10% RBC AChE 

inhibition following acute (single day, 24 hours) and steady state (21-day) exposures for a 

variety of exposure scenarios for chlorpyrifos and/or chlorpyrifos oxon. For OPs, repeated 

exposures generally result in more AChE inhibition at a given administered dose compared to 

acute studies. Moreover, AChE inhibition in repeated dosing guideline toxicology studies with 

OPs show a consistent pattern of inhibition reaching steady state at or around 2-3 weeks of 

exposure in adult laboratory animals (U.S. EPA, 2002). This pattern observed with repeated 

dosing is a result of the amount of inhibition comes at equilibrium (or steady state) with the 

production of new enzyme. As such, AChE studies of 2-3 weeks generally show the same 

degree of inhibition with those of longer duration (i.e., up to 2 years of exposure). 

Separate PoDs have been calculated for dietary (food, drinking water), residential, and 

occupational exposures by varying inputs on types of exposures and populations exposed. 

Examples of inputs include: duration; route (dermal, oral, inhalation); body weights which vary 

by lifestage; exposure duration (hours per day, days per week); and exposure frequency [events 

per day (eating, drinking)]. The predicted PoDs, used in this revised risk assessment can be seen 

in Table 4.8.4. 
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Exposure and Risk Assessment 

The general approach for the chlorpyrifos exposure and risk assessment can be described as 

follows: The PBPK-PD model was used to calculate acute (24 hour) and steady state (21 day) 

points of departure dose levels which correspond to 10% RBC ChEI for the index lifestages 

relevant to chlorpyrifos risk assessment (children of various ages which differ due to exposure 

pattern, and adult females of childbearing age). The PBPK-PD PoD predictions for each 

exposure route and pathway were modeled separately (e.g., for residential exposure dermal, 

inhalation, and incidental oral calculations). 

PoDs are divided by the total uncertainty factors to derive a population adjusted dose (PAD). 

There are potential risks of concern when estimated dietary risk exceeds 100% of the PAD. For 

the dietary (food only) assessment, the only residue of concern is chlorpyrifos (the oxon 

metabolite is not an expected residue on foods). The chlorpyrifos total uncertainty factors are 

100X for adult females (10X FQPA SF and 10X intra-species extrapolation factor) and 40X for 

the other relevant populations (10X FQPA SF and 4X intra-species extrapolation factor). The 

chlorpyrifos exposure values resulting from dietary modeling are compared to the PAD. 

For the dietary (water only) assessment, the only residue of concern assumed to occur in drinking 

water is chlorpyrifos oxon. It is assumed that all chlorpyrifos in water is converted to 

chlorpyrifos oxon upon treatment. The chlorpyrifos oxon total uncertainty factors are 100X for 

adult females (10X FQPA SF and 10X intra-species extrapolation factor) and 50X for the other 

relevant populations (10X FQPA SF and 5X intra-species extrapolation factor). A DWLOC 

approach is employed to determine the risk estimates for drinking water. Drinking water is 

addressed in the aggregate sections of the risk assessment. 

For the residential and occupational assessment, margins of exposure (MOEs) were calculated by 

comparing the PoDs to the calculated exposures for each scenario and applicable level of 

personal protection (e.g., varied levels of protective clothing and equipment). The resulting 

MOEs were then compared to the appropriate level of concern (LOC) which are 100 for adult 

females and 40 for the other index lifestages evaluated in this risk assessment. If calculated 

MOEs were less than the applicable LOC then a risk of concern was identified for that exposure 

scenario. 

Dietary Risk Assessment 

Highly refined acute and steady state dietary (food only; parent chlorpyrifos only) exposure 

analyses were performed using the Dietary Exposure Evaluation Model (DEEM) and the 

Calendex models, respectively. Most food residues used were based upon U.S. Department of 

Agriculture’s Pesticide Data Program (PDP) monitoring data. Percent crop treated and empirical 

food processing factors were used where available. Acute dietary (food only) risk estimates are 

all <100 % of the acute PAD for food (aPADfood) at the 99.9th percentile of exposure. The 

subgroup with the highest exposure was females (13-49 years old) at 3.2 % aPADfood. For the 

steady state dietary (food only) exposure analyses, children (1-2 years old) was the population 

subgroup with the highest exposure at 9.7% of the ssPADfood at the 99.9th percentile of exposure. 
Chlorpyrifos Human Health Risk Assessment D424485 

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Residential (Non-occupational) Exposures 

The residential exposure assessment for chlorpyrifos has been updated to reflect the use of the 

PBPK-PD model and updates to EPA’s SOPs for Residential Exposure Assessment. Current 

chlorpyrifos residential uses include a granular ant mound use which is applied by commercial 

applicators only and as a roach bait which is available for homeowner users in child-resistant 

packaging. Additionally, chlorpyrifos is labeled for golf course turf applications and for public 

health aerial and ground-based fogger ultra-low volume (ULV) mosquito adulticide applications. 

Residential post-application exposure is anticipated from playing golf on chlorpyrifos-treated 

courses and from exposures which can occur following aerial and ground-based ULV mosquito 

adulticide use. Chemical-specific turf transferable residue (TTR) data were used in the 

residential post-application assessment. The residue of concern for residential post-application 

exposure is the parent compound chlorpyrifos. A quantitative assessment was not performed for 

the roach bait product and ant mound treatments as residential exposures for these uses are 

negligible. 

The assessment of steady state golfer post-application exposures (dermal only) to chlorpyrifos 

treated turf for the lifestages adults (females 13-49 years old), children (6 to < 11 years old), and 

youths (11 to < 16 years old), results in no risks of concern (i.e., for children and youths, MOEs 

are ≥ 40; for adults, MOEs are ≥ 100 on the day of application). For the assessment of postapplication exposures from public health mosquitocide applications, no combined risks of 

concern were identified, even on the day of application, for adults (dermal and inhalation) and 

children (dermal, incidental oral, and inhalation). 

In addition to the assessment of residential exposures for golf course and mosquitocide uses, the 

potential for post-application exposures to residential bystanders (who live on, work in, or 

frequent areas adjacent to chlorpyrifos-treated agricultural fields) from spray drift and 

volatilization were also considered. The potential risks from spray drift and the impact of 

potential risk reduction measures were assessed in a July 20123

 memorandum. To increase 

protection for children and other bystanders, chlorpyrifos technical registrants voluntarily agreed 

to lower application rates and adopt other spray drift mitigation measures such as buffer zones.4

 

The spray drift risk assessment results have been updated to incorporate the PBPK-PD modeling 

approach. The resulting buffer distances (feet) necessary to reach the level of concern for adults 

(females 13-49 years old) and children (1 to < 2 years old) (i.e., for adults, MOEs are ≥ 100; for 

children, MOEs are ≥ 40) with use of certain application rates, nozzle droplet types, and 

application methods range from 0 to 25 feet. The estimated buffer distances based on this 

revised assessment are less than previously predicted in July 2012. 

 

3

 J. Dawson, W. Britton, R. Bohaty, N. Mallampalli, and A. Grube. Chlorpyrifos: Evaluation of the Potential Risks 

from Spray Drift and the Impact of Potential Risk Reduction Measures. 7/13/12. U.S. EPA Office of Chemical 

Safety and Pollution Prevention. D399483, D399485. 

4

 R. Keigwin. Spray Drift Mitigation Decision for Chlorpyrifos (059101). 7/2012. U.S. EPA Office of Chemical 

Safety and Pollution Prevention. EPA-HQ-OPP-2008-0850-0103. 
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In January 2013, a preliminary assessment of the potential risks from chlorpyrifos volatilization 

was conducted.5 However, this assessment was revised in June 20146 following submission of 

two vapor phase inhalation toxicity studies which indicate no adverse effects occurred even at 

the saturation concentration for chlorpyrifos and chlorpyrifos oxon.7

 Because these new studies 

demonstrated that no toxicity occurred even at the saturation concentration, which is the highest 

physically achievable concentration, there are no anticipated risks of concern from exposure to 

the volatilization of either chlorpyrifos or chlorpyrifos oxon. 

Aggregate Assessment and Risk Estimates 

The Agency has considered aggregate exposures and risks from combined food, drinking water, 

and residential exposures to chlorpyrifos and chlorpyrifos oxon. The acute aggregate assessment 

includes only food and drinking water. The steady state aggregate assessment includes exposures 

from food, drinking water and residential uses. Exposure to the parent compound chlorpyrifos is 

expected for food and residential uses. Exposure to either chlorpyrifos or chlorpyrifos oxon may 

be expected from drinking water sources. The drinking water assessment assumed 100% 

conversion of chlorpyrifos to the more toxic chlorpyrifos oxon (the predominant chlorpyrifos 

transformation product formed during drinking water treatment (e.g., chlorination)). 

For acute and steady state aggregate assessments, a DWLOC approach was used to calculate the 

amount of exposure which could occur without exceeding the risk level of concern (i.e., the 

available space in the total aggregate risk cup for exposures to chlorpyrifos oxon in drinking 

water after accounting for exposures to parent chlorpyrifos from food and residential uses). The 

calculated DWLOCs were then compared to the estimated drinking water concentrations 

(EDWCs) of oxon modeled under a variety of conditions. When the EDWC is less than the 

DWLOC, there are no risk concerns for exposures to the pesticide in drinking water which also 

indicates aggregate exposures are not of concern. Conversely, when the EDWC is greater than 

the DWLOC, then potential risks of concern have been identified. For chlorpyrifos, DWLOCs 

were calculated for both the acute and steady state aggregate assessments for infants, children, 

youths and adult females. However, only the steady state DWLOCs were compared to the 

EDWCs as the steady state aggregate risk estimates are protective of any acute exposures. The 

lowest DWLOC calculated was 3.9 ppb for infants (<1 year old) for the steady state exposure 

duration. This water concentration is the concentration used for comparison to the EDWCs. 

Drinking water concentrations of chlorpyrifos oxon above 3.9 ppb indicate a potential risk 

 

5

 R. Bohaty, C. Peck, A. Lowit, W. Britton, N. Mallampalli, A. Grube. Chlorpyrifos: Preliminary Evaluation of the 

Potential Risks from Volatilization. 1/31/13. U.S. EPA Office of Chemical Safety and Pollution Prevention. 

D399484, D400781. 

6

 W. Britton. W. Irwin. J. Dawson. A. Lowit. E. Mendez. Chlorpyrifos: Reevaluation of the Potential Risks from 

Volatilization in Consideration of Chlorpyrifos Parent and Oxon Vapor Inhalation Toxicity Studies. 6/25/2014. U.S. 

EPA Office of Chemical Safety and Pollution Prevention. D417105. 

7

 W. Irwin. Review of Nose-Only Inhalation of Chlorpyrifos Vapor: Limited Toxicokinetics and Determination of 

Time-Dependent Effects on Plasma, Red Blood Cell, Brain and Lung Cholinesterase Activity in Femal CD(SD): Crl 

Rats. U.S. EPA Office of Chemical Safety and Pollution Prevention. 6/25/14. D411959. TXR# 0056694. EPA 

MRID# 49119501. 

W. Irwin. Review of Nose-Only Inhalation of Chlorpyrifos-Oxon Vapor: Limited Toxicokinetics and Determination 

of Time-Dependent Effects on Plasma, Red Blood Cell, Brain, and Lung Cholinesterase Activity in Female 

CD(SD):Crl Rats. U.S. EPA Office of Chemical Safety and Pollution Prevention. 6/25/14. D415447. TXR# 

0056869. EPA MRID# 49210101. 
Chlorpyrifos Human Health Risk Assessment D424485 

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concern. 

EFED used modeling to perform a national screening bounding exercise to illustrate the range of 

estimated EDWC. Two maximum label rate application scenarios were selected to represent high 

and low end exposures, i.e., tart cherries at five applications totaling 14.5 pounds per acre per 

year, and bulb onions at a single application of one pound per acre per year, respectively. For 

surface water, the application of chlorpyrifos to tart cherries resulted in a 21-day average 

concentration that exceeded the drinking water level of comparison (DWLOC of 3.9 ppb). For 

surface water, applications to bulb onions resulted in a 21-day average concentration below the 

DWLOC. For groundwater, both tart cherry and onion scenarios resulted in EDWCs below the 

DWLOC. 

To investigate whether other chlorpyrifos application scenarios may result in concentrations that 

exceed the DWLOC, a screen of all available surface water modeling scenarios was completed 

considering three different application dates and a single application at several different 

application rates that ranged from one to six pounds. This analysis showed that even with only 

one application, several chlorpyrifos uses may exceed the DWLOC at rates lower than maximum 

labeled rates (both single as well as yearly), including an application rate of one pound per acre 

per year. The analysis also showed that the DWLOC exceedances are not expected to be 

uniformly distributed across the country. 

Further analysis was conducted to look at the spatial distribution of EDWCs at a regional level, 

as well as by using a drinking water intake watershed approach. This exercise demonstrated that 

chlorpyrifos applications will result in variable drinking water exposures that are highly localized 

and that the highest exposures generally occur in small hydrologic regions where there is a high 

percent cropped area on which chlorpyrifos use could occur. 

Finally, EDWCs were compared to monitoring data. This analysis showed that when modeling 

scenarios are parameterized to reflect reported use and EDWCs are adjusted to reflect percent 

cropped area, the EDWCs are within an order of magnitude of the measured concentrations 

reported in the monitoring data. Therefore, although there are uncertainties associated with the 

model input parameters for which conservative assumptions were made (e.g., one aerobic aquatic 

metabolism half-life value multiplied by the uncertainty factor of three, stable hydrolysis, 100% 

of the cropped waters is treated at the same time, and use of the Index Reservoir as the receiving 

waterbody), these assumptions do not appear to lead to an overly conservative estimate of 

exposure. In addition, evaluation of the monitoring data further illustrates that exposures are 

highly localized. Additional work can be done to examine EDWCs on a regional and/or 

watershed scale to pinpoint community drinking water systems where exposure to chlorpyrifos 

oxon as a result of chlorpyrifos applications may pose an exposure concern. 

[For complete details of the DWA see Bohaty, R. 12/23/14, D424487, Chlorpyrifos: Updated 

Drinking Water Assessment for Registration Review.] 

Occupational Exposures 

Based on the anticipated chlorpyrifos use patterns and current labeling, a variety of equipment 
Chlorpyrifos Human Health Risk Assessment D424485 

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and application techniques can potentially be used. These are expected to result in both 

occupational handler and post-application exposures. The 2011 occupational exposure 

assessment has been updated based on a number of changes that have occurred since that time, 

including use of the PBPK-PD modeling approach and incorporation of updated metrics (e.g., 

unit exposures for some scenarios). For the occupational risk assessment, the residue of concern 

is the parent chlopyrifos. Exposure to the oxon is only an issue under very limited circumstances 

in greenhouses for postapplication workers.

Current labels generally require that handlers use normal work clothing (i.e., long sleeved shirt 

and pants, shoes and socks) and coveralls, chemical resistant gloves, and dust/mist respirators. 

Also, some products are marketed in engineering controls such as water soluble packets. In 

order to determine what level of personal protection is required to alleviate risk concerns and to 

ascertain if label modifications are needed, steady state exposure and risk estimates were 

calculated for occupational handlers of chlorpyrifos for a variety of scenarios at differing levels 

of personal protection including engineering controls. In this assessment, a total of 285 total 

occupational handler exposure scenarios were assessed and 132 of them are not of concern (i.e., 

MOEs are ≥ 100) at current product label requirements. Risks of concern for 27 additional 

exposure scenarios could potentially be mitigated if additional personal protective equipment or 

engineering controls are used. Risks of concern were estimated for 126 exposure scenarios and 

remain a concern regardless of the levels of personal protection and engineering controls 

considered. These risks could be mitigated using the highest level of personal protection 

available and by modification to other label requirements such as application rate or, in some 

cases, deleting certain types of application equipment. 

Additionally, occupational handler exposure for seed treatment activities were assessed 

(combined dermal and inhalation). Of the 68 exposure scenarios assessed, 36 resulted in risk 

estimates which were not of concern (i.e., MOEs are ≥ 100) at the level of personal protection 

currently required by product labels. Secondary handler (seed planter) scenarios were also 

evaluated and none were of concern at current label requirements. The remaining 32 seed 

treatment scenarios are of potential concern. 

Steady state occupational post-application exposures and risks were assessed for any crops where 

hand labor is anticipated following applications of chlorpyrifos. The assessment was completed 

using seven chlorpyrifos dislodgeable foliar residue (DFR) studies. Chlorpyrifos parent 

compound is the residue of concern for occupational post-application exposures that occur 

outdoors; however, it may be possible that the formation of chlorpyrifos oxon is greater and its 

degradation slower in greenhouses when compared to the outdoor environment. Occupational 

post-application assessments were performed for: 1) exposures to the parent compound 

chlorpyrifos in outdoor environments (uses other than greenhouse), 2) exposures to the parent 

chlorpyrifos (only) in greenhouses and 3) exposures to both the parent and the oxon metabolite 

in greenhouses. 

Current labels require a Restricted Entry Interval (REI) of 24 hours from most crops and 

activities, but in some cases such as tree fruit, REIs are up to 5 days after application. All postapplication worker risks have been updated in the current assessment. Results indicate that no 

REI increase is required for the majority of outdoor environment crops and commodities with a 
Chlorpyrifos Human Health Risk Assessment D424485 

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labeled REI (i.e., 43 of 55 total). However, for 12 crops, activities such as irrigation, hand 

harvesting, scouting, and thinning result in risks of concern up to as many as 10 days following 

application. For the assessment of post-application exposures to parent chlorpyrifos (only) from 

ornamental production in greenhouses, 4 formulations were assessed: emulsifiable concentrate, 

microencapsulated liquid, wettable powder in WSP, and total release fogger. An REI increase of 

up to 5 days may be needed to alleviate risks from with use of the emulsifiable concentrate, 

wettable powder in WSP and total release fogger formulations. For the microencapsulated liquid 

formulation, the estimated REIs range from 0 to > 35 days after treatment (the completion of the 

monitoring period) dependent on the exposure activity considered. 

Due to uncertainty regarding the formation of chlorpyrifos oxon in greenhouses, HED also 

estimated risks for reentry into treated greenhouses (all 4 formulations) for the parent 

chlorpyrifos plus chlorpyrifos oxon using a total toxic residue approach. The total toxic residue 

approach was conducted with use of chlorpyrifos DFR studies and an assumed daily fraction, 

5%, of chlorpyrifos oxon. When the total toxic residue approach is used to evaluate risks from 

exposure to the oxon with use of the emulsifiable concentrate, wettable powder in WSP, and 

total release fogger formulations, results indicate that an REI increase up to 6 days may be 

needed to alleviate risk concerns. For the microencapsulated liquid formulation, REIs range 

from 3 to > 35 days after treatment (the completion of the monitoring period) dependent on the 

exposure activity considered. 

Tolerances and MRLs 

The establishment of tolerances are being recommended for residues of chlorpyrifos on cotton 

gin byproducts, aspirated grain fractions, milled corn byproducts, and milled wheat byproducts. 

In addition, the current tolerance definition should be updated to be consistent with current 

policy. 

U.S. tolerances and Codex maximum residue limits (MRLs) are based on the analysis of residues 

of chlorpyrifos only. There are differences for some commodities between the U.S. and Codex. 

Canadian MRLs are for chlorpyrifos only for some commodities and for both parent chlorpyrifos 

and its metabolite TCP (3,5,6-trichloro-2-pyridinol; not a U.S. residue of concern) for other 

commodities. The potential for tolerance harmonization will be revisited during Registration 

Review. 

2.0 HED Recommendations 

2.1 Data Deficiencies 

Toxicology 

None. 

Residue Chemistry 
Chlorpyrifos Human Health Risk Assessment D424485 

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860.1500: 

Separate magnitude of the residue studies for lemons are needed after application of Lorsban 4E 

and 75% WDG formulations in order to reevaluate the existing tolerance for chlorpyrifos for the 

citrus fruit crop group. 

Magnitude of the residue studies are needed to establish a tolerance for residues of chlorpyrifos 

on wheat hay. 

860.1520: 

Processing studies are needed for soybean meal, hulls and refined oil. 

Occupational/Residential 

No new data requirements have been identified for chlorpyrifos; however, in the 2011 

preliminary HHRA, additional studies to address the uncertainties regarding the formation and 

degradation of chlorpyrifos oxon in greenhouses were recommended. To date, those data have 

not been submitted. In the absence of the recommended data, and to account for the potential for 

oxon to form in greenhouses, EPA has used a conservative total toxic residue approach for parent 

chlorpyrifos plus the chlorpyrifos oxon. 

2.1 Tolerance Considerations 

2.2.1 Enforcement Analytical Method 

The methods in the PAM Volume II are adequate to analyze the residue of concern for tolerance 

enforcement purposes, chlorpyrifos only. The limit of detection of these methods is adequate to 

cover the lowest tolerance level included in the 40 CFR 180.342 for detection of chlorpyrifos 

only, 0.01 ppm. In addition, chlorpyrifos is completely recovered using FDA multiresidue 

protocols D and E (nonfatty matrices) and partially recovered using multiresidue method 

protocol E (fatty matrices). 

2.2.2 International Harmonization 

Current U.S. tolerances for chlorpyrifos are listed in 40 CFR§180.342 and are summarized in 

Appendix 8 of this document. The Codex Alimentarius Commission and Canada have 

established Maximum Residue Limits (MRLs) for chlorpyrifos. Mexico adopts U.S. tolerances 

and/or Codex MRLs for its export purposes. U.S. tolerances and Codex MRLs are based on the 

analysis of residues of chlorpyrifos. Canada MRLs are for chlorpyrifos for some commodities 

and for both parent chlorpyrifos and its metabolite TCP (3,5,6-trichloro-2-pyridinol) which is not 

a U.S. residue of concern, for other commodities. 

With the exception of apple commodities, Canada MRLs are currently not harmonized with the 

U.S. tolerances. This is because the Canada MRL residue definition is for the combined residues 
Chlorpyrifos Human Health Risk Assessment D424485 

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of chlorpyrifos and the TCP metabolite and in the U.S. the tolerance definition is just for 

chlorpyrifos (TCP is not a residue of concern for the U.S.). Harmonization between the U.S. 

tolerances and Codex MRLs is only possible for corn, field, grain; cranberry; egg; sorghum, 

grain, grain; sorghum, grain, stover; and wheat, grain. In addition, two commodities of the Leafy 

Vegetable (CG 5) can be harmonized with the Codex, head cabbage, and Chinese cabbage (type 

petsai). A summary of the U.S. and international tolerances and MRLs is included in Appendix 

8 of this document. 

2.2.3 Recommended/Reassessed Tolerances 

The following tolerances for chlorpyrifos on cotton gin byproducts and aspirated grain fractions 

are necessary to address residues found in field trials: 

Cotton, gin byproducts........15 ppm 

Grain, aspirated fractions.....22 ppm 

The following tolerances should be reinstated to address residues of chlorpyrifos on the milled 

byproducts of corn and wheat: 

Corn, milled byproducts.......0.1 ppm 

Wheat, milled byproducts.....1.5 ppm 

Table 2.2.3 Recommended Tolerances for Chlorpyrifos 

Commodity Established Tolerance 

(ppm) 

Recommended 

Tolerance (ppm) 

Comments 

Correct Commodity Definition 

Aspirated grain fractions none 22 

Cotton, gin byproducts none 15 

Corn, milled byproducts none 0.1 

Wheat, milled byproducts none 1.5 

On 5/27/09 HED established interim guidance on writing tolerance expressions for enforcement 

purposes. According to the guidance, the tolerance expression in the 40 CFR § 180.342 should 

be revised to read as follows: 

“(a) General. (1) Tolerances are established for residues of chlorpyrifos, including 

its metabolites and degradates, in or on the commodities in the table below. 

Compliance with the tolerance levels specified below is to be determined by 

measuring only chlorpyrifos.” 

The current tolerance expression reads “chlorpyrifos per se (O,O -diethyl O -(3,5,6-trichloro-2-

pyridyl) phosphorothioate”. 

3.0 Introduction 
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3.1 Chemical Identity 

Table 3.1 Chlorpyrifos Degradate/ Residues of Concern Nomenclature. 

Chlorpyrifos 

IUPAC name O,O-diethyl O-3,5,6-trichloro-2-pyridyl phosphorothioate 

CAS name O,O-diethyl O-(3,5,6-trichloro-2-pyridinyl) phosphorothioate 

CAS registry number 2921-88-2 

End-use product (EP) Lorsban 75% WDG and Lorsban 50% WP 

 

TCP Metabolite/Degradate 

(Residue of Concern for 

Canada) 

IUPAC Name 

3,5,6 Trichloro-2-pyridinol 

Oxon 

Metabolite/Degradate 

Common Name 

Chlorpyrifos Oxon 

IUPAC Name 

O,O-diethyl. O-3,5,6-

trichloro-2-pyridyl 

phosphate 

3.2 Physical/Chemical Characteristics 

Technical chlorpyrifos is a white crystalline solid. Chlorpyrifos is stable in neutral and acidic 

aqueous solutions; however, stability decreases with increasing pH. Chlorpyrifos is practically 

insoluble in water, but is soluble in most organic solvents (i.e., acetone, xylene and methylene 

chloride). Chlorpyrifos is moderately volatile based on its vapor pressure of 1.87x10-5 mmHg at 

25oC. See Appendix 7. 

Laboratory studies show chlorpyrifos is susceptible to hydrolysis under alkaline conditions and 

that volatilization and photo-degradation are not likely to play a significant role in the dissipation 

of chlorpyrifos in the environment. Nonetheless, chlorpyrifos has been detected in air samples, 

and so volatilization may play more of a role in dissipation than laboratory studies indicate. The 

major route of dissipation appears to be aerobic and anaerobic metabolism, as well as 

partitioning to the soil (partition coefficient of 6040). The aerobic aquatic metabolism half-life is 

30.4 days (~6% remaining in 4 months). The water peak half-lives were ~1 day in a monitoring 

study (MRID 44711601). Based on available data, chlorpyrifos degrades slowly in soil under 

O

P

S

N

Cl Cl

Cl OC2H5 OC2H5

N OH

Cl Cl

Cl
Chlorpyrifos Human Health Risk Assessment D424485 

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both aerobic and anaerobic conditions. Degradation begins with cleavage of the phosphorus 

ester bond to yield 3,5,6-trichloro-2-pyridinol (TCP). Field dissipation studies show that 

chlorpyrifos is moderately persistent under field conditions—dissipation half-life less than 60 

days. Chlorpyrifos is only slightly soluble in water (1400 ppb). However, if it reaches aquatic 

environments the Log Kow (4.7) indicates that chlorpyrifos may bioaccumulate in fish and other 

aquatic organisms. A fish bioaccumulation study shows that chlorpyrifos is absorbed by fish; 

however, it rapidly depurates when exposure ceases. 

Oxidation of chlorpyrifos to chlorpyrifos oxon could potentially occur through photolysis, 

aerobic metabolism, and chlorination as well as other oxidative processes. Chlorpyrifos oxon is 

expected to have similar fate characteristics as chlorpyrifos except chlorpyrifos oxon is more 

soluble in water and undergoes hydrolysis faster. The hydrolysis half-life of chlorpyrifos oxon is 

significantly shorter than that observed for chlorpyrifos (5 days vs 81 days). Chlorpyrifos oxon 

hydrolyses to form TCP. For chlorpyrifos, water purification (chlorination) has been shown to 

be a major route of chlorpyrifos oxon formation and degradation. 

3.3 Anticipated Exposure Pathways 

Chlorpyrifos applications may be made directly to growing crops (food and feedstuffs) which 

may result in human exposure to chlorpyrifos in food and to chlorpyrifos oxon in drinking water 

(from surface and ground water sources). Registered uses that may result in residential (nonoccupational) exposures to chlorpyrifos include aerial and ground-based fogger adult 

mosquitocide applications and golf course turf applications. There are also potential exposures 

for residential bystanders who live on, work in, or frequent areas adjacent to chlorpyrifos-treated 

agricultural fields from spray drift and volatilization. In occupational settings, exposure may 

occur while handling the pesticide prior to application, as well as during application. There is 

also a potential for post-application exposure for workers re-entering treated fields. 

3.4 Consideration of Environmental Justice 

Potential areas of environmental justice concerns, to the extent possible, were considered in this 

human health risk assessment, in accordance with U.S. Executive Order 12898, “Federal Actions 

to Address Environmental Justice in Minority Populations and Low-Income Populations,” 

(http://epa.gov/compliance/ej/resources/policy/exec_order_12898.pdf). As a part of every 

pesticide risk assessment, OPP considers a large variety of consumer subgroups according to 

well-established procedures. In line with OPP policy, HED estimates risks to population 

subgroups from pesticide exposures that are based on patterns of that subgroup’s food and water 

consumption, and activities in and around the home that involve pesticide use in a residential 

setting. Extensive data on food consumption patterns are compiled by the U.S. Department of 

Agriculture (USDA) under the NHANES/WWEIA (What We Eat in America) Survey; 2003-

2008 and are used in pesticide risk assessments for all registered food uses of a pesticide. These 

data are analyzed and categorized by subgroups based on age, season of the year, ethnic group, 

and region of the country. Additionally, OPP is able to assess dietary exposure to smaller, 

specialized subgroups and exposure assessments are performed when conditions or 
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circumstances warrant. Whenever appropriate, non-dietary exposures based on home use of 

pesticide products and associated risks for adult applicators and for toddlers, youths, and adults 

entering or playing on treated areas post-application are evaluated. Potential exposures from 

spray drift and via volatilization have also been considered in this risk assessment. The spray 

drift assessment is revised and previously implemented measures such as buffer zones were 

refined. 

4.0 Hazard Characterization and Dose-Response Assessment 

This section provides summary information and weight of evidence findings integrating multiple 

lines of evidence from experimental toxicology and epidemiology with respect to AChE/ChE 

inhibition (acetylcholinesterase/cholinesterase) and neurodevelopmental outcomes. This section 

also describes the use of a robust physiologically-based pharmacokinetic-pharmacodynamic 

(PBPK-PD) model for deriving points of departure (PoDs) and refined intra-species factors. 

Details of the science and data analysis that support these conclusions can be found in 

Appendices 1-6, 10, and 11. Code for the PBPK-PD model can be found in the docket for this 

risk assessment. 

Appendix 1: Evaluation of Experimental Toxicology Data 

Appendix 2: Detailed Review and Synthesis of Three Children’s Environmental Health Cohort 

Studies

Appendix 3: Epidemiology Study Specific Evaluations 

Appendix 4: Detailed Summary Tables of Children’s Environmental Health Epidemiology 

Studies 

Appendix 5. Summary of OPP’s ChE Policy & Use of BMD Modeling 

Appendix 6. Columbia Center for Children’s Environmental Health (CCCEH) Epidemiology 

Data Acquisition “Raw Data” Request 

Appendix 10. Dose Reconstruction Analysis 

Appendix 11. New Literature on Chlorpyrifos since the 2012 FIFRA SAP Meeting 

4.1 Introduction & Background 

Mode of action (MOA) and adverse outcome pathways (AOPs) provide important concepts and 

organizing tools for risk assessment (Boobis et al., 2008; Seed et al., 2005; Sonich-Mullin et al., 

2001; Meek et al, 2014; Ankley et al, 2010). MOAs/AOPs describe a set of measureable key 

events that make up the biological processes leading to an adverse outcome and the causal 

linkages between such events. An AOP further defines the initial step in the process as the 

molecular initiating event (MIE; Ankley, et al., 2010). Fundamentally, MOA and AOP are 

different terms for basically the same concept. Consistent with the World Health 

Organization/International Programme on Chemical Safety, for this risk assessment, the terms 

are considered equivalent and interchangeable (Meek et al, 2014). For chlorpyrifos two different 

adverse outcomes provide the focus of the revised chlorpyrifos risk assessment: AChE/ChE 

inhibition and neurodevelopmental outcomes. 
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It is well established that AChE inhibition is the MOA/AOP for the cholinergic toxicity of 

organophosphate pesticides (OPs), including chlorpyrifos. Because AChE inhibition is the 

initiating event for this MOA/AOP, using AChE inhibition as a regulatory endpoint is protective 

of downstream cholinergic effects. Moreover, given the sensitivity of AChE inhibition data for 

OPs, these data have historically been considered to be protective of other potential toxicities 

and/or MOAs/AOPs for OPs. 

Figure 1. Adverse outcome pathway for OPs 

Newer lines of research on chlorpyrifos, notably epidemiological studies in mothers and 

children, have raised some uncertainty about the Agency’s risk assessment approach for 

chlorpyrifos with regard to the potential for neurodevelopmental effects. It is this uncertainty 

which provides the foundation for retaining the 10X FQPA Safety Factor as described in Section 

4.6 and 4.7. 

Over the last several years, the Agency has taken a stepwise, objective and transparent approach 

to evaluate, interpret, and characterize the strengths and uncertainties associated with all the lines 

of scientific information related to the potential for adverse neurodevelopmental effects in infants 

and children as a result of prenatal exposure to chlorpyrifos. The stepwise evaluation began with 

the September 2008 FIFRA SAP involving a preliminary review of the literature for 

chlorpyrifos, with a particular focus on women and children (USEPA, 2008). Subsequent 

Agency activities have involved developing approaches for performing risk assessment of semivolatile pesticides (USEPA, 2009), and developing the draft “Framework for Incorporating 

Human Epidemiologic & Incident Data in Health Risk Assessment” for integration of 

epidemiology with other types of experimental data (USEPA, 2010; FIFRA SAP 2010a,b). In 

early 2011, the FIFRA SAP reviewed the PBPK-PD model being used in this assessment to 

conduct quantitative risk assessment with estimates of human ChE inhibition following exposure 

to chlorpyrifos and/or the oxon from a variety of exposure pathways (FIFRA SAP 2011). In 

summer 2011, the Agency released its preliminary human health risk assessment that focused on 

the AChE inhibiting potential of chlorpyrifos (USEPA, 2011). This focus was consistent with 

the recommendation from the 2008 SAP that AChE data provide the most appropriate endpoint 

and dose-response data for deriving PoDs for purposes of risk assessment. Moreover, because of 

the Agency’s long experience with assessing the potential risk to chlorpyrifos and other OPs, and 

because the dose response approaches based on AChE inhibition used in the 2011 preliminary 

assessment had been vetted by numerous SAPs, there was confidence in that approach. 

In 2012, the Agency convened another meeting of the FIFRA SAP focused on chlorpyrifos 

which incorporated the newest experimental data related to AChE inhibition and both cholinergic 

and non-cholinergic adverse outcomes, including neurodevelopmental studies on behavior and 

cognition effects (FIFRA SAP 2012). Similarly, the Agency also performed a more in-depth 

Target 

Tissue dose 

Phosphorylation 

of the active site 

of AChE

Neurotoxicity Accumulation of 

acetylcholine 
Chlorpyrifos Human Health Risk Assessment D424485 

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analysis of the biomonitoring data and of epidemiologic studies from three major children’s 

health cohort studies in the U.S., as well as plausible hypotheses on MOAs/AOPs leading to 

neurodevelopmental outcomes. Following the 2012 SAP meeting, the Agency solicited 

additional input from federal experts in the areas of MRI (Magnetic Resonance Imaging) and 

neurobehavioral testing in children to further clarify results obtained by examination of the 

epidemiological cohorts.8

 Also, the potential for exposure to lead and other environmental 

chemicals to affect the interpretation of the results from the Columbia University studies was 

investigated and EPA inquired about potential availability of additional information from 

investigators at Columbia University (Appendix 6). This effort has involved extensive 

collaboration across EPA and also within the Federal government. 

Aspects of this risk assessment represent innovative approaches to pesticide human health risk 

assessment. For example, this risk assessment uses the draft “Framework for Incorporating 

Human Epidemiologic & Incident Data in Health Risk Assessment” (U.S. EPA, 2010) to develop 

a weight of evidence analysis integrating quantitative and qualitative findings across many lines 

of evidence, including experimental toxicology studies, epidemiology studies and PBPK-PD 

modeling, does not provide support for the reduction of the 10X FQPA Safety Factor. In 

addition, the Agency is explicitly using the robust PBPK-PD model to estimate human 

equivalent doses/concentrations and intra-species factors to chlorpyrifos and/or its oxon from 

multiple pathways (e.g. food, water, occupational, residential) of exposure. 

4.2 Types of Studies Available for Analysis 

Chlorpyrifos and its oxon are widely studied and thus have an extensive database of scientific 

studies. Included in the database are: guideline studies, special studies conducted by the 

registrant, and literature studies across many scientific areas reflective of different levels of 

biological organization (e.g. metabolism, MOA/AOP, in vitro and in vivo experimental 

toxicology, biomonitoring, epidemiology), various species (mouse, rabbit, dog, non-rodent, 

human) and across lifestages (fetal, postnatal, pregnant and non-pregnant adult). As reflected in 

the 2000 risk assessment, 2008 and 2012 draft issue papers for the SAP, the 2011 preliminary 

risk assessment, and this revised risk assessment, EPA has reviewed numerous studies for 

chlorpyrifos and the oxon and provided those citations in the various documents for public 

evaluation. 

In recent years, the National Academy of Sciences has encouraged the Agency to move towards 

systematic review processes to enhance the transparency of scientific literature reviews that 

support chemical-specific risk assessments to inform regulatory decision making9

. EPA’s Office 

of Chemical Safety and Pollution Prevention is currently developing systematic review policies 

and procedures. As part of the revised human health risk assessment, the Agency has reviewed 

and updated the experimental toxicology literature search since the 2012 SAP using the concepts 

consistent with systematic review such as detailed tracking of search terms and which literature 

have been included or excluded. The literature review was conducted in PubMed for the time 

 

8 http://www.regulations.gov/#!documentDetail;D=EPA-HQ-OPP-2008-0850-0170 9

 NRC 2011. “Review of the Environmental Protection Agency’s Draft IRIS Assessment of Formaldehyde” ; 

NRC 2014. “Review of EPA's Integrated Risk Information System (IRIS) Process” 
Chlorpyrifos Human Health Risk Assessment D424485 

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period January 2012 up to August 2014 and was supplemented with ToxLine and Google 

Scholar (Appendix 11). 

4.3 Absorption, Distribution, Metabolism, & Excretion (ADME) 

The metabolism and pharmacokinetic (PK) profiles of chlorpyrifos and its oxon have been 

extensively studied in in vitro systems, in vivo laboratory animals, as well as humans. This large 

body of PK information is used in the PBPK-PD model which is described in more detail in 

Section 4.8. Only limited summary information is provided here. 

Chlorpyrifos undergoes metabolic transformations mainly by the liver microsomal enzymes (i.e., 

cytochrome P450s). Although, chlorpyrifos is lipophilic, its extensive metabolism into water 

soluble metabolites does not lead to any accumulation of the parent material or its metabolites in 

the body tissues. The initial metabolic action of chlorpyrifos is desulfuration, resulting in 

bioactivation of the parent compound to the more toxic and potent AChE inhibitor, the oxon 

form (Figure 2). However, the oxon is unstable and is rapidly deactivated through hydrolytic 

cleavage by a process called dearylation releasing 3,5,6-trichloro-2-pyridinol (TCPy). 

Simultaneous with the desulfuration process, dearylation acts on both the parent chlorpyrifos as 

well as on the oxon metabolite leading to the release of TCPy. TCPy is further conjugated to 

form glycine or glucuronide conjugates and eliminated into the urine. TCPy is the major excreted 

metabolite and used as the biomarker in PK, biomonitoring, and epidemiology studies. 

Diethylphosphate (DEP) is another metabolite often used in biomonitoring studies, but since it is 

produced by a number of OPs, DEP is not a specific marker for chlorpyrifos. An important 

aspect of the chlorpyrifos PBPK model is that chlorpyrifos and its oxon are approximately 98-

99% protein bound in the blood. 

There are several enzymes that have roles in the metabolism and toxicity of chlorpyrifos. In 

addition to inhibition of ChE, the oxon binds stoichometrically to B-esterases, the most 

important of which are butyrylcholinesterse (BuChE; abundant in blood, brain, and other tissues) 

and carboxylesterases (highest levels in liver). These B-esterases function as a scavenger, or 

“sink”, of the oxon and may lessen its entry in the brain or peripheral targets to inhibit AChE. 

Another group of important enzymes in the detoxification of chlorpyrifos is the A-esterases; one 

such A-esterase is paraoxonase (i.e., PON1). These esterases are calcium-activated enzymes that 

are distributed in various tissues including the liver, brain and blood. These enzymes act on the 

oxon by hydrolyzing it before reaching its target AChE enzyme. The cytochrome P450 family of 

microsomal enzymes (CYPs) is also responsible for its metabolic activation and deactivation of 

chlorpyrifos. Glutathione-dependent enzymes play an important role in the secondary 

metabolism of chlorpyrifos producing water soluble metabolites that are readily excreted into the 

urine. 
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Figure 2. Major metabolic pathways of chlorpyrifos metabolism (Reproduced from Timchalk et al, 

2002)

As summarized below and supported by the PBPK-PD model, there is lifestage sensitivity, 

particularly to the very young following acute exposures. This sensitivity is not derived from 

differential inhibition of the AChE enzyme itself as supported by in vitro studies (Benke and 

Murphy, 1975; Chanda et al., 1995; Mortensen et al., 1996; Atterberry et al., 1997). Rat fetuses 

and juveniles and human fetuses, infants, and young children have lower capacity to detoxify 

chlorpyrifos than adults. Specifically, in rats, A-esterase activity is virtually nonexistent in the 

fetus (Lassiter et al., 1998) and increases from birth to reach adult levels around post-natal day 

21 (PND21) (Mortensen et al., 1996; Li et al., 1997). Mortenson et al (1996) showed that in the 

plasma level of A-esterase in 4-day old rats was 1/11 that of adult animals. The animal data 

regarding the role of carboxylesterase in mediating OP toxicity are also quite extensive (e.g., 

Clement, 1984; Fonnum et al., 1985; Maxwell, 1992 a, b). Fetal rats and mice possess very little 

carboxylesterase activity with increasing activity as the postnatal rodent matures, reaching adult 

values around puberty (Lassiter et al., 1998; Morgan et al., 1994; Moser et al., 1998; Karanth and 

Pope, 2000; Zhu et al., 2009). The temporal pattern of A-esterase and carboxylesterase activity 

correlates well with studies on OP sensitivity. Several studies have shown an increased 

sensitivity of newborn rats to OP compounds which are detoxified via the A-esterase and/or 

carboxylesterase pathways (e.g., Gagne and Brodeur, 1972; Benke and Murphy, 1975; Pope et 

al., 1991; Chambers and Carr, 1993; Padilla et al., 2000; 2002; Karanth and Pope, 2000). 

Cl

Cl Cl

CH3

CH3

O

O

S

P

O N

O

Cl

Cl Cl

CH3

CH3

O

O P

O N

AChE Inhibition

Toxicity

CH3

CH3

O

O

O

P

OH

CH3

CH3

O

O

S

P

OH

Cl

Cl Cl

OH N

s

Cl

Cl Cl

Conjugates-O N

Chlorpyrifos

Chlorpyrifos-oxon

A-esterase (PON-1)

B-esterase

Diethylphosphate

Diethylthiophosphate

3,5,6-trichloro-2-pyridinol 

(TCP)

Sulfate or glucuronides of TCP

Cl

Cl Cl

CH3

CH3

O

O

S

P

O N

O

Cl

Cl Cl

CH3

CH3

O

O P

O N

AChE Inhibition

Toxicity

CH3

CH3

O

O

O

P

OH

CH3

CH3

O

O

S

P

OH

Cl

Cl Cl

OH N

s

Cl

Cl Cl

Conjugates-O N Cl

Cl Cl

Conjugates-O N

Chlorpyrifos

Chlorpyrifos-oxon

A-esterase (PON-1)

B-esterase

Diethylphosphate

Diethylthiophosphate

3,5,6-trichloro-2-pyridinol 

(TCP)

Sulfate or glucuronides of TCP

C

Y

P4

CYP450 
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While there are fewer data in human tissues which could evaluate age-related maturation of 

carboxylesterase and A-esterase expression, there are numerous studies that present age 

differences in liver P-450 metabolism in humans that also play a role in OP metabolism (e.g., 

review by Hines, 2007). Studies evaluating maturational expression and activity of 

carboxylesterases in human liver tissues show lowest levels in fetal tissues, with increasing 

activity during childhood, although there are large individual differences and thus high 

variability (Pope et al., 2005; Yang et al., 2009; Zhu et al., 2009). Adult tissues were 

approximately 10-fold and 4-fold more active in hydrolyzing certain chemicals than fetal and 

child (0-10 years) tissues, respectively, and enzyme expression differences were much greater 

(Yang et al., 2009). In contrast, in Pope et al. (2005), the differences in activity were relatively 

small (and not statistically significant) between children ages 2–24 months and adults (20–36 

years); however, that youngest age evaluated in the study was 2 months old and this individual 

had the lowest level of carboxylesterase. 

Serum A-esterase levels also appear to be very low in human infants and children compared to 

adults (Augustinsson and Barr, 1962; Cole et al., 2003; Huen et al., 2009; Mueller et al., 1983; 

Ecobichon and Stephens, 1973; Gonzalez et al., 2012; Holland et al, 2006; Chen et al, 2003). 

While some studies have shown that PON1 activity reaches adult levels by about 2 years of age 

(e.g., Cole et al., 2003), recent papers have reported slightly lower levels as late as 9 years 

(Gonzalez et al., 2012). Reasons for these differences could include type of measurement (i.e., 

genotyping vs phenotyping), assay substrate, sample sizes, and population factors (e.g., 

ethnicity). 

With respect to distribution to the fetus during gestation, there are multiple studies on 

chlorpyrifos (Mattsson et al., 1998, 2000;; Qiao et al, 2002) and other OPs (USEPA, 2006) 

which show that the pregnant dam exhibits similar or more AChE inhibition than the fetus at a 

given dose to the dam. As such, for AChE inhibition, protecting against AChE inhibition in the 

pregnant female is expected to be protective for AChE inhibition in the fetus. Biomonitoring 

data from rats and humans illustrate fetal exposure levels are similar to maternal levels which 

support these findings. Specifically, Whyatt et al (2003) have shown that levels of chlorpyrifos 

in maternal blood are similar to the levels measured in human umbilical cord blood. In Hunter et 

al. (1999), pregnant rats were orally dosed with chlorpyrifos for 5 days during late gestation at 

doses of 3 or 7 mg/kg/day, and the amounts of chlorpyrifos, chlorpyrifos-oxon, and TCPy were 

quantified in fetal and maternal brain and liver. No chlorpyrifos or its oxon were detected. The 

concentration of TCPy in the fetal brain was higher than the TCPy concentration in the maternal 

brain in time-course and dose-response studies but in liver the maternal levels were higher. In a 

study by Mattsson et al (1998, 2000), concentrations of chlorpyrifos, the oxon, and TCPy were 

measured in the blood of maternal and fetuses and TCPy levels in dam and fetal blood were 

similar and chlorpyrifos levels were approximately 2-fold higher in maternal blood than fetal 

blood. In another gavage gestational exposure study, Akhtar et al (2006) exposed rats to 

chlorpyrifos from GD 0-20 in fetal and maternal tissues on GD21 at high doses (9.6-15 

mg/kg/day); while there is a high degree of variability in the Akhtar et al study, dams and fetuses 

showed similar levels of chlorpyrifos in liver and brain. 
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4.4 Summary of Experimental Toxicology Studies 

Summarized below are key findings from experimental toxicology studies on AChE inhibition 

and neurodevelopmental outcomes. Details can be found in Appendix 1. Chlorpyrifos has also 

been evaluated for other adverse outcomes such as reproductive toxicity, developmental toxicity, 

cancer, genotoxicity, dermal toxicity, inhalation toxicity, and immunotoxicity. The summary of 

these findings can be found in the 2000 risk assessment which supported the Registration 

Eligibility Decision and the 2011 preliminary risk assessment. These adverse outcomes are less 

sensitive than AChE inhibition and neurodevelopmental effects and are thus not discussed in 

detail here. 

4.4.1 AChE Inhibition in Experimental Laboratory Animal Studies 

For the 2008 and 2012 SAP reviews and the 2011 preliminary risk assessment, the Agency 

performed comprehensive reviews of the literature for AChE inhibition. This risk assessment 

has been updated with newer studies. A summary of EPA’s ChE policy with respect to the use 

of such data in risk assessment is provided in Appendix 5. AChE inhibition remains the most 

robust quantitative dose response data and thus continues to be the critical effect for the 

quantitative risk assessment. This approach is consistent with the advice of the SAP from 2008 

and 2012. The Agency has conducted benchmark dose (BMD) analysis of numerous studies 

using empirical approaches previously accepted by the FIFRA SAP (USEPA, 2002) and 

consistent with the 2006 OP cumulative risk assessment (USEPA, 2006) and other single 

chemical OP risk assessments. Details on ChE studies and related analyses can be found in 

Appendix 1 and the preliminary HHRA (USEPA, 2011). 

There are many chlorpyrifos studies evaluating AChE inhibition in red blood cell (RBC) or brain 

in multiple lifestages (gestational, fetal, post-natal, and non-pregnant adult), multiple species (rat, 

mouse, rabbit, dog, human), methods of oral administration (oral gavage with corn oil, dietary, 

gavage via milk) and routes of exposure (oral, dermal, inhalation via vapor and via aerosol). In 

addition, chlorpyrifos is unique in the availability of ChE data from peripheral tissues in some 

studies (e.g., heart, lung, liver). There are also literature studies comparing the in vitro ChE 

response to a variety of tissues (Chambers, 2013) which show similar sensitivity and intrinsic 

activity. Across the database, brain AChE tends to be less sensitive than RBC AChE or 

peripheral ChE. In oral studies, RBC AChE inhibition is generally similar in response to 

peripheral tissues. Thus, the in vitro data and oral studies combined supports the continued use 

of RBC AChE inhibition as the critical effect for quantitative dose-response assessment. 

As with many OPs, female rats tend to be more sensitive than males to these AChE effects. For 

chlorpyrifos, there are data from multiple studies which provide robust RBC AChE data in 

pregnant, lactating, and non-pregnant female rats from oral exposure [e.g., DNT, reproductive, 

and subchronic rats], respectively. The BMD10/BMDL1010 values from these studies range from 

0.05/0.04 to 0.18/0.12 mg/kg/day (Table 4 in Appendix 1). Studies are available in juvenile pups 

which show age-dependent differences, particularly following acute exposures, in sensitivity to 

 

10 BMD10 is the estimated dose to yield 10% inhibition in RBC AChE inhibition compared to controls or background 

levels. The BMDL10 is the lower 95% confidence limit on the BMD10. 
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chlorpyrifos and its oxon (Tables 1, 2, 3, and 4 in Appendix 1). As discussed above, this 

sensitivity is not derived from differences in the AChE enzyme itself but instead are derived 

largely from the immature metabolic clearance capacity in the juveniles. 

Multiple route-specific studies for the dermal and inhalation routes are available. Dermal AChE 

data are available from a 21-day study and 4-day probe study (MRID 40972801) in rats which 

together establish a NOAEL of 5 mg/kg/day and LOAEL of 10 mg/kg/day. Two subchronic 

inhalation toxicity studies (MRID Nos.40013901, 40166501, 40908401) in the rat are available 

using vapor phase chlorpyrifos which show no ChE effects up to a concentration of 20.6 ppb 

(287 μg/m3 or 0.082 mg/kg/day (MRID 40013901, 40166501, 40908401). Multiple acute 

inhalation studies are also available. In a special acute inhalation study, female rats were 

exposed by nose only11 to atmospheric concentrations of up to 53.9 mg/m3

 of particulate 

chlorpyrifos for six hours and allowed an additional 72 hours to recover (MRID No: 48139303 

Hotchkiss et al. 2010, TXR # 0055409). Consistent and significant lung ChE inhibition were 

noted at the lowest concentration tested of 3.7 mg/m3

, which is a LOAEL. RBC and brain ChE 

inhibition were noted at ≥ 12.9 mg/m3

 and 53.9 mg/m3, respectively, indicating they are less 

sensitive than lung and plasma ChE inhibition following acute inhalation exposures. 

Since the 2011 preliminary risk assessment, two acute inhalation studies on the saturated vapor 

have been performed on the parent chlorpyrifos and chlorpyrifos oxon (MRID 49119501 and 

49210101, respectively). In these studies, female rats were exposed by nose only to a saturated 

vapor of chlorpyrifos or its oxon for 6 hours to a time-weighted concentration of 17.7 ppb (0.254 

mg/m3) (MRID 49119501) or 2.58 ppb (35.3 μg/m3

) (MRID 49210101), respectively. There were 

no statistically-significant decreases in ChE activity in the RBC, lung, brain or plasma tissues. 

These acute studies along with the subchronic inhalation studies with vapor phase chlorpyrifos 

support a conclusion that acute exposure to the saturated vapor of chlorpyrifos or its oxon do not 

result in hazard. 

4.4.2 Neurodevelopmental Outcomes in Laboratory Animals 

There is a considerable and growing body of literature on the effects of chlorpyrifos on the 

developing brain of laboratory animals (rats and mice) indicating that gestational and/or 

postnatal exposure may cause persistent behavioral effects into adulthood. These data provide 

support for the susceptibility of the developing mammalian brain to chlorpyrifos exposure. In 

the 2008 and 2012 SAP reviews, the Agency evaluated the neurobehavioral studies available at 

that time; the literature review has been updated for the revised risk assessment (Appendix 1). 

Papers considered by EPA as addressing long-term outcomes from developmental exposure 

include only those where chlorpyrifos is administered during the pre-weaning period (gestational 

and/or postnatal) and the offspring are examined at some time after weaning. That is, papers 

reporting evaluations shortly after birth or during the pre-weaning period do not reflect long-term 

consequences and may also be confounded by AChE/ChE inhibition during concurrent or recent 

exposure. In addition, the Agency focused its efforts on studies using relatively low doses (e.g., 

1 mg/kg/day), that is, doses that would not be expected to produce a considerable degree of brain 

AChE inhibition and resultant cholinergic toxicity. These constraints aid in the unencumbered 

 

11 Mass median aerodynamic diameter/geometric standard deviation was 1.9/1.51 , respectively 
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evaluation of longer-term effects compared to acute impacts of AChE inhibition. In total, the 

Agency has reviewed 31 papers generated from 14 different laboratories on areas such as 

cognitive function, anxiety/emotion, social behaviors/interaction, and motor activity. Twentyfive papers were reviewed for the 2012 SAP, and another six have been published and reviewed 

by ORD since then. The review of the new papers can be found in Appendix 1. 

In spite of considerable differences in study design, upon review of the published literature a 

pattern of neurodevelopmental adverse outcomes emerges. Although the manifestations of these 

effects differ (nature and/or severity), these differences are likely due to the variability in study 

designs including developmental period of exposure, dosing scenarios, testing methods, ages of 

testing subjects, specific equipment used, choice of dependent variable, statistical analyses--all of 

which are critical features of all developmental neurotoxicity studies. While behavioral changes 

were consistently reported, they were somewhat inconsistent, possibly due to experimental 

design differences. Given the wide array of testing that has been conducted, some variability is 

not unexpected and in fact, the consistency of finding neurological effects is striking. At both 

the 2008 and 2012 SAP meetings, the Panel agreed that exposure to doses of 1 mg/kg/d and 

greater, during some developmental period, produced significant and long-term effects on animal 

behavior. 

These studies report a range of neurobehavioral changes in rats and mice following 

developmental exposure to chlorpyrifos. Obvious species differences have not emerged, 

although effective doses are similar (1-6 mg/kg/d). Changes in various aspects of cognitive tests 

indicate perturbations of learning and/or memory, even though in some cases these may be 

manifested as improved function. Likewise, alterations in domains such as anxiety and social 

interactions may differ in direction of change, but are still suggestive of impacts on normal 

neuronal processing. There is replication of some effects across studies, and with some of the 

newer papers, across laboratories as well. Activity measures, on the other hand, still provide 

results as varied as the different measures of assessment. Taken together, these data do not 

provide evidence for a specific profile of effects but instead suggest more global alterations in 

neurobehavioral function. 

All testing was conducted at various times after weaning (adolescents to adults), and there is a 

presumption that the effects are permanent; however, no study has directly addressed this issue, 

and there is a range in test ages. Dose-response is not always evident, since many studies only 

use one dose, and of those using two or more doses, there is not always a monotonic response. 

While there are demonstrated differences in uptake and persistence of chlorpyrifos given 

subcutaneously vs. oral, and with different oils or DMSO as vehicle, the developmental literature 

does not provide obvious differences in outcome based on these factors. Likewise, the 

experimental literature has not shown that any specific developmental period is critical overall to 

the long-term outcomes, since similar effects are shown with different exposure periods. For 

example, cognitive changes in the radial arm maze were observed following gestational and early 

postnatal (PND1-4), but not late (PND11-14), exposure (Aldridge, et al., 2005; Icenogle, et al., 

2004; Levin, et al., 2002; Levin, et al., 2001). However, cognitive deficits were reported with 

the Morris water maze following both gestational and late postnatal exposures (BillauerHaimovitch, et al., 2009; Jett, et al., 2001; Turgeman, et al., 2011). Similarly, some changes in 

anxiety and social behaviors were reported at both gestational and postnatal exposure periods. 
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Overall, these data do not clearly show specific critical periods of exposure, or definitive 

sensitive behavioral outcomes. Unfortunately, no laboratory has provided systematic 

comparisons across exposure period, dosing regimen, and age of testing; such studies would 

improve understanding of the impact of these critical factors. 

These studies have almost exclusively focused on doses that could produce some degree, 

however minimal, of AChE inhibition. For example, a number of papers from Duke University 

researchers use a dose of 1 mg/kg/d administered 1-4 days after birth (e.g., Aldridge et al., 2005; 

Levin et al., 2001), but a recent publication (Slotkin et al., 2013) has reported that 5-10% 

inhibition of brain AChE activity is measured at 2 hours after the last dose. Given that this dose 

is widely used in many studies from several laboratories, this information clearly shows that 

while this dose does not induce toxic effects, it does however have a small but significant effect 

on pup brain AChE. Another study of relatively low doses administered chlorpyrifos in feed to 

pregnant rats, and even the lowest intake of 0.36 mg/kg/d produced about 20-25% RBC ChE 

inhibition (Ohishi et al., 2013). Thus it is not possible to know whether effects would be present 

at lower doses, since they have not been adequately studied. 

Overall, across the literature on neurodevelopmental outcomes and including most recent 

publications, there continue to be inconsistencies in effects in relation to functional domains, 

dosing paradigms, and gender-specificity. The only studies reporting effects use doses that 

inhibit fetal/pup brain ChE activity to some degree, even though there are also negative effects at 

the same doses. The broad profile of neurological effects that have been reported do not aid in 

the development of a specific AOP (AChE inhibition or other mechanisms), and existing 

experimental studies have not been designed to examine and track possible mechanisms from 

early initiating events to the final neurological outcome. 

4.4.3 Plausible hypotheses on MOA/AOP for neurodevelopmental outcomes 

Numerous studies on the possible mechanistic aspects of neurodevelopmental effects have been 

published. The results have led some research groups to propose that changes in brain 

connectivity and/or neurochemistry may underlie the long-term in vivo neurobehavioral changes 

observed into adulthood. While multiple biologically plausible hypotheses are being pursued by 

researchers, no one pathway has sufficient data to be considered more credible than the others. 

The SAP concurred with the Agency in 2008 and 2012 about the lack of definable key events in 

a MOA/AOP leading to neurobehavioral effects. The Agency has considered the new literature 

since the 2012 SAP related to mechanistic hypotheses as described below (Appendix 11), and 

note that such a MOA/AOP still cannot be established. 

 Acetylcholinesterase (AChE) as a morphogen: The classically understood role of AChE 

is the rapid hydrolysis of acetylcholine at synapses in the brain and at neuromuscular 

junctions, thereby regulating cholinergic neurotransmission. Consistent with this role, 

AChE is predominant at cholinergic synapses at neurons and in muscle, and inhibition of 

its catalytic activity results in the signs and symptoms of cholinergic overstimulation. 

Several lines of evidence suggest that AChE can also serve as a morphogen, influencing 

the growth of cells during neurodevelopment distinct from its role as an esterase. 
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Alterations in the expression or structure of the AChE protein can disrupt various aspects 

of neuronal differentiation and growth, as has recently been shown in vitro (using 

NG108-15 cell line) following exposure to another OP, paraoxon (Campanha et al., 

2014). While perturbation of the morphogenic activity of AChE is a plausible adverse 

outcome pathway for chlorpyrifos, a number of questions remain, including effective 

concentrations compared to those that inhibit catalytic activity of AChE. There is, 

however, no direct evidence showing that disruption of the morphogenic function of 

AChE can alter axon or dendritic growth in vivo. While limited in vivo studies using 

zebrafish indicate that chlorpyrifos or its metabolite chlorpyrifos oxon can disrupt axonal 

growth (Yang et al. 2011), it has not been demonstrated that this effect is due to alteration 

of the morphogenic function of AChE versus other potential mechanisms. 

 Cholinergic system: There are several lines of evidence showing that signaling through 

cholinergic receptors is involved in neurodevelopment. Activation of muscarinic and/or 

nicotinic cholinergic receptors can regulate neural progenitor cell proliferation and 

differentiation (Resende & Adhikari, 2009), and in vivo studies demonstrate that 

cholinergic signaling is likely involved in brain morphogenesis (Hohmann & BergerSweeney, 1998). While ChE inhibitors can affect cholinergic signaling by inhibition of 

the catalytic activity of AChE and subsequent increase in acetylcholine, some inhibitors, 

including chlorpyrifos and chlorpyrifos oxon, can also directly interact with cholinergic 

receptors. Thus, direct interaction with cholinergic receptors by chlorpyrifos represents a 

potential adverse outcome pathway for disruption of neurodevelopment distinct from 

AChE/ChE inhibition. Some OPs have been shown to directly interact with cholinergic 

muscarinic receptors at relatively low concentrations. The muscarinic receptors are 

members of the G-protein receptor family and five subtypes (m1-m5) have been 

identified. Ward et al. (1993) examined the relationship between ChE inhibition and 

direct binding to muscarinic receptors for a series of OPs and their active oxon 

metabolites. The results indicated a strong correlation between AChE activity of OPs, 

including chlorpyrifos and chlorpyrifos oxon, and the ability to compete for CD binding 

sites (m2 receptors) in rat brain homogenates. Binding affinities of the oxons were in the 

nanomolar range, at or below concentrations that inhibited AChE (Huff et al, 1994); 

specifically, chlorpyrifos oxon had a binding affinity of 22 nM in rat striatum and 2 nM 

in rat cortex (Huff, et al., 1994; Ward & Mundy, 1996). In total, these studies suggest 

that direct interactions with muscarinic receptors, and especially the m2 subtype, 

represent an alternative site of action for OPs including chlorpyrifos and chlorpyrifos 

oxon, with the oxon forms having high affinity. Together, the studies cited above outline 

a plausible adverse outcome pathway for chlorpyrifos and chlorpyrifos oxon to affect 

brain development via actions at the m2 subtype of muscarinic receptors. However, while 

there are studies showing that chlorpyrifos oxon can affect neurite outgrowth in vitro and 

decrease cell proliferation and differentiation both in vitro (Jameson et al , 2006; Qiao et 

al, , 2001; Song, et al., 1998) and in vivo (Dam et al, , 1998; Qiao, et al., 2003), there is 

no experimental evidence that these effects are a result of direct actions on the m2 

receptor. 

 Endocannabinoid system: Several lines of research have suggested that disruption of the 

endocannabinoid (EC) system due to chlorpyrifos exposure could play a role in its acute 
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and/or long-term toxicity, and could also be extended to potential developmental toxicity. 

The EC system modulates neurotransmission as well as playing a morphogenic role 

during development of the nervous system. Chemicals (e.g., drugs of abuse) which act 

on this system, produce long-term neurodevelopmental disorders in animal models and 

human studies. Chlorpyrifos also interacts with this system, both in vitro and in vivo. By 

this hypothesis, the EC system represents a possible adverse outcome pathway for 

developmental effects of chlorpyrifos. There is a body of studies on the interaction of 

OPs with relevant enzymes but only two studies have examined the effects of 

chlorpyrifos on the EC system in developing animals. Carr et al. (2011, 2013) has dosed 

preweanling rats for 5 or 7 days (1-5 mg/kg/day, p.o.), and showed that endocannabinoidrelated enzymes were inhibited in rat brain tissue taken 4 to 48 hours after the last dose. 

Interestingly, fatty acid amid hydrolase (FAAH) showed a greater degree and more 

persistent inhibition compared to AChE inhibition measured in the same rats. A more 

recent publication (Carr et al., 2014) repeated these findings using a lower dose (0.5 

mg/kg/d for 7 days), still showing significant FAAH inhibition but with no measurable 

AChE inhibition. This suggests a greater sensitivity of the EC system, at least in terms of 

the hydrolase compared to AChE activity, in the pups. However, there were no other 

ages tested, no downstream or correlative measure of changes in EC system function, and 

no subsequent neurodevelopmental effects that could be linked to the action. Additional 

studies along these lines are needed. 

 Reactive Oxygen Species: The production of reactive oxygen species (ROS) and 

resulting cellular damage has been proposed as a mechanism for a wide variety of 

neurotoxicants. Due to lower levels of protective enzymes and antioxidants, and 

relatively low numbers of glia relative to the adult, the developing brain may be 

particularly sensitive to neural cell damage caused by oxidative stress. In addition, recent 

work suggests that ROS can act as second messengers. Relatively small changes in the 

oxidative status of the cell (redox potential) can lead to changes in redox sensitive 

signaling pathways that regulate cell physiology. In the nervous system, redox signaling 

is involved in the regulation of neurodevelopmental processes including neural stem cell 

proliferation and differentiation (Le Belle et al., 2011; Vieira et al, 2011). A number of 

studies suggest that chlorpyrifos and chlorpyrifos oxon can induce oxidative stress in 

various neural cell types. Thus, generation of reactive oxygen species and/or alteration of 

cellular redox potential by chlorpyrifos represent a possible initiating event leading to 

developmental neurotoxicity. Data from both in vitro studies with neuronal cells 

(including neural precursors) and in vivo studies in developing brain demonstrate that 

chlorpyrifos can induce oxidative stress. The in vitro data suggests that this effect may 

not be due to AChE inhibition, since the parent compound chlorpyrifos is either 

equipotent or more potent than the oxon (for example, Crumpton et al., 2000). There was, 

however, no concurrent analysis of AChE inhibition in most of these studies. Several 

known developmental neurotoxicants have been shown to disrupt neural precursor cell 

proliferation in vitro through a common pathway that is initiated by increasing the 

oxidative state of the cell (Li et al, 2007), and the antioxidant vitamin E protected PC12 

cells from the anti-proliferative effect of chlorpyrifos (Slotkin et al, 2007). Thus, the in 

vitro data suggest that chlorpyrifos can affect a critical neurodevelopmental process, at 

least in part, via generation of ROS. Though limited, in vivo studies show both direct 
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evidence (lipid peroxidation) and indirect evidence (alteration in the expression of 

oxidative stress response genes) of oxidative stress in the developing brain after exposure 

to chlorpyrifos. Recent evidence suggests that oxidative stress can alter 

neurodevelopment in vitro and in vivo by the dysregulation of signaling pathways 

controlling neuroprogenitor cell function (Le Belle, et al., 2011; Vieira, et al., 2011). It 

has been demonstrated in vivo that antioxidant treatment can attenuate the induction of 

oxidative stress produced by chlorpyrifos in adult rats (Singh and Panwar, 2014), but 

there are as yet no such studies addressing its developmental neurotoxicity. Thus, there is 

the potential for initiation of an AOP via induction of oxidative stress, but supportive 

studies in developing animals have not been reported. 

 Serotonergic system: Beyond its classical neurotransmitter actions, serotonin has other 

roles during development. In their review, Thompson and Stanwood (2009) described 

serotonin as a pleiotropic molecule, meaning that it can produce multiple, diverse effects, 

regulating different functions at different times during development. The serotonergic 

system is integral in many developmental processes including, but not limited to, 

neurogenesis, migration, and differentiation, synaptogenesis, and cardiac development 

before assuming its more well-known function as a neurotransmitter in the adult nervous 

system (reviewed in Frederick & Stanwood, 2009). Serotonin also plays crucial roles in 

thalamocortical patterning (reviewed in (Frederick & Stanwood, 2009). As serotonin is 

present extremely early in development, it is thought that it modulates cellular function 

even before neurogenesis. Later in development, serotonin is temporarily taken up by socalled transient serotonergic neurons mainly involved in sensory processing, and is 

involved in activity-dependent patterning of the brain. Later in development, serotonin 

has also been shown to modulate differentiation in the brain. There are numerous studies 

of the effects of perinatal chlorpyrifos administration on the patency of the serotonergic 

system coming from both Duke University and Istituto Superiore di Sanita in Italy; 

however, there have been no additional reports since the 2012 SAP. Endpoints in various 

brain regions include serotonin levels, serotonin turnover, serotonin receptor levels, 

serotonin reuptake receptor levels, serotonin elicited second messenger activity, gene 

expression of serotonin receptor and metabolism related genes, serotonin related 

behavioral assessments, and behavior after serotonergic drug challenge. All the data 

indicate that there are acute, as well as permanent, effects of neonatal chlorpyrifos 

treatment on the maturation of the serotonergic nervous system. The effects are often 

gender-specific, region-specific and dose-related. 

There is ample evidence that chlorpyrifos exposure during development causes 

permanent changes in the serotonergic nervous system; there are, however, few papers 

that assessed concurrently the ChE inhibition (either brain or blood) in those same 

animals. In some cases, although ChE activity was not assessed concurrently, a dosing 

regimen was used that had been characterized previously with regard to ChE activity. It 

does appear, however, that most of the studies on the effects of chlorpyrifos on the 

serotonergic nervous system were conducted with doses of chlorpyrifos that likely 

produced inhibition of ChE activity. 
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As many steps in this chlorpyrifos AOP are possible and plausible, and in laboratory 

animals the serotonergic nervous system is sufficiently sensitive to low doses of 

chlorpyrifos during development to alter its function, it is plausible that exposure to 

chlorpyrifos during development could alter brain development and the function of the 

serotonergic nervous system. Although chlorpyrifos effects on the serotonergic nervous 

system in laboratory animals likely is initiated within 24 hours (Slotkin & Seidler, 2007), 

the actual initiating event of this potential adverse outcome pathway is unknown. 

 Tubulin, Microtubule Associated Proteins and Axonal Transport: Microtubules, one 

component of the dynamic cytoskeletal scaffolding within each cell, are composed of 

heterodimers of α- and β-tubulin, as well as microtubule associated proteins. The 

microtubule associated proteins appear to have three main functions: (1) to stabilize the 

microtubules; (2) to aid in tubulin dissociation and (3) to act as motor proteins moving 

substances forward and backward along the microtubules (Avila et al, , 1994; Pellegrini 

& Budman, 2005; Sánchez et al, 2000). Not only does the microtubule cytoskeleton 

determine neuronal morphology (Matus, 1988, 1990; Sánchez, et al., 2000), but the 

dynamic reorganization of the microtubules and microtubule associated proteins within a 

cell may also coordinate neurite extension/retraction, as well as growth cone 

advancement. In addition to these integral roles in brain structure and growth, 

microtubules and the microtubule associated motor proteins kinesin (Hirokawa & Noda, 

2008) and dynein (Vallee et al, 2004) also provide a “railway” for transport of materials 

throughout the cell, i.e., axonal transport (Fukushima et al, 2009), another process which 

is integral to the health of the central and peripheral nervous system, playing a pivotal 

role in neuronal network formation and synapse maturation (Hirokawa & Takemura, 

2004). The construction of an adverse outcome pathway using chlorpyrifos-induced 

effects on tubulin and microtubule associated proteins is still in its infancy. While it is 

thought that tubulin, microtubule associated proteins and axonal transport are integral to 

nervous system development and maintenance, there is no experimental evidence that 

perturbations of these endpoints by chlorpyrifos during development has neurotoxic 

outcomes. 

Overall, a definitive mode of action or adverse outcome pathway leading to effects on the 

developing brain cannot yet be established because of insufficient data establishing the causal 

linkages among different levels of biological organization to adversity. For example, while there 

is in vitro evidence relating binding of chlorpyrifos or the chlorpyrifos oxon to AChE and the 

subsequent decrease in neurite outgrowth at the cellular level, the relationship between neurite 

outgrowth and neurodevelopmental consequences has not been established. As described in the 

NRC report, “Toxicity Testing in the 21st Century” (NRC, 2007), to develop an adverse outcome 

pathway not only is it necessary to establish plausible relationships among the key events, but 

quantitative relationships also need to be established. In other words, how much of a change in 

one key event is needed to result in an adverse effect at the next level of biological organization? 

Thus, certain exposures to a chemical may impact normal physiological responses in a way that 

may not necessarily be adverse, and thus, the AOP concept requires an understanding of 

adaptive/homeostatic capacity of biological systems and their limits, relative to concentration 

and duration of exposure. 
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4.5 Epidemiology Studies in Mothers and Children 

4.5.1 Review of Children’s Environmental Health Epidemiology Studies 

In this chlorpyrifos human health risk assessment (HHRA), EPA is including epidemiologic 

research results from three prospective birth cohort studies. These include: 1) The Mothers and 

Newborn Study of North Manhattan and South Bronx performed by the Columbia Children’s 

Center for Environmental Health (CCCEH) at Columbia University; 2) the Mt. Sinai Inner-City 

Toxicants, Child Growth and Development Study or the “Mt. Sinai Child Growth and 

Development Study;” and 3) the Center for Health Assessment of Mothers and Children of 

Salinas Valley (CHAMACOS) conducted by researchers at University of California Berkeley. In 

these epidemiology studies, mother-infant pairs were recruited for the purpose of studying the 

potential health effects of environmental exposures during pregnancy on subsequent child 

development. Importantly, each of these cohorts evaluated the association between prenatal 

chlorpyrifos or OP exposure with adverse neurodevelopmental outcomes in children through age 

7 years. 

These studies reflect different types of exposed groups in the total population which strengthens 

the weight of the evidence considerations regarding this stream of information. The CCCEH 

Mother’s and Newborn study and the Mt. Sinai Child Growth and Development study 

participants were likely exposed to chlorpyrifos through the diet and through residential use of 

the pesticide for indoor pest control. In the residential setting, study populations were most likely 

exposed through indoor residential use of the pesticide during the study time period and 

additionally exposed to chlorpyrifos via the oral route through ingesting residues in the diet and 

from hand-to-mouth contact with in-home chlorpyrifos-contaminated surfaces, as well as 

possible dermal or inhalation exposure through contact with treated areas in the home 

environment (Berkowitz et al., 2003; Whyatt et al., 2003; Whyatt et al., 2009; Whyatt et al., 

2007). In contrast, CHAMACOS cohort participants were employed as farm laborers or were 

residing in homes with farm laborers. The CHAMACOS study participants likely experienced 

exposure to OPs through the diet and from occupational exposure (primarily inhalation and 

dermal routes), as well as probable indirect take-home exposures (the “tracking in” of pesticide 

residues through shoes and clothing, augmented by poor hygiene practices) (Bradman et al., 

2007). Reported use of chlorpyrifos in the CHAMACOS region was about 10% of total 

pesticide use (Eskenazi et al., 2004). In each of the children’s health cohorts, the biological 

measurements in these cohorts were comparable to the general population (National Health and 

Nutrition Examination Survey; NHANES12). 

The Columbia CCCEH study measured parent chlorpyrifos in cord blood, and other indicators 

(e.g., air sampling, behavioral information), as etiologic measures of exposure, while the other 

two birth cohorts measured non-specific urinary metabolites of chlorpyrifos and other OPs 

(TCPy, dialkyl phosphate metabolites) in the mothers to estimate pesticide exposure. Therefore, 

EPA considers the CCCEH Mothers and Newborn Study research results as most relevant to the 

chlorpyrifos HHRA; the other two cohorts provide important supporting information. 

 

12 http://www.cdc.gov/nchs/nhanes.htm 
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EPA performed its review and critical evaluation of these epidemiology studies in an open and 

transparent manner. The Agency presented an iterative evaluation of these data at two meetings 

of the FIFRA SAP, as well as discussed strengths and limitations of these studies in the 

preliminary human health risk assessment upon which EPA sought public comment in 2011 

(EPA-HQ-OPP-2008-0850). In September 2008, EPA presented its initial review and assessment 

of the available epidemiological data (1999-2007) as well as supporting experimental studies as 

to the neurodevelopmental toxicity of chlorpyrifos to the Panel.13 In April 2012, EPA presented 

an expanded and updated review of the available epidemiologic data (1999-2011) concerning the 

effect of chlorpyrifos exposure on children’s environmental health in conjunction with a review 

of recent experimental studies as well as hypothesized adverse outcome pathways (AOP).14

Published observational studies and supplemental analyses produced subsequent to the 

September 2008 FIFRA SAP evaluation expanded the knowledge base of potential long-term 

sequelae of prenatal chlorpyrifos exposure. Data included the results of supplemental analyses 

recommended by the 2008 SAP, additional data concerning intelligence measures at age 7 years 

from each study cohort, the effect of PON1 genotypic and phenotypic status on infant 

neurodevelopment, and several methodological studies relating to the accuracy and reliability of 

exposure and confounding variables. Both the 2008 and 2012 SAP panels agreed with EPA’s 

assessment, and concluded that “chlorpyrifos likely played a role” in the observed 

neurodevelopmental outcomes. 

EPA has considered the strengths and limitations of these studies, and believes that random or 

systematic errors in the design, conduct or analysis of these studies were unlikely to fully explain 

observed positive associations between in utero chlorpyrifos exposure and adverse 

neurodevelopmental effects observed at birth and through childhood (age 7 years). EPA believes 

these are strong studies which support a conclusion that chlorpyrifos likely played a role in these 

outcomes. Although it cannot be stated the chlorpyrifos is the sole contributer to these 

outcomes; co-exposure to other OPs and mixtures of environmental exposures may also 

contribute to these outcomes. 

4.5.2 Review of Study Design and Research Methods 

The study design and research methods of these prospective cohort studies are summarized in 

this section. Appendices 2, 3, and 4 reflect a more detailed evaluation of these studies, study 

specific critical evaluations, and accompanying detailed evidence table for the included studies, 

respectively. These cohort studies each enrolled pregnant women from approximately 1997 

through1999, measured both environmental exposure to the pesticide during pregnancy and also 

estimated internal dose during pregnancy and at delivery, and prospectively assessed associations 

in their newborns and young children through age 7 years. Each study includes several hundred 

(approximately 100-400) mother-infant pairs; these sample sizes are sufficient to perform 

statistically valid analyses. 

 

13 http://www.epa.gov/scipoly/sap/meetings/2008/091608_mtg.htm 14 http://www.epa.gov/scipoly/sap/meetings/2012/041012meeting.html 
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EPA focuses its review on research results from these three epidemiological cohort studies into 

the chlorpyrifos HHRA due to the considerable strengths in study design, conduct, and analyses 

demonstrated in these investigations. Investigators from each study cohort utilized a similarly 

strong study design (prospective birth cohort); measured pesticide exposure using several 

different methods including environmental indicators as well as specific and non-specific 

biomarkers of chlorpyrifos; ascertained developmental outcomes using validated assessment 

tools well-established in both clinical and research settings; and, measured, analyzed, selected 

and statistically adjusted for potentially confounding variables including socio-economic status 

and other environmental exposures using reasonable and appropriate methods. Limitations exist 

as well. These studies utilized a one-time measure (or the average of two measures) of 

chlorpyrifos or OP exposure to assess prenatal pesticide exposure throughout the gestational 

period, were unable to assess the influence of mixtures (co-occurring exposures in the relevant 

biological time window), and reflect a small sample size to fully evaluate the effect of more than 

one simultaneous exposure on neurodevelopment, i.e., evidence of effect modification. The lack 

of understanding of the critical window of exposure for these effects is a significant overarching 

uncertainty. Balancing the strengths as well as the limitations, EPA believes these data present an 

informative body of evidence with some notable consistencies across studies. 

As noted, two major uncertainties in environmental epidemiology studies are the accurate and 

reliable measurement of exposure and potential confounding variables such as the influence of 

mixtures. The researchers with each of the three cohorts have provided supplemental 

methodological research to address these areas to the extent possible. Across the three children’s 

health cohorts, study authors measured parent chlorpyrifos, TCPy and DEP to estimate 

chlorpyrifos and/or OP exposure in relation to neurodevelopment. There is uncertainty as to the 

extent measurement of non-specific metabolites of OP or chlorpyrifos accurately reflects 

chlorpyrifos exposure; for this reason, the Columbia CCCEH study provides the most valuable 

data for specifically assessing chlorpyrifos. The findings presented below in relation to DEP 

(chlorpyrifos or diazinon) and TCPy (chlorpyrifos or chlorpyrifos-methyl) provide supporting 

information. The Columbia CCCEH study does not estimate post-natal exposure to chlorpyrifos 

among child participants, therefore the influence of early life and childhood chlorpyrifos 

exposure is unaccounted for in these analyses. However, the pre- and post-natal chlorpyrifos 

levels would have to be strongly correlated to significantly affect the reported epidemiological 

risk estimates; the time period of the study reflects a rapid decline in use of the pesticide due to 

the voluntary cancellation of chlorpyrifos in the residential market. Further, the CHAMACOS 

cohort measured early life OP exposure in relation to neurodevelopment and did not observe 

significant associations (Eskenazi et al., 2007). The CHAMACOS cohort investigators also 

measured acetyl and butyl ChE as supplemental indicators of OP exposure. To overcome the 

limitations of a one-time measure of chlorpyrifos exposure, CCCEH authors performed 

additional analyses supporting the correlation of this measure with other measures of 

chlorpyrifos during the pregnancy period (TCPy15 in meconium, air concentration of 

chlorpyrifos, and maternal urinary concentration of chlorpyrifos during the third trimester). 

Together, these multiple measures of exposure and internal dose improve our understanding of in 

utero chlorpyrifos exposure, although there are still uncertainties associated with the 

biomonitoring data collected at or near time of chlorpyrifos applications. 

 

15 Internal measures of TCPy (3,5,6-trichloro-2-pyridinol) need to evaluated with caution as they may also result 

from exposure to chlorpyrifos, chlorpyrifos-methyl, or TCPy in the environment. 
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Potential confounding bias is another major uncertainty within environmental epidemiology 

studies. Confounding variables, exposures that could be related to both chlorpyrifos exposure 

and neurodevelopmental outcomes such as blood lead, may result in an incorrect epidemiological 

risk estimate. Across these cohort studies, investigators collected relevant information 

concerning demographic characteristics and other environmental exposures, and were, to the 

extent possible with the existing information, able to effectively hold constant the influence of 

these other variables when estimating the association between prenatal chlorpyrifos and adverse 

neurodevelopmental outcomes. Control of these variables is important to reduce the chances of a 

false positive study result. Overall, statistical analyses were judged to be appropriate and 

reasonable (not overly large number of statistical model variables) to the research question by 

EPA and expert Panel reviews (FIFRA SAP 2008 and 2012). 

EPA notes that researchers from the three birth cohort studies also investigated the possible role 

of prenatal chlorpyrifos exposure and fetal growth. These results were not consistent across these 

cohorts. Authors with CCCEH Mothers and Newborn Study observed evidence of an inverse 

association, i.e., increasing cord blood chlorpyrifos was associated with decreased measures of 

birth weight and length, while authors with the Mt. Sinai and CHAMACOS cohorts reported 

either no association, or evidence of a positive relationship, respectively (Berkowitz et al., 2004; 

Eskenazi et al., 2004; R. M. Whyatt et al., 2004). Inconsistent results may be due to differences 

across study groups in exposure profiles as well as dissimilar methods of prenatal chlorpyrifos 

exposure assessment (Needham, 2005). The varied study results across cohorts and the limited 

biological rationale between reduced fetal growth and adverse neurodevelopment reduces the 

relevance of this evidence. The database of laboratory animal studies does not suggest that fetal 

growth is a key endpoint for concern with chlorpyrifos. Given the lack of consistency among 

cohorts for the fetal growth metrics and the lack of corroborating animal data, the proposed link 

between fetal growth and neurodevelopment is tenuous recalling that EPA’s focus in the 

chlorpyrifos HHRA is on neurodevelopmental outcomes. These results are summarized below. 

4.5.3 Neurological and Neurodevelopmental Health Effects in Children 

4.5.3.1 The CCCEH Mothers and Newborn Study 

Analysts with the CCCEH Mothers and Newborn Study have performed both etiologic studies 

into the relationship between prenatal chlorpyrifos exposure and adverse neurodevelopmental 

outcomes in children from infancy through early childhood, as well as important methodological 

research as to the validity and reliability of exposure and confounding variable measurement and 

statistical modeling. The result of this body of work extends the knowledge of the long-term 

sequelae of prenatal chlorpyrifos exposure and reduces uncertainty in important aspects of these 

analyses. Both are briefly described in this section. 

Researchers across the three children’s health cohorts utilized the Bayley Scales of Infant 

Development II (BSID-II) to generate a Mental Development Index (MDI) and a Psychomotor 

Development Index (PDI) to assess neurodevelopment in early childhood. In the CCCEH 
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Mothers and Newborn study, Rauh et al. 2006 investigated MDI and PDI at 12, 24, and 36 

months of age. Children were categorized as having either high (>6.17pg/g) or low (<6.17pg/g) 

prenatal exposure, using categories informed by results of the previous study on birth 

characteristics (R. M. Whyatt et al., 2004). Authors reported that the difference in MDI scores 

was “marginally significant” (p = .06) between the “high” and “low” exposed groups; the high 

exposed group scoring an average of 3.3 points lower than the low exposed (V. A. Rauh et al., 

2006). Regarding the PDI score (motor skills), none of the 12 or 24 month PDI scores showed 

significant effects, but the 36 month score was significantly related to chlorpyrifos exposure. 

Researchers noted that the effects were most pronounced at the 36 month testing period. Within 

the 36 month testing period, the likelihood of highly exposed children developing mental delays 

were significantly greater (MDI: 2.4 times greater (95% CI: 1.12-5.08, p = .02) and PDI: 4.9 

times greater (95% CI: 1.78-13.72; p = .002)) than those with lower prenatal exposure (V. A. 

Rauh et al., 2006). 

Further, in supplemental analyses suggested by the 2008 FIFRA SAP, authors illustrated that 

when diazinon was added to the statistical model, the effect of chlorpyrifos on the MDI and PDI 

measure was greater (increasing the magnitude chlorpyrifos risk estimate for MDI and PDI 50-

200% in the same direction (positively away from the null)) suggesting diazinon is a strong 

confounding variable in this association (correlation with chlorpyrifos 0.63), (R. Whyatt & Rauh, 

2011). Lastly, Rauh et al. (2006) also investigated attention-problems in infants. When analyzing 

the 36-month child behavior checklist (CBCL) (behavioral) scores, significant differences were 

observed between the high and low chlorpyrifos exposure groups in the general category of 

attention-problems (p=0.010), and in the more specific DSM-IV scale for ADHD problems (p 

=0.018). 

To measure intelligence among school aged children, authors from each of the three children’s 

health cohorts used the Wechsler Intelligence Scale for Children, 4th edition (WISC-IV). The 

instrument measures four areas of mental functioning: the Verbal Comprehension Index, the 

Perceptual Reasoning Index, the Working Memory Index, and the Processing Speed Index. A 

Full-Scale IQ score combines the four composite indices. WISC-IV scores are standardized 

against U.S. population-based norms for English and Spanish-speaking children. In the CCCEH 

Mothers and Newborn Study, Rauh et al. (2011) evaluated the relationship between prenatal 

chlorpyrifos exposure and neurodevelopment among 265 of the cohort participants who had 

reached the age of 7 years and had a complete set of data including prenatal maternal interview 

data, prenatal chlorpyrifos marker levels from maternal and/or cord blood samples at delivery, 

postnatal covariates, and neurodevelopmental outcome data (Rauh et al., 2011). While models 

were developed using continuous measures of both prenatal chlorpyrifos exposure and Wechsler 

scores, for ease of interpretation, investigators reported that for each standard deviation increase 

in exposure (4.61 pg/g) there is a 1.4% reduction in Full-Scale IQ and a 2.8% reduction in 

Working Memory. 

To ascertain whether observed differences in neurodevelopment by prenatal chlorpyrifos 

exposure may be explained by differences in brain morphology between exposed groups, 

investigators compared MRI brain images between high and low chlorpyrifos exposed child 

study participants (Rauh et al., 2012). Authors determined there were distinct morphological 

differences in brain areas associated with these neurodevelopmental outcomes. The pilot study 
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included only 40 child participants due to strict inclusion and exclusion criteria, and the high cost 

of performing the imaging studies on each child. EPA convened a Federal Panel of experts to 

perform a written peer-review of this study.16 The Federal Panel concurred with the authors’ 

conclusions in general; however the Federal Panel also noted that significantly greater and more 

sophisticated MRI imaging studies would be needed to link the morphological changes indicated 

in this study with specific functional outcomes noted in the CCCEH IQ study. Therefore, while 

generally supportive of the epidemiologic findings, additional study is needed to make specific 

links with areas of brain development change. 

As mentioned above, CCCEH researchers have also performed epidemiologic methods research 

which in many ways reduces uncertainties related to key exposure and confounding measures 

within these studies. The extent to which the one (or the average of two) estimate of chlorpyrifos 

or DAP exposure during pregnancy reflects exposure(s) over critical windows of development 

during pregnancy is an uncertainty in these data. To address this uncertainty, investigators 

assessed the inter- and intra- individual variability in exposure measurements over time and 

determined that intra-individual variation is low such that the individual exposure profiles may 

be relatively stable over time (Whyatt et al., 2007). In a separate study, researchers measured the 

correlation between chlorpyrifos exposure measures taken at different time points in pregnancy 

as well as between internal dose (biomarker) and external exposure. They demonstrated that the 

one-time measure of cord blood chlorpyrifos levels at birth were moderately well correlated with 

measures of exposure earlier in pregnancy (meconium, 14-weeks gestation) and with 

environmental exposures (air concentration) during the last trimester of pregnancy (Whyatt et al. 

2009). This additional information supports the validity of using the one-time measure of 

chlorpyrifos in cord blood as an approximation of in utero pesticide exposure during gestation as 

a method to correctly rank study participants by pesticide exposure category. 

The appropriate control of potentially confounding variables is vital to an accurate assessment of 

the association under study. Both important characteristics of socio-economic status (SES) and 

estimates of other environmental exposures are important variables to consider in this analysis as 

inaccurate measurement of modeling of either type of variable could lead to a false positive 

result. With regard to both of these types of factors, the CCCEH researchers were able to 

perform additional analyses to clarify the estimated association, and, therefore, to reduce 

uncertainty in the interpretation of this study. SES is difficult to measure because the effects of 

variables such as race, class, education are related in complex ways. The CCCEH investigators 

augmented the understanding of SES using regression methods that model individual as well as 

group level variable contribution of overall model variability (Lovasi et al., 2011). This allows 

researchers to better isolate the effect of chlorpyrifos on neurodevelopment, holding constant the 

influence of socio-demographic influences. Using this approach, investigators demonstrated that 

the association between prenatal chlorpyrifos and neurodevelopmental outcomes remained 

significantly, positively related. 

In addition, the authors were able to measure and model important environmental exposures 

including environmental tobacco smoke (ETS), polyaromatic hydrocarbons (PAHs), 

methylmercury and other ChE-inhibiting pesticides such as diazinon and propoxur (Whyatt & 

Rauh, 2011). Given the known relation between both lead and also methyl mercury and 

 

16 http://www.regulations.gov/#!documentDetail;D=EPA-HQ-OPP-2008-0850-0170. 
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neurodevelopment, the authors performed supplemental analyses. The results of these analyses 

showed that neither pre-natal nor post-natal blood lead levels or methyl mercury levels measured 

in cord blood were significantly correlated with chlorpyrifos, and was therefore not considered a 

confounding variable in the association of interest within the Columbia cohort (Whyatt & Rauh, 

2011; Rauh et al., 2006). This additional work significantly reduces uncertainty in research 

results. 

Lastly, the authors demonstrated that chlorpyrifos exposure measures in the time periods 

immediately before and after the voluntary cancellation of chlorpyrifos for residential use 

reduced substantially (Whyatt & Rauh, 2011; Whyatt et al., 2004). CCCEH researchers 

illustrated that prior to the voluntary cancellation there were >80% detectable levels of 

chlorpyrifos in cord blood, in the time period after the cancellation, only 16% of the measured 

values were greater than the level of detection (LOD). Furthermore, although reported 

household use of any pesticide in general did not change over the same time period in this study, 

among newborns born before 1 January 2001, 34% had combined high level exposure levels 

whereas among newborns born after 1 January 2001, only one participant (1.5%) was in the 

highest chlorpyrifos exposure group—a difference that was highly significant (chi squared = 50, 

p < 0.001) (Whyatt et al., 2004). 

In summary, CCCEH researchers, examined several aspects of infant and early childhood 

neurodevelopment in relation in chlorpyrifos exposure during the gestational period. The authors 

reported a 2-4 fold increase in reduced mental and also psychomotor development in infants 

exposed to chlorpyrifos in utero and these results are most strong at 36 months of age. At three 

years of age, the authors also demonstrated statistically significant evidence of differences in the 

proportion of pervasive developmental disorder diagnoses between children in the high and low 

exposure groups (+/- 6.17 pg/g). At seven years of age, the authors observed reduced measures 

of intelligence by increasing measures of prenatal chlorpyrifos exposure (1.4-2.4% decrease in 

intelligence score per 4.6 pg/g change, or 1 SD, of chlorpyrifos). Preliminary data from a small 

pilot study indicate, in principle, that differences in brain morphology between high and low 

chlorpyrifos exposed groups may exist, however, it is unclear whether these morphological 

differences are directly responsible for functional neurodevelopmental decrements observed (a 

conclusion supported by EPA’s Federal Review Panel). In addition, these investigators 

performed several methodological investigations which reduce key uncertainties in these data, 

specifically with regard to exposure measurement error and potential confounding bias. This 

work reduces the probability that positive associations reported above are inaccurate, i.e., they 

are false positive results. 

 4.5.3.2 Supporting Epidemiological Evidence: The Mt. Sinai and 

CHAMACOS Cohorts 

As previously noted, for the purpose of informing the chlorpyrifos human health risk assessment, 

EPA has focused its review on the Columbia Mother’s and Newborn Study among the three birth 

cohort studies reviewed due to researchers’ measurement of parent chlorpyrifos per se. However, 

EPA also believes that it is valuable to comprehensively consider the results of all three of the 

children’s environmental health cohorts together, given each study cohort contributes different 
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and unique information to the chlorpyrifos HHRA assessment. The Mt. Sinai and CHAMACOS 

cohorts examined other health outcomes (e.g., the Brazelton index of neonatal development and 

attentional difficulties in early childhood) and performed supplemental analyses (e.g., effect 

modification by PON1 status) not reflected in the CCCEH Mothers and Newborn Study 

database. 

Researchers with both the Mt. Sinai and CHAMACOS cohorts evaluated neonatal neurological 

functioning in association with prenatal OP exposure; CCCEH did not conduct these 

measurements. To measure indices of abnormal neonatal behavior and/or neurological integrity 

authors used outcome measures derived from the Brazelton Neonatal Behavioral Assessment 

Scale (BNBAS), a neurological assessment of 28 behavioral items and 18 primitive reflexes. 

This tool was administered to infants 2-5 days post-partum by trained neonatologists in the 

hospital setting using similar environmental conditions. The authors with both study groups 

observed an increased number of abnormal reflexes in relation to increasing measures of OP 

exposure (Engel et al., 2007; Young et al., 2005). Among the other 27 measures in the BNBAS, 

neither study group reported evidence of any other positive associations. The authors also 

observed evidence of potential effect modification by PON1 activity level in the relation between 

DAPs and neonatal neurodevelopment in which infants of mothers who are slower metabolizers 

have greater risk of abnormal reflexes (Young et al 2005; Engel et al 2007). However, EPA notes 

these studies are likely under-powered to make a statistically robust estimate of this statistical 

interaction. 

Each of the three children’s health cohorts utilized the Bayley scale (BSID-II) to generate an 

MDI and PDI score to assess neurodevelopment in early childhood (6-36 months). Within the 

Mt. Sinai study, authors administered the BSID-II to participating children at 12 and 24 months 

and observed that prenatal total DAP metabolite level was associated with a decrement in mental 

development at 12 months among blacks and Hispanics children; however, these associations 

either attenuated or were non-existent at the 24-month visit (Engel et al., 2011). In the 

CHAMCAOS cohort, Eskenazi et al. (2007) observed that prenatal DAP levels were adversely 

associated with MDI, and at 24 months of age these associations reached statistical significance. 

In this study, neither prenatal DAPs nor maternal TCPy were associated with PDI (motor skills), 

nor did authors observe evidence of different risk by PON1 status (Eskenazi et al., 2010). The 

CHAMACOS cohort also investigated attention problems in early childhood using three 

different assessment tools: maternal report of child behavior at 3.5 and 5 years of age; direct 

assessment of the child at 3.5 and 5 years; and by a psychometrician’s report of the behavior of 

the child during testing at 5 years. In this study population, higher concentrations of OP 

metabolites in the urine of pregnant women were associated with increased odds of attention 

problems and poorer attention scores in their children at age 5 years (Eskenazi et al., 2007). 

Regarding intelligence measures, both research groups observed an inverse association between 

prenatal OP exposure and intelligence measures. In the Mt. Sinai study, prenatal maternal DEP 

urinary metabolite concentrations were associated with slight decrements in FSIQ, Perceptual 

Reasoning, and Working Memory between the ages of 6 and 9 years, and difference in 

intelligence measures by putative PON1 status were also noted (Engel et al., 2011). Similarly, in 

the CHAMACOS cohort, Bouchard et al. (2011) observed evidence of an association between 

prenatal exposures to OPs as measured by urinary DAP (total DAP, DEP, and DMP) metabolites 
Chlorpyrifos Human Health Risk Assessment D424485 

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in women during pregnancy, and decreased cognitive functioning in children at age 7. In this 

study, children in the highest quintile of maternal DAP concentrations had a statistically 

significant 7 point difference in IQ points compared with those in the lowest quintile. 

Results from the Mt. Sinai and CHAMACOS cohort studies support the results of the CCCEH 

study in several ways. Both of these studies also investigated infant mental and psychomotor 

development. With regard to mental development (as measured by the Bayley scale), both of 

these epidemiology studies reported increased risk of delays in appropriate mental development 

in relation to increasing DAPs, and the effects appeared to be greater at older ages tested; 

however, neither of these cohorts reported a link of psychomotor development. Similar to the 

CCCEH cohort study results, the CHAMACOS investigators also reported a significant 

increased odds of pervasive developmental disorder and attention difficulties in association with 

prenatal OP exposure; the Mt. Sinai study did not evaluate this outcome. Each study group 

reported an inverse relationship between increasing chlorpyrifos or OP exposure and intelligence 

measures using the same Wechslar scale of intelligence, when testing study participants at the 

same age and developmental time period. Researchers with each cohort reported evidence of an 

inverse exposure response relation, although the results were not consistently statistically 

significant. Taken together, these results bolster the findings reported by CCCEH’s study 

authors. 

4.5.4 Dose Reconstruction 

In 2012, the SAP recommended that the Agency conduct a dose reconstruction analysis for the 

CCCEH study to help characterize the extent to which participants in the cohort may or may not 

have experienced RBC AChE inhibition. EPA has conducted such analysis and it is summarized 

here. In this analysis EPA has estimated potential exposures for pregnant women who may have 

used chlorpyrifos containing products and also to investigate possible post-natal exposures for 

children in the cohort. The primary goal was to estimate the degree to which adverse effects 

could be attributed to doses lower than those below the level eliciting elicit 10% inhibition of 

RBC AChE, which is the benchmark response level that has been used to establish the points of 

departure previously used for regulatory decision making. 

Prior to 2000, chlorpyrifos was one of the most widely used insecticides for indoor pest control 

so there are many possible ways which exposures could have occurred as a result of these types 

of uses (e.g., use of a total release fogger, aerosol can sprays, or crack and crevice treatment by a 

pest control operator). Dose estimates for these uses were calculated based on the Agency’s 

SOPs for Residential Exposure Assessment17. This SOP describes, on a scenario basis, specific 

algorithms and data used to predict the potential exposures which could have occurred in 

individuals in the cohort from these now canceled uses of chlorpyrifos. The potential for RBC 

AChE inhibition was then investigated using a chlorpyrifos specific PBPK-PD model that is 

described below. The scenarios which were considered represent the highest potential 

exposures for each population being evaluated. They include: 

 

17See http://www.epa.gov/pesticides/science/residential-exposure-sop.html for further information. These SOPs 

provide the basis for health protective risk assessments for residential scenarios used routinely by EPA for 

regulatory purposes. 
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 Pregnant women who may have purchased a consumer aerosol can product and applied it 

in their homes; 

 Exposures to pregnant women who may have had contact with residues in their homes 

after a previous treatment; and 

 Exposures to young children (aged 1 to 2 years old) who may have contact with residues 

in their homes after a previous treatment. 

The results of this analysis are presented below for pregnant women who potentially applied 

consumer products (Table 4.5.4.1). Details of the analysis can be found in Appendix 10. 

Results for pregnant women and children 1 to < 2 years old exposed from contact to residues 

following broadcast applications to hard surfaces in their homes are presented in Tables 4.5.4.2 

and 4.5.4.3, respectively. Peak RBC AChE inhibition for pregnant women resulting from 

combined dermal and inhalation exposure occurring during the application, assuming continuous 

exposure for one hour, of a consumer product was 0.0012%. Similarly, peak RBC AChE 

inhibition for pregnant women (75 kg) resulting from dermal exposure occurring from contact 

with previously treated areas was 0.45% and from inhalation exposure immediately after 

application was 0.005%; combined peak % RBC AChE inhibition for pregnant women was 

0.45%. For young children (11 kg), peak RBC AChE inhibition associated with the exposures 

from being in previously treated areas results in RBC AChE inhibition from dermal exposure of 

0.14%, from mouthing behaviors of 2.2%, and from inhalation exposure of 0.014%; combined 

peak inhibition for children 1 to < 2 years old was 2.7%. 

This dose reconstruction analysis only includes exposure to indoor uses of chlorpyrifos. At the 

time of recruitment into the CCCEH study, women could have been exposed to additional ChEinhibiting pesticides indoors and likely were exposed to higher levels of ChE-inhibiting 

pesticides in food compared to current food residue levels of ChE-inhibiting pesticides (data 

from 1998 to 2012 indicate that total annual pounds of OPs applied to food crops has been 

steadily declining). For example, Whyatt et al (2003) report that 50% of cord blood samples in 

the CCCEH had detectable levels of diazinon, although all indoor uses of diazinon were removed 

from product labels in December 2002 due to concerns over human health. In addition other OPs 

were detected in some women at low frequency (0.5%-2%); included among these are some of 

the most potent OPs (methyl parathion, parathion, terbufos, phorate). The Agency’s dose 

reconstruction analysis supports a qualitative conclusion that it is unlikely that > 10% RBC 

AChE inhibition would have occurred in the CCCEH participants; however, it is important to 

interpret the results in 4.5.4.1-4.5.4.3 with caution as it is possible that additional sources of 

exposure to OPs are not accounted for in this analysis. 

Overall, the dose reconstruction results support a conclusion that indoor application of 

chlorpyrifos, when used as allowed prior to cancellation from the residential marketplace in 

2000, likely would not have resulted in RBC AChE inhibition greater than 10% in pregnant 

women or young children. These findings are pertinent to the weight-of-evidence discussion for 

the FQPA 10X Safety Factor. 
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Table 4.5.4.1 Residential Handler (Pregnant Women in the Columbia Cohort) Estimated Exposures and Predicted % 

RBC AChE Inhibition (Route-specific and Combined) 

Exposure 

Scenario Formulation Amount Handled 

Dermal Dose 

(mg/kg/day) 

Dermal: 

Peak 

% RBC 

AChE 

Inhibition 

Inhalation 

Dose 

(mg/kg/day) 

Inhalation: 

Peak 

% RBC 

AChE 

Inhibition 

Combined Peak 

% RBC AChE 

Inhibition 

Broadcast 

Ready to Use: 

1 % Aerosol 

Spray Can 

1 can 0.049 0.0007% 0.030 0.0008% 0.0012% 

Table 4.5.4.2 Residential Post-application (Pregnant Women in the Columbia Cohort) Estimated Dermal and Inhalation 

Exposures and Predicted % RBC AChE Inhibition (Route-specific and Combined) 

Exposure 

Scenario Formulation Dermal Dose (mg/kg/day) 

Dermal: 

 Peak 

% RBC AChE 

Inhibition 

Airborne 

Concentration of 

Chlorpyrifos 

(mg/m3) 

Inhalation: 

Peak 

% RBC AChE 

Inhibition 

Combined: 

Peak 

% RBC AChE 

Inhibition 

Broadcast 

(Hard 

Surfaces) 

1% PCO18

Application 

or RTU 1 16 

oz can 

0.71 0.45% 0.092 0.0049% 0.45% 

Table 4.5.4.3 Residential Post-application (Children in the Columbia Cohort) Estimated Dermal and Inhalation 

Exposures and Predicted % RBC AChE Inhibition (Route-specific and Combined)

Exposure 

Scenario Formulation 

Dermal 

Dose 

(mg/kg/day) 

Dermal: 

Peak 

% RBC 

AChE 

Inhibition 

HTM 

Dose 

(mg/kg/day) 

HTM Peak 

% RBC 

AChE 

Inhibition 

(mg/day) 

Airborne 

Concn. of 

Chlorpyrifos 

(mg/m3) 

Inhalation: 

Peak 

% RBC 

AChE 

Inhibition 

Combined: 

Peak 

% RBC 

AChE 

Inhibition 

Broadcast 

(Hard 

Surfaces) 

1% PCO 

Application 

or RTU 1 16 

oz can 

1.3 0.14% 0.10 2.2% 0.092 0.014% 2.7% 

4.5.4. Summary of EPA’s Conclusions Regarding Children’s Environmental 

Health Cohort Studies 

Across these three children’s environmental health studies, authors consistently identified 

associations with neurodevelopmental outcomes in relation to chlorpyrifos exposure. There is 

evidence of delays in mental development in infants (24-36 months), attention problems and 

pervasive developmental disorder in early childhood, and intelligence decrements in school age 

children who were exposed to chlorpyrifos or OP during gestation. Investigators reported strong 

measures of statistical association across several of these evaluations (odds ratios 2-4 fold 

increased in some instances), and observed evidence of exposures-response trends in some 

instances, e.g., intelligence measures. Researchers with CCCEH study demonstrated that 

 

18 PCO: pest control operator; RTU: ready-to-use 
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environmental and biological measures of chlorpyrifos exposure decreased significantly in the 

time period subsequent to the voluntary cancellation of the pesticide. 

These studies reflect several strengths and some notable limitations; however, upon analysis, 

EPA believes that it is more likely that research results were under-estimated, rather than overestimated. Errors (or mistakes) in the measurement of exposure and confounding variables are 

major sources of biases that may result in an inaccurate measurement of risk. Within CCCEH 

study, researchers illustrated that due to low intra-individual variability in exposure over time, 

and the correlation between the (one-time) measure of cord blood chlorpyrifos and other 

measures of chlorpyrifos exposure throughout pregnancy, a one-time measure of exposure may 

accurately rank or order the study participants by chlorpyrifos exposure group in this cohort. 

EPA notes that given the nature of the study design, the most likely effect of any exposure 

measurement error would be to under-estimate rather than over-estimate risk (i.e. nondifferential exposure misclassification). The lack of a post-natal estimate of chlorpyrifos 

exposure in the CCCEH study is a limitation; however, supporting data from the CHAMACOS 

cohort indicate post-natal OP exposure is not significantly related to neurodevelopmental 

outcomes. Further, both SES and other environmental exposures may be potential confounding 

variables in these studies, i.e., they may be related to both chlorpyrifos exposure and 

neurodevelopment. However, EPA believes that authors were able to appropriately measure and 

model the effect of these variables on the study outcomes. 

Therefore, as a result of the Agency’s review and critical assessment of these data, including 

expert elicitation to characterize known uncertainties in these studies, EPA concludes that 

chlorpyrifos likely played a role in the neurodevelopmental outcomes observed in these 

epidemiology studies. It cannot be stated with certainty, however, that chlorpyrifos is the sole 

contributor to these effects. 

4.6 Weight of Evidence Analysis 

Sections 4.1-4.5 summarized key scientific information on the PK profile of chlorpyrifos and its 

oxon along with adverse health effects in animals and humans related to the effects of 

chlorpyrifos and its oxon on two different adverse health outcomes, AChE inhibition and 

potential for neurodevelopmental effects. The Agency has conducted a WOE analysis 

implementing the draft “Framework for Incorporating Human Epidemiologic & Incident Data in 

Health Risk Assessment” focused on two key scientific questions: 1) the degree to which 

scientific data suggest that chlorpyrifos causes long-term neurodevelopmental effects from fetal 

or early life exposure and 2) the degree to which adverse effects can be attributed to doses lower 

than those which elicit 10% inhibition of AChE, i.e., the dose levels previously used for 

regulatory decision making. 

4.6.1 Summary of the draft “Framework for Incorporating Human 

Epidemiologic & Incident Data in Health Risk Assessment” 

In 2010, OPP developed a draft “Framework for Incorporating Human Epidemiologic & Incident 

Data in Health Risk Assessment” which provides the foundation for evaluating multiple lines of 
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scientific evidence in the context of the understanding of the adverse outcome pathway (or mode 

of action (U.S. EPA, 2010). The draft framework, which includes two key components: 

problem formulation and use of the MOA/AOP frameworks, was reviewed favorably by the SAP 

in 2010 (FIFRA SAP, 2010). 

OPP’s draft framework is consistent with updates to the World Health Organization/International 

Programme on Chemical Safety mode of action/human relevance framework, which highlight 

the importance of problem formulation and the need to integrate information at different levels of 

biological organization (Meek et al, 2014). Consistent with recommendations by the NRC in its 

2009 report on Science and Decisions19, OPP’s draft framework describes the importance of 

using problem formulation at the beginning of a complex scientific analysis. The problem 

formulation stage starts with planning dialogue with risk managers to identify goals for the 

analysis and possible risk management strategies. This initial dialogue provides the regulatory 

context for the scientific analysis and helps define the scope of such an analysis. The problem 

formulation stage also involves consideration of the available information regarding the pesticide 

use/usage, toxicological effects of concern and exposure pathways and duration along with key 

gaps in data or scientific information. Specific to chlorpyrifos, the 2008 and 2012 SAP reviews 

represent the problem formulation analyses for the weight-of-evidence (WOE) evaluation. 

Appendix 1 specifically describes a conceptual framework which integrates pesticide use/usage, 

toxicological effects of concern and exposure pathways and duration in on effects of chlorpyrifos 

to key lifestages. 

MOA (Boobis et al., 2006; Boobis et al., 2008; USEPA, 2005; Simon et al, 2014; Meek et al, 

2014) and AOP (Ankley et al., 2010) provide important concepts in this integrative analysis. 

Both a MOA and an AOP are based on the premise that an adverse effect caused by exposure to 

a compound can be described by a series of causally linked biological key events that result in an 

adverse human health or ecological outcome. One of the key components of the Agency’s draft 

framework is the use the MOA framework /AOP concept as a tool for organizing and integrating 

information from different sources to inform the causal nature of links observed in both 

experimental and observational studies. Specifically, the modified Bradford Hill Criteria (Hill, 

1965) are used to evaluate the experimental support that establishes key events within a mode of 

action or an adverse outcome pathway, and explicitly considers such concepts as strength, 

consistency, dose response, temporal concordance and biological plausibility in a weight of 

evidence analysis. Section 4.4.3 and Appendix 1 provide the state of science on MOAs/AOPs 

for both AChE mediated cholinergic toxicity and neurodevelopmental effects. 

4.6.2 Integration Across Multiple Lines of Evidence 

Dose-response relationships & temporal concordance. 

Since the MOA(s)/AOP(s) is/are not established for neurodevelopmental outcomes as described 

in section 4.4.3, it is not possible to describe the concordance in key events or biological steps 

leading to neurodevelopmental outcomes. As such, the quantitative linkages between MIEs, 

 

19 NRC (National Research Council). (2009). Science and decisions: Advancing risk assessment. Washington, DC: 

The National Academies Press. http://www.nap.edu/openbook.php?record_id=12209
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intermediate steps, and ultimately the adverse outcome (i.e., neurodevelopmental effects) cannot 

be determined. Experimental toxicology studies in rodents suggest that long-term effects from 

chlorpyrifos exposure may occur. Due to the dose selections in most of these in vivo studies 

evaluating effects such as behavior and cognition, it is not known whether such adverse effects 

would be shown at doses lower than those which elicit 10% RBC AChE inhibition. It is notable, 

however, that comparing the lowest NOAEL observed in the in vivo animal studies (0.2 

mg/kg/day; Billauer-Haimovitch et al., 2009) for the neurodevelopmental outcomes to the 

repeated dosing reliable BMDL10 ranging from 0.05-0.17 mg/kg/day for RBC AChE inhibition 

suggests that AChE inhibition is a sensitive endpoint. 

Within the epidemiology studies, the relationship in time between prenatal chlorpyrifos exposure 

and adverse neurodevelopmental outcomes is concordant. Specifically, with regard to the 

children’s environmental health epidemiology studies, each of the three study cohorts utilized a 

prospective birth cohort study design in which mothers were recruited into study prior to the 

birth of the infants and development and identification of adverse effects; therefore, it is known 

with certainty that exposure preceded effect. In addition, because the time period under study 

within these cohorts, and specifically the Columbia University (CCCEH) study, spanned the 

point in time in which pesticide manufacturers voluntarily cancelled the use of chlorpyrifos in 

the home environment, researchers were able to show the change in exposure before (high use 

period) and after (low/no use period) the period of removal of chlorpyrifos products from the 

residential marketplace. Moreover, prior to the voluntary cancellation there were >80% 

detectable levels of chlorpyrifos in cord blood but in the time period after the cancellation only 

16% of the measured values were greater than the LOD; there was only one child born in the 

time period subsequent to the voluntary cancellation of chlorpyrifos in the residential 

marketplace for whom the cord blood chlorpyrifos level was in the upper-tertile of precancellation exposure levels. The significantly reduced proportion of measured values greater 

than the limit of detection as well as the observation of an absence of an association between 

prenatal chlorpyrifos exposure among infants born after the voluntary cancellation of 

chlorpyrifos support the hypothesis that chlorpyrifos is related to these outcomes. However, as 

noted by study authors, EPA and the FIFRA SAP (2012), this could also be due to inadequate 

sample size to detect a small to modest effect among the group of infants born after the voluntary 

cancellation. 

With respect to the timing of exposure, the cord blood and other (meconium) measures from the 

Columbia University study provide evidence that exposure did occur to the fetus during gestation 

but the actual level of such exposure during the critical window(s) of susceptibility is not known. 

While significant uncertainties remain about the actual exposure levels experienced by mothers 

and infant participants in the three children’s health cohorts, particularly during the time period 

prior to the voluntary cancellation of indoor residential uses of chlorpyrifos containing pesticide 

products, exposures measured in the range reported in the epidemiology studies (pg/g plasma) 

are likely low enough that is unlikely to result in AChE inhibition. The FIFRA SAP (2012) 

concurred with the conclusion that measured levels of chlorpyrifos among epidemiology study 

participants were unlikely to have resulted in AChE inhibition. The urinary TCPy concentrations 

among mothers were comparable to the general population levels measured in NHANES. 

Comparing cord blood concentrations with the concentrations in which AChE inhibition was 

observed in adult volunteers indicates AChE inhibition would likely not have occurred at levels 
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observed in the epidemiology studies (6.17 pg/g). Therefore, while uncertainty exists as to actual 

chlorpyrifos exposure at (unknown) critical windows of exposure, EPA believes it is unlikely 

mothers enrolled in the birth cohort studies experienced RBC AChE inhibition. 

This biomarker data from the Columbia University studies are supported by the Agency’s dose 

reconstruction analysis using the PBPK-PD model. In accordance with the recommendation of 

the FIFRA SAP (2012), the Agency conducted a dose reconstruction analysis of residential uses 

available prior to 2000 for pregnant women and young children inside the home. Based on the 

output from the PBPK-PD model, for the highest exposure considered (i.e., indoor broadcast use 

of a 1% chlorpyrifos formulation), <10% RBC AChE inhibition in pregnant women and young 

children would be expected from residential uses. It is noteworthy that all estimates of exposure 

based on conservative assumptions lead to predicted AChE inhibition levels < 10%. 

Strength, consistency, and specificity. 

As stated in the EPA neurotoxicity guidelines20, direct extrapolation of developmental 

neurotoxicity results from laboratory animals to humans is limited by the lack of knowledge 

about underlying toxicological mechanisms and the relevance of these results to humans. EPA 

notes consistencies across these two databases, although challenges of making a direct 

comparison between neurodevelopmental domain inter-species remain. It can be assumed that 

developmental neurotoxicity effects in animal studies indicate the potential for altered 

neurobehavioral development in humans, although the specific types of developmental effects 

seen in experimental animal studies may not be the same as those that may be produced in 

humans. However, considering the toxicological and epidemiological data in the context of three 

major neurodevelopmental domains (specifically, cognition, motor control, and social behavior), 

insights can be gained. For example, chlorpyrifos studies in rats and/or mice have reported 

impaired cognition (spatial learning and working memory; e.g., Icenogle et al., 2004, BillauerHaimovitch et al., 2009); changes in locomotor activity levels (exploration, rearing; e.g., Levin et 

al., 2002; Ricceri et al., 2003); and altered social interaction (aggression, maternal behavior; 

Venerosi et al., 2006, 2010); and effects on brain morphometrics (MRID no. 44787301; Chen et 

al, 2012). Similarly, epidemiologic investigations have reported effects on cognition (Bayley 

scale indices; Rauh et al. 2006; Eskenazi et al. 2007), abnormal motor development in neonates 

(reflexes, Brazelton score; Young et al., 2005; Engel et al. 2007), altered social development 

(e.g., ADHD; Rauh et al. 2006; Bouchard et al. 2010), and MRI brain scans (Rauh et al., 2012). 

It is notable that the laboratory animal studies vary in experimental designs such as species, 

strain, gender, dosing regimens (age, routes, vehicle), and test parameters (age, protocol). 

Likewise, observational epidemiology studies vary by population characteristics (race/ethnicity, 

SES, and pesticide use/exposure profile), co-exposures (mix of chemicals, windows of 

exposure), and method of exposure and outcome assessment. Given the differences across 

laboratory animal and epidemiology studies, the qualitative similarity in research findings is 

striking. 

In contrast, quantitatively, there are notable differences between animals and humans. 

Specifically, in animals, the doses most often used in the behavior studies (1 and 5 mg/kg/day) 

are sufficient to elicit approximately ≥10% brain inhibition and ≥30% in RBC inhibition , 

 

20 http://www.epa.gov/raf/publications/pdfs/NEUROTOX.PDF 
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depending on the study design, age of the animal, and sampling time. In the epidemiology 

studies, based on the comparisons with biomonitoring data and the results of the dosereconstruction analysis, it is unlikely that RBC AChE would have been inhibited by any 

meaningful or measurable amount, if any at all, and most likely none in the brain (although the 

Agency has not investigated the degree to which exposure to multiple AChE-inhibiting 

pesticides indoors simultaneously could impact this conclusion). This key difference in dose 

response between the experimental toxicology and epidemiology studies poses challenges in 

interpreting such data. There are a number of possible hypotheses such as: 1) limitations of 

experimental laboratory studies which have limited statistical power due to relatively small 

sample sizes; 2) humans display a broader array of behaviors and cognitive abilities than rats, 

thus limiting the sensitivity of the rat studies; and 3) in the epidemiology studies, the timing of 

chlorpyrifos application and blood collections are not coupled---thus higher levels of blood 

chlorpyrifos were likely missed (albeit the results of the dose reconstruction analysis reduce the 

likelihood of this hypothesis). 

In making a weight-of-evidence analysis, it is important to consider the strength of the statistical 

measures of association between prenatal chlorpyrifos exposure and adverse neurodevelopmental 

outcomes through childhood (epidemiology) and possibly into adulthood (animal studies). It is 

also important to consider the strength of the integrated qualitative and quantitative evidence, the 

consistency of the observed associations across epidemiology studies and considering both 

animal and human data support the conclusion that chlorpyrifos plays a role in adverse 

neurodevelopmental outcomes. While it cannot be stated that chlorpyrifos alone is the sole 

contributor to the observed outcomes (specificity), since other environmental, demographic or 

psychosocial exposures may also play a part in these outcomes, this does not obviate the 

contribution of prenatal chlorpyrifos exposure in the development of adverse 

neurodevelopmental outcomes as echoed by the FIFRA SAP (2012). 

The CCCEH study, which measures chlorpyrifos specifically, provides a number of notable 

associations. Regarding infant and toddler neurodevelopment, the CCCEH authors also reported 

statistically significant deficits of 6.5 points on the Bayley Psychomotor Development Index 

(PDI) at 3 years of age when comparing high to low exposure groups (Rauh et al., 2006). 

Notably these decrements in PDI persist even after adjustment for group and individual level 

socioeconomic variables (Lovasi et al., 2011). These investigators also observed increased odds 

of mental delay (OR=2.4; 95% CI: 1.1-5.1) and psychomotor delay (OR=4.9; 95% CI: 1.8-13.7) 

at age three when comparing high to low exposure groups (Rauh et al., 2006). Rauh et al (2006) 

also reported extremely large odds ratios for attention disorders (OR=11.26; 95% CI: 1.79-

70.99), ADHD (OR=6.50; 95% CI: 1.09-38.69), and PDD (OR=5.39; 95% CI: 1.21-24.11) when 

comparing high to low chlorpyrifos exposure groups (Rauh et al., 2006). EPA notes that the 

magnitude of these results are so large that they are unlikely to be affected by residual 

confounding although limited sample sizes resulted in imprecise estimates. 

Importantly, across the three children’s environmental health birth cohorts, decrements in 

intelligence measures were identified in relation to increasing levels of prenatal chlorpyrifos 

exposure. Authors from the Columbia cohort (CCCEH) reported statistically significant 

decreases of 1.4% in full scale IQ and 2.8% in working memory among seven-year olds for each 

standard deviation increase in chlorpyrifos exposure (Rauh et al., 2011). These results persist 
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even when performing sensitivity analyses including only those with detectable chlorpyrifos 

levels. In addition, no evidence was provided of mediation by child behavior on the measure of 

working memory instrument. 

Biological plausibility and coherence. EPA’s Cancer Guidelines (2005) includes guidance which 

are also applicable to this current evaluation of chlorpyrifos. The Guidelines indicate: 

“evaluation of the biological plausibility of the associations observed in epidemiologic 

studies reflects consideration of both exposure-related factors and toxicological evidence 

relevant to identification of potential modes of action (MOAs). Similarly, consideration of the 

coherence of health effects associations reported in the epidemiologic literature reflects 

broad consideration of information pertaining to the nature of the biological markers 

evaluated in toxicologic and epidemiologic studies. [p. 39].” 

The Cancer Guidelines further state that “lack of mechanistic data, however, is not a reason to reject 

causality [p. 41].” 

At this time, a MOA(s)/AOP(s) has/have not been established for neurodevelopmental outcomes. 

This growing body of literature does demonstrate, however, that chlorpyrifos and/or its oxon are 

biologically active on a number of processes that affect the developing brain. Moreover, there is 

a large body of in vivo laboratory studies which show long-term behavioral effects from early 

life exposure. EPA considers the results of the toxicological studies relevant to the human 

population, as qualitatively supported by the results of epidemiology studies. The lack of 

established MOA/AOP pathway does not undermine or reduce the confidence in the findings of 

the epidemiology studies. The CCCEH data are not considered in isolation, but rather are 

strengthened when considered in concert with the results from the other two cohort studies, as 

noted by the FIFRA SAP (July 2012). As noted above, the CHAMACOS and Mt. Sinai cohorts 

that measured neurological effects at birth (the Brazelton index), observed an association with 

chlorpyrifos (Engel et al., 2007; Young et al., 2005). Similarly, while not consistent by age at 

time of testing (ranging from 6 month to 36 months across the three cohorts), each cohort 

reported evidence of impaired mental and psychomotor development. Attentional problems and 

ADHD were reported by both Mt. Sinai and CHAMACOS investigators. Finally, each of the 

three cohort study authors observed an inverse relation between the respective prenatal measures 

of chlorpyrifos and intelligence measures at age 7 years. 

Although uncertainties remain as articulated above, these uncertainties are diminished in the 

context of the qualitative similarity between the databases, and the concern for long-term 

neurodevelopmental effects as a result of prenatal, perinatal and possibly early life exposure. 

4.7 Safety Factor for Infants and Children (FQPA Safety Factor) 

The key issues being considered by the Agency in the WOE are 1) whether chlorpyrifos causes 

long-term effects from fetal or early life exposure and 2) whether adverse effects can be 

attributed to doses lower than those which elicit 10% inhibition of AChE). When taken together 

the evidence from 1) the experimental toxicology studies evaluating outcomes such as behavior 
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and cognitive function; 2) mechanistic data on possible adverse outcome pathways/modes of 

action; and 3) epidemiologic and biomonitoring studies lead to the following: 

o Qualitatively, the Agency concludes that these lines of evidence together support a 

conclusion that exposure to chlorpyrifos results in adverse neurodevelopmental outcomes in 

humans, at least under some conditions. 

o Quantitatively, the dose –response relationship of AChE inhibition across different life stages 

is established, but MOAs/AOPs for neurodevelopmental outcomes are not established. 

The database of in vivo animal toxicology neurodevelopmental studies on adverse outcomes 

includes only a small number of studies at doses lower than 1 mg/kg/day. Despite this 

uncertainty, the Agency noted that the BMD values in adult (pregnant and nonpregnant) 

female rats (0.05-0.15 mg/kg/day) are generally 10-fold or more lower than the doses where 

effects on neurodevelopmental outcomes in laboratory rats are observed. 

With respect to the mechanistic data, there are some effects which are similarly sensitive or 

more sensitive than AChE inhibition. The fact that there are, however, sparse data to support 

the in vitro to in vivo extrapolation, or the extrapolation from biological perturbation to 

adverse consequence significantly limits their quantitative use in risk assessment. 

As noted above, the lack of an established MOA/AOP makes quantitative use of the 

epidemiology study in risk assessment challenging, particularly with respect to doseresponse, critical duration of exposure, and window(s) of susceptibility. Despite this 

uncertainty, the cord blood and other measures (meconium) provide evidence of exposure to 

the fetus during gestation. Moreover, exposure levels in the range measured in the 

epidemiology studies (pg/g) are likely low enough that is unlikely to result in AChE 

inhibition, as supported by the dose reconstruction analysis of residential use prior to 2000 

(although the agency has not investigated the degree to which exposure to multiple AChEinhibiting pesticides indoors simultaneously could impact this conclusion). 

Given the totality of the evidence, the agency concludes that chlorpyrifos likely played a role in 

the neurodevelopmental outcomes reported by the Columbia University investigators but 

uncertainties such as the lack of an established MOA/AOP for neurodevelopmental effects and 

the exposure to multiple AChE-inhibiting pesticides precludes definitive causal inference. 

However, there is sufficient uncertainty in the human dose-response relationship for 

neurodevelopmental effects which prevents the Agency from reducing or removing the statutory 

10X FQPA Safety Factor. The FQPA 10X Safety Factor will be retained for infants, 

children, youths, and women of childbearing age for all exposure scenarios.

4.8 Dose-Response Assessment 

4.8.1 Durations of Exposure, Critical Windows of Exposure, & Temporality 

of Effects 
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In risk assessment, exposure is evaluated in conjunction with the toxicology profile. More 

specifically, a variety of toxicokinetic and toxicodynamic factors are considered. In the case of 

chlorpyrifos, exposure can occur from a single exposure (e.g., eating a meal) or from repeated 

days of exposure (e.g., worker, residential). 

With respect to AChE inhibition, these effects can occur from a single exposure or from repeated 

exposures. For OPs, repeated exposures generally result in more AChE inhibition at a given 

administered dose compared to acute studies. Moreover, AChE inhibition in repeated dosing 

guideline toxicology studies with OPs show a consistent pattern of inhibition reaching steady 

state at or around 2-3 weeks of exposure in adult laboratory animals (U.S. EPA, 2002). This 

pattern observed with repeated dosing is a result of the amount of inhibition comes at 

equilibrium with production of new enzyme. As such, AChE studies of 2-3 weeks generally 

show the same degree of inhibition with those of longer duration (i.e., up to 2 years of exposure). 

Thus, for most of the human health risk assessments for the OPs, the agency is focusing on the 

critical durations range from a single day up to 21 days (i.e., the approximate time to reach 

steady state for most OPs). As described below, points of departure for various lifestages, 

routes, and scenarios have been derived at the acute and steady state durations. 

With respect to effects on the developing brain, very little is known about the duration of 

chlorpyrifos exposure needed to precipitate adverse effects in the developing brain. There are 

critical windows of vulnerability (Rice & Barone, 2000; Rodier, 2004) with regard to toxicant 

effects on brain development. This vulnerable period in humans spans early pregnancy to 

adolescence (Rice & Barone, 2000). In fact, evidence shows that synapse formation peaks quite 

late in human brain development at 4-8 years of age (Glantz et al, 2007). Within these 

vulnerable periods there are key neurodevelopmental processes (e.g. cell division, migration, 

differentiation, synaptogenesis, and myelination) and each of these is region and stage specific. 

Consequently, the time of toxicant exposure will be a major determinate in the spectrum of 

neurotoxic effects. Because of the dynamic processes in the developing brain (i.e., vulnerable 

windows) it is difficult to determine if the effect or differences in effects is due to duration of 

exposure or if different vulnerable windows were affected. As such, it is impossible at this time 

to rule out even a single day of high exposure to chlorpyrifos having a potential adverse 

neurodevelopmental effect in humans. 

For the chlorpyrifos risk assessment, PoDs for various lifestages, routes, and scenarios have 

been derived at the acute and steady state durations. 

4.8.2. Introduction to the PBPK-PD Model 

As described in detail in EPA’s 2006 document entitled, “Approaches for the Application of 

Physiologically Based Pharmacokinetic (PBPK) Models and Supporting Data in Risk 

Assessment,” physiologically based pharmacokinetic (PBPK) modelling is a scientifically sound 

and robust approach to estimating the internal dose of a chemical at a target site and as a means 

to evaluate and describe the uncertainty in risk assessments. PBPK models consist of a series of 

mathematical representations of biological tissues and physiological processes in the body that 
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simulate the absorption, distribution, metabolism, and excretion (ADME) of chemicals that enter 

the body. Examples of PBPK model applications in risk assessments include interspecies 

extrapolation, intra-species extrapolation, route-to-route extrapolation, estimation of response 

from varying exposure conditions, and high-to-low dose extrapolation. PBPK models can be 

used in conjunction with an exposure assessment to improve the quantitative characterization of 

the dose-response relationship and the overall risk assessment. These models can also be used to 

evaluate the relationship between an applied dose and biomonitoring data. 

The chlorpyrifos PBPK-PD model includes the description of a molecular initiating event in the 

cholinergic toxicity MOA/AOP: AChE inhibition. Thus, the PBPK-PD model can be used to 

predict the critical dose metrics associated with cholinergic toxicity following chlorpyrifos 

exposure: RBC and brain AChE inhibition. Also predicted by the model are chlorpyrifos, its 

oxon, and TCPy levels in various tissues, such as plasma and urine. Age-specific parameters are 

incorporated allowing for lifestage-specific evaluations from infant through adulthood. The 

model can be run in two modes: deterministic and variation. In the deterministic mode, the 

output accounts for human specific metabolism and physiology thus obviating the need for the 

inter-species extrapolation factor for all for all age groups. In variation mode, distributions for16 

parameters, which are critical for determining human variations in RBC AChE inhibition, are 

incorporated and thus the output accounts for intra-species extrapolation for infants, toddler, 

youths, and non-pregnant adults. As described below, the agency is not using the PBPK-PD 

model for pregnant females due to uncertainty associated with lack of appropriate mathematical 

equations to account for physiological changes during pregnancy that could impact population 

variability. 

4.8.3 Description & Structure of the PBPK-PD Model 

The PBPK-PD model for chlorpyrifos that was originally developed by Timchalk and coworkers 

in 2002 (Timchalk et al., 2002a, b) has been refined over the years as more data has become 

available (Busby-Hjerpe et al., 2010; Cole et al., 2005; Garabrant et al., 2009; Lee et al., 2009; 

Lowe et al., 2009; Lu et al., 2010; Marty et al., 2007; Timchalk and Poet, 2008; Timchalk et al., 

2005; Timchalk et al., 2006). The model will not be described in detail here as it is described in 

numerous publications, including a report reviewed by the FIFRA SAP in 2011; summary 

information is provided here. All model code for the PBPK-PD model are provided in the public 

docket for the chlorpyrifos risk assessment. Developers of the chlorpyrifos PBPK-PD model 

sponsored a third-party quality assurance assessment to verify model parameter values and their 

respective sources. The Agency has conducted a review of this third-party assessment by 

randomly checking a subset of the values and sources in the model parameters. The agency also 

conducted a mass balance analysis. Minor inconsistencies were identified, and developers of the 

chlorpyrifos PBPK-PD model have since made the corrections, or provided additional references 

to justify their parameterization choices. 

In recent collaborative research effort between Battelle Pacific Northwest National Laboratory 

and Dow (DAS et al., 2011; Poet et al, 2014; MRIDs 49074901, 48913401), the chlorpyrifos 

PBPK-PD model has been expanded from the ‘typical adult’ model to include other lifestages, 

specific examples were infants (6 months), children (3 year-olds), and adults (30 year olds). 
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Body weight can be calculated at any given age using a modified Gompertz growth function for 

both male and female (Smith et al., 2014). Subsequently, tissue volumes, blood flows to tissue, 

metabolism rates, ChE enzyme activities, and exposure doses are calculated as a function of 

body weight for infants through adulthood, which in turn is also a function of age. Furthermore, 

the PBPK-PD model was refined to include probabilistic capabilities to allow for refining the 

intra-species extrapolation factor. The FIFRA SAP reviewed the initial multiple lifestage, 

probabilistic model at its February 2011 meeting and recommended some additional 

improvements (FIFRA SAP, 2011; Hinderliter et al, 2011; Price et al, 2011). In response to the 

SAP, DAS made multiple changes and performed additional analyses, including a global 

sensitivity analysis, improvements to the quantitative approach to evaluating population 

variability across individuals at a given age, and an uncertainty analysis on metabolism data from 

human hepatic microsomes and plasma to address variation in response that occurs from 

metabolism (Dow, 2014a, b). Recently, code has been developed to simulate tissue dosimetry 

from dermal and inhalation exposure routes (Poet et al, 2014). The deterministic multi-route, 

lifestage PBPK-PD model code used in the chlorpyrifos risk assessment was submitted to the 

agency in 2013. The model code to evaluate population variability and to derive intra-species 

data-derived extrapolation factors was most recently updated and submitted in 2014 (Dow, 

2014a, b) 

The chlorpyrifos PBPK-PD model includes descriptions of the ADME of chlorpyrifos, and its 

metabolites, oxon and TCPy (Figure 3). The metabolic profile for chlorpyrifos is provided above 

in Figure 2. The PBPK-PD model contains descriptions of metabolism to account for 

chlorpyrifos, its oxon, and TCPy in liver, blood, brain, small intestine, lungs, diaphragm, and 

skin. Model parameterization was achieved most often by extrapolation from in vitro studies 

(animal and human tissues), in some incidences by extrapolation from rat in vivo studies, and, for 

TCPy pharmacokinetics, by fitting to human data21. The PD portion of the model is an extension 

of the PBPK model and relates the prediction of the formation of oxon to activity changes in 

AChE, BuChE, and carboxylesterase in brain, diaphragm, liver, lungs, plasma, and RBC. 

To simulate exposures to infants and children, the model incorporates age-specific body weight 

prediction (Leucke et al. (2007) and Young et al., (2009)), which is then used to scale the tissue 

volumes, blood flows to tissues, and ChE activities (Figure 4). In addition to age-related 

physiology, the model also incorporates age-specific metabolism based on in vitro data, allowing 

the model to assess changes in metabolism from infancy to adulthood. Total Vmax of human 

enzymatic metabolism of CPF to TCPy, CPF to CPF-oxon, and CPF-oxon to TCPy in the liver 

over various ages were scaled to age-dependent volume of the liver (Table 4.8.3.1) and 

concentration of microsomes in the liver, which is approximately 37 mg/g tissue (Barter et al., 

2008). Thus, even in cases where the enzymatic measures show no in vitro age- or 

physiological-dependences (on a per mg protein basis), the total enzymatic capacity of the 

individual varies with age. In plasma, however, in vitro PON1 metabolism of CPF-oxon to TCPy 

was age-specific on a per ml plasma volume basis, as described previously. Therefore, the age-

 

21 Two human deliberate dosing studies (Nolan et al. 1982; Kisicki et al, 1999) are available which have been 

reviewed by EPA’s Human Studies Review Board 

http://www.epa.gov/osa/hsrb/files/june2009finalreport92609.pdf

http://www.epa.gov/osa/hsrb/files/meeting-materials/apr-13-14-2011/appendix1.pdf

 These studies have been used in the development and evaluation of the PBPK-PD model. 
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specific Vmax was scaled to age-dependent volume of the blood, resulting in plasma metabolism 

varying by almost 2 orders of magnitude. Values and sources of model parameters can be found 

in Tables 4.8.3.1-4.8.3.2 and were extracted from Dow 2011 and MRID 49248201. 

Figure 3. PBPK/PD model (Typical Adult model) structure. The shaded compartments denote 

tissues which contain B-esterases (bottom panel). Tissue volumes and enzyme activities (Vmax) 

change with age based on liver and/or blood compartmental growth (Extracted from Dow, 2011). 

Figure 4.Schematic of age and body weight dependences in the PBPK-PD model. All compartment volumes and 

blood flows vary with age and body weight. In vivo metabolic rates are scaled based on tissue size (measured in 
Chlorpyrifos Human Health Risk Assessment D424485 

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vitro values scaled to describe tissue-specific (brain, blood, and liver) metabolism); in blood, PON1 metabolism of 

oxon is not only blood volume but also age-dependent (Extracted from Dow, 2011). 
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Table 4.8.3.1. Pharmacokinetic model parameters. 

Parameter Value Source 

Tissue Ontogeny 

All Scaled by age and body 

weight See Table 4.8.3.2 

Flows (L/hr/kg tissue volume) 

Cardiac Output Summed from total tissue 

flow See “Blood flows” in this section

Brain 30.6 Price et al., 2003 

Diaphragm 85.2 Luecke et al., 2007 

Fat 1.45 Luecke et al., 2007, Cowles et al., 1971, 

Price et al., 2003 

Liver 50.4 Price et al., 2003 

Rapidly Perfused 61.8 Adrenal/Spleen average: Price et al., 2003 

Slowly Perfused 1.80 Bone: Price et al., 2003 

Partition Coefficients for CPF (tissue:blood) 

Brain 16.5 Lowe et al., 2009 

Diaphragm 3.85 Lowe et al., 2009 

Fat 250 Lowe et al., 2009 

Liver 12.8 Lowe et al., 2009 

Rapidly Perfused 16.5 Lowe et al., 2009 

Slowly Perfused 3.85 Lowe et al., 2009 

Partition Coefficients for CPF-oxon (tissue:blood) 

Brain 5.6 Lowe et al., 2009 

Diaphragm 1.8 Lowe et al., 2009 

Fat 75 Lowe et al., 2009 

Liver 4.5 Lowe et al., 2009 

Rapidly Perfused 5.6 Lowe et al., 2009 

Slowly Perfused 1.8 Lowe et al., 2009 

Hepatic CYP Metabolic Constants (per tissue wt) 

CPF → TCPy Vmax 

(nmol/hr/kg) 1580 Measured in vitro, Median: See Table 

4.8.3.2 

CPF → TCPy Km 

(μM) 53.8 Measured in vitro, Median: See Table 

4.8.3.2 

CPF → Oxon Vmax 

(μmol/hr/kg) 689 Measured in vitro, Median: See Table 

4.8.3.2 

CPF → Oxon Km 

(μM) 85.8 Measured in vitro, Median: See Table 

4.8.3.2 

Intestinal CYP Metabolic Constants (per tissue wt) 

CPF → TCPy Vmax 

(μmol/hr/kg) 36.8 Poet et al., 2003 
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Parameter Value Source 

CPF → TCPy Km 

(μM) 55 Poet et al., 2003 

CPF → Oxon Vmax 

(μmol/hr/kg) 10.0 Poet et al., 2003 

CPF → Oxon Km (μM) 8.1 Poet et al., 2003 

Brain CYP Metabolic Constants (per tissue wt)

CPF → TCPy Vmax 

(μmol/hr/kg) 3.85 Extrapolated: CPF metabolism/mechanism of action 

CPF → TCPy Km 

(μM) 5.38 See Section 2: CPF metabolism/mechanism of action 

CPF → Oxon Vmax 

(μmol/hr/kg) 0.91 Extrapolated: CPF metabolism/mechanism of action 

CPF → Oxon Km 

(μM) 8.60 See CPF metabolism/mechanism of action 

PON1 Metabolic Constants 

Plasma Vmax 

(μmol/hr/kg) Logistic Fit Measured in vitro, Median: See Table 4.8.3.2 

Plasma Km (μM) 192 Measured in vitro, Median: See Table 

4.8.3.2 

Liver Vmax 

(μmol/hr/kg) 3902 Measured in vitro, Median: See Table 4.8.3.2 

Liver Km (μM) 498 Measured in vitro, Median: See Table 

4.8.3.2 

Intestine Vmax 

(μmol/hr/mg) 246 Poet et al., 2003 

Intestine Km (μM) 328 Poet et al., 2003 

Oral Absorption 

Stomach →Intestine 0.5 Fitted: Nolan et al., Timchalk et al., 2002 

Intestinal Absorption 0.2 Fitted: Nolan et al., Timchalk et al., 2002 

Plasma Protein Binding (%) 

CPF 99 Lowe et al., 2009 

Oxon 99 Lowe et al., 2009 

TCPy Compartmental Model 

Vd (L) 0.2×BW1.117 Fitted: Nolan et al., 1984, Timchalk et al., 

2002 

Ke (/hr) 0.013 Fitted: Nolan et al., 1984, Timchalk et al., 

2002 

Cholinesterase Degradation Rates (hr-1) 

Butyryl 0.004 Fitted (repeat dose data in rats: DOW 

unpublished) 

Acetyl 0.01 Standardized, Timchalk et al., 2002b 

Bimolecular Inhibition Rate (μM hr-1) 

Butyryl 2000 Timchalk et al., 2002 
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Parameter Value Source 

Acetyl 220 Kousba et al. (2007) 

Enzyme Turnover Rate (hr-1) 

Butyryl 1.17x107 Maxwell et al., 1987, Timchalk et al., 2002b 

Acetyl 3.66x106 Maxwell et al., 1987, Timchalk et al., 2002b 

Carboxyl 1.086x105 Maxwell et al., 1987, Timchalk et al., 2002b 

Enzyme Turnover Rate (hr-1) 

Enzyme Turnover Rate (hr-1) 

Butyryl 11700000 Maxwell et al., 1987, Timchalk et al., 2002b 

Acetyl 3660000 Maxwell et al., 1987, Timchalk et al., 2002b 

Carboxyl 108600 Albers et al., 2010 

Enzyme Activity (μmol/kg/hr) 

Brain ACHE 440000 Hojring et al., 1976 

Diaphragm ACHE 77400 Maxwell et al., 1987, Timchalk et al., 2002b 

Liver Carboxyl 1920000 Maxwell et al., 1987, Timchalk et al., 

2002b; Pope et al., 2005 

Plasma Carboxyl NA Li et al., 2005 

Brain Butyryl 46800 Maxwell et al., 1987, Timchalk et al., 2002b 

Diaphragm Butyryl 26400 Maxwell et al., 1987, Timchalk et al., 2002b 

Liver Butyryl 30000 Maxwell et al., 1987, Timchalk et al., 2002b 

Plasma Butyryl 263000 Sidell et al., 1975 

Enzyme Reactivation Rate (hr-1) 

Butyryl 0.0014 Carr and Chambers, 1996, Timchalk et al., 

2002b 

Acetyl 0.014 Carr and Chambers, 1996, Timchalk et al., 

2002b 

Carboxyl 0.014 Carr and Chambers, 1996, Timchalk et al., 

2002b 

Enzyme Aging Rate (hr-1) 

Butyryl 0.0113 Carr and Chambers, 1996, Timchalk et al., 

2002b 

Acetyl 0.0113 Carr and Chambers, 1996, Timchalk et al., 

2002b 

Carboxyl 0.0113 Carr and Chambers, 1996, Timchalk et al., 

2002b 
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Table 4.8.3.2. Compartmental Growth. 

Compartment Fraction of Body Weight Equation1 Sourc

Param

Eqn. Format V0+V1×BW+V2×BW2+V3×BW3+V4×BW4+V5×BW5+V6×BW6 (BW: 

 V0 V1 V2 V3 V4 V5 V6 

Blood Leucke 9.15e-2 -8.59e-7 1.25e-11 -6.46e-17 - - - Youn

Young 8.97e-2 -3.50e-7 6.54e-13LifeStage 

8.970e-2 -3.500e-7 6.540e-13 - - - - 

Brain Leucke 1.19e-1 -3.51e-6 4.28 e-11 -1.82 e-16 - - Fit to 

(bwt

≤

2009 kg) anbased al. 20

Young 1.41e-1 -5.54e-6 9.30 e-11 -6.83e e-16 1.80e-21LifeStage 

1.216e-1 -3.465e-6 4.354e-11 2.463e-16 5.132e-22

Diaphragm Leucke 3.000e-4 - - - - - - Lueck

Young NA - - - - - - LifeStage 

3.000e-4 - - - - - - 

Fat* Female Leucke 5.91e-2 1.20e-5 -5.80e-10 1.12e-14 -6.36e-20 - - Fit to 

Lafor

Young 1.84e-2 and e

-6.86e-6 2.46e-10 -2.11e-15 7.58e-21 -9.94e-27 - 
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Compartment Fraction of Body Weight Equation1 Sourc

Param

Eqn. Format V0+V1×BW+V2×BW2+V3×BW3+V4×BW4+V5×BW5+V6×BW6 (BW: 

 V0 V1 V2 V3 V4 V5 V6 

LifeStage 

9.217e-02 1.401e-05 -6.787e-10 1.540e-14 -1.558e19 7.249e-25 1.273e- 30 on eq

(Lafo

Male Leucke 3.95e-2 1.59e-5 -6.99e-10 1.09e-14 -5.26e-20 - - Fit to 

Lafor

and eon eq(Lafo

Young 1.61e-2 -3.59e-6 -8.28e-11 -3.57e-16 4.73e-22 - - LifeStage 

3.484e-2 2.803e-5 -1.422e-9 2.892e-14 -2.718e19 1.203e-24 -2.036e- 30

Liver Leucke 3.49e-2 -3.23e-7 2.13e-12 - - - - Fit to 

(body et al. value

Young 4.25e-2 -1.01e-6 1.99e-11 -1.66e-16 4.83e-22 - - LifeStage 

3.917e-2 -6.789e-7 1.082e-11 -7.393e-17 1.701e-22 - - 

Rapid Lung Leucke 1.67e-2 -9.96e-8 -1.09e-13 1.13e-17 - - - Youn

Young 1.860e-2 -4.550e-8 - - - - - LifeStage 

1.860e-2 -4.550e-8 - - - - - 

Kidney Leucke 7.31e-3 -8.29e-8 2.13e-12 - - - - Youn

Young 7.26e-3 -6.69e-8 3.33e-13 - - - - LifeStage 

7.260e-3 -6.690e-8 3.330e-13 - - - - 

Pancreas

e Leucke 1.17e-3 -1.18e-8 1.81e-13 - - - - Brow

Young 1.48e et al. 

-3 - - - - - - 

LifeStage 

1.480e-3 - - - - - - 

Spleen Leucke 3.05e-3 -2.09e-8 1.24e-13 - - - - Youn

Young 3.12e-3 5.57e-9 - - - - - LifeStage 

3.120e-3 5.570e-9 - - - - - 
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Compartment Fraction of Body Weight Equation1 Sourc

Param

Eqn. Format V0+V1×BW+V2×BW2+V3×BW3+V4×BW4+V5×BW5+V6×BW6 (BW: 

 V0 V1 V2 V3 V4 V5 V6 

GI Leucke 1.93e-2 -4.42e-7 9.28e-12 -4.88e-17 - - - Brow

Young NA - - - - - - LifeStage 

1.650e-2 - - - - - - 

Slow Non-Fat adipose* 

Leucke NA NA NA NA NA NA NA Fit to 

Lafordata/0and eon eq(Lafo

Young NA NA NA NA NA NA NA LifeStage 

2.044e-1 2.617e-5 -1.542e-9 3.268e-14 -3.116e19 1.387e-24 -2.35e- 30

Muscle Leucke 9.61e-2 -4.88e-6 3.05e-10 -3.62e-15 1.22e-20 - - Fit to 

JanssextrapFig 2

Young 9.68e-1 -3.32e-6 1.83e-10 -1.24e-15 - - - LifeStage 

1.251e-1 1.458e-5 -2.927e-10 2.114e-15 -5.250e21 - - 

Skin Leucke 1.07e-1 -3.26e-6 6.11e-11 -5.43e-16 1.83e-21 - - Youn

Young 1.03e-1 -2.56e-6 3.68e-11 -2.580e-16 8.620e-22 -1.100e-27 - LifeStage 

1.030e-1 -2.560e-6 3.680e-11 -2.580e-16 8.620e-22 -1.100e-27 - 

Bone Marrow Leucke 5.19e-2 8.06e-7 -1.96e-10 7.63e-15 -1.08e-19 5.14e-25 NA Brow

Young NA NA NA NA NA NA NA yellow LifeStage 

2.100e-2 - - - - - - 
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4.8.4 Use of the PBPK-PD Model 

Evaluation of PBPK-PD models intended for risk assessments includes a review of the model 

purpose, model structure, mathematical representation, parameter estimation (calibration), and 

computer implementation (USEPA, 2006b). The chlorpyrifos PBPK-PD model has been 

through several quality assurance reviews by various individuals or groups, including the agency, 

and found that the mathematical equations in this model were adequate to reasonably predict 

both blood/urine dosimetry and ChE inhibition in two controlled, deliberate oral human dosing 

studies (Nolan et al., 1984; Kisicki et al., 1999) and a dermal human study (Nolan et al., 1984). 

The PBPK-PD model predictions for rats inhaled chlorpyrifos compared well with animal data 

(Hotchkiss et al., 2013) with respect to chlorpyrifos, oxon, and TCPy concentrations in plasma, 

and ChE in plasma, RBC and brain (Poet et al., 2014). In addition, the agency has continued to 

critically evaluate the model code and related inputs during the risk assessment process. 

Significant improvements have been made to the PBPK-PD model in response to the 2008, 2011, 

and 2012 SAPs, the agency, and peer reviewers from academic journals in addition to the input 

of new data. The agency believes that the model is sufficiently robust for use in human health 

risk assessment. Because age-specific parameters are incorporated allowing for lifestagespecific evaluations from infant through adulthood, in the deterministic mode, the output 

accounts for human specific metabolism and physiology thus obviating the need for the interspecies extrapolation factor. The deterministic model can be used to simulate an “average 

individual” for all age groups. As such, as described below, the agency is using the PBPK-PD 

model to determine the points of departure (PoDs) for all age groups (See Table 4.8.4 below). 

At the 2011 SAP meeting, the Panel specifically noted the lack of maternal and fetal PK and PD 

compartments in the current PBPK-PD model to inform about tissue dosimetry and AChE 

inhibition during lactation (FIFRA SAP 2011). As described in detail below, the agency has 

assessed exposure to bottle-feeding infants exposed to the oxon through water used with infant 

formula. With respect to chlorpyrifos or oxon exposure to infants through breast milk, any 

exposure to chlorpyrifos would be far lower than drinking water levels predicted by EFED. 

Thus, the agency is already accounting for oral exposure to chlorpyrifos to infants via bottlefeeding and a lactation component in the PBPK-PD model is not necessary. 

The SAP also noted the lack of maternal and fetal PK and PD compartments in the current 

PBPK-PD model to inform about tissue dosimetry and AChE inhibition to pregnant women and 

their fetuses (FIFRA SAP 2011). With respect to exposure to the fetus during gestation, there 

are multiple studies on chlorpyrifos (Mattsson et al., 1998, 2000) and other OPs (U.S. EPA, 

2006a) which show that the pregnant dam exhibits similar or more AChE inhibition than the 

fetus at a given dose to the dam. As such, for AChE inhibition, by protecting against AChE 

inhibition in the pregnant female is expected to be protective for AChE inhibition in the fetus. 

Biomonitoring data from rats and humans support the findings of the AChE studies. 

Specifically, Whyatt et al (2003) have shown that levels of chlorpyrifos in maternal blood are 

similar to the levels measured in human umbilical cord blood (Whyatt et al, 2003). With respect 

to the pregnant dam during gestation, metabolic activities and physiological parameters can be 

altered during pregnancy (for citations, see Appendix 1). While the current PBPK-PD model 
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accounts for age-related growth from infancy to adulthood by using polynomial equations to 

describe tissue volumes and blood flows as a function of age, the model does not include any 

descriptions on physiological, anatomical and biochemical changes associated with pregnancy. 

Due to the uncertainty in extrapolating the current model predictions among women who may be 

pregnant, the agency is applying the standard 10X intra-species extrapolation factor for 

women of child bearing age. 

4.8.4.1 Derivation of Human Equivalent Doses/Concentrations 

In typical risk assessments, PoDs are derived directly from laboratory animal studies and interand intra-species extrapolation is accomplished by use of 10X factors. In the case of 

chlorpyrifos and its oxon, PBPK-PD modeling is being used as a data-derived approach to 

estimate PoDs for all age groups and Data-Derived Extrapolation Factors (DDEF) for intraspecies extrapolation for some groups (USEPA, 2014). The agency typically uses a 10% 

response level for AChE inhibition in human health risk assessment. This response level is 

consistent with the 2006 OP cumulative risk assessment (USEPA, 2006a) and other single 

chemical OP risk assessments. As such, the model has been used to estimate exposure levels 

resulting in 10% RBC AChE inhibition following single day (acute; 24 hours) and 21-day 

exposures for a variety of exposure scenarios (For discussion of durations see Section 4.8.1). 

The PBPK-PD model accounts for PK and PD characteristics to derive age, duration, and route 

specific PoDs (Table 4.8.4 below). Separate PoDs have been calculated for dietary (food, 

drinking water), residential, and occupational exposures by varying inputs on types of exposures 

and populations exposed. Specifically, the following characteristics have been evaluated: 

duration [acute, 21 day (steady state)]; route (dermal, oral, inhalation); body weights which vary 

by lifestage; exposure duration (hours per day, days per week); and exposure frequency [events 

per day (eating, drinking)]. 

For each exposure scenario, the appropriate body weight for each age group or sex was modeled 

as identified from the Exposure Factors Handbook (USEPA, 2011) for occupational and 

residential exposures and from the NHANES/What We Eat in America (WWEIA) Survey22 for 

dietary exposures. All body weights used are consistent with those assumed for dietary, 

occupational and residential exposure assessments. For infants from birth to < 1 year old, the 

agency has selected the body weight for the youngest age group, birth to < 1 month old, 4.8 kg 

(Exposure Factors Handbook, Table 8-3, mean body weight for the birth to < 1 month age 

group). For children between 1-2 years old, the body weight was set to 12.6 for dietary 

exposures (NHANES/WWEIA) and was set to 11 kg for residential exposures (Exposure Factors 

Handbook, Table 8-3, mean body weight for the 1 to < 2 year old age group). For female adults, 

the body weight was set to 72.9 kg for dietary exposures (NHANES/WWEIA) and was set to 69 

kg for residential exposures (Exposure Factors Handbook, Table 8-5, mean body weight for 

females 13 to < 49 years old). 

 

22http://www.ars.usda.gov/Services/docs.htm?docid=13793
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The agency assesses dietary exposures for children 6-12 years old, and children between 6-11 

years old for residential exposures. For purpose of aggregate assessment, these age groups are 

combined. The body weight for children 6-12 years old was set to 37.1 for dietary exposures 

(NHANES/WWEIA); the body weight for children 6-11 years old was set to 32 kg for residential 

exposures (Exposure Factors Handbook, Table 8-3, mean body weight for the 6 to < 11 year old 

age group). The agency assesses dietary exposures for youths 13-19 years old, and youths 

between 11-16 years old for residential exposures. For purpose of aggregate assessment, these 

age groups are combined. For youths 13-19 years old, the body weight was set to 67.3 kg for 

dietary exposures (NHANES/WWEIA); the body weight for youths 11-16 years old was set to 

57 kg for residential exposures (Exposure Factors Handbook, Table 8-3, mean body weight for 

the 11 to < 16 year old age group). 

The following scenarios were evaluated: dietary exposure to the oxon exposures via drinking 

water (24-hour and 21-day exposures for infants, children, youths, and female adults); exposure 

to chlorpyrifos exposures via food (24-hour and 21-day exposures for infants, children, youths, 

and female adults); 21-day residential exposures to chlorpyrifos via skin for children, youths, and 

female adults; 21-day residential exposures to chlorpyrifos via hand-to-mouth ingestion for 

children 1- 2 years old; 21-day residential exposures to chlorpyrifos via inhalation for children 1 

-2 years old and female adults. 

Dietary exposure was estimated for 7 days/week. For the dietary cases in which people are 

exposed to oxon via drinking water, the daily water consumption volume was set to 0.688557 L 

for infants, children between 1-2 year old, and children 6-12 years old, and 1.71062 L for youths 

13-19 years old and female adults. Infants and children were assumed to consume water six 

times a day; youths and female adults were assumed to consume water four times a day. For the 

dietary cases in which people are exposed to chlorpyrifos via food, the eating event was set to 

one meal per day. The daily volumes consumed and number of daily consumption events for all 

populations are mean values by age group based on USDA What We Eat in America, NHANES 

survey for dietary exposures. The mean daily water consumption for children 1- 2 years old, 

0.35 L, and children 6-12 years old, 0.58 L, was less than that for the infants, 0.688557 L; 

however, the infant daily water consumption volume was selected to be protective for PBPK-PD 

PoD derivation for these age groups. For youths 13-19 years old, the mean daily water 

consumption, 0.93 L, was less than that for the female adults; however, the adult daily water 

consumption was also selected to be protective. 

For all residential dermal exposures to chlorpyrifos, the fraction of skin in contact with 

chlorpyrifos was set to 50%. A daily shower (i.e., washing off the chlorpyrifos) was assumed 

following chlorpyrifos exposure. All residential exposures were set to be continuous for 21 days. 

For residential exposures via golfing on treated turf, the daily exposure time is assumed to be 4 

hours/day; for residential exposures via contact with turf following public health mosquitocide 

application, the daily exposure duration is assumed to be 1.5 hours. For residential inhalation 

exposures following public health mosquitocide application, the exposure duration was set to 1 

hour per day for 21 days. The exposure times selected are based on those recommended in the 

2012 Residential SOPs. 
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In addition to dietary and residential exposures, the PBPK-PD model was also used to estimate 

exposure levels resulting in 10% RBC AChE inhibition following occupational exposures (Table 

4.8.4). Dermal exposures for workers assumed even distribution across the entire body surface 

area. A daily shower (i.e., washing off the chlorpyrifos) was assumed following chlorpyrifos 

exposure. The worker was assumed to be a female adult between the ages of 13 to 49, and had a 

body weight of 69 kg. This worker is exposed to chlorpyrifos either via inhalation or skin for 8 

hours/day, 5 days/week, for a total of 21 days. 
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Table 4.8.4. Chlorpyrifos PBPK Modeled Doses (PoDs) Corresponding to 10% RBC AChE Inhibition

RA Type 

Exposure 

Pathway 

(all chlorpyrifos 

unless noted) 

Infants 

( < 1 yr old) 

Young Children 

(1 - 2 years old) 

Children 

(Residential:6-11 years old; Dietary:6-12 years old) 

Youths 

(Residential:11-16 years old; Dietary:13-19 years old) 

(

Acute Steady State (21 day) Acute Steady State (21 day) Acute Steady State (21 day) Acute Steady State (21 day) A

Dietary Drinking Water (oxon conc, ppb) 1,183 217 3,004 548 7,700 1,358 4,988 878 5

Food (ug/kg/day) 600 103 581 99 530 90 475 80 4

Residential (Golfers) 

Dermal 

(ug/kg/day) 25,750 13,950 

Residential (Mosquitocide Application) 

Dermal 

(ug/kg/day) 134,250 

Oral 

(ug/kg/day) 101 

Inhalation 

(concn. in air 

mg/m3) 

 2.37 

Occupational 

Dermal 

(ug/kg/day) 

Inhalation 

(ug/kg/day) 

*PoDs and exposure and risk estimates for females 13-49 yrs covers all youths >13 yrs 
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4.8.4.2 Intra-species extrapolation 

With respect to intra-species extrapolation, the PBPK-PD model can be run in ‘variation’ mode 

which allows for age-specific parameters to vary across a distribution of values. The model will 

not be described in detail here as it is described in multiple recent publications, including a 

detailed report reviewed by the FIFRA SAP in 2011; summary information is provided here. All 

model code for the PBPK-PD variation model are available to the public. 

Significant improvements have been made to the PBPK-PD model in response to the 2008, 2011, 

and 2012 SAPs, the agency, and peer reviewers from academic journals in addition to the input 

of new data. At the 2011 SAP, the panel was critical of some aspects of how Dow proposed to 

assess intra-species extrapolation. DAS made multiple changes, including the addition of a 

global sensitivity analysis, improvements to the quantitative approach to evaluating population 

variability across individuals at a given age, and an uncertainty analysis on metabolism data from 

human hepatic microsomes to address variation in response that occurs from metabolism. In 

addition, Dow performed additional analysis leading to further refinement of the derivation of 

intra-species extrapolation across multiple dose levels by incorporating human variation in 12 

additional parameters in the predictions of RBC AChE responses to chlorpyrifos exposures. 

Of the more than 120 parameters in the PBPK-PD model, 16 parameters were selected for 

varying in the DDEF intra-species analysis. They were selected using local and global 

sensitivity analyses (MRID 49248201, Dow, 2014a,b). The distributions for these 16 are 

provided in Table 4.8.4.2 (below). Interindividual variations for the 16 sensitive parameters 

(listed above) were assumed to follow a lognormal distribution. The distributions are truncated 

at far extreme values only to permit the model to compute but functionally not truncated with 

respect to assessing human variability. References cited in the table are listed in the report 

“Development of Chemical Specific Adjustment Factors for Chlorpyrifos and Chlorpyrifos 

Oxon” (MRID number 49248201) and also provided in Dow, 2014a,b,c. 

hepatic CYP450 activation of 

chlorpyrifos to chlorpyrifos oxon 

total blood 

volume 

RBC ChE 

degradation rate 

transfer rate of chlorpyrifos or 

oxon from the stomach to the 

intestine 

hepatic PON1 detoxification of 

chlorpyrifos oxon to TCPy 

hepatic blood 

flow 

RBC ChE 

reactivation rate 

volume of the liver 

PON1 detoxification of chlorpyrifos 

oxon to TCPy in plasma 

RBC AChE 

inhibition 

rate 

intestinal CYP 

bioactivation to 

chlorpyrifos oxon 

hepatic carboxyl basal 

activity rate 

hepatic PON1 detoxification of 

chlorpyrifos oxon to TCPy 

hematocrit intestinal CYP 

detoxification to 

TCPy 

hepatic carboxyl reactivation 

rate 

Of these 16, four metabolism-related parameters (hepatic CYP450 activation of chlorpyrifos to 

chlorpyrifos oxon, hepatic CYP450 detoxification of chlorpyrifos oxon to TCPy, hepatic PON1 

detoxification of chlorpyrifos oxon to TCPy, PON1 detoxification of chlorpyrifos oxon to TCPy 

in plasma) were found to drive more than 80% of the total variation in RBC AChE inhibition. 
Chlorpyrifos Human Health Risk Assessment D424485 

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The human variability for these four parameters were assessed using in vitro data from 30 human 

hepatic microsome samples and 20 human plasma samples (Smith et al., 2011). Twenty of the 

hepatic microsome samples came from individuals < 12 years of age; and 10 of the samples 

came from adults > 17 years old. Ten of the plasma sample came from individuals < 2 years of 

age; and 10 of the samples came from adults. Because the findings from Smith et al (2011) 

account for more than 80% of the total variation in RBC AChE inhibition, it was determined that 

evaluating the uncertainty associated with the data (i.e., small number of samples compared to 

the large U.S. population) from this study was important to having confidence in the DDEFs 

derived from the variation model. Although some other in vitro studies shown in Table 4.8.4.2 

also have small numbers of samples, these parameters make relatively small contributions to the 

overall variability. As such, additional quantitative uncertainty analysis on these in vitro studies 

is not needed. 

The uncertainty associated with these four critical parameters were incorporated in the 

subsequent Monte Carlo analysis by generating 50 sets of unbounded parametric distributions 

using the following approach. First, the parametric bootstrap approach was used to sample 1000 

values, with replacement, from the in vitro data. Then, this process was repeated for 50 

iterations, and the resulting 50 sets of distribution all have equally probable sets of means and 

coefficient of variation as the observed data, except for the coefficient of variation of the plasma 

PON1 metabolism rate. Since the liver is the origin of PON1 in plasma, the variation of the 

plasma PON1 metabolism rate was set to be the same as the hepatic PON1 metabolism rate. 

Even though the distributions have similar means and coefficient of variation as the observed 

data, they included values outside of the range of the observed data because the distributions 

were assumed to be unbounded. These 50 sets of distributions, for each of the four parameters, 

were found to cover the entire range of the observed data; and the ratios of maximum value to 

minimum value in the simulated distributions were at least three times the ratios of maximum 

value to minimum value in the observed data. 

 
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Table 4.8.4.2 Sixteen parameters in variation model. Extracted from Dow, 2014c 

Parameter Mean value Standard

Deviation CV Variability Reference

Total Blood Volume (L/kg body 

weight) 0.08 0.0022 0.027 P3M; Price et al., 2003 

Plasma PON1 (μmol/hr×L) 162000 92000 0.57 Smith et al., 2011 

Hepatic Blood Flow (L/hr×kg 

tissue) 50 14 0.27 Materne et al., 2000 

RBC ChE Inhibition Rate 

(l/μmol×hr) 100 17 0.17 

Dimitriadis and 

Syrmos, 

2011 

Hepatic PON1 (μmol/hr×kg 

tissue) 154000 88000 0.57 Smith et al, 2011 

Hematocrit (%) 0.45 0.031 0.068 P3M; Price et al., 2003 

RBC ChE Degradation Rate (l/hr) 0.01 0.0014 0.14 Chapman et al., 1968 

Hepatic P450 Bioactivation to 

Oxon (μmol/hr×kg tissue) 690 410 0.59 Smith et al., 2011 

Hepatic P450 Detoxification to 

TCPy (μmol/hr×kg tissue) 1500 800 0.53 Smith et al., 2011 

RBC ChE Reactivation Rate 

(l/hr) 0.014 0.0050 0.36 Mason et al., 2000 

Intestinal CYP Bioactivation to 

Oxon (μmol/hr×kg tissue) 82 43 0.52 Obach et al., 2001 

Intestinal CYP Detoxification to 

TCPy (μmol/hr×kg tissue) 53 28 0.52 Obach et al., 2001 

Transfer Rate to Intestine (hr-1) 0.31 0.081 0.26 Singh et al., 2006 

Volume of the Liver (L/kg body 

weight) 0.032 0.0010 0.032 P3M; Price et al., 2003 

Hepatic Carboxyl Basal Activity 

Rate (l/hr/kg tissue) 1270000 460000 0.36 Pope et al., 2005 

Hepatic Carboxyl Reactivation 

Rate (l/hr) 0.014 0.0050 0.36 Mason et al., 2000 
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According to EPA’s Data-Derived Extrapolation Factor guidance, when calculating a DDEF 

intra-species extrapolation (USEPA, 2014), administered doses leading to the response level of 

interest (10% change in RBC AChE inhibition) are compared between a measure of average 

response and response at the tail of the distribution representing sensitive individuals. DAS has 

conducted an analysis to derive the oral doses that cause 10% RBC AChE inhibition in both 

adults and 6-month old infants (example provided in Figure 5a, b). The ratio of the adult ED10 to 

the infant ED10 was then used to derive intraspecies extrapolation factors. In the subsequent 

Monte Carlo simulations, the target age group is six month old individuals. Some model 

parameters are specific to this age group (e.g., PON1 metabolism in plasma), and some 

parameters are scaled by body weight that reflect this age group (e.g., tissue volume). Based on 

the 5th percentile of the distributions, the DDEF for intraspecies extrapolation is 2.8X for 

chlorpyrifos and 3.1X for the oxon (Dow, 2014b). Based on the 99th percentile of the 

distributions, the DDEF for intraspecies extrapolation is 4X for chlorpyrifos and 5X for the oxon 

(Dow, 2014b). For this revised HHRA, the 99th percentile is being used to account for 

sensitivities (i.e., the intra-species factor is 4X for chlorpyrifos and 5X for the oxon for all 

groups except women who are pregnant or may become pregnant). As shown in Figure 5b, at 

the 99th-ile, only 1% of infants will experience 10% or greater RBC AChE inhibition at the POD. 

Figure 5a. Simulated population of 6 month olds for intra-species extrapolation DDEF derivation. Percent RBC 

AChE inhibition from exposure to single oral doses of chlorpyrifos ranging from 0.05 to 5.0 mg/kg/day (X and Y 

axes provided on the log scale). 

0

10

20

30

40

50

60

70

80

90

100

0.05 0.50 5.00

Percent inhibition cmpared to bacground

Single Oral Dose (mg/kg)
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Figure 5b. Simulated population of 6 month olds for intra-species extrapolation DDEF derivation. Percent RBC 

AChE inhibition from exposure to single oral doses of chlorpyrifos ranging from 0.05 to 1.0 mg/kg/day. Green lines 

represent the infant acute PoD for chlorpyrifos, the PoD adjusted for the 3X and 4X intraspecies factors for the 95 

and 99th-%ile, respectively. 

In summary, for the chlorpyrifos HHRA, the PBPK-PD model has been used to derive PODs for 

RBC AChE inhibition for various populations, durations, and routes (Table 4.8.4). As such, the 

interspecies factor is not needed. To account for variations in sensitivities, an intra-species factor 

of 4X for chlorpyrifos and 5X for the oxon is applied for all groups except women of 

childbearing age. For women of childbearing age, the typical 10X intra-species factor is being 

applied due the lack of appropriate information and algorithms to characterize physiological 

changes during pregnancy. The 10X FQPA SF is being applied to women of childbearing age, 

infants, youths, 
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5.0 Dietary Exposure and Risk Assessment 

5.1 Residues of Concern Summary and Rationale 

The qualitative nature of the residue in plants and livestock is adequately understood based on 

acceptable metabolism studies with cereal grain (corn), root and tuber vegetable (sugar beets), 

and poultry and ruminants. The residue of concern, for tolerance expression and risk assessment, 

in plants (food and feed) and livestock commodities is the parent compound chlorpyrifos. 

Based on evidence (various crop field trials and metabolism studies) indicating that the 

metabolite chlorpyrifos oxon would be not be present in edible portions of the crops (particularly 

at periods longer than the currently registered PHIs), it is not a residue of concern in food or feed 

at this time. Also, the chlorpyrifos oxon is not found on samples in the USDA PDP monitoring 

program. In fact, from 2007 to 2012, out of several thousand samples of various commodities, 

only one sample of potato showed presence of the oxon at trace levels, 0.003 ppm where the 

LOD was 0.002 ppm, even though there are no registered uses of chlorpyrifos on potato in the 

U.S. 

The oxon metabolite was not found in milk or livestock tissues in cattle and dairy cow feeding 

studies, at all feeding levels tested, and is not a residue of concern in livestock commodities. 

Oxidation of chlorpyrifos to chlorpyrifos oxon could potentially occur through photolysis, 

aerobic metabolism, and chlorination as well as other oxidative processes. Because of the 

toxicity of the oxon and data indicating that chlorpyrifos rapidly converts to the oxon during 

typical drinking water treatment (chlorination), the drinking water risk assessment considers the 

oxon as the residue of concern in treated drinking water and assumes 100% conversion of 

chlorpyrifos to chlorpyrifos oxon (see DWA, D424487). 

The chlorpyrifos degradate TCP is not considered a residue of concern for this assessment as it 

does not inhibit cholinesterase (a separate human health risk assessment has been performed for 

TCP, which has its own toxicity database. TCP (derived from triclopyr, chlorpyrifos, and 

chlorpyrifos-methyl) was previously assessed on 6/6/2002 (W. Donovan, D283101)). 

Table 5.1 Summary of Metabolites and Degradates to be included in the Risk Assessment and 

Tolerance Expression 

Matrix Residues included in Risk 

Assessment 

Residues included in 

Tolerance Expression 

Plants Primary Crop Chlorpyrifos Chlorpyrifos 

Rotational Crop Chlorpyrifos Chlorpyrifos 

Livestock Ruminant Chlorpyrifos Chlorpyrifos 

Poultry Chlorpyrifos Chlorpyrifos 

Drinking Water Chlorpyrifos Oxon Not Applicable 
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5.2 Food (Residue Chemistry) Profile 

As mentioned in the 2011 assessment, newer crop field trial studies had been submitted for 

cotton gin byproducts (MRID 46651202), tart cherries (MRID 46651201), aspirated grain 

fractions for soybean, sorghum and wheat (MRID 46640901), and grass forage and hay as part of 

a data call in related to the 2002 chlorpyrifos IRED. These studies were reviewed previously and 

regulatory conclusions are included here for all the commodities with the exception of grass 

forage and hay (original petition withdrawn). The data are considered adequate to conclude that 

tolerances of 15 ppm and 22 ppm would cover any residues of chlorpyrifos on cotton gin byproducts and aspirated grain fractions, respectively, and would support the current tolerance level 

for tart cherries under the condition that only dormant/delayed dormant and trunk spray 

applications are allowed on the label of the 75% WDG end use product. 

The dietary burden to livestock was recalculated to consider residues at tolerance level in the 

feedstock commodities (aspirated grain fractions and cotton gin byproducts) and to use the most 

current version of Table 1 of the OPPTS Test Guidelines 860.1000, released on June 2008. 

Based on the residues observed in the feeding study of cattle beef, dairy cow, swine and poultry 

at the 1x level or higher, HED concludes that the possible residues observed on livestock 

commodities (from animals fed with feedstock that may contain residues resulting from legal 

applications) are covered by the current tolerances established in the 40 CFR §180.342. 

 

There are some outstanding residue chemistry data requirements for chlorpyrifos. A magnitude 

of the residue study to establish a tolerance for wheat hay was required in the previous RED and 

has not been received. For soybean, processing studies are required to determine if tolerances are 

needed for chlorpyrifos in soybean meal, hulls and refined oil. 

Studies submitted to support the registration of a microencapsulated formulation of chlorpyrifos 

showed over-tolerance residues after a foliar application of Lorsban 4E end use product to lemon 

with a rate of 6 lb ai/A. The maximum residue observed was 1.41 ppm while the tolerance for 

citrus fruit (CG 10) is 1.0 ppm. The label of the existing Lorsban 4E end use product (44.9% 

chlorpyrifos) allows a maximum application rate of 6.4 lb ai/A and addition of oil to the spray 

mixture. Under these use conditions residues over tolerance may occur. Magnitude of the 

residue studies are needed with lemon after application of Lorsban 4E and 75% WDG 

formulations separately in order to reevaluate the tolerance for the citrus fruit crop group. 

Since the 2011 preliminary risk assessment, cotton processing studies have been submitted 

(MRID 00037455). The cotton processing study is considered scientifically acceptable. 

Following 16 broadcast foliar applications of the 4 lb ai/gal EC formulation of chlorpyrifos made 

at a total rate of 13.0 lb ai/A using aerial equipment, quantifiable (trail average) residues of 

chlorpyrifos were 0.103 ppm in/on cottonseed and 0.074 ppm in hulls harvested at a 18-day PHI. 

Residues of chlorpyrifos were nonquantifiable (<0.01 ppm) in solvent extracted meal and 

refined-bleached oil. A comparison of the residues in cottonseed with those in processed 

cottonseed fractions indicated that residues of chlorpyrifos do not concentrate in hulls 

(processing factor of 0.7x), solvent extracted meal (<0.1x), or refined-bleached oil (<0.1x). 

Separate tolerances are not needed for the cotton processed commodities. This data requirement 

is considered fulfilled. 
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A tolerance was previously established for chlorpyrifos in wheat milling fractions at 1.5 ppm. 

Also, for corn milled byproducts a tolerance of 0.1 ppm was previously recommended based on 

concentration factors from 1.25x in grits to 2x in flour (D188151, S. Knizner, 8/20/1993). 

Tolerances for residues of chlorpyrifos on wheat milled byproducts (1.5 ppm) and corn milled 

byproducts (0.1 ppm) should be included in the 40 CFR §180.342 as the current tolerance levels 

on the associated raw agricultural commodities, wheat grain and corn grain, are insufficient to 

cover any residues that may occur on these livestock feedstuffs. 

[See Negrón-Encarnación, 5/24/11, D388164, Chlorpyrifos. Registration Review Action for 

Chlorpyrifos. Summary of Analytical Chemistry and Residue Data for complete details 

regarding label revision recommendations, chemistry data requirements and tolerance 

recommendations]. 

5.3 Water Residue Profile 

EFED has provided a revised drinking water assessment (DWA) for chlorpyrifos (Bohaty, R., 

12/23/14, D424487, Chlorpyrifos: Updated Drinking Water Assessment for Registration Review) 

which includes the EDWCs that are used in the aggregate assessment. The DWA also serves as 

the human health aggregate risk assessment since the EDWCs are compared to the DWLOC 

within that document. 

See the Aggregate section 7.0 below for a summary of the DWA including the aggregate 

assessment results (comparison of DWLOCs to EDWCs). For complete details on the 

assumptions, results, and characterization of the drinking water and aggregate assessment refer to 

EFED’s DWA (2014, D424487). 

5.4 Dietary (Food Only) Risk Assessment 

The general approach for the chlorpyrifos exposure and risk assessment can be described as 

follows: The PBPK-PD model was used to predict acute (24 hour) and steady state (21 day) 

points of departure dose levels (PoDs) which correspond to 10% RBC ChEI for the index 

lifestages relevant to chlorpyrifos risk assessment (children of various ages which differ due to 

exposure pattern, and adult females of childbearing age). The PoDs are then divided by the total 

uncertainty factor to determine the population adjusted dose (PAD). For food, the residue of 

concern is chlorpyrifos (the oxon metabolite is not an expected residue on foods). The 

chlorpyrifos total uncertainty factors are 100X for adult females (10X FQPA SF and 10X intraspecies extrapolation factor) and 40X for the other populations (10X FQPA SF and 4X intraspecies extrapolation factor). The chlorpyrifos exposure values resulting from dietary modeling 

are compared to the PAD. There are potential risks of concern when estimated dietary risk 

exceeds 100% of the PAD. 

For the dietary risk assessment for food only, the exposure values resulting from Dietary 

Exposure Evaluation Model (DEEM) and the Calendex model are compared to the PBPK-PDbased acute PAD and steady state PAD, respectively. When estimated dietary risk estimates 

exceeds 100% of the PAD there may be a risk concern. 
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5.4.1 Description of Residue Data Used in Dietary (Food Only) Assessment 

Acute and steady state dietary (food only) exposure analyses for chlorpyrifos were conducted 

using the Dietary Exposure Evaluation Model (DEEM) and Calendex software with the Food 

Commodity Intake Database (FCID) (D. Drew, 11/18/14, D424486, Chlorpyrifos Acute and 

Steady State Dietary (Food Only) Exposure Analysis to Support Registration Review). This 

software uses 2003-2008 food consumption data from the U.S. Department of Agriculture’s 

(USDA’s) National Health and Nutrition Examination Survey, What We Eat in America, 

(NHANES/WWEIA). The most recent previous dietary assessment was performed in 2011 to 

support chlorpyrifos registration review (D. Soderberg, 6/30/11, D388166, Chlorpyrifos: Revised 

Acute (Probabilistic) and Chronic Dietary Exposure and Risk Assessments for Food Only (with 

and without Food Handling Use included) and for Water Only for the Registration Review 

Action – Typical Use Rates/Water Included). This current analyses reflect the latest consumption 

data as well as more recent food monitoring and percent crop treated data. These analyses was 

performed for the purpose of obtaining food exposure values for comparison to the chlorpyrifos 

doses predicted by the PBPK-PD model to cause RBC ChEI. The acute and steady state exposure 

analyses do not include drinking water which is assessed separately as discussed in Section 7. 

All residues in food are assumed to be parent chlorpyrifos since the chlorpyrifos oxon is not 

typically found in foods in monitoring data or crop field trials. Food exposures are based only 

upon field and livestock use of chlorpyrifos and do not incorporate potential exposure from food 

handling establishment (FHE) uses since no active registered FHE uses have been identified by 

BEAD. The previous (2011) dietary risk assessment did include a chronic analysis for FHE use 

based on <2% establishments treated (BEAD could not confirm that there was any actual usage 

although there was a registered use at the time) and half the analytical limit of detection (1⁄2 LOD; 

0.01 ppm) based on all nondetectable residues in a chlorpyrifos FHE study. The exposure from 

any potential FHE uses may be considered negligible compared to exposures from field uses. 

Both the acute and steady state dietary exposure analyses are highly refined. The large majority 

of food residues used were based upon U. S. Department of Agriculture’s Pesticide Data 

Program (PDP) monitoring data except in a few instances where no appropriate PDP data were 

available. In those cases, field trial data or tolerance level residues were assumed. The same data 

were used for both the acute and steady state analyses. BEAD of OPP provided percent crop 

treated information. Food processing factors from submitted studies were used as appropriate. 

5.4.2 Percent Crop Treated Used in Dietary Assessment 

The acute and steady state dietary exposure assessment used percent crop treated information 

from BEAD’s Screening Level Usage Analysis (SLUA; May 1, 2014). 

5.4.3 Acute Dietary (Food Only) Risk Assessment 

Chlorpyrifos acute (food only) dietary exposure assessments were conducted using the Dietary 

Exposure Evaluation Model software with the Food Commodity Intake Database DEEMFCIDTM, Version 3.16, which incorporates consumption data from USDA’s National Health and 
Chlorpyrifos Human Health Risk Assessment D424485 

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Nutrition Examination Survey, What We Eat in America, (NHANES/WWEIA). This dietary 

survey was conducted from 2003 to 2008. Acute dietary risk estimates are presented below for 

the sentinel population subgroups for acute risk assessment: infants (< 1 year old), children (1-2 

years old), youths (6-12 years old) and adults (females 13-49 years old). The assessment of these 

index lifestages will be protective for the other population subgroups. 

Acute dietary (food only) risk estimates are all <100 % of the acute PAD for food (aPADfood) at 

the 99.9th percentile of exposure. The subgroup with the highest risk estimate was females (13-49 

years old) at 3.2 % aPADfood. 

Table 5.4.3 Acute Dietary (Food Only) Exposure and Risk Estimates for 

Chlorpyrifos 

Population 

Subgroup 

aPoDfood

1

(ug/kg/day) 

aPADfood

2

(ug/kg/day) 

Food 

Exposure3

(ug/kg/day) 

% of aPADfood

Infants 

(< 1 yr) 600 15 0.273 1.8 

Children 

(1-2 yrs) 581 14 0.423 3.0 

Youths 

(6-12 yrs) 530 13 0.189 1.4 

Adults 

(Females 13-

49 yrs) 

469 4.7 0.150 3.2 

1 acute point of departure; daily dose predicted by PBPK-PD model to cause RBC ChEI of 10% for acute dietary (food) 

exposures. 

2aPAD= acute population adjusted dose = PoD (Dose predicted by PBPK-PD model to cause 10% RBC ChEI) ÷ total UF; Total 

uncertainty factor =100X for females 13-49 yrs (10X intraspecies factor and 10X FQPA uncertainty factor) and 40X for other 

populations (4X intraspecies factor and 10X FQPA uncertainty factor). 3 Acute food only exposure estimates from DEEM (at 99.9th percentile). Refined with monitoring 

data and %CT. 

5.4.4 Steady State Dietary (Food Only) Risk Assessment 

A chlorpyrifos steady state dietary (food only) exposure analysis was conducted using CalendexFCIDTM. HED’s steady state assessment considers the potential risk from a 21-day exposure 

duration using a 3-week rolling average (sliding by day) across the year. For this assessment, the 

same food residue values used in the acute assessment were used for the 21-day duration. In the 

Calendex software, one diary for each individual in the WWEIA is selected to be paired with a 

randomly selected set of residue values for each food consumed. The steady state analysis 

calculated exposures for the sentinel populations for infant, child, youths, and adult (infants <1 

yr, children 1-2 yrs, youths 6-12 yrs, females 13-49 yrs). 

Calendex reported dietary exposures for each population subgroup at several percentiles of 

exposure ranging from 10th percentile to 99.9th percentile. The dietary (food only) exposures for 
Chlorpyrifos Human Health Risk Assessment D424485 

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chlorpyrifos were all <100% ssPADfood (all populations, at all percentiles of exposure). Only the 

99.9th percentile of exposure is presented in Table 5.4.4 below. Calendex results for other 

percentiles of exposure can be found in D424486. 

For the steady state dietary (food only) exposure analyses, children (1-2 years old) was the 

population subgroup with the highest risk estimate at 9.7% of the ssPADfood at the 99.9th

percentile of exposure. 

Table 5.4.4 Steady State Dietary (Food Only) Exposure and Risk 

Estimates for Chlorpyrifos 

Population 

Subgroup 

SS PoDfood

1

(ug/kg/day) 

ssPADfood

2

(ug/kg/day) 

Food 

Exposure3

(ug/kg/day) 

% of 

ssPADfood

Infants 

(< 1 yr) 103 2.6 0.186 7.2 

Children 

(1-2 yrs) 99 2.5 0.242 9.7 

Youths 

(6-12 yrs) 90 2.2 0.128 5.8 

Adults 

(Females 13-

49 yrs) 

78 0.78 0.075 9.6 

1 Steady state point of departure; daily dose predicted by PBPK-PD model to cause RBC ChEI of 10% for steady state (21 day) 

dietary (food) exposures. 

2ssPAD= Steady state population adjusted dose = PoD (Dose predicted by PBPK-PD model to cause 10% RBC ChEI) ÷ total UF; 

Total uncertainty factor =100X for females 13-49 yrs (10X intraspecies factor and 10X FQPA uncertainty factor) and 40X for 

other populations (4X intraspecies factor and 10X FQPA uncertainty factor). 3Steady state (21 day) food only exposure estimates from Calendex (at 99.9th percentile). Refined with monitoring data and %CT. 

6.0 Residential (Non-Occupational) Exposure/Risk Characterization 

The 2011 residential assessment has been updated23 to reflect the use of the PBPK-PD modeling 

approach and EPA’s SOPs for Residential Pesticide Exposure Assessment (USEPA, 2012). In 

1997, the registrant, Dow AgroSciences, voluntarily agreed to cancel chlorpyrifos registrations 

for indoor broadcast use and direct pet treatments, except pet collars. In December 2001, the 

majority of the remaining chlorpyrifos residential products were subject to voluntary phase 

out/cancellation. Current chlorpyrifos residential uses include a granular ant mound use 

(commercial applicator only) and roach bait in child-resistant packaging (homeowner 

applicator). Additionally, chlorpyrifos is labeled for public health aerial and ground-based 

fogger ULV mosquito adulticide applications and for golf course turf applications. For the 

purpose of residential exposure assessment, the parent compound chlorpyrifos is the residue of 

concern. 

 

This assessment also includes a retrospective dose reconstruction of the exposures which could 

 

23 W. Britton. Chlorpyrifos: Updated Occupational and Residential Exposure Assessment for Registration Review. 

12/29/2014. U.S. EPA Office of Chemical Safety and Pollution Prevention. D424484. 
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have potentially occurred as a result of previous (now cancelled) chlorpyrifos uses in indoor 

residential environments (see Section 4.5.4). 

6.1 Residential Handler Exposure 

HED uses the term “handlers” to describe those individuals who are involved in the pesticide 

application process. HED believes that there are distinct tasks related to applications and that 

exposures can vary depending on the specifics of each task. Residential (non-occupational) 

handlers are addressed somewhat differently by HED as homeowners are assumed to complete 

all elements of an application without use of any protective equipment. 

Based upon review of all chlorpyrifos registered uses, only the roach bait products can be applied 

by a homeowner in a residential setting but the application of roach bait products has not been 

quantitatively assessed because these exposures are negligible and do not pose a risk concern. 

The roach bait product is designed such that the active ingredient is contained within a bait 

station which eliminates the potential for contact with the chlorpyrifos-containing bait material. 

6.2 Residential Post-Application Exposure 

There is the potential for post-application exposures as a result of being in an environment that 

has been previously treated with chlorpyrifos. Chlorpyrifos can be used in areas frequented by 

the general population including golf courses. It is also applied as an aerial and ground-based 

ULV mosquito adulticide applications directly in residential areas. These uses were previously 

assessed in the 2011 preliminary risk assessment. Post-application exposure from residential ant 

mound treatment is not quantitatively assessed as exposures are considered to be negligible and 

do not pose a risk concern; these products can only be applied professionally and direct exposure 

with treated ant mounds is not anticipated. 

The residential post-application exposure scenarios and application rates assessed in 2011 remain 

unchanged. However, the post-application exposure assessment has been updated to reflect the 

following changes: 1) use of the PBPK-PD model for determining toxicological PoDs, 2) use of 

the 2012 Standard Operating Procedures for Residential Pesticide Exposure Assessment24, 3) 

use of the AgDISP model for estimation of airborne concentrations and residue dissipation 

following chlorpyrifos mosquito adulticide applications, 4) updated methodology for 

determining the airborne concentration of active ingredient following ground-based mosquito 

adulticide applications, and 5) use of updated body weights for all residential populations 

assessed. 

In addition, steady state durations of exposure are assumed in the updated residential assessment. 

The steady state endpoint selection for chlorpyrifos overlaps HED’s traditional short-term 

exposure duration endpoint selection and is considered health protective for both short- and 

intermediate- term exposures (additional explanation is available in section 4.8.1 above). 

The quantitative exposure/risk assessment for residential post-application exposures is based on 

 

24 http://www.epa.gov/pesticides/science/USEPA-OPP-HED_Residential%20SOPs_Oct2012.pdf
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the following scenarios: 

Golf Course Use (Emulsifiable concentrate (EC) and Granular (G) formulations) 

 Children 6 to < 11 years old, youths 11 to < 16 years old, and adult post-application 

dermal exposure from contact with treated turf while golfing. 

Public Health Mosquito Adulticide Use (aerial and ground applications) 

 Children 1 to < 2 years old and adult post-application dermal exposure from contact with 

turf following the deposition of chlorpyrifos residues from public health mosquito 

adulticide application. 

 Children 1 to < 2 years old and adult post-application inhalation exposure from airborne 

chlorpyrifos following public health mosquito adulticide application. 

 Children 1 to < 2 years old post-application incidental oral (hand-to-mouth) exposure 

from contact with turf following the deposition of chlorpyrifos residues from public 

health mosquito adulticide application. 

 Children 1 to < 2 years old post-application incidental oral (object-to-mouth) exposure 

from contact with toys containing residues from turf following the deposition of 

chlorpyrifos residues from public health mosquito adulticide application. 

A series of assumptions and exposure factors served as the basis for completing the residential 

post-application risk assessment and have been described in brief. The assumptions and factors 

are described in detail in the updated occupational and residential exposure and risk assessment 

(D424484). 

Exposure Duration: Residential post-application exposures to chlorpyrifos are assumed to be 

steady state (i.e., 21 days or longer). 

The application of mosquitocide in residential areas may result in the potential for postapplication inhalation exposures. The aerosolized particulate remaining following application is 

assumed to persist for no longer than one hour in proximity to the application source and, 

accordingly, would be most appropriately defined as acute in duration. However, this 

assessment assumes that post-application inhalation exposures are steady state, which is a highly 

conservative approach given how infrequently mosquitocides are repeatedly applied to the same 

locations and how rapidly aerosols dissipate after these types of applications. The parameters 

used to define this exposure scenario in the PBPK-PD model conservatively reflect daily, one 

hour exposures for 21 days. 

Application Rates: In order to seek clarification of chlorpyrifos usage, the Agency compiled a 

master use summary document reflective of the use profile of all active product labels. The 

document, among other information, presents all registered uses of chlorpyrifos and 

corresponding maximum single application rates, equipment types, re-entry intervals (REIs), 

etc. This assessment assumes that the detailed information on application rates and use patterns 

presented in Appendix 9 (Master Use Summary Document) will be implemented on all 

chlorpyrifos labels and is the basis of the occupational and residential risk assessment. If, for 

any reason, the final chlorpyrifos labels contain higher application rates, the actual risks posed by 
Chlorpyrifos Human Health Risk Assessment D424485 

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those products may exceed the risks estimated in this assessment.

Body Weights: The body weights assumed for this assessment differ from those used in 2011 

residential exposure assessment and are based on the recommendations of the 2012 Residential 

SOPs. These body weights are the same as selected for derivation of PBPK-PD PoDs for use in 

assessment of residential exposures. 

The standard body weights are as follows: youths 11 to < 16 years old, 57 kg; children 6 to < 11 

years old, 32 kg; and children 1 to < 2 years old, 11 kg. For adults when an endpoint is not sexspecific (i.e., the endpoints are not based on developmental or fetal effects) a body weight of 80 

kg is typically used in risk assessment. However, in this case, a female-specific body weight of 

69 kg was used. While the endpoint of concern, RBC AChE inhibition, is not sex-specific, the 

female body weight was used due to concerns for neurodevelopmental effects related to early life 

exposure to chlorpyrifos. 

Post-application exposures from golfing have been assessed using the 2012 Residential SOPs 

and with use of exposure data from a chemical-specific turf transferable residue (TTR) study. 

The study was conducted with an emulsifiable concentrate, a granular, and a wettable powder 

formulation. Only the emulsifiable concentrate and granular data were used because there are no 

currently registered wettable powder formulations. The study was conducted in 3 states, 

California, Indiana and Mississippi, with use of the emulsifiable concentrate and wettable 

powder formulations. Exposure was estimated by normalizing Day 0 TTR measures from study 

application rates to the current maximum application rate allowable by the label. Chlorpyrifos 

oxon residues were not analyzed. 

The post-application exposure potential from public health mosquito adulticide applications has 

been considered for both ground based truck foggers and aerial applications. For assessment of 

the mosquito adulticide use, the algorithms and inputs presented in the 2012 Residential SOP 

Lawns/Turf section were used coupled with the available TTR data described above. The 

deposition of chlorpyrifos from these applications are not based on the application rate alone, but 

also using the AgDISP (v8.2.6) model (aerial applications, the currently recommended model for 

assessment of mosquito adulticide applications) or empirical data (ground applications) to 

determine how much pesticide is deposited on residential lawns as a result of mosquito adulticide 

treatments at the maximum application rates for each. The TTR data are then used to determine 

the fraction of the total residue deposited following the mosquitocide application which can 

result in exposures to impacted individuals. Inhalation exposures are also estimated using 

AgDrift for aerial application and a recently developed well-mixed box (WMB) model approach 

for outdoor foggers. 

As described above, the AgDISP (v8.2.6) model was used to estimate the deposition of 

chlorpyrifos from aerial applications and the airborne concentration of chlorpyrifos following 

public health mosquitocide application. AgDISP predicts the motion of spray material released 

from aircraft, and determines the amount of application volume that remained aloft and the 

amount of the resulting droplets deposited on the surfaces in the treatment area, as well as 

downwind from the treatment area. The model also allows for the estimation of air 

concentrations in the breathing zones of adults and children for use in calculating the postapplication inhalation risks to individuals residing in areas being treated by aerial application of 
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chlorpyrifos. The aerial fraction of the mosquito adulticide application rate applied (0.010 lb 

ai/A) is 0.35 (i.e., 35 percent of application rate is deposited on turf); and the airborne 

concentration at the breathing height of adults and children of chlorpyrifos 1 hour following 

aerial mosquito adulticide application is 0.00060 mg/m3

. 

Empirical data were used to derive the ground-based deposition of chlorpyrifos following public 

health mosquitocide application. These data, conducted by Moore et al. (1993)25 and Tietze et 

al. (1994)

26, measured the deposition of malathion via ultra-low volume (ULV) ground 

equipment as applied for mosquito control. Based on these data, an off-target deposition rate of 

5 percent of the application rate was used by HED to evaluate ground-based ULV applications 

(i.e., 5 percent of the target application rate deposits on turf). A value slightly higher than the 

mean values for both studies was selected because of the variability in the data and the limited 

number of data points. The adjusted application rate was then used to define TTR levels by 

scaling the available TTR data as appropriate. 

In order to calculate airborne concentrations from ULV truck fogger applications, HED used the 

2012 Residential SOPs for Outdoor Fogging/Misting Systems, with minimal modification to the 

well-mixed box (WMB) model. The WMB model allows for the estimation of air concentrations 

in the breathing zones of adults and children for use in calculating the post-application inhalation 

exposure to individuals residing in areas being treated by ground application of chlorpyrifos. 

This methodology is a modification of the previous method used in the 2011 occupational and 

residential exposure assessment to evaluate post-application inhalation exposure resulting from 

truck mounted mosquito fogger. The revised methodology more accurately accounts for 

dilution. 

Combining Exposure and Risk Estimates 

Since dermal, incidental oral, and inhalation exposure routes share a common toxicological 

endpoint, RBC AChE inhibition, risk estimates have been combined for those routes. The 

incidental oral scenarios (i.e., hand-to-mouth and object-to-mouth) should be considered interrelated and it is likely that they occur interspersed amongst each other across time. Combining 

these scenarios with the dermal and inhalation exposure scenarios would be unrealistic because 

of the conservative nature of each individual assessment. Therefore, the post-application 

exposure scenarios that were combined for children 1 < 2 years old are the dermal, inhalation, 

and hand-to-mouth scenarios (the highest incidental oral exposure expected). This combination 

should be considered a protective estimate of children’s exposure to pesticides. 

Summary of Residential Post-application Non-Cancer Exposure and Risk Estimates 

The assessment of steady state golfer post-application exposures (dermal only) to chlorpyrifos 

treated turf for the lifestages adults, children 6 to < 11 years old, and youths 11 to < 16 years old, 

 

25 J.C. Moore, J.C. Dukes, J.R. Clark, J. Malone, C.F. Hallmon, and P.G. Hester. Downwind Drift and 

Deposition of Malathion on Human Targets From Ground Ultra-Low Volume Mosquito Sprays; Journal 

of the American Mosquito Control Association; Vol. 9, No. 2 (June, 1993)

26 N.S. Tietze, P.G. Hester, and K.R. Shaffer. Mass Recovery of Malathion in Simulated Open Field 

Mosquito Adulticide Tests: Archives of Environmental Contamination and Toxicology; 26: 473-477 

(1994)
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results in no risks of concern (i.e., children 6 to < 11 and youths 11 to < 16 years old, MOEs are 

≥ 40; adults, MOEs are ≥ 100). For the assessment of post-application exposures from public 

health mosquitocide applications, no combined risks of concern were identified for adults 

(dermal and inhalation) and children 1 to < 2 years old (dermal, incidental oral, and inhalation). 

A summary of risk estimates is presented in Table 6.2 below 

Table 6.2. Residential Post-application Non-cancer Exposure and Risk Estimates for Chlorpyrifos. 

Lifestage 

Post-application Exposure 

Scenario Application 

Rate1

State 

(TTR 

Data) 

Dose 

(mg/kg/day)3 MOEs4 Combined 

Routes5 

Combined 

MOEs6 

Use Site Route of 

Exposure 

Adult 

(Females) 

Golf Course 

Turf Dermal 

1.0 

(Emulsifiable 

Concentrate) 

CA 0.010 1,200 

NA NA 

IN 0.0069 1,700 

MS 0.012 1,000 

Mean 0.0095 1,200 

Youths 11 

to < 16 

years old 

CA 0.010 1,400 

IN 0.0069 2,000 

MS 0.012 1,200 

Mean 0.0096 1,500 

Children 

6 to < 11 

years old 

CA 0.012 1,900 

IN 0.0082 2,800 

MS 0.014 1,600 

Mean 0.011 2,000 

Adult 

(Females) 

1.0 

(Granular) CA 

0.0088 1,400 

Youths 11 

to < 16 

years old 

0.0088 1,600 

Children 

6 to < 11 

years old 

0.010 2,200 

Adult 

(Females) 

Aerial and 

Ground Based 

ULV 

Mosquitocide 

Applications 

Dermal 

0.010 

(Aerial) 

MS 0.00052 46,000 X 

8,400 Inhalation NA 0.00060 

(mg/m3) 10,300 X 

Children 

1 to < 2 

years old 

Dermal MS 0.00088 150,000 X 

Inhalation NA 2,300 2 0.00060 

(mg/m3) 4,000 X 

Hand-to-Mouth MS 0.000018 5,600 X 

Object-to-Mouth MS 6.1x10-6 180,000 NA NA 

Soil Ingestion NA2 1.3x10-6 4,900,000 NA NA 

Adult 

(Females) 

Dermal 

0.010 

(Ground) 

MS 0.000074 320,000 X 

1,200 Inhalation NA 0.0051 

(mg/m3) 1,200 X 

Children 

1 to < 2 

years old 

Dermal MS 0.00013 1,100,000 X 

Inhalation NA 460 2 0.0051 

(mg/m3) 460 X 

Hand-to-Mouth MS 2.8x10-6 39,000 X 

Object-to-Mouth MS 8.6x10-7 1,300,000 NA NA 

Soil Ingestion NA2 1.7x10-8 34,000,000 NA NA 

1 Based on the maximum application rates registered for golf course turf and ULV mosquito adulticide uses. 

2 The airborne concentrations of chlorpyrifos following ULV mosquito adulticide applications was determined with use of the 

AgDISP (v8.2.6) model. 

3 Dose (mg/kg/day) equations for golfing and mosquitocide applications are provided in Appendices B and C of the updated 

occupational and residential exposures assessment. For calculation of doses (i.e., dermal, hand-to-mouth, and object-to-mouth)

from exposure to ULV mosquito adulticide, TTR data was used. The MS TTR data was selected for use because it is the worst 
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case and, as a result, most protective of human health. Additionally, the fraction of chlorpyrifos residue deposited following

mosquitocide application, 35% (0.35), was determined with use of the AgDISP (v8.2.6) model and used for dose calculation. 

The fraction of chlorpyrifos deposited following ground ULV application, 5% (0.050), is based on surrogate exposure data 

(malathion). For dose estimation from exposures to golfing on treated turf, on the TTR data was used. Doses have been 

presented for all State sites, including the mean of all State sites. 

4 MOE = PoD (mg/kg/day) ÷ Dose (mg/kg/day). 

5 X indicates the exposure scenario is included in the combined MOE 

6 Combined MOE = 1 ÷ (1/dermal MOE) + (1/inhalation MOE) + (1/incidental oral MOE), where applicable. 

NA = Not applicable. 

6.3 Residential Bystander Post-application Inhalation Exposure 

6.3.1 Spray Drift 

In the 2011 occupational and residential exposure assessment the potential risks to bystanders 

from spray drift were identified as possible concerns. Spray drift is the movement of aerosols 

and volatile components away from the treated area during the application process. The 

chemical may then be deposited on areas such as home lawns. Post-application exposures may 

occur to adults (dermal) or children (dermal and incidental oral) during activities on treated turf. 

The potential risks from spray drift and the impact of potential risk reduction measures were 

assessed in July 2012.27 This evaluation supplemented the 2011 assessment where limited 

monitoring data indicate risks to bystanders. To increase protection for children and other 

bystanders, chlorpyrifos technical registrants voluntarily agreed to lower application rates and to 

other spray drift mitigation measures.28 As of December 2012, spray drift mitigation measures 

and use restrictions appear on all chlorpyrifos agricultural product labels. Because the use of the 

PBPK-PD model impacts spray drift risks, an updated assessment was conducted. 

Table 6.3.1 presents the buffer distances (feet) necessary to reach the level of concern for adults 

and children 1 to < 2 years old (i.e., adults, MOEs are ≥ 100; children 1 to < 2 years old, MOEs 

are ≥ 40) with use of certain application rates, nozzle droplet types, and application methods. The 

estimated buffer distances are less than those agreed to by the technical registrants in July 

2012. All drift risk estimates are presented in the occupational and residential exposure 

assessment (Appendix D of D424484). 

Table 6.3.1. Summary of Spray Drift Buffers for Chlorpyrifos1

Application 

Rate 

 (lb ai/A) 

Nozzle 

Droplet 

Type 

Adult Buffer Summary 

Children 1 to < 2 Years Old Buffer 

Summary 

(Dermal + Incidental Oral) 

Buffers Necessary to reach MOE of 100 

(Feet) 

Buffers Necessary to reach MOE of 40 

(Feet) 

Aerial Groundboom Airblast Aerial Groundboom Airblast 

6.0 

Medium or 

Coarse 

NA2 NA 0 NA NA 25 

4.3 NA 0 0 NA 0 10 

4.0 NA 0 0 NA 0 10 

3.76 NA 0 0 NA 0 10 

3.0 Coarse or NA 0 0 NA 0 0 

 

27 J. Dawson, W. Britton, R. Bohaty, N. Mallampalli, and A. Grube. Chlorpyrifos: Evaluation of the Potential Risks 

from Spray Drift and the Impact of Potential Risk Reduction Measures. 7/13/12. U.S. EPA Office of Chemical 

Safety and Pollution Prevention. D399483, D399485.

28 R. Keigwin. Spray Drift Mitigation Decision for Chlorpyrifos (059101). 7/2012. U.S. EPA Office of Chemical 

Safety and Pollution Prevention. EPA-HQ-OPP-2008-0850-0103. 
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Table 6.3.1. Summary of Spray Drift Buffers for Chlorpyrifos1

Application 

Rate 

 (lb ai/A) 

Nozzle 

Droplet 

Type 

Adult Buffer Summary 

Children 1 to < 2 Years Old Buffer 

Summary 

(Dermal + Incidental Oral) 

Buffers Necessary to reach MOE of 100 

(Feet) 

Buffers Necessary to reach MOE of 40 

(Feet) 

Aerial Groundboom Airblast Aerial Groundboom Airblast 

Very Coarse 

3.0 Medium NA 0 0 NA 0 0 

2.3 Coarse or 

Very Coarse 50 0 0 0 0 0 

2.3 Medium 100 0 0 10 0 0 

2.0 Coarse or 

Very Coarse 25 0 0 0 0 0 

2.0 Medium 75 0 0 0 0 0 

1.5 Coarse or 

Very Coarse 25 0 0 0 0 0 

1.5 Medium 50 0 0 0 0 0 

1.0 Coarse or 

Very Coarse 10 0 0 0 0 0 

1.0 Medium 25 0 0 0 0 0 

1 Per December 2012 spray drift mitigation memorandum, aerial application of greater than 2 lb ai/A is only 

permitted for Asian Citrus Psylla control, up to 2.3 lb ai/A. 

2 NA is not allowable. 

6.3.2 Volatilization 

In January 2013, a preliminary assessment of the potential risks from volatilization was 

conducted.29 The assessment evaluated the potential risks to bystanders, or those who live and/or 

work in proximity to treated fields, from inhalation exposure to vapor phase chlorpyrifos and 

chlorpyrifos-oxon emitted from fields following application of chlorpyrifos. The results of the 

January 2013 assessment indicated that offsite concentrations of chlorpyrifos and chlorpyrifosoxon may exceed the target concentration based on the toxicological endpoints used at that 

time.30

One significant area of uncertainty described in the preliminary assessment was the use of the 

aerosolized chlorpyrifos inhalation toxicity study -- as opposed to chlorpyrifos vapor -- for 

evaluation of lung AChE resulting from field volatilization. Because field volatilization is the 

production and release of vapor into the atmosphere after sprays have settled on treated soils and 

plant canopies, the vapor, rather than the aerosol, is the relevant form for evaluation of bystander 

volatilization exposures. However, EPA lacked chlorpyrifos vapor toxicity data at the time it 

conducted the preliminary volatilization assessment in 2013. Following the release of the 

preliminary volatilization assessment, Dow AgroSciences LLC conducted, high quality nose-

 

29 R. Bohaty, C. Peck, A. Lowit, W. Britton, N. Mallampalli, A. Grube. Chlorpyrifos: Preliminary Evaluation of the 

Potential Risks from Volatilization. 1/31/13. U.S. EPA Office of Chemical Safety and Pollution Prevention. 

D399484, D400781. 

30EPA MRID# 48139303:Acute Inhalation Exposure of Adult Crl:CD(SD) Rates to Particulate Chlorpyrifos 

Aerosols: Kinetics of Concentration-Dependent Cholinesterase (ACHE) Inhibition in Red Blood Cells, Plasma, 

Brain and Lung; Authors: J. A. Hotchkiss, S. M. Krieger, K. A. Brzak, and D. L. Rick; Sponsor: Dow AgroSciences 

LLC. 
Chlorpyrifos Human Health Risk Assessment D424485 

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only vapor phase inhalation toxicity studies for both chlorpyrifos and chlorpyrifos-oxon31 to 

address this uncertainty. 

In June 2014, a reevaluation of the 2013 preliminary volatilization assessment was conducted to 

present the results of the vapor studies and their impact. In the vapor studies, female rats were 

administered a saturated vapor, meaning that the test subjects received the highest possible 

concentration of chlorpyrifos or chlorpyrifos-oxon which can saturate the air in a closed system. 

At these saturated concentrations, no statistically significant inhibition of AChE activity was 

measured in RBC, plasma, lung, or brain at any time after the six-hour exposure period in either 

study. Under actual field conditions, indications are that exposures to vapor phase chlorpyrifos 

and its oxon would be much lower as discussed in the January 2013 preliminary volatilization 

assessment. 

Because these new studies demonstrated that no toxicity occurred even at the saturation 

concentration, which is the highest physically achievable concentration, then there is no 

anticipated risks of concern from exposure to the volatilization of either chlorpyrifos or 

chlorpyrifos oxon. In June 2014, the January 2013 volatilization assessment was revised to 

reflect these findings.32

7.0 Aggregate Exposure/Risk Characterization 

In accordance with the FQPA, HED must consider and aggregate (add) pesticide exposures and 

risks from three major sources: food, drinking water, and residential exposures. In an aggregate 

assessment, exposures from relevant sources are added together and compared to quantitative 

estimates of hazard, or the risks themselves can be aggregated. The durations of exposure 

identified for chlorpyrifos uses are acute and steady state. The acute aggregate assessment 

includes food and drinking water only. The steady state aggregate assessment includes food, 

drinking water, and residential exposures.

A drinking water level of comparison (DWLOC) approach to aggregate risk was used to 

calculate the amount of exposure available in the total ‘risk cup’ for chlorpyrifos oxon in 

drinking water after accounting for any chloropyrifos exposures from food and/or residential use. 

The DWLOC approach used a reciprocal MOE calculation method for adults (females of 

childbearing age) since the target MOEs are the same for all relevant sources of exposure, i.e., 

100X for residential dermal and for dietary food and water. This entailed calculating the MOE 

 

31W. Irwin. Review of Nose-Only Inhalation of Chlorpyrifos Vapor: Limited Toxicokinetics and Determination of 

Time-Dependent Effects on Plasma, Red Blood Cell, Brain and Lung Cholinesterase Activity in Femal CD(SD): Crl 

Rats. U.S. EPA Office of Chemical Safety and Pollution Prevention. 6/25/14. D411959. TXR# 0056694. EPA 

MRID# 49119501. 

W. Irwin. Review of Nose-Only Inhalation of Chlorpyrifos-Oxon Vapor: Limited Toxicokinetics and Determination 

of Time-Dependent Effects on Plasma, Red Blood Cell, Brain, and Lung Cholinesterase Activity in Female 

CD(SD):Crl Rats. U.S. EPA Office of Chemical Safety and Pollution Prevention. 6/25/14. D415447. TXR# 

0056869. EPA MRID# 49210101. 

32 W. Britton. W. Irwin. J. Dawson. A. Lowit. E. Mendez. Chlorpyrifos:Reevaluation of the Potential Risks from 

Volatilization in Consideration of Chlorpyrifos Parent and Oxon Vapor Inhalation Toxicity Studies. 6/25/2014. U.S. 

EPA Office of Chemical Safety and Pollution Prevention. D417105. 
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for water (MOEwater) by deducting the contributions from food (MOE food) and dermal 

(MOEdermal) from the aggregate MOE (MOEagg) of 100. The aggregate MOE value is the same as 

target MOE (level of concern). The DWLOC is then calculated by dividing the PoDwater by the 

MOEwater. The general reciprocal MOE formula is as follows: 

MOEagg = 1/ [(1/MOEwater) + (1/MOEfood) + (1/MOEdermal)] 

MOEwater = 1/ [(1/MOEagg) – ((1/MOEfood) + (1/MOEdermal))] 

DWLOC= PoDwater/ MOEwater 

When target MOEs (levels of concern) are not the same across the relevant sources of exposure, 

the reciprocal MOE approach for calculating DWLOCs is not appropriate; instead an aggregate 

risk index (ARI) method is used. This ARI method was employed for infants, children and 

youths aggregate assessments for chlorpyrifos uses since the target MOEs for the relevant 

sources of exposure are not the same i.e., the target MOE for dietary food and for residential 

dermal exposures is 40X while the target MOE for drinking water exposure is 50X. In this 

approach, the aggregate, or ‘total’, ARI value is assigned as 1 (HED is generally concerned when 

any calculated ARIs are less than 1). Similar to the reciprocal MOE approach, the ARIs for food 

and dermal are deducted from the aggregate ARI to determine the ARI for water. The water ARI 

is multiplied by the target MOE for water to determine the calculated water MOE (MOEwater). 

The DWLOC is then calculated by dividing the PoDwater by the MOEwater. The general ARI 

method formula is as follows: 

ARIs for food or dermal are calculated as ARIfood or dermal = [(MOEfood or dermal)/(MOEtarget for food or dermal)]. 

ARIagg = 1/ [(1/ARIwater) + (1/ARIfood) + (1/ARIdermal)] 

ARIwater= 1/[(1/ARIagg) – ((1/ARIfood) + (1/ARIdermal) )]; Where ARIagg=1 

MOEwater = ARIwater x MOEtarget. 

DWLOC= PoDwater/ MOEwater 

 ARIs for food and dermal are calculated as ARIfood or dermal = [(MOEfood or dermal)/(MOEtarget for food or dermal)]. 

Aggregate risk estimate calculations and related DWLOCs are based on guidance in the HED 

SOP 99.5 (Updated “Interim Guidance for Incorporating Drinking Water Exposure into 

Aggregate Risk Assessments,” 8/1/99). 

7.1 Acute Aggregate Risk 

The acute aggregate assessment includes only food and drinking water. DWLOCs were 

calculated for infants, children, youths, and adults. The lowest acute DWLOC calculated was for 
Chlorpyrifos Human Health Risk Assessment D424485 

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infants (<1 year old) at 24 ppb. 

Table 7.1 Acute Aggregate (Food and Drinking Water) 

Calculation of DWLOCs1,2 

Population 

Food 

Exposure 

(chlorpyrifos)3

Drinking 

Water 

Exposure 

(chlorpyrifos)4

Acute 

DWLOC 5

(ppb chlorpyrifos 

oxon) MOE ARI MOE ARI 

Infants1

(<1 yr) 2200 55 50 1.0 24 

Children1

(1-2 yrs) 1400 35 50 1.0 60 

Youths1

(6-12 yrs) 2800 70 50 1.0 150 

Adults2

(Females 13-

49 yrs) 

3100 NA 100 NA 53 

1

 DWLOCs for infants, children and youths are calculated using the ARI (Aggregate Risk Index) approach since 

target MOEs are different for drinking water (chlorpyrifos oxon target MOE=50) and for food and residential 

(chlorpyrifos target MOE= 40) exposure. 

2 DWLOCs for adults (females 13-49 yrs) are calculated using the reciprocal MOE approach since the target MOEs 

are the same for drinking water (chlorpyrifos oxon target MOE=100) and for food and residential (chlorpyrifos 

target MOE= 100) exposure. 

3 FOOD: MOEfood = PoDfood (ug/kg/day) (from Table 4.8.4)/ Food Exposure (ug/kg/day) (from Table 5.4.3). 

ARIfood = [(MOEfood)/(MOEtarget)]. 4 WATER (ARI approach): ARIwater= 1/[(1/ARIagg) – ((1/ARIfood) + (1/ARIdermal) )]; Where ARIagg=1 (Note:HED is 

generally concerned when calculated ARIs are less than 1). 

MOEwater = ARIwater x MOEtarget.

WATER (Reciprocal MOE approach): MOEwater = 1/ [(1/MOEagg) – ((1/MOEfood) + (1/MOEdermal))]; Where MOEagg =Target 

MOE. 

5 DWLOC: DWLOC ppb= PoDwater (ppb; from Table 4.8.4) /MOEwater

7.2 Steady State Aggregate Risk 

The steady state aggregate assessment includes dietary exposures from food and drinking water 

and dermal exposures from residential uses (dermal exposures represent the highest residential 

exposures). DWLOCs were calculated for infants, children, youths, and adults. The lowest 

steady state DWLOC calculated was for infants (<1 year old) at 3.9 ppb. 
Chlorpyrifos Human Health Risk Assessment D424485 

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Table 7.2 Steady State Aggregate (Food, Drinking Water, Residential) 

Calculation of DWLOCs1,2 

Population 

Food 

Exposure 

(chlorpyrifos)3

Dermal 

Exposure 

(chlorpyrifos)4

Drinking 

Water 

Exposure 

(chlorpyrifos 

oxon)5

Steady State 

DWLOC 6

(ppb chlorpyrifos 

oxon) 

MOE ARI MOE ARI MOE ARI 

Infants1

(<1 yr) 550 14 NA NA 55 1.1 3.9 

Children1

(1-2 yrs) 410 10 NA NA 55 1.1 10 

Youths1

(6-12 yrs) 700 18 1600 40 54 1.1 16 

Adults2

(Females 13-

49 yrs) 

1000 NA 1000 NA 125 NA 7.5 

1

 DWLOCs for infants, children and youths are calculated using the ARI (Aggregate Risk Index) approach since 

target MOEs are different for drinking water (chlorpyrifos oxon target MOE=50) and for food and residential 

(chlorpyrifos target MOE= 40) exposure. 

2 DWLOCs for adults (females 13-49 yrs) are calculated using the reciprocal MOE approach since the target MOEs 

are the same for drinking water (chlorpyrifos oxon target MOE=100) and for food and residential (chlorpyrifos 

target MOE= 100) exposure. 

3 FOOD: MOEfood = PoDfood (ug/kg/day) (from Table 4.8.4)/ Food Exposure (ug/kg/day) (from Table 5.4.4). 

ARIfood = [(MOEfood)/(MOEtarget)]. 4 DERMAL: MOEdermal = PoDdermal (ug/kg/day) (from Table 4.8.4)/ Dermal Exposure (ug/kg/day) (from Table 6.2). 

ARIdermal = [(MOE dermal)/(MOEtarget)]. 5 WATER (ARI approach): ARIwater= 1/[(1/ARIagg) – ((1/ARIfood) + (1/ARIdermal) )]; Where ARIagg=1 (Note:HED is 

generally concerned when calculated ARIs are less than 1). 

MOEwater = ARIwater x MOEtarget.

 WATER (Reciprocal MOE approach): MOEwater = 1/ [(1/MOEagg) – ((1/MOEfood) + (1/MOEdermal))]; Where MOEagg =Target 

MOE. 

6 DWLOC: DWLOC ppb= PoDwater (ppb; from Table 4.8.4) /MOEwater

7.3 Aggregate Risk Estimates 

The acute aggregate assessment includes food and drinking water only. The steady state 

aggregate assessment includes food, residential and drinking water exposures. Acute and steady 

state risk estimates for dietary (food only) exposures or for residential only (dermal, inhaltion 

and incidental oral) exposures to chlorpyrifos are not of concern. Steady state risk estimates from 

food and residential exposures combined are also not of concern. For the chlorpyrifos aggregate 

assessments that include drinking water along with the food and residential components, a 

DWLOC approach is used to calculate the amount of exposure available in the total ‘risk cup’ for 

exposures to the chlorpyrifos oxon in drinking water after accounting for any exposures from 

parent compound chlorpyrifos from food and/or residential use. 

Typically, for a DWLOC aggregate risk assessment, the calculated DWLOC is compared to the 

EDWC. When the EDWC is less than the DWLOC, there are no risk concerns for exposures to 
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the pesticide in drinking water. Conversely, when the EDWC is greater than the DWLOC, there 

may be a risk concern. For chlorpyrifos, DWLOCs were calculated for both the acute and steady 

state aggregate assessments for infants, children, youths and adult females. However, the acute 

DWLOCs were not compared to EDWCs. The steady state DWLOCs and risk estimates for 

drinking water will be protective of any acute aggregate exposures because the oxon 

concentrations expected to result in 10% ChEI are considerably higher (up to 5.7x higher) for 

acute exposures than for steady state. In addition, the steady state DWLOC calculations include 

the residential exposure component. Consequently, the calculated acute DWLOCs are much 

higher than those for the steady state. For example, for infants, the acute DWLOC is 24 ppb 

while the steady state DWLOC is 3.9 ppb (Tables 7.1 and 7.2). In fact, the lowest DWLOC 

calculated for any duration or population was the 3.9 ppb value (infants) and is the concentration 

used for comparison to the EFED-modeled EDWCs. Drinking water concentrations of 

chlorpyrifos oxon above 3.9 ppb may be of concern. 

EFED has summarized the drinking water and aggregate assessment below (for complete details 

on the assumptions, results, and characterization of the screening analysis refer to EFED’s DWA, 

(Bohaty, R. 12/23/14, D424487, Chlorpyrifos: Updated Drinking Water Assessment for 

Registration Review). 

Residue Definition 

A national screening level drinking water assessment was completed for the registration review 

of chlorpyrifos, with focus on the agricultural uses. The primary drinking water residue of 

concern is chlorpyrifos-oxon, the predominant chlorpyrifos transformation product formed 

during drinking water treatment (e.g., chlorination). To illustrate the range of EDWCs, two 

maximum label rate application scenarios were selected to represent high and low end exposures, 

i.e., tart cherries at 5 applications totaling 14.5 pounds per acre per year, and bulb onions at a 

single application of one pound per acre per year, respectively. The application of chlorpyrifos to 

tart cherries resulted in concentrations in surface water that exceeded the DWLOC; whereas, 

chlorpyrifos applications to bulb onions result in concentrations below the DWLOC. 

Concentrations in groundwater are not expected to exceed the DWLOC. 

To investigate whether other chlorpyrifos application scenarios may result in concentrations that 

exceed the DWLOC, a screen of all available surface water modeling scenarios was completed 

considering three different application dates and a single application at several different 

application rates that ranged from one to six pounds. This analysis showed that even with only 

one application, several chlorpyrifos uses may exceed the DWLOC at rates lower than maximum 

labeled rates (both single as well as yearly), including an application rate of one pound per acre 

per year. The analysis also showed that the DWLOC exceedances are not expected to be 

uniformly distributed across the country. 

Further analysis was conducted to look at the spatial distribution of EDWCs at a regional level, 

as well as by using a drinking water intake watershed approach. This exercise demonstrated that 

chlorpyrifos applications will result in variable drinking water exposures that are highly localized 

and that the highest exposures generally occur in small hydrologic regions where there is a high 

percent cropped area on which chlorpyrifos use could occur. 
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Finally, EDWCs were compared to monitoring data. This analysis showed that when modeling 

scenarios are parameterized to reflect reported use and EDWCs are adjusted to reflect percent 

cropped area, the EDWCs are within an order of magnitude of the measured concentrations 

reported in the monitoring data. Therefore, although there are uncertainties associated with the 

model input parameters for which conservative assumptions were made (e.g., one aerobic aquatic 

metabolism half-life value multiplied by the uncertainty factor of three, stable hydrolysis, 100% 

of the cropped watershed is treated on the same day, and use of the Index Reservoir as the 

receiving waterbody), these assumptions do not appear to lead to an overly conservative estimate 

of exposure. In addition, evaluation of the monitoring data further illustrates that exposures are 

highly localized. Additional work can be done to examine EDWCs on a regional and/or 

watershed scale to pinpoint community drinking water systems where exposure to chlorpyrifosoxon as a result of chlorpyrifos applications may pose an exposure concern. 

EDWCs are provided for chlorpyrifos and chlorpyrifos-oxon. Chlorpyrifos EDWCs were 

multiplied by 0.9541 (molecular weight correction factor) and 100% (maximum conversion 

during water purification) to generate chlorpyrifos-oxon EDWCs. Essentially, the concentration 

of chlorpyrifos and chlorpyrifos-oxon are the same. A 100% conversion factor for the oxidation 

of chlorpyrifos to chlorpyrifos-oxon was used as an approximation based on empirical bench 

scale laboratory data that indicate chlorpyrifos rapidly oxidizes to form chlorpyrifos-oxon almost 

quantitatively during typical water treatment (chlorination).33 There are limited data available on 

the removal efficiency of chlorpyrifos prior to oxidation or the removal efficiency of 

chlorpyrifos-oxon during the drinking water treatment process. Based on empirical data showing 

that more than 75 percent of community water systems use chlorination to disinfect drinking 

water in the United States34, the assumption of exposure to chlorpyrifos-oxon equivalent to 100% 

conversion of chlorpyrifos is not considered overly conservative. It is possible that some 

drinking water treatment procedures, such as granular activated carbon filtration and water 

softening (increased rate of chlorpyrifos-oxon hydrolysis at pH > 9) could reduce the amount of 

chlorpyrifos-oxon in finished drinking water; however, these treatment methods are not typical 

practices across the country for surface water. 

While there is the potential to have both chlorpyrifos and chlorpyrifos-oxon present in finished 

drinking water, limited (or no) information is available to readily quantify how much of each 

form remains in the finished water. In the absence of available information, all the chlorpyrifos 

that enters a drinking water treatment facility is assumed to remain after treatment, and is 

converted to chlorpyrifos-oxon during treatment. 

Although chlorpyrifos-oxon has a hydrolysis half-life of 5 days, the drinking water treatment 

simulation half-life for chlorpyrifos-oxon is approximately 12 days.35,36,37 Therefore, once 

 

33 Duirk, S. E.; Collette, T. W.; Degradation of Chlorpyrifos in Aqueous Chlorine Solutions: Pathways, Kinetics, 

and Modeling. Environ. Sci. Technol., 2006, 40(2), 546-550. 34 Community Water System Survey 2006; U.S. Environmental Protection Agency, Washington, DC 20460 May 

2009 (survey data) 35 Tunink, A. Chlorpyrifos-oxon: Determination of hydrolysis as a function of pH, 2010 (MRID 48355201; 

acceptable) 

36 Wu, J.; Laird, D. A. Abiotic Transformation of Chlorpyrifos to Chlorpyrifos Oxon in Chlorinated Water. Environ. 

Toxcol.Chem., 2003, 22(2), 261-264. 37 Tierney, D. P.; Christensen, B. R.; Culpepper, V. C. Chlorine Degradation of Six Organophosphate Insecticides 
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chlorpyrifos-oxon forms during treatment, little transformation is expected to occur before 

consumption (during drinking water distribution). There is a wide range of treatment processes 

and sequences of treatment processes employed at community water systems across the country 

and there are limited data available on a community water system specific basis to assess the 

removal or transformation of chlorpyrifos during treatment. These processes are not specifically 

designed to remove pesticides and pesticide transformation products including chlorpyrifos and 

chlorpyrifos-oxon. In general, drinking water treatment processes, with the exception of 

activated carbon,38 have been shown to have little impact on removal of pesticide residues. 

Additional discussion of drinking water treatment can be found in the Drinking Water 

Treatment section of the DWA or in the preliminary DWA. 

Aggregate 

National Screen 

The 2011 preliminary DWA, as well as the additional analyses completed as part of this 

assessment, indicate that exposure to chlorpyrifos-oxon in drinking water derived from surface 

water may pose an exposure concern. Since a large number of chlorpyrifos uses were identified 

in the preliminary DWA as triggering a concern, a bounding estimate of exposure was completed 

using a screening level national assessment approach. This was done to determine which 

currently registered uses could result in exposure to chlorpyrifos-oxon in drinking water that 

exceed the DWLOC. 

Use of chlorpyrifos on tart cherries is expected to result in the highest EDWC. EDWCs for 

chlorpyrifos and chlorpyrifos-oxon39 are reported below for Tier I groundwater and Tier II 

surface water model simulations. Because chlorpyrifos is used on a number of agricultural crops, 

as well as turf, a drinking water intake percent cropped area (PCA) adjustment factor of 1 was 

used.40,41 While the model input values42 have been updated since the preliminary assessment, 

the results presented in 7.3.1 are similar to those previously reported. 

 

 

and Four Oxons in Drinking Water Matrix. Submitted by Syngenta Crop Protection, Inc. 2001. 38 Progress Report on Estimating Pesticide Concentrations in Drinking Water and Assessing Water Treatment 

Effects on Pesticide Removal and Transformation: A Consultation. FIFRA Scientific Advisory Panel Meeting, 

September 29, 2000; SAP Report No. 2001-02 February 12, 2011. 

39 Chlorpyrifos EDWCs were multiplied by 0.9541 (molecular weight correction factor) and 100% (maximum 

conversion during water purification) to generate chlorpyrifos-oxon EDWCs. Additional details on the potential 

impacts on drinking water treatment on chlorpyrifos and chlorpyrifos-oxon see the Additional Analyses section on 

Drinking Water Treatment. 

40 U.S. Environmental Protection Agency Brady, D. Guidance on Development and Use of Community Water 

System Drinking Water Intake Percent Cropped Area Adjustment Factors for Use in Drinking Water Exposure 

Assessments: 2014 Updated, September 12, 2014. 

41 U.S. Environmental Protection Agency, Bohaty, R., Carleton, J., Crk, T., Echeverria, M., Ruhman, M., Thawley, 

M., Thurman, N., Villanueva, P., White, K., Development of Community Water System Drinking Water Intake 

Percent Cropped Area Adjustment Factors for Use in Drinking Water Exposure Assessments: 2014 Update, 

September 9, 2014. 

42 Guidance for Selecting Input Parameters in Modeling the Environmental Fate and Transport of Pesticides, 

Version 2.1, October 22, 2009. 
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Table 7.3.1. Estimated Drinking Water Concentrations Resulting from the Use of Chlorpyrifos 

Residue 

Surface Water Groundwater 

1-in-10 Year 

Peak 

Concentration 

μg/L 

21-day Average 

Concentration 

μg/L 

1-in-10 Year 

Annual Average 

Concentration 

μg/L 

30 Year 

Annual 

Average 

Concentration 

μg/L 

SCI-GROW 

Tier I 

Concentration 

μg/La 

Michigan Tart Cherries 

Chlorpyrifos 129 83.8 39.2 29.7 0.16 

Chlorpyrifosoxon 

123 80.0 37.4 28.3 0.15 

Georgia Bulb Onion 

Chlorpyrifos 6.2 3.1 1.2 0.8 0.01 

Chlorpyrifosoxon 

5.9 3.0 1.1 0.8 0.01 

a. SCI-GROW resulted in higher EDWCs than PRZM-GW simulations. 

In order to better define the extent to which other chlorpyrifos use scenarios may result in an 

exposure concern, the Health Effects Division developed a 21-day average DWLOC of 3.9 μg/L 

for chlorpyrifos-oxon43 that could be compared to model output values. Uncertainties in this 

approach include potential temporal aspects of relative concentrations from day to day on AChE 

inhibition, geospatial distribution of exposure as a result of variability in use, environmental 

factors, and drinking water treatment processes. 

Previous risk assessments44 suggest that typical upper bound application rates for chlorpyrifos 

are similar to the maximum single application rates for a wide range of crops; however, often the 

number of typical applications per year is lower than the maximum number of applications 

currently permitted on product labels (i.e., summarized in Master Use Summary Document). 

Considering this information, a screening analysis (see the DWA for details) was completed to 

determine which chlorpyrifos uses do not exceed the DWLOC, based on a single application of 

chlorpyrifos per year at 1 and 4 pounds of chlorpyrifos per acre. The results for 1 and 4 pounds 

per acre are reported here as a representation of the range of potential chlorpyrifos application 

rates, bearing in mind that chlorpyrifos can be applied at lower and higher single rates (e.g., an 

application rate of 6 pounds per acre on citrus). This analysis showed that over a 30 year period, 

the current maximum application rate scenarios, as well as maximum single application rates for 

a wide range of chlorpyrifos use scenarios, may result in multiple 21-day average concentrations 

that exceed the DWLOC (Error! Reference source not found.). 

 

43 The average 21-day concentration of chlorpyrifos-oxon necessary to cause 10% AChE inhibition was determined 

by HED to be 217 ppb. This value was divided by the safety factors (50x; 4.3 ppb) and then the contribution from 

food (0.4 ppb) was subtracted out to give to derive a DWLOC (3.9 ppb). 

44 Dawson, J., Bohaty, R., Mallampalli, N. Evaluation of the Potential Risks from Spray Drift and the Impact of 

Potential Risk Reduction Measures, June 20, 2012 PC 059101 DP 399483 and 399485. 
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Figure 7.3.1. National Screen: Exceedance Count/Percent (one application per year) 

Regional Screen 

Although there are several exceedances of the DWLOC on a national basis, the incidence of high 

exposures is expected to be highly localized. While it is currently extremely challenging to 

assess exposure on a local scale due to the unavailability of data and wide range of 

characteristics [i.e., environmental such as soil, weather, etc. or otherwise (e.g., drinking water 

treatment process)] that affect the vulnerability of a given community drinking system to 

chlorpyrifos-oxon contamination, a method was developed to examine the potential geospatial 

concentration differences for two Hydrological Unit Code (HUC) 2 Regions – HUC 2 Region 

17: Pacific Northwest and HUC 2 Region 3: South Atlantic-Gulf, in order to identify use patterns 

that may result in EDWCs that exceed the DWLOC on a regional basis.45 This analysis 

considered all potential chlorpyrifos use sites within the HUC 2 regions based on the National 

Agricultural Statistics Service cropland data layers and survey data. Due to the uncertainty 

associated with the urban uses (e.g., wide area/general outdoor treatment) that are represented by 

the impervious scenarios, modeled results from the impervious scenarios are not included in this 

analysis. Additional clarification from the registrants is needed in order to determine if these uses 

pose an exposure concern. 

For HUC 2 Region 17, four chlorpyrifos use patterns were identified as a potential concern based 

on maximum single application rates of 1 and 4 pounds per acre. However, for HUC 2 Region 3, 

several chlorpyrifos use scenarios were identified that could exceed the DWLOC. 

Watershed Screen 

The uses that exceeded the DWLOC from the regional screening exercise for HUC 2 Region 3 

were further explored for one example by utilizing the drinking water intake (DWI) watershed 

database. This example shows an overlap of potential chlorpyrifos use sites that may result in an 

exceedance of the DWLOC with watersheds that supply source water for community drinking 

water systems. In addition, this analysis shows that exposure is not uniform within a HUC 2 

Region and that some watersheds are more vulnerable than others. Watershed vulnerability is 

 

45 http://water.usgs.gov/GIS/huc.html 
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expected to be greatest for smaller watersheds with high percent cropped areas. Smaller 

community water systems are generally more vulnerable due to short distribution times and the 

reliance of chlorination to treat source surface water as well as limited access to other treatment 

methods such as granular activated carbon. 

Monitoring Data Analysis 

Water monitoring data from the USGS National Water-Quality Assessment Program (NAWQA), 

USEPA/USGS Pilot Reservoir Monitoring Program, USDA Pesticide Data Program (PDP), and 

California Department of Pesticide Regulation (CDPR) were evaluated in the preliminary DWA 

with reference to an acute exposure to chlorpyrifos and its degradation product chlorpyrifosoxon. The monitoring data showed chlorpyrifos detections at low concentrations, generally not 

exceeding 0.5 μg/L. For example, USGS NAWQA, which contains an extensive monitoring 

dataset for chlorpyrifos and chlorpyrifos oxon, reports a peak chlorpyrifos detection of 0.57 μg/L 

in surface water with a detection frequency of approximately 15%. CDPR has detected 

chlorpyrifos concentrations greater than 1 μg/L in surface water on several occasions, with an 

observed peak chlorpyrifos concentration of 3.96 μg/L. Sampling frequencies in these 

monitoring programs were sporadic and generally range from once per year to twice per month. 

Since the preliminary assessment, water monitoring data from Washington State Department of 

Ecology and Agriculture (WSDE/WSDA) Cooperative Surface Water Monitoring Program46,47, 

Dow AgroSciences (MRID 44711601), and Oregon Department of Environmental Quality were 

evaluated and are presented as part of this update. The previously referenced data have also been 

re-examined to consider short-term exposure (i.e., 21-day average concentrations) considering 

the importance of the single day exposure and the temporal relationship of exposure. A summary 

of all surface water monitoring data examined to date for chlorpyrifos are presented in Error! 

Reference source not found.. Some of the monitoring programs analyzed for chlorpyrifos-oxon; 

however, the number of detections as well as the concentrations were generally much lower. 

Since the majority of the conversion of chlorpyrifos to chlorpyrifos-oxon is assumed to occur 

during drinking water treatment, and not in the environment, the monitoring data presented in 

Error! Reference source not found. are limited to chlorpyrifos. 

 

 

46 Sargeant, D, Dugger, D. Newell, E., Anderson, P, Cowles, J. Surface Water Monitoring Program for Pesticides in 

Salmonid-Bearing Streams 2006-2008 Triennial Report, February 2010 (Washington State Department of Ecology 

and Washington State Department of Agriculture) 

https://fortress.wa.gov/ecy/publications/summarypages/1003008.html; 

http://agr.wa.gov/PestFert/natresources/docs/swm/2008_swm_report.pdf 47 Sargeant, D., Newell, E., Anderson, P., Cook, A. Surface Water Monitoring Program for Pesticides in SalmonidBearing Streams 2009-2011 Triennial Report, February 2013 (Washington State Department of Ecology and 

Washington State Department of Agriculture) http://agr.wa.gov/FP/Pubs/docs/377-SWM2009-11Report.pdf 
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Table 7.3.1. Surface Water Monitoring Data Summary for Chlorpyrifos 

Monitoring Data Scale

Years of 

Sampling 

(number of 

samples)

Detection 

Frequency

(%)

Maximum 

Concentration

(μg/L)

USGS NAWQA National 1991-2012 

(30,542) 15 0.57 

California Department of Pesticide 

Regulation State 1991-2012 

(13,121) 

20 3.96 

Washington State Department of 

Ecology and Agriculture 

Cooperative Surface Water 

Monitoring Program 

State 2003-2013 

(4,091) 8.4 0.4 

USDA Pesticide Data Program National 

2004-2009 

(raw water; 

1,178) 

2001-2009 

(finished 

water; 2,918) 

0 na 

USGS-EPA Pilot Drinking Water 

Reservoir National 1999-2000 (323) 5.3 0.034 

Oregon Department of 

Environmental Quality 

Watershed 

(Clackamas) 

2005-2011 

(363) 13 2.4 

MRID 44711601 

Watershed 

(Orestimba 

Creek) 

1996-1997 

(1,089) 61 2.22 

In general, the monitoring data include sampling sites that represent a wide range of aquatic 

environments including small and large water bodies, rivers, reservoirs, and urban and 

agricultural locations, but are limited for some areas of the United States where chlorpyrifos use 

occurs. Also, the sampling sites, as well as the number of samples, vary by year. In addition, the 

vulnerability of the sampling site to chlorpyrifos contamination varies substantially due to use, 

soil characteristics, weather and agronomic practices. None of the monitoring programs 

examined to date were specifically designed to target chlorpyrifos use (except the Registrant 

Monitoring Program MRID 44711601); therefore, peak concentrations (and likely 21-day 

average concentrations) of chlorpyrifos and chlorpyrifos-oxon likely went undetected in these 

programs. See the Revised Chlorpyrifos Preliminary Registration Review Drinking Water 

Assessment dated June 30, 2011 for further details on the monitoring programs discussed here. 

http://www.regulations.gov/#!documentDetail;D=EPA-HQ-OPP-2008-0850-0026 
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In general, sampling frequency needs to be approximately equal to the duration of exposure 

concern.48 The chlorpyrifos monitoring data evaluated thus far also show that as sample 

frequency increases, so does the detection frequency. This is evident in the registrant-submitted 

monitoring data, as well as examination of individual sampling sites within the various datasets. 

Therefore, while there are many individual samples collected and analyzed for chlorpyrifos (or 

chlorpyrifos-oxon) across the United States, it would not be appropriate to combine these data 

sources to generate exposure estimates or to use these datasets to represent exposure on a 

national or even regional basis. Thus, comparing the monitoring data results to the DWLOC 

would not be a reasonable approach for the reasons given above, including limited sample 

frequency, limited use information, and sampling site variability, on a national or even a regional 

basis. Model estimated concentrations should be considered suitable upper bound concentrations 

for chlorpyrifos and chlorpyrifos-oxon. 

Additionally, model simulations were completed to represent two different water monitoring 

datasets - Washington State Department of Ecology and Agriculture (WSDE/WSDA) 

Cooperative Surface Water Monitoring Program and Dow AgroSciences (MRID 44711601) 

Orestimba Creek. For both of these water monitoring programs, enough information was 

available, including chlorpyrifos use information as well as the PCA, to parameterize the model. 

In these simulations, the modeled EDWCs were within an order of magnitude of the measured 

concentrations. This suggests that the modeling results are not overly conservative and supports 

the use of the model to estimate chlorpyrifos-oxon concentrations in drinking water. 

Additional modeling can be done to pinpoint regions or watersheds where EDWCs may exceed 

the DWLOC. This would include completing the regional assessment presented here for all 

HUC 2 Regions and crop uses, as well as considering multiple applications per year. 

Nevertheless, based on the current analysis, concentrations of chlorpyrifos-oxon in drinking 

water are expected to be highly localized primarily in small watersheds with high PCA. 

Summary 

In summary, examination of chlorpyrifos agricultural use across the country indicates that there 

are a number of uses that may result in potential exposure to chlorpyrifos-oxon in finished 

drinking water at levels that exceed the DWLOC. The EDWCs for tart cherries and bulb onion 

reported here are expected to provide a reasonable bounding estimation of exposure based on 

maximum rates included in Master Use Summary Document. This analysis showed that the 

maximum use scenario for tart cherries exceeds the DWLOC, while it does not for bulb onions. 

The rate used for the bulb onion simulation was 1 pound chlorpyrifos per acre; therefore, a 

screen of all Surface Water Concentration Calculator modeling scenarios was done using a single 

application of chlorpyrifos. This analysis showed that exceedances are expected even for one 

application of chlorpyrifos applied at 1 pound per acre per year. 

 

48 U.S. Environmental Protection Agency. Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) Scientific 

Advisory Panel: Problem Formulation for the Reassessment of Ecological Risks from the Use of Atrazine, June 12-

14, 2012, Docket Number: EPA-HQ-OPP-2012-0230. 
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While there are uncertainties associated with the model input parameters for which conservative 

assumptions were made (e.g., one aerobic aquatic metabolism half-life value multiplied by the 

uncertainty factor of three, stable hydrolysis, 100% of the cropped watershed is treated on the 

same day, and use of the Index Reservoir as the receiving waterbody) these assumptions do not 

appear to lead to an overly conservative estimate of exposure based on a comparison of model 

estimates with measured concentrations. Comparison of model estimated concentrations with 

measured concentrations suggests that model estimates are consistent with measured 

concentrations when actual application rates and representative Surface Water Concentration 

Calculator (SWCC) scenarios are considered and a PCA adjustment factor is applied to the 

model estimates. This modeling/monitoring comparison suggests that when growers use 

maximum application rates, or even rates much lower than maximum, chlorpyrifos-oxon 

concentrations in drinking water could pose an exposure concern for a wide range of chlorpyrifos 

uses. However, these exposures are not expected to be uniformly distributed across the country. 

Additional analyses are needed in order to pinpoint the exact community water systems where 

concentrations may be of concern. 

8.0 Cumulative Exposure/Risk Characterization 

Section 408(b)(2)(D)(v) of the Federal Food, Drug, and Cosmetic Act (FFDCA), as amended by 

the Food Quality Protection Act (1996) stipulates that when determining the safety of a pesticide 

chemical, EPA shall base its assessment of the risk posed by the chemical on, among other 

things, available information concerning the cumulative effects to human health that may result 

from dietary, residential, or other non-occupational exposure to other substances that have a 

common mechanism of toxicity. The reason for consideration of other substances is due to the 

possibility that low-level exposures to multiple chemical substances that cause a common toxic 

effect by a common mechanism could lead to the same adverse health effect as would a higher 

level of exposure to any of the other substances individually. A person exposed to a pesticide at a 

level that is considered safe may in fact experience harm if that person is also exposed to other 

substances that cause a common toxic effect by a mechanism common with that of the subject 

pesticide, even if the individual exposure levels to the other substances are also considered safe. 

Chlorpyrifos is a member of the organophosphate (OP) class of pesticides. Other members of 

this class of pesticides are numerous and include azinphos methyl, diazinon, chlorpyrifos-methyl, 

dichlorvos, dicrotophos, dimethoate, disulfoton, methamidophos, methidathion, monocrotophos, 

naled, oxydemeton-methyl, phorate, phosmet, pirimiphos-methyl, and trichlorfon to name a few. 

EPA considers organophosphates to express toxicity through a common biochemical interaction 

with cholinesterase which may lead to a myriad of cholinergic effects and, consequently the 

organophosphate pesticides should be considered as a group when performing cumulative risk 

assessments. HED published the final guidance that it now uses for identifying substances that 

have a common mechanism of toxicity (FR 64(24) 5796-5799, February 5, 1999) “Proposed 

Guidance of Cumulative Risk Assessment for Chemicals that have a Common Mechanism of 

Toxicity” was made available for public comment in the Federal Register (65 FR 40644, June 

30, 2000). The Agency presented this approach to the FIFRA Scientific Advisory Panel in late 

September, 2000. The SAP reviewed revised methods used to conduct a preliminary cumulative 

risk assessment for organophosphate pesticides in 2002 (US EPA, 2002), found at 
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http://www.epa.gov/scipoly/sap/2002/index.htm. 

The Agency has completed a cumulative risk assessment for OPs, (US EPA, 2001), a revised 

cumulative risk assessment for OPs, (US EPA, 2002), and an updated OP cumulative risk 

assessment (US EPA, August 2006) which can be found on the Agency's web site 

http://www.epa.gov/pesticides/cumulative/rra-op/. The cumulative effects of exposure to 

multiple OPs, including chlorpyrifos, are evaluated in those documents. Prior to the completion 

of registration review, OPP will update the OP CRA to incorporate any toxicity and exposure 

information available since 2006. 

9.0 Occupational Exposure/Risk Characterization 

9.1 Steady State Occupational Handler Risk 

The term handlers is used to describe those individuals who are involved in the pesticide 

application process. There are distinct job functions or tasks related to applications and 

exposures can vary depending on the specifics of each task. Job requirements (amount of a 

chemical used in each application), the kinds of equipment used, the target being treated, and the 

level of protection used by a handler can cause exposure levels to differ in a manner specific to 

each application event. Based on the anticipated use patterns and current labeling, types of 

equipment and techniques that can potentially be used, occupational handler exposure is 

expected from chlorpyrifos use. For purpose of occupational handler assessment, the parent 

chlorpyrifos is the relevant compound. 

The 2011 occupational exposure assessment for chlorpyrifos (W. Britton, 6/27/11, D388165, 

Chlorpyrifos: Occupational and Residential Exposure Assessment) has been updated based on a 

number of changes that have occurred since that time, including the following: 1) use of the 

PBPK-PD model for determining toxicological PoDs, 2) inclusion of use patterns based on a 

master use document developed for chlorpyrifos, 3) use of revised guidance for all of the 

currently recommended unit exposures for standard EPA occupational handler exposure 

scenarios49, Occupational Pesticide Handler Unit Exposure Surrogate Reference Table – 

Revised March 2013, 4) use of the revised seed treatment policy, Interim Guidance (SOP 15.1) 

Amount of Commercial Seed Treated per Day, as was updated March 21, 2013 and 5) use of 

updated adult (female) body weight. 

In addition, steady state durations of exposure are assumed in the updated occupational 

assessment. HED typically classifies exposures from 1 to 30 days as short-term and exposures 30 

days to six months as intermediate-term. The steady state endpoint selection for chlorpyrifos 

overlaps HED’s traditional short-term exposure duration endpoint selection and is considered 

health protective for both short- and intermediate- term exposures. 

Full details regarding the updated occupational assessment can be found in D4244847

. A 

summary of the assumptions and conclusions are provided below. 

 

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The updated quantitative exposure/risk assessment developed for occupational handlers is based 

on the following scenarios: 

Mixer/Loaders:

(1a) Liquids for Aerial/ Chemigation Applications 

(1b) Liquids for Airblast Applications 

(1c) Liquids for Groundboom Applications 

(1d) Liquids for Wide Area Aerial Applications 

(1e) Liquids for Wide Area Ground Applications 

(2a) Wettable Powder (in WSP) for Aerial/ Chemigation Applications 

(2b) Wettable Powder (in WSP) for Airblast Applications 

(2c) Wettable Powder (in WSP) for Groundboom Applications 

(3a) Granulars for Aerial Applications 

(3b) Granulars for Tractor Drawn Spreader Applications 

(3c) Granulars with SmartBox® for Aerial Applications 

(3d) Granulars with SmartBox® for Tractor Drawn Spreader Applications 

(4a) Water Dispersible Granulars (in WSP) for Aerial/ Chemigation Applications 

(4b) Water Dispersible Granulars (in WSP) for Airblast Applications 

(4c) Water Dispersible Granulars (in WSP) for Groundboom Applications 

Applicators: 

(5a) Aerial Liquid Applications 

(5b) Wide Area Aerial Applications 

(5c) Aerial Granular Applications 

(6a) Airblast Applications 

(6b) Wide Area Ground Applications 

(7a) Groundboom Applications 

(7b) Tractor Drawn Granular Applications 

(7c) Tractor Drawn Granular Applications with SmartBox® 

(8) Paint Brush/Roller Applications 

(9) Cattle Ear Tag Applications 

(10) Bait Station Applications 

(11) Total Release Fogger 

Mixer/Loader/Applicators: 

(12a) Liquid: Manually Pressurized Handwand 

(12b) Liquid: Wettable Powder (in WSP) for Manually Pressurized Handwand 

(12c) Liquid: Microencapsulated for Manually Pressurized Handwand 

(12d) Liquid: Backpack Sprayer 

(12e) Liquid: Wettable Powder (in WSP) for Backpack Sprayer 

(12f) Liquid: Microencapsulated for Backpack Sprayer 

(12g) Liquid: Mechanically Pressurized Handgun 

(12h) Liquid: Wettable Powder (in WSP) for Mechanically Pressurized Handgun 
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(12i) Liquid: Microencapsulated for Mechanically Pressurized Handgun 

(13a) Granulars: Hand Dispersal 

(13b) Granulars: Rotary Spreader 

(13c) Granulars: Spoon 

(13d) Granulars: Belly Grinder 

(13e) Granulars: Belly Grinder 

(14a) Water Dispersible Granular (in WSP): Manually Pressurized Handwand 

(14b) Water Dispersible Granular (in WSP): Backpack Sprayer 

(14c) Water Dispersible Granular (in WSP): Mechanically Pressurized Handgun 

 Flaggers: 

(15a) Flagging for Aerial Spray Applications 

(15b) Flagging for Aerial Granular Applications 

Occupational Handler Exposure Data and Assumptions 

A series of assumptions and exposure factors served as the basis for completing the occupational 

handler risk assessments. 

Application Rates: In order to seek clarification of chlorpyrifos usage, the Agency compiled a 

master use summary document reflective of the use profile of all active product labels. The 

document, among other information, presents all registered uses of chlorpyrifos and 

corresponding maximum single application rates, equipment types, re-entry intervals (REIs), 

etc. HED believes that the detailed information on application rates and use patterns presented in 

Appendix 9 (Master Use Summary Document) will be implemented on all chlorpyrifos labels 

and is the basis of the occupational and residential risk assessment. If, for any reason, the final 

chlorpyrifos labels contain higher application rates, the actual risks posed by those products may 

exceed the risks estimated in this assessment.

Body weights: For adults, when an endpoint is not sex-specific a body weight of 80 kg is 

typically used in occupational risk assessment. However, in this case, a female-specific body 

weight of 69 kg was used. While the endpoint of concern, RBC AChE inhibition, is not sexspecific, the female body weight was used due to concerns for neurodevelopmental effects 

related to early life exposure to chlorpyrifos. 

Unit Exposures: It is the policy of HED to use the best available data to assess handler exposure. 

Sources of generic handler data, used as surrogate data in the absence of chemical-specific data, 

include PHED 1.1, the AHETF database, the Outdoor Residential Exposure Task Force 

(ORETF) database, or other registrant-submitted occupational exposure studies. Some of these 

data are proprietary (e.g., AHETF data), and subject to the data protection provisions of FIFRA. 

The standard values recommended for use in predicting handler exposure that are used in this 

updated assessment, known as “unit exposures”, are outlined in the “Occupational Pesticide 

Handler Unit Exposure Surrogate Reference Table50”, which, along with additional information 

 

50 Available: http://www.epa.gov/opp00001/science/handler-exposure-table.pdf
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on HED policy on use of surrogate data, including descriptions of the various sources, can be 

found at the Agency website.51

Area Treated or Amount Handled: For the updated assessment, the daily areas treated were 

defined for each handler scenario (in appropriate units) by determining the amount that can be 

reasonably treated in a single day (e.g., acres, square feet, or gallons per day). When possible, the 

assumptions for daily areas treated are taken from the HED Science Advisory Committee on 

Exposure (ExpoSAC) Policy 9.1: Standard Values for Daily Acres Treated in Agriculture, which 

was completed on July 5, 2000. However, standard values are not available for numerous 

scenarios. The area treated assumptions identified in the 2011 occupational and residential 

exposure assessment are largely the same as used for the updated except for the following 

changes: 

 Paint Roller/Brush: the 2011 assessment assumed 110 gallons for treatment of sewer 

manhole covers, the current assumes 20 gallons for coverage of 50 manholes; 3 gallons 

applied for all other uses as was done previously; 

 Fire Ant Mounds: the previous assessment assumed 25 mounds were treated, the current 

assumes that an area of 1,000 square feet is treated. 

Exposure Duration: As described previously, the steady state approach is used and is 

appropriate for chlorpyrifos given the toxicological and exposure profile. The steady state 

approach accounts for the short-term exposure duration, as well as for workers that are exposed 

over longer periods of time (i.e., intermediate-term exposures). Long-term exposures are not 

expected based on the current chlorpyrifos uses. 

Mitigation/Personal Protective Equipment: Estimates of dermal and inhalation exposure were 

calculated for various levels of personal protective equipment (PPE). The lowest tier is 

designated as the baseline exposure scenario (i.e., long-sleeve shirt, long pants, shoes, socks, and 

no respirator). If risks are of concern at baseline attire, then increasing levels of personal 

protective equipment or PPE (e.g., gloves, double-layer body protection, and respirators) are 

evaluated. If risks remain a concern with maximum PPE, then engineering controls (e.g., 

enclosed cabs or cockpits, water-soluble packaging, and closed mixing/loading systems) are 

evaluated. This approach is used to ensure that the lowest level of risk mitigation that provides 

adequate protection is selected, since the addition of PPE and engineering controls involves an 

additional expense to the user and – in the case of PPE – also involves an additional burden to 

the user due to decreased comfort and dexterity and increased heat stress and respiratory stress. 

An assessment of handler exposure from the use of chlorpyrifos total release foggers was not 

performed because dermal and inhalation exposure is expected to be negligible based on the use 

pattern for this method of application. A total release fogger is an aerosol pesticide device 

designed to automatically release its total content in one operation for the purpose of creating a 

permeating fog within a space to deliver the pesticide throughout the space. Therefore, total 

release aerosols do not need any other application equipment (PR NOTICE 98-6, 1998). 

Furthermore, the total release fogger product (EPA Reg. No. 499-405) directions state that once 

 

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cans are activated to, “leave greenhouse at once,” and that, “all human occupants must be 

removed before treatment.” In accordance with a common integrated pest management (IPM) 

and preventive safety practice, if multiple foggers are to be activated, the chlorpyrifos total 

release fogger label directs the use to “start with the can farthest away from the exit door.” 

The assessment of handler exposure from the use of chlorpyrifos ear tag and roach bait stations 

were also not conducted. Dermal exposure is expected to be negligible if gloves are worn, and 

inhalation exposure from these uses is not anticipated. 

Exposure Data for Handler Exposure Scenarios: HED uses unit exposures to assess handler 

exposures to pesticides. Unit exposures are estimates of the amount of exposure to an active 

ingredient a handler receives while performing various handler tasks and are expressed in terms 

of micrograms or milligrams of active ingredient per pounds of active ingredient handled. HED 

has developed a series of unit exposures that are unique for each scenario typically considered in 

our assessments (i.e., there are different unit exposures for different types of application 

equipment, job functions, and levels of protection). The current assessment makes use of the 

document, Occupational Pesticide Handler Unit Exposure Surrogate Reference Table – Revised 

March 2013, was publically released to provide guidance for all of the currently recommended 

unit exposures for standard EPA occupational handler exposure scenarios.52 A summary of all 

exposure data used to conduct the occupational handler risk assessment is presented in the 

updated occupational and residential exposure assessment. 

In addition to this surrogate data, one non-chemical specific exposure study was used for the 

updated assessment. The exposure study was used to assess the use of SmartBox® technology 

for the mixing/loading of granular formulations for aerial and ground applications, and the 

application by ground equipment. Separate data evaluation records (DERs) were completed for 

the mixing/loading53 and applicator54 studies. A summary of the study is presented in the updated 

occupational and residential exposure assessment. 

Summary of Occupational Handler Exposure Data Gaps: Consistent with the 2011 occupational 

and residential exposure assessment, no new data gaps have been identified for the assessment of 

occupational handler exposure/risk in addition to those identified in the Registration Eligibility 

Decision (RED) for chlorpyrifos (finalized 7/31/06; IRED issued 2/2002). Based on a 90-day 

progress report submitted (August 28, 2003) by the technical registrant, Dow AgroSciences, 

many of the data requirements are anticipated to be supported by the generation of data by the 

Agricultural Handlers Exposure Task Force (AHETF). These data requirements include 

exposure data for: 

 seed treatment uses; 

 mixing wettable powders for aerial/chemigation application; 

 loading and applying granulars for aerial application; 

 

52 http://www.epa.gov/opp00001/science/handler-exposure-data.html 53 L. LaSota. Evaluation of Loader Exposure Data Submitted as a Part of the Conditional Registration of Fortress 

5G (Chlorethoxyfos). EPA MRID: 44180401. 1/23/1997. D232274. 

54 J. Arthur. Review of “Chlorethoxyfos Applicator Exposure Study during Application of Fortress® 5G Granular 

Insecticide using the Smartbox System during Corn Planting in the Midwest.” EPA MRID: 443013-01. 7/27/1998. 

D238361. 
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 groundboom application; and 

 human flaggers for aerial application. 

AHETF data for groundboom application and for mixing/loading liquid formulations have been 

submitted and reviewed by HED and used to estimate exposure/risk for occupational handlers 

from the exposure scenario and, therefore, satisfy the data requirement. AHETF data for seed 

treatment and planting treated seed has been submitted and is currently under review by HED. 

The requirement for exposure data for mixing wettable powders for aerial/chemigation 

applications should be waived because the formulation is no longer supported by the registrant. 

An exposure study for loading granular formulations is being planned under AHETF. A study 

protocol was submitted and reviewed by the Human Studies Review Board (HSRB) November 

2014. It does not appear, however, that flaggers are being supported by DAS as part of their 

prior response to the data requirements. 

In accordance with 40CFR158, the following data are generally required for all occupational or 

residential pesticide uses: dislodgeable foliar residue (DFR) data are required for all occupational 

(e.g., crop, nursery, greenhouse use sites) or residential (e.g., ornamental and vegetable gardens, 

pick your own farms, retail tree farms) uses that could result in post-application exposure to 

foliage; and turf transferable residue (TTR) data are required for all occupational (e.g., sod 

farms, golf courses, parks, and recreational areas) or residential turf uses that could result in postapplication exposure to turf. Adequate DFR and TTR data are available for chlorpyrifos. 

Occupational Handler Non-Cancer Exposure and Risk Estimate Equations 

The algorithms used to estimate non-cancer exposure and dose for occupational handlers can be 

found in the updated occupational and residential exposure assessment (D424484). 

Combining Exposures/Risk Estimates: 

Dermal and inhalation risk estimates were combined in this assessment, since the toxicological 

endpoint, RBC AChE inhibition, is the same for these exposure routes. 

Summary of Occupational Handler Non-Cancer Exposures and Risk Estimates 

Steady state exposure and risks calculated for occupational handlers (other than commercial seed 

treatment) for all use patterns are presented in the updated occupational and residential exposure 

assessment (Appendix G of D424484). 

Current labels generally require that handlers use normal work clothing (i.e., long sleeved shirt 

and pants, shoes and socks) and coveralls, chemical resistant gloves, and dust/mist respirators. 

Also, some products are marketed in engineering controls such as water soluble packets. In 

order to determine what level of personal protection is required to alleviate risk concerns and to 

ascertain if label modifications are needed, steady state exposure and risk estimates were 

calculated for occupational handlers of chlorpyrifos for a variety of scenarios at differing levels 

of personal protection including engineering controls. In this assessment, a total of 285 total 

occupational handler exposure scenarios were assessed and 132 of them are not of concern (i.e., 

MOEs are ≥ 100) at current product label requirements. Risks of concern for 27 additional 
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exposure scenarios could potentially be mitigated if additional personal protective equipment or 

engineering controls are used. Risks of concern were estimated for 126 exposure scenarios and 

remain a concern regardless of the levels of personal protection and engineering controls 

considered. These risks could be mitigated using the highest level of personal protection 

available and by modification to other label requirements such as application rate or, in some 

cases, deleting certain types of application equipment. 

Commercial Seed Treatment 

Based upon the registered seed treatment uses of chlorpyrifos, occupational handler exposure is 

expected for individuals involved in commercial seed treatment. Post-application exposure for 

agricultural (field) workers is not expected based on the seed treatment use of chlorpyrifos. 

The 2011 occupational commercial seed treatment assessment has been updated. All 

occupational exposure scenarios assessed, algorithms, and unit exposures used (taken from HED 

ExpoSAC Policy 14: SOPs for Seed Treatment) are the same as were used previously in the 2011 

occupational and residential exposure assessment for the assessment of commercial seed 

treatment. However, the assessment of occupational seed treatment uses has been updated to 

reflect the use of the guidance document, Interim Guidance (SOP 15.1) Amount of Commercial 

Seed Treated per Day, updated March 21, 2013, as well as the updated default adult (female) 

body weight. Also, seed treatment risk was estimated with use of PBPK-PD modeled PoDs 

(dermal and inhalation) specific for occupational assessment. 

The steady state approach is appropriate for the assessment of commercial seed treatment with 

chlorpyrifos given the toxicological and exposure profile. The steady state endpoint selection for 

chlorpyrifos accounts for HED’s traditional short-term exposure duration endpoint selection, as 

well as being appropriately health protective for workers that are exposed over longer periods of 

time (i.e., intermediate-term exposures). 

Summary of Commercial Seed Treatment Exposures and Risk Estimates 

Steady state exposure and risks calculated for occupational handlers performing seed treatment 

activities in commercial settings and for occupational secondary handlers from planting 

chlorpyrifos-treated seeds are presented in the updated occupational and residential exposure 

assessment (Appendix H of D424484). 

Of the 68 exposure scenarios assessed, 36 resulted in risk estimates which were not of concern 

(i.e., MOEs are ≥ 100) at the level of personal protection currently required by product 

labels. Secondary handler (seed planter) scenarios were also evaluated and none were of concern 

at current label requirements. The remaining 32 seed treatment scenarios are of potential 

concern. 

9.2 Steady State Occupational Post-Application Risk 
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9.2.1 Dermal Post-Application Risk 

HED uses the term post-application to describe exposures that occur when individuals are 

present in an environment that has been previously treated with a pesticide (also referred to as reentry exposure). Such exposures may occur when workers enter previously treated areas to 

perform job functions, including activities related to crop production, such as scouting for pests 

or harvesting. Post-application exposure levels vary over time and depend on such things as the 

type of activity, the nature of the crop or target that was treated, the type of pesticide application, 

and the chemical’s degradation properties. In addition, the timing of pesticide applications, 

relative to harvest activities, can greatly reduce the potential for post-application exposure. 

The 2011 occupational post-application exposure assessment has been updated to include the 

following: 1) a PBPK-PD modeled dermal PoD specific for occupational assessment, 2) the 

master use document, 3) the updated adult (female) default body weight and 4) the changes 

relating to agricultural transfer coefficients (TC) as described in the Science Advisory Council for 

Exposure (ExpoSAC) Policy 3 – Revised March 2013. 55

Occupational Post-application Dermal Exposure Data and Assumptions 

A series of assumptions and exposure factors served as the basis for completing the occupational 

post-application risk assessments. The majority of assumptions and exposure factor detailed in 

the 2011 exposure assessment are the same, except for changes relating to agricultural transfer 

coefficients (TC) as described in the Science Advisory Council for Exposure (ExpoSAC) Policy 3 

– Revised March 2013 and the standard adult body weight assumptions. 

Post-application Exposure Data: The 2011 occupational and residential exposure assessment 

made use of seven chemical-specific DFR studies that encompass the use of five different 

formulations and 12 different crops. Specifically, the DFR studies examine the use of 1) 

emulsifiable concentrate formulations on sugar beets, pecans, citrus, sweet corn, cotton, and turf; 

2) wettable powder formulations on almonds, apples, pecans, cauliflower, tomato and turf; 3) 

granular formulations on sweet corn and turf; 4) a total release aerosol formulation on 

ornamentals; and 5) a microencapsulated formulation on ornamentals. The submitted studies 

were reviewed by HED. Despite limitations, HED recommended the use of all or some of the 

data in the studies to assess post-application risks to chlorpyrifos except for the tomato DFR 

data. Summaries for all DFR studies can be referenced in the updated occupational and 

residential exposure assessment (Appendix I of D424484). 

The current assessment makes use of the same DFR data and crop pairings as was done for the 

2011 occupational and residential exposure assessment. For example, DFR data for an 

individual crop was applied to that specific crop, as well as to crops in the same crop grouping 

(e.g., cauliflower data was used for cauliflower and all other cole crops). For other crops for 

which no crop-specific or crop group-specific data are available, the DFR data for the crop 

deemed the closest match were used as surrogates to calculate potential exposure (e.g., 

cauliflower data were also used for strawberries, cranberries, and leafy vegetables). 

Additionally, whenever possible, a label use was assessed using DFR data for the same 

 

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formulation type. A full description of the criteria for selection of DFR data for assessment of 

post-application exposures to individual crops/crop groupings can be referenced in Section 2.4.3 

of the 2011 occupational and residential exposure assessment (D388165). 

For occupational post-application dermal exposures, the parent compound is the residue of 

concern; dermal exposure to the oxon on foliar surfaces from reentry into an outdoor 

environment previously treated with chlorpyrifos is not anticipated and, therefore, has not been 

assessed. However, based on study results (see Section 9.2.2), it may be possible that the 

formation of the oxon is greater and its deactivation slower in greenhouses when compared to the 

outdoor environment and that an assessment may be needed for exposure to the oxon in 

greenhouse settings. In the 2011 Preliminary Human Health Risk Assessment, additional studies 

to address uncertainties regarding the formation of chlorpyrifos oxon and its decay following 

applications in greenhouses were recommended. To date, no additional data have been 

submitted. Due to uncertainty regarding the formation of chlorpyrifos oxon in greenhouses, HED 

estimated risks for reentry into (microencapsulated and total release fogger formulation) treated 

greenhouses for parent chlorpyrifos only, as well as for parent chlorpyrifos plus chlorpyrifos 

oxon using a total toxic residue approach. This conservative approach is assumed to result in a 

health protective assessment of post-application exposures to chlorpyrifos oxon in greenhouses. 

Summary of Occupational Post-application Exposure Data Gaps: 

No new occupational data requirements have been identified for chlorpyrifos; however, as 

described above, additional data were recommended for the evaluation of chlorpyrifos oxon and 

no data have been submitted to date. 

Summary of Occupational Post-application Dermal Exposure and Risk Estimates 

Steady state occupational post-application dermal risk estimates are presented in the updated 

occupational and residential exposure assessment (Appendix J of D424484) based on the PBPKPD model predicted dermal PoD. 

Current labels require a Restricted Entry Interval (REI) of 24 hours from most crops and 

activities, but in some cases such as tree fruit, REIs are up to 5 days after application. All postapplication worker risks have been updated in the current assessment. Results indicate that no 

REI increase is required for the majority of outdoor environment crops and commodities with a 

labeled REI (i.e., 43 of 55 total). However, for 12 crops, activities such as irrigation, hand 

harvesting, scouting, and thinning result in risks of concern up to as many as 10 days following 

application. 

For the assessment of post-application exposures to parent chlorpyrifos (only) from ornamental 

production in greenhouses, 4 formulations were assessed: emulsifiable concentrate, 

microencapsulated liquid, wettable powder in WSP, and total release fogger. An REI increase of 

up to 5 days may be needed to alleviate risks from with use of the emulsifiable concentrate, 

wettable powder in WSP and total release fogger formulations. For the microencapsulated liquid 

formulation, the estimated REIs range from 0 to > 35 days after treatment (the completion of the 

monitoring period) dependent on the exposure activity considered. 
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9.2.2 Occupational Post-application Dermal Exposure/Risk Estimates: 

Chlorpyrifos Oxon 

Chlorpyrifos is activated by desulfuration, reacting in bioactivation to the more toxic and potent 

acetyl cholinesterase (AChE) inhibitor, chlorpyrifos oxon. The oxon is highly unstable due to 

rapid deactivation through hydrolytic cleavage by a process called dearylation which releases 

TCP. Workers reentering an indoor environment (i.e., greenhouses) previously treated with 

chlorpyrifos could potentially be exposed to the oxon as chlorpyrifos degrades. Available 

exposure data indicate chlorpyrifos oxon may form in indoor environments.56 Toxicity 

adjustment factors (TAFs) were used to estimate the potency of chlorpyrifos oxon relative to 

chlorpyrifos. HED determined the oxon to be between 11.9 (acute) and 18 (chronic) times more 

toxic than the parent. 

Dermal exposure to the oxon on foliar surfaces from reentry into an outdoor environment (e.g., 

field crops and orchards) previously treated with chlorpyrifos is not anticipated and, therefore, 

has not been assessed. No occupational exposure studies (handler, post-application, or DFR) 

were identified that quantified the levels of oxon present in the environment. However, a search 

of open literature for the 2011 assessment resulted in 4 plant metabolism studies which measured 

surface residues. Three plant metabolism studies57 measured leaf surface residues of the oxon in 

outdoor environments that were either well below the parent, not detectable, or detected at a 

level just above the level of detection (LOD). The potential for exposure to the oxon is further 

minimized due to rapid deactivation of the oxon to TCP. Further, the dietary exposure risk 

assessment58 conducted in support of registration review concludes the following, “all residues in 

food are assumed to be parent chlorpyrifos since the chlorpyrifos oxon is not typically found in 

foods in monitoring data or crop field trials.” 

The 4th plant metabolism study, a tomato and green bean greenhouse metabolism study, was less 

definitive than the other three plant metabolism studies regarding oxon presence. HED is 

concerned that the formation of the oxon may be greater and its deactivation to TCP slower in 

greenhouses when compared to the outdoor environment. The study results indicate that oxon 

residue is from 9 to 14X less than the parent from fruit analyzed on the day of application in flat 

and asymmetric roof greenhouses. The proportion of oxon to parent is less for all days which 

measurable levels were observed (all but 8 and 15 days after application). The oxon was 

detected until day 5 with levels between 5 and 6X below that of the parent. It should be noted 

that residues of chlorpyrifos and oxon were measured from analysis of whole fruit samples. 

HED typically assesses occupational post-application exposure and risk based upon the potential 

for transfer from surface residues. The whole fruit samples, which include surface residues, as 

 

56 J.L. Martinez Vidal, et al. 1998. Diminution of Chlorpyrifos and Chlorpyrifos Oxon in Tomatoes and Green 

Beans Grown in Greenhouses. J. of Agric. and Food Chem. 46 (4), 1440–1444. 57 Iwata, Y. et al. 1983. Chlorpyrifos Applied to California Citrus: Residue Levels on Foliage and On and In Fruit. 

J. Agric. Food Chem. 31(3), 603-610. 

H. Jin and G.R. Webster. 1997. Persistence, Penetration, and Surface Availability of Chlorpyrifos, Its Oxon, and 

3,5,6-Trichloro-2-pyridinol in Elm Bark. 45(12), 4871-4876. 

R. Putnam, et al. 2003. The Persistence and Degradation of Chlorthalonil and Chlorpyrifos in a Cranberry Bog. J. 

Agric. Food Chem. 51(1), 170-176. 

58 D. Drew. Chlorpyrifos: Acute and Steady State Dietary (Food Only) Exposure Analysis to Support Registration 

Review. 11/18/2014. U.S. EPA Office of Chemical Safety and Pollution Prevention. D424486. 
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well as residues which may have been contained within the fruit sample, may overestimate the 

amount of oxon on the fruit surface. Regardless, the 2011 occupational and residential exposure 

assessment recommended additional data to measure the chlorpyrifos and oxon residues on leaf 

surfaces following treatment with a liquid formulation in greenhouses in order to address these 

uncertainties and more accurately address the risk potential for exposure from occupational 

reentry into greenhouses treated with chlorpyrifos. To date, no data has been submitted to 

address these uncertainties. As a result, HED has conducted an assessment of occupational postapplication exposures in greenhouses that uses conservative assumptions of oxon formation. 

In order to account for the formation of and potential increased toxicity from exposure to 

chlorpyrifos oxon, a total toxic residue approach was applied which combines chlorpyrifos and 

chlorpyrifos oxon (expressed as toxicity equivalents). The total toxic residue approach59

estimates the chlorpyrifos oxon equivalent residues by 1) assuming a specific fraction of the 

measured chlorpyrifos dislodgeable foliar residues are available as the oxon and 2) factoring in 

the relative potency of chlorpyrifos oxon with use of a TAF. It was conservatively assumed that 

5% (0.05) of the total chlorpyrifos present as DFR in greenhouses is available for worker contact 

during post-application activities. This assumption is based on a review of available TTR and 

DFR data for other OPs where both the parent and metabolite were measured in residue 

samples. Five percent was found to be the high-end value for the percent of parent that 

metabolized during the course of the residue studies. The chronic TAF (which is appropriate for 

steady state assessment) of 18 was derived from BMD analysis of inhibition of RBC AChE in 

adult female rats (adult male rats not examined) observed in the repeated phase of the CCA 

study. Once predicted, these total toxic (dislodgeable foliar) residues are used to estimate 

exposures from post-application activities in greenhouse and risks are estimated with used of the 

steady state PoD for occupational exposures, 4.9 mg/kg/day. 

Summary of Occupational Post-application Dermal Exposure and Risk Estimates with Use of 

Total Toxic Residue Approach 

Due to uncertainty regarding the formation of chlorpyrifos oxon in greenhouses, HED also 

estimated risks for reentry into treated greenhouses (all 4 formulations) for the parent 

chlorpyrifos plus chlorpyrifos oxon using a total toxic residue approach. When the total toxic 

residue approach is used to evaluate risks from exposure to the oxon with use of the emulsifiable 

concentrate, wettable powder in WSP, and total release fogger formulations, results indicate that 

an REI increase up to 6 days may be needed to alleviate risk concerns. For the 

microencapsulated liquid formulation, REIs range from 3 to > 35 days after treatment (the 

completion of the monitoring period) dependent on the exposure activity considered. 

9.2.3 Inhalation Post-application Risk 

There are multiple potential sources of post-application inhalation exposure to individuals 

performing post-application activities in previously treated fields. These potential sources 

include volatilization of pesticides and resuspension of dusts and/or particulates that contain 

pesticides. As described in Section 6.3.2 of this document, a quantitative risk assessment is not 

 

59 Total DFR (μg/cm2

) = [ Chlorpyrifos DFR (μg/cm2) * TAF] + [ Chlorpyrifos DFR (μg/cm2)] 
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required for chlorpyrifos or chlorpyrifos oxon due to the lack of toxicity of these chemicals when 

in the vapor state, even at the saturation concentration. 

The Worker Protection Standard for Agricultural Pesticides contains requirements for protecting 

workers from inhalation exposures during and after greenhouse applications through the use of 

ventilation requirements.[40 CFR 170.110, (3) (Restrictions associated with pesticide 

applications)]. 

A post-application inhalation exposure assessment is not required as exposure is expected to be 

negligible. Seed treatment assessments provide quantitative inhalation exposure assessments for 

seed treaters and secondary handlers (i.e., planters). It is expected that these exposure estimates 

would be protective of any potential low-level post-application inhalation exposure that could 

result from these types of applications. 

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11. List of Appendices 

Appendix 1. Evaluation of Experimental Toxicology Data 

Appendix 2. Detailed Review and Synthesis of Three Children’s Environmental Health Cohort 

Studies

Appendix 3. Epidemiology Study Specific Evaluations 

Appendix 4. Detailed Summary Tables of Children’s Environmental Health Epidemiology 

Studies 

Appendix 5. Summary of OPP’s ChE Policy & Use of BMD Modeling 

Appendix 6. Columbia Center for Children’s Environmental Health (CCCEH) Epidemiology 

Data Acquisition “Raw Data” Request 

Appendix 7. Physical/Chemical Properties 

Appendix 8. Current U.S. Tolerances and International Residue Limits 

Appendix 9. Master Use Summary Document 

Appendix 10. Dose Reconstruction Analysis 

Appendix 11. New Literature on Chlorpyrifos since the 2012 FIFRA SAP Meeting 
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Appendix 1. Evaluation of Experimental Toxicology Data 

 
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1.0. Introduction 

Chlorpyrifos (0,0-diethyl-0-3,5,6-trichloro -2-pyridyl phosphorothioate) is a broad-spectrum, chlorinated 

organophosphate (OP) insecticide. In 2000, most residential uses were voluntarily cancelled by Dow 

AgroSciences but agricultural use remains. In 2011, the Agency released a preliminary human health risk 

assessment for chlorpyrifos. The focus of the 2011 risk assessment was on the cholinesterase (ChE) 

inhibiting potential of chlorpyrifos including in young animals. Like other OPs, chlorpyrifos binds to and 

phosphorylates the enzyme acetylcholinesterase (AChE) in both the central (brain) and peripheral nervous 

systems. This can lead to accumulation of acetylcholine and, ultimately, at sufficiently high doses, to 

clinical signs of toxicity. Consistent with the focus on ChE inhibition, in the 2011 preliminary risk 

assessment, EPA evaluated the extensive database of ChE data for multiple lifestages and selected points 

of departure based on consideration of all quality and reliable data. 

In 2008 and 2012, the Federal Insecticide, Fungicide and Rodenticide Act (FIFRA) Scientific Advisory 

Panel (SAP) reviewed two draft science issue papers on the human health effects of chlorpyrifos which 

provided a preliminary review of the scientific literature on experimental toxicology and epidemiology 

studies available since the 2000 risk assessment. These draft issue papers considered a growing body of 

literature with laboratory animals (rats and mice) indicating that gestational and/or early postnatal 

exposure to chlorpyrifos may cause persistent effects into adulthood in addition to epidemiology studies 

evaluating prenatal chlorpyrifos exposure in mother-infant pairs that have reported associations with birth 

outcomes, childhood neurobehavioral and neurodevelopment outcomes in the offspring when evaluated in 

neonates, infants, and young children. In 2008 and 2012, the SAP agreed with the Agency that although 

the epidemiology studies were high quality with respect to design, conduct and analyses and provided 

information for hazard characterization, ChE inhibition remained the most robust dose response data for 

deriving points of departure. This appendix incorporates components from the draft SAP issues papers, 

comments from the SAP and from the public on those drafts and new studies since 201260. 

In 2010, the Agency developed a draft “Framework for Incorporating Human Epidemiologic & Incident 

Data in Health Risk Assessment” which provides the foundation for evaluating multiple lines of scientific 

evidence in the context of the understanding of the adverse outcome pathway (or mode of action; (U.S. 

Environmental Protection Agency, 2010). The draft framework, which includes two key components: 

problem formulation and use of the mode of action/adverse outcome pathway frameworks, was reviewed 

favorably by the SAP in 2010 (FIFRA SAP, 2010). OPP’s draft framework is consistent with updates to 

the World Health Organization/International Programme on Chemical Safety mode of action/human 

relevance framework which highlight the importance of problem formulation and the need to integrate 

information at different levels of biological organization (Meek et al, 2014). Consistent with 

recommendations by the NRC in its 2009 report, OPP’s draft framework describes the importance of 

using problem formulation at the beginning of a complex scientific analysis. The problem formulation 

stage starts with planning dialogue with risk managers to identify goals for the analysis and possible risk 

management strategies. This initial dialogue provides the regulatory context for the scientific analysis 

and helps define the scope of such an analysis. The problem formulation stage also involves 

consideration of the available information regarding the pesticide use/usage, toxicological effects of 

concern and exposure pathways and duration along with key gaps in data or scientific information. 

Specific to chlorpyrifos, the 2008 and 2012 SAP reviews represent the problem formulation analyses for 

the weight of evidence (WOE) analysis provided in the main body of the risk assessment. 

 

60 The agency is aware of multiple review articles related to topics in this appendix published in the 

scientific literature (Prueitt et al, 2011; Goodman et al, 2013; Li et al, 2012; Li et al, 2014; Mink et al, 

2014; Reiss et al, 2012). EPA has conducted an independent review of the scientific literature. 
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Mode of action (Boobis et al., 2006; Boobis et al., 2008; U.S. EPA, 2005; Simon et al, 2014; Meek et al, 

2014) and adverse outcome pathway (Ankley et al., 2010) provide important concepts in this integrative 

analysis. Both a mode of action and an adverse outcome pathway are based on the premise that an 

adverse effect caused by exposure to a compound can be described by a series of causally linked 

biological key events that result in an adverse human health or ecological outcome. One of the key 

components of the agency’s draft framework is the use the mode of action framework /adverse outcome 

pathway concept as a tool for organizing and integrating information from different sources to inform the 

causal nature of links observed in both experimental and observational studies. Specifically, the modified 

Bradford Hill Criteria are used to evaluate the experimental support that establishes key events within a 

mode of action or an adverse outcome pathway, and explicitly considers such concepts as strength, 

consistency, dose response, temporal concordance and biological plausibility in a weight of evidence 

analysis. 

The draft framework is oriented around the source to outcome pathway (Figure App 1-1) discussed in the 

National Research Council’s (NRC) report on toxicity testing in the 21st Century (National Research 

Council, 2007). 
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Figure App

1‐1. Source to effects pathway (adapted from NRC, 2007)

Chemicals Molecular

Target

Cellular

Response

Tissue/

Organ

Indiv

Pharmaco‐  kinetics

In vitro studies

Structure Activity Relationship

s

Toxicity Pathwa

ys

Human Incidents

In vivo stud
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2.0. Conceptual Framework 

As an aid in organizing the available information and to identify the complex scientific issues 

being considered, the agency developed a conceptual framework. This conceptual framework 

(Figure App 1-2) is consistent with the source-to-outcome pathway and provides the foundation 

for the analysis being undertaken by the agency to delineate outcomes derived from AChE 

inhibition in different lifestages and from alternative modes/mechanisms (Figure App 1-1). This 

conceptual framework provides key information on which lifestages are thought be the most 

susceptible to chlorpyrifos, the exposure pathway by which these individuals are exposed, 

possible adverse outcome pathways and related critical duration(s) of exposure leading from 

exposure to adverse health outcome. 

Figure App 1-2. Conceptual framework considering chlorpyrifos exposure, lifestages, critical durations, 

modes of action/adverse outcome pathways, and health outcomes of interest. 
Chlorpyrifos Human Health Risk Assessment D424485 

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2.1. Adverse Outcomes 

AChE inhibition is the well-established cholinergic mode of action for OPs and is typically used 

as the critical effect in hazard characterization for members of this class of pesticides. The 2008 

and 2012 SAPs concurred with the agency that AChE/ChE data when taking into account 

sensitive lifestages remains the most robust dose response data for use in derivation of points of 

departure. However, over the last 10-15 years, experimental toxicology studies on 

neurotoxicological as well as epidemiology studies have been published which suggest that the 

developing brain of the fetus and young children may also be affected. There are a significant 

number of literature studies evaluating neurobehavioral outcomes in experimental animals (rats, 

mice). These studies vary in their study design but many involve gestational and/or early 

postnatal dosing with behavioral evaluation in adulthood. Epidemiological studies from three 

cohorts of mothers and children, funded in part by EPA and National Institutes of Environmental 

Health, have reported birth and neurodevelopmental outcomes associated with prenatal 

exposures to OPs. Two of these (Mt. Sinai and Berkeley) have focused primarily upon nonspecific urinary biomarkers of OP exposure, the dialkyl phosphate metabolites (i.e., DAPs), and 

thus present some uncertainty as to the extent these results relate to chlorpyrifos per se. The 

Columbia University investigators are studying a cohort of mothers and children in inner-city 

New York and have published multiple papers on associations among level of prenatal 

chlorpyrifos exposure and birth and neurodevelopmental outcomes at multiple time points from 

birth through age seven years. With respect to neurodevelopmental outcomes, multiple 

investigators have reported hypothesized modes/mechanisms of action. In the conceptual 

framework, both adverse health outcomes of interest for chlorpyrifos are being tracked—AChE 

inhibition and potential for neurodevelopmental effects. 

2.2. Potentially Sensitive Lifestages 

The agency believes that pregnant women and their fetuses, newborns, and young children 

represent potential sensitive lifestages for exposure to chlorpyrifos. This conclusion is based on 

multiple lines of evidence. 

 Pregnant Women: 

Chlorpyrifos RBC AChE data from pregnant rats provides the most sensitive data for use 

in deriving a point of departure from repeated dosing studies. Although it is unclear 

whether metabolic changes during pregnancy are sufficient to affect metabolism at 

environmental exposure, some have suggested that there may be reduced ability to 

detoxify chlorpyrifos and/or the oxon may affect sensitivity during pregnancy. 

Metabolic activities can be altered during pregnancy (Anderson, 2006; Anger & PiquetteMiller, 2008; Bologa, et al, 1991; Carpintero et al, 1996; Czekaj et al, 2000, 2005; 

Dickmann et al., 2008a, b; Ejiri et al, 2005; Ferre et al., 2006; Hines, 2007; Homma et al., 

2000; Howard & Sugden, 1993; Tsutsumi et al., 2001). For example, Chanda et al. (2002) 

showed that pregnant female rats had lower plasma, brain, and liver carboxylesterase 

activity compared to non pregnant females. Regarding A-esterase activity, Ferre et al.

(2006) showed that the paraoxonase (i.e., A-esterase in serum decreased from a 

nonpregnant background of 146 U/L to 111 U/L in late gestation, indicating 76% of 

normal activity in late gestation pregnant women. Carpintero et al. (1996), however, 
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found that phenyl acetate metabolism increased from 23.6 to 33.5 kat/g in the third 

trimester, but it must be kept in mind that phenyl acetate metabolism is not necessarily a 

specific measure of A-esterase activity. Data in mice support the findings of Ferre et al. 

(2006) in humans suggesting a reduction in A-esterase activity during pregnancy. In 

mice, Weitman et al. (1983) found that PON1 activity after exposure to parathion was 50 

nmol/min/ml in non-pregnant females, but it decreased as low as 14 nmol/min/ml during 

gestation (Weitman, et al., 1983). With regard to plasma butylcholinesterase (BuChE) 

activity, Howard et al. (1978) have shown that in six healthy pregnant women levels of 

plasma BuChE activity dropped by approximately 30% during the first trimester, but 

returned to close to pre-pregnancy levels in the third trimester. Similarly, other 

investigators have also reported decreases in plasma BuChE activity in pregnant women 

(de Peyster et al, 1994; Venkataraman et al , 1990; Whittaker, Crawford, & Lewis, 1988). 

Evans et al. (1988) showed that serum ChE levels in 39 of 44 pregnant women dropped 

after conception; in 20 of those women, the decline in ChE activity continued throughout 

pregnancy. 

Pregnancy is a remarkably dynamic biological process in which rapid changes occurring 

in both the developing system (embryo/fetus) and the mother can significantly impact the 

pharmacokinetics of chemicals. In addition to the potential for decreased clearance of 

chemicals due to immature metabolic systems in the developing embryo/fetus (mentioned 

above), other pregnancy-related changes can also have a pronounced effect on 

pharmacokinetics. For a typical human pregnancy, total body weight gain is in the order 

of 10-30% while cardiac output can increase as much as 50% (Corley et al, 2003a, b; 

Young et al., 1997a, b). A significant fraction of the body weight gain is due to increases 

in total body water and fat that can lead to a greater volume of distribution by simple 

dilution. For instance, increases in total body water dilute plasma proteins which can 

increase the free fraction of highly plasma protein-bound chemicals (e.g., chlorpyrifos) 

available for distribution. Increases in fat, on the other hand, can provide a larger storage 

volume for lipophilic chemicals (Corley et al., 2003). Additionally, significant changes 

take place in the maternal circulatory system to accommodate the development of 

placental blood flow which undergoes considerable increases throughout pregnancy 

along with embryo/fetus which increases in volume over a billion-fold from conception 

to birth (Young et al., 1997). 

 Fetuses, Infants, Toddlers & Young Children: 

With respect to distribution to the fetus during gestation, there are multiple studies on 

chlorpyrifos (Mattsson et al., 1998, 2000; Qiao et al., 2002) and other OPs (USEPA, 

2006) which show that the pregnant dam exhibits similar or more AChE inhibition than 

the fetus at a given dose to the dam. As such, for AChE inhibition, protecting against 

AChE inhibition in the pregnant female is expected to be protective for AChE inhibition 

in the fetus. Biomonitoring data from rats and humans illustrate fetal exposure levels are 

similar to maternal levels which support these findings. Specifically, Whyatt et al., 

(2003) have shown that levels of chlorpyrifos in maternal blood are similar to the levels 

measured in human umbilical cord blood. In Hunter et al., (1999), pregnant rats were 

orally dosed with chlorpyrifos for 5 days during late gestation at doses of 3 or 7 

mg/kg/day, and the amounts of chlorpyrifos, chlorpyrifos-oxon, and TCPy were 

quantified in fetal and maternal brain and liver. No chlorpyrifos or its oxon were 
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detected. The concentration of TCPy in the fetal brain was higher than the TCPy 

concentration in the maternal brain in time-course and dose-response studies but in liver 

the maternal levels were higher. In a study by Mattsson et al., (1998, 2000), 

concentrations of chlorpyrifos, the oxon, and TCPy were measured in the blood of 

maternal and fetuses and TCPy levels in dam and fetal blood were similar and 

chlorpyrifos levels were approximately 2-fold higher in maternal blood than fetal blood. 

In another gavage gestational exposure study, Akhtar et al., (2006) exposed rats to 

chlorpyrifos from GD 0-20 in fetal and maternal tissues on GD21 at high doses (9.6-15 

mg/kg/day); while there is a high degree of variability in the Akhtar et al study, dams and 

fetuses showed similar levels of chlorpyrifos in liver and brain. 

With respect to neurodevelopmental outcomes, numerous epidemiological investigations 

have observed a link between early life exposure to chlorpyrifos and adverse effects on 

neurodevelopment through age seven years. Epidemiology studies focused on exposure 

during gestation but the impact of post-natal exposure can not be ruled out. Overall, 

these studies are strong, well-conducted studies in which likely sources of error or bias 

would more likely tend to underestimate, rather than overestimate an effect size (e.g., 

relative risk measure). In addition, there a growing body of literature with laboratory 

animals (rats and mice) indicating that gestational and/or early postnatal exposure to 

chlorpyrifos may cause persistent effects into adulthood. 

Potential sensitivity, particularly observed in acute, single dose exposures, is largely 

derived from immature metabolic systems that have less capacity to detoxify the parent 

OP and its toxicologically active metabolite chlorpyrifos oxon. This sensitivity is not 

derived from differential inhibition of the AChE enzyme itself as supported by in vitro 

studies (Benke and Murphy, 1975; Chanda et al., 1995; Mortensen et al., 1996; 

Atterberry et al., 1997). Rat fetuses and juveniles and human fetuses, infants, and young 

children have lower capacity to detoxify chlorpyrifos than adults. Specifically, in rats, Aesterase activity is virtually nonexistent in the fetus (Lassiter et al., 1998) and increases 

from birth to reach adult levels around PND21 (Mortensen et al., 1996; Li et al., 1997). 

Mortenson et al., (1996) showed that in the plasma level of A-esterase in 4-day old rats 

was 1/11 that of adult animals. The animal data regarding the role of carboxylesterase in 

mediating OP toxicity are also quite extensive (e.g., Clement, 1984; Fonnum et al., 1985; 

Maxwell, 1992 a, b). Fetal rats and mice possess very little carboxylesterase activity with 

increasing activity as the postnatal rodent matures, reaching adult values around puberty 

(Lassiter et al., 1998; Morgan et al., 1994; Moser et al., 1998; Karanth and Pope, 2000; 

Zhu et al., 2009). The temporal pattern of A-esterase and carboxylesterase activity 

correlates well with studies on OP sensitivity. Several studies have shown an increased 

sensitivity of newborn rats to OP compounds which are detoxified via the A-esterase 

and/or carboxylesterase pathways (e.g., Gagne and Brodeur, 1972; Benke and Murphy, 

1975; Pope et al., 1991; Chambers and Carr, 1993; Padilla et al., 2000; 2002; Karanth 

and Pope, 2000). 

While there are fewer data in human tissues which could evaluate age-related maturation 

of carboxylesterase and A-esterase expression, there are numerous studies that present 

age differences in liver P-450 metabolism in humans that also play a role in OP 

metabolism (e.g., review by Hines, 2007). Studies evaluating maturational expression 

and activity of liver carboxylesterases in human liver tissues show lowest levels in fetal 
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tissues, with increasing activity during childhood, although there are large individual 

differences and thus high variability (Pope et al., 2005; Yang et al., 2009; Zhu et al., 

2009). Adult tissues were approximately 10-fold and 4-fold more active in hydrolyzing 

certain chemicals than fetal and child (0-10 years) tissues, respectively, and enzyme 

expression differences were much greater (Yang et al., 2009). In contrast, in Pope et al., 

(2005), the differences in activity were relatively small (and not statistically significant) 

between children ages 2–24 months and adults (20–36 years); however, that youngest age 

evaluated in the study was 2 months old and this individual had the lowest level of 

carboxylesterase. 

Serum A-esterase levels also appear to be very low in human infants and children 

compared to adults (Augustinsson and Barr, 1963; Cole et al., 2003; Huen et al., 2009; 

Mueller et al., 1983; Ecobichon and Stephens, 1973; Gonzalez et al., 2012; Holland et 

al., 2006; Chen et al., 2003). While some studies have shown that PON1 activity reaches 

adult levels by about 2 years of age (e.g., Cole et al., 2003), recent papers have reported 

slightly lower levels as late as 9 years (Gonzalez et al., 2012). Reasons for these 

differences could include type of measurement (i.e., genotyping vs phenotyping), assay 

substrate, sample sizes, and population factors (e.g., ethnicity). 

There are critical windows of vulnerability (Rice & Barone, 2000; Rodier, 2004) with 

regard to toxicant effects on brain development. This vulnerable period in humans spans 

early pregnancy to adolescence (Rice & Barone, 2000). In fact, evidence shows that 

synapse formation peaks quite late in human brain development at 4-8 years of age 

(Glantz et al., 2007). Within these vulnerable periods there are key neurodevelopmental 

processes (e.g. cell division, migration, differentiation, synaptogenesis, and myleination) 

and each of these is region and stage specific. Consequently, the time of toxicant 

exposure will be a major determinate in the spectrum of neurotoxic effects 

 
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3.0. Adverse Outcome Pathways: AChE Inhibition & Plausible Pathways Leading to 

Neurodevelopmental Outcomes 

Mode of action and adverse outcome pathways provide important concepts in the draft 

“Framework for Incorporating Human Epidemiologic & Incident Data in Health Risk 

Assessment” (U.S. EPA, 2010). As mentioned above, both a mode of action and an adverse 

outcome pathway (AOP) include a set of measureable key events that describe the biological 

processes leading to an apical effect. Figure App1-3 is a graphical presentation of a generic 

adverse outcome pathway (Ankley et al., 2010). This figure is an extension of the source-tooutcome pathway and provides additional detail on the types of scientific information from 

various levels of biological organization used in establishing an adverse outcome pathway. As 

shown in the conceptual framework, two different adverse outcomes in laboratory animals are 

being evaluated: AChE/ChE inhibition and neurodevelopmental outcomes. Each of these is 

discussed below. 

Figure App1-3. Generic Adverse Outcome Pathway (Adapted from Ankley et al., 2010) 

3.1. Adverse Outcome Pathway: AChE Inhibition 

3.1.1. Initiating Event & Health Outcomes 

AChE inhibition is the well-established mode of action for OPs and is typically used as the 

critical effect in hazard characterization for members of this class of pesticides. Specifically, the 

initiating event for OP mediated neurotoxicity is interaction at the serine residue at the active site 

of the AChE. This interaction leads to accumulation of acetylcholine and ultimately to clinical 

signs of neurotoxicity at high doses. In experimental toxicity studies in laboratory animals, high 

acute doses produce signs of autonomic stimulation, ataxia, fasciculations, tremors, respiratory 
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difficulties, and convulsions. The Agency’s recent review of human incident reporting databases 

(Recore & Oo, 2011) indicates that humans exposed to high levels of chlorpyrifos may complain 

of gastrointestinal and neurological effects consistent with acute AChE poisoning. Specifically, 

individuals have reported: nausea, vomiting, diarrhea, tremors, headaches, dizziness, numbness 

and tingling sensations, muscle spasm, shortness of breath, and seizures (Recore & Oo, 2011). 

Although inhibition of AChE as the initiating event in this adverse outcome pathway has long 

been established, the quantitative dose response linkage between AChE inhibition and toxicity 

has been shown to be more variable. In other words, the amount of AChE inhibition required to 

elicit such responses from OP exposure is not consistent but instead varies among different OPs, 

different study designs, and different neurotoxic outcomes. Specific to chlorpyrifos, there are 

limited human data which inform the quantitative dose-response linkage between AChE 

inhibition and apical neurotoxic effects. There are two human deliberate dosing studies in 

human subjects (Nolan et al., 1982)61 which provides plasma ChE and RBC AChE data from a 

single oral dose (0.5 mg/kg). AChE/ChE activity (plasma and RBC) was monitored at 2, 6, 12, 

24 hours, and 2, 3, 4, 8, 14, 22, 27, and 30 days post dose. Despite plasma ChE inhibition of 83-

89% at the peak time of effect, no clinical signs of toxicity or inhibition of RBC ChE activity 

were observed. Kisicki et al. (1999) provides data from six human subjects exposed to 

chlorpyrifos in a gelatin capsule at dose levels of 0 (lactose-filled gelatin capsule, 2 control 

groups), 0.5, 1.0, or 2.0 mg/kg; RBC AChE activity was monitored at 2, 4, 8, 12, 24, 36, 48, 72, 

96, 120, 144, and 168 hours post dose. In the Kisicki et al. (1999) study RBC AChE inhibition 

changes were only seen in one person. 

3.1.2. Dose Response Analysis for AChE Inhibition 

In the 2011 preliminary risk assessment, the agency used typical reference dose and margin of 

exposure approaches where points of departure are derived from toxicology studies in laboratory 

animals and standard 10X factors for inter- and intra-species extrapolation. In the revised risk 

assessment, the agency is using a more sophisticated approach with the PBPK-PD model. As 

part of the 2008 SAP, the agency performed a comprehensive review of the literature on 

available AChE data across multiple lifestages, durations, and routes (FIFRA SAP, 2008b). This 

literature review was considered by the SAP at that time and provided part of the scientific 

rationale for the Panel’s recommendation that the agency continue to use AChE data in the most 

sensitive lifestages for dose-response analysis. The literature review was updated prior to the 

2011 preliminary risk assessment, the 2012 SAP review, and again for the 2014 revised risk 

assessment. Section 3.1.2.1 provides a summary of the benchmark dose (BMD) modeling on 

selected oral AChE studies conducted for the preliminary HHRA (2011). Section 3.1.2.2 

provides additional dose-reponse data extracted from the 2008 draft issue paper reviewed by the 

FIFRA SAP. 

3.1.2.1. Summary of BMD Modeling Results 

 

61 Nolan (1982) and Kisicki et al (1999) has been reviewed by the Human Studies Review Board (HSRB) and found 

to be ethically and scientifically conducted (http://www.epa.gov/osa/hsrb/files/june2009finalreport92609.pdf; 

http://www.epa.gov/osa/hsrb/files/meeting-materials/apr-13-14-2011/appendix1.pdf). 
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For the 2011 preliminary risk assessment, the Agency conducted BMD modeling on selected oral 

AChE studies62. These studies were selected based on the availability of at least two treatment 

groups in addition to a control group and those that showed good dose response. The agency has 

also focused on those studies where AChE was measured at or near the time of peak effect, 

typically within one to several hours after dosing. AChE data measured 24 hours or longer after 

dosing will underestimate the amount of AChE inhibition and are thus not appropriate for 

deriving points of departure. In addition, the studies used in the dose response assessment were 

selected as they represented a variety of ages, lifestages, and durations. The agency focused on 

those studies representing single and repeating dosing in post-natal exposure to rat ages (PND 10 

and older); PND10 and older is considered to be approximately concordant with human 

postnatal exposure (Benjamins & McKhann, 1981; Dobbing & Smart, 1974). AChE data from 

adult laboratory animals (both pregnant and non-pregnant) from single dose and repeating dosing 

studies has also been collected. The focus has been on AChE data from rats as these are 

generally the most robust and sensitive studies. Data in fetuses for chlorpyrifos have not been 

evaluating using BMD techniques as these studies tend to be less sensitive than those in postnatal juveniles and adults. The agency has conducted BMD analysis on available data for blood 

(RBC), brain (whole or sections as appropriate), and peripheral (i.e., heart) tissues. 

Specifically, the agency used the decreasing exponential dose-response model similar to that 

used for the OP and N-methyl carbamate cumulative risk assessments and multiple risk 

assessments for individual AChE inhibiting pesticides (U.S. EPA, 2006, 2007). The use of this 

empirical dose-response procedure has been previously reviewed and supported by the FIFRA 

SAP on several occasions (2001, 2002, 2005a, 2005b, 2008bThe agency typically uses a 10% 

decrease in brain and peripheral ChE inhibition as the response level in deriving points of 

departure. This 10% response level is called the benchmark response level (BMR). This level 

has been shown to be a level which is protective for cholinergic outcomes for OPs and can also 

be reliably measured in most guideline experimental toxicology studies (U.S. EPA, 2002, 2006). 

The agency has calculated BMD10s and BMDL10s where the BMD10 is the estimated dose where 

AChE is inhibited by 10% compared to background and the BMDL10 is the lower confidence 

bound on the BMD10. As a matter of science policy, the agency uses the BMDL, not the BMD, 

for use as the point of departure since the BMDL accounts for variability of the data (U.S. EPA, 

2012). 

For the assessment of infants and children, the most robust studies are 1) the comparative 

cholinesterase63 (CCA) study (MRID 48139301, also referred to as Marty et al., 2012) which 

provides brain and RBC data in PND11 pups and 2) (Moser et al., 2006) which provides data 

from brain and whole blood ChE data in PND17 pups. In both of these studies, the rat pups were 

dosed directly and ChE was measured at the time of peak effect. In addition, two studies 

(Timchalk et al., 2006; Zheng et al., 2000) provide supporting data. These two studies show 

AChE data in post-natal rats which are consistent with the comparative cholinesterase study and 

Moser et al. (2006). However, Timchalk et al. (2006) and Zheng et al. (2000) are not robust 

datasets for dose-response modeling (U.S. EPA, 2011). For the assessment of adults, single 

dosing AChE data from the comparative cholinesterase study (MRID 48139301) provide the 

 

62 Note: This appendix focuses on chlorpyrifos studies. See the 2011 preliminary HHRA for oxon results. 63 Comparative cholinesterase (CCA) studies are specially designed studies in juvenile and adult rats which provide 

direct comparative ChE/AChE data for evaluating lifestage sensitivity. For OPs, data are typically collected from 

acute dosing (typically PND11, young adult) and from 11-days repeated dosing (PND11-21 and young adults).
Chlorpyrifos Human Health Risk Assessment D424485 

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most robust data for BMD analysis64. Tables App 1-1 and 1-2 provide a summary of the BMD 

results for the single dosing studies in post-natal rat pups and adult rats, respectively. Generally 

for the both the pups and the adults, the BMD and BMDL estimates for the blood measures are 

lower than those for the brain. In addition, the results from the pups are approximately 2-fold 

lower than those for adults (i.e., when comparing the same compartment, administration vehicle). 

Table App1-2. Results of BMD Modeling of Rat Pup Brain and Blood ChE Inhibition following a 

Single Oral Dose of Chlorpyrifos

Table App1-3. Results of BMD Modeling of Adult Rat Brain and RBC AChE Inhibition following 

a Single Oral, Dose of Chlorpyrifos

Reference

Sex &

Administration

Method

Endpoint BMD10  (mg/kg) BMDL10 (mg/kg)

CCA                    

(MRID 48139301)

Female  

Gavage   Brain AChE 4.11 2.26

CCA                    

(MRID 48139301)

Female

Gavage RBC AChE 1.9 1.2

 

64 Only adult female rats were tested in MRID 48139301. 

Reference Sex & Age at

Dosing Endpoint BMD10 (mg/kg) BMDL10 (mg/kg)

Moser et al.,

2006  

Male PND 17

Gavage Brain AChE   1.89 1.54

Moser et al.,

2006  

Male PND 17

Gavage

Whole Blood

AChE   0.62 0.43

CCA Study         

(MRID 48139301)

Male PND 11  

Gavage Brain AChE 2.13 1.53

CCA Study         

(MRID 48139301)

Male PND 11

Gavage    RBC AChE 0.82 0.65

CCA Study         

(MRID 48139301)

Female PND 11  

Gavage Brain AChE 2.18 1.56

CCA Study         

(MRID 48139301)

Female PND 11  

Gavage RBC AChE   0.96 0.75

CCA Study         

(MRID 48139301)

Male PND 11  

Milk Brain AChE 4.4 2.4

CCA Study         

(MRID 48139301)

Male PND 11  

Milk RBC AChE 0.47 0.36

CCA Study         

(MRID 48139301)

Female PND 11  

Milk Brain AChE 1.42 0.91

CCA Study         

(MRID 48139301)

Female PND 11  

Milk RBC AChE   0.5 0.36
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CCA                    

(MRID 48139301)

Female

12 hr diet   Brain AChE 4.47 3.30

CCA                    

(MRID 48139301)

Female

12 hr diet   RBC AChE 1.03 0.6

For BMD analysis of repeated dosing studies, in the preliminary risk assessment, the Agency 

selected the repeated dosing portions of the comparative cholinesterase study (MRID 48139301) 

in juvenile and non-pregnant adult rats;, the data from pregnant dams in the developmental 

neurotoxicity rat study (Maurissen et al., 2000) (MRID 44556901) and the data from pregnant 

dams in the special cholinesterase study in which dams where administered chlorpyrifos from 

gestation day 6 through lactation day 10 (GD6 – LD10) (Mattsson et al., 2000; MRID 

44648101). Although the Zheng et al. (2000) data are not robust data for BMD modeling, the 

repeated dosing portion of this study provides additional supportive data for the findings from 

the comparative cholinesterase study in both pups and adults. 

  

Table App1-4. Results of BMD Modeling of Pup Rat Brain and RBC AChE Inhibition following 

11-days Repeated Oral, Gavage Doses of Chlorpyrifos

Reference

Sex &

Age/Duration of

Dosing

Endpoint BMD10  

(mg/kg/day)

BMDL10

(mg/kg/day)

CCA               

(MRID 48139301)  

Female PND 11‐21

Gavage Brain AChE 0.80 0.69

CCA               

(MRID 48139301)

Female PND 11‐21

Gavage RBC AChE 0.17 0.15

CCA               

(MRID 48139301)

Male  PND 11‐21

Gavage Brain AChE 0.63 0.52

CCA               

(MRID 48139301)

Male PND 11‐21

Gavage RBC AChE 0.11 0.09

Table App1-5. Results of BMD Modeling of Adult, Female Rat Brain, RBC and Heart 

Cholinesterase Inhibition following Repeat Oral Doses of Chlorpyrifos

Reference Sex, Age/Duration &

Administration method Endpoint BMD10  (mg/kg/day)

BMDL10

(mg/kg/day)

Dow (MRID 44556901;

Maurissen et al., 2000)

Dams, GD6‐20,  

Gavage Brain AChE 0.65 0.54

Dow (MRID 44556901;

Maurissen et al., 2000)

Dams, GD6‐20,  

Gavage RBC AChE 0.0665 0.03

Dow (MRID 44648101;  

Mattsson et al., 2000) Dams, GD6‐20, Gavage Brain AChE 1.1 0.8

Dow (MRID 44648101;  

Mattsson et al., 2000) Dams, GD6‐20, Gavage RBC AChE   0.14 0.08

 

65 The BMD results from this dataset are not considered reliable due to high variability in the data & BMD values 

below the tested doses. 
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Dow (MRID 44648101;  

Mattsson et al., 2000) Dams, GD6‐20, Gavage Heart ChE 0.85 0.22

Dow (MRID 44648101;  

Mattsson et al., 2000)

Dams, GD6‐LD10

Gavage Brain AChE 1.13 0.89

Dow (MRID 44648101;  

Mattsson et al., 2000)

Dams, GD6‐LD10

Gavage RBC AChE 0.050 0.044

Dow (MRID 44648101;  

Mattsson et al., 2000)

Dams, GD6‐LD10

Gavage Heart ChE 0.21 0.18

CCA  (MRID 48139301)    Adult, non‐pregnant

11 daysGavage Brain AChE 1.03 0.95

CCA  (MRID 48139301)    Adult, non‐pregnant

11 days Gavage RBC AChE 0.45 0.35

Subchronic Oral 

(MRID 40952801) 

Dietary, non‐pregnant

female, Day 44

RBC AChE 0.180 0.117

Subchronic Oral 

(MRID 40952801) 

Dietary, non-pregnant 

female, Day 91 

RBC AChE 0.15 0.090

As shown in Tables App 1-3 and 1-4, there is no meaningful difference between BMD10s and 

BMDL10s from pups and adults in repeated dosing studies. This is not unexpected given that 

over the duration of the repeated dosing study, the metabolic system in pups is maturing closer to 

adult levels. Thus, it is not unexpected that as the metabolic system matures that the degree of 

sensitivity decreases. Typically, studies submitted for pesticide registration and most studies 

from the public literature only measure brain and/or blood ChEs. It is rare for data from 

peripheral tissues to be available for consideration. Chlorpyrifos is unique in that multiple 

studies are available which provide such peripheral data. It is notable that the BMDs and 

BMDLs for heart ChE are more sensitive than brain and closer in magnitude than those for RBC 

AChE. This finding supports the use of the blood measures for extrapolating risk. 

There is good concordance in the estimates across the BMD10 and BMDL10 estimates across the 

adult studies evaluated in this analysis. Notably, brain AChE estimates range from 

approximately 0.5 to 1 mg/kg/day and the RBC AChE estimates range from 0.03 to 0.35 

mg/kg/day. In addition, for the OP cumulative risk assessment (U.S. EPA, 2006), the agency 

performed BMD modeling using a sophisticated meta-analysis approach on brain AChE data 

from adult, non-pregnant female and male rats from one subchronic oral toxicity study and two 

chronic oral toxicity studies (MRID nos. 40952801, 40952802, and 42172802). The BMD results 

reported in the OP cumulative risk assessment for chlorpyrifos are BMD10s of 1.48 and 1.50 

mg/kg/day and BMDL10s of 1.26 and 1.27, mg/kg/day respectively in females and males. The 

values for brain AChE inhibition reported in the cumulative assessment from subchronic and 

chronic dosing are quite similar to those reported in Table 1-4 above for both pregnant and nonpregnant females and males in the selected studies and add further support to the analysis 

conducted for the preliminary risk assessment for chlorpyrifos. 
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3.1.2.2. Summary of Additional AChE/ChE Data (Extracted from USEPA, 2008) 

Table App1-5. Summary of repeated studies evaluating gestational exposure to maternal rats and 

fetuses (Extracted from USEPA, 2008). 

Study Route 

(vehicle) 

Time of 

exposure 

Time of 

measurement 

post-dosing 

Dose Fetal 

inhibition

Maternal 

inhibition Compartment

Mattsson 

et al., 

1998, 

2000 

oral 

gavage 

(corn 

oil) 

GD6-20 4 hrs 1 

mg/kg/day

8% (NS) 10%/7% 

(p<0.02) forebrain 

0% 12%/7% 

(NS) hindbrain 

5% (NS) 87%/85% 

(p<0.02) RBC 

4% (NS) 77%/60% 

(p<0.02) plasma 

0% 49%/50% 

(p<0.02) heart 

Hoberman 

et al., 

1998a,b; 

Maurissen 

et al., 

2000 

oral 

gavage 

(corn 

oil) 

GD6-20 4-5 hrs 1 

mg/kg/day

N/A 68.9% plasma 

N/A 84.4% RBC 

N/A 17.9% brain 

Mattsson 

et al., 

1998, 

2000 

oral 

gavage 

(corn 

oil) 

GD6-

PND1 2 hrs 1 mg/kg/day

5% (NS) 6% (NS) forebrain 

0% 6% (NS) hindbrain 

0% 90% 

(p<0.02) RBC 

5% (NS) 80% 

(p<0.02) plasma 

2% (NS) 40% 

(p<0.02) heart 

Qiao et 

al., 2002 

s.c. 

injection 

(DMSO) 

GD 17-

20 GD 21 1 mg/kg/day

3% (NS) brainstem N/A 

6% (NS) forebrain N/A 
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Table App1-6. Summary of acute studies evaluating post-natal exposure to juvenile rats 

(Extracted from USEPA, 2008).

Study Route 

(vehicle) Age 

Time of 

measurement 

post-dosinga

Dose Inhibition Compartment 

Dam et al. 

(2000) 

s.c. injection 

(DMSO) 

 

PND 1 2 hrs 1 mg/kg 

70% (M); 25% 

(F) brainstem 

80% (M); 10% 

(F) cerebellum 

60% (M); 35% 

(F) forebrain 

Betancourt 

and Carr 

(2004) 

oral gavage 

(corn oil) PND 1 12 hrs 1.5 mg/kg 58% forebrain 

Timchalk et 

al. (2006) oral gavage PND 5 3 hrs 1 mg/kg 

22.1% brain 

45.7% RBC 

62.1% plasma 

Zheng et al, 

2000 

 

oral gavage 

(peanut oil) 

PND 7 4 hrs 1.5 mg/kg 

17% frontal cortex 

32% RBC 

51% plasma 

Timchalk et 

al. (2006) or al gavage PND 12 6 hrs 1 mg/kg 

5.2% brain 

27.0% RBC 

33.4% plasma 

Timchalk et 

al. (2006) oral gavage PND 17 24 hrs 1 mg/kg 

2.1% brain 

15% RBC 

21.9% plasma 

Moser et al. 

2006 

oral gavage 

(corn oil) PND17 4.5 hrs 

0.5 mg/kg 0% 

brain 

2 mg/kg 10% 

0.5 mg/kg 10% 

whole blood 

2 mg/kg 40% 

a. Reported time of peak effect 
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Table App1-7. Summary of repeated studies evaluating post-natal exposure to juvenile rats 

(Extracted from USEPA, 2008). 

Study Route 

(vehicle) 

Time of 

exposure 

Time of 

measurement 

post-dosing 

Dose Inhibition Compartment 

Guo-Ross et 

al. (2007) 

oral gavage 

(corn oil) PND 1-4 4 hrs 

1 

mg/kg/day 25% 

brain 

1.5 

mg/kg/day 45% 

Betancourt 

and Carr 

(2004) 

oral gavage PND 1-3 24 hrs 1.5 

mg/kg/day 27% forebrain 

Song et al. 

(1997) 

s.c. 

injection 

(DMSO) 

PND 1-4 24 hrs 1 

mg/kg/day 24% brainstem 

Richardson 

and Chambers 

(2005) 

oral gavage 

(corn oil) PND 1-6 6 hrs 1.5 

mg/kg/day 49% 

brain (excluding 

cerebellum and 

medulla-pons) 

Betancourt 

and Carr 

(2004) 

oral gavage PND 1-6 24 hrs 1.5 

mg/kg/day 28% forebrain 

Betancourt 

and Carr 

(2004) 

oral gavage PND1-11 24 hrs 1.5 

mg/kg/day None forebrain 

Richardson 

and Chambers 

(2005) 

oral gavage 

(corn oil) PND 1- 12 12 hrs 1.5 mg/kg/day 43% 

brain (excluding 

cerebellum and 

medulla-pons) 

Richardson 

and Chambers 

(2005) 

oral gavage 

(corn oil) PND 1-21 24 hrs 1.5 

mg/kg/day 36% 

brain (excluding 

cerebellum and 

medulla-pons) 

Zheng et al 

(2000) 

oral gavage 

(peanut oil) PND7-20 4 hrs 1.5 

mg/kg/day 

42% frontal cortex 

57% RBC 

59% plasma 
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3.2. Adverse Outcome Pathway: Neurodevelopmental Outcomes 

With respect to modes of action/adverse outcome pathways leading to neurodevelopmental 

effects, at the present time, there is no established series of causal key events at a biological level 

of organization relevant to the risk assessment (i.e., adverse neurodevelopmental effects from 

gestational and/or postnatal exposure). For the 2014 revised HHRA, the agency conducted an 

updated literature review on the experimental toxicology studies for chlorpyrifos (Appendix 11) 

for studies published since the 2012 SAP meeting. Some of the new studies since 2012 have 

been integrated in this section. Despite the newest studies, the agency does not believe that any 

of the current lines of research support a coherent set of key events and that much work remains 

to elucidate the modes of action and adverse outcome pathways of chlorpyrifos toxicity. 

Even though a rigorous demonstration of an adverse outcome pathway for chlorpyrifos has not 

been constructed at this time, there are multiple studies from numerous laboratories that show 

that exposure to a variety of OPs (including chlorpyrifos, other pesticides, nerve gases) during 

the developmental period affects events in nervous system development (see below). These 

studies conducted to date generally report a correlation between OP exposure and a tested effect 

without consideration of temporal concordance (i.e., the sequence of biological events leading to 

effects) and quantitative linkages (i.e., degree of change that results in neurodevelopmental 

consequences). Some of this experimental work specific to chlorpyrifos is summarized below, 

and may be shown in the future actually to be steps in the same adverse outcome pathways and 

not discreet pathways. However, at this time, the experimental studies below do not provide all 

the information needed to demonstrate linkages among initiating event(s), subsequent events at 

the molecular and cellular level, and the adverse outcome of interest and are thus described 

separately. 

3.2.1. Biologically Plausible Toxicity Pathways Leading to Neurodevelopmental Outcomes 

3.2.1.1. Acetylcholinesterase (AChE) as a morphogen 

The classically understood role of AChE is the rapid hydrolysis of acetylcholine at synapses in 

the brain and at neuromuscular junctions, thereby regulating cholinergic neurotransmission. 

Consistent with this role, AChE is predominant at cholinergic synapses at neurons and in muscle, 

and inhibition of its catalytic activity results in the signs and symptoms of cholinergic 

overstimulation. Several lines of evidence, however, suggest that AChE can also serve as a 

morphogen, influencing the growth of cells during neurodevelopment. Alterations in the 

expression or structure of the AChE protein can disrupt various aspects of neuronal 

differentiation and growth. Because chlorpyrifos can interact with AChE, perturbation of this 

morphogenic activity represents a plausible adverse outcome pathway leading to developmental 

neurotoxicity. Additional for the plausibility of this adverse outcome pathways is found in the 

chlorpyrifos developmental neurotoxicity study (MRID 44556901) where PND66 pups exposed 

during gestation and lactation at the 1 and 5 mg/kg/day groups exhibited significant dose- and 

treatment-related decreases in measurements of the parietal cortex in female offspring. 

One of the first indications that AChE had roles other than the termination of cholinergic 
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neurotransmission was the timing and distribution of its expression in the developing nervous 

system. In certain brain regions containing non-cholinergic neurons and few cholinergic 

synapses, AChE levels are still high. In addition, AChE is highly expressed throughout the brain 

during periods of active axonal outgrowth in the absence of other cholinergic markers and before 

synaptic connections are made (Bigbee et al., 1999; Brimijoin & Koenigsberger, 1999). For 

example, AChE levels are high during early cerebellar development while there is very little 

evidence for the presence of cholinergic cells or cholinergic neurotransmission (Appleyard & 

Jahnsen, 1992; Parvari, 1983). A second line of evidence for a morphogenic role arose after the 

cloning and sequencing of rodent and human AChE. Surprising sequence homologies were 

found with a family of proteins lacking a catalytically active esterase site but pocessing a similar 

extacellular domain. Several of the homologs of AChE are extracellular matrix components 

essential to neuronal adhesion, axon guidance, and synapse formation (Brimijoin & 

Koenigsberger, 1999; Grisaru et al., 1999). Thus, the sequence of AChE is similar to other 

proteins which have a morphogenic role during the development of the nervous system. Finally, 

experimental evidence indicates that manipulation of AChE levels or activity can influence 

neuronal growth in vitro. Studies in cultured neuroblastoma cells (Koenigsberger et al., 1997) or 

PC12 cells (Grifman et al., 1998) have shown that antisense suppression of AChE decreased 

neurite outgrowth, and that neurite outgrowth could be rescued or increased by transfection with 

sense AChE. In cultures of rat dorsal root ganglion cultures, treatment with an antibody against 

AChE (which did not inhibit the catalytic activity) decreased neurite outgrowth, and neurite 

outgrowth was restored upon removal of the antibody (Bigbee et al., 1999). Expression in frog 

embyros of a recombinant human AChE lacking the ability to hydrolyze acetylcholine resulted in 

increased neurite outgrowth from cultured spinal neurons (Sternfeld et al., 1998). Thus, the 

catalytic ability of AChE does not seem to be required for its morphogenic properties. 

Studies using pharmacologic inhibition of AChE activity to alter neurite outgrowth also suggest 

a dissociation of the catalytic and morphogenic activities. Several laboratories using different in 

vitro systems have demonstrated that some potent AChE inhibitors including BW284c51 supress 

neurite outgrowth while other, equally potent AChE inhibitors like ecothiophate do not (Bigbee 

et al., 1999; Koenigsberger, et al., 1997; Layer et al., 1993). These differential effects may be 

related to the degree of chemical binding to separate sites on the AChE enzyme. There are at 

least two sites that can bind inhibitors: the catalytic (active) site and the peripheral anionic site 

(Grisaru et al., 1999). It has been suggested that inhibitors that bind directly to the peripheral 

anionic site, or that bind to the catalytic site in such a way that changes the conformation of the 

peripheral anionic site, will alter the morphogenic function of AChE (Bigbee et al., 1999; Yang 

et al., 2008). Thus inhibitors that bind only to the catalytic site (e.g. edrophonium, tacrine) did 

not affect neurite outgrowth, while ligands that bind to the peripheral anionic site (propidium, 

gallamine) or both sites (BW284c51) inhibited neurite outgrowth (Koenigsberger et al., 1997; 

Munoz et al., 1999). At least one study indicates that chlorpyrifos oxon can bind at both sites 

(Kousba et al., 2004). 

Data showing a potential action of chlorpyrifos on the morphogenic function of AChE are 

derived primarily from in vitro studies. Chlorpyrifos (as well as the oxon and TCPy) has been 

examined for effects on neurite outgrowth using a number of cell lines and in primary neuronal 

cultures. In some cases concurrent assessments of cholinesterase inhibition or cytotoxicity were 

performed. Li and Cassida (1998) examined the effect of chlorpyrifos oxon on neurite outgrowth 
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in the PC12 cell line. In this study cells were exposed prior to, but not during the active phase of 

neurite growth, which was assessed after 5 days. Chlorpyrifos oxon inhibited neurite outgrowth 

by 50%, but only at a relatively high concentration (200 uM) that was both cytotoxic and 

decreased cholinesterase activity. Flaskos et al. (2011) showed that neurite outgrowth in 

differentiating N2a cells was observed at μM concentrations of the oxon which also resulted in 

inhibition of AChE in the same cell line. Sachana et al. (2005, 2008) examined the effect of 3 

uM chlorpyrifos on the initiation of neurite growth in mouse N2a neuroblastoma cells. The 

number of cells exhibiting neurites after 4 or 8 hours of exposure (considered as a measure of 

cell differentiation) was decreased by approximately 50% in the absense of cytotoxicity. 

Cholinesterase activity was not measured. Axelrad et al. (2002) exposed mouse NB2a cells to 

chlorpyrifos for 24 hr and directly measured neurite length. Chlorpyrifos inhibited neurite 

outgrowth by 50% at 25 uM. Again, cholinesterase activity was not measured. While the studies 

cited above suggested that chlorpyrifos and chlorpyrifos oxon could affect measures related to 

neuronal differentiation and neurite growth, they were not specifically designed to examine the 

morphogenic role of AChE. 

Using PC12 cells, Das and Barone (1999) examined the concentration-related effects of 

chlorpyrifos, chlorpyrifos oxon, and TCPy on both neurite outgrowth and cholinesterase 

inhibition. Exposure to chlorpyrifos for 24 hr inhibited neurite outgrowth at a concentration (3 

uM) 10-fold below that which inhibited cholinesterase activity, while chlorpyrifos oxon inhibited 

both measures at equivalent concentrations (1 nM). TCPy, which is inactive against AChE, 

inhibited neurite growth at 5 uM. Similar studies of chlorpyrifos and its metabolites were 

performed in a series of experiments in the Lein laboratory using primary neuronal cultures 

derived directly from the mammalian nervous system. Using rat sympathetic neurons, Howard et 

al. (2005) showed that 24 hr exposure to chlorpyrifos and chlorpyrifos oxon decreased axonal 

outgrowth at concentrations (0.001 uM and 0.001 nM, respectively) well below the 

concentrations that inhibited AChE activity (1 uM and 1 nM). In the same study chlorpyrifos, 

chlorpyrifos oxon and TCPy enhanced dendrite outgrowth. A follow-up study from the same 

laboratory used sensory neurons from the dorsal root ganglion, which extend only axons (Yang, 

et al., 2008). Similar results were observed, with both chlorpyrifos and chlorpyrifos oxon 

decreasing axonal outgrowth at concentrations below those that inhibited AChE activity. To 

establish whether the target of chlorpyrifos and chlorpyrifos oxon was AChE or some other 

molecule, Yang et al. (2008) repeated the experiments in cultures from AChE-null animals. 

Chlorpyrifos and chlorpyrifos oxon had no effect in the AChE-null cultures, suggesting that 

inhibition of neurite outgrowth required AChE. These later three studies provide the most 

convincing evidence for selective inhibition of the morphogenic activity of AChE. It is not yet 

clear whether the effects are a direct action of chlorpyrifos on AChE, or the result of in vitro

conversion to the active oxon form. It should be mentioned that studies from the Slotkin 

laboratory suggest an effect of chlorpyrifos on neurite outgrowth in vitro (Song et al., 1998). In 

those experiments, however, neurite outgrowth is inferred from biochemical measurements and 

cholinesterase activity is not assessed concurrently. 

Testing the hypothesis that exposure to chlorpyrifos (or any chemical) can inhibit the 

morphogenic function of AChE in vivo and alter brain development is difficult. Neuronal 

differentiation and the subsequent development of axonal and dendritic networks occurs in a 

temporal- and region-specific manner. In the absence of prior information extensive studies 
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would be required to survey brain morphology during both the prenatal and postnatal period. In 

addition, the methods needed to assess the morphology of axonal and dendritic growth in vivo

and detect potentially subtle chemical-induced changes are tedious and time consuming. 

Nevertheless, some attempts have been made to detect chlorpyrifos-induced changes in neurite 

growth in vivo. In particular, several laboratories have examined the effect of chlorpyrifos and 

chlorpyrifos oxon on neuronal morphogenesis in developing zebrafish. Jacobson et al. (2010) 

exposed zebrafish embryos at 3 hr post fertilization to 300 nM chlorpyrifos oxon. When 

examined 24 hr later (1 day post fertilization), AChE was inhibited by 50%. Gross morphology 

was only slightly affected and muscular development (including the neuromuscular junction) 

was normal; however, there was a decrease in the number of Rohon-Beard sensory neurons 

accompanied by abnormal extension of their axons. As a follow up to this study and to their in 

vitro work described above, Yang et al. (2011) exposed zebrafish embryos from 24 to 72 hr post 

fertilization to chlorpyrifos and chlorpyrifos oxon. Chlorpyrifos had no effect, but chlorpyrifos 

oxon significantly decreased AChE activity (30 nM), decreased touch-induced swimming (100 

nM) and inhibited axon growth from both sensory neurons and motor neurons (100 – 1000 nM). 

These two studies show that exposure to chlorpyrifos oxon in an intact, developing organism can 

alter neurite outgrowth. However, because catalytic activity was also inhibited, it is not clear that 

this effect was solely due to disruption of the morphogenic role of AChE. 

As noted above, neurite outgrowth has been assessed indirectly in vitro. This method has also 

been applied in vivo in mammals. Qiao et al. (2003) exposed rats to 1 or 5 mg/kg chlorpyrifos on 

gestational days 17-20. In a previous study from the same laboratory this exposure paradigm 

resulted in a non-significant 5% decrease in AChE activity after 1 mg/kg and a significant 50% 

decrease in AChE activity after 5 mg/kg when assessed 24 hr after the last dose (Qiao et al., 

2002). The ratio of membrane protein to total protein in several brain regions was assessed as a 

surrogate index of neurite outgrowth. The results indicated that there was no effect of treatment 

during early postnatal development (PND 4-21), but that both doses decreased this ratio on PND 

30 and 60. While the authors interpret this biochemical measurement as a decrease in neurite 

growth, there has not been data presented to correlate the changes in the membrane protein to 

total protein ratio with actual quantification of neurite length. In addition, no effects were 

observed early in development when axons and dendrite growth is high. 

In summary, while perturbation of the morphogenic activity of AChE is a plausible adverse 

outcome pathway for chlorpyrifos, a number of questions remain. There is substantial evidence 

for a morphogenic role of AChE in nervous system development distinct from its role as an 

esterase to hydrolyze acetylcholine. In vitro evidence suggests that AChE can regulate neurite 

outgrowth, and that cholinesterase inhibitors (including chlorpyrifos and its metabolites) can 

interfere with this process at concentrations that do not inhibit the esterase activity. There is, 

however, no direct evidence showing that disruption of the morphogenic function of AChE can 

alter axon or dendritic growth in vivo. While limited in vivo studies using zebrafish indicate that 

chlorpyrifos or its metabolite chlorpyrifos oxon can disrupt axonal growth, it has not been 

demonstrated that this effect is due to alteration of the morphogenic function of AChE versus 

other potential mechanisms. 

3.2.1.2. Cholinergic system 

There are several lines of evidence showing that signaling through cholinergic receptors is 
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involved in neurodevelopment. Activation of muscarinic and/or nicotinic cholinergic receptors 

can regulate neural progenitor cell proliferation and differentiation (Resende & Adhikari, 2009), 

and in vivo studies demonstrate that cholinergic signaling is likely involved in brain 

morphogenesis (Hohmann & Berger-Sweeney, 1998). While cholinesterase inhibitors can affect 

cholinergic signaling by inhibition of the catalytic activity of AChE and subsequent increase in 

acetylcholine, some, including chlorpyrifos and chlorpyrifos oxon, can also directly interact with 

cholinergic receptors. Thus, direct interaction with cholinergic receptors by chlorpyrifos 

represents a potential adverse outcome pathway for disruption of neurodevelopment distinct 

from AChE/ChE inhibition. 

Some OPs have been shown to directly interact with cholinergic muscarinic receptors at 

relatively low concentrations. The muscarinic receptors are members of the G-protein receptor 

family and five subtypes (m1-m5) have been identified. Volpe et al. (1985) reported that 

nanomolar concentrations of several OPs decreased binding of quinuclidinyl benzilate (QNB), 

which binds equally to all five subtypes. This effect was noncompetitive and occurred at only a 

small fraction of the total QNB binding sites. A further study from the same laboratory proposed 

these receptor subtypes to be m2 and/or m3 ( Katz & Marquis, 1989). Bakry et al. (1988) found 

that a number of OP nerve agents and an OP therapeutic agent inhibited QNB binding to rat 

brain membranes with low potency, but competitively inhibited binding of cis-methyldioxolane 

(CD) with high potency. In rat cardiac tissue, OPs also competitively inhibited CD binding with 

very high potency (Silveira et al., 1990). CD is a muscarinic agonist that binds to the high 

affinity state of the m2 receptor in mammalian brain and heart (Closse et al., 1987; Vickroy et 

al., 1984; Watson, 1986). Subsequent research using Chinese hamster ovary (CHO) cells 

transfected with cDNA for the five distinct genes for muscarinic receptors (ml-m5) has identified 

the CD binding site as the high affinity state (GDP bound G-protein) of the m2 receptor (Huff & 

Abou-Donia, 1994). In light of these findings, Ward et al. (1993) examined the relationship 

between cholinesterase inhibition and direct binding to muscarinic receptors for a series of OPs 

and their active “oxon” metabolites. The results indicated a strong correlation between 

anticholinesterase activity of OPs, including chlorpyrifos and chlorpyrifos oxon, and the ability 

to compete for CD binding sites (m2 receptors) in rat brain homogenates. Binding affinities of 

the oxons were in the nanomolar range, at or below concentrations that inhibited AChE (Huff et 

al., 1994); specifically, chlorpyrifos oxon had a binding affinity of 22 nM in rat striatum and 2 

nM in rat cortex (Huff et al., 1994; Ward & Mundy, 1996). In total, these studies suggest that 

direct interactions with muscarinic receptors, and especially the m2 subtype, represent an 

alternative site of action for OPs including chlorpyrifos and chlorpyrifos oxon, with the oxon 

forms having high affinity. 

Further studies determined whether binding of OPs to the m2 receptor had functional 

consequences on downstream second messenger signaling. The m2 and m4 receptors are coupled 

to an inhibitory G-protein (Gi) and activation inhibits adenylate cyclase and decreases cAMP 

formation. In contrast, m1, m3, and m5 receptors increase PI hydrolysis via the stimulatory Gprotein Gp (Lameh et al., 1990). Jett et al. (1991) reported that paraoxon could inhibit both CD 

binding and cAMP formation in rat striatum. The effects of paraoxon were similar to the 

classical muscarinic agonist carbachol, and were blocked by the muscarinic antagonist atropine. 

Similar results were observed in rat frontal cortex for paraoxon and malaoxon (Ward & Mundy, 

1996). In addition, Ward and Mundy (1996) observed that the OPs had no effect on PI 
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hydrolysis. These data suggest that OPs can act as agonists at the m2 receptor (but not the m1, 

m3, or m5 receptor) and decrease cAMP formation. Huff et al. (1994) extended these findings to 

chlorpyrifos oxon, which had an IC50 for inhibition of cAMP of 155 nM. Unlike the other OPs, 

however, the effects of chlorpyrifos oxon were not completely blocked by atropine; an 

observation confirmed by Ward and Mundy (1996). Thus, like other OPs chlorpyrifos oxon can 

act directly as an agonist at the m2 receptor, but may also act at another site downstream of the 

receptor to inhibit adenylate cyclase. 

Cholinergic receptor signaling has been shown to be involved in apoptosis, cell proliferation and 

neuronal differentiation (Resende & Adhikari, 2009). While the m2 receptor subtype is widely 

expressed in proliferating neuroepithelial cells in the ventricular zone of embryonic brain (Ma et 

al., 2004), evidence for a specific role of in the modulation of neurodevelopment is derived 

primarily from in vitro studies. Neural precursor cells derived from embryonic rat cortex 

expressed the m2 receptor, and exposure to muscarinic agonists increased cell proliferation and 

enhanced differentiation into neurons (Ma et al., 2000). Using a P19 embryonic cell line as a 

model for neurogenesis, Resende et al. (2008) used pharmacologic agonists and antagonists to 

demonstrate the ability of m2 receptors to induce cell differentiation. The m2 receptor is highly 

expressed in developing cells of the dorsal root ganglia, and is thought to regulate both neuronal 

and non neuronal cell differentiation (Biagioni et al., 2000; Tata et al., 2003). Another line of 

evidence for involvement of m2 receptor signaling in neurodevelopment is the role of cAMP in 

neurite outgrowth. Studies in both cell lines and primary neuronal cultures show that activation 

of adenylate cyclase and subsequent formation of cAMP stimulate neurite outgrowth (Kamei & 

Tsang, 2003; Mattson et al., 1988), while inhibition of adenylate cyclase decreases neurite 

outgrowth (Tam et al., 2006; Wong et al., 1991). Thus, in vitro evidence supports a role of m2 

receptor signaling in neurodevelopment at the cellular level. However, evidence showing that 

manipulation of m2 receptors can alter brain development in vivo is lacking. 

Because they can also influence neurodevelopment, it should be noted that there are several 

studies demonstrating that OPs including chlorpyrifos and chlorpyrifos oxon can bind directly to 

and desensitize nicotinic receptors (Katz et al., 1997; Smulders et al., 2004). This binding, 

however, occurs at relatively high concentrations (5 – 30 uM) and has not been demonstrated in 

neuronal tissue. 

In summary, in vitro studies have shown that chlorpyrifos oxon can bind to and activate m2 

receptors at levels similar to those that inhibit AChE activity. Other work has shown that m2 

receptor signaling can regulate various aspects of neurodevelopment. Together, the studies cited 

above outline a plausible adverse outcome pathway for chlorpyrifos and chlorpyrifos oxon to 

affect brain development via actions at the m2 subtype of muscarinic receptors. However, while 

there are studies showing that chlorpyrifos oxon can affect neurite outgrowth in vitro and 

decrease cell proliferation and differentiation both in vitro (Jameson et al., 2006; Qiao et al., 

2001; Song et al., 1998) and in vivo (Dam et al., 1998; Qiao, et al., 2003), there is no 

experimental evidence that these effects are a result of direct actions on the m2 receptor. 

3.2.1.3. Endocannabinoid system

Several lines of research have suggested that disruption of the endocannabinoid (EC) system due 
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to chlorpyrifos exposure could play a role in its acute and/or long-term toxicity, and could also 

be extended to potential developmental toxicity. The EC system modulates neurotransmission as 

well as playing a morphogenic role during development of the nervous system. Chemicals e.g., 

drugs of abuse, which act on this system, produce long-term neurodevelopmental disorders in 

animal models and human studies. Chlorpyrifos also interacts with this system, both in vitro and 

in vivo. By this reasoning, the EC system represents a possible adverse outcome pathway for 

developmental effects of chlorpyrifos. 

The EC system includes inhibitory G-protein-coupled receptors (CB1 and CB2), endogenous 

ligands (2-arachidonoylglycerol, or 2-AG, and anadamide, or AEA), and hydrolases 

(monoacylglycerol, or MAG, and fatty acid amide hydrolase, or FAAH) that end the receptor 

actions of these ligands. CB1 receptors are predominant in certain brain regions, and the EC 

system modulates neuronal transmission and is involved in several physiological processes 

including appetite, pain sensation, mood, and cognition (Wilson & Nicoll, 2002). Emerging 

lines of research indicate that during development, this system plays a major role in controlling 

the morphological and functional specification of neurons (e.g., Fride, 2008; Harkany et al., 

2007; Harkany et al., 2008a, b; reviewed in Campolongo et al., 2009). Its involvement in 

neurogenesis, proliferation, and axon guidance suggests a role in overall neuronal connectivity. 

There are epidemiological data on neurodevelopmental outcomes following prenatal exposure to 

cannabinoids such as cannabis and THC through maternal drug abuse, but interpretations are 

often somewhat difficult given co-exposures to other neurotoxicants (e.g., ethanol). Despite 

limitations, an overall picture has emerged of long-lasting impaired cognitive processes, 

including attention and problem-solving deficits, as well as anxiety and depressive symptoms in 

humans (e.g., Campolongo et al., 2009; Fried & Smith, 2001; Jutras-Aswad et al., 2009; Trezza 

et al., 2008). In addition, animal models have indicated various changes in neurotransmission of 

catecholaminergic and indolaminergic systems, ontogeny of motor function, and cognitive 

function in adults developmentally exposed to cannabis as well as other CB1 agonists (e.g., 

WIN55,212-2) (reviewed in Campolongo et al., 2011; Marco et al., 2009). This supports the 

possibility that other chemicals, unrelated to cannabis, that also act on the EC system early in 

development could lead to long-term neurological dysfunction. Despite the number of studies in 

this area, however, the specific responsible cellular responses have not been delineated, and most 

studies have not evaluated basic dose-response such as correlations of the degree of receptor 

binding with any structural or functional outcomes. 

In addition to drugs of abuse (e.g., cannabis), there are several classes of chemicals that act on 

the EC system, including organosulfonyl fluorides and some OPs (e.g., Casida & Quistad, 2004; 

Segall et al., 2003). OPs phosphorylate and thereby block the action of the hydrolases FAAH 

and MAG. This inhibition of the hydrolysis of endogenous cannabinoids could prolong their 

actions on the receptor, which inhibits several neurotransmitter systems, including ACh. OPs 

also bind the CB1 receptor and block the binding of specific receptor ligands. The OPs do not 

bind at the agonist site, however, and may have no inherent agonist or antagonists consequences; 

however, this has not been adequately studied. Thus, prolongation of the ligand residence time, 

plus blockade of ligand binding at the receptor, could lead to downstream modulation of 

cholinergic activity and signaling, separate from AChE inhibition. It is unclear which actions 

could predominate in vivo. 
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Casida and colleagues have produced several papers relating FAAH inhibition to that of AChE 

and neurotoxic esterase (NTE), and considered its involvement in acute cholinergic, 

intermediate, and delayed toxicity syndromes (e.g., Casida & Quistad, 2004; Quistad et al , 2006; 

Quistad et al, 2002a, b; Quistad et al 2001; Segall, et al., 2003). Several studies have included 

chlorpyrifos. They have reported that in vitro chlorpyrifos oxon inhibits FAAH and MAG with 

IC50s of 40 nM and 34 nM, respectively, compared to an AChE IC50 of 19 nM. Other OPs 

(paraoxon, dichlorvos, diazoxon) have higher hydrolase inhibitory actions in vitro (IC50s 540-

14,000 nM). In vitro receptor binding assays of CB1 agonists reveal an IC50 of 14 nM for 

chlorpyrifos oxon, and higher IC50s (>64 nM) for other OPs (chlorpyrifos methyl oxon, 

diazoxon, dichlorvos). 

Casida’s group has also dosed adult mice with OPs and evaluated motor behavior and 

cholinergic signs, followed by ex vivo biochemical assays. These studies report inhibition of 

FAAH activity and CB1 binding only at highly toxic doses of chlorpyrifos and/or chlorpyrifos 

oxon. Chlorpyrifos dose-response differs somewhat across studies, but in general, FAAH is 

inhibited more than MAG and AChE, and CB1 binding is moderately inhibited, but only at doses 

producing cholinergic signs. Chlorpyrifos inhibition of FAAH does not correlate with the 

hypomotility (akinesia, rigidity) produced by the EC agonist amandamide. Furthermore, 

chlorpyrifos does not potentiate the effects of amandamide, suggesting that it does not appear to 

have functional impacts in the EC system. In contrast, treatment with other chemicals that do 

produce marked (>76%) FAAH inhibition does indeed potentiate anandamide effects in mice; 

however, chlorpyrifos doses that would be required to produce this level of FAAH inhibition are 

likely to be lethal. This line of research has led Casida and colleagues to conclude that 

chlorpyrifos actions on the EC system in vivo are less important to its acute toxicity than AChE 

inhibition. 

A series of studies from the laboratories of Pope and colleagues has evaluated the influence of 

CB1 agonists administered in vivo on the signs of toxicity produced by several OPs (Baireddy et 

al, 2011; Nallapaneni et al, 2006, 2008; Pope et al, 2010; Wright et al, 2010). They reported that 

CB1 agonists and other cannabinomimetics block the acute signs of exposure to paraoxon or 

DFP (chlorpyrifos was not tested). Studies with chlorpyrifos show increased extracellular levels 

of 2-AG but not amandamide in hippocampus. Somewhat different conclusions were drawn, 

however, in a paper showing that the toxicity and AChE inhibition produced by an acute dose of 

chlorpyrifos is not altered in mice without the CB1 receptor (knockout) compared to their wild 

type littermates. Pope et al. also measured chlorpyrifos oxon-induced ACh release from these 

tissues and saw only a small difference. These somewhat contradictory results suggest that 

potential EC involvement in the acute toxicity of certain OPs is still uncertain. 

A limited number of studies have examined chlorpyrifos effects on the EC system in developing 

animals. Carr et al. (2011) dosed preweanling rats for 5 days (1-5 mg/kg/day, p.o.) and took 

brain tissue 4 hr after the last dose. The lowest dose produced 40% inhibition of FAAH, 14% 

inhibition of MAG, and only 18% inhibition of AChE; in addition, the highest dose eliminated 

FAAH activity and produced about 52-55% inhibition of AChE. This suggests a greater 

sensitivity of the EC system, at least in terms of the hydrolase compared to AChE activity, in the 

pups. Carr et al (2013) confirmed the findings in 2011 paper and showed that FAAH recovers 
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more faster than brain AChE and MAGL. Another recent publication (Carr et al., 2014) repeated 

these findings using a lower dose (0.5 mg/kg/d for 7 days), still showing significant FAAH 

inhibition but with no measurable AChE inhibition. Additional studies along these lines are 

needed. 

In summary, most of the research in this area has evaluated acute responses in the EC system, 

even though such changes during a critical period of development could have long-term 

consequences. While some steps along a chlorpyrifos adverse outcome pathway for 

developmental neurotoxicity are plausible, there are remaining questions as to whether the EC 

system is sufficiently sensitive to low doses of chlorpyrifos to alter its function during 

development. Despite the increasing understanding of the EC system, there are no data during 

development on 1) inhibition of the CB1 receptor at any time during development (animal 

studies used only agonists); 2) inhibition of FAAH during development; 3) time-course and 

dose-response of OPs in vivo compared to AChE inhibition; and 4) whether these actions on the 

receptors and/or hydrolases actually change EC signaling to a point that would impact 

downstream nervous system development. 

3.2.1.4. Reactive Oxygen Species

The production of reactive oxygen species (ROS) and resulting cellular damage has been 

proposed as a mechanism for a wide variety of neurotoxicants. Due to lower levels of protective 

enzymes and antioxidants, and relatively low numbers of glia relative to the adult, the developing 

brain may be particularly sensitive to neural cell damage caused by oxidative stress. In addition, 

recent work suggests that ROS can act as second messengers. Relatively small changes the 

oxidative status of the cell (redox potential) can lead to changes in redox sensitive signaling 

pathways that regulate cell physiology. In the nervous system, redox signaling is involved in the 

regulation of neurodevelopmental processes including neural stem cell proliferation and 

differentiation (Le Belle et al., 2011; Vieira et al, 2011). A number of studies suggest that 

chlorpyrifos and chlorpyrifos oxon can induce oxidative stress in various neural cell types. Thus, 

generation of reactive oxygen species and/or alteration of cellular redox potential by chlorpyrifos 

represent a possible initiating event leading to developmental neurotoxicity. 

Both chlorpyrifos and chlorpyrifos oxon have been shown to induce oxidative stress in cells in 

vitro (Ojha and Srivastava, 2014; Ki et al, 2013; Chiapella et al, 2013). Lee et al (2012) 

exposed PC12 cells to chlorpyrifos and suggest that neuronal cell death is partly due to MAPK 

activation via ROS generation. In addition, a series of studies using PC12 cells were performed 

by the Slotkin laboratory. Crumpton et al. (2000) reported that acute exposure of undifferentiated 

PC12 cells to 1.5 – 150 uM chlorpyrifos for 10 min resulted in a rapid increase in ROS 

production. Exposure to 30 uM chlorpyrifos oxon for 10 min had no effect. Consistent with the 

generation of ROS, further studies demonstrated an increase in oxidative damage (lipid 

peroxidation) in both undifferentiated and differentiated PC12 cells after exposure to 

chlorpyrifos (3 – 100 uM) for 24 hr (Qiao, et al, 2005). Finally, Slotkin and Seidler (2009) used 

microarrays to examine the expression of genes involved in the response to oxidative stress after 

exposure of PC12 cells to 30 uM chlorpyrifos for 24 or 72 hr. They observed a significant effect 

on transcription of genes including glutathione peroxidase, glutathione-S-transferase, and 

superoxide dismutase, which was greater in differentiated cells compared to undifferentiated 
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cells. PC12 cells were also used by a separate group of researchers who reported that very high 

concentrations of chlorpyrifos (1.4 – 14 mM) increased ROS production during a 5 hr exposure 

period (Geter et al., 2008). ROS production has also been reported in other neuronal cell types. 

Giodano et al. (2007) examined the effects of chlorpyrifos and chlorpyrifos oxon on ROS levels 

and lipid peroxidation in primary cultures of cerebellar granule cells from mice. Exposure to 1 

uM chlorpyrifos for 60 min increased both ROS and lipid peroxidation, and a similar response 

was observed with 1 uM chlorpyrifos oxon. Saulsbury et al. (2009) examined oxidative stress in 

oligodendrocyte progenitor cells, which are the precursors to glial cell types in the brain. 

Exposure to 15 – 120 uM chlorpyrifos for 3 hr resulted in a significant increase in ROS 

production. These in vitro studies suggest that exposure of cells to the micromolar concentrations 

of the parent compound chlorpyrifos (which is not a potent AChE inhibitor) can result in 

oxidative stress. The active metabolite chlorpyrifos oxon was found to be active in cerebellar 

granule cells but not in PC12 cells. Inhibition of AChE was not determined in these studies. 

A limited number of studies have examined the oxidative stress response in rat brain after in vivo

exposure to chlorpyrifos. Bagchi et al. (1995) dosed adult female rats p.o. twice with 41 mg/kg 

chlorpyrifos, once at 0 hr and once at 21 hr. Animals were sacrificed at 24 hr. Both ROS 

production and lipid peroxidation were increased in homogenates from whole brain. In a followup study, the same dosing paradigm resulted in an up-regulation of heat shock protein, which in 

some cases can be a response to oxidative stress (Bagchi et al., 1996). There are two studies of 

oxidative stress in response to chlorpyrifos exposure in developing animals. Slotkin et al., (2005) 

used three dosing paradigms: s.c. exposure of pregnant rats on GD 17-20 followed by collection 

of the fetal brain on GD 21, s.c. exposure of rat pups on PND 1-4 followed by brain collection on 

PND 5, or s.c. exposure of rat pups on PND 11-14 followed by brain collection on PND 15. 

Lipid peroxidation was measured in brain regions including the brainstem, forebrain and 

cerebellum. There was a 20% increase in lipid peroxidation in the brain only after exposure to 5 

mg/kg chlorpyrifos during the later postnatal period (PND 11-14) and only in males. AChE 

inhibition was not assessed. Ray et al. (2010) dosed 7 day old rat pups p.o. with 0.1, 0.5, 1, or 2 

mg/kg chlorpyrifos and examined gene expression in the forebrain 24 hr later using microarrays. 

Only the highest dose inhibited AChE (25%). Gene expression changes were observed at all 

doses, and ‘oxidative stress’ was one of the six canonical pathways that was significantly 

affected. In a recent study, Ambali and Ayo (2012) showed that in vivo treatment to adult rats 

with vitamin C and chlorpyrifos together mediated the muscle ChE inhibition and 

neurobehavioral effects of chlorpyrifos following 17 weeks of gavage dosing at 10.6 mg/kg/day. 

Kalender et al. (2012) also showed that chlorpyrifos effects at a dose of 5 mg/kg/day could be 

modulated by co-treatment with catechin and quercetin. In recent papers (Basha and Poojary, 

2012a, b; Poojary and Basha, 2012), it is hypothesized that cold temperatures induces oxidative 

stress which increases the toxicity from chlorpyrifos. The oral doses used by these investigators 

are relatively high; replication of these findings is needed by another laboratory, particularly at 

lower doses, to provide additional credence to the hypothesis. 

Data from both in vitro studies with neuronal cells (including neural precursors) and in vivo

studies in developing brain demonstrate chlorpyrifos can induce oxidative stress. The in vitro

data suggests that this effect may not be due to AChE inhibition, since the parent compound 

chlorpyrifos is either equipotent or more potent than the oxon. There was, however, no 

concurrent analysis of AChE inhibition in most of these studies. Several known developmental 
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neurotoxicants have been shown to disrupt neural precursor cell proliferation in vitro through a 

common pathway that is initiated by increasing the oxidative state of the cell (Li et al, 2007), and 

the antioxidant vitamin E protected PC12 cells from the anti-proliferative effect of chlorpyrifos 

(Slotkin et al, 2007). Thus, the in vitro data suggest that chlorpyrifos can affect a critical 

neurodevelopmental process, at least in part, via generation of ROS. Though limited, in vivo

studies show both direct evidence (lipid peroxidation) and indirect evidence (alteration in the 

expression of oxidative stress response genes) of oxidative stress in the developing brain after 

exposure to chlorpyrifos. Recent evidence suggests that oxidative stress can alter 

neurodevelopment in vitro and in vivo by the dysregulation of signaling pathways controlling 

neuroprogenitor cell function (Le Belle, et al., 2011; Vieira, et al., 2011). Thus, while there is the 

potential for initiation of an adverse outcome pathway via induction of oxidative stress, it has yet 

to be demonstrated in vivo that treatments preventing the induction of oxidative stress can 

ameliorate the developmental neurotoxicity of chlorpyrifos. 

3.2.1.5. Serotonergic system

The serotonergic system (reviewed in Rho & Storey, 2001; Serotonin Receptors in 

Neurobiology, 2007) includes 7 distinct families of serotonin receptors regulating diverse 

functions such as neuronal excitability, appetite control, memory, learning, circadian rhythm, 

sexual behavior, anxiety, aggression, and respiration. While the majority of the receptors are 

either negatively or positively coupled to adenylate cyclase, the 5-HT3 serotonergic receptor is a 

ligand-gated ion channel. Serotonin receptors are distributed throughout the brain and body. 

Synthesized from L-tryptophan, the rate limiting step in serotonin biosynthesis is catalyzed by 

tryptophan hydroxylase. After release, the action of serotonin is primarily terminated by reuptake 

into the presynaptic terminal via monoamine transporters. Various drugs inhibit the reuptake of 

serotonin, effectively increasing the available synaptic concentrations and actions: Ecstasy, 

amphetamine, cocaine, tricyclic antidepressants and the newer antidepressants: SSRIs (selective 

serotonin reuptake inhibitors). 

Beyond its classical neurotransmitter actions (described above), serotonin has other roles during 

development. In their review, Thompson and Stanwood (2009) described serotonin as a 

pleiotropic molecule, meaning that it can produce multiple, diverse effects, regulating different 

functions at different times during development. The serotonergic system is integral in many 

developmental processes including, but not limited to, neurogenesis, migration, and 

differentiation, synaptogenesis, and cardiac development before assuming its more well-known 

function as a neurotransmitter in the adult nervous system (reviewed in Frederick & Stanwood, 

2009). Serotonin also plays crucial roles in thalamocortical patterning (reviewed in Frederick & 

Stanwood, 2009). As serotonin is present extremely early in development, it is thought that it 

modulates cellular function even before neurogenesis. Later in development, serotonin is 

temporarily taken up by so-called transient serotonergic neurons mainly involved in sensory 

processing, and is involved in activity-dependent patterning of the brain. Later in development, 

serotonin has also been shown to modulate differentiation in the brain. 

Perturbation of the development of the serotonergic system in laboratory animals causes 

permanent changes in the neurochemical, behavioral and structural aspects of the adult nervous 

system (reviewed in Borue et al, 2007; Daubert & Condron, 2010; Frederick & Stanwood, 2009; 
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Oberlander et al, 2009). Moreover, it is generally recognized that the development stage of 

serotonergic perturbation determines the developmental outcome (reviewed in Oberlander, et al., 

2009; Olivier et al, 2011), meaning that the time at which the developing animal experienced the 

insult to the serotonergic nervous system would determine the degree and the result. The review 

by Daubert and Condron (2010) includes a table summarizing many laboratory studies where 

different aspects of the serotonergic system were manipulated (either through knock-out, 

conditional knock-out, or chemical treatment) during development accompanied by the 

behavioral and morphological results of that manipulation. For example, SERT (serotonin 

reuptake receptor) knock-out mice had alterations in sensory patterning, sleep pattern 

abnormalities, and long-term behavioral deficits (Li, 2006), and animals treated with SSRIs 

during development show many similarities to the SERT knock-out mice. Developmental 

exposure to SSRIs in mice produced permanent (adulthood) effects on behavior (Lisboa et al, 

2007; Noorlander et al., 2008) and brain chemistry (Noorlander, et al., 2008) and changes in 

morphology in the hippocampus (Zheng et al., 2011) and somatosensory cortex (Lee, 2009). 

Because of the extensive usage of SSRI antidepressants in the human population, there is a rich 

literature SSRI effects during pregnancy and lactation (reviewed in Alwan & Friedman, 2009; 

Ellfolk & Malm, 2010; Gentile, 2005; Oberlander, et al., 2009; Olivier, et al., 2011; Tuccori et 

al., 2009). The main effects of SSRI usage are on cardiac development, and while there is some 

support for concluding that actions on the serotonergic system early in development can lead to 

long-term neurological dysfunction, there are only few studies that have studied this relationship 

in humans in detail. A recent study has noted that children born to mothers who used SSRIs have 

increased likelihood for social-behavioral abnormalities (Klinger et al., 2011). 

There are numerous studies of the effects of perinatal chlorpyrifos administration on the patency 

of the serotonergic system. Endpoints in various brain regions include serotonin levels (Aldridge 

et al, 2005a; Slotkin & Seidler, 2007b, 2007c), serotonin turnover (Aldridgeet al., 2005a; Slotkin 

& Seidler, 2007b, 2007c), serotonin receptor levels (Aldridge et al, 2005b; Aldridge et al, 2003; 

Aldridge et al, 2004; Slotkin & Seidler, 2005), serotonin reuptake receptor levels (Aldridge, et 

al., 2005b; Aldridge, et al., 2003; Aldridge, et al., 2004; Slotkin & Seidler, 2005), serotonin 

elicited second messenger activity (Aldridge et al., 2005b; Aldridge et al., 2003; Aldridge et al., 

2004), gene expression of serotonin receptor and metabolism related genes (Slotkin & Seidler, 

2007a; Xu et al, 2012), serotonin related behavioral assessments (Aldridge et al, 2005; Chen et 

al., 2011; Venerosi et al, 2010), and behavior after serotonergic drug challenge (Aldridgeet al., 

2005; Venerosi, et al., 2010). Chlorpyrifos was administered during gestation (Aldridgeet al., 

2005a; Aldridge et al., 2003; Aldridge et al., 2004; Slotkin & Seidler, 2007c; Venerosi et al., 

2010), postnatally (Aldridge, et al., 2005; Aldridge et al., 2005a, 2005b; Aldridge et al., 2003; 

Aldridge et al., 2004; Slotkin & Seidler, 2005, 2007a, 2007b) or during adolescent (Chen, et al., 

2011) and the animals were assessed either shortly after dosing ceased (Aldridge et al., 2003; 

Slotkin & Seidler, 2007a), or after a matter of weeks (Aldridgeet al., 2005b; Aldridge et al., 

2003; Chen, et al., 2011; Slotkin & Seidler, 2007b, 2007c) or in adulthood (Aldridge et al., 

2005a; Aldridge et al., 2004; Slotkin & Seidler, 2005; Venerosi et al., 2010). All the data indicate 

that there are acute, as well as permanent, effects of neonatal chlorpyrifos treatment on the 

maturation of the serotonergic nervous system. The effects are often gender-specific, regionspecific and dose-related. For example, a group of papers found permanent effects of late 

gestational chlorpyrifos administration in rats (Aldridge, et al., 2005a; Aldridge et al., 2004) or 
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mice (Venerosi et al., 2010) on the development of the serotonergic nervous system. In rats 

(Aldridge et al., 2005a; Aldridge et al., 2004), chlorpyrifos was administered on GD17-20 (1 or 5 

mg/kg, sc in DMSO), and the adult brain showed changes in brain regional serotonin content (5 

mg/kg group), increased serotonin turnover (1 and 5 mg/kg groups), increases in 5-HT1A and 5-

HT2 receptor populations (1 mg and 5 mg/kg groups), increases in serotonin reuptake receptor (1 

and 5 mg/kg groups), and changes in the adenylate cyclase response to serotonin in the cerebral 

cortex and mid brain (1 mg/kg group)—all indicative of permanent changes in the serotonergic 

tone of the adult brain. In a related study in mice (Venerosi et al., 2010), chlorpyrifos was also 

administered during late gestation, but at a higher dose and by a different route (GD14-17; 6 

mg/kg, peanut oil, gavage); the offspring were assessed upon reaching adulthood ( 90 days). 

The chlorpyrifos-treated rats showed minor differences in the behavioral tests, but when the tests 

were combined with a fluvoxamine (serotonin reuptake inhibitor) pharmacological challenge, 

there were marked differences in responses between the animals that had been exposed to 

chlorpyrifos during gestation and those that had not—another indication that developmental 

chlorpyrifos exposure had permanently altered the serotonergic tone of the adult brain. 

There is ample evidence that chlorpyrifos exposure during development causes permanent 

changes in the serotonergic nervous system; there are, however, few papers that assessed 

concurrently the cholinesterase inhibition (either brain or blood) in those same animals. In some 

cases, although cholinesterase activity was not assessed concurrently, a dosing regimen was used 

that had been characterized previously with regard to cholinesterase activity. It does appear, 

however, that most of the studies on the effects of chlorpyrifos on the serotonergic nervous 

system were conducted with doses of chlorpyrifos that likely produced marked inhibition of 

cholinesterase activity. There is, however, one of the dosing regimens [GD17-20 (daily 

subcutaneous dosing with chlorpyrifos in DMSO] that has been shown to produce no fetal brain 

cholinesterase inhibition measured 24 hours after the last dose at the 1 mg/kg , and inhibition 

(~20%) at 2 mg/kg (Qiao et al., 2002). As noted above, this level of gestational exposure to 

chlorpyrifos permanently altered the development of the serotonergic nervous system (Aldridge 

et al., 2005a; Aldridge et al., 2004). 

In summary, with regard to development of the chlorpyrifos adverse outcome pathway for the 

serotonergic nervous system: the serotonergic system is intregal to mammalian brain 

development and function; perturbation of that system during development leads to lasting 

effects in mammals; and exposure to chlorpyrifos during the perinatal period can permanently 

change the function of serotonergic system. Therefore, as many steps in this chlorpyrifos adverse 

outcome pathway are possible and plausible, and in laboratory animals the serotonergic nervous 

system is sufficiently sensitive to low doses of chlorpyrifos during development to alter its 

function, it is plausible that exposure to chlorpyrifos during development could alter brain 

development and the function of the serotonergic nervous system. Although chlorpyrifos effects 

on the serotonergic nervous system in laboratory animals likely is initiated within 24 hours 

(Slotkin & Seidler, 2007a), the actual initiating event of this potential adverse outcome pathway 

is unknown. 

3.2.1.6. Tubulin, Microtubule Associated Proteins and Axonal Transport 
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Microtubules, one component of the dynamic cytoskeletal scaffolding within each cell, are 

composed of heterodimers of α- and β-tubulin, as well as microtubule associated proteins. The 

microtubule associated proteins appear to have three main functions: (1) to stabilize the 

microtubules; (2) to aid in tubulin dissociation and (3) to act as motor proteins moving 

substances forward and backward along the microtubules (Avila et al, 1994; Pellegrini & 

Budman, 2005; Sánchez et al, 2000). Not only does the microtubule cytoskeleton determine 

neuronal morphology (Matus, 1988, 1990; Sánchez, et al., 2000), but the dynamic reorganization 

of the microtubules and microtubule associated proteins within a cell may also coordinate neurite 

extension/retraction, as well as growth cone advancement. In addition to these integral roles in 

brain structure and growth, microtubules and the microtubule associated motor proteins kinesin 

(Hirokawa & Noda, 2008) and dynein (Vallee et al, 2004) also provide a “railway” for transport 

of materials throughout the cell, i.e., axonal transport (Fukushima et al, 2009), another process 

which is integral to the health of the central and peripheral nervous system, playing a pivotal role 

in neuronal network formation and synapse maturation (Hirokawa & Takemura, 2004). 

During brain development, the pattern of expression of microtubules (Bond & Farmer, 1983; 

Denouletet al, 1986; Meininger & Binet, 1989; Moskowitz & Oblinger, 1995), the posttranslational modification of the tubulin (Fukushima, et al., 2009; Lee et al, 1990), and the 

spectrum of microtubule associated proteins (Fischer & Romano-Clarke, 1990; Nunez, 1986; 

Sánchez, et al., 2000) are all developmentally regulated. Because the phosphorylation of both 

microtubules and microtubule associated proteins are integral to their functional roles in the 

nervous system (Avila, et al., 1994; Fischer & Romano-Clarke, 1990; Fukushima, et al., 2009; 

Sánchez, et al., 2000), there is concern that exposure to OPs may affect the phosphorylation 

status and consequently the function of these molecules. 

One of the shortcomings of constructing this adverse outcome pathway is that there are no 

studies on tubulin dynamics, microtubule associated proteins or axonal transport in developing 

animals, but because tubulin dynamics, microtubule associated proteins and axonal transport are 

so integral to the development and maintenance of the nervous system, the available adult data 

are first given in detail and then summarized below. 

Adult rats treated for 14 days with chlorpyrifos (sc in peanut oil; 2.5, 10, 18, or 25 mg/kg; all 

doses produced plasma cholinesterase inhibition) show depressed anterograde and retrograde 

axonal transport in the sciatic nerve 6 days after cessation of treatment (Terry et al., 2003). This 

same laboratory followed up (Terry et al., 2007) with another, slightly different, study where 

axonal transport was assessed in adult rats given chlorpyrifos repeatedly (sc, 3% DMSO, 97% 

peanut oil vehicle, every other day for 30 days; 18 mg/kg). Depressed bidirectional axonal 

transport was noted after 1 dose, after the 30 days of dosing or 2 weeks after cessation of 

treatment. This dose produced about 30-50% brain cholinesterase inhibition 14 days after last 

dose. In that same year, the Terry laboratory published an in vitro paper (Gearhart et al, 2007) 

studying the effect of chlorpyrifos or chlorpyrifos oxon on kinesin-dependent microtubule 

motility (the mechanism of anterograde axonal transport). Using tubulin and kinesin prepared 

from bovine brain, they assessed kinesin-mediated movement of microtubules. This was done by 

either preincubating the tubulin or the kinesin fraction with chlorpyrifos or chlorpyrifos oxon and 

then combining the fractions and assessing movement of the microtubules. They obtained the 

same interesting pattern using chlorypyrifos or chlorpyrifos oxon: there was no effect if 
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preincubated with microtubules, but marked effect (increased detachment of the microtubules) if 

preincubated with kinesin. This assay contains no cholinesterase activity, thereby precluding 

inhibition assessment. A recently published in vitro paper (Middlemore-Risher et al, 2011) 

assessed the effect of chlorpyrifos or chlorpyrifos oxon transport of mitochondria in rat cortical 

neurons. They found dose-related effects of chlorpyrifos or chlorpyrifos oxon on total distance 

moved and number of moving mitochondria. All doses produced cholinesterase inhibition except 

lowest dose of chlorpyrifos (1 uM) or chlorpyrifos oxon (5 nM), and those doses still affected 

movement. Interestingly the toxic effect was not altered by co-treatment of either a nicotinic or 

muscarinic receptor blocker, an indication that the depressed transport was not mediated through 

cholinergic receptors. Subsequent to demonstrating that in vitro chlorpyrifos oxon appeared to 

alter tubulin dynamics (Grigoryan & Lockridge, 2009) and also covalently bound to tubulin 

(Grigoryan et al., 2008), the Lockridge laboratory assessed tubulin and microtubule associated 

proteins in the brains of mice that had been dosed in vivo with chlorpyrifos or chlorpyrifos oxon 

(Jiang et al., 2010). Repeated (14 days) dosing with chlorpyrifos (sc, 3% DMSO/97% peanut oil, 

3 mg/kg) produced a transient decrease in blood AChE activity, no change in blood 

carboxylesterase activity and a steady decline in blood BuChE activity. Brain β-tubulin from the 

animals treated with chlorpyrifos had been covalently modified (diethosyphosphorylated) on 

tyrosine 281 (the residue also found in the in vitro study (Grigoryan et al., 2009). This same 

paper also described treating adult mice with a single dose of chlorpyrifos oxon (3 mg/kg; ip); 

this dose produced toxic signs and marked AChE and BuChE inhibition in the plasma. Brain 

tubulin was then purified, polymerized, the proteins separated by gel electrophoresis and the 

proteins identified by mass spectrophometry. The polymerized tubulin was also subjected to 

atomic force microscopy nanoimaging to assess the structure (width and height) of the 

microtubules. Microtubules from chlorpyrifos oxon- treated brains were missing 6 microtubule 

associated proteins. This observation was supported by the nanoimaging showing that 

microtubules from chlorpyrifos oxon-treated animals were narrower and shorter than control 

brain microtubules and also had fewer attached (decorated) proteins than do the microtubules 

from control brains. It interesting to note that an in vitro assessment (Prendergast et al., 2007) of 

the effects of chlorpyrifos oxon tubulin/microtubule associated protein dynamics in a 8 day old 

rat hippocampal slice preparation showed all concentrations of chlorpyrifos oxon decreased 

mircrotubule associated protein- staining and decreased microtubule associated protein mediated 

tubulin polymerization by about 45%; all chlorpyrifos-oxon doses inhibited AChE. 

In summary, in the late 2000s, a number of papers were published on the in vitro modification of 

various proteins by chlorpyrifos or chlorpyrifos oxon (Grigoryan, Li, et al., 2009; Li et al., 2009), 

including tubulin (Grigoryan, Li, et al., 2009; Grigoryan & Lockridge, 2009; Grigoryan, 

Schopfer, et al., 2009; Grigoryan, et al., 2008). Although interesting and provocative, these 

studies were usually conducted with exceedingly high concentrations (high micromolar to 

millimolar) of the OP compound, making the connection to a “real world” human exposure 

tenuous. In 2010, however, Jiang and coworkers published an in vivo study in which they found 

covalently labeled tubulin in the brains of mice treated with chlorpyrifos or chlorpyrifos oxon, 

showing the real-world possibility of chlorpyrifos-induced covalent modification of brain tubulin 

in mammals at doses that produced minimal blood cholinesterase inhibition (Jiang, et al., 2010). 

Other studies have shown perturbations in tubulin dynamics, perturbations in the amount of 

tubulin protein or microtubule associated proteins, and changes in axonal transport elicited by 

treatment with chlorpyrifos or its oxon. Submicromolar concentrations of chlorpyrifos oxon 
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markedly inhibited tubulin polymerization in vitro (Prendergast et al., 2007), while in vitro

treatment with chlorpyrifos oxon (but not chlorpyrifos) reduced that amount tubulin in rat C6 

glioma cells (Sachana et al., 2008). Decreases in the amount of microtubule associated proteins 

after chlorpyrifos or chlorpyrifos oxon treatment has been reported by three separate studies: two 

in vitro studies both showed decreases after chlorpyrifos oxon treatment (Prendergast et al., 

2007; Sachana et al., 2008), and one in vivo study showed extensive reduction in specific 

microtubule associated proteins in the brains of mice treated with chlorpyrifos oxon (Jiang et al., 

2010). Depressed axonal transport has been noted by many laboratories both in vivo (Terry et al., 

2007; Terry et al., 2003) and in vitro (Gearhart et al., 2007; Middlemore-Risher, et al., 2011) 

using both chlorpyrifos (in vivo and in vitro studies) and chlorpyrifos oxon (in vitro studies). In 

two instances (Gearhart et al., 2007; Middlemore-Risher et al., 2011), the effects of the 

chlorpyrifos and oxon were noted at concentrations of chlorpyrifos or chlorpyrifos oxon that did 

not inhibition cholinesterase activity (Middlemore-Risher, et al., 2011) or in systems that were so 

basic (purified microtubule/kinesin) that no cholinesterase was present (Gearhart et al., 2007). 

The construction of an adverse outcome pathway using chlorpyrifos-induced effects on tubulin 

and microtuble associated proteins is still in its infancy mainly because although it is thought that 

tubulin, microtubule associated proteins and axonal transport are integral to nervous system 

development and maintainence, there is no experimental evidence that perturbations of these 

endpoints by chlorpyrifos during development has neurotoxic outcomes. 

3.2.1.7 Summary/Conclusions 

There are a number of known developmentally neurotoxic chemicals with well-established 

relationships between exposure and neurological disorders in humans for which a definitive 

mode of action has not been established: for example lead, methyl mercury, and ethanol 

(Alfonso-Loeches & Guerri, 2011; Castoldi et al., 2008; Farina et al., 2011; Johansson et al., 

2007; Verstraeten et al., 2008). Even today, despite with thousands of published papers on these 

three, accepted, developmental neurotoxicants, no coherent adverse outcome pathway or 

pathways can be constructed, because there are a multitude of initiating toxic events and cellular 

responses put forth, and they are not positively connected with one another. The only adverse 

outcome pathway available for any form of neurotoxicity has been developed for domoic acid in 

adult animals (Watanabe et al., 2011). 

As can be seen from the section above, there are several lines of evidence for actions of 

chlorpyrifos distinct from the classical mode of action of cholinesterase inhibition. This 

information has been generated from model systems representing different levels of biological 

organization, and provide support for molecular initiating events (binding to the morphogenic 

site of AChE, muscarinic receptors, or tubulin), cellular responses (alterations in neuronal 

proliferation, differentiation, neurite growth, or intracellular signaling) and responses at the level 

of the intact nervous system (serotonergic tone, axonal transport). In some cases these effects 

occur at chlorpyrifos exposure levels below or equivalent to those which result in cholinesterase 

inhibition in the same test system. When taken in aggregate, data from individual studies can be 

used to develop plausible hypotheses for a mode of action leading to developmental 

neurotoxicity (e.g. inhibition of the morphogenic action of AChE leading to reduced neurite 

outgrowth and subsequent miswiring or dysmorphology of the brain); however, as is the case for 
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many other developmental neurotoxicants, most of these studies have not been designed with the 

specific goal of construction or testing an adverse outcome pathway. Thus, there are not 

sufficient data available to test rigorously the causal relationship between effects of chlorpyrifos 

at the different levels of biological organization in the nervous system. Moreover, there is still 

lack of data on the toxic moiety (ies) impacting neurodevelopment (i.e., chlorpyrifos, 

chlorpyrifos oxon and/or TCPy) (Flaskos, 2012). This is not surprising in light of the complex, 

intricate, and interrelated dynamic processes ongoing in the developing brain and multiple 

critical windows for exposure. 

3.2.2. Adverse Outcomes in Laboratory Animals: Effects on the Developing Brain 

There is a considerable and growing body of literature on the effects of chlorpyrifos on the 

developing brain of laboratory animals (rats and mice) indicating that gestational and/or 

postnatal exposure may cause persistent behavioral effects into adulthood. These data provide 

support for the susceptibility of the developing mammalian brain to chlorpyrifos exposure. The 

literature was summarized and a preliminary review was provided by the Agency for the 2008 

SAP with a more extensive review in 2012 and updated in 2014. The Panel agreed that exposure 

to doses of 1 mg/kg/d and greater, during some developmental period, produced significant and 

long-term effects on behavior. While behavioral changes were consistently reported, they were 

somewhat inconsistent, most likely due to experimental design differences. Such factors include 

route of exposure, developmental period of exposure, test methods, choice of dependent variable, 

statistical analyses, and so on, which are critical features of all developmental neurotoxicity 

studies. 

In this section the agency reviews newer literature, i.e., papers published since 2008, to build on 

the earlier SAP review. The new information provided is evaluated in terms of concordance 

with, or divergence from, the previous, pre-2008 findings. Overall, these new studies serve to 

strengthen the findings on which the 2008 SAP made their conclusions and further documents 

the scope of long-term behavioral effects resulting from chlorpyrifos exposure during 

development. 

Text and tables in 3.2.2.2 were extracted from the 2012 draft SAP issue paper. Section 3.2.2.3 

provides a summary of the newest studies published between 2012 and 2014. 

Papers considered herein by the Agency as addressing long-term outcomes from developmental 

exposure include only those where chlorpyrifos is administered during the preweaning period 

(either gestational and/or postnatal) and the offspring are examined at some time after weaning. 

That is, papers reporting evaluations shortly after birth or during the pre-weaning period do not 

reflect long-term consequences and may also be confounded by AChE/ChE inhibition during 

concurrent or recent exposure; thus, those data are not discussed further here. In addition, the 

Agency focused its efforts on studies using relatively low doses, and that did not (or would not 

be expected to) produce a considerable degree of brain AChE inhibition and resultant cholinergic 

toxicity. These constraints aid in the unencumbered evaluation of longer-term effects compared 

to acute impacts of AChE inhibition, but it does limit the use of some papers. 

At the time of the 2012 SAP meeting, the following papers were identified and evaluated by the 
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Agency; (Abou-Donia et al., 2006; Aldridge, Levin, et al., 2005; Billauer-Haimovitch et al., 

2009; Braquenier, Quertemont, Tirelli, & Plumier, 2010; Carr et al., 2001; Chakraborti, Farrar, & 

Pope, 1993; Chanda & Pope, 1996; Dam et al, 2000; Haviland, Butz, & Porter, 2010; Icenogle et 

al., 2004; D. A. Jett, Navoa, Beckles, & McLemore, 2001; Johnson, Chambers, Nail, 

Givaruangsawat, & Carr, 2009; Laviola, Adriani, Gaudino, Marino, & Keller, 2006; Levin et al., 

2002; Levin et al., 2001; Maurissen, et al., 2000; Muto, Lobelle, Bidanset, & Wurpel, 1992; 

Ricceri et al., 2003; Ricceri et al., 2006; Venerosi, Calamandrei, & Ricceri, 2006; Venerosi et al., 

2008; Venerosi, et al., 2010; Venerosi, Ricceri, Scattoni, & Calamandrei, 2009; Zhang et al., 

2011). It is important to appreciate the various experimental designs, encompassing species, 

gender, exposure scenarios, ages of assessment, and test apparatuses. Of the papers included in 

the 2012 analysis, four papers and one study from a fifth paper did not meet the criteria stated 

above, and were excluded from further consideration herein. 

Specifically, these excluded studies are: 

1) Muto et al. (1992) used a chlorpyrifos product that was formulated in xylene, a known 

developmental and reproductive toxicant, yet the vehicle control group received saline, 

not xylene. Thus, the findings of that study cannot be attributed to chlorpyrifos alone. 

Furthermore, all testing took place before weaning, no later than PND16; 

2) A gestational-exposure study (GD12-19) used a dose (25 mg/kg/d sc) that produced 90% 

brain inhibition in the dams at the end of dosing, and ~20% brain inhibition in offspring 

on PND3 (Chanda & Pope, 1996). The reported motor effects are confounded with the 

concurrent cholinesterase inhibition on PND1 and 3, and no testing was conducted at 

later ages; 

3) A postnatal study (Chakraborti, et al., 1993) used a very high dose (40 mg/kg/d sc) given 

directly to the pups postnatally (PND7, 11, 15, 19), with up to 60% brain inhibition 

evident four days after dosing; 

4) A gestational study (Venerosi, et al., 2009) only tested pups up to PND12, reporting 

altered motor ontogeny and changes in ultrasonic vocalizations; and 

5) The “postnatal” study described by ( Jett, et al., 2001) involved dosing after weaning, 

while the rats were being tested in the Morris water maze. Note, part of this paper 

remains in consideration, since the “preweaning” study from that paper met the criteria 

for inclusion. 

 

3.2.2.1		Developmental Impacts on Neurological Domains

Because many of these papers report a number of positive as well as negative findings, the 

agency had previously taken the approach of comparing responses that were observed following 

various exposures to a common dose, 1 mg/kg/d (FIFRA SAP, 2008a; U.S. EPA, 2011). A more 

robust approach is taken here, to include important factors such as dose-response and differences 

in exposure scenarios. In order to evaluate effects on domains of neurological functions, broken 
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down by exposure period, the papers have been summarized as such in Section 3.2.2.1.5. It is 

well-known that exposures during different critical periods of development can result in very 

different outcomes (Adams et al., 2000; Rice & Barone, 2000). Unfortunately, development of 

the nervous system does not occur in distinct non-overlapping temporal periods with which to 

attribute the apical behavioral changes observed. In the text and tables below, the papers from 

2008 to 2012 are described and evaluated in terms of their contribution to and concordance with, 

previous papers using similar measures. The agency’s evaluation follows several Bradford Hill 

criteria including consistency of findings across domains and dose response. The latter was 

considered as either graded magnitude of effects across doses or significant findings at a higher 

but not lower dose; unfortunately, many of the chlorpyrifos studies have evaluated only one 

dose. 

These studies include measures that evaluate the following neurobehavioral domains: cognitive 

function, anxiety and emotion, social behavior and maternal interactions, and motor activity. 

These are discussed in greater detail below; overall, the newer data were in general agreement 

with previous studies. 

3.2.2.1.1 Cognitive Function 

A few of the new studies evaluated effects on cognitive function, using some of the same 

procedures used in the earlier studies (radial arm maze, Morris water maze). The earlier findings 

are summarized below, along with comparisons to the newer studies. 

Radial Arm Maze 

The radial arm maze is a spatial task requiring the subject to enter different arms for food 

reinforcer, which is located in only certain arms. A widely used test of learning and memory, 

both working errors (re-entering baited arms after taking the food) and reference errors (entering 

arms that are never baited) are recorded over days of training. From 2001-2005, a series of 

behavioral studies from a single laboratory (Duke University) described effects on radial arm 

maze performance in adult rats that were treated with chlorpyrifos during gestational or postnatal 

development. Both working and reference memory are altered in rats treated during gestation or 

early postnatally, and gender differences were described in all but one study. Across these 

studies, significant differences were detected in one or the other type of error, or both, and error 

selectivity was not fully consistent across studies. Following early gestational exposure (GD9-

12), high-dose (5 mg/kg/d) rats of both sexes showed increased working and reference memory 

errors early in training (Icenogle, et al., 2004). With later gestational exposure (GD17-20), only 

females showed more errors; however, this was observed only at the low dose (1 mg/kg/d, not 5 

mg/kg/d) (Levin, et al., 2002). With early postnatal exposure (PND1-4, 1 mg/kg/d), two separate 

studies showed similar results in that error rate was increased in males (worse performance) but 

decreased in females (better performance) (Aldridge, Levin, et al., 2005; Levin, et al., 2001). 

Only the late postnatal exposure (PND11-14, 5 mg/kg/d) was without effect on this measure 

(Levin, et al., 2001). 

Several new studies have confirmed chlorpyrifos impacts on learning and memory. In a new 
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study using the radial arm maze and longer postnatal exposures (PND1-21), male, but not 

female, rats showed more working memory errors at all doses (lowest dose, 1 mg/kg/d) 

(Johnson, et al., 2009). This was evident later in training, indicating that the task was not learned 

as well as in controls. Reference memory errors showed the same gender-specific pattern as seen 

in two Duke studies (Aldridge, Levin, et al., 2005; Levin, et al., 2001), in that chlorpyrifostreated females showed fewer errors during training, and males showed more. The altered 

reference memory pattern was only seen in the mid and high dose groups, which had 

incrementally increasing doses (up to 4 or 6 mg/kg/d) during the exposure period. In this study, 

the low-dose group had experienced about 14% brain AChE inhibition on PND20, with no 

inhibition evident when radial arm maze training began (PND36). On the other hand, the mid 

and high-dose rats showed some residual AChE inhibition at that time, making it challenging to 

separate the memory deficits from concurrent AChE holinesterase inhibition. 

A study using mice exposed gestationally (GD17-20, 1 or 5 mg/kg/d) also used a radial arm 

maze, although comparisons are difficult due to differences in length of training and data 

presentation (Haviland, et al., 2010). Reference memory errors showed some differences across 

trial, dose, and sex; these are not interpreted by the authors as a meaningful effect, and may 

indeed be spurious. The authors used these data to compare with their novel foraging task, 

which used a modification of a radial arm maze and examined the rate of learning that a reward 

(food) is present (recognition) as well as its location (positional learning). Both of these 

parameters were altered by chlorpyrifos exposure. The low dose (1 mg/kg/d) females were 

delayed in learning to recognize the reward, but there were no statistically significant differences 

at that dose in positional learning (despite the authors’ claim of an effect). On the other hand, 

low-dose males showed accelerated food recognition, and increased positional learning that was 

evident during only two sessions. The high-dose groups (both sexes) showed the same pattern of 

changes, with somewhat greater magnitude of differences, indicating dose-response. Thus, 

effects were observed on a spatial learning task, albeit a different apparatus and procedure. Note 

that the gender effect was reversed from that reported above for rats in the radial arm maze – 

these differences could be due to species or testing apparatus. 

Morris Water Maze 

The Morris water maze is a different type of spatial learning task, which can be varied to assess 

different types of learning and memory. In this test, the animals are trained over days to swim to 

the location of a submerged platform to escape from the water, and learning is evident by faster 

latencies and other measures of memory for the platform position. A recent paper using 

gestational exposure (GD9-18) in mice resulted in slower learning in the offspring, although a 

clear dose-response was not evident (Billauer-Haimovitch, et al., 2009). The authors report an 

overall effect of chlorpyrifos treatment, but state that only the lower doses (1, 3 mg/kg/d, but not 

5, 10 mg/kg/d) were individually significant. While the magnitude of effect does not appear to 

be pronounced in the first study (visual inspection of Figure 3 in the paper), the finding was 

repeated, and was more obvious, in two additional studies using a single dose (3 mg/kg/d). 

Another recent paper from the same laboratory again reported slower learning in mice exposed 

during gestation (3 mg/kg/d; GD9-18) (Turgeman et al., 2011). Thus, there are internal 

replications of this finding across several studies, albeit in the same laboratory. 
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These recent findings, plus another study available only in Chinese66, extend an earlier report of 

rats that showed deficits in the Morris water maze (beginning on PND23) following exposure on 

PND7, 11, and 15 (0.3, 7 mg/kg/d ) (Jett, et al., 2001). The 2008 SAP, however, was critical of 

this paper, for reasons described below. 

Summary 

Taken together, these studies in rats and mice show altered cognitive function using wellaccepted tests of spatial learning and memory (radial arm maze, Morris water maze). The 

direction of change may be sex-specific and dependent on the timing of exposure. Often these 

changes suggest impaired learning and/or memory. While enhanced function is also apparent in 

some studies, such changes are evidence of alterations in memory processes nonetheless. 

Several of these findings have recently been replicated across studies and laboratories. Effects 

were also reported in a spatial foraging task (Haviland, et al., 2010), but direct comparison 

between that and the radial arm maze is difficult. Earlier papers have reported that other 

cognitive tasks (spontaneous or delayed alternation, passive avoidance) are not altered, but there 

are no new studies using these other tasks. These outcomes are summarized in App 1-8. 

 

66 Another new study also evaluated Morris water maze learning, using rats exposed postnatally (PND11-14, 5 

mg/kg/d, route uncertain) (Zhang et al., 2011). The abstract states that learning and memory impairments were 

observed, but the rest of the paper cannot be critically evaluated at this time (article is in Chinese) and so cannot be 

combined with other findings. 
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Table App. 1-8. Summary of Outcomes on Cognitive Tests in Male (M) and Female (F) Rodents (Extracted from 

Early gestation 

GD9-12; 9-18 

Late gestation 

GD 17-20 

Perinatal 

GD6-LD10 

Early postnatal 

PND1-4 

Late pos

PND11

7,11,

Radial Arm Maze 

Cognitive deficit - rat, M&F, doseresponse6

Cognitive deficit - rat, F not M, no dose-response4

Cognitive deficit in M, improved function in F - rat3

Cognitive deficit in M, improved function in F - rat7

No effect – M&F3

Morris Water Maze 

Cognitive deficit - mouse, M&F, no dose-response8 Cognitive deficit – mouse, M&F11

Cognitive rat, M&F, response2 

Foraging Maze 

Cognitive deficit in F, improved function in M – mouse, doseresponse in F not M10

T-maze Spontaneous Alternation 

No effect – rat, M&F6

No effect – rat, M&F4

No effect – rat, M&F3

No effect – M&F3

Delayed Spatial Alternation 

No effect – rat,M&F1

Passive Avoidance No effect – mouse, only M tested5 No effect – 

only M tes

1 Maurissen et al., 2000 2 Jett et al., 2001 
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3 Levin et al., 2001 4 Levin et al., 2002 5 Ricceri et al., 2003 6 Icenogle et al., 2004 7 Aldridge et al., 2005 8 Billauer-Haimovitch et al., 2009 9 Johnson et al., 2009 10 Haviland et al., 2010 11 Turgeman et al., 2011 
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3.2.2.1.2 Anxiety/Emotion 

Anxiety and despair/affect have been modeled in animals using a number of different tests, 

including those that measure avoidance of aversive areas, investigation of novel areas, response 

to forced activity, and response to preferred substances. Several new studies assessed anxiety, 

despair, and affect. While different procedures were used in most cases, there was some 

concordance in outcomes. These are described below. 

Tests of Anxiety 

Several studies have suggested that chlorpyrifos alters measures of anxiety. The early studies 

came from the laboratories of Duke University and ISS (Italy). Three newer studies, all in mice, 

add to the findings in this area: two were from the ISS laboratory and one came from a third 

laboratory (Belgium). Earlier studies employed an elevated plus maze, which has open and 

enclosed areas: being fearful of bright open areas, rodents tend to stay in the enclosed arms. 

Decreased anxiety is inferred from changes such as increased time in the open arms, decreased 

head dipping, and other measures. Using this paradigm, lower anxiety was reported in rats 

(males, not females) exposed postnatally to 1 mg/kg/d (PND1-4)(Aldridge, Levin, et al., 2005; 

Ricceri, et al., 2003) and in mice (females, not males) exposure postnatally to 3 mg/kg/d 

(PND11-14) (Ricceri, et al., 2006). On the other hand, increased anxiety was measured in mice 

exposed gestationally (GD15-18), but only in the low-dose (3 mg/kg/d, not 6 mg/kg/d) males 

(not females) (Ricceri, et al., 2006). There were no changes in anxiety behaviors in rats exposed 

gestationally (GD9-12, 1 or 5 mg/kg/d) (Icenogle, et al., 2004). 

Three new studies have used a different apparatus, the light-dark box, to measure anxiety, but the 

principle is the same in that rodents typically prefer the dark chamber. In one study (Braquenier, 

et al., 2010), both the elevated plus maze and the light-dark box were used, providing direct 

comparisons between these tests. Mice were exposed both gestationally and lactationally 

(GD15-LD14, 0, 0.2, 1, or 5 mg/kg/d), and only female offspring were tested. When tested in 

the light-dark box, the middle dose group only (1 mg/kg/d) showed decreased time in the center 

of the light side, which was considered to reflect increased anxiety. The same dose group moved 

back and forth between the sides less often, which could reflect general activity levels but was 

also interpreted by the authors as greater anxiety. The time in the dark and light sides, however, 

did not differ. Littermates were tested in the elevated plus maze, and again the middle dose 

group showed less time spent in the open arms and fewer open arm entries, supporting a 

conclusion of increased anxiety, but only at the middle dose. The lack of dose-response was not 

addressed by the authors. 

A study in which mice were treated gestationally (GD14-17, 6 mg/kg/d) (Venerosi, et al., 2010) 

showed no difference in time spent on either side of the light-dark box, but females (not males) 

spent more time in the tunnel connecting the sides. This finding, along with a few other 

measures that did not reach statistical significance, was interpreted by the authors as increased 

anxiety. Another study by this group (Venerosi, et al., 2008) exposed mice postnatally (PND11-

14, 3 mg/kg/d), then bred the female offspring as adults and allowed them to deliver normally. 

On postpartum day 2, the dams were removed from the pups and tested in the light-dark box. 

They reported a decreased latency and higher proportion of mice entering the light side, 

indicating decreased anxiety. Time in the dark or light sides was not reported. 
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Despair/Affect 

Only a few studies have also included measures of despair and affect, and the results are not 

completely consistent. Most recently, behavioral despair was measured using a forced 

swimming procedure (Venerosi, et al., 2010). In that study, gestational exposure to chlorpyrifos 

did not alter any baseline responses, and there are no other studies using similar measures with 

which to compare. There have been no new studies that could add to, and aid in the 

interpretation of, the findings reported by others: decreased preference for chocolate milk in rats, 

or no effect on novelty exploration in mice (Aldridge, Levin, et al., 2005; Ricceri, et al., 2003). 

Summary 

Taken together, these assessments suggest that, in both rats and mice, changes in anxiety are 

dependent on exposure period. Specifically, postnatal exposure decreases anxiety, whereas 

increased anxiety is observed following late gestational exposure or a longer gestational plus 

postnatal exposure. Some inconsistencies are evident, such as lack of dose-response in a few 

studies and effects on one sex or the other (same laboratory, similar dosing regimen). Thus, 

while the data are not fully consistent, overall there is evidence for long-term changes in anxiety 

behavior following chlorpyrifos exposure, as shown in Table App 1-9. 
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Table App 1-9 Summary of Anxiety/Emotion Outcomes in Male (M) and Female (F) Rodents (Extracted from USEPA, 2012)

Early gestation 

GD9-12 

Late gestation 

GD14-17; GD15-18 

Early postnatal 

PND1-4 

Late postnata

PND11-14 

Elevated Plus Maze (anxiety) No effect – rat, M&F1

Decreased anxiety - mouse, M not F, no dose-response5

Decreased anxiety - rat, M not F2

Decreased anxiety mouse, F not M, dresponse5

Light-Dark box (anxiety) 

Increased anxiety - mouse, F not M7

Decreased anxiety mouse, dams6

Despair No effect – mouse, M&F7Novelty/preference 

Decreased anxiety - rat, M&F2

No effect – mouse, M&F4

No effect – mouseM&F4

1 Icenogle et al., 2004 2 Aldridge et al., 2005 3 Braquenier et al., 2010 4 Ricceri et al., 2003 5 Ricceri et al., 2006 6 Venerosi et al., 2008 7 Venerosi et al., 2010 
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3.2.2.1.3 Social Behaviors/Interactions 

Conspecific behaviors are not typically studied in the context of developmental neurotoxicity 

studies, and methods for such assessments are not well-standardized (Cory-Slechta, et al., 2001). 

Aggressive, social, and parental behaviors have been studied following developmental exposure 

to chlorpyrifos, all of which were conducted in the ISS laboratory using mice. Even within this 

one laboratory, however, various testing methods have been used. These include: 1) social 

investigation of nulliparous female:female pairs; 2) social investigation and agonistic behaviors 

in male:male pairs; 3) maternal behaviors towards pups, induced by placing nulliparous females 

with foster litters; 4) maternal behaviors in lactating dams towards their own pups, and 5) 

agonistic behaviors in lactating dam:male pairs. In most studies, measures include social 

(interactive) as well as nonsocial behaviors (e.g., grooming, exploration). Within each grouping, 

there are often many measures taken, and treatment effects have sometimes been reported on just 

a few, suggesting that the changes are subtle. There are few systematic comparisons of 

chlorpyrifos effects across these varied behaviors using similar dosing regimens. 

Maternal Behavior 

Two recent papers focused on maternal behavior, measuring actions of the lactating dam towards 

her pups, nesting activity, and agonistic behaviors towards an intruder male. In one study 

(Venerosi, et al., 2008), mice were exposed postnatally (PND11-14, 3 mg/kg/d), mated as adults, 

and the dams were tested. They reported that the treated dams showed delayed start of nesting, 

decreased latency to lick pups, as well as fewer defensive postures and more social investigation 

of an intruder male. This latter effect was replicated in that decreased aggressive behaviors were 

also reported for lactating dams that had been exposed in their own fetal period (GD14-17, 6 

mg/kg/d) (Venerosi, et al., 2010). These findings were interpreted by the authors as impaired 

maternalistic behaviors. 

Social/Agonistic Behavior 

Evaluations of male or female same-sex social behaviors generally show no effect of postnatal 

(PND1-4 or PND11-14) chlorpyrifos exposures (Ricceri, et al., 2003; Venerosi, et al., 2006), a 

finding that was recently confirmed (Venerosi, et al., 2008; Venerosi, et al., 2010). On the other 

hand, increased investigation of the stranger mouse was reported following gestation exposure 

(GD15-18)(Venerosi, et al., 2006), and increased solicitation behaviors were observed following 

late postnatal exposure (PND11-14) (Ricceri, et al., 2003). Thus, there are contradictory reports 

of changes in female social behavior, being either increased or not altered. The only study of 

male social behavior showed no effect. 

Earlier studies reported that male agonistic behaviors were consistently increased in mice 

exposed either postnatally (PND1-4, PND11-14) or gestationally (GD15-18); the lowest 

effective dose was 1 mg/kg/d administered during either postnatal period (Ricceri, et al., 2003; 

Ricceri, et al., 2006). None of the newer studies addressed male behaviors. These findings are 

summarized in Table 1-10. 
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Summary 

Overall, decreased aggressive behaviors of dams that had been exposed during their development 

are a common finding across several exposure periods, as are increased male agonistic behaviors. 

Social and/or maternal behaviors in females are less consistently or convincingly altered. 
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Table App 1-10. Summary of Social/Interactive Behavior Outcomes in Male (M) and Female (F) Mice (Extracted 

Exposure/effect Late gestation GD14-17, 15-18 Early postnatal PND1-4 

Female:female social Increased investigation, doseresponse3 No effect1

Increarespon

No effNo eff

Male:male social Increased aggressive postures, dose-response2 Increased agonistic behavior, differs across time-course, dose- response1

IncrearesponIncreatime, nIncrearesponNo eff

Induced maternal No effect2 Increa

respon

Natural maternal DecreaNatural maternal:male Decreased aggression5 Decrea

DR dose-response, if not mentioned, only one dose tested a apparently only social, no agonistic, behaviors measured1 Ricceri et al., 2003 2 Ricceri et al., 2006 3 Venerosi et al., 2006 4 Venerosi et al., 2008 5 Venerosi et al., 2010
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3.2.2.1.4 Motor activity 

Most of the developmental studies of chlorpyrifos have evaluated motor activity of some sort; 

these were presented and summarized in detail in the 2008 SAP report. In general, activity levels 

have been recorded in terms of: 1) locomotion; 2) response latencies or other activity measures 

in the course of testing in various apparatuses (t-maze, radial arm maze, etc); and 3) habituation 

of activity over the session in activity chambers. In general, the earlier studies reported that 

activity is increased, decreased, or not altered, in either both or just one sex. Recent studies add 

to this literature, but none provide information to better explain these varied and contradictory 

outcomes. 

Activity Devices 

Evaluations of locomotor activity in open fields or other activity devices have been made in 

numerous studies across quite a few laboratories. Looking across developmental period and 

gender, there have been similar reports of increased, decreased, or no change in various measures 

of activity (e.g., exploration, rearing, etc). Generalizations of effect could not be made in the 

2008 preliminary review. 

Two recent studies have used an open field to measure activity. Braquenier et al. (2010) reported 

no change of activity levels in female mice that had been exposed from late gestation through 

late lactation (GD15-LD14). While Zhang et al. (Zhang, et al., 2011) reported decreased activity 

in rats, those data cannot be evaluated at this time due to the language barrier. 

Ancillary Activity Measures 

As with activity measured specifically in activity devices, various measures of activity during 

other tests have shown increases, decreases, or no effect, in both or only one sex, with no 

obvious association to exposure period. It is important to consider, for example, that the time it 

takes to visit the arms of a radial arm maze is a somewhat different behavior than exploratory 

movement in a novel chamber. Thus, these activity measures may not be fully comparable, and 

the evaluations described here should be interpreted with caution. 

Three recent papers reported assessments of activity during cognitive and social behavior testing. 

There was no change noted in postnatally exposed rats during radial arm maze testing (PND1-

21) (Johnson, et al., 2009), or in gestationally exposed mice during foraging testing (GD17-20) 

(Haviland, et al., 2010). Mice exposed postnatally (PND11-14) showed decreased exploratory 

behaviors during the acclimatization phase of social testing (i.e., before being exposed to another 

mouse); however, this was only significant in the first block of the test session (Venerosi, et al., 

2008). 
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Habituation 

Habituation, or decrease in activity level during the course of a test session, has only been 

specifically evaluated in the a few laboratories. As with other activity outcomes, the data are 

specific to exposure period and gender. Within the same laboratory, habituation is either faster 

(early gestation, both sexes) (Icenogle, et al., 2004), slower (late gestation, females only; late 

postnatal, both sexes) (Levin, et al., 2002; Levin, et al., 2001), or not altered (early postnatal) 

(Levin, et al., 2001). A perinatal study (GD6-LD10) also reported no change in habituation; 

however, the data were not statistically analyzed in the same way, so results are not comparable 

(Maurissen et al., 2000). This measure has not been addressed in more recent studies. 

Summary 

There remains to be inconsistencies in the various measures of motor activity, but it is important 

to note the numerous differences in procedures and apparatuses in which activity was measured. 

Given this, generalizable conclusions and summaries cannot be made at this time. 
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Table App 1-11. Summary of Motor Activity Outcomes in Male (M) and Female (F) Rodents (Extracted from USEPA, 2012)

Early gestation 

GD9-12 

Late gestation 

GD14-16; 14-17; 

15-18; 17-20 

Perinatal 

GD6-LD10; GD15-

LD14 

Early postnatal 

PND 1-4 

Late postn

PND11-1

Activity chambers 

No effect – rat, M&F6

Increased activity - mouse, only M tested, doseresponse13No effect - mouse8

No effect – rat, M&F10

No effect – rat, M&F11No effect – mouse, only F tested2

No effect - mouse, M&F12Decreased activity - rat, M not F4No effect – rat, M&F9

Increased actimouse, only M tested, doseresponse13

Increased actimouse, M&F, response12Increased actirat, M&F4No effect – raM&F9

Activity measures 

Increased activity - rat, M&F, doseresponse6

No effect - mouse, only F tested14

No effect - mouse, M&F5

Increased activity - rat, M&F, DR10

No effect - mouse, M&F13

No effect – mouse, only F tested2

Increased activity - rat, M not F1Increased activity - mouse, M&F12Decreased activity - rat, M not F9

Decreased actmouse, M&F1Decreased actrat, M not F9

Increased actimouse, M&F1

No effect – mM&F13

Habituation rate 

Increased habituation - rat, M&F, doseresponse6

Decreased habituation - rat, F not M, doseresponse10No effect – mouse8

No effect – rat, M&F11

No effect – rat, M&F9

Decreased habituation - rM&F9

1 Aldridge et al., 2005 2 Braquenier et al., 2010 

3 Carr et al., 2001 4 Dam et al., 2000 
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5 Haviland et al., 2010 6 Icenogle et al., 2004 7 Johnson et al., 2009 8 Laviola et al., 2006 9 Levin et al., 2001 10 Levin et al., 2002 11 Maurissen et al., 2000 12 Ricceri et al., 2003 13 Ricceri et al., 2006 14 Venerosi et al., 2008 
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3.2.2.1.5 Detailed information on behavioral studies (Extracted from USEPA, 2012). Table App 1-12. In vivo studies of chlorpyrifos administered during gestation; effects described are only tweaning. Bold indicates functional domain that showed effects 

Study & laboratory 

Species & strain 

Dose, route, vehicle 

Dosing period AChE 

inhibition 

Domain Age of testing Outcome, 

effective dose & gender 

Abou-Donia et al., 2006 Duke 

Rat SD 1 mg/kg/d 

dermal in 70% ethanol 

GD4-20 0-30% increase 

brain (regions) activity at PND90, F not M 

Neuromotor function 

PND90 Decreased grip 

time, M&F; Decreased performance on inclined plane, not M; 

Icenogle et al., 2004 Duke 

Rat SD 1, 5 mg/kg/d sc 

in DMSO 

GD9-12 Not measured Motor activity PND28-42 Faster habituat

in figure-8, 5 mg/kg/d, M&F 

PND28-56 Lower latencie

(faster) in t-maearly in testing1&5 mg/kg/d, M&F 

PND>91 Increased cente

crosses in elevated plus maze (more active), 5 mg/kg/d, M&F 

Cognition PND28-56 No effect on tmaze 

spontaneous alternation, M&

PND56-91 Increased radia

arm maze working & reference memory errorsearly in trainin5 mg/kg/d, M&

Neuromotor function 

PND>91 No effect on 

acoustic startle 
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Study & laboratory 

Species & strain 

Dose, route, vehicle 

Dosing period AChE 

inhibition 

Domain Age of testing Outcome, 

effective dose & gender reflex or prepuinhibition, M&

Anxiety & Emotion 

PND>91 No effect on tim

in sides of elevated plus maze, M&F 

BillauerHaimovitch et al., 2009 Israel 

Mouse HS/lbg 1, 3, 5, 10, 20 

mg/kg/d sc in DMSO 

GD9-18 Not measured Cognition PND75 Longer latencie

(slower learninin Morris watemaze, 1&3 mg/kg/d, M&F 

Turgeman et al., 2011 Italy 

Mouse HS/lbg 3 mg/kg/d GD9-18 Not measured Cognition PND80 Longer latencie

(slower learninin Morris watemaze, M&F 

Laviola et al., 2006 Italy 

Mouse, wild type on C57/Bl6 background; Study included reeler homoand heterozygous mice 

Oxon 5 mg/kg/d sc in osmostic pump with DMSO 

GD14-16 Not measured Motor activity PND>70 No difference i

activity levels during habituation, before drug challenges 
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Study & laboratory 

Species & strain 

Dose, route, vehicle 

Dosing period AChE 

inhibition 

Domain Age of testing Outcome, 

effective dose & gender 

Venerosi et al., 2010 Italy 

Mouse CD-1 6 mg/kg/d po in 

peanut oil 

GD14-17 Not measured Anxiety & 

emotion 

PND90 Increased time 

tunnel between sides in light-dbox, F not M;  Decreased latency to enter dark side (p=0.05) (increased anxiety), F not 

No effect on forced swimmitask, M&F 

Social behavior & maternal interaction

Adult F, after mating on postpartum day 8 

Decreased attacbehavior, increased sociasniffing behavito intruder mal

Haviland et al., 2010 UWM 

Mouse Swiss Webster 

1, 5 mg/kg/d sc in DMSO 

GD17-20 Not measured Cognition PND60-81 Slower learning 

of food recognition & position, 1&5 mg/kg/d, F not M; Faster food recognition learning, 1 not mg/kg/d, M no
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Study & laboratory 

Species & strain 

Dose, route, vehicle 

Dosing period AChE 

inhibition 

Domain Age of testing Outcome, 

effective dose & gender F; Spurious changin radial arm maze (says no effect, only 9 trials) 

Motor activity PND60-81 No effect on 

foraging rate, M&F 

Levin et al., 2002 Duke 

Rat SD 1, 5 mg/kg/d sc 

in DMSO 

GD17-20 Not measured Motor activity PND28-42 Lower latencie

(faster) in t-maearly in testing1&5 mg/kg/d, M&F 

Slower habituation in figure-8, 1&5 mg/kg/d, F onl

PND56-91 Lower latencie

(faster) in radiaarm maze durincertain blocks only, 1&5 mg/kg/d, M&F 

Cognition PND28-42 No effect on tmaze 

spontaneous alternation, M&

PND56-91 Increased radia

arm maze working & reference memory errorsmg/kg/d only, F not M 
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Study & laboratory 

Species & strain 

Dose, route, vehicle 

Dosing period AChE 

inhibition 

Domain Age of testing Outcome, 

effective dose & gender 

Ricceri et al., 2006 Italy 

Mouse CD-1 3, 6 mg/kg/d po 

in peanut oil Plus vehicle on PND11-14 

GD15-18 Dam on GD19 

(pilot study): 40% brain ChE inhibition at 6 mg/kg/d, serum inhibition at both doses Pup on GD19: no brain inhibition, serum inhibition at both doses Pup on PND15: no brain or serum inhibition 

Motor activity PND70 Increased 

crossing in opefield, 6 mg/kg/only M tested 

PND120 No effect on 

elevated plus maze activity, M&F 

Anxiety& emotion 

PND120 Decreased head 

dips in elevated plus maze (decreased anxiety), 3 mg/kg/d only, M not F 

Social behavior & maternal interaction

PND75-80 Increased 

offensive postubehaviors, 6 mg/kg/d, only M tested; No effect on nosocial behavior

PND90 No effect on 

induced maternbehaviors 

Venerosi et al., 2006 Italy 

Mouse CD-1 3, 6 mg/kg/d sc 

in peanut oil Plus vehicle on PND11-14 

GD15-18 Cites Ricceri 

2006 findings 

Social behavior & maternal interaction

PND120 Increased 

ultrasonic vocalizations, 6 mg/kg/d; only tested Increased sociainvestigation, 6 mg/kg/d 

a Sample sizes and analyses refer only to behavioral measures b Graded response across doses or else effect at higher but not lower dose(s) 

M, F, Male, Female 
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Table App 1-13. In vivo studies of chlorpyrifos administered postnatally. Effects described are only those 

weaning. Bold indicates functional domain showed effects. 

Study & laboratory 

Species & strain 

Dose, route, vehicle 

Dosing period 

AChE inhibition 

Domain Age of 

testing 

Outcome, effective dose & gender 

Notesa 

Dam et al., 2000 Duke 

Rat SD 1 mg/kg/d sc 

in DMSO 

PND1-4 ~60% brain 

(regions) inhibition in M, ~20% in F; more at 2 hr than 4 hr after single dose on PND1 

Motor activity 

PND21, 30 

Decreased open field activity & rearing, M not F 

5-7/sex/Maybe dosing bexplicitAlso sompreweantesting 

Levin et al., 2001 Duke 

Rat SD 1 mg/kg/d sc 

in DMSO 

PND1-4 Not measured Motor 

activity 

PND28-42 

Longer latencies (slower) in t-maze, M not F 

10/sex/dSplit-litNo ACh involvem

No effect on habituation in figure-8, M&F 

Cognition PND28-

42 

No effect on tmaze spontaneous alternation, M&F 

PND56-91 

Increased radial arm maze working & reference errors, early in training, M not F; Decreased radial arm maze working & reference errors, F not M 

Adridge et al., 2005 Duke 

Rat SD 1 mg/kg/d sc 

in DMSO 

PND1-4 Not measured Cognition PND64+ Increased radial 

arm maze working & reference memory errors, M not F; Decreased working memory 

9/sex/doProbabllitter donot expl
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errors in radial arm maze, F not M 

Anxiety & emotion

PND52-53 

Increased open arm time in elevated plus maze (decreased anxiety), M not F 

PND54 Decreased 

chocolate milk preference (anhedonia), M&F 

Motor activity 

PND52-53 

Increased center crossings in elevated plus maze, M not F 

PND64+ No differences in 

radial arm maze latencies, M&F 

Ricceri et al., 2003 Italy 

Mouse CD1 

1, 3 mg/kg/d sc in DMSO 

PND1-4 20, 23% brain 

inhibition at low & high dose 1 hr after dose on PND4; no inhibition at 4, 24 hr 

Motor activity 

PND25 No effect on open 

field activity 

Split-litn=8-18/says all all tests are unexdifferensample across tBonferrcorrectehocs folANOVA effects wp>0.05; Also sompreweantesting 

PND35-38 

Increased activity at door opening in 2-chamber box, only 3 mg/kg/d tested, M&F 

Anxiety & emotion 

PND38 No effect on 

novelty preference in 2-chamber box, only 3 mg/kg/d tested, M&F 

Social behavior & maternal interaction 

PND45 No effect 

investigative, affiliative, or soliciting behaviors, M&F; Decreased selfgrooming, 1&3 
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mg/kg/d, M&F Increased frequency & duration agonistic behavior sum, differs across time, 1&3 mg/kg/d, M not F 

Cognition PND60 No effect on 

passive avoidance, only M tested 

Dam et al., 2000 Duke 

Rat SD 5 mg/kg/d sc 

in DMSO 

PND11-14 ~20-30% brain 

(regions) inhibition, M&F, more at 4 hr than 2 hr after single dose on PND11 

Motor activity 

PND21, 30 

Increased open field rearing & grooming, M&F 

5-7/sex/Maybe dosing bexplicitFigure 3 male ongender differensignifica

Levin et al., 2001 Duke 

Rat SD 5 mg/kg/d sc 

in DMSO 

PND11-14 Not measured Motor 

activity 

PND28-42 

Longer latencies (slower) in t-maze, M not F 

10/sex/dSplit-litACh syinvolvem

Decreased habituation (slower) in figure8, M&F 

Cognition PND28-

42 

No effect on tmaze spontaneous alternation, M&F 

PND56-91 

No effect on radial arm maze working or reference memory, M&F 

Ricceri et al., 2003 Italy 

Mouse CD1 

1, 3 mg/kg/d sc in DMSO 

PND11-14 No brain 

inhibition at 1, 4, 24 hr after dose 

Motor activity 

PND25 Increased distance 

traveled in open field, 1&3 mg/kg/d, M&F 

Split-litn=8-18/says all all tests 
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PND35-38 

Increased activity following door opening in 2-chamber box, only 3 mg/kg/d tested, M&F 

are unexdifferensample across tBonferrcorrectehocs folANOVA effects wp>0.05 

Anxiety & emotion 

PND38 No effect on 

novelty preference in 2-chamber box, only 3 mg/kg/d tested, M&F 

Social behavior & maternal interaction 

PND45 No effect on 

investigative or affiliative behaviors, M&F; Increased soliciting behavior, 3 mg/kg/d, M&F 

Increased frequency & duration agonistic behaviors throughout, 1&3 mg/kg/d, M not F 

Cognition PND60 No effect on 

passive avoidance, only M tested 

Ricceri et al., 2006 Italy 

Mouse CD-1 

1, 3 mg/kg/d sc in peanut oil Plus vehicle to dam GD15-18 

PND11-14 24 hr after last 

dose (PND15): no brain inhibition, serum inhibition both doses 

Motor activity 

PND70 Increased crossing 

in open field, only 1st 5 min, 3 

mg/kg/d, only M tested 

Litter as 10 litters/gdose, posplit-littSays aneffect in sexes busignificamales 

PND120 No effect on 

elevated plus maze activity, M&F 

Social behavior & maternal 

PND75-80 

Increased attack behaviors, 3 mg/kg/d, only M 
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interaction tested; 

No effect on M non-social behaviors 

PND90 Increased induced 

maternal behaviors, 1&3 mg/kg/d 

Anxiety PND120 Increased time in 

open arms in elevated plus maze (decreased anxiety), 3 mg/kg/d, F only 

Venerosi et al., 2006 Italy 

Mouse CD-1 

1, 3 mg/kg/d sc in peanut oil Plus vehicle to dam GD15-18 

PND11-14 Not measured, 

cites Ricceri 2006 findings 

Social behavior & maternal interaction

PND120 No effect on 

ultrasonic vocalizations or social investigation, only F tested 

Litter as 10 litters/gdose, posplit-litt

Venerosi et al., 2008 Italy 

Mouse CD-1 

3 mg/kg/d sc in peanut oil 

PND11-14 Not measured Social 

behavior & maternal interaction

PND40-45 

No effect on social interaction with same-sex conspecific, M&F 

Litter as Split-lit2/sex/doN=15 liN=9-14some mtests; Female mated omaternaafter giv

LD1-7 In lactating dams: 

Longer latency to build nest; Shorter latency to lick pups; Increased time digging (nonmaternal behavior) 

LD7 In lactating dams: 

Decreased defensive, increased social investigative, & increased digging behaviors when 
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exposed to intruder male 

Anxiety & emotion 

LD2 In lactating dams: 

Faster entry & more mice entering light side (decreased anxiety) 

Motor activity 

PND40-45 

Decreased distance moved during acclimation before social test, 1st time block, 

M&F 

Johnson et al., 2009 MSU 

Rat SD Incrementing 

doses: low, 1 mg/kg/d; mid, 1 up to 4 mg/kg/d; hi, 1.5 up to 6 mg/kg/d Po in corn oil 

PND1-21 PND20 (time 

after dose not given) – 14-53% brain (hippocampal) inhibition, all doses significant; PND30-40, mid & high doses still show inhibition (17-23%); No inhibition PND50; M&F same 

Cognition PND36-

60 

Increased radial arm maze working memory errors, hi dose throughout training, all doses during last week, M only; Decreased radial arm maze reference memory errors, middle to end of training, mid & hi doses, F only; Increased radial arm maze reference errors, middle week on training, mid & hi doses, M only 

Split-litn=10-14/sex/dTable 1 PND20

Motor activity 

PND36-60 

No effect on response time in radial arm maze 

Carr et al., 2001 MSU 

Rat SD Incrementing 

doses: low, 3 mg/kg/d; 

PND1-21, every other day 

PND25, 30 – 14-26% brain (regions) 

Motor activity 

PND25, 30 

Decreased open field activity, mid & hi, M&F 

Betweendosing; N=5 litt
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mid, 3 up to 6 mg/kg/d; hi, 3 up to 12 mg/kg/d Po in corn oil 

inhibition at low dose; all doses significant, M&F same 

2/sex frlitter; Used wfactor in Also sompreweantesting 

Jett et al., 2001 JHU 

Rat LE 0.3, 7 

mg/kg/d sc in peanut oil 

PND7, 11, 15 “preweaning study” 

No brain inhibition on PND7 (3 hr after dose), 8, 16, or 28 

Cognition PND24-

28 

Longer latencies to find platform in Morris water maze, 7 mg/kg/d throughout, 0.3 mg/kg/d on day 1 (p=0.05), M&F; Less time spent in training quadrant, 7 mg/kg/d, M&F 

Betweendosing arandom N=8-10“2 or mused”; “Postwestudy nopresente

a Sample sizes and analyses refer only to behavioral measures b Graded response across doses or else effect at higher but not lower dose(s) 

M, F, Male, Female 
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Table App 1-14. In vivo studies of chlorpyrifos administered during gestation and postnatally, continuous 

exposures. Effects described are only those measured after weaning. Bold indicates functional domain sh

Study & laboratory 

Species & strain 

Dose, route, vehicle 

Dosing period 

AChE inhibition 

Domain Age of 

testing 

Outcome, effective dose & gender 

Notesa 

Maurissen et al., 2000 Dow 

Rat SD 0.3, 1, 5 

mg/kg/d po in corn oil 

GD6-LD10 

Dam on GD20, 4-5 hr after dose, 45% blood but no brain inhibition at 0.3; greater blood and brain inhibition at higher doses 

Neuromotor function 

PND22 Increased auditory 

startle latency did not meet lower p-value, 5 mg/kg/d, M&F; “effects may have been real” – p-value =0.03 

Litter a1/sex/liN=8/sexdelayed alternatrats at bN=20/sfor startmotor a(same rages); alpha seAlso sopreweanMaternaat 5 mg

PND61 No effect on auditory 

startle measures, M&F 

Motor activity 

PND21 and PND60 

No effect on total counts or habituation at either age, M&F; High dose “may have had ..slight increase in activity on PNDs 17, 21, and 60” 

Cognition PND22-

24 and PND61-91 

No effect on spatial delayed alternation learning at either age, M&F 

Braquenier et al., 2010 Belgium 

Mouse CD-1 

0.2, 1, 5 mg/kg/d po in corn oil 

GD15-LD14 

Pup on PND1, 3 hr after dose to dam, 14% brain inhibition, only 5 mg/kg/d group tested 

Motor activity 

“Adult” No effect on 

locomotor activity 

1/litter test; ontested; N=8-10Dunnethoc afteeffect p

PND80 No effect on elevated 

plus maze total arm entries 

Anxiety & emotion 

“Adult” Decreased time in 

chamber center in light-dark box and decreased box switches (increased anxiety), 1mg/kg/d only; 
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Study & laboratory 

Species & strain 

Dose, route, vehicle 

Dosing period 

AChE inhibition 

Domain Age of 

testing 

Outcome, effective dose & gender 

Notesa 

PND80 Decreased time & 

entries in elevated plus maze open arm (increased anxiety), 1 mg/kg/d only 

Ricceri et al., 2006 Italy 

Mouse CD-1 

3, 6 mg/kg/d po in peanut oil Plus 1, 3 mg/kg/d sc in peanut oil on PND11-14 

GD15-18 “pre” plus PND11-14 “post” 

Dam on GD19 (pilot study): 40% brain ChE inhibition at 6 mg/kg/d, serum inhibition at both doses Pup on GD19: no brain inhibition, serum inhibition at both doses Pup on PND15: no brain or serum inhibition 

Motor activity 

PND70 Increased crossing in 

open field, 6 mg/kg/d prenatal plus 1, not 3 mg/kg/d, postnatal 

 

PND120 No pre-post 

interaction on elevated plus maze activity, M&F 

Anxiety & emotion 

PND120 No pre-post 

interaction on elevated plus maze, M&F 

Social behavior & maternal interaction 

PND75-80 

No pre-post interaction on social behaviors, M&F 

PND90 No pre-post 

interaction on induced maternal behaviors 

Venerosi et al., 2006 Italy 

Mouse CD-1 

3, 6 mg/kg/d sc in peanut oil Plus 1, 3 mg/kg/d sc in peanut oil on PND11-14 

GD15-18 “pre” plus PND11-14 “post” 

Cites Ricceri 2006 findings 

Social behavior & maternal interaction 

PND120 Increased ultrasonic 

vocalizations & social investigation at 6 mg/kg/d prenatally reversed by 1&3 mg/kg/d postnatally 

 
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a Sample sizes and analyses refer only to behavioral measures b Graded response across doses or else effect at higher but not lower dose(s) 

M, F, Male, Female 
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3.2.2.2. Update on Developmental Neurotoxicity Studies of Chlorpyrifos as of August 2014 

There are six new studies since early 2012, and five of these are from laboratories that have not 

published behavioral studies of this type before. Note, however, that most of these laboratories 

have published neurochemical, molecular, or in vitro studies with chlorpyrifos or other OPs. 

Most of these studies use the same dosing paradigms as earlier studies. 

Of particular interest is a recent publication from the Duke laboratory (Slotkin et al., Neurotox. 

Teratol. 37:1-12, 2013) that reported 5-10% inhibition of fetal brain ChE activity at 2 hr after the 

last dose of 1 mg/kg/d sc in DMSO administered PND1-4. This dose is widely used in many of 

these studies, and this information clearly shows that while this dose does not induce toxic or 

developmental effects, it does have a small but significant effect on fetal brain ChE. 

A critical review of the six papers follow: 

1) Chen X-P, Chen W-Z, Wang F-S, Liu J-X. (2012) Selective cognitive impairments are related 

to selective hippocampus and prefrontal cortex deficits after prenatal chlorpyrifos exposure. 

Brain Res. 1474:19-28. 

This study represents a new laboratory to the field. ICR mice were used, no information on how 

many pregnant females they started with. Chlorpyrifos (1, 5 mg/kg/d) was given GD13-17 sc in 

DMSO. The paper does not specify if litters were culled or sex distribution within litters. 

Littermates were randomly re-distributed across litters every 7 days, there is no information 

given as to whether littermates were allocated to different dams or if the litter stayed as a unit. 

On PND20, 8 litters were chosen for testing. It says that littermates were divided into groups, 

but it is not clear if all pups in a litter tested together – figure legend does say n=8 mice per 

group. It is also not clear how pups were sampled, whether there were equal numbers of males 

and females, or whether the statistics accounted for littermates. 

Behavioral testing was conducted in a T-maze using an alternating procedure, i.e., for each trial 

the baited arm alternated. They conducted 10 trials/session, 2 sessions/ day (does not say how 

far apart), for 3 days for each inter-trial interval (ITI). Training used the shortest ITI (no time 

between trials), then used 10, 20, 40, and 60 sec ITIs. It does not give the order of presentation 

of the different ITIs. Win-shift failure, or errors, are described as going to the wrong arm, which 

triggered training that is not clearly described. Lose-shift failure, or errors, was defined as 

continuing to go to the wrong arm. Training began on PND45 (after 4-5 days acclimation to the 

apparatus) and continued 15 days. 

Adult offspring showed no difference in percent correct on first choice. It reports a sex effect 

and then analyzes them separately, but not clear if there was a sex*trial interaction. Lose-shift 

errors were significant in males, showed a trend in females – they reported male effects at longer 

delays (40, 60 sec) and effect in females only at 10 sec. Only the high dose was significant. 

Procedural details are not well-explained, especially the training that was initiated after each 

choice error. Figure legend states 8 mice per group, which could imply one mouse from each 

litter, but unclear if they were still littermates. Additional uncertainties in the statistics include 

how littermates and sex were handled and whether there were significant dose*trial, sex*dose, or 

dose*sex*trial interactions. Even in controls, there is not an evident delay-retention gradient, in 

that 60 sec performance looks similar to 0 sec, suggesting that even 60 sec may have been too 
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short a delay. T-maze data are usually somewhat variable, yet the data show remarkable, and too 

much, consistency. This raises questions as to the actual procedure used, and whether the 

apparent treatment effects are real. 

2) Cole TB, Fisher JC, Burbacher TM, Costa LG, Furlong CE. (2012) Neurobehavioral 

assessment of mice following repeated postnatal exposure to chlorpyrifos-oxon. Neurotoxicol. 

Teratol. 34:311-322. 

This study represents another new laboratory to conduct DNT studies, although they are an 

experienced laboratory for studies of PON1 and OPs. This study used only the oxon in PON1-

knockout mice, thus it is not exactly comparable to other chlorpyrifos studies. However, given 

that the knockout mice were assumed to be more sensitive, and the current interest in the oxon, 

the study is relevant. 

This was part of a larger study with PON-/-, +/+, and humanized PONs, but only the PON-/- mice 

were tested behaviorally. The study was done in two cohorts and only findings that were seen in 

both were reported. On PND4 mice were culled to three males and three females. Pups were 

directly dosed (sc in DMSO; 0.15, 0.18, 0.25 mg/kg/d) on PND4-21: all mice in a litter received 

the same dose. One male and one female per litter were allocated to a set of tests, another male 

and female per litter allocated to another set of tests, designated sets “A” and “B”. Before 

weaning, all mice were tested for ontogeny of righting reflex, cliff avoidance, and negative 

geotaxis. Tests for set “A” were: rotarod PND23, pre-pulse acoustic startle reflex PND25 and 

50, fear conditioning PND62-64, and water radial arm maze PND77-116. Set “B” tests were: 

open field PND25, Morris water maze PND70-98, and served as no-shock controls in fear 

conditioning. All procedures were standard and appropriate for these tests. It is not clear 

whether sex was nested or analyzed as a within-litter variable. 

Littermates of these mice were used to measure brain AChE activity on PND22 (day after last 

dose), and showed 10%, 16%, and 27% inhibition at the three increasing doses. Body weight 

was decreased at 0.25 mg/kg/d, which appeared during dosing and persisted through adulthood. 

Hyperkinetic responses (jumping, darting) were noted in all dose groups within 5 min of dosing 

beginning PND15. Of all the neurobehavioral and cognitive tests, only decreased startle latency 

was detected in the 0.18 and 0.25 groups on PND50. Also on that day only the 0.18 group 

showed an effect on pre-pulse amplitude, but no effect of the pre-pulse inhibition. The high dose 

of 0.25 mg/kg/d showed no behavioral effects. 

The study was conducted appropriately, and generally analyzed correctly except that it appears 

that sex was not nested. The graphs do not show males and females separately (and it is unclear 

which it is, or if it’s combined) but supplemental data show sex-related differences in some of 

the tests where it’s expected (e.g., fear conditioning). The authors discuss the shorter startle 

latency as representing stronger synaptic connections, but do not adequately address the lack of 

dose response. Generally the lack of oxon effects stands in contrast to many of the studies that 

report effects of chlorpyrifos using the same behavioral tests. 

3) Vatanparast J, Naseh M, Baniasadi M, Haghdoost-Yazdi H. (2013) Developmental exposure 

to chlorpyrifos and diazinon differentially affect passive avoidance performance and nitric oxide 

synthase-containing neurons in the basolateral complex of the amygdala. Brain Res. 1494:17-27 
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This paper is from another laboratory that is new to this field. Wistar rats received 1 mg/kg/d sc 

in DMSO either on GD15-18 or directly to pups on PND1-4. Adult offspring (PND60) were 

tested in an open field followed by passive avoidance. After two habituation trials, rats were 

subjected to shock when they crossed over. This was repeated every 2 min until they did not 

cross in 120 sec. At 24 hr they were retested. Note that rats that did not go into the dark side 

during the habituation trials were eliminated, this results in a selection bias that could have been 

related to dose but no information is given. Dependent variables were trials to criterion on the 

day of training, and step-through latency and time in dark side (directly correlated with latency) 

at 24 hr. Litters were culled and 1 male and 1 female per litter were tested with at least 12 

litters/treatment. Analysis uses sex as a factor but not nested within litter. 

There were no changes in activity, nor were there changes in trials to criterion. In the retention 

test, prenatally dosed rats showed decreased latency, similarly in males and females. There were 

no effects in postnatally treated rats. 

Group means for trials to criterion are very close to 1, meaning that most rats met it in 1-2 trials; 

therefore, there was not much dynamic range with which to detect effects. However, the 24-hr 

retention data show an obvious treatment effect. Passive avoidance has not been tested 

previously in rats, but there were no effects in mice treated postnatally (Ricceri et al., 2013). 

4) Mullen BR, Khialeeva E, Hoffman DB, Ghiani CA, Carpenter EM. (2013) Decreased reelin 

expression and organophosphate pesticide exposure alters mouse behaviour and brain 

morphology. ASN NEURO 5:27-42. 

This study is from a new laboratory, and focuses on chlorpyrifos interactions in mice having 

decreased or normal expression of the reelin gene which has been associated with autism. 

Results of chlorpyrifos treatment in the reelin+/+ mice (normal expression) are evaluated here. 

Osmotic minipumps were implanted on GD13.5, releasing 6 mg/ml oxon in PBS, apparently 

over 3 days. There was no extrapolation to administered dose. Reelin+/+ and +/- were bred, 

resulting in litters with mixed genotypes. There were 9 oxon and 13 vehicle litters, and there 

were at least 6 mice per treatment/genotype group. It is unclear how many males and females 

tested came from the same litter. AChE was measured in fetal heads on GD16.5. On PND7, 

litters were separated from the dam and USVs were measured. On PND30 there was a 30-min 

open field session, also a social interaction test. Marble burying was measured on PND60. 

Measured were collected using an overhead video system. Outliers were rejected (no criterion 

given) and data were analyzed with t-tests and “aggregate” ANOVAs (no information where 

each approach was used). 

AChE activity was decreased by about 15% (not stated whether this was significant). There was 

a decrease in the number and duration of USVs, similarly in males and females. Males only 

showed decreased sniffing duration in the open field. Females only showed an increased in 

sniffing at stranger 1 in the social interaction test, but no difference when stranger 2 was 

introduced. There was no change in marble burying. There were several other non-significant 

differences which they interpret as being effects. 

The only statistically significant changes were seen with sniffing, and this was not consistent in 
Chlorpyrifos Human Health Risk Assessment D424485 

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males vs females. It is not described what algorithm was used to define sniffing in the software 

using an overhead camera, and this information is not available at the manufacturer’s website. 

Sniffing is a questionable measure of activity, and in the social interaction test, a better measure 

is time spent with the other mice, yet this was not altered. The data are not particularly 

convincing. 

5) Ohishi T, Wang L, Akane H, Itahashi M, Nakamura D, Yafune A, Mitsumori K, Shibutani M. 

(2013). Reversible effect of maternal exposure to chlorpyrifos on the intermediate granul cell 

progenitors in the hippocampal dentate gyrus of rat offspring. Reprod. Toxicol. 35:125-136. 

This is a new laboratory to do these studies. SD rats were given chlorpyrifos in the diet from 

GD10-PND21, at 2.8, 14, 70 ppm. AChE was measured in dams at weaning and in male 

offspring on PND21 and PND77. Preweaning physical and righting reflex milestones were 

measured. Detailed clinical observations were noted at PND29, 48, 71. Also on PND71, rats 

were subject to FOB-type measures: sensory responses (click, visual approach, touch, tail pinch, 

pupil), righting reflex, landing foot splay, and forelimb and hindlimb grip strength. Motor 

activity was also measured that day in 1-hr session but it does not specify type of chamber or 

cage. Biel maze testing on PND45-58 looked at latencies and choice errors. Behavioral tests 

used 1 male and 1 female per litter, and litter was used as the unit of measure for stats. However, 

it does not appear that sex was considered as a factor in the analyses, and repeated measures 

were not used where it should have been. This study design is generally consistent with 

developmental neurotoxicity test guidelines, and is comparable to the Maurissen et al. (2000) 

paper. 

Maternal intakes over exposure were calculated to be 0.36, 1.86, and 9.18 mg/kg/d. In dams 

(PND21), AChE was decreased in RBC (all doses, up to ~80% inhibition), plasma (14 and 70 

ppm, similar magnitude), and brain (70 ppm only, similar magnitude). On PND21 in offspring, 

AChE was inhibited in RBC and plasma (14 and 70 ppm, ~70% inhibition) and brain (70 ppm 

only, ~50%). There were no treatment effects on milestones or FOB-like measures. There was 

increased body weight in both sexes during postnatal period but only in 14 ppm group, along 

with faster eye opening (2.8 and 14 ppm) and faster incisor eruption (14 ppm). Motor activity 

was different in 2 intervals in 14 ppm males, otherwise there were no effects. There were 

decreased errors in one trial in 2.8 ppm males in the Biel maze, and increased latency in the 

straight channel on the first trial only in the 2.8 and 14 ppm groups. The high dose of 70 ppm 

had no effects. 

This study had no doses where AChE was not at all inhibited, but brain ChE was inhibited only 

at the high dose in both dams and pups. Magnitude of inhibition was quite high, but there was 

no mention of toxic signs, and dams gained weight normally (Note: this finding is somewhat 

unexpected). The pre-weaning weight and development differences at 14 ppm are in the 

opposite of the expected direction, and suggest differences in actual gestational age. The few 

significant intervals and trials were not dose-related and most likely were spurious due to the 

multiple tests run, instead of doing a repeated-measures ANOVA. If the appropriate statistics 

were used, it is doubtful there would be any significant differences. 

6) Levin ED, Cauley M, Johnson JE, Cooper EM, Stapleton HM, Ferguson PL, Seidler FJ, 

Slotkin TA. (2014). Prenatal dexamethasone augments the neurobehavioral teratology of 
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chlorpyrifos: Significance for maternal stress and preterm labor. Neurotoxicol. Teratol. 41:35-

42. 

This laboratory published extensively in this area in the early 2000s. The focus of this study is 

interactive effects of prenatal dexamethasone and postnatal chlorpyrifos; however, for this 

review only the effects in the chlorpyrifos alone group compare to control are considered. Dams 

received saline sc GD17-19, then chlorpyrifos 1 mg/kg/d sc was given to pups on PND1-4. 

Litters were culled after birth and redistributed within treatment group every few days. Figure 

legend (only) states 11-12 per treatment, but never states that one male and one female came 

from each litter; however, that is the usual procedure used in this laboratory. The same offspring 

went through all behavioral tests: T-maze exploration in week 4, motor activity (figure 8 

chamber) week 5, novel environment feeding in week 7, novel object recognition in week 9. 

There were no effects of chlorpyrifos on T-maze or the novel environment feeding, but note that 

while there were no significant differences, the abstract and discussion say that there were 

effects. Chlorpyrifos increased motor activity in males only, but the change in habituation did 

not reach significance (although the authors claim there is an effect). On novel object 

recognition, there was an increased overall exploration in males only, but no effect on 

differential time between novel and familiar objects. 

The study was generally conducted properly, using many of the same methods from earlier 

publications. Effects are somewhat exaggerated in that non-significant differences are presented 

as effects. The increased motor activity likely accounts for increased activity in the novel object 

test, and there is no evidence of a cognitive effect. 

The effects described in this study disagree with more than half of the findings from earlier 

studies in this laboratory using the same dosing paradigm. Specifically, the effects and the 

associated functional domains here are: 1) Activity – Increased figure-8 activity and increased 

exploration in novel object test, males only, and no effect on T-maze exploration, which agree 

with increased elevated plus maze activity, males only (Aldridge et al., 2005) but not with 

decreased T-maze activity in males (Levin et al., 2001) or decreased open field activity in males 

(Dam et al., 2000); 2) Cognition – No effect on T-maze alternation or novel object recognition, 

which agree with no effect on T-maze alternation (Levin et al., 2001) but disagree with deficit in 

radial arm maze performance in males, and improved function in females; 3) Anxiety/Emotion – 

No effect on novel environment feeding which disagrees with evidence of decreased anxiety in 

elevated plus maze and anhedonia suggested by decreased chocolate milk preference (Aldridge 

et al., 2005). The authors do not discuss these discrepancies. 

Overall summary: 

 For half of the studies, the lowest dose was 1 mg/kg/d, and two studies used the oxon, 

making it difficult to compare dose levels. Only one study used a lower dose, 0.36 mg/kg/d, in 

feed, and even this level was sufficient to produce a great degree of RBC ChE inhibition. The 

functional effects are summarized below (See Table App 1-15). 

1) Cognition – Of these studies, cognition was generally unaffected except for an effect on 

passive avoidance; interestingly, previous studies reported no passive avoidance effects. There 

were no effects on other cognitive tests that did show changes in earlier studies, as well as no 

effects in new tests being used for the first time. Thus, what appeared to be fairly consistent 

reports of cognitive changes from earlier studies is not so conclusive. 
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2) Anxiety & emotion – The only study which evaluated this domain reported no effect, in 

contrast to previous reports, including one from the same laboratory. 

3) Social & interactive behavior – Only one study measured this and reported increased 

interaction with a stranger mouse (albeit with a questionable measure), whereas earlier studies 

showed mixed effects on other tests of social investigation. 

4) Activity – Most of these studies report no change in activity, with only one report of increased 

activity and another of decreased sniffing (a questionable measure of activity). Combined with 

the previous literature, this continues to show inconsistent changes in motor activity levels and 

habituation. 

5) Neuromotor development and function were generally unaltered except for one measure that 

did not show a dose-response. Few other studies have addressed this. 

Conclusions: There continue to be inconsistencies in effects in relation to functional domains, 

dosing paradigms, and gender-specificity. The only studies reporting effects used doses that 

inhibited fetal/pup brain ChE activity to some degree, even though there were many negative 

effects at these same doses. 
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Table App 1-15. Summary of newest studies. Bolded domains indicate significance in some measure with

Study & laboratory 

Species & strain 

Dose, route, vehicle 

Dosing period 

AChE inhibition 

Domain Age of testing Outcome, effective dose, 

gender 

Notes 

Chen et al., 2012 Hangzhou China 

Mouse ICR 

1, 5 mg/kg/d sc in DMSO 

GD13-17 Not 

measured 

Cognition PND45-60 Increased lose-shift errors in 

T-maze delayed alternation at 5 mg/kg/d M, trend F 

Litter alloand statisttreatment Control dashow experetention dlonger ITI

Levin et al., 2014 

Rat SD 1 mg/kg/d 

sc in DMSO 

PND1-4 (saline vehicle GD17-19) 

Not measured 

Cognition PND28; 

PND63 

No effect on T-maze alternation; No effect on differential exploration in novel object recognition 

12-14/dosmore than each sex flitter; litteredistributfew days Did not nelitter Study addCPF and dexamethainteractionPresents nsignificant effects 

Activity PND28; 

PND35; PND63 

No effect on T-maze alternation; Increased total activity in figure-8, M only; Increased overall activity in novel object recognition, M only 

Anxiety & emotion 

PND49 No effect on novel 

environment feeding 

Vatanparast et al., 2013 

Rat Wistar 1 mg/kg/d 

sc in DMSO 

GD15-18 Not 

measured 

Activity PND60 No effect in open field, M&F 1 male and 

female eacbut sex noin litter foanalyses 

Cognition PND60-63 Decreased step-through 

latency at 24 hr in passive avoidance, M&F 

PND1-4 Activity PND60 No effect in open field, M&F 

Cognition PND60-63 No effect in passive 

avoidance, M&F 

Mullen et al., 2013 

Reelin+/+

mice 

6 mg/ml oxon in PBS, with osmotic minipmp 

~GD13.5-16.5 

~15% inhibition in fetal heads GD16.5 

Activity PND30 Decreased sniffing in open 

field, F not M 

Sampling F from littclear, sex nested in s

Social PND30 Increased sniffing of first 

stranger in social interaction test, F not M 

Anxiety & emotion 

PND60 No change in marble burying, 

M&F 
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Ohishi et al., 2013 

Rat SD 2.8, 14, 70 

ppm in diet, giving 0.36, 1.86, 1.98 mg/kg/d 

GD10-PND21 

Dams on PND21: inhibition (max ~80%) in RBC (all doses), plasma (14 and 70), brain (70) Pups on PND21: inhibition (max ~70%) in RBC and plasma (14 and 70), brain (70) No inhibition on PND77 

Activity PND71 Changes in activity in two 

intervals only, 14 ppm, M not F, likely spurious 

Did not nelitter; Appsex was noin analysis repeated mwas not us

Cognition PND56-58 Decreased errors in one trial 

only in Biel maze, 2.8 ppm, M not F, likely spurious 

Neuromotor 

PND56-58 Preweaning & PND29, 48, 71 

Increased latency in Biel maze in straight channel, 2.8 and 14 only, M not F, likely spurious No effect on many measures of development, sensorimotor, neuromuscular function 

Cole et al., 2012 

PON-/-

mice 

0.15, 0.18, 0.25 mg/kg/d oxon sc in DMSO 

PND4-21 10%, 

16%, 27% inhibition in littermates on PND22 

Cognition PND62-64; 

PND77-116; PND70-98 

No effect on fear conditioning, M&F; No effect on water radial arm maze, M&F; No effect on Morris water maze, M&F 

 

Motor activity 

PND25 No effect in open field 

Neuromotor function 

Preweaning; PND23; PND25, 50; 

No effect on reflex ontogeny, M&F; No effect on rotarod, M&F; Decreased acoustic startle latency on PND50, 0.18 & 0.25 mg/kg/d, M&F; Altered pre-pulse startle, 0.18 mg/kg/d only, M&F 
Chlorpyrifos Human Health Risk Assessment D424485 

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Appendix 2. Detailed Review and Synthesis of Three Children’s 

Environmental Health Cohort Studies 

NOTE: Appendix 2 contains the Detailed Review and Synthesis of the Three Children’s 

Environmental Health Cohort Studies. This information was presented to the FIFRA 

Scientific Advisory Panel (SAP) in April of 2012; however, it is supplemented by both an 

external Federal panel review performed June –September 2012, and also the results of a 

on-site meeting with staff of the Columbia Children’s Center for Environmental Health 

(CCCEH) in April 2013. The CCCEH is one of the three children’s environmental health 

cohorts included in the EPA review. EPA has considered the FIFRA SAP input regarding 

the analysis and interpretation of these epidemiologic studies in the completion of the 

revised chlorpyrifos HHRA (April 2012 FIFRA SAP review of this information can be 

found at: http://www.epa.gov/scipoly/sap/meetings/2012/april/041012minutes.pdf.) 

Information concerning the Federal letter review of a subsequent study published by 

CCCEH researchers in addition to other questions can be found in the Federal docket at: 

http://www.regulations.gov/#!documentDetail;D=EPA-HQ-OPP-2008-0850-0170. The 

Federal panel review considered the results of a Magnetic Resonance Imaging (MRI) 

study which detailed alterations in brain morphology in children by in utero chlorpyrifos 

exposure level. Within the Federal letter review, EPA also specifically requested 

feedback upon areas of child neurodevelopment testing for which the April 2012 FIFRA 

SAP expressed a lack of expertise to fully consider. Lastly, in April 2013, EPA scientists 

met with CCCEH researchers to address a series of questions generated by the April 2012 

FIFRA SAP, the June 2013 Federal letter review, and EPA deliberations including the 

need for obtaining certain “raw data” or original analytic data files to address important 

uncertainties in the use of these data in the HHRA. Specifically, questions regarding 

whether additional information may be available through the CCCEH data collection 

efforts to further characterize chlorpyrifos exposure of CCCEH study participants and to 

better characterize other environmental exposures within the study population. The 

results of this meeting are encapsulated in the EPA Memorandum “Columbia Center for 

Children’s Environmental Health (CCCEH) Epidemiology Data Acquisition (“Raw Data” 

Request) and is included as Appendix 6 to the revised chlorpyrifos HHRA. The input 

provided from the two reviews and the results of further inquiry of the CCCEH 

researchers by EPA is encapsulated herein. The combined force of this material informed 

EPA’s deliberations concerning the content of the revised chlorpyrifos HHRA. 
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1.0 Scope and Purpose 

In September 2008, EPA presented to the FIFRA SAP its preliminary review of available 

epidemiologic investigations of prenatal exposure to chlorpyrifos in association with measures of 

fetal growth and adverse neurodevelopmental effects in three major prospective children’s health 

cohorts in the U.S.67 These are: 1) The Mother’s and Newborn Study of North Manhattan and 

South Bronx performed by Columbia University researchers referred in this document as 

“Columbia Mother’s and Newborn Study;” 2) the Mt. Sinai Inner-City Toxicants, Child Growth 

and Development Study or the “Mt. Sinai child growth and development study;” and, 3) Center 

for Health Assessment of Mother’s and Children of Salinas Valley (CHAMACOS) conducted by 

researchers at University of California Berkeley, the “CHAMACOS study.” In this document, 

EPA updates and expands its targeted evaluation of this important line of evidence regarding 

chlorpyrifos developmental neurotoxicity. 

At the previous meeting, the Panel agreed with EPA’s conclusions that “chlorpyrifos likely 

played a role in the birth and developmental outcomes noted in the three cohort studies” (pp. 37 

Meeting Minutes). In support of this statement, the Panel offered that investigations performed 

within these three epidemiological cohorts utilized a similarly strong study design (prospective 

cohort); measured exposure using several different methods including specific and non-specific 

biomarkers of chlorpyrifos; ascertained developmental outcomes using validated assessment 

tools common to both clinical and research settings; and, analyzed, selected and statistically 

adjusted for potentially confounding variables using reasonable and appropriate methods. 

Overall, the Panel noted that the epidemiological database at that time presented an informative 

body of evidence with some notable consistencies across studies. Areas of inconsistency were 

also observed, and judged, in part, to be due to differing methods of measurement and 

evaluation, as well as dissimilar exposure profiles (i.e., residential versus direct and indirect 

occupational (farm laborer) exposure). Importantly, the Panel at the 2008 FIFRA SAP meeting 

also stated that it could not conclude that chlorpyrifos was the sole contributor to these outcomes, 

as co-exposure to other organophosphate pesticides and mixtures of environmental exposures 

may also have played a part in these outcomes. 

For the purpose of informing the chlorpyrifos risk assessment, the Panel concurred with the 

Agency with respect to the primacy of the Columbia Mother’s and Newborn Study among the 

three birth cohort studies, although the Panel also encouraged the Agency to comprehensively 

consider the results of the three children’s health cohorts. While the Columbia Mother’s and 

Newborn Study researchers measured the parent compound chlorpyrifos, as opposed to nonspecific organophosphate metabolites, the other cohorts examined health outcomes (the 

Brazelton index of neonatal development) and performed supplemental analyses (e.g., effect 

modification by PON1 status) not reflected in the Columbia Mother’s and Newborn Study 

database. Therefore, the Panel expected EPA could strengthen its understanding of the potential 

developmental neurotoxicity of chlorpyrifos by considering the three children’s health cohort 

studies together. Additionally, the Panel in 2008 suggested supplemental statistical analyses to 

enhance understanding of epidemiological study results in the risk assessment context (See 

Attachment 1). The Panel also generally noted both strengths and limitations of these studies, 

and offered that random or systematic errors in the design, conduct or analysis of these studies 

 

67 See Meeting Minutes at: http://www.epa.gov/scipoly/sap/meetings/2008/september/sap0908report.pdf. 
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were unlikely to fully explain observed associations. However, the Panel also noted that absent 

the available toxicological and epidemiological databases was a well understood and defined 

mode of action as to the role of chlorpyrifos exposure in the etiology of adverse infant and child 

neurodevelopmental outcomes. 

In the revised chlorpyrifos HHRA, EPA expands and updates its review of the available 

epidemiologic data concerning the effect of chlorpyrifos exposure on children’s environmental 

health in conjunction with a review of recent experimental studies and hypothesized adverse 

outcome pathways (AOP). Observational studies published subsequent to the September 2008 

FIFRA SAP evaluation extend the knowledge base of potential long-term sequalae of prenatal 

chlorpyrifos exposure. Specifically, in April 2011, researchers with each of the three prospective 

children’s health cohort studies concurrently published results of their respective investigations 

of prenatal chlorpyrifos exposure and measures of intelligence among school aged children 

approximately 7-years (Bouchard et al., 2011; Engel et al., 2011; V. Rauh et al., 2011). 

Additionally, researchers with the Mt. Sinai Child Growth and Development study contributed 

their evaluation of organophosphate exposure and mental and psychomotor development, and 

authors with the CHAMACOS study published an evaluation of chlorpyrifos exposure and both 

fetal growth and neurodevelopment measures in young children as modified by paraoxonase-1 

(PON1) genotype and phenotype (Eskenazi et al., 2010; Harley et al., 2011). 

Researchers have also performed epidemiologic methods research which in many ways reduces 

uncertainties related to key measures within these studies. Within the Columbia Mothers and 

Newborn study, investigators published results of analyses evaluating the validity of prenatal 

chlorpyrifos exposure measures in the time periods immediately after the voluntary cancellation 

of chlorpyrifos for residential use (R. M. Whyatt et al., 2009; R. M. Whyatt et al., 2007), as well 

as employed innovative statistical techniques to further assess the potential confounding bias of 

socio-economic status (SES) in the relation between prenatal chlorpyrifos exposure and adverse 

neurodevelopmental health outcomes (Lovasi et al., 2011). Overall, these additions to the 

epidemiologic database concerning children’s health effects in combination with other lines of 

evidence, add to the body of knowledge available to inform the ways in which chlorpyrifos 

exposure may be related to adverse neurodevelopment outcomes in children. 

2.0 Summary of Epidemiology Findings 

In this section, EPA summarizes and critically reviews epidemiologic studies of prenatal 

chlorpyrifos exposure and subsequent fetal growth and child development evaluated within the 

three prospective children’s health cohorts described above. Specifically, in this section the 

design, conduct and methods of analysis of each cohort study are briefly presented; individual 

study results are summarized by type of health outcome investigated; and strengths and 

limitations of these investigations are discussed. Appendix 3 of the chlorpyrifos human health 

risk assessment (HHRA) includes detailed study reviews and critical analysis of each 

investigation and Appendix 4 includes extensive evidence tables summarizing details of each 

investigation. The following section 3.0 reflects EPA’s synthesis and evaluation of the current 

chlorpyrifos epidemiology database from the three children’s health cohorts. The results of the 

April 2012 FIFRA SAP review, the June 2012 Federal letter review, and the April 2013 on-site 

meeting with CCCEH study staff are integrated throughout the document where appropriate. In 
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accordance with the draft “Framework for Incorporating Epidemiology in Risk Assessment,”68

this analysis considers the strengths and limitations reflected in each cohort and research study as 

well as modified Bradford Hill considerations for the synthesis of these data. 

2.1 Overview of Design and Methods of Children’s Health Studies 

These cohorts were recruited for the purpose of studying the potential health effects of 

environmental exposures during pregnancy on subsequent child development: The Columbia 

University’s Mother’s and Newborn Cohort (Lovasi et al., 2011; V. Rauh et al., 2011; V. A. 

Rauh et al., 2006; R. M. Whyatt et al., 2009; R. M. Whyatt et al., 2007; R. M. Whyatt et al., 

2004); The Mount Sinai Hospital Children’s Environmental Health Cohort (Berkowitz et al., 

2004; Engel et al., 2007; Engel et al., 2011); and UC Berkeley’s the Center for the Health 

Assessment of Mothers and Children of Salinas (CHAMACOS) Cohort (Bouchard et al., 2011; 

Eskenazi et al., 2004b; Eskenazi et al., 2010; Eskenazi et al., 2007; Harley et al., 2011; Marks et 

al., 2010; Young et al., 2005). These studies enrolled pregnant women at baseline and 

prospectively assessed associations in their newborns and young children. Multiple reports have 

been published based on the findings in each cohort. 

The three study populations reflect different exposure profiles. The Columbia Mother’s and 

Newborn study and the Mt. Sinai Child Growth and Development study participants were likely 

exposed to chlorpyrifos through residential use of the pesticide for indoor pest control. In the 

residential setting, chlorpyrifos was among the most widely used household pesticides in the US 

during the time period of these epidemiologic investigations. However, pesticide companies 

voluntary cancelled indoor residential uses of chlorpyrifos-containing pesticide products on 

December 31, 2000, during the time period of these cohort investigations. In the agricultural 

setting, chlorpyrifos was registered for use on commonly consumed crops such as corn, almonds, 

apples and oranges. Therefore, these study populations were most likely additionally exposed to 

chlorpyrifos via the oral route through ingesting residues in the diet and from hand-to-mouth 

contact with in-home chlorpyrifos-contaminated surfaces, as well as possible dermal exposure 

through contact with treated areas in the home environment (R. M. Whyatt et al., 2003; R. M. 

Whyatt et al., 2009; R. M. Whyatt et al., 2007). In contrast, CHAMACOS cohort participants 

were employed as farm laborers or were residing in homes with farm laborers. These participants 

likely experienced either occupational exposure through the inhalation and dermal routes, as well 

as probable indirect exposure through drinking water and take-home exposures (Bradman et al., 

2007). In each of the children’s health cohorts, the prevalence of pesticide exposure was high; 

however, reported use of chlorpyrifos in the CHAMACOS region was modest (10% of total 

pesticide use) (Eskenazi et al., 2004a). 

 

2.1.1. The Mother’s and Newborns Study of North Manhattan and South Bronx (Columbia 

University) 

Researchers with the Columbia Mother’s and Newborn Study evaluated the association between 

prenatal exposure to pesticides including chlorpyrifos and developmental outcomes in children 

through age 7 years. In this birth cohort study, participants were recruited during early pregnancy 

(≤20th week) among African-American and Dominican women age 18-35 years, and registered 

 

68 http://www.epa.gov/scipoly/sap/meetings/2010/020210meeting.html#materials 
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for prenatal care and delivery at either New York Presbyterian Medical Center or Harlem City 

hospitals. Women who smoked, had a history of drug abuse, diabetes, hypertension, or HIV 

infection were excluded from participation in the study, as were women who resided in New 

York City for less than 1 year. The study samples represented in the reports reviewed were 

recruited between 1998 and 2004, a period which overlaps the voluntary cancellation of 

chlorpyrifos use in the residential environment. 

In this cohort, authors measured chlorpyrifos exposure in several different biological and 

environmental matrices. These include chlorpyrifos parent compound in cord blood; the 

chlorpyrifos metabolite 3,5,6-trichloro-2-pyridinol (TCPy) in maternal and infant urine and 

meconium; and chlorpyrifos in personal and stationary air monitoring samples. In epidemiologic 

analyses, investigators consistently utilized cord blood measures of chlorpyrifos as the measure 

of prenatal exposure. Chlorpyrifos levels in umbilical cord blood samples were sampled as close 

to the time of delivery as possible, and within 2 days post-partum. Cord blood plasma 

chlorpyrifos levels were imputed from maternal blood levels for newborns for whom no cord 

blood sample was obtained because correlation was high (>80%). Quantification of chlorpyrifos 

levels in plasma were conducted by the Centers for Disease Control and Prevention (CDC). 

Information regarding basic demographics, socio-economic status, and pregnancy related 

measures, among other factors was collected through self-report questionnaire at the time of 

enrollment. 

2.1.2. Inner-City Toxicants, Child Growth and Development Study (Mt. Sinai Hospital) 

Researchers with the Mt. Sinai Children’s Health and Development study conducted a 

prospective birth cohort study in which they enrolled primiparous women presenting for prenatal 

care with singleton pregnancies at the Mount Sinai prenatal clinic and two private practices and 

who delivered their infants at Mount Sinai Hospital in New York City between May 1998 and 

July 2001. Mothers were excluded if they had any of the following characteristics: an initial 

prenatal visit after 26 weeks of gestation, serious chronic diseases, a serious pregnancy 

complication that could affect fetal growth and development. Additional, participants were 

excluded for risky health behaviors including alcohol consumed greater than two alcoholic 

beverages per day or illicit drug use. Mothers and infants were also excluded if the child was 

born with a congenital malformation or severe prematurity. 

To measure prenatal pesticide exposure, researchers implemented a self-report questionnaire to 

solicit information regarding pesticide usage, in-home pest pressure and other exposure 

characteristics. In addition, in the early third trimester, participants were asked to provide a urine 

sample at the time of a routine clinical blood draw. Using this biological sample, authors 

measured urinary concentration of pesticide metabolites including both TCPy (Berkowitz et al., 

2004), and also non-specific measures of organophosphate exposure, dialkyl phosphates (DAPs) 

(Engel et al., 2007; Engel et al., 2011). Authors adapted analytical methods to conduct laboratory 

measurement of TPCy; DAPs were measured by CDC using published methods (Barr et al., 

2002). Using maternal and infant (cord blood) blood sample, researchers measured PON1

enzymatic activity levels, and conducted genotyping analysis to determine prevalence of PON1

variant alleles. 
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Potentially confounding variables were measured through self-report questionnaire and included 

in final statistical models if variables were known to be associated with either pesticide exposure 

or fetal growth. Authors also measured concentration of other pesticides in biological matrices 

including metabolites of pyrethroid exposure (PBA), pentachlorophenol (Berkowitz et al., 2004), 

as well as other organophosphates (malathion) and organochlorines compounds including 

polychlorinated biphenyls (PCBs) (Engel et al., 2007). However, authors did not present 

analyses concerning potential confounding effect of co-exposure to other environmental 

chemicals. 

2.1.3 Center for Health Assessment of Mother’s and Children of Salinas Valley, 

CHAMCOS (University of California/Berkley) 

The Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) cohort 

is comprised by participants who live and work in the Salinas Valley, CA. The Salinas Valley is 

a major center of agricultural production in the United States with approximately 500,000 

pounds of organophosphate pesticides applied annually (California EPA. Pesticide Use 

Reporting 2001 Summary Data, 2002. (www.cdpr.ca.gov/docs/pur/pur01rep/01_pur.htm)), 

chlorpyrifos, however, was not frequently used in agriculture in this area during the time period 

of this study. This cohort is comprised of low-income, predominantly Mexican-American (or 

Mexican immigrant) women employed as farm laborers or living with someone employed as a 

farm laborer. Enrollment of the Center for the Health Assessment of Mothers and Children of 

Salinas (CHAMACOS) cohort took place at regional community clinics. Women considered 

eligible for the study were less than 20 weeks gestation, aged 18 years or older, Medi-Cal 

eligible, fluent in English and/or Spanish, and planning to deliver at Natividad Medical Center. 

Excluded from analyses were women with gestational or preexisting diabetes, hypertension, twin 

births, or stillbirths. 

Data collection was performed using several different tools. Authors administered self-report 

questionnaires to study participants to obtain information regarding demographic characteristics, 

work history and health behaviors. To assess exposure, participants were asked to provide 

biological samples (urine, blood). Specifically, maternal and fetal exposure to organophosphate 

pesticides was assessed by measurement of organophosphate dialkyl phosphate metabolites 

(DAPs) and seven different pesticide-specific metabolites, including TCPy, in maternal urine 

during two periods in the pregnancy. Maternal urine samples collected between 5 and 27 weeks 

gestation and again between 18 and 39 weeks. The post-delivery urines were collected within 1 

week of delivery for 73% of the sample, with the remainder obtained up to 176 days afterwards. 

Total DAP, including dimethyl phosphates (DMP), and diethyl phosphates (DEP) levels were 

determined for each participant for each of the two pregnancy urine samples, and because the 

measures did not differ substantially, these values were averaged in epidemiologic analyses to 

estimate prenatal pesticide exposure. Quantification of organophosphate metabolites was 

conducted by the Centers for Disease Control and Prevention (CDC) labs (Barr et al., 2002). 

In this cohort, authors also measured cholinesterase (ChE) in whole blood and butyl 

cholinesterase (BChE) in plasma as a surrogate for organophosphate pesticide exposure. PON1 

was also measured in blood. Researchers also measured concentration of other environmental 

chemicals including PCBs, lead, DDT/DDE, HCB, and PBDEs; however, these compounds were 
Chlorpyrifos Human Health Risk Assessment D424485 

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not included in final models as potential confounding variables. 

2.2 Summary of Research Results 

Across these three children’s health cohorts, authors have assessed the relation between 

measures of prenatal chlorpyrifos exposure and various measures of fetal growth and infant and 

child neurodevelopment. In this section, research results are briefly summarized by health 

outcome category. A more detailed description of the study design, methods and analysis, as well 

as research results and a critique of individual study strengths and limitations are presented in 

Appendix 3. An evidence table delineating key study features is presented in Appendix 4. Table 

1 briefly summarizes these results. 

2.2.1 Measures of Fetal Growth 

Authors with each of the three respective children’s health cohorts measured prenatal 

chlorpyrifos exposure in association with fetal growth including birth weight, birth length, head 

circumference, gestational age and Ponderal index. To ascertain birth characteristics, authors 

linked with medical records at respective participating hospitals. Across these children’s health 

cohorts, authors observed inconsistent evidence of an association; however, differing exposure 

profiles as well as dissimilar methods of prenatal chlorpyrifos exposure assessment likely played 

a role in this observation (Needham, 2005). 

 
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Table 1 Summary of Findings in the Columbia Mothers and Newborn Cohort, the Mount Sinai Hospital 

Children’s Environmental Health Cohort, and the UC Berkeley Center for the Health Assessment of Mothers 

and Children of Salinas Studies of Prenatal Organophosphate Pesticide Exposure and Child Development

   Columbia

Cohort Mount Sinai Cohort UC Berkeley Cohort

Markers of

Exposure:

Chlorpyrifo

s TCPy DAPS TCPy DAPS

Birth Length

Inverse

(Null post

2000)

Null  ‐‐  Positive

(NS) Positive

Birth Weight

Inverse

(Null post

2000)

Null  ‐‐  Positive

(NS)   Null

Head Circumference

at Birth Null Null(Inverse*)   ‐‐  Null Positive(Inverse*)

Gestational Age  ‐‐  Null  ‐‐  Null Null

(Inverse*)

BNBAS Newborn

(Abnormal reflexes) ‐‐  ‐‐  Positive*  ‐‐  Positive

BNBAS Newborn

(Neurodevelopment

)

‐‐  ‐‐  Null  ‐‐  Null

Bayley Scores  

6 months (MDI/PDI) ‐‐  ‐‐  ‐‐  Null/Inverse (NS)

Inverse

(NS)/

Inverse

(NS)

Bayley Scores  

12 months

(MDI/PDI)

Null / Null  ‐‐ 

Inverse**/

Inverse (NS)  

Inverse

(NS)/

Inverse (NS)

Inverse

(NS)/

Inverse

(NS)

Bayley Scores  

24 months

(MDI/PDI)

Null / Null  ‐‐ 

Inverse

(NS)/Null  

Inverse(NS)

/

Inverse (NS)

Inverse/

Inverse

(NS)  

Bayley Scores  

36 months

(MDI/PDI)

Inverse/

Inverse ‐‐  ‐‐  ‐‐  ‐‐ 

Pervasive

Development

Disorder (PDD)

Positive  

(36 mo.) ‐‐  ‐‐  ‐‐  Positive  (24 mo.)

Mental

Development ‐‐  ‐‐  Null  ‐‐  ‐‐ 
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   Columbia

Cohort Mount Sinai Cohort UC Berkeley Cohort

Markers of

Exposure:

Chlorpyrifo

s TCPy DAPS TCPy DAPS

(WPPSI‐III, age 6

years)

Mental

Development

(WISC‐IV, age 7‐9

years)

Inverse

(Full‐scale

IQ and

Working

Memory)

‐‐ 

Inverse (NS)

(Full scale IQ,

perceptual

reasoning,

working

memory and

processing

speed)

‐‐ 

Inverse

(FSIQ,

working

memory,

processin

g speed,

verbal

perceptua

l

reasoning

)  

Odds of ADHD/

attention and

behavior problems  

Positive (36

mo.) ‐‐  ‐‐  ‐‐ 

Positive

(NS 3

years)

Positive (5

years)

*Interaction observed between pesticide markers and PON1 activity or genotype

**Interaction observed between pesticide markers and race

NS=Not statistically significant

Inverse= Higher levels of exposure associated with adverse health outcomes (measurement value or score decreased)

Positive = Higher levels of exposure associated with adverse health outcome (measurement value or score increased)

Null = No association observed

MDI= Mental Development Index

PDI = Psychomotor Development Index  

 
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Researchers with the Columbia Mothers and Newborn study observed an association between 

decreased fetal growth and prenatal chlorpyrifos measures among 314 mother-infant pairs 

selected for this study (R. M. Whyatt et al., 2004). Controlling for potential confounders, for 

each log unit increase in cord plasma chlorpyrifos levels, birth weight decreased by 42.6 g (95% 

CI: –81.8 to –3.8) and birth length decreased by 0.24 cm (95% CI: –0.47 to –0.01). Whyatt et al. 

(2004) did not report an association with head circumference. Combined exposure to both 

chlorpyrifos and diazinon (adjusted for relative potency using US EPA cumulative risk 

assessment methods) were also significantly inversely associated with birth weight and length (p 

< 0.05). When births were stratified by time period prior to or after the voluntary cancellation of 

chlorpyrifos for residential use, researchers no longer observed evidence of an association (p > 

0.8) among births which took place in the cancellation period (n=77). In supplementary analyses, 

authors replicated this analysis with additional post-cancellation era births (total n=193) and 

similarly did not observe evidence of an association with chlorpyrifos in the later time period (R. 

Whyatt & Rauh, 2011) (See Attachment 1). Overall, authors suggest that prenatal chlorpyrifos 

exposures may have impaired fetal growth among this inner-city, low income cohort. While 

suggestive of a conclusion that cessation of chlorpyrifos exposure explains the different 

associations with fetal growth before and after the period of the voluntary cancellation of 

chlorpyrifos, EPA notes that this finding may also be suggestive of a possible threshold effect, or 

it could also be due to a lack of statistical power to assess associations by time period. 

Within the Mt. Sinai Child Growth and Development study, Berkowitz et al. (2004) assessed the 

association between prenatal chlorpyrifos exposure measured as urinary TCPy and subsequent 

risk of impaired fetal growth (Berkowitz et al., 2004). Authors also evaluated potential effect 

modification by PON1 genotype and phenotype in the relation of interest. Among 404 births 

which took place between 1998 and 2002, authors found no statistically significant associations 

between fetal growth including birth weight, or birth length or head circumference and 

chlorpyrifos (estimated as TCPy) exposure. However, Berkowitz et al. (2004) did observe 

evidence of heterogeneity of effect by PON1 activity level. Specifically, researchers observed a 

small, but statistically significant reduction in head circumference among children of mothers 

with levels of chlorpyrifos above the limit of detection and also in the lowest tertile of PON1 

activity (least able to metabolize exogenous exposures such as OP pesticides). In the subgroup of 

infants whose mothers had TCPy levels greater than the level of detection, those with low 

maternal PON1 had an average (SD) head circumference of 33.3cm (1.5cm) which was 

significantly smaller than those with medium (34.0cm (1.5cm)) and high (34.1cm (1.6cm)) 

maternal PON1 activity after adjusting for race/ethnicity, infant sex, and gestational age (p = 

0.014), although the statistical interaction was not significant. Authors did not observe evidence 

of heterogeneity of effect by PON1 in the relation between other measures of birth outcomes 

including birth weight and birth length and pesticide exposure. Neither maternal PON1 genetic 

polymorphisms nor infant paraoxonase levels were associated with reduced head size. 

Within the CHAMACOS cohort, among 488 participants enrolled between 1999-2000 Eskenazi 

et al. (2004) did not observe a significant adverse relationship between fetal growth and any 

measure of in utero organophosphate pesticide exposure (Eskenazi et al., 2004b). On the 

contrary, investigators reported positive associations between birth length and head 

circumference associated with non-specific organophosphate exposure measures (DAPs). 

Researchers observed decreases in gestational age associated with measures of in utero pesticide 
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exposure: urinary DMP metabolites (β = –0.41 weeks per log10 unit increase; 95% CI: –0.75––

0.02; p = 0.02), which reflects exposure to dimethyl organophosphate compounds such as 

malathion, but not chlorpyrifos. Authors did not observe an association using TCPy as a measure 

of chlorpyrifos exposure; however they did report an increased risk of preterm delivery with 

decreasing cholinesterase concentrations, a third biomarker of organophosphate exposure. To 

identify “critical windows” of fetal development when exposure may have a greater impact, the 

authors analyzed the associations of outcomes and metabolite levels measured during moving 6-

week windows of pregnancy (e.g., 5–10 weeks, 6–11 weeks, 7–12 weeks) using a series of 

multiple regression analyses. No period of greater impact was observed in this largely null study. 

In a follow-up analysis within this cohort, Harley et al. (2011) performed the same analysis 

however evaluating the potential effect modifying role of PON1 genotype and phenotype in the 

relation between prenatal pesticide and chlorpyrifos exposure (DAP, TCPy, ChE) and fetal 

growth (Harley et al., 2011). In this study, infants’ (but not mother's) PON1 genotype and PON1 

activity modified the association between gestational age and head circumference. Infants with 

the susceptible PON1-108 TT genotype had shorter gestational age (beta =-0.5 weeks, 95%CI: -

0.9, 0.0) and smaller head circumference (beta=-0.4 cm, 95% CI: -0.7, 0.0) than those without 

the susceptible genotype (PON1-108 CC genotype). Infants’ arylesterase and paraoxonase activity 

were positively associated with gestational age. Maternal DAP concentrations were associated 

with shorter gestational age among infants of the susceptible PON1-108 TT genotype, although 

only the interaction between PON1-108 genotype and DEP metabolite concentrations was 

statistically significant (p-value for interaction = 0.09). However, maternal DAP concentrations 

were associated with larger birth weight (p-value for interaction = 0.06) and head circumference 

(p-value for interaction =0.01) in infants with non-susceptible genotypes. The authors conclude 

that infants with certain PON1 genotypes (e.g., PON1-108 TT) may be more susceptible to effects 

of in utero organophosphate pesticide exposure. 

2.2.2 Brazelton Neonatal Neurological Functioning 

Researchers with both the Mt. Sinai Child Growth and Development study and the CHAMACOS 

cohort evaluated neonatal neurological functioning in association with prenatal chlorpyrifos 

exposure. To measure indices of abnormal neonatal behavior and/or neurological integrity 

authors used outcome measures derived from the Brazelton Neonatal Behavioral Assessment 

Scale (BNBAS). The BNBAS includes 28 behavioral items and 18 primitive reflexes which 

assesses the infant across several different developmental areas. This tool was administered to 

infants within days after birth and before they left the hospital (2-5 days post-partum). 

Examinations were conducted by trained neonatologists in the hospital setting using similar 

environmental conditions. The Mt. Sinai Child Development study and the CHAMACOS cohort 

evaluated this outcome measure; researchers with the Columbia Mother’s and Newborn study 

did not measure the relation. 

Among the 438 infants eligible to participate in the Mt. Sinai evaluation, Engel et al. 2007 

observed an association between generic (DAPS) biomarkers of prenatal organophosphate 

pesticides and an increased number of abnormal primitive reflexes which are considered a 

critical marker of neurologic integrity. Controlling for confounding factors, subjects with 

prenatal total diethylphosphates levels above the median (24.7 nmol/L) delivered infants who 
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were 2.3 times more likely to have at least two abnormal reflexes (95% CI: 1.1, 5.0). 

Associations with other DAPs were also reported. Notably, no statistically significant adverse 

associations were found for any of the dialkylphosphate metabolites and other domains of the 

Brazelton assessment tool in the author’s primary analyses, e.g., habituation, range of state, 

orientation. Authors also observed evidence of potential effect modification by PON1 activity 

level in the relation between DAPs and neonatal neurodevelopment. Specifically, in the first 

tertile of paraoxonase 1 expression (slowest metabolizers), the relative risks (RR) of having 

abnormal reflexes were significantly related to DAP and DMP levels; the risk estimate for DEP 

and the number of abnormal reflexes within the lowest PON1 level was not significant. In the 

CHAMACOS cohort, Young et al. (2005) observed a statistically significant association between 

organophosphate pesticide exposure and the reflex cluster of the BNBAS (Young et al., 2005). 

The proportion of infants with more than three abnormal reflexes was 3-fold increased among 

those with prenatal DEP exposure (DEP: OR = 3.4, 95% CI = 1.2, 9.9). Similar to Engel et al. 

(2007), no other associations between prenatal DAPs and other aspects of the Brazelton 

assessment were noted (other neurodevelopmental domains) (Engel et al., 2007). No adverse 

associations were found between postnatal urinary metabolite levels and any of the 

developmental outcomes. 

2.2.3. Bayley Scale of Mental and Psychomotor Development 

Researchers across the three children’s health cohorts utilized the Bayley Scales of Infant 

Development II (BSID-II) to generate a Mental Development Index (MDI) and a Psychomotor 

Development Index (PDI) to assess neurodevelopment in early childhood. As a complement to 

this measure of mental development and behavior, authors also used the 99-item Child Behavior 

Checklist (CBCL). In addition, because the quality of the home environment is a key determinant 

of child development, authors also used the Home Observation for Measurement of the 

Environment (HOME) instrument. This instrument collects data regarding the physical and 

interactive qualities of the child’s home as a measure of mental stimulation and interactions. 

Results from each of these measurement tools were used to model the relation between prenatal 

chlorpyrifos exposure and adverse neurodevelopmental outcomes in infancy and toddlerhood (6-

36 months of age). 

Rauh et al. (2006) investigated Mental Development Index (MDI) and a Psychomotor 

Development Index (PDI) at 12, 24, and 36 months of age within the Columbia Mother’s and 

Newborn Study. Children were categorized as having either high (>6.17pg/g) or low 

(<6.17pg/g) prenatal exposure, using categories informed by results of the previous study on 

birth characteristics (R. M. Whyatt et al., 2004). Authors reported that the difference in MDI 

scores was “marginally significant” (p = .06) with the exposed group scoring an average of 3.3 

points lower. When the same multivariate regression models were calculated regarding the PDI 

scores, none of the 12 or 24 month PDI scores showed significant effects, but the 36 month score 

was significantly related to chlorpyrifos exposure. Investigators also calculated estimates of 

adjusted risk for developmental delays (MDI and PDI) related to chlorpyrifos exposure and 

illustrated that before 36 months of age, delays were no more likely in the highly exposed group, 

but, at the 36 month milestone, the likelihood of highly exposed children developing mental 

delays were 2.4 times greater (95% CI: 1.12-5.08, p = .02) and motor delays were 4.9 times 

greater (95% CI: 1.78-13.72; p = .002) than those with lower prenatal exposure. Using general 
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linear modeling (GLM), authors analyzed developmental trajectories of the effects and results 

were consistent with the 12, 24, and 36 month milestone analyses indicating that the age effects 

most significantly occurred at the later phase of the 3 year period. In supplemental analyses 

suggested by the 2008 FIFRA SAP, authors illustrated that diazinon was a strong confounding 

variable in this association (correlation with chlorpyrifos 0.63), increasing the magnitude 

chlorpyrifos risk estimate for MDI and PDI 50-200% in the same direction (away from the null) 

(R. Whyatt & Rauh, 2011) (Attachment 1). 

Within the Mt. Sinai Children’s Environmental Health study, authors administered the BSID-II 

to participating children at 12 and 24 months. Among 404 women originally enrolled between 

May, 1998 and July, 2001 (n = 404), children of these mothers returned for neurodevelopment 

assessments at ages 12 months (n = 200), 24 months (n = 276) of age. Using generalized linear 

models, authors found that prenatal total dialkylphosphate metabolite level was associated with a 

decrement in mental development at 12 months among blacks and Hispanics children. The 

associations appeared to be strongest among children of mothers who carried the PON1 Q192R 

QR/RR genotype. The authors concluded that their findings are suggestive of an association 

between prenatal exposure to organophosphates and decrements in cognitive development, 

particularly perceptual reasoning, with evidence of effects beginning at 12 months and 

continuing through early childhood, with PON1 being a potentially important susceptibility 

factor for these deleterious effects. The authors did not observe any effect modification by PON1 

enzyme activity level. In general, associations observed during the 12-month follow-up were 

either attenuated or non-existent at the 24-month visit. 

In the CHAMACOS cohort, Eskenazi et al 2007, the authors report on the relationship between 

prenatal and child urinary organophosphate metabolite levels and child neurodevelopment at 

ages 6, 12, and 24 months of age. Controlling for indicators such as the psychometrician 

conducting the assessment and the location of assessment, age at assessment, sex, duration of 

breast-feeding, HOME score, and household income, parity and indicator of maternal 

intelligence (PPVT score), authors observed that prenatal DAP levels were adversely associated 

with MDI, while early life DAP levels were positively associated with MDI. At 24 months of 

age, these associations reached statistical significance (per 10-fold increase in prenatal DAPs: β

= –3.5 points; 95% CI: –6.6 to –0.5; child DAPs: β = 2.4 points; 95% CI: 0.5 to 4.2). In a 

subsequent study, investigators did not observe evidence of effect modification by PON1 status 

in the relation between prenatal DAPs and child neurodevelopment as measured by the Bayley 

Scale (Eskenazi et al., 2010). Neither prenatal nor child DAPs were associated with PDI or 

CBCL attention problems. Both prenatal and postnatal DAPs were associated with risk of 

pervasive developmental disorder (per 10-fold increase in prenatal DAPs: OR = 2.3, p = 0.05; 

child DAPs OR = 1.7, p = 0.04). TCPy as a biomarker of chlorpyrifos exposure was not 

significantly associated with any neurodevelopment outcome in this study. 

2.2.4 Attention Problems 

Also within the CHAMACOS cohort, Marks et al. (2010) conducted a study to investigate the 

association between urinary dialkyl phosphate (DAP) metabolites in pregnant women and their 

children, as a marker of organophosphate exposure, and attention-related outcomes among 348 

children who had available data at 3.5 and/or 5 years and met inclusion criteria (Marks et al., 
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2010). Attention-related health outcomes were measured through maternal report of child 

behavior at 3.5 and 5 years of age; direct assessment of the child at 3.5 and 5 years; and by a 

psychometrician’s report of the behavior of the child during testing at 5 years. To identify 

children whose behaviors were most suggestive of possible ADHD within the cohort, a 

composite ADHD variable was defined that combined the results of the maternal report (CBCL), 

child testing (K-CPT), and the psychometrician report (Hillside). In this study population, higher 

concentrations of organophosphate metabolites in the urine of pregnant women were associated 

with increased odds of attention problems and poorer attention scores in their children at age 5 

years. Prenatal DAPs were non-significantly associated with maternal report of attention 

problems and ADHD at age 3.5 years but were significantly related at age 5 years (CBCL 

attention problems: β = 0.7 points; 95% CI: 0.2–1.2; ADHD: β = 1.3; 95% CI: 0.4–2.1). Prenatal 

DAPs were associated with scores on the K-CPT ADHD Confidence Index > 70th percentile 

(OR = 5.1; 95% CI: 1.7–15.7 and with a composite ADHD indicator of the various measures 

(OR = 3.5; 95% CI: 1.1–10.7). Some outcomes exhibited evidence of effect modification by sex, 

with associations found only among boys. Children’s concurrent total DAP and DMP metabolite 

levels at 3.5 years and 5 years were unrelated to attention outcomes, and but child DEP 

concentrations at 5 years were adversely associated with the composite measure of attention (OR 

= 2.0; 95% CI:1.1–3.6). The results of this investigation by Marks et al. (2010) in the 

CHAMACOS cohort are suggestive of a detrimental association between prenatal 

organophosphate exposure, as measured by maternal urinary metabolite levels, and attentional 

difficulties at age 5 years using three different measures of this neurodevelopmental outcome. 

2.2.5. Intelligence Measures 

To measure intelligence among school aged children, authors from each of the three children’s 

health cohorts used the Wechsler Intelligence Scale for Children, 4th edition (WISC-IV). The 

instrument measures four areas of mental functioning: the Verbal Comprehension Index, the 

Perceptual Reasoning Index, the Working Memory Index, and the Processing Speed Index. 

These indices are associated with, but distinct from, overall intelligence quotient (IQ) and are 

sensitive to cognitive deficits related to learning and working memory. A Full-Scale IQ score 

combines the four composite indices. A General Ability Index score is a summary score of 

general intelligence, similar to Full-Scale IQ, but excludes contributions from both Working 

Memory Index and Processing Speed Index. WISC-IV scores are standardized against U.S. 

population-based norms for English and Spanish-speaking children. 

Rauh et al. (2011) evaluated the relationship between prenatal chlorpyrifos exposure and 

neurodevelopment among 265 of the cohort participants who had reached the age of 7 years and 

had a complete set of data including prenatal maternal interview data, prenatal chlorpyrifos 

marker levels from maternal and/or cord blood samples at delivery, postnatal covariates, and 

neurodevelopmental outcome data (V. Rauh et al., 2011). While models were developed using 

continuous measures of both prenatal chlorpyrifos exposure and Wechsler scores, for ease of 

interpretation, investigators reported that for each standard deviation increase in exposure 

(4.61pg/g) there is a 1.4% reduction in Full-Scale IQ and a 2.8% reduction in Working Memory. 

In the Mt. Sinai Child Growth and Development study, Engel et al. 2011 used generalized linear 

models to analyze the relationship between total diethylphosphates (DEPs), total 
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dimethylphosphates (DMPs), total dialkylphosphate metabolites (DAPs) measured in maternal 

urine during the third trimester, and subsequent cognitive/intelligence development evaluated at 

age 7 years. In this study, prenatal maternal DEP urinary metabolite concentrations were 

associated with slight decrements in FSIQ, Perceptual Reasoning, and Working Memory 

between the ages of 6 and 9 years. Among children of mothers with the susceptible PON1

genotype, DAP and DMP urinary metabolite concentrations were associated with poorer scores 

on Perceptual Reasoning and FSIQ. 

In the CHAMACOS cohort, Bouchard et al. (2011) observed evidence of an association between 

prenatal exposures to organophosphate pesticides as measured by urinary DAP metabolites in 

women during pregnancy, and decreased cognitive functioning in children at age 7 (Bouchard et 

al., 2011). Authors observed this finding using total DAP, DMP, and DEP metabolites to 

estimate pesticide exposure. Children in the highest quintile of maternal DAP concentrations had 

an average deficit of 7.0 IQ points compared with those in the lowest quintile. Authors reported 

the associations were linear with no threshold. Urinary DAP concentrations in childhood 

(postnatal) were not associated with cognitive scores in this cohort of children. Of note, the 

following known or suspected neurotoxicants were measured: polybrominated diphenyl ethers 

(PBDEs), polychlorinated biphenyls (PCBs), p,p’-dichlorodiphenyltrichlorethane (DDT), p,p’- 

dichlorodiphenyltrichlorethylene (DDE), and lead. Lead was measured in maternal blood at 26 

weeks gestation, in cord blood for a subset of participants, and children’s blood a 2 years of age. 

These other environmental exposures including blood lead concentration did not change the 

magnitude and direction of the associations observed between DAP/DEP and IQ measures 

among school-aged children. 

2.2.6 MRI Imaging Study 

In April 2012, CCCEH researchers published a study utilizing magnetic resonance imaging 

(MRI) technology to identify and contrast anatomical characteristics of the cortical region of the 

brain including cortical surface area and thickness in relation to prenatal pesticide exposure (V. 

A. Rauh et al., 2012). Between June and September 2012, EPA sought additional expertise 

within the Federal government as to the quality of the MRI data collection procedures used in 

this study as well as the appropriateness of the authors’ interpretation of the MRI studies in 

association with chlorpyrifos exposure. EPA’s critical review and analysis of this MRI study can 

be found in Appendices 3 and 4; the results of the Federal Panel review concerning EPA’s 

questions about the study can be found at: 

http://www.regulations.gov/#!documentDetail;D=EPA-HQ-OPP-2008-0850-0170. 

In this study, authors CCCEH birth cohort study evaluated whether there were areas of 

morphological change in the pediatric brain in regions of the brain known to be associated with 

learning, cognition and social behavior in association with prenatal chlorpyrifos (CPF) exposure 

(V. A. Rauh et al., 2012). These are functional neurodevelopmental outcomes that have been 

adversely associated with chlorpyrifos exposure in previous observational research (V. A. Rauh 

et al., 2006; V. Rauh et al., 2011), as well as some experimental studies, as noted by the authors. 

Specifically, in this study, authors evaluated 1) whether morphological changes in the brain 
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including cortical thickness and differences in the cerebral brain surface area and volume were 

related to prenatal chlorpyrifos exposure (the main effect), and, 2) whether the relation between 

the exposure and morphological changes in the brain were modified by either full scale 

intelligence quotient (FSIQ) score (evidence of effect modification by IQ) and, 3) in preliminary 

analyses, whether changes in the normal sexual differentiation of the brain exist by exposurelevel (V. A. Rauh et al., 2012). 

Authors concluded that while overall brain size did not differ by prenatal chlorpyrifos exposure, 

certain areas of the brain were enlarged among those more highly exposed to chlorpyrifos 

including superior temporal, posterior middle temporal, and inferior post-central gyri, and 

superior frontal gyrus, gyrus rectus, cuneus, and pre-cuneus in the mesial views of the right 

hemisphere of the cerebrum. Further, they noted a difference exposure-response relation between 

cerebral surface distances and CPF exposure, by FSIQ wherein the relation between brain area 

enlargements and FSIQ were positively correlated in the low CPF group (greater regional brain 

size correlated with greater IQ), but an inverse relation among the high CPF group. In 

supplemental analyses, authors illustrated that areas of regional brain enlargement among those 

more highly exposed to chlorpyrifos were due to underlying white matter enlargements. 

Researchers also noted sporadic differences in cortical thickness wherein higher CPF exposure 

was related to reduced cortical thickness. Preliminary analyses also displayed a disruption of 

normal male-larger-than-female areas as well as female-larger-than-male in certain areas of the 

brain known to differ by child sex in normal brain. Overall, researchers concluded that 

differences in brain structure (regional cerebral size and thickness) exist between CPF exposure 

groups, and the differences (high>low CPF) in regional brain size may be due to enlargement of 

underlying white matter. However, authors did not speculate as to the underlying biological 

explanation of the effect of CPF exposure on intelligence or with morphological changes in key 

areas of the brain linked with learning, cognition and social behavior. 

2.3. Strength and Limitations of Children’s Health Cohort Studies 

There are several strengths reflected in the design, conduct and analysis decisions made by 

investigators with each of the three children’s health studies, as well as some limitations to 

consider. Strengths of these studies include the prospective nature of these investigations, 

extensive data collection including several different measures of prenatal chlorpyrifos exposure 

which have been validated, and use of neurodevelopmental outcome ascertainment tools which 

have been validated in different populations and commonly used in both clinical and research 

settings, among other strengths noted in this section. Key limitations reflect the difficulty in 

measuring multi-dimensional characteristics such as socio-economic status accurately, the 

challenge of conducting environmental epidemiology studies in populations exposed to several 

different compounds and mixtures, and the ability to assess effect modification (e.g., genetic 

variability) with precision and accuracy. Considering both the strengths and limitations of the 

respective investigations, EPA puts forward epidemiologic inference at the conclusion of this 

section. 

Study authors from each of the respective long-term studies hypothesized a role for 

environmental exposures, including but not limited to pesticides generally and chlorpyrifos 

specifically, in the etiology of adverse fetal growth and neurodevelopmental outcomes. Study 
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authors stated their motivation was the knowledge that these populations were highly exposed to 

pesticides and other environmental contaminants, and that prior research had linked in utero 

environmental exposure to adverse neurodevelopmental outcomes. Cohorts were each funded 

through the EPA and National Institute of Environmental Health Science (NIEHS) Children’s 

Environmental Health and Disease Prevention Research Centers69. Cohort studies were 

comprised of several hundred mother-infant pairs (range: n=102 to n=488) and were likely 

sufficiently statistically powered to detect hypothesized main effects, but studies may have 

lacked statistical power to perform stratified analyses, e.g., effect modification by genetic 

variant. This is especially true for the MRI imaging study performed within the CCCEH (Rauh et 

al. 2012). The pilot nature of the study, inclusion of only 40 child participants from the overall 

cohort, severely limited the evaluation of statistical interactions. Similarly, statistical methods 

utilized across these investigations were appropriate to the data collected and research question 

hypothesized, however statistical analyses were discussed in varying degrees of detail by 

separate study groups. EPA notes that in some instances, additional information is provided in 

supporting methods paper, e.g., approaches to handling non-detects in large datasets (Arunajadai 

& Rauh, 2012). Statistical model selection was generally parsimonious in nature, and 

investigators performed multiple sensitivity analyses (not always shown) within each study to 

further explore and eliminate alternative explanations for observed results, e.g., confounding by 

blood lead, variable transformations. Concerning the main effects hypothesized, both design and 

analysis decisions made by researchers enhanced the probability of identifying an association, if 

an association exists. For the association under study, measurement errors likely occurred due to 

the challenge of estimating exposure during the critical window of development. 

Selection criteria were clearly defined and appropriate across the three cohorts. Investigators 

limited participants to individuals within a similar racial and ethnic group characteristics, income 

and education level, and geographic area, and who reported low prevalence of risky health 

behaviors (alcohol use, smoking during pregnancy), and who did not report major medical comorbidities which may have adversely affected fetal growth and development (e.g., gestational 

diabetes). Therefore, authors sought to control through design (restriction) variability in the 

sample population to isolate the effect of chlorpyrifos exposure on fetal outcomes. However, the 

many selection criteria applied may have affected the generalizability of study results. The 

selected study population is narrowly defined and dissimilar to the general U.S. population 

across many demographic and exposure related characteristics. However, in comparison with 

biomonitoring levels in the U.S. general population, exposure among these cohorts was greater, 

but within a similar range. In addition, the external validity of the study findings may be limited 

if the organophosphate pesticide exposure-fetal development association is modified by factors 

that are more, or less, prevalent in the study populations relative to the population(s) to which 

inference is being made, or to populations with a substantially different exposure range. 

All three studies are prospective cohort studies in which exposure measurements were obtained 

prenatally and/or at delivery, and fetal growth and neurodevelopmental outcomes were assessed 

subsequently. This study design eliminates or reduces several potential sources of bias. This 

design eliminates temporal bias, i.e., uncertainty as to whether exposure precedes the adverse 

outcome, and, and also reduces the effect of differential exposure measurement error (a type of 

 

69 http://epa.gov/ncer/childrenscenters/
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information bias) as it is unlikely exposure measurement would be differentially assessed by 

(unmeasured, future) health outcome. 

Researchers utilized similar tools to measure adverse health outcomes across the studies. The use 

of similar outcome measures across studies aids comparability of studies and assessment of the 

consistency of results. Neurodevelopmental outcome assessment tools utilized were common to 

both clinical and research settings and most have been validated in both English and Spanish 

speaking populations. While measures of fetal growth are somewhat objective to measure, 

neurodevelopmental outcomes measured within these studies are more difficult to assess. While 

administered consistently within these investigations, tests of mental development, cognition, 

psychomotor development, and intelligence tools are somewhat subjective in nature, and may be 

affected by child’s anxiety (Bayley Scales) and influenced by cultural factors (Wechsler). 

However, the homogeneity of the study population may reduce the potential influence of a 

cultural bias or differences in assessment tool response based on external factors, and these are 

among the best (gold standard) of tools to assess these types of developmental issues. Authors 

employed appropriate quality assurance and control measures such as training of test 

administrators, periodic evaluation of adjudicators, consistent environments in which tests were 

administered, and in many instances included an indicator variable for test administrator in 

statistical analysis to adjust for slight differences in examiner effect. . Federal letter reviewers in 

September 2012 concurred that the neurodevelopmental testing utilized in these studies were 

reasonable and appropriate70. With the exception of the child behavior checklist (CBCL), errors 

in the measurement of health outcomes were likely non-differential in nature, leading to an 

attenuation of the risk estimates. Overall, across these studies researchers utilized the best 

available outcome measurement tools for neurodevelopment health effects, and implemented the 

evaluations in a consistent, standardized manner with trained health professionals and/or study 

staff. The results of the Federal Panel review of June-September 2012 supported this conclusion. 

Federal panelists agreed that these are acceptable tools and appropriate to the age range of the 

child participants in these cohort studies. The tools allowed researchers to address the question as 

to whether neurodevelopment scores differed by chlorpyrifos exposure.71 

Across the three children’s health cohorts, study authors measured parent chlorpyrifos, TCPy and 

diethyl phosphate (DEP) to estimate chlorpyrifos and/or organophosphate exposure. Chlorpyrifos 

metabolites (TCPy and DAPs) are likely more accurate and objective indicators of 

organophosphate pesticide exposure than other exposure ascertainment methods such as selfreport, but uncertainty remains as to the extent measurement of non-specific metabolites reflects 

chlorpyrifos exposure. TCPy is a metabolite specific to chlorpyrifos, but can also be produced as 

a result of chlorpyrifos-methyl exposure or environmental exposure to TCPy itself (Morgan et 

al., 2005; Wilson, Chuang, Lyu, Menton, & Morgan, 2003). Urinary dialkyl phosphate (DAP) 

metabolites of organophosphorus insecticides are markers of exposure to organophosphate 

pesticides generally; chlorpyrifos and diazinon are metabolized to diethyl phosphates (DEP), 

while other organophosphates are metabolized to dimethyl phosphate (DMP). For risk 

assessment purposes, it is difficult to infer chlorpyrifos effects specifically from urinary DAPs 

and to a lesser extent the TCPy metabolite. Different exposure measurements may in part explain 

inconsistencies across study findings, as noted by others (Needham, 2005). The three birth 

 

70 http://www.regulations.gov/#!documentDetail;D=EPA-HQ-OPP-2008-0850-0170. 71 See http://www.regulations.gov/#!documentDetail;D=EPA-HQ-OPP-2008-0850-0170. 
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cohorts recruited participants who likely have chlorpyrifos exposures that are higher than the 

general population, and the exposures in these cohorts may also be more consistent over time.

Inner-city populations report frequent use of pesticides, most applied at least once per week, 

during the time period of these studies (R. M. Whyatt et al., 2003; R. M. Whyatt et al., 2002). In 

New York City, location of two of the three children’s health cohort studies, housing authority 

reported common use of chlorpyrifos for indoor pest control during the time period of these 

studies, prior to the time period of the voluntary cancellation of these uses. In the CHAMACOS 

cohort, chlorpyrifos metabolite (TCPy) was detected in 77% of urine samples (Bradman et al., 

2005; Eskenazi et al., 2004a). Despite being considered to have greater opportunities for 

exposure relative to the general population, the average levels of chlorpyrifos biomarkers in the 

three cohorts were generally comparable to the U.S. general population, which may indicate a 

difference between actual and measured exposure. Nevertheless, it is not clear to what extent the 

use of one or two biomarker measurements conducted in the reported studies reflects exposure(s) 

over critical windows of development during pregnancy. This remains an uncertainty within 

these studies. As previously mentioned, chlorpyrifos exposure was assessed by quantification of 

biomarkers primarily in one- or two-samples of maternal urine taken during the third trimester, 

and also, in one cohort, in a single maternal blood or cord blood sample obtained at the time of 

delivery. If the exposure is chronic, a biomarker measured at a single time point may provide a 

representative dosimeter, even if the toxicant has a short half-life, as is the case for chlorpyrifos 

and organophosphate pesticides generally. However, if pesticide exposures are sporadic or 

otherwise vary over short time scales, the biomarker measurement may not be representative of 

“usual” exposure or of the exposure during critical periods of fetal development. In the UC 

Berkeley CHAMACOS cohort (Eskenazi et al., 2004a), prenatal urine was collected between 5 

and 27 weeks gestation and again between 18 and 39 weeks. The within-person standard 

deviation in the DAP metabolites was approximately three times larger than the between-person 

standard deviation, and concluded that a single biomarker assessment, as was conducted in the 

other two cohorts, “may not accurately reflect exposures over the entire pregnancy.” 

This potential limitation was also assessed in two validation studies conducted by Whyatt et al 

within the Columbia Mother’s and Newborn Study. The first of these studies (R. M. Whyatt et 

al., 2007) assessed within- and between-home variability in indoor-air insecticides over the final 

2 months of pregnancy among a subset of participants. Authors observed little within-home 

variability and no significant difference in air concentrations within homes over time (p ≥ 0.2); 

between-home variability accounted for 92% of the variance in the indoor air levels of 

chlorpyrifos. Indoor and maternal personal air insecticide levels were highly correlated (r = 0.7–

0.9, p < 0.001). While this study provides some evidence that assessment of chlorpyrifos 

exposure at a single time point may be reasonable, the study took place during a period of rapid 

decreasing indoor use of chlorpyrifos, and may not reflect variability in indoor air concentration 

prior to the period of cancellation. In addition to inhalational exposure, participants were likely 

also exposed by ingesting residues in the diet and from hand-to-mouth contact with surfaces 

contaminated with the pesticide. If true, then high correlation of chlorpyrifos air exposure over 

time may not indicate a true consistency in exposure levels. 

The variability in exposure measures and the validity of different biomarkers of exposure was 

evaluated in a second study (R. M. Whyatt et al., 2009) which evaluated trends over time in 

multiple biomarkers of prenatal chlorpyrifos exposure. Authors measured TCPy levels in repeat 
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prenatal urine samples and determined they were positively, but only moderately correlated (r = 

0.23–0.56), and within-subject variability exceeded between-subject variability (intraclass 

correlation coefficient = 0.43). This indicates that variability in individual exposure over time 

may be considerable. Indoor air levels explained only 19% of the variance in prenatal urine 

TCPy (p = 0.001) in this study. However, these researchers also demonstrated the presence of 

TCPy in meconium; meconium is fecal matter accumulated in intestines of developing fetus 

from week 13 of gestation and released immediately prior to or within a few days after delivery. 

This matrix is considered to be an integrated measure of exogenous exposures to the fetus during 

the gestational period. Importantly, investigators reported a moderate and significant correlation 

between maternal blood and urine collected in the later part of the pregnancy period, infant cord 

blood collected at delivery, and meconium (R. M. Whyatt et al., 2009). As noted in earlier 

sections, the critical windows of effect for these specific outcomes is unknown at this time, and 

may span the period of early pregnancy through early childhood (Rice & Barone, 2000; Rodier, 

2004). The correlation among several biomarkers of chlorpyrifos exposure reflecting different 

windows of exposure from week 13 through delivery (e.g., correlation between meconium and 

infant cord blood r=0.33, p=0.01, n=56), suggests a one-time measure of exposure may 

accurately rank participants with respect to prenatal exposures, at least within a 2- to 3-fold level 

of variation as suggested by authors (R. M. Whyatt et al., 2009). Additionally, the correlation 

between the analytic measure of exposure and a measure reflective of exposure during a large 

proportion of the gestational period suggests the exposure measures may also reflect exposure 

during the several likely critical windows of development in the mid- to latter period of 

gestation. However, to the extent participants experienced “peak” exposures at key periods of 

development, this information would not likely be reflected in the one-time measure, and likely 

lead to non-differential measurement error. 

Bias due to confounding occurs primarily when risk factors for the outcome are unequally 

distributed among exposure groups, but are not themselves caused by the exposure, and these 

factors are not controlled in either the design or the analysis of the study. The greatest concern is 

for unmeasured confounders, those variables not measured within the study that may be related 

to both the exposure and disease of interest but are unknown to the investigator. Residual 

confounding can also arise due to errors in the measurement or categorization of confounders, 

even after apparent adjustment for these factors in the study, i.e., these are factors not measured 

well enough. Among the major potential unmeasured or poorly measured confounding variables 

in the chlorpyrifos and neurodevelopmental association are: 1) difficult to measure aspects of the 

social environment (e.g., chronic stress, socioeconomic conditions), and 2) environmental coexposures that may be determinants of neurodevelopment (e.g., lead exposure, mercury 

exposure, air pollution, tobacco smoke exposure, maternal alcohol intake, and exposure to other 

pesticides). The selection of cohorts that are homogenous with respect to many demographic and 

social factors significantly reduces the potential for residual or unmeasured confounding factors 

related to socio-economic status to have influenced these results as they have been controlled 

through restriction (another method of adjustment). For example, socioeconomic status cannot 

confound the main association in a cohort of individuals living under the same socioeconomic 

condition, i.e., there is little variation in the study population on this factor. However, close 

examination of descriptive tables in these publications indicates some degree of variability 

remains. Therefore, authors appropriately evaluated and included in final models several 

individual-level markers for socio-economic status such as education, income, and race. 
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Additionally, within the Columbia Mother’s and Newborn study, authors performed new 

analyses using hierarchal regression techniques to model variability due to SES in the relation 

between prenatal chlorpyrifos exposure and mental and motor development (the Bayley Scale) 

(Lovasi et al., 2011). These techniques utilize individual- as well as group-level variables to 

model SES. In this research, authors did not observe a significant difference in risk estimates, 

indicating that the role of SES as a major confounder in the Columbia cohort is appropriately 

adjusted. However, others argue that the multi-dimensional nature of a characteristic such as SES 

can never be fully captured through commonly used variables, and suggest within a term such as 

SES there could be components that act as both positive and negative confounding variables 

(Bellinger, 2011). However, at this time, there is no compelling evidence that lack of control of 

the confounding influence of SES significantly biased the reported risk estimates. 

Additionally, authors have measured several other environmental chemicals and in some 

instances considered these other exposures as potential confounding variables (not always 

shown). Researchers collected information from participants regarding biomarkers of other 

organophosphates, environmental tobacco smoke (ETS), blood lead, PAH, methylmercury (R. 

Whyatt & Rauh, 2011) (See Attachment 1), pyrethroids/pyrethrins, organochlorines such as 

DDT/DDE and hexachlorobenzene, PBDE, and polychlorinated biphenyls. While authors 

evaluated these environmental exposures in relation to both chlorpyrifos and outcomes, none 

were included as confounders in final models due to lack of statistical evidence of confounding. 

Within the Columbia Mothers and Newborn study, authors performed additional analyses 

suggested by the 2008 FIFRA SAP to evaluate the role of other organophosphate exposures, 

specifically diazinon and propoxur, in the relation between chlorpyrifos and both birth 

characteristics and mental and motor delays (Bayley scores) (R. Whyatt & Rauh, 2011) (See 

Attachment 1). Chlorpyrifos and diazinon biomarkers were highly correlated in this cohort 

(r=63%). While the effect sizes reported for the reported relation between both birth length and 

also birth weight remained unchanged from published reports when diazinon was added to the 

model, significant differences were observed in the relation with neurodevelopmental functions 

(mental (MDI) an PDI (motor) scores). Specifically, authors reported a 50-200% increase in 

effect sizes with MDI and PDI, respectively, i.e., observed adverse effects became more 

pronounced with diazinon added to the model (See Attachment 1). Limited evidence of 

confounding by propoxur was also observed, but the correlation between chlorpyrifos and 

propoxur was moderate (23%), the confounding effect was not significant. In addition, neither 

pre- nor post-natal blood lead levels or methyl mercury levels measured in cord blood were 

significantly correlated with chlorpyrifos, and was therefore not considered a confounding 

variable in the association of interest within the Columbia cohort (V. A. Rauh et al., 2006; R. 

Whyatt & Rauh, 2011). 

The appropriate measurement and controlling of potentially confounding variables was a major 

line of inquiry in the EPA initiated Federal Panel review in 2012. On this point, reviewers agreed 

with EPA that factors relating to socio-economic status were adequately measured and 

characterized by authors. In addition, reviewers did not note other major potential confounders 

inadequately measured or controlled in analysis. With respect to blood lead, reviewers of the 

MRI study indicated the sample size may have been inadequate to fully control for the effect of 

low-levels of lead exposure. As a result of this comment, EPA contacted researchers with the 

CCCEH to gain additional information as to the association between chlorpyrifos and blood lead 
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in the cohort (additional data collection beyond the published study data). In further written and 

oral communication with the CCCEH staff, EPA learned that when the correlation between the 

two environmental exposures was assessed among a larger sub-set of the cohort than previously 

published (n=300), the correlation between chlorpyrifos and cord blood lead remained extremely 

low (See Appendix 6 to the revised chlorpyrifos HHRA). This information reinforces EPA’s 

understanding that lead exposure is not likely a confounding variable in this study. 

However, uncertainties remain as to the role of other (unmeasured) environmental compounds as 

potential confounding variables in this association. None of the authors evaluated the possible 

effect modifying role of other environmental chemicals in the relation between chlorpyrifos and 

neurodevelopment, nor were studies suited to examining the effect of mixtures (simultaneous 

combined exposures at potentially relevant critical windows of development). New 

investigations are needed to address these hypotheses, if relevant. While the possibility of 

unknown, unmeasured positive confounding bias can never be completely excluded, given the 

evidence available it is unlikely this potential bias may entirely explain observed associations. 

Selection bias occurs when study participants are either selected or lost to observation as a result 

of a third, unmeasured factor that is associated with both the exposure and outcome of interest. In 

these prospective cohort studies, selection bias is most likely induced not by selection into the 

study, but by selection out of the study, i.e. due to attrition of study participants and missing data. 

Because data are often not obtained on those that are lost to follow-up or for whom data is 

otherwise missing, it is difficult to determine whether or not, and to what extent, the observed 

associations are biased due these factors. The remedy is simple, in theory – follow the entire 

cohort and obtain all relevant data – but often difficult in practice, particularly for long-term 

cohort studies such as those reviewed here. Across these studies, the amount of missing data 

varied but was great in some studies. To address missingness in data, authors imputed values in 

several instances, and performed sensitivity analyses with and without imputed data to illustrate 

comparability in reported results. In addition, authors were able to illustrate differences between 

those included and excluded were comparable on several major characteristics, but some were 

not evaluated such as blood lead levels. Therefore, while it is difficult to ascertain the degree to 

which selection bias due to missing data may have influenced these study results, authors 

employed appropriate analytic tools to address missingness in data to the extent possible. 

Regarding external validity, the inclusion and exclusion criteria employed may limit the 

generalizability of study results. This is particularly true of the MRI imaging study, which was a 

pilot effort. 

Information bias arises due to misclassification or error in the measurement of either the 

exposure or outcome of interest, or the accurate measurement of confounding variables. 

Qualities of the outcome ascertainment method, the exposure assessment method and the 

measurement of confounding variables were reviewed above. In summary, regarding exposure 

measurement error the true relevant exposure in these studies is prenatal chlorpyrifos to the 

developing fetus. Sources of exposure measurement errors include 1) the single measurement 

chlorpyrifos or its metabolites during the third trimester, 2) error arising from differences 

between measured biomarker levels and actual chlorpyrifos or chlorpyrifos oxon exposure, even 

at the one time point, 3) unmeasured time- and space- dependent patterns of chlorpyrifos 

exposure, 4) uncertainty regarding the critical period for chlorpyrifos effects on development, 5) 
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missing exposure data, 6) laboratory errors, and 7) imputation of missing exposure levels. 

Measurement errors in the ascertainment of chlorpyrifos exposure are likely to have occurred in 

the studies reviewed. However, because of the prospective study designs employed, the errors 

are unlikely to result in falsely positive findings, because the probability and magnitude of these 

errors are likely to be independent of the outcome status of participants. Measurement errors in 

the ascertainment of the health outcomes were also likely to have occurred, perhaps more for the 

neurocognitive outcomes, than for the indicators of fetal growth and development, and also are 

unlikely to have spuriously positive findings. These measurement errors are more likely to have 

resulted in false negative findings, if a causal association truly exists. 

While the MRI study by Rauh et al. 2012 follows upon both previous epidemiological research 

and experimental toxicology studies that suggest an association between chlorpyrifos exposure in 

the pre-natal and early life periods with adverse neurodevelopmental outcomes observed in 

children through school age, EPA believes that the use of these data to inform specific 

underlying biological mechanisms or pathways of the potential toxic action of CPF is limited; 

additional and more sophisticated imaging would have to be performed to determine links to the 

very specific types of neurodevelopmental effects posited by authors in the Rauh et al. (2012) 

study. 

2.3.1. Epidemiologic Inference of Combined Children’s Health Cohorts 

As stated earlier, the three children’s health cohorts considered herein have several strengths as 

well as limitations to consider in the interpretation of these studies. Within these studies, there 

are several factors that would tend to under-estimate the actual association (possibly leading to 

false negative association), as well as some characteristics that may lead to over-estimation 

(possibly leading to a false positive association). However, it must also be noted that 

methodological research and supplemental analyses (primarily within the Columbia Mothers and 

Newborn study) performed subsequent to the 2008 FIFRA SAP deliberations concerning these 

children’s health cohort studies have reduced and further characterized important sources of 

uncertainty. As noted in the Introduction to this paper, currently scientists cannot determine with 

accuracy the critical window of exposure for these outcomes (early gestation through early 

childhood). Across the children’s health cohorts, researchers used one or two measure(s) of 

exposure to estimate gestational exposure during the critical window of development, and 

investigators assessed the main exposure using non-specific biomarkers of chlorpyrifos. 

Undoubtedly, exposure measurement error occurred. However, exposure validation studies 

illustrate some degree of correlation across exposure measures made at different periods of 

gestation (e.g., meconium, week 13 through delivery; maternal urine during last 8-weeks of 

pregnancy; and cord blood at delivery). Additionally, given the prospective nature of these 

studies, it is unlikely the measurement error was differential by outcome, i.e., non-differential 

exposure misclassification leading to biased estimates toward the null is anticipated. Finally, as 

noted above the degree of missingness in some key variables across these studies may have 

resulted in a form of selection bias which may have also lead to an under-estimation of effects. 

However, given the data are missing, it is difficult to assess the magnitude and direction of this 

error on study results. Low sample size may have limited the ability of researcher to identify 

sources of effect modification or perform stratified analyses with accuracy. 
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Conversely, there may have been factors at play which led to an inflated estimate of the true 

association. Factor that may lead to false positive associations include unmeasured or poorly 

measured confounding factors which are positively associated with both chlorpyrifos use and 

adverse neurodevelopmental outcomes (positive confounding variables). The measurement of 

socio-economic factors and other environmental exposures experienced either pre- or post-natal 

environment are most likely among these potential factors, although others may exist. However, 

when additional adjustment was made for these factors using both individual- and group-level 

variables, the magnitude and direction of the associations remained stable (diazinon, propoxur, 

blood lead, methyl mercury, SES, post-natal exposure) (Lovasi et al., 2011; V. A. Rauh et al., 

2006; R. M. Whyatt et al., 2004; R. Whyatt & Rauh, 2011) (See Attachment 1). In the instances 

in which authors assessed the role of early childhood exposure to chlorpyrifos or other 

environmental contaminants as potentially confounding variables in the relation between prenatal 

exposure and neurodevelopmental outcomes, confounding bias was not observed. However, not 

all investigations evaluated the role of post-natal pesticide exposure, and authors note the 

limitation. Selection bias due to missing or drop outs from the study could also have influenced 

the observation of a false positive association, and this is again difficult to assess in the absence 

of data. However, researchers uniformly discussed and described the comparison of those 

included and excluded from individual analyses based in past of missing data using several 

important factors, and in many instances reported a level of comparability which is reassuring. 

The observation of positive associations as a result of multiple comparisons may also be a factor 

to consider; however the a priori identification of research questions of interest and the 

consistency of findings across several neurodevelopmental domains argues against a large role 

for multiple statistical testing to explain positive findings. 

Overall, these are well performed studies which are shielded from several major sources of bias 

in the interpretation of results due to the strong design, conduct and analyses utilized in these 

investigations. While factors are present across these studies which may have led to either false 

positive or negative associations, it is notable that positive associations were observed as EPA 

believes the possibility of under-estimation of effect size is more likely than factors that would 

lead to over-estimation of effect size. Authors have taken significant steps to address major 

sources of uncertainty that may lead to over-estimation of effects, i.e., positive confounding bias 

as a result of poorly measured factors related to socio-economic status and other environmental 

chemical exposures. 

3.0. Chlorpyrifos Epidemiology Synthesis and Evaluation 

In this chapter, EPA reviewed the results of epidemiological investigations of the association 

between prenatal chlorpyrifos exposure and adverse effects upon fetal growth and neonatal and 

early childhood neurodevelopmental outcomes across three major prospective children’s health 

cohort studies in the U.S. In accordance with the OPP’s draft “Framework for the Incorporation 

of Epidemiology into Regulatory Risk Assessment”72, including the use of the modified 

Bradford Hill considerations in judging the potential causal nature of observed associations, in 

this section EPA considers the totality of the epidemiological evidence from these cohorts. To 

perform this analysis, EPA considered the strength of the associations observed and the presence 

of exposure-response trends, the temporality of the observed associations, and the degree to 

 

72 http://www.epa.gov/scipoly/sap/meetings/2010/020210meeting.html#materials 
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which alternative explanations have been considered and eliminated as explanatory factors, 

among other considerations. Issues of biological plausibility and specific mechanisms of action 

which may explain a causal role for chlorpyrifos in adverse neurodevelopmental outcomes, while 

broadly considered within the context of these investigations, are discussed in depth in the 

revised chlorpyrifos HHRA and supporting documents. EPA notes that while the pilot MRI study 

by the CCCEH researchers supports other findings from these cohorts, additional and more 

sophisticated imaging would have to be performed to determine links to the very specific types 

of neurodevelopmental effects posited by authors in the Rauh et al. (2012) study. 

As noted previously, each investigation is a prospective cohort study in which prenatal exposures 

occurred prior to the developmental of either fetal growth anomalies or neonatal or early 

childhood neurological delays. Therefore, in each study exposure preceded the health effect, risk 

may be directly calculated, and measurement error is more likely non-differential in nature, i.e., 

likely under-estimation rather than over-estimation of risk effect. Table 1 summarizes the 

associations observed for chlorpyrifos markers and neurodevelopmental outcomes in the three 

cohorts. Within this database, moderately strong associations have been observed, and in many 

instances measured with precision. The relation between both neonatal neurological development 

as measured using the Brazelton index (number of abnormal reflexes) and also mental and 

psychomotor development among toddlers (Bayley Scale for 24-36 months) with prenatal 

chlorpyrifos exposure is elevated approximately 2- to 4- fold among those more highly exposed. 

This observation was consistent across cohorts with respect to the neonatal period, but not across 

measures using the Bayley Scale. Considering adverse effect in the 24-36 months period of 

development, the effects became more pronounced over time in one cohort (the Columbia 

Mother’s and Newborn study), and became less pronounced over time in the Mt. Sinai Child 

Development study using non-specific biomarker of chlorpyrifos exposure (DAPs). However, it 

is notable the significant effects were seen when chlorpyrifos parent compound was measured 

directly; and, the effect size became significantly greater when diazinon was considered in the 

final statistical model. Notable decrements in intelligence measures were consistently observed 

across all three cohorts. While effect sizes measured as beta-coefficients are reflective of average 

change in intelligence score per unit change in chlorpyrifos exposure, and may appear modest in 

effect size, EPA notes that according to these models, the adverse influence at the extremes of 

the chlorpyrifos exposure distribution, if truly present in nature, would be more deleterious. 

Strong, consistent evidence of a positive association between prenatal chlorpyrifos and 

attentional problems, pervasive developmental disorder, and ADHD-like symptoms was 

observed in these studies, although not always measured with precision. 

An association is consistent when a similar magnitude and direction of results are replicated in 

studies in different settings using different methods. If a relationship is causal, one may expect to 

observe it consistently in different studies and among different populations. Several consistencies 

were noted across these studies. Higher levels of the chlorpyrifos-specific metabolite TCPy were 

associated with prevalence of abnormal reflexes among newborns, as assessed using the 

Brazelton Neonatal Behavioral Assessment Scale (BNBAS), in both the Mount Sinai Hospital 

and UC Berkeley (CHAMACOS) Cohorts. The BNBAS assessment was not conducted in the 

Columbia Mothers and Newborns cohort. TCPy levels were consistently not associated with 

BNBAS indicators of newborn neurodevelopment such as behavioral domains other than those 

observed for number of abnormal reflexes. In addition, associations between pesticide markers 
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and the Mental Development Index (MDI) portion of the Bayley Scores at 12, 24, and 36 months 

varied between small, largely non-statistically significant decrements, and null. However, in 

supplemental (post-publication) analyses, authors illustrated that adjustment for other 

organophosphate pesticides in the relation between prenatal chlorpyrifos and MDI strengthened 

the magnitude of the effect significantly at the 36 months time point (R. Whyatt & Rauh, 2011) 

(See Attachment 1). Associations between early life organophosphate pesticide exposure 

biomarkers and decrements in mental development in childhood (age 7-9 years) were observed 

in all three studies, although the relationships were not consistently statistically significant. 

Some notable inconsistencies were also observed. These may be explained by differing exposure 

biomarkers, timing of biomarker collection, or other limitations of study design and 

measurement, e.g., the magnitude of non-differential exposure misclassification. However, 

inconsistency in results may also argue against a true causal association. Associations between 

markers of prenatal pesticide exposure and fetal growth were not consistent across the three 

studies, as noted in the FIFRA SAP 2008 meeting. In the Mount Sinai Child Growth and 

Development cohort, no statistically significant associations were observed in primary analyses, 

although a modest decrease in average head circumference with increasing TCPy was noted 

among those with low PON1 activity. In the Columbia Mothers and Newborn Study (R. M. 

Whyatt et al., 2004), chlorpyrifos levels in maternal blood were modestly associated with 

decreased fetal growth. In contrast, among the UC Berkeley CHAMACOS study participants, 

modest increased birth weight and head circumference were associated with in utero DAP 

concentrations (Eskenazi et al., 2004; Harley et al., 2011). In fact, stratification by PON1 status 

seemed to enhance these positive associations, such that in the non-susceptible groups (i.e., 

PON1-108 CC, PON1192 RR, and high arylesterase activity), increasing DAP concentrations were 

significantly associated with increased birth weight and head circumference. The pesticide 

biomarkers differed between the two studies; cord blood chlorpyrifos levels were assessed in the 

Columbia Mothers and Newborn study, while DAPs were assessed in the UC Berkeley 

CHAMCOS cohort. It is possible that high urinary DAP concentrations may be an indication of 

rapid detoxification and excretion of organophosphate pesticides, rather than a marker of high 

exposure, particularly among the participants that are less potentially less susceptible to pesticide 

effects due to their PON1 genotype or PON activity level, as suggested by study authors. 

Although consistency of associations across studies set in different populations, and employing 

different methods has been used as a criterion for causality, it is not the only compelling factor. 

In fact, heterogeneity across studies is expected to occur between studies in the presence of effect 

modification, if the prevalence of the effect modifier varies between the populations being 

assessed. Apparent inconsistencies could also be due to chance differences, and due to the 

presence of biases operating in the studies under consideration. 

There was evidence for an exposure-response relationship in several of the studies reviewed, 

whereby increasing levels of exposure were associated with increasingly large decrements in 

measures of neurodevelopment (V. Rauh et al., 2011; V. A. Rauh et al., 2006). For the analyses 

in which continuous distributions of exposure markers were used, the levels were often, but not 

always, log transformed prior to entry in statistical models. The estimated association between 

the biomarker level and the outcome in these cases is not linear; rather, the association between 

the log-transformed exposure and the outcome is linear. In such cases, results are interpretable 
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as changes in mean level/risk of the outcome of interest for a given percentage increase in the 

exposure. Examination of departures from linearity of exposure-response relationships was 

reported in only a small subset of the reviewed articles. For example, Rauh et al. (2011) reported 

that, in the Columbia cohort, “the dose-effect relationships between CPF [chlorpyrifos] exposure 

and log-transformed Working Memory Index and Full-Scale IQ scores are linear across the range 

of exposures in the study population, with no evidence for a threshold” (V. Rauh et al., 2011). 

Departures from linearity were not statistically significant. Two possible explanations for this 

finding are that 1) the exposure response is linear on the scale assessed, or that 2) the studies did 

not have sufficient power to detect departures from linearity in the shape exposure-outcome 

relationship. The smoothed exposure-response curve superimposed upon the scatter plot of logtransformed working memory appears to be monotonically decreasing; however, a similar curve 

superimposed upon the scatter plot of log-transformed IQ score and chlorpyrifos levels appears 

flat if not slightly positive, before turning negative beyond an inflection point at about 5 pg/gram 

of chlorpyrifos. As such, it may be that both explanations for the null finding regarding 

departures from linearity are operating in this study. As another example, Bouchard et al used 

cubic splines to evaluate the shape of dose-response curves, test the linearity assumption, and 

investigate potential thresholds (Bouchard et al., 2011). Again, no statistically significant 

departures from linearity were observed. 

Due to the voluntary cancellation by registrants of indoor residential uses of chlorpyrifoscontaining pesticide products, children born after the year 2000 were likely exposed to far lower 

levels of chlorpyrifos, on average, than children born prior to 2001. In their report, Whyatt et al. 

(2004) took advantage of this “natural experiment” by stratifying their analyses on birth date 

prior to, or after, January 1, 2001. Although reported household use of pesticides in general did 

not change over the same time period in this study, maternal chlorpyrifos in maternal air samples 

were significantly lower after the voluntary cancellation (4.9 ng/m3

), relative to those taken 

before 2001 (8 ng/m3; p<0001). Associations between birth outcomes and organophosphate 

insecticide levels in maternal personal air samples were not statistically significant in both unstratified analyses and among subgroups stratified by birth prior to or after January, 2001. 

However, the associations between birth weight and length and cord plasma chlorpyrifos were 

highly significant (p ≤ 0.007) among newborns born before the 2001 cancellation of registrants. 

Among newborns born after January 2001, no association with fetal growth was observed (p > 

0.8) (R. M. Whyatt et al., 2004). Supplemental analysis suggested this observation persisted 

among additional number of infants born after the period of the voluntary cancellation (n=193) 

(R. Whyatt & Rauh, 2011) (Attachment 1). This finding by Whyatt et al provides evidence that 

the association “can be altered (prevented or ameliorated) by an appropriate experimental 

regimen,” as advocated by AB Hill in the assessment of causality in observational studies. 

Changes in exposure were reflected in decreasing chlorpyrifos levels, and appear to have been 

associated with changes in developmental outcomes. However, as noted by the 2008 FIFRA SAP 

Panel in their report, this study was not designed to test this hypothesis, specifically. 

It must also be considered that given the many selection criteria applied within each of the three 

children’s health cohorts, the generalizability of study results may have been affected. These 

studies reflect a somewhat narrowly defined sub-population who likely experienced a greater 

range of exposures and higher peak exposures than the general U.S. population; a comparison of 

maternal urinary concentration of TCPy in the three children’s health cohorts with the general 
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population (NHANES) illustrates higher exposures occurred among the cohort participants. The 

exception is with the urinary concentration data collected by Whyatt et al. (2009) which were 

lower than the U.S. general population; however, these data were collected 2001-2004, a period 

after the voluntary cancellation of indoor residential uses of chlorpyrifos. In addition, the 

external validity of the study findings may be limited if the organophosphate pesticide exposurefetal development association is modified by factors that are more, or less, prevalent in the study 

populations relative to the population(s) to which inference is being made, or to populations with 

a substantially different exposure range. Generalizability may also be affected by low sample 

size such as in the brain imaging (MRI) study. In the studies reviewed, the exploration of geneby-environment, and phenotype-by-environment interactions is a strength of many of the 

analyses, although as noted above the studies are uniformly underpowered to assess such 

interactions. There was limited assessment of interaction between chlorpyrifos and other 

environmental co-exposures conducted in these investigations including blood lead levels. 

4.0 Conclusions 

The FIFRA SAP panel convened in September 2008 concluded that given the strengths of these 

studies, the effect of biases likely present, and rigorous statistical analyses performed, the 

observation of associations across these studies can be interpreted as a conclusion that 

chlorpyrifos likely played a role in the neurodevelopmental outcomes observed among children 

more highly exposed to chlorpyrifos in the in utero environment. Since that time, researchers 

with each of the three children’s health cohorts have published several new etiologic and 

methodological studies and performed supplemental analyses as suggested by the 2008 Panel 

which extend the knowledge base, and reduce several major sources of uncertainty present in the 

2008 database (i.e., confounding by SES, exposure validation in the Columbia Mothers and 

Newborn study, further evaluation of PON1 genotype and PON activity level, pilot MRI imaging 

study). In the current document, EPA utilized the draft “Framework for Incorporating 

Epidemiology into Risk Assessment”73, including use of modified Bradford Hill considerations, 

to assess the strengths and limitations of the studies in this database on the association of interest, 

and to clarify the epidemiologic evidence in support of a causal role for chlorpyrifos in infant 

and child adverse neurodevelopmental outcomes. 

In EPA’s analysis of the strengths and weaknesses of these studies, the chance that positive 

associations observed are false positives due to systematic errors in the studies cannot be 

excluded; however, it is more likely that error present in these studies would lead to the underestimation of the true association. Therefore, while alternative explanations for positive 

association can be hypothesized (e.g., additional unmeasured or poorly measured positive 

confounding variables), these explanations are judged to be less plausible than the alternative 

that associations have been missed or under-estimated due to non-differential measurement error 

and low sample size across exposure strata. In occupational settings, exposure measurement error 

has been shown to more greatly influence epidemiology study results than unknown or 

unmeasured confounding variables (Blair et al., 2011). Additionally, the elimination of temporal 

bias due to the prospective study design employed within each of the three children’s health 

cohorts assures that prenatal exposures preceded neurodevelopmental outcomes measured at 

birth, and in early and later childhood through age 7 years. The strength of the associations 

 

73 http://www.epa.gov/scipoly/sap/meetings/2010/020210meeting.html#materials 
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measured in some studies was notably strong. However, associations in many instances were 

weak to moderate, possibly due to measurement error. Associations with neurodevelopmental 

outcomes were consistently identified with respect to the number of abnormal reflexes in the 

neonatal period, the presence of mental and behavioral issues as well as gross motor delays were 

pronounced especially in later toddler years of 24-36 months, and the observation of intelligence 

decrements were seen across the three cohorts using different measures of prenatal chlorpyrifos 

exposure, although not consistently statistically significant. EPA notes that other 

organophosphates may be involved as well as the CHAMACOS and Mt. Sinai Child 

Development study utilized dialkyl phosphates which measure exposure to several different 

organophosphates, and that use of chlorpyrifos in the CHAMACOS study region was low at the 

time of the study. However, links with diethyl phosphate which result from either chlorpyrifos or 

diazinon exposure were observed as well. 

In summary, while the strengths and limitations of the studies would be more likely to lead to an 

under-estimation of the true effect, the possibility of false positive associations cannot be entirely 

ruled out. The temporal association, strength of the statistical associations observed and presence 

of exposure response across some (but not all) of the investigations, as well as some notable 

consistencies in findings within this epidemiologic database tend to support a role for 

chlorpyrifos in this relation. It is true that the Columbia Mothers and Newborn study is the only 

cohort to have measured chlorpyrifos parent compound directly; replication in other cohorts that 

use this exposure metric would aid causal inference. The presence of factors which may have 

over-estimated effects, the lack of consistency in many domains (e.g., fetal growth), and the lack 

of a clear mechanism of action may argue against a true association. Overall, the current 

database supports the conclusion from 2008 Panel that “chlorpyrifos likely played a role in the 

birth and developmental outcomes noted in the three cohort studies.” Additionally, the 

subsequent studies made available since the time of the 2008 evaluation helps to clarify the 

potential role for chlorpyrifos in adverse neurodevelopmental health effects. 

 
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Appendix 2. Attachment 1 
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UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

WASHINGTON, D.C. 20460 

 OFFICE OF CHEMICAL SAFETY AND 

 POLLUTION PREVENTION 

 

MEMORANDUM

Date: June 30, 2011 

SUBJECT: Chlorpyrifos Correspondence with Columbia Researchers: (1) Responses to 

Scientific Advisory Panel (SAP) comments (Whyatt and Rauh 2010), and 

(2) Responses to Dow AgroSciences inquiries (Whyatt 2010). 

PC Code: 059101 DP Barcode: D391257 

Decision No.: NA Registration No.: NA 

Petition No.: NA Regulatory Action: NA 

Risk Assessment Type: Single Chemical Aggregate Case No.: NA 

TXR No.: 0056066 CAS No.: NA 

MRID No.: NA 40 CFR: NA

 

FROM: Deborah Smegal, MPH, 

 Immediate Office (IO) 

 Health Effects Division (7509P) 

TO: Danette Drew, Risk Assessor 

 RABV 

 Health Effects Division (7509P) 

Backrgound 

On September 16-19, 2008, the Agency held a FIFRA Scientific Advisory Panel (SAP) meeting 

(SAP 2008) to discuss the Agency's Evaluation of the Toxicity Profile of Chlorpyrifos, which 

includes more recent and extensive research on various aspects of chlorpyrifos including its 

neurological effects in animals and humans following gestational and post-natal exposures, its 

pharmacokinetics, and mechanism of action. Details can be found at the Scientific Advisory 

Panel website (http://www.epa.gov/scipoly/sap/meetings/2008/091608_mtg.htm), and in the 

Chlorpyrifos Final SAP report 

(http://www.epa.gov/scipoly/sap/meetings/2008/september/sap0908report.pdf.) 

Attached is a response to the SAP comments on the “Epidemiologic Appendix: Columbia Study” 

that Deborah Smegal summarized and sent to Robin Whyatt and Virginia Rauh, dated 1/28/2010. 

In addition, the Columbia researchers prepared responses to questions raised by Dow 

AgroSciences. 
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Columbia Response to the SAP Queries 

2/19/2010 

The following is a response to the Scientific Advisory Panel comments on the 

“Epidemiologic Appendix: Columbia Study” that Deborah Smegal summarized and sent 

to Robin Whyatt and Virginia Rauh, dated 1/28/2010. 

1. SAP comment: EPA should continue its collaboration with Columbia researchers in 

analyzing the epidemiologic data ...[and] should seek clarification of the reported 

exposure levels . . . EPA should use the epidemiological cohort data for bounding 

exposure levels and in conjunction with PBPK models, address current or potential 

human exposures and to determine the final RfD/RfC . . . PBPK models enable 

estimation of exposure dose metric for multiple sources (e.g., air, food, water etc). 

EPA should pursue simpler PBPK model in short-term. 

Response: We are happy to continue the collaboration. In addition, as described in our letter, Dr. 

Dale Hattis at Clark University is conducting PBPK modeling of the Columbia data under a U.S. 

EPA Star Grant. The grant is entitled “Use of Biomarkers and Physiologically Based 

Pharmacokinetic [PBPK] Modeling in Risk Analysis for Developmental Effects of Chlorpyrifos”. 

We believe that the PBPK model being developed by Dr. Hattis will prove extremely helpful to 

EPA in addressing the SAP comments and in clarifying the exposure concentrations that 

correspond to the chlorpyrifos levels in umbilical cord blood at which we begin to see statistically 

significant adverse effects on fetal growth and child neurocognitive development. Thus this data 

should be helpful in bounding the exposure data as recommended by the SAP. We recommend 

that EPA contact Dr. Hattis. 

2. SAP comment: Determine if the chlorpyrifos effect on birth weight would result in 

more infants in “low birth weight” category. 

Response: We are not able to address this question as our cohort was predominantly full term by 

design. This is because we did not conduct our environmental monitoring until the 3rd trimester of 

pregnancy and completion of the environmental monitoring was a prerequisite for enrollment. For 

example, of the subjects in our 2004 analyses (Whyatt et al., EHP, 112:1125-1132, 2004), only 

12/314 (3.8%) of the women delivered prior to 37 weeks gestation and only 12/314 (3.8%) of the 

infants were <2500 grams at birth. We, therefore, do not have adequate statistical power to evaluate 

the relationship between prenatal chlorpyrifos exposure and low birth weight. 

3. SAP comment: Ascertain whether there are statistically significant differences in 

neurodevelopmental outcomes at 3 yrs between chlorpyrifos levels in cord blood 

below LOD and those with chlorpyrifos >6.17 pg/g . . . Verify/clarify there is no 

suggestion of an effect as the most highly exposed group and the group with values 

below the LOD had lower MDI and PDI scores than did the middle levels. 

Response: Table 1 below provide the same type of information as Table 2 in the 2006 Pediatrics 

paper (Rauh et al., Pediatrics, 118:e1845-e1859, 2006) with respect to the % of subjects whose 

tests scores indicated developmental delay with respect to the PDI and the MDI, as a function of 
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chlorpyrifos exposure. However, here we provide the % delayed for all four exposure groups, 

rather than just the ‘high’ and ‘low’ exposure groups shown in the paper. For both the PDI and 

MDI the rate of delay is clearly highest in the high chlorpyrifos group (Group 4).For the 36 month 

PDI, the rate of developmental delay is relatively low in all 3 low-exposure groups, and is about 

three times higher in the highest exposure group. And for the 36 month MDI, while the <LOD 

group experienced a higher rate of delay than group 2 and 3, the very highest rate of delay is among 

the most exposed children. In univariate analyses, the difference in % delay between the highest 

exposure (>6.17pg/g) group versus the lowest exposure (<LOD) group, was statistically significant 

for PDI (chi-square p=.018, 1 sided; p=.031 two-sided), but not significant for MDI. However, 

importantly, there was no significant difference in the rate of delay between groups 1, 2 and 3 for 

either the PDI (chi-squared= 0.97, p=0.61) or MDI (chi-squared= 4.4, p=0.11). And the rate of 

developmental delay in terms of both PDI and MDI in group four was significantly greater than in 

groups 1-3 combined, as reported in our paper. 

Table 1. The proportion of children at age 36 month who were at risk of delay (score < 85) on the 

Psychomotor Development Index (PDI) or Mental Development Index (MDI) of the Bayley Scales of Infant 

Development by chlorpyrifos exposure group

Group 1 

(<LOD) 

Group 2 (1st tertile > 

LOD) 

Group 3 (2nd tertile > 

LOD) 

Group 4 (3rd tertile > 

LOD) 

PDI 7/80 (8.8%) 3/65 (4.6%) 3/38 (7.95) 11/45 (24.4%) 

MDI 30/80 (37.5%) 14/65 (21.5%) 11/39 (28.2%) 20/44 (45.5%) 

4. SAP comment: Conduct an analysis of exposure levels as dependent variable and year 

(an ordinal variable) as the primary independent variable, while adjusting for any 

factors associated with both exposure levels and year of study. 

Response: We have already published on changes in indoor and personal air levels, as well as in 

multiple biomarker levels by year of monitoring and have enclosed the most relevant articles ( 

Whyatt et al., 2007; Whyatt et al., 2009). In addition, Dr. Hattis has also recently completed 

analyses looking at changes in chlorpyrifos levels by year in the context of other factors such as 

season of monitoring. As stated above, we recommend that EPA contact Dr. Hattis to review his 

results. 

 

5. SAP comment: EPA should request additional details on the loss to follow-up in each 

of the 4 exposure groups and provide additional details on how the exposure groups 

were combined. 

Response: Subjects were eligible for inclusion in the Pediatric paper if they had been born by 

7//12/02 and thus had reached age three years by the time of the analyses and also had: (1) measures 

of chlorpyrifos in blood at birth; (2) neurologic assessments at least once between ages 12-36 

months and (3) data on model covairates. A total of 381 subjects in the cohort had been born by 

7/12/02 and had birth chlorpyrifos blood measure; of these 254 (67%) met the above criteria for 

inclusion, including 228 who had developmental outcomes at age 36 months. The remaining 

subjects had either been lost to follow-up (accounting for approximately 14%) or were missing 

outcome and/or covariate data. Table 2 below shows the 254 subjects included in the analyses over 

all 381 subjects by the chlorpyrifos groups. (The numbers per group just for the 228 subjects with 

outcome data at 36 months are given in the Pediatric manuscript.) Group 1 includes all subjects 
Chlorpyrifos Human Health Risk Assessment D424485 

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with chlorpyrifos blood levels below LOD; the remaining subjects were ranked into tertiles based 

on ascending levels (Groups 2-4). However, as describe in the Pediatric manuscript, this grouping 

was done on subjects born prior to 1/1/01 to be consistent with our prior publication (see Whyatt 

et al. 2004). This was essential in order to compare results across analyses moving forward, given 

that chlorpyrifos concentrations were changing rapidly following the ban and thus we needed 

consistent cutpoints. Groups 1-3 were combined and compared with group 4 for the analyses 

presented in the Pediatrics manuscript. 

Table 2. Subjects included in the Pediatric paper over all cohort subjects born at or before 7/12/02 who had 

chlorpyrifos measured in cord blood at birth 

Group 1 (<LOD) Group 2 (1st tertile > LOD) Group 3 (2nd tertile > LOD) Group 4 (3rd tertile > LOD) 

88/121 (72%) 72/127 (56.7%) 44/63 (69.8%) 50/70 (71.4%) 

6. SAP comment: Post ban declines in effects are based on 1 subject being classified as 

highly exposed, which lacks statistical power and may be sensitive to the cutoff value 

chosen to classify subjects as highly exposed. Can an analysis be conducted to look at 

relationships between more equally sized exposure groups that separate between the 

lower exposure levels? 

0

2

4

6

8

10

chlopyrifos

1999 68/71 (96%)

2001 26/86 (30%)

2000 101/120 (84%)

2002 18/59 (31%)

Figure 1. Chlorpyrifos levels in maternal and umbilical cord 

blood samples (pg/gram) at delivery by year of birth

2003 0/58 (0%)

2004 0/30 (0%)

Cord blood (N=395) Year N > LOD (%)

0

2

4

6

8

10

chlopyrifos

Maternal blood (N=424) Year N > LOD (%)

1999 93/110 (85%)

2001 18/71 (25%)

2000 88/110 (80%) 

2002 14/36 (39%)

2003 0/43 (0%)

2004 0/25 (0%)

Response: We believe the SAP has misunderstood that our post ban declines were based not on 

the change in the number of subjects that fell into the highly exposure group but on the decrease 

we had seen in chlorpyrifos concentrations in both maternal and cord blood among all cohort 

subjects by year of birth (see Figure 1). As can be seen in the above figure, chlorpyrifos levels 

had dropped dramatically by 2001. Our 2004 study categorized the full cohort available at that 

time (most born prior to1/1/01) into four groups based on ascending levels of chlorpyrifos in 

umbilical cord blood. The only significant difference we saw was between infants in the highest 

exposure group (group 4) compared to those with levels below the limit of detection (group 1). By 

contrast, there was no significant difference in birth outcomes between subjects in the intermediate 
Chlorpyrifos Human Health Risk Assessment D424485 

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exposure groups (2 or 3) compared to group 1 (see Whyatt et al. EHP, 112: 1125-1132. 2004). At 

the time we conducted the analyses for our 2004 paper, only n=77 infants were born after 1/1/01, 

which limited our power to determine whether or not there was any continuing effect of prenatal 

chlorpyrifos exposure on birth outcomes after2001. In April 23, 2008, we presented undated 

analyses to EPA that included more infants (n=193) born after 1/1/01. Results are shown in Table 

3 below and are consistent with our 2004 publication. Specifically, we saw a highly significant 

decrease in birth weight for each log unit increase in umbilical cord 

Table 3. Change in birth weight for each log unit increase 

inchlorpyrifos concentrations in umbilical cord plasma among 

babies born before and after 1/1/01

B (95% CI) P-value 

Born before 1/1/01 (n=233) -71/4 (-121/1, -21.8 P=0.005 

Born after 1/1/01 (n=193) 32.7 (-59.3, 124.7) P=0.48 

chlorpyrifos concentrations among babies born before 1/1/01, but no association between 

chlorpyrifos and birth outcomes among infants born after 1/1/01. Of those infants born after 1/1/01, 

only 16% had chlorpyrifos levels in cord blood above the LOD and only one of these was in the 

previously defined ‘high exposure’ group. It is obvious that this marked reduction in chlorpyrifos 

exposure, and the virtual elimination of high exposure levels after 1/1/01, is due to the chlorpyrifos 

ban , . To address the SAP comments further, we have conducted analyses that categorize infants 

born after 1/1/01 into those with chlorpyrifos above and below the LOD. Keeping in mind that 

there was only one child with high (group 4) chlorpyrifos exposure in this post-ban sample, it is 

not surprising that there was no significant difference in birth weight between the <lod group and 

the small group with some exposure during this post-ban period. These findings thus support the 

hypothesis that chlorpyrifos exposures following the ban had dropped below concentrations that 

have adverse effects on fetal growth. However, it is also possible that our sample size is simply 

too small to detect relatively small but real negative effects on birth outcomes associated with the 

much reduced exposure levels after 1/1/01. 

7. SAP comment: The panel felt it might be helpful to have a longitudinal component to the 

study to determine if the adverse effect persisted in children who exhibited poor birth 

outcomes or neurobehavioral deficits. 

Response: As described in the attached letter, we have just completed statistical analyses on the 

relationship between prenatal chlorpyrifos exposure and outcomes on the Wechsler Intelligence 

Scale for Children (Fourth Edition) and the Child Behavior Check List, which have been 

administered to cohort children at age 7 years. We anticipate that a manuscript on these findings 

will be completed and ready for submission within several months. 

8. SAP comment: It would be useful to examine the results of a statistical analysis that 

includes all three AChE-inhibiting insecticides in the analysis model as dichotomous 

variables (above or below the LOD) in combination with continuous measurements for these 

variables. This is not likely to change the results but could be helpful in illustrating threshold 

or dose response effects . . . Are all 3 AChE inhibiting insecticides correlated (if lack of 

correlation—less likely would confound cyp and neuro outcomes) . . . Conduct additional 
Chlorpyrifos Human Health Risk Assessment D424485 

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analysis controlling for multiple exposures examining additive effects to elucidate the role of 

other Ops. This is important for AChEI to be considered as mode of action. 

Response: It should be noted that our 2004 paper (Whyatt et al., EHP, 112: 1125-1132) looked at 

the additive effects of chlorpyrifos and diazinon on birth outcomes using the U. S. EPA guidelines 

for conducting cumulative risk assessments for the organophosphates. However, in response to the 

SAP comments, we have now conducted a number of additional analyses both to evaluate the 

correlations among the three AChE inhibiting insecticides (chlorpyrifos, diazinon and propoxur) 

detected in our cohort and to look at the effects of chlorpyrifos on both birth outcomes and child 

neurocognitive development after controlling for diazinon and propoxur. Specifically we have 

devloped 4 additional models for birth weight and birth length and 4 models for the psychomotor 

development index (PDI) and mental development index (MDI) on the Bayley Scales of Infant 

Development at age 36 months. All models controlled for the same covariates as in our 2004 and 

2006 publications. Model 1 includes only chlorpyrifos as the independent variable (i.e. is identical 

to the data in our initial publications); model 2 includes chlorpyrifos and diazinon; model 3 

includes chlorpyrifos and 2-isopropoxyphenol (the chemical-specific metabolite of propoxur); and 

model 4 includes all three acetylcholinesterase inhibiting insecticidess Results are presented 

below: 

Correlations among pesticides: For infants born prior to the chlorpyrifos phase out (the relevant 

time period), there was a modest correlation between 2-isopropoxyphenol (the chemical specific 

metabolite of propoxur) and both chlorpyrifos (r=0.25, p<0.001, n=222) and diazinon (r=0.21, 

p=0.002, p=227) in umbilical cord blood samples. However, there was a higher correlation 

between chlorpyrifos and diazinon (r=0.63, p<0.0001, n=222), incdicating that approximately 33% 

shared variabilitybetween these two pesticides. Given this relationship between chlorpyrifos and 

diazinon, the question raised by the SAP—whether the chlorpyrifos effects we report could be due 

to confounding by diazinon—is therefore very appropriate. This question is addressed in the new 

analyses reported below. 

Results on birth outcomes. Results are presented in Tables 4 and 5 below. As can be seen, 

chlorpyrifos remained inversely associated with birth weight and length in all models after 

controlling for diazinon and 2-isopropoxyphenol, and the magnitude of the significant negative 

effect of chlorpyrifos on infant size remained relatively constant across the models, indicating that 

neither of these two other acetylcholinesterase inhibitors was confounding the relationship 

between chlorpyrifos and birth outcomes. Further neither diazinon nor 2-isopropoxyphenol, either 

individually or in combination, was associated with birth weight, although the inverse association 

reported previously between 2-isopropoxyphenol and birth length (see Whyatt et al., 2004) 

remains of borderline significant (p=0.05). Consistent with our prior findings, birth weight 

decrease significantly by 198 grams among infants in the highest compared to the lowest 

chlorpyrifos groups (Group 4 versus Group 1) after controlling for both diazinon and 2-

isopropoxyphenol, as well as the other model covariates (p<0.05). These analyses thus indicate 

that it is primarily prenatal chlorpyrifos and not the other two acetylcholinesterase-inhibiting 

insecticides that is having an adverse effect on fetal growth. Further, the findings suggest that the 

effect of chlorpyrifos on fetal growth may well be mediated by mechanisms other than 

acetylcholinesterase inhibition, given the lack of an association between the other two 

acetylchlolinesterase inhibitors and birth weight. 
Chlorpyrifos Human Health Risk Assessment D424485 

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Table 4. Decrease in birth weight for each log-unit increase in insecticide levels in umbilical cord blood at 

birth among infants born prior to 1/01/01. Values shown are regression coefficient (B), 95% confidence 

interval, and p-value 

 Model 1 Model 2 Model 3 Model 4 

(ln)Chlorpyrifos -67.3 (-17.8, -

116.8) 

p=0.008 

-65.9 (-5.7, -

126) 

p=0.03 

-60.9 (-10.0, -

111.8) 

p=0.02 

-60.2 (0.9, -121.3) 

p=0.054 

(ln)Diazinon -4.7 (107.7, -

117.1) 

p=0.94 

-2.5 (110, -114) 

p=0.96 

(ln)2-isopropoxyphenol 

 

-50.9 (46.1, -

147.8) 

p=0.30 

-50.8 (46.5, -114.9) 

p=0.30 

All models controlled for gestational age, parity, maternal pre-pregnancy weight and net weight gain during 

pregnancy, maternal self-reported environmental tobacco smoke in the home, gender of the newborn and 

season of delivery. 

 

Table 5. Decrease in birth length for each log-unit increase in insecticide levels in umbilical cord blood at 

birth among infants born prior to 1/01/01. Values shown are regression coefficient (B), 95% confidence 

interval, and p-value 

 Model 1 Model 2 Model 3 Model 4 

(ln)Chlorpyrifos -0.43 (-0.14,-

0.73) 

p=0.004 

-0.46 (-0.1, -

0.82) 

p=0.01 

-0.36 (-0.06,, -

0.66) 

p=0.02 

-0.40 (-0.75, -

0.35) 

p=0.03 

(ln)Diazinon 0.09 (0.76, -

0.58) 

p=0.80 

 0.12 (-0.78, 055) 

p=0.73 

(ln)2-isopropoxyphenol -0.57 (0.002, -

1.14) 

p=0.051 

-0.58 (-.001, -

1.15) 

p=0.05 

All models controlled for gestational age, parity, maternal pre-pregnancy weight and net weight gain during 

pregnancy, maternal self-reported environmental tobacco smoke in the home, gender of the newborn and 

season of delivery. 

Results on PDI and MDI are presented in Table 6 and 7 below. The findings were quite 

surprising. There was no significant association between 2-isopropoxyphenol and delay on either 

the PDI or MDI. However, when diazinon was added to the model, the negative effect of 

chlorpyrifos on developmental delay for both the PDI and MDI became much stronger. 

Specifically, the odds ratio almost doubled for PDI and increased by 40%-60% for the MDI. These 

results again indicate that it is chlorpyrifos and not the other two acetylcholinesterase insecticides 

that is having an adverse effect on child mental and motor development and further that the effect 

size appears considerably greater than we had estimated before. The findings again support the 

suggestion, as discussed by the SAP, and as is suggested by the experimental evidence, that the 

effects of chlorpyrofos on child development may well be by a mode of action other than 

acetylcholinesterase inhibition. 

Table 6. Effects of chorpyrifos on the odds of ps;ychomotor delay on the Bayley Scales of Infant Development 

at age 36 month. Values shown are regression coefficient (B), 95% confidence interval, and p-value s 

Model 1 Model 2 Model 3 Model 4 

Chlorpyrifos2 4.5 (1.6, 12.7) 8.4 (2.4, 30.2) 4.0 (1.4, 11.8) 7.9 (2.1, 29.1 ) 
Chlorpyrifos Human Health Risk Assessment D424485 

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p=0.004 p=0.001 p=0.01 p=0.002 

(ln)Diazinon 0.45 (0.18, 1.13) 

p = 0.09 

 0.4 (0.2, 1.1) 

p=0.08 

(ln)2-isopropoxyphenol 1.3 (0.65, 2.8) 

p=0.4 

1.4 (0.67, 3.0) 

p=0.37 

All models controlled for race/ethnically, gender, ETS, gestational age, maternal IQ, maternal education, 

HOME score1

OR = Odds ratio 2

>6.17 pg/g versus ≤ 6.17 pg/g in umbilical cord blood 

Table 7. Effects of chorpyrifos on the odds of mental delay on the Bayley Scales of Infant Development at 

age 36 months. Values shown are regression coefficient (B), 95% confidence interval, and p-value 

 Model 1 Model 2 Model 3 Model 4 

Chlorpyrifos2 2.3 (1.1, 5.2) 

p=0.03 

3.8 (1.5, 9.4) 

p=0.004 

2.0 (0.9, 4.5) 

p=0.11 

3.2 (1.3, 8.2 ) 

p=0.02 

(ln)Diazinon 0.57 (0.35, 0.95) 

p = 0.03 

 0.6 (0.3, 0.9) 

p=0.03 

(ln)2-isopropoxyphenol 1.5 (0.9, 2.5) 

p=0.12 

1.5 (0.9, 2.6) 

p=0.11 

All models controlled for race/ethnically, gender, ETS, gestational age, maternal IQ, maternal education, 

HOME score1

OR = Odds ratio 2

>6.17 pg/g versus ≤ 6.17 pg/g in umbilical cord blood 

9. SAP comment: Is it possible to determine quantitative contribution of chlorpyrifos in 

exposure to mixture by accounting for differences in chemicals, potency, pharmacokinetics 

and possible chemical interactions. 

Response: Dr. Hattis can address this question after his PK model is implemented and calibrated. 

10. SAP comment: Look at intermediate markers of biologically effective dose and early 

effect that further link exposure and outcome and or a clearer idea of mode of action from 

exposure studies. 

Response: As described in our letter, we have been conducting Magnetic Resonance Imaging 

(MRI) scans on cohort children with either high prenatal chlorpyrifos exposure (n=20) or no 

exposure to any of the contaminants that have been associated with adverse child cognitive 

development among the CCCEH cohort (n=20). The MRI results will provide a marker of biologic 

effect. These analyses are being undertaken under the direction of Dr. Bradley Peterson, Director 

of Child and Adolescent Psychiatry at the New York State Psychiatric Institute. We anticipate that 

a manuscript on the results will be submitted to a peer-reviewed journal by Aug 1 2010. 
Chlorpyrifos Human Health Risk Assessment D424485 

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COLUMBIA CENTER 

FOR CHILDREN'S 

ENVIRONMENTAL 

HEALTH 

MAILMAN SCHOOL OF PUBLIC HEALTH 

Columbia University 

Memo to: Deborah Smegal 

From: Robin Whyatt 

Date: March 8, 2010 

We are pleased that our answers to the questions raised by the Science Advisory Panel (SAP) 

will be of value to EPA as you move forward to update the chlorpyrifos risk assessment. Below 

are our responses to the questions raised by Dow Agrosciences which you also asked us to 

address in your email of March 5,2010. Please let us know if you need any additional 

information or if we can be of more help. 

Question one: The Columbia birth cohort includes cord blood chlorpyrifos levels for most 

children and urine from many mothers (Whyatt et aL. 2005,2009). However, the authors state 

that "we found no association between chlorpyrifos levels in maternal and cord blood and TCPy 

levels in maternal urine samples during pregnancy or after delivery." (Whyatt et aL. 2009, pg 

563). Since the health effect papers are based solely upon chlorpyrifos levels in blood, and 

because blood chlorpyrifos measurements have not been reported in any other human exposure 

study at these trace-level concentrations, this is a critical point to evaluate with full transparency. 

Specific questions include: 

a. Why is there no association between blood chlorpyrifos and urinary TCPy? 

b. How many mother infant pairs in 1999 have both urine TCPy and blood chlorpyrifos? 

c. Are the 1999 urine and blood levels correlated? 

d. Are the 2000 urine and blood levels correlated? 

e. Are the 2001 urine and blood levels correlated? 

f. With personal monitoring, air monitoring, blood chlorpyrifos and urinaryTCPy, what is the 

estimated internal dose for the infant-mother pairs in each year of measurement? 

Answer: Please note that the primary biomarker we have measured in our cohort is chlorpyrifos 

concentrations in maternal and umbilical cord blood. We did not measure TCPy in maternal 

urine samples except for a small subset of subjects. Specifically, as described in our 2009 

manuscript (Whyatt et aI., EHP, 117;559-567, 2009), we measured TCPy in repeat urine 

samples collected from only n=97 women during pregnancy. These urine samples were collected 

among women who delivered between 2001-2004. Thus the answer to la through 1d above is 

that no mother/infant pairs have both chlorpyrifos in blood and TCPy in urine in either 1999 or 
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2000 and we thus do not have the data to look at the correlation between TCPy in urine and 

chlorpyrifos in blood among subjects who delivered prior to 2001. For subjects who delivered in 

2001, we can look at this relationship. It should be noted, however, that these data are not 

particularly helpful in evaluating the correlations between urinary TCPY and blood chlorpyrifos 

concentrations, due to small sample size. But more importantly, chlorpyrifos in blood samples 

had already dropped substantially by 2001 following the residential ban. Specifically, by 2001 

among subjects that had both TCPy measured in maternal urine and chlorpyrifos measured in 

blood, chlorpyrifos was only detected in 24% of maternal blood samples and 19% of umbilical 

cord blood samples (See Figure 2, Whyatt et at., 2009). By contrast, as shown in Figure 1 from 

in the Columbia Response to the SAP Queries (2/19/2010), chlorpyrifos was detected in 98% and 

85% of maternal and umbilical cord blood samples, respectively, during 1999 and 84% and 80% 

during 2000. Given these caveats, the results for 2001 are as follows: For these subjects, the 

average chlorpyrifos concentrations in two-week integrated indoor air samples collected over 

the final 6-8 weeks of pregnancy were positively correlated with average TCPy levels in 

maternal prenatal spot urine samples (r=0.5, p=0. 005). However, there was no correlation 

(p>0.2) between TCPy levels and chlorpyrifos in either maternal blood samples (n=29) or cord 

blood samples (n=21) at delivery. Maternal and umbilical cord blood levels were highly 

correlated (r=0.85, p<0.001, n=21). 

Regarding question "If" above, please note, that as discussed in our answers to the SAP 

querries, Dr. Dale Hattis at Clark University is conducting PBPK modeling of the Columbia 

data under a Us. EPA Star Grant. The grant is entitled “Use of Biomarkers and Physiologically 

Based Pharmacokinetic [PBPK] Modeling in Risk Analysis for Developmental Effects of 

Chlorpyrifos ". We believe that the PBPK model being developed by Dr. Hattis will prove helpful 

in clarifying the exposure concentrations that correspond to the chlorpyrifos levels in umbilical 

cord blood at which we begin to see statistically significant adverse effects on fetal growth and 

child neurocognitive development and also in estimating the internal dose for the infant/mother 

pairs in each year of measurement. Dr. Hattis estimates that his analyses should be completed 

relatively shortly. We recommend that you contract Dr. Hattis. 

Question Two: In the Rauh et al. (2006) paper the authors note a nonlinear dose trend with using 

4 exposure groups. The final analyses are limited to two exposure groups. An analysis of 4 

exposure groups is more information from a risk perspective and is critical for full transparency 

of the data. Analysis with 4 exposure groups for the results presented in Tables 2-8 should be 

replicated and made publically available. 

Answer: We have already addressed this comment in our answer # 3 from the Columbia 

Response to the SAP Queries (2/19/2010). To reiterate, we prepared a new table (Table 1 below) 

which provided the same type of information as Table 2 in the 2006 Pediatrics paper (Rauh et 

at., Pediatrics, 118:el 845..;el 859, 2006) with respect to the % of subjects whose tests scores 

indicated developmental delay with respect to the PDI and the MDI, as a function of chlorpyrifos 

exposure. However, we provided the % delayed for all four exposure groups, rather than just 

the 'high' and 'low' exposure groups shown in the paper. For both the PDI and MDI the rate of 

delay is clearly highest in the high chlorpyrifos group (Group 4). For the 36 month PDI, the rate 

of developmental delay is relatively low in all 3 low-exposure groups, and is about three times 

higher in the highest exposure group. And for the 36 month MDI, while the <LOD group 

experienced a higher rate of delay than 2 and 3, the very highest rate of delay is among 

the most exposed children. In univariate analyses, the difference in % delay between highest 
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exposure (>6.17pg/g) group versus the lowest exposure group (<LOD) was statistically significant 

for PDI (chi-square p=0.018, 1 sided; p-0.031 two-sided), but not significant for MDI 

However, importantly, was no significant difference in the rate of delay between groups 1, 

2 and 3 for either the PDI (chi-squared=0.97, p=0. 61) or MDI (chi-squared=4.4, p=0.11). 

And the rate of developmental delay in terms of both PDI and MDI in group four was significantly 

greater than in groups 1-3 combined, as reported in our paper. 

Table 1. The proportion of children at age 36 month who were at risk of delay (score < 85) on the 

Psychomotor Development Index (PDI) or Mental Development Index (MDI) of the Bayley Scales of Infant 

Development by chlorpyrifos exposure group

Group 1 

(<LOD) 

Group 2 (1st tertile > 

LOD) 

Group 3 (2nd tertile > 

LOD) 

Group 4 (3rd tertile > 

LOD) 

PDI 7/80 (8.8%) 3/65 (4.6%) 3/38 (7.95) 11/45 (24.4%) 

MDI 30/80 (37.5%) 14/65 (21.5%) 11/39 (28.2%) 20/44 (45.5%) 

Question Three: In the Rauh et al. (2006) paper, the authors compare results of the Bayley 

Mental Development Index (MDI) and Bayley Psychomotor Development (PDI) in different 

years. Using significance testing only, they claim there is a “preban” difference and no "postban" 

difference. This natural experiment is important to control for the many confounding factors 

associated with the home environment of these children. A more comprehensive and transparent 

analysis of these data over would provide information about role of chlorpyrifos and MDI and 

PDI this unique population. 

Answer: Please note that most cohort children in the 2006 paper by Rauh et at. were born prior 

to the residential ban on chlorpyrifos. Specifically, as described in our answers to SAP queries, 

subjects were eligible for inclusion in the Pediatric paper if they had been born by 7/12/02 and 

thus had reached age three by the time of the analyses and also had: (1) measures of 

chlorpyrifos in blood at birth; (2) neurologic assessments at least once between ages 12-36 

months and (3) data on model covariates. Therefore, we were not able to undertake statistical 

analyses relating the changes in chlorpyrifos concentrations to changes in MDI and PDI 

following the residential ban. However, authors concurred that this is an important research 

question and state that “larger epidemiologic studies will to assess the full impact of 

the ban (see page e1854 of the Pediatric paper)." As discussed in our February 17th letter to 

Dr. Jack Fowle, we are currently preparing a manuscript on the relationship between prenatal 

chlorpyrifos exposure and outcomes on the Weschler Intelligence Scale for Children (Fourth 

Edition) and the Child Behavior Check List, which have been administered to cohort children at 

age 7 years. However, we also plan to undertake additional analyses of the 36th month MDI and 

PDI in relationship to prenatal chlorpyrifos exposures following the ban, since many more 

children in our cohort have now completed the 3 year developmental assessments and we 

have the sample size to undertake the analysis outlined above, while controlling for the 

many potential confounders. 

Question Four: The exposure estimates in the Columbia and CHAMACOS studies are reported 

differently, thus preventing a meta-analysis from being undertaken. Both studies reported Bayley 

MDI and Bayley PDI results at 24 months and neither was statistically significant (Eskenazi et al. 

2007). This can be interpreted as a tendency but underpowered, or as a chance finding. Obtaining the 
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raw data and pooling the results should be undertaken and has several benefits. 

 

a. The exposure estimates can be unified (either by calculating an estimated dose or 

estimating a TCP level from the blood data). The dose cut points can be set to at least 3 

doses to determine the presence of a dose response relationship. 

b. The power will doubled by combining the two cohorts, thereby increasing the statistical 

power. 

Answer: As discussed in our February 17th letter to Jack Fowle, a multi-site study has been 

funded the National Institute of Environmental Health Sciences ($568,000 total costs) 

that will pool the children's centers at the University of California-Berkeley, 

Columbia University (our children’s center) and the Mount School of Medicine to address 

questions about relationships across the three cohorts between prenatal organophosphate 

exposure, fetal growth and child neurocognitive development. Dr. Brenda Eskenazi at Berkeley 

is the principal investigator on project.
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Appendix 3. Epidemiology Study Specific Evaluations 

APPENDIX 3 

Study Specific Evaluations of Children’s Health Epidemiology Studies 

Article 1. Whyatt et al. (2004) 

Prenatal Insecticide Exposures and Birth Weight and Length among an Urban Minority 

Cohort. Robin M. Whyatt, Virginia Rauh, Dana B. Barr, David E. Camann, Howard F. 

Andrews, Robin Garfinkel, Lori A. Hoepner, Diurka Diaz, Jessica Dietrich, Andria Reyes, 

Deliang Tang, Patrick L. Kinney, and Frederica P. Perera. Environmental Health 

Perspectives. 112:1125–1132 (2004). 

Study Summary 

In this report, Whyatt et al. report on the relationship between pesticide exposures and 

developmental outcomes in newborns in the Columbia Mother’s and Newborn Study to include 

additional insecticides (the organophosphate diazinon and the carbamate propoxur), a larger 

sample size (n = 314 mother–newborn pairs), and insecticide measurements in maternal personal 

air sampled in the home, as well as in umbilical cord plasma sampled at delivery. 

Study participants were recruited during early pregnancy (≤20th week) among African-American 

and Dominican women age 18-35 years, and registered at New York Presbyterian Medical 

Center and Harlem City. Women who smoked, had a history of drug abuse, diabetes, 

hypertension, or HIV infection were excluded from participation in the study, as were women 

who resided in New York City for less than 1 year. The study sample presented in this report was 

recruited between 1998 and 2002, a period which straddles the December, 2000 voluntary 

cancellation by registrants of indoor residential chlorpyrifos use. 

Insecticides, including chlorpyrifos, were measured in 48-hr personal air samples collected 

during the third trimester of pregnancy. Analyses of air samples for pesticide levels were carried 

out at the Southwest Research Institute. 

Chlorpyrifos levels in umbilical cord blood samples were available for 256 newborns, sampled 

as close to the time of delivery as possible, and within 2 days post-partum. Cord blood plasma 

chlorpyrifos levels were imputed from maternal blood levels for 31 newborns for whom no cord 

blood sample was obtained. Quantification of chlorpyrifos levels in plasma were conducted by 

the Centers for Disease Control and Prevention (CDC). 

Insecticide levels in both maternal personal air and blood samples were available for 259 of the 

314 mother–newborn pairs (82%). Measures in either personal air or blood were available for the 

remaining 55 pairs (18%). Pesticide levels in personal air and blood samples below the level of 

detection (LOD) were assigned a value of half the LOD; levels were then log2-transformed for 

statistical analysis. For pesticides in blood, 31% of chlorpyrifos levels were below the LOD. 

Infants with below LOD cord blood chlorpyrifos levels were used as a comparison group and 

compared to groups with increasing levels, categorized by tertile of above LOD levels. 

Infants’ weight, length, and head circumference were assessed at birth and obtained for this study 
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through medical record abstraction. 

Multiple linear regression analyses were performed to evaluate associations between prenatal 

insecticide exposure and the indicators of infant development at birth. Covariates were selected 

from variables known to be associated with insecticide exposure or fetal growth. Effect 

modification by race/ethnicity was evaluated, but not observed. 

Controlling for potential confounders, the researchers found no association between maternal 

personal air insecticide levels and birth weight, length. Head circumference was not 

significantly associated with maternal personal air or cord blood chlorpyrifos levels. However, 

for each log unit increase in cord plasma chlorpyrifos levels, birth weight decreased by 42.6 g 

(95% CI: –81.8 to –3.8) and birth length decreased by 0.24 cm (95% CI: –0.47 to –0.01). 

Combined measures of (ln)cord plasma chlorpyrifos and diazinon (adjusted for relative potency) 

were also inversely associated with birth weight and length (p < 0.05). Birth weight averaged 

186.3 g less (95% CI: –375.2 to –45.5) among newborns with the highest compared with lowest 

26% of exposure levels (combined p-value = 0.01). When stratified by date of birth prior to or 

after 2001 (before and after the voluntary cancellation by registrants of indoor residential 

chlorpyrifos use was implemented), associations between birth weight and length and cord 

plasma chlorpyrifos were statistically significant (p ≤ 0.007) among newborns born before the 

cancellation. Among newborns born after January 2001, exposure levels were substantially 

lower, and no associations with fetal growth outcomes were observed (p > 0.8). Results suggest 

that prenatal chlorpyrifos exposures may have impaired fetal growth among this inner-city, low 

income cohort. 

Study Review 

This was a very well conducted study with numerous strengths, and some noted limitations. The 

prospective nature of the study limits the influence of systematic errors in the exposure 

assessment on the association observed, such that mismeasurement of chlorpyrifos exposure 

would likely be non-differential with respect to the indicators of fetal growth. 

A notable strength of this study, relative to investigations in other cohorts, is the direct 

measurement of chlorpyrifos in cord blood and personal air samples, rather than non-specific 

markers of organophosphate pesticide exposure. Errors in estimation of cord blood chlorpyrifos 

levels were likely to have been non-differential with respect to the indicators of fetal 

development. However, authors did not formally assess the variability in the exposure estimate 

resulting from the imputation of missing cord blood chlorpyrifos levels. 

A limitation of the specific biomarker exposure indicators in this study is the single sampling 

period (at delivery). It is not clear to what extent this single measurement reflects exposures over 

critical windows of fetal growth and development during pregnancy. If the exposure is chronic, a 

biomarker measured at a single time point can provide a representative dosimeter, even if the 

toxicant has a short half-life, as is the case for chlorpyrifos. However, if pesticide exposures are 

sporadic or otherwise vary over short time scales, the biomarker measurement will not be 

representative of “usual” exposure, or of the exposure during critical periods of fetal 

development. An observation effect could also influence the validity of the 48-hour personal air 
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sampling if, for example, the act of wearing the dosimeter inhibited participants from using 

pesticides or otherwise altered their behavior patterns in a manner that affected exposure levels. 

These limitations are assessed in subsequent validation studies conducted within this cohort 

(Whyatt, 2007 and Whyatt, 2009). 

The health outcomes in this study, birth weight, birth length, and head circumference were 

obtained through direct linkage with the mothers’ and infants’ computerized medical records, 

and were likely to have been accurately measured. Any errors in outcome measurement would 

have occurred in the clinic, and are likely to be small and likely to be non-differential with 

respect to pesticide exposure. 

The investigators collected data on, and adjusted for, important potential confounders. Covariate 

selection proceeded appropriately by assessing relationships between potential confounders and 

the indicators of fetal growth, change-in-estimate of the main effect of chlorpyrifos exposure, 

and assessments of model fit. Covariates included in final models were race/ethnicity, 

gestational age, parity, maternal pre-pregnancy weight, and net weight gain during pregnancy, 

maternal self-reported environmental tobacco smoke in the home, sex of the newborn, and 

season of delivery. Models for head circumference also included an indicator for delivery type 

(vaginal versus cesarean section). 

Maternal exposure to polycyclic aromatic hydrocarbons (PAHs) during pregnancy was not 

included in the final models because it did not improve model fit. However, to ensure that PAH 

exposure did not confound the pesticide associations with fetal development, models including 

PAHs were evaluated. The authors’ consideration of other pesticides and environmental 

contaminants as covariates in their multivariate models is a notable strength of this study. 

Confounding by maternal smoking behaviors was controlled by restriction (i.e., excluding 

smokers from the study sample). Confounding by unmeasured factors may have been similarly 

controlled by design, this being a relatively homogeneous population with respect to 

determinants of fetal growth, compared to the general population. For example, the authors 

found it unnecessary to adjust for maternal alcohol consumption (which was measured), partly 

because few women in the study drank alcohol during pregnancy. 

Bias due to confounding by unmeasured determinants of fetal growth is possible in this 

observational study, for example, confounding by factors related to socioeconomic condition. 

Annual household income, maternal education, maternal marital status, material hardship during 

pregnancy, and degree of housing disrepair were evaluated as possible markers for 

socioeconomic status but were not included in final models because they were not associated 

with birth outcomes, did not improve the model fit, and did not substantially alter parameter 

estimates, confidence intervals (CIs), or significance levels of variables included in the final 

models. This observation that indicators of socioeconomic condition were not associated with 

the health outcomes is somewhat unexpected, although perhaps explainable by the relative 

homogeneity of these factors in this study population (control through restriction of study 

population). 

In categorical analyses, infants with the lowest tertile of observable cord blood levels had, as a 
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group, larger heights and weights at birth, compared to infants with chlorpyrifos levels below the 

level of detection, although the increases were not statistically significant. Among those with 

higher cord blood levels, increasing tertiles of chlorpyrifos were significantly associated with 

decreasing birth weight and height. This is suggestive of a threshold effect (a hockey stick doseresponse curve). Departures from linearity in the exposure-response relationship were not 

assessed in models including (ln)chlorpyrifos entered as continuous variables. 

The authors observed an association between chlorpyrifos and fetal growth only when the 

biomarkers were used as indicators of prenatal exposure and not when environmental levels (in 

maternal 48-hr personal air sampling conducted during the third trimester) were used. 

Researchers note several possible explanations for this discrepancy, including the notion that the 

biomarkers are integrating dosimeters of exposure. This may be particularly important for 

insecticide exposure by routes other than inhalation in the home (e.g., dermal, ingestion, and 

occupational exposures). In addition, the authors speculate that the biomarkers may provide 

better dosimeters of the dose to the developing fetus, relative to the airborne exposures. 

The voluntary cancellation by registrants of indoor residential chlorpyrifos use introduces 

exogenous variation in the exposure of interest. No associations were observed between the fetal 

growth indicators and chlorpyrifos levels among infants born after the implementation of the 

voluntary cancellation of chlorpyrifos containing in home pesticide products. This finding is 

similarly suggestive of a possible threshold effect (decreasing dose-levels before and after 

voluntary cancellation), although it could also be due to a lack of statistical power to assess 

associations at lower levels of exposure in the relatively small subsample (n=77). 

The statistical analysis appeared to be conventional and appropriate. However, the authors did 

not present nor discuss regression diagnostics to assess the degree to which their models met or 

violated the assumptions implicit in linear models (i.e. homoscedasticity, normality of error term 

distribution, independence of error terms, and linearity of the exposure-response relationship), 

other than to state that investigators log transformed chlorpyrifos levels. As discussed 

previously, there is some evidence in the data to suggest a non-linear exposure response 

relationship (i.e., threshold) between the natural log of cord blood chlorpyrifos levels and birth 

weight and length, but no formal statistical testing of this possibility was presented or discussed. 

Bias due to differential errors in the selection of study participants is unlikely to have occurred in 

this prospective study with low loss to follow-up. Appropriate exclusion criteria were applied, 

although these design decisions may reduce external validity (generalizability) when making 

inference to other populations using the associations observed in this narrowly defined study 

population. 

The results presented in this expanded analysis confirm the authors’ earlier findings of an inverse 

association between chlorpyrifos levels in umbilical cord plasma and birth weight and length in 

this study population of non-smoking African-American and Dominican women, age 18-35 

years, living in New York City (Whyatt et al., 2003). Systematic errors in the selection of study 

participants or in the measurement of key analytic variables (exposure and outcome) are unlikely 

to explain this observed association. The possibility of unmeasured or poorly measured 

potentially confounding variables cannot be completely excluded, although study authors 
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thoroughly evaluated this possibility in the data available. Data suggest a threshold effect may be 

possible, although authors did not formally evaluate this characteristic of the data. 

 
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Works Cited 

Whyatt, R. M., Barr, D. B., Camann, D. E., Kinney, P. L., Barr, J. R., Andrews, H. F., . . . Perera, 

F. P. (2003). Contemporary-use pesticides in personal air samples during pregnancy and blood 

samples at delivery among urban minority mothers and newborns. Environ Health Perspect, 

111(5), 749-756. 

Whyatt RM, Garfinkel R, Hoepner LA, Holmes D, Borjas M, Williams MK, Reyes A, Rauh VA, 

Perera FP, Camann DE. Within- and Between-Home Variability in Indoor-Air Insecticide Levels 

during Pregnancy among an Inner-City Cohort from New York City. Environmental Health 

Perspectives. 115:383–389 (2007). 

Whyatt RM, Garfinkel R, Hoepner LA, Andrews H, Holmes D, Williams MK, Reyes A, Diaz D, 

Perera FP, Camann DE, Barr DB. A Biomarker Validation Study of Prenatal Chlorpyrifos 

Exposure within an Inner-City Cohort during Pregnancy. Environal Health Perspectives. 

117:559–567 (2009). 

 
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Article 2. Berkowitz et al. (2004) 

In Utero Pesticide Exposure, Maternal Paraoxonase Activity, and Head Circumference. 

Gertrud S. Berkowitz, James G. Wetmur, Elena Birman-Deych, Josephine Obel, Robert H. 

Lapinski, James H. Godbold, Ian R. Holzman, Mary S. Wolff. Environmental Health 

Perspectives. 112:388–391 (2004) 

Study Summary 

To assess the relationship of exposure to pesticides during pregnancy and subsequent risk of 

impaired growth and neurodevelopment, Berkowitz and colleagues (Mount Sinai Child 

Development Study) investigated the relationship between prenatal pesticide exposure and infant 

growth and neurodevelopment in a prospective, multiethnic cohort study of mothers and infants 

delivered at Mount Sinai Hospital in New York City. 

The study population was drawn primarily from East Harlem and consisted largely of young 

Hispanic women (predominantly Puerto Rican), but also included African-American and 

Caucasian women. Mothers were enrolled during early pregnancy from a prenatal clinic and two 

private practices at Mount Sinai Hospital from March 1998 to March 2002. The study included 

only primiparous with singleton births. Mothers were excluded if they had any of the following: 

an initial prenatal visit after 26 weeks of gestation; serious chronic diseases; a serious pregnancy 

complication that could affect fetal growth and development; alcohol consumed greater than two 

alcoholic beverages per day; or, illicit drug use. Mothers and infants were also excluded if the 

child was born with a congenital malformation or severe prematurity. A total of 479 prenatal 

patients were recruited; 75 were excluded because of medical complications, premature birth, an 

infant with birth defects, inability to collect biologic specimens before birth, change of hospital 

or residence outside New York City, or refusal to continue to participate. 

Pesticide exposure was assessed by a prenatal questionnaire and measurement of urinary 

concentration of 3,5,6-trichloro-2-pyridinol (TCPy), a metabolite of chlorpyrifos exposure. The 

survey was administered, and maternal urine and blood specimens were collected, during the 

third trimester. Cord blood samples were also obtained at birth. Measurement of paraoxanase-1 

(PON1) genotype and phenotype was made using maternal and infant cord blood samples. PON1 

is an enzyme that can detoxify chlorpyrifos before it can inhibit acetylcholinesterase in the 

peripheral and central nervous systems. The investigators assessed birth weight, length, head 

circumference, and gestational age of the newborns for 404 participating mother-child pairs. 

Almost half of the participants in the study, (46%) reported that they, or a household member, 

had used indoor pesticides during the pregnancy; 72% were classified as exposed using a 

composite index defined as indoor pesticide use in the household or exterminator application, 

fumigation, or pesticide use in common areas. Detectable levels of urinary 3,5,6-trichloro-2-

pyridinol (TCPy; a metabolite of chlorpyrifos) were observed in 44% of the study participants. 

The authors constructed generalized linear models to evaluate the associations among pesticide 

exposure and PON1 activity with average birth weight, birth length, head circumference, and 

gestational age, adjusting for selected covariates (race/ethnicity, infant sex, and gestational age). 

The authors found no statistically significant associations between pesticide exposure based 
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either on questionnaire responses or biomarkers of pesticide exposure (TCPy) on fetal growth or 

gestational age. 

Researchers also evaluated the potential effect modifying role of PON1 genotype and phenotype 

in the relation of interest. Stratifying participants by presence of PON1 polymorphisms and 

tertiles of maternal PON1 activity, researchers reported a small but statistically significant 

reduction in head circumference among children of mothers with levels of chlorpyrifos above the 

limit of detection and also in the lowest tertile of PON1 activity, i.e., slowest metabolic activity. 

However, the interaction between TCPy level and PON1 activity was not statistically significant. 

In the subgroup of infants whose mothers had TCPy levels greater than the level of detection, 

those with low maternal PON1 had an average (SD) head circumference of 33.3cm (1.5cm) 

which was significantly smaller than those with medium (34.0cm (1.5cm)) and high (34.1cm 

(1.6cm)) maternal PON1 activity after adjusting for race/ethnicity, infant sex, and gestational age 

(p = 0.014). A similar trend was observed with head circumference when maternal PON1 activity 

was considered alone; the adjusted means were 33.5 cm for the low PON1, 33.9 cm for medium 

PON1, and 34.1 cm for high PON1 activity (p = 0.004). Maternal PON1 genetic polymorphisms 

were not associated with reduced head size. No trends were seen for birth weight or birth length 

for TCPy or the other metabolite levels when maternal paraoxonase level was taken into account. 

Infant paraoxonase activity had no association with any of the fetal growth measures. 

Because small head size has been found to be predictive of subsequent cognitive ability, the 

authors conclude that chlorpyrifos may have a detrimental effect on fetal neurodevelopment 

among mothers who exhibit low PON1 activity. 

Study Review 

The largely null study had notable strengths and some limitations; notably authors did not 

discuss any limitations of their study in the published manuscript. The prospective nature of the 

study limits the influence of errors in the self-reported exposure classification on the association 

observed; as such errors would likely be non-differential with respect to determinants of the 

health outcome under investigation. 

The authors assessed pesticide exposure using both subjective questionnaire-based methods and 

biomarkers of exposure which are objective, sensitive and specific surrogates of pesticide 

exposure. Biomarkers of specific pesticides (i.e., TCPy) were assessed in a single maternal urine 

sample provided during the third trimester. The authors do not discuss the impact of policies 

aimed at reducing use of chlorpyrifos on the prevalence and magnitude of pesticide exposures 

during the time period of this study; it is likely that use of chlorpyrifos exposure was decreasing 

over the time period of the study, as noted in other similar studies of the same time period. The 

authors do not deliberate on the degree to which the biomarkers assessed represent usual 

exposure to pesticides versus recent, acute exposure(s), nor do they discuss the relative 

importance of assessment of exposure during the third trimester vis-à-vis fetal growth and 

development. The critical window of exposure for fetal growth and neurodevelopment health 

effects is not fully characterized. If sources and patterns of exposure remain static, a single 

measurement would be a good indicator of pesticide exposure during pregnancy, even if the 

pesticides have short half-lives in vivo. If, however, sources and exposure patterns change over 
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short time-spans, perhaps on the order of days to weeks, the biomarker assessment would not 

necessarily provide a good indicator of the relevant exposure. The authors classified biomarker 

levels according to the assays’ levels of detection (above versus below). Falsely negative 

exposure characterization would occur if mothers were exposed only early in pregnancy, and the 

pesticides were fully metabolized prior to the third trimester. Incorrect classification of 

participants as exposed, when in fact they were not exposed, was unlikely to have occurred using 

the biomarkers. Like other sources of measurement error in this study, errors in the biomarker 

indicators of exposure in this study are likely to have resulted in a non-differential 

misclassification, with respect to fetal growth and development. 

The health outcomes in this study, birth length, birth weight, head circumference, and gestational 

age were likely to have been accurately measured in this study, as they were obtained through 

direct linkage with the computerized medical record. Any errors in outcome measurement would 

have occurred in the clinic, and are likely to be small and certainly non-differential with respect 

to pesticide exposure. 

To some extent, the recruitment process resulted in a population that was likely homogeneous 

with respect to confounders of the in utero pesticide exposure-fetal growth relationship 

(compared to the general population), and the potential for confounding was thus limited by 

design. The authors were also able to collect data on, and adjust for, important covariates, 

including race/ethnicity, infant sex, and gestational age. The authors also controlled for birth 

weight or birth length in their assessment of head circumference and pesticide exposure. 

Covariates used in the analyses were selected, according to the authors, from variables known to 

be associated with either pesticide exposure or fetal growth. However, factors related to 

socioeconomic condition of those assessed and co-contaminants to which the study participants 

may have been exposed were not addressed in detail in the article. To the extent that such factors 

may be determinants of fetal growth and development and correlated with (but not caused by) 

pesticide exposure, they may confound the associations of interest in the study. If these factors 

are positively associated with both pesticide exposure and diminished fetal development, they 

would bias the true association upward (towards larger mean differences associated with 

exposure). However, this is unlikely to have occurred in this largely null study. 

Only factors that were statistically significant (p < 0.05) were included in the final models. The 

use of a statistical test as a means to select covariates from a larger set of potential confounders is 

discouraged in some epidemiologic circles (Miettinen 1976, Breslow and Day 1980, Greenland 

1989). The assessment of confounding is generally not considered to be a statistical endeavor, in 

part because the association between the confounder and the outcome is just one factor that 

determines the degree of confounding, and also because the statistical significance of an 

association is a function not only of the magnitude of the association but also of other factors 

(e.g., sample size, and sample variability) which are irrelevant to bias due to confounding. 

Moreover, the use of significance testing in the selection of confounders treats false negative 

errors (i.e. the deleting a confounder) as secondary to false positive error (i.e., inclusion of a nonconfounder). This implicit ranking of error is backward, as deleting a confounder introduces bias 

and is justified only if the bias induced by its exclusion is tolerably small or deemed to be worth 

the precision gain, whereas including a non-confounder only (potentially) reduces precision by 

increasing the size of the model. This is a small consideration in this study, and different 
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approaches are not likely to qualitatively change the study findings or conclusions. Notably, 

smoking was not included as a potentially confounding variable in this analysis as the prevalence 

of smoking was low in this cohort. According to the authors, the inclusion of smoking in final 

models did not alter risk estimates and added to variance as stated by the authors. 

Bias due to differential errors in the selection of study participants is unlikely to have occurred in 

this prospective study with no loss to follow-up at the time of this study. Appropriate exclusion 

criteria were applied, including mothers with no prenatal medical visit prior to 26 weeks of 

gestation, mothers with serious chronic diseases (e.g., diabetes, hypertension, thyroid disease), 

mothers who developed a serious pregnancy complication that could affect fetal growth and 

development, and mothers who consumed more than two alcoholic beverages per day or who 

used illicit drugs, mothers with multiple births, and infants born with a congenital malformation 

or severe prematurity. The subjective, survey-based pesticide exposure assessment was fortified 

by objective indicators of exposure (i.e. urinary biomarkers). 

The generalizability of the study may be limited as consequence of the particulars of the study 

population assessed and the numerous exclusion criteria applied. Making valid inference using 

these results requires an assumption that the pesticide exposure-fetal development association is 

the same in this population as the population to which the results are being generalized. This 

study population consisted of primiparous women, recruited early on in their pregnancies from 

the prenatal clinic and two private practices at Mount Sinai Hospital in New York from March 

1998 to March 2002, consisted predominantly of young Hispanic women with low education. 

Sixty-eight percent were less than 25 years old. Just under half were Hispanic (49.8%), although 

substantial numbers of White (21.0%) and African-American (27.7%) women also participated. 

Just under half (46.8%) classified themselves as being either single, divorced, widowed, or 

separated, and just over half (50.1%) had no more than a high school education. The exclusion 

criteria applied to potential study participants further focused the population assessed, relative to 

the general population of pregnant women. In the likely situation that the prevalence of 

important modifiers of the exposure-fetal development association in this focused study 

population is different from that in other populations of concern, generalizability will be limited. 

Important modifiers to consider may include time spent in the home, patterns of food preparation 

and consumption, health consciousness, the baseline health status of the mother (and father), and 

a host of socioeconomic determinants. The investigators finding of effect modification by PON1 

activity is illustrative of limits on the generalizability of the study; populations who have high 

levels of paraoxonase activity may vary by genotypic and phenotypic determinants. These 

factors were not discussed in the report, but it has been noted previously that there is 10- to 40-

fold inter-individual variability in rates of paraoxon hydrolysis (Playfer et al 1976). The 

potential limitation vis-à-vis generalizability is offset by a strength; the study has increased 

statistical efficiency, relative to one set in a general population. 

Because small head size has been found to be predictive of subsequent cognitive ability, the 

authors conclude that chlorpyrifos may have a detrimental effect on fetal neurodevelopment 

among mothers who exhibit low PON1 activity. Implicit in this conclusion is the notion that 

small head size is a good surrogate for fetal neurodevelopment. To support their conclusion, the 

authors note that head circumference has been shown to correlate with brain weight, and that 

brain size and head circumferences are both predictive of IQ and cognitive ability. They 
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additionally cite research associating brain volume with attention-deficit/hyperactivity disorder. 

Although head circumference was significantly associated with cognitive ability, the differences 

in head size across tertiles of exposure were notably small (i.e. less than one centimeter); the 

clinical relevance of such small changes in the context of even a small amount of measurement 

error in assessment of head size is unclear. 

The authors were not surprised that infant paraoxonase levels in their study were not associated 

with birth outcomes because PON1 is not well expressed until after birth, and infant PON1 levels 

are much lower during the first year of life than maternal levels. The expression of PON1 in 

infants is approximately one-third that of their mothers (Chen et al. 2003). They speculate that 

lower PON1 activity in infants may make them more susceptible to PON1-related toxicity, 

including exposure to organophosphate pesticides, relative to adults. 

The net effects of confounding, selection bias, and information bias, are unlikely to have 

qualitatively changed the results of this well conducted, prospective cohort study. Non-specific 

biomarkers of exposure, as were assessed in this study, are likely more objective, sensitive, and 

likely also more accurate and reliable indicators of pesticide exposure, relative to exposure 

assessment methodologies that rely on self-report. However, to the extent that prenatal 

chlorpyrifos is the true exposure of interest, the assessment of non-specific organophosphate 

pesticide metabolites in a single sample of prenatal urine is a primary potential source of 

exposure measurement error. Almost certainly, such biases would not have resulted in the 

observation of a falsely positive association between pesticide exposure and fetal growth. More 

likely to have occurred is an attenuation of the true (unobserved) associations, toward (observed) 

associations that are relatively smaller, or null. Indeed, the findings of this study are either null, 

or associations of small magnitude. To contextualize these findings, the authors cite literature 

describing the finding that even small increases in prenatal or early childhood exposures to other 

environmental toxicants (lead and polychlorinated biphenyls) are associated with significant 

decrements in IQ and academic achievement up to 11 years of age. The validity of this analogy is 

speculative. The external validity of the study findings may be limited if the organophosphate 

pesticide exposure-fetal development association is modified by factors that are more, or less, 

prevalent in this young, urban, minority population, relative to the population(s) to which 

inference is being made. That said, the decision to focus on this population is reasonable, as it is 

a population likely to be highly exposed, and perhaps also at high risk of cognitive and 

behavioral deficits enhancing the likelihood of observing an association if an association exists. 

 
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Works Cited 

Breslow NE, Day NE. Statistical Methods in Cancer Research: Volume II: The Design and 

Analysis of Cohort Studies. Lyon: IARC, 1987. 

Chen J, Kumar M, Chan W, Berkowitz G, Wetmur J. Increased influence of genetic variation on 

PON1 activity in neonates. Environtal Health Perspectives. 2003;111:1403–1409. 

Engel SM, Berkowitz GS, Barr DB, Teitelbaum SL, Siskind J, Meisel SJ, Wetmur JG, Wolff 

MS. Prenatal Organophosphate Metabolite and Organochlorine Levels and Performance on the 

Brazelton Neonatal Behavioral Assessment Scale in a Multiethnic Pregnancy Cohort. American 

Journal of Epidemiology. 2007;165:1397-1404. 

Engel SM, Wetmur J, Chen J, Zhu C, Barr DB, Canfield RL, Wolff MS. Prenatal Exposure to 

Organophosphates, Paraoxonase 1, and Cognitive Development in Childhood. Environmental 

Health Perspectives 2011;119:1182-1188 

Greenland, S. Modeling and Variable Selection in Epidemiologic Analysis. American Journal of 

Public Health. 1989; 79:340-349. 

Miettinen OS. Stratification by a multivariate confounder score. American Journal of 

Epidemiology 1976;104:609- 620. 

Playfer J, Eze LC, Bullen MF, Evans DAP. Genetic polymorphism and inter-ethnic variability of 

plasma paraoxonase activity. J Med Genet. 1976;13:337–342. 
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Article 3. Eskenazi et al. (2004) 

Association of in Utero Organophosphate Pesticide Exposure and Fetal Growth and 

Length of Gestation in an Agricultural Population. Brenda Eskenazi, Kim Harley, Asa 

Bradman, Erin Weltzien, Nicholas P. Jewell, Dana B. Barr, Clement E. Furlong, Nina T. 

Holland. Environmental Health Perspectives. 115:792–798 (2007). 

Study Summary 

In this article by Eskenazi et al, the authors report on the relationship between prenatal urinary 

organophosphate metabolite levels and fetal growth and gestational duration in the CHAMACOS 

cohort of Mexican-American mothers and their children living Salinas Valley, an agricultural 

region of California. 

Enrollment of the Center for the Health Assessment of Mothers and Children of Salinas 

(CHAMACOS) cohort took place at community clinics. A total of 601 out of 1,130 eligible 

women agreed to participate and were enrolled between October, 1999 and October, 2000, with 

538 being followed through to delivery of a live born infant, whose birth weight was measured. 

The study populations consisted largely of low-income, agricultural workers. Women considered 

eligible for the study were less than 20 weeks gestation, aged 18 years or older, Medi-Cal 

eligible, fluent in English and/or Spanish, and planning to deliver at Natividad Medical Center. 

Excluded from analyses were women with gestational or preexisting diabetes (n = 26), 

hypertension (n = 15), twin births (n = 5), or stillbirths (n = 3). One additional woman for whom 

birth weight information was out of range (< 500 g) was also excluded. The final analytic sample 

consisted of 488 mother-infant pairs. 

Maternal/fetal exposure to organophosphate pesticides was assessed by measurement of 

organophosphate dialkyl phosphate metabolites (DAPs) and seven different pesticide-specific 

metabolites in maternal urine during pregnancy, including 3,5,6-trichloro-2-pyridinol (TCPy), a 

metabolite specific to chlorpyrifos. Cholinesterase (ChE) in whole blood and butyryl 

cholinesterase (BChE) in plasma collected from mothers during pregnancy, at delivery, and from 

umbilical cord blood was also assessed. Maternal urine samples were collected between 5 and 

27 weeks gestation and again between 18 and 39 weeks. The dialkyl phosphates measured were 

dimethylphosphate, dimethyldithiophosphate, dimethylthiophosphate (DMPs), diethylphosphate, 

diethyldithiophosphate, and diethylthiophosphate (DEPs). Total DAPs and DMPs were available 

for 485 women; DEP metabolite levels were available for 486 women. Quantification of 

organophosphate metabolites using gas chromatography-tandem mass spectrometry and isotope 

dilution calibration was conducted by the Centers for Disease Control and Prevention (CDC) 

labs. 

Total DAP metabolite level was defined as the sum of the molar concentrations of the six DAP 

metabolites, dimethylphosphate (DMP) metabolite level as the sum of the molar concentrations 

of the three dimethylphosphate metabolites only, and diethylphosphate (DEP) metabolite level as 

the sum of the molar concentrations of the three diethylphosphate metabolites. Total DAP, DMP, 

and DEP levels were determined for each participant for each of the two pregnancy urine 

samples. Individual metabolite levels below the limit of detection (LOD) were assigned a value 
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of the LOD divided by the square root of two, and this value was included in each sum. Total 

DAP, DMP, and DEP metabolite levels were log10 transformed. DAP, DMP, and DEP values 

were created by averaging the two log-transformed pregnancy measures. For specific 

metabolites, if a woman was below the LOD for both measurements, she was classified as below 

LOD for the average. If she was above the LOD at either or both measurements, her 

measurements were averaged and classified in relation to the median. For 19 women with only 

one DAP measurement in pregnancy, the single measure was used. 

Similar to the analytic treatment of dialkyl phosphate metabolite, authors assigned a value of the 

LOD divided by the square root of two for TCPy measurements below LOD. Because a large 

proportion of women had non-detectable levels of TCPy, the chlorpyrifos-specific metabolite 

was categorized into three groups: TCPy less than LOD for both pregnancy measurements, and, 

for those with at least one detectable level, subdivided below and above the median of the 

average pregnancy level. 

Infant birth weight, crown–heel length, and head circumference were obtained from hospital 

delivery logs and medical records. Infant ponderal index, a measure of proportionality of growth, 

was calculated as (birth weight in grams × 100)/(length in centimeters). Gestational age was 

obtained from medical records and was based on ultrasound procedures for 25% of women. Low 

birth weight was defined as < 2,500 g. Preterm delivery was defined as birth at less than 37 

completed weeks of gestation. A “small for gestational age” (SGA) birth was defined as birth 

weight less than the 10th percentile for gestational age according to ethnicity, parity, and infant 

sex. 

Multiple linear regression models were developed to assess associations between the markers of 

pesticide exposure and length of gestation, birth weight, length, head circumference, and 

Ponderal index. Multivariate logistic regression was used to evaluate associations between 

exposure measurements and low birth weight, preterm delivery, and SGA births. Covariates in 

the models included including maternal age, pregnancy weight gain, week of initial prenatal care, 

parity, infant sex, mother’s country of birth, body mass index, and family income. All models of 

birth weight, length, head circumference, and Ponderal index were adjusted for linear and 

quadratic terms for gestational age. 

The women in this study averaged 25 years of age (SD = 5); two-thirds were multiparous, 80% 

were married, 79% had not graduated from high school, 58% were overweight or obese, 88% 

preferred to speak Spanish, and 84% were born in Mexico, with more than half residing in the 

United States for < 5 years. Almost all of the women were living within 200% of the poverty 

level. Very few women reported smoking (6%), drug use (2%), or alcohol consumption (1%) 

during pregnancy. Approximately 28% of the women had worked in the fields during the 

pregnancy, and another 14% had worked at other jobs in agriculture. Eighty-five percent of the 

women in the study had agricultural workers living in their homes during their pregnancy. The 

median total DAP, DMP, DEP and total DAP metabolite levels for the study population were 

136 nmol/L, 101 nmol/L, and 22 nmol/L, respectively. Only one woman had no detectable DAP 

levels and TCPy was detected in 77% of maternal urine samples. The median level for TCPy was 

3.3 μg/L. 
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The mean (± SD) duration of gestation was 38.9 ± 1.7 weeks; mean birth weight was 3,449 ± 516 

g; mean body (crown–heel) length was 50.2 ± 2.7 cm; mean head circumference was 34.1 ± 1.5 

cm; and mean Ponderal index was 2.7 ± 0.3 g/m3. A total of 3.7% (n = 18) of children were born 

of low birth weight; 4.8% (n = 23) were SGA births, and 6.6% (n = 32) were preterm. 

The authors did not observe an adverse relationship between fetal growth and any measure of in 

utero organophosphate pesticide exposure. On the contrary, they found positive associations 

between body length and head circumference associated with some exposure measures, but not 

chlorpyrifos. They did observe decreases in gestational duration associated with two measures 

of in utero pesticide exposure: urinary DMP metabolites (β = –0.41 weeks per log10 unit 

increase; 95% CI: –0.75––0.02; p = 0.02), which reflects exposure to dimethyl organophosphate 

compounds such as malathion, not chlorpyrifos. 

Study Review 

The study has numerous strengths; most notably the prospective design the longitudinal 

assessment of both exposure and outcomes. Strengths in the study design also include the use of 

multiple exposure biomarkers including quantification of non-specific (DAPs), chlorpyrifosspecific (TCPy) metabolites, cholinesterase (acetyl and butyl), and other environmental coexposures. 

The ascertainment and analysis of urinary organophosphate pesticide markers was appropriate. 

Quantification was conducted at the CDC, using published methods. DAP metabolites are likely 

more accurate and objective indicators of organophosphate pesticide exposure, relative to other 

ascertainment methods such as self-report. Although they are sensitive indicators of exposure, it 

is difficult to infer chlorpyrifos effects specifically from urinary DAPs. Urinary metabolite levels 

may reflect exposure not only to a host of organophosphate parent compounds, but also to 

potentially less toxic environmental metabolites. Also, because exposure to pesticides varies 

considerably from day to day, DAPs from spot urine samples may not represent average 

exposure over time. However, the prenatal exposure measures were, with some exceptions, the 

average of two measurements, and thus may better reflect chronic exposure during the pregnancy 

than a one-time exposure measure. 

Errors in the assignment of exposure in this prospective study will likely have resulted in 

attenuation of observed associations. The likelihood of these errors is high, and the magnitude of 

the bias induced difficult to assess. Under an assumption of no other biases, the negative 

findings, those for the association between children DAP levels and fetal development outcomes, 

for example, could have resulted from exposure measurement error. 

To identify “critical windows” of fetal development when exposure may have a greater impact, 

the authors analyzed the associations of outcomes and metabolite levels measured during moving 

6-week windows of pregnancy (e.g., 5–10 weeks, 6–11 weeks, 7–12 weeks) using a series of 

multiple regression analyses. No period of greater impact was observed in this largely null study. 

The indicators of fetal growth and development (infant birth weight, length, and head 

circumference, Ponderal index, and gestational age) were obtained from hospital delivery logs 
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and medical records, or calculated using data from these records. They are likely to have been 

accurately and objectively measured. Although partially self-reported, any errors in gestational 

age are unlikely to be associated with pesticide exposures in this prospective study. 

The statistical analysis used to assess the associations between the markers of exposure and fetal 

growth and development were appropriate. However, the authors did not present nor discuss 

regression diagnostics to assess the degree to which their models met or violated the assumptions 

implicit in linear models (i.e. homoscedasticity, normality of error term distribution, 

independence of error terms, and linearity of the exposure-response relationship), other than to 

state that they log transformed exposure marker levels. The authors also conduct secondary and 

sensitivity analyses to assess influence of modeling decisions on their findings, and generally 

found their results to be robust. 

A reasonable set of exclusion criteria were applied which likely resulted in recruitment of a study 

population that was relatively homogeneous with respect to confounders of the in utero pesticide 

exposure-fetal growth relationship (compared to the general population), and the potential for 

confounding was thus limited by design. For example, smoking, alcohol use, and illicit drug use 

were not included in the models because very few women reported use and controlling for these 

variables did not alter the results. 

Bias due to confounding was also controlled in the analysis, by assessing and adjusting for risk 

factors for impaired fetal growth and development that are potentially associated with pesticide 

exposure, including maternal age, pregnancy weight gain, week of initial prenatal care, parity, 

infant sex, mother’s country of birth, body mass index, and family income. Variables were 

selected as potential confounders for the multivariate models based on associations reported in 

the literature, and included in final models if they changed the coefficient associated with the 

exposure by 10% or more. Inclusion of indicators for environmental tobacco smoke exposure, 

consumption of caffeinated beverages, history of low birth weight and history of preterm 

delivery, did not alter the relationship of pesticide metabolites and birth outcome and were not 

included in final models. In addition to restriction of confounding by design, the selection of the 

CHAMACOS population, which consists mostly of children from low-income families, also 

served to increase the relative statistical efficiency of the study, as this population is at high risk 

of developmental deficits, compared to the general population. 

The results of this study by Eskenazi et al (2004) in the CHAMACOS cohort failed to 

demonstrate an adverse relationship between fetal growth and in utero organophosphate 

pesticide exposure as assessed by multiple measures of prenatal pesticide exposure. The 

prospective study design and reasonable methods used protect from certain false-positive 

inducing biases, although it is possible that (non-differential) exposure measurement error 

attenuated the associations. Misclassification of the fetal growth outcomes was unlikely to have 

biased the results. Residual confounding by mismeasured or unmeasured factors is possible in 

this observational study, although it is unlikely that such confounding would have resulted in the 

null finding. 
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Article 4. Harley et al. (2011) 

Association of Organophosphate Pesticide Exposure and Paraoxonase with Birth 

Outcome in Mexican-American Women. Kim G. Harley, Karen Huen, Raul Aguilar 

Schall1, Nina T. Holland, Asa Bradman, Dana Boyd Barr, Brenda Eskenazi. PLoS ONE 

6(8)1-10 (2011). 

Study Summary 

In this article by Harley et al, the authors present a follow-up assessment of the relationship 

between prenatal urinary organophosphate metabolite levels and fetal growth and gestational age 

in the CHAMACOS cohort of Mexican-American mothers and their children living Salinas 

Valley, an agricultural region of California.(Eskenazi et al, 2004) They evaluate whether 

paraoxonase (PON1), a key enzyme involved in detoxification of organophosphate pesticides, 

could be an effect modifier of the association between indicators of maternal organophosphorus 

pesticide exposure (DAP metabolite concentrations) and poorer fetal growth previously reported 

in this cohort. They additionally examine the main effects of maternal and child PON1 genotypes 

and PON1 activity on birth outcomes. 

Enrollment of the Center for the Health Assessment of Mothers and Children of Salinas 

(CHAMACOS) cohort took place at community clinics. A total of 601 out of 1,130 eligible 

women agreed to participate and were enrolled between October, 1999 and October, 2000, with 

538 being followed through to delivery of a live born infant, whose birth weight was measured. 

The study populations consisted largely of low-income, agricultural workers. Women considered 

eligible for the study were less than 20 weeks gestation, aged 18 years or older, Medi-Cal 

eligible, fluent in English and/or Spanish, and planning to deliver at Natividad Medical Center. 

Excluded from analyses were women with gestational or preexisting diabetes (n = 26), 

hypertension (n = 15), twin births (n = 5), or stillbirths (n = 3), and one infant with a birth < 500 

grams. In addition, mother-infant pairs with missing PON1 genotype and activity levels were 

excluded (n =21), leaving a final sample size of 467. Of these, PON1 genotype information was 

available for 451 mothers and 436 infants, and PON1 activity data were available for 371 

mothers and 324 infants. 

Maternal/fetal exposure to organophosphate pesticides was assessed by measurement of 

organophosphate dialkyl phosphate metabolites (DAPs). Maternal urine samples were collected 

between 5 and 27 weeks gestation and again between 18 and 39 weeks. The dialkyl phosphates 

measured were dimethylphosphate, dimethyldithiophosphate, dimethylthiophosphate (DMPs), 

diethylphosphate, diethyldithiophosphate, and diethylthiophosphate (DEPs). 

Quantification of organophosphate metabolites using gas chromatography-tandem mass 

spectrometry and isotope dilution calibration was conducted by the Centers for Disease Control 

and Prevention (CDC) labs. Total DAP metabolite level was defined as the sum of the molar 

concentrations of the six DAP metabolites, dimethylphosphate (DMP) metabolite level as the 

sum of the molar concentrations of the three dimethylphosphate metabolites only, and 

diethylphosphate (DEP) metabolite level as the sum of the molar concentrations of the three 

diethylphosphate metabolites. For eight women, missing metabolite values were imputed using 
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linear regression to predict the missing metabolite level as a function of the other known 

metabolites for a given participant at a given time point. 

The variables for total DAP, DMP, and DEP concentrations were log-transformed to reduce the 

impact of outlier points. Metabolite measurements from the two sampling time points were 

averaged to create a summary measure of in utero organophosphate pesticide exposure. 

The primary analyses were conducted using DAP concentrations unadjusted for creatinine, 

although sensitivity analyses included creatinine as a covariate in the models. Total DAPs and 

DMPs were available for 485 women; DEP metabolite levels were available for 486 women. 

Maternal and umbilical cord blood samples were collected at the time of delivery. Genotyping 

of the PON1-108 and PON1192 SNPs was performed using genomic DNA extracted from blood 

clots and Taqman PCR methods. 

Two PON1 enzyme activity assays (arylesterase and paraoxonase) were performed using 

spectrophotometric methods. Arylesterase activity measures the rate of hydrolysis of the 

substrate phenyl acetate, and reflects the quantity of PON1 enzyme. Paroaxonase activity 

measures the rate of hydrolysis of paraoxon, and thus reflects a combination of the catalytic 

efficiency and quantity of the PON1 enzyme. 

Birth outcome data, including infant birth weight, length, head circumference, and gestational 

age, were abstracted from medical records by a registered nurse. 

Linear regression models were constructed to examine the main associations of PON1 genotype 

and activity with length of gestation, infant birth weight, birth length, and head circumference, 

independent of pesticide exposure. Maternal and child PON1 genotypes were entered into 

models as categorical variables; for PON1-108, the CC genotype was the reference, and for 

PON1192, the RR genotype was the reference. Arylesterase and paraoxonase activity in maternal 

and umbilical cord blood were examined as continuous variables, normalized by dividing by the 

standard deviation. 

The associations of PON1 activity with birth outcomes were assessed within each PON1192

genotype by stratifying by genotype and by testing for interaction between activity and genotype. 

The presence of effect modification of associations between maternal DAP metabolite and birth 

outcomes by PON1 genotype and activity was assessed by stratification, and use of interaction 

terms. PON1 genotype (as a categorical variable) or activity (as a continuous variable) and crossproduct terms were included in models previously used to assess pesticide associations in the 

2004 report by Eskenazi et al. (Eskenazi et al, 2004) For PON1 genotype, two cross-product 

terms were included in the model (e.g. QR*DAP concentrations and RR*DAP concentrations). 

Similar cross-product terms were generated using tertiles of PON1 activity. Statistical 

significance for interaction was set at p=0.1. In addition, effect modification by PON1 was 

assessed by conducting linear regressions on data stratified by PON1 genotype and by tertiles of 

enzyme activity. 
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Models of PON1 genotype included maternal pre-pregnancy BMI and maternal weight gain 

during pregnancy as covariates. Models of PON1 enzyme activity also included maternal age and 

country of birth, assay temperature, and PON1192 genotype. In models of birth weight, length and 

head circumference, the authors also controlled for linear and quadratic gestational age to 

examine associations independent of gestational duration. 

As previously summarized, women in this cohort were predominantly Spanish-speaking and 

born in Mexico; 41% of the women worked in agriculture during pregnancy. More than 60% of 

families were living below the federal poverty threshold, and 89% of women had never 

completed high school. The mean maternal age was 25.5 years (SD = 5.0). All women had 

detectable levels of organophosphate pesticide metabolites in their urine during pregnancy. The 

geometric mean for the DAP concentrations during pregnancy (for the average of the two 

sampling periods) was 146 nmol/L (95% CI: 133, 160); of this, a larger proportion was DMP 

metabolites (GM = 109 nmol/L; 95% CI =98, 120) than DEPs (GM= 23 nmol/L; 95% CI = 21, 

25). The allele frequencies of the PON1192 Q allele and the PON1-108 T allele were 50% and 

46%, respectively, and did not differ between mothers and newborns. The mean arylesterase and 

paraoxonase activity was 33.6 U/mL (SD = 16) and 256.6 U/L (SD = 165), respectively, for 

infants and 136.6 U/mL (SD = 44) and 989.0 U/L (SD = 616) for mothers. 

In this study, infants’ (but not mother's) PON1 genotype and PON1 activity were associated with 

gestational age and head circumference: Infants with the susceptible PON1-108 TT genotype had 

shorter gestational age ( =-0.5 weeks, 95%CI: -0.9, 0.0) and smaller head circumference ( =-

0.4 cm, 95% CI: -0.7, 0.0) than those with the PON1-108 CC genotype. Infants’ arylesterase and 

paraoxonase activity were positively associated with gestational age. Maternal DAP 

concentrations were associated with shorter gestational age among infants of the susceptible 

PON1-108 TT genotype, although only the interaction between PON1-108 genotype and DEP 

metabolite concentrations was statistically significant (p-value for interaction = 0.09). However, 

maternal DAP concentrations were associated with larger birth weight (p-value for interaction = 

0.06) and head circumference (p-value for interaction =0.01) in infants with non-susceptible 

genotypes. The authors conclude that infants with certain PON1 genotypes (e.g., PON1-108 TT) 

may be more susceptible to effects of in utero organophosphate pesticide exposure. 

Study Review 

This study improves on the work by Eskenazi et al (2004) by including an assessment of effect 

modification of the association between prenatal pesticide marker levels and birth outcomes by 

PON1 genotype and activity. Additional strengths of this study include the prospective design, 

the assessment of pesticide markers at two time-points during pregnancy, and the assessment of 

PON1 activity by two different methods. 

The authors speculate that a possible reason that they did not observe an association between 

DEPs and gestational age in the earlier study by Eskenazi is that it was “masked” by the effects 

of PON1. They note that the observation of an interaction only for DEP concentrations is 

plausible, since the known biologically relevant action of PON1 is with two DEP pesticides 

(chlorpyrifos and diazinon). 
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The finding of an independent effect of PON1 genotype on birth outcomes is consistent with 

finding of reductions in average gestational age and head circumference at birth associated with 

the PON1-108 TT genotype in the Mount Sinai cohort, as reported by Berkowitz et al. (Berkowitz 

et al, 2004). The association between infant PON1 (arylesterase) activity and gestational age was 

null in the Mount Sinai cohort. This is in contrast to the positive association reported by Harley 

et al. Maternal PON1 (arylesterase) activity was not associated with birth outcomes in the Haley 

study, but positively associated with head circumference in the Berkowitz et al study. Finally, the 

interactions observed in the two studies differed. Berkowitz et al reported a decrease in head 

circumference associated with chlorpyrifos marker levels among newborns of mothers with low 

arylesterase activity in the Mount Sinai cohort. Harley et al, in contrast, observed a statistically 

significant interaction between DEP levels and gestational age among infants in the UC Berkeley 

CHAMACOS cohort with the TT PON1 genotype, but they did not observe significant 

interaction between PON1 activity and birth outcomes. In addition to measuring different 

indicators of pesticide exposure (TCPy), the PON1 genotypes and activity assessed for the 

Mount Sinai cohort differed somewhat from those assessed in the UC Berkeley cohort; 

Berkowitz et al did not assess PON1192 (only PON1-108) and paraoxonase activity (only 

arylesterase activity) 

In the Columbia Mothers and Newborn Study (Whyatt et al, 2004), organophosphate pesticide 

levels in maternal blood were associated with decreased fetal growth, while in the UC Berkeley 

CHAMACOS study, modest increases in birth weight and head circumference were associated 

with in utero DAP concentrations. In fact, stratification by PON1 status seemed to enhance these 

positive associations, such that in the non-susceptible groups (e.g. PON1-108 CC, PON1192 RR, 

and high arylesterase activity), increasing DAP concentrations were significantly associated with 

increased birth weight and head circumference. 

The finding of a positive association between DAPs and DMPs and both head circumference and 

birth weight among those with the CT or CC PON1 genotypes, and a positive association 

between head circumference and DAPs among infants with RR PON1 genotypes is perplexing. 

The authors speculated that it is possible that, among the less susceptible groups, high urinary 

DAP concentrations may be an indication of rapid detoxification and excretion of 

organophosphate pesticides, rather than a marker of high exposure. 

The ascertainment and analysis of urinary organophosphate pesticide markers was appropriate. 

Quantification was conducted at the CDC, using published methods. However, the use of DAP 

metabolites to measure exposure has some limitations, and these were discussed by the authors. 

Although they are sensitive and relatively specific indicators of organophosphate exposure, it is 

difficult to infer chlorpyrifos effects specifically from urinary DAPs. Urinary metabolite levels 

may reflect exposure not only to a host of organophosphate parent compounds, but also exposure 

to the less toxic metabolites themselves. Because exposure to pesticides varies considerably from 

day to day, DAPs from spot urine samples may not represent average exposure over time. 

However, the prenatal exposure measures were, with some exceptions, the average of two 

measurements taken early and late pregnancy, and thus may better reflect chronic exposure 

during the pregnancy. Additionally, DAP metabolites in urine may overestimate 

organophosphate pesticide exposure if they reflect exposure to both pre-formed DAPs in the 

environment and the parent organophosphate compounds. 
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Errors in the assignment of exposure in this prospective study will likely have resulted in 

attenuation of observed associations. The likelihood of these errors is high, and the magnitude of 

the bias induced difficult to assess. Under an assumption of no other biases, the negative 

findings, those for the association between children’s DAP levels and attention outcomes, for 

example, could have resulted from exposure measurement error. 

The indicators of fetal growth and development (infant birth weight, length, and head 

circumference, and gestational age) were obtained from hospital medical records, and 

ascertained by a registered nurse. They are likely to have been accurately and objectively 

measured, although the potential for errors in their measurement was not assessed. Although 

partially self-reported, any errors in gestational age are unlikely to be associated with pesticide 

exposures, PON1 genotype, or PON1 activity in this prospective study. 

The statistical analysis used to assess the associations between the markers of exposure and fetal 

growth and development were appropriate. However, despite a reasonable sample size for 

assessing main effects, the Harley et al study likely lacked sufficient statistical power to assess 

effect modification by PON1 genotype and PON1 activity, as the CHACAMOS Cohort study 

was not originally designed to assess interactions between prenatal pesticide exposures and these 

co-factors. As a result, the analysis is at risk of failing to detect effect modification if effect 

modification is truly present. 

The authors did not present nor discuss regression diagnostics to assess the degree to which their 

models met or violated the assumptions implicit in linear models (i.e. homoscedasticity, 

normality of error term distribution, independence of error terms, and linearity of the exposureresponse relationship), other than to state that they log-transformed DAP levels to reduce the 

impact of outlier observations. The authors did conduct secondary and sensitivity analyses to 

assess influence of modeling decisions on their findings, including adjustment for creatinine. 

A reasonable set of exclusion criteria were applied which likely resulted in recruitment of a study 

population that was relatively homogeneous with respect to confounders of the relationship 

between in utero pesticide exposure and fetal growth (compared to the general population), and 

the potential for confounding was thus limited by design. For example, smoking, alcohol use, 

and illicit drug use were not included in the models because very few women reported use and 

controlling for these variables did not alter the results. 

The authors reported that factors known, a priori, to be associated with birth outcomes in this 

population were considered potential confounders, although they did not enumerate these 

variables. Covariates were retained in final models if they were also associated with PON1

genotype or activity (in the study population, presumably) or if their exclusion from the model 

changed the main effect coefficient by more than 10%. The authors’ final models for birth 

weight and head circumference adjusted for timing of urine collection, timing of entry into 

prenatal care, maternal age, parity, country of birth, household income, pre-pregnancy body mass 

index, maternal weight gain, infant sex, and gestational age using both linear and quadratic 

terms. Gestational age models adjusted for timing of urine collection, timing of entry into 

prenatal care, maternal age, parity, country of birth, and household income. 
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The authors controlled for gestational age in models of birth weight, length, and head 

circumference to examine associations independent of gestational duration. It is possible that 

gestational age is an intermediate on the causal pathway. If this is the case, the coefficients from 

these models can be interpreted as estimates of the ‘direct-effect’ of prenatal urinary DAPs and 

fetal growth outcomes due to causal pathways other than those mediated by gestational age. 

These pathway-specific analyses have a potential limitation that was not discussed by the 

authors. Namely, there is the potential for inducing bias if gestational age and fetal growth have 

unmeasured common causes (i.e. an unknown co-exposure) (Cole and Hernan, 2002). 

In addition to restriction of confounding by design, the selection of the CHAMACOS population, 

which consists mostly of children from low-income families, also served to increase the relative 

statistical efficiency of the study, as this population is at high risk of neurodevelopmental 

deficits, compared to the general population. 

The results of this study by Harley et al in the CHAMACOS cohort demonstrate modest adverse 

associations between some measures of PON1 and fetal growth, as well as providing some 

evidence of effect modification of the relationship between organophosphate pesticide exposure 

markers and fetal growth. The prospective study design and reasonable methods used protect 

from certain false-positive inducing biases, although it is possible that exposure measurement 

error attenuated the associations. Misclassification of the fetal growth outcomes was unlikely to 

have biased the results. Residual confounding by mismeasured or unmeasured factors, including 

bias induced by adjusting for an intermediate variable (gestational age), is possible in this study, 

and may account for the observed associations. 

 
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Work Cited 

Berkowitz GS, Wetmur JG, Birman-Deych E, Obel J, Lapinski RH, Godbold JH, Holzman IR, 

Wolff MS. In Utero Pesticide Exposure, Maternal Paraoxonase Activity, and Head 

Circumference. Environmental Health Perspectives. 2004;112:388–391. 

Cole SR and Hernan MA. Fallibility in Estimating Direct Effects. Int. J. Epidemiol. 2002; 31 (1): 

163-165. 

Eskenazi B, Harley K, Bradman A, Weltzien E, Jewell NP, Barr DB, Furlong CE, Holland NT. 

Association of in Utero Organophosphate Pesticide Exposure and Fetal Growth and Length of 

Gestation in an Agricultural Population. Environmental Health Perspectives. 2004;115:792–798. 

Whyatt RM, Rauh V, Barr DB, Camann DE, Andrews HF, Garfinkel R, Hoepner LA, Diaz D, 

Dietrich J, Reyes A, Tang D, Kinney PL, Perera FP. Prenatal Insecticide Exposures and Birth 

Weight and Length among an Urban Minority Cohort. Environmental Health Perspectives. 

2004;112:1125–1132. 
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Article 5. Engel et al. (2007) 

Prenatal Organophosphate Metabolite and Organochlorine Levels and Performance on the 

Brazelton Neonatal Behavioral Assessment Scale in a Multiethnic Pregnancy Cohort. 

Stephanie M. Engel, Gertrud S. Berkowitz, Dana B. Barr, Susan L. Teitelbaum, Jodi 

Siskind, Stefanie J. Meisel, James G. Wetmur and Mary S. Wolff. American Journal of 

Epidemiology. 165:1397-1404, 2007. 

Study Summary 

Engel et al conducted a study leveraging the Children’s Environmental Health Study cohort data 

to investigate the impact of pesticide exposure on pregnancy outcomes and child 

neurodevelopment in an inner-city multiethnic cohort of women recruited during pregnancy. The 

study enrolled primiparous women presenting for prenatal care with singleton pregnancies at the 

Mount Sinai prenatal clinic and two private practices and who delivered their infants at Mount 

Sinai Hospital between May 1998 and July 2001. Participants in this study were predominantly 

young, unmarried, Hispanic and African American women with low educational attainment. 

This is the second of three published analyses of prenatal pesticide exposure and child growth 

and development in the Mount Sinai Hospital Children’s Environmental Health Study. The first 

(Berkowitz et al 2004) reported on assessments of birth weight, length and head circumference. 

The third (Engel et al 2011) reports on assessments of infant development at age 12 and 24 

months and child development at ages 6-9 years. 

The study population was drawn primarily from East Harlem and consisted largely of young 

Hispanic women (predominantly Puerto Rican), but also included African-American and 

Caucasian women. Mothers were enrolled during early pregnancy from the Prenatal Clinic and 

two private practices at Mount Sinai Hospital from May 1998 to July 2001. The study included 

only primiparous with singleton births. Mothers were excluded if they had any of the following: 

an initial prenatal visit after 26 weeks of gestation, serious chronic diseases, a serious pregnancy 

complication that could affect fetal growth and development; alcohol consumed greater than two 

alcoholic beverages per day, illicit drug use. Mothers and infants were also excluded if the child 

was born with a congenital malformation or severe prematurity. A total of 479 prenatal patients 

were recruited; 75 were excluded because of medical complications, premature birth, an infant 

with birth defects, inability to collect biologic specimens before birth, change of hospital or 

residence outside New York City, or refusal to continue to participate. 

The authors assessed the relationship between total diethylphosphates (DEPs), total 

dimethylphosphates (DMPs), total dialkylphosphate metabolites (DAPs), as well as malathion 

dicarboxylic acid (MDA) and indices of abnormal neonatal behavior and/or neurological 

integrity using outcome measures derived from the Brazelton Neonatal Behavioral Assessment 

Scale (BNBAS), which was administered to the infants of participating mothers within days after 

birth and before they left the hospital. The authors also assessed modification of the associations 

between pesticide metabolite levels and metrics of neurodevelopment by maternal paraoxonase 1 

activity. 

Environmental exposures, sociodemographic characteristics, medical history, and lifestyle 
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factors were assessed by a prenatal questionnaire administered to the mothers during the third 

trimester. Participating mothers provided urine samples during the 3rd trimester prenatal visit 

which were provided to the CDC in order to assay six dialkylphosphate metabolites and 

malathion dicarboxylic acid (MDA). The investigators imputed values for samples with one or 

more missing values using methods described by Eskenazi et al. (2004), extending the method to 

also include imputation of metabolite values for women who were missing two metabolites in a 

class. Diethyl- and dimethylphosphate metabolites were summed on a molar basis (as nm/liter) to 

obtain total diethylphosphates (DEPs) and total dimethylphosphates (DMPs), respectively, and 

together to obtain total dialkylphosphates (DAP) levels. All biomarkers except MDA were 

included in analyses as log10 linear terms in the initial model selection stage. Results were 

reported for DEPs dichotomized at the median level. MDA was dichotomized at the limit of 

detection because only 21.6% of the population had detectable values. Maternal plasma 

paraoxonase 1 activity was classified in tertiles. 

A high prevalence of pesticide use was reported in this cohort of pregnant women residing in 

New York City. The study cohort was enrolled between 1998 and 2002, which, the authors point 

out, overlaps with a period when chlorpyrifos and diazinon were being phased out of residential 

use. However, the authors note that in 1997, chlorpyrifos was the most heavily used insecticide 

by pest control operators at the New York City Housing Authority. The authors noted that all of 

the women who participated in personal air monitoring of their home had detectable levels of 

diazinon, chlorpyrifos, carbamate propoxur, and o-phenylphenol. 46% of women in the cohort 

reported that pesticides had been applied in their home during their pregnancy. Total 

diethylphosphates were calculated as the sum of diethyldithiophosphate, diethylphosphate, and 

diethylthiophosphate. In this cohort, the DEPs were detectable in 88.8% of the mothers, with a 

median (IQR) of 24.7 nm/liter (8.9–52.8 nm/liter). Total dimethylphosphates were calculated as 

the sum of dimethyldithiophosphate, dimethylphosphate, dimethylthiophosphate. DMPs were 

detectable in 90.2% of the mothers, with a median (IQR) of 47.8 nm/liter (15.6–149.2 nm/liter). 

Total dialkylphosphates were detectable in 96.5% of the mothers, with a median (IQR) of 82.0 

nm/liter (35.2–194.7 nm/liter). 

To assess infants’ neurodevelopment, the investigators conducted a behavioral assessment using 

the Brazelton Neonatal Behavioral Assessment Scale (BNBAS). All BNBAS examinations were 

completed within 5 days of delivery, and 86.1% were conducted within two days of delivery. 

The BNBAS includes 28 behavioral items and 18 primitive reflexes. The exam does not yield a 

single score but instead assesses the infant across different developmental areas; infants' 

behaviors were scored according to seven domains: 

1) Habituation - the ability to respond to and inhibit discrete stimuli while asleep; 

2) Orientation -attention to visual and auditory stimuli and quality of overall alertness; 

3) Motor - motor performance and quality of movement and tone; 

4) Range of state - a measure of infant arousal and state lability; 

5) Regulation of state - the ability to regulate state in the face of increasing levels of 

stimulation; 

6) Autonomic stability - signs of stress related to homeostatic adjustments of the central 

nervous system; 

7) The number and type of abnormal primitive reflexes 
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Of the 308 infants evaluated, all 18 primitive reflexes assessed in the BNBAS, 64% had all reflexes 

within the normal range, 18% had one abnormal reflex, 8% had two abnormal reflexes, and 10% had 

three or more abnormal reflexes. The majority of abnormal reflexes were due to hypotonicity (which 

includes a reflex not elicited after stimulation). Authors do not discuss the prevalence of other common 

causes of hypotonicity such as hypothyroidism, hypoglycemia, hypoxic encephalopathy. 

The authors observed an association between both generic and pesticide-specific biomarkers of 

prenatal organophosphate pesticides and an increased number of abnormal primitive reflexes 

which are considered a critical marker of neurologic integrity. Notably, no statistically 

significant adverse associations were found for the dialkylphosphate metabolites and any 

measured behavior in the author’s primary analyses. 

They did observe, however, an interaction between paraoxonase 1expression levels and total 

dimethylphosphates on risk of abnormal reflexes, such that infants born to women in the first 

(interaction p = 0.002) and second (interaction p = 0.01) tertiles (slower metabolizers) had a 

greater risk of abnormal reflexes than infants of those in the highest tertile (fast metabolizers). In 

the first tertile of paraoxonase 1 expression, the relative risks (RR) of having abnormal reflexes 

were 1.78 (1.01, 3.14) associated with total diethylphosphates, 1.96 (1.27, 3.03) for total 

dialkylphosphates, and total dialkylphosphates 2.38 (1.37, 4.15). In the second tertile of 

paraoxonase 1 expression, the corresponding relative risks (RR) were 1.42 (0.85, 2.35) for total 

diethylphosphates, 1.66 (1.03, 2.65), for total dialkylphosphates, and total dialkylphosphates 1.75 

(0.96, 3.17). No increased risk of abnormal reflexes with increasing exposure was found for 

women in the highest tertile of paraoxonase 1 expression, nor was a similar interaction observed 

between total diethylphosphates and paraoxonase 1. 

Higher levels of total diethylphosphates and total dialkylphosphates, too, were associated with an 

increase in the risk of having abnormal reflexes. Subjects with prenatal total diethylphosphates 

levels above the median delivered infants who were 2.3 times more likely to have at least two 

abnormal reflexes (95% CI: 1.1, 5.0). Among women with lower tertiles of paraoxonase 1 

expression (i.e., slow organophosphate pesticide metabolizers) the authors also observed a 

significantly increased risk of abnormal reflexes with increasing exposure. 

Relative to the first quartile, quartiles 2–4 of total diethylphosphates, total dimethylphosphates, 

and total dialkylphosphates were associated with an increased proportion of abnormal reflexes, 

although the associations did not increase monotonically and varied in their strength and 

precision. The authors speculated that this may due to a threshold effect. 

With the exception of MDA, there appeared to be stronger associations between metabolite 

levels and abnormal reflexes for examinations performed after the first day of life, although only 

the total dimethylphosphates interaction p-valueinteraction= 0.10. In contrast, an interaction 

(pinteraction = 0.10) with MDA showed that, for infants examined before day 2, the relation 

between MDA and abnormal reflexes was stronger (RR= 2.51, 95% CI: 1.61,3.90) compared 

with that for infants examined later (RR= 1.34, 95%CI: 0.72, 2.49). 

The authors noted that their findings were remarkably consistent with those of Young et al. 

(Neurotoxicology, 2005) who reported that for each log10 unit increase in total 
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dialkylphosphates, there was a 26% increased risk of abnormal reflexes, (compared to 32% 

increased risk in this study). The magnitudes of association were also similar for total 

diethylphosphates (RRYoung = 1.25, RRcurrent study = 1.49) and total dimethylphosphates (RRYoung = 

1.20, RRcurrent study) = 1.13). Both studies reported stronger associations between dialkylphosphate 

metabolites and abnormal reflexes for older children, although, in the present study, the 

interaction with day of administration was not statistically significant. 

The association between MDA, a malathion-specific metabolite, and abnormal reflexes appeared 

to be age dependent in this study, with the most pronounced effect observed among infants 

evaluated within the first day of life. The authors noted that this finding conflicts with the 

suggestion of later effects for dialkylphosphate metabolites, including dimethylphosphates, 

which encompasses nonspecific metabolites of malathion. The authors concluded that it is 

unclear what pesticides may be driving this disparity. 

Study Review 

This was a well conducted prospective study conducted in a young, predominantly minority 

population. The study design, analytic approach, and statistical analyses were appropriate. The 

authors used a sensitive albeit non-specific marker of chlorpyrifos pesticide exposure 

(DEP/DAP). While the analytic methods appear sound, authors assessed these markers using 

only a single maternal urine specimen taken during the third trimester. However, in their 

discussion, the authors note that organophosphate pesticide metabolites have short half-lives. If 

sources and patterns of exposure remain static, a single measurement would be a good indicator 

of pesticide exposure during pregnancy. If, however, sources and exposure patterns change over 

short time-spans, perhaps on the order of days to weeks, such an exposure assessment would not 

necessarily provide a good indicator of the relevant exposure. Like the many other sources of 

error possible in this study, this, too is likely to result in a non-differential misclassification of 

the exposure, with respect to the disease outcome. Serial measurements of urinary biomarkers of 

pesticide exposure in the third trimester would shed light on these patterns, although the benefits 

of such information are offset by the added cost, logistical complexity expense, and burden on 

the study participants. 

The Neonatal Behavioral Assessment Scale (NBAS), used to ascertain the health endpoints in 

this study, is designed to examine individual differences in newborn behavior 

(http://www.brazelton-institute.com/intro.html). Examinations took place in a quiet, semidarkened, warm room adjacent to the neonatal nursery, or in the mother’s private room. 

Examiners in this study were either trained and certified by the Brazelton Institute or trained by a 

certified examiner. It was not specified how many examinations were conducted by the certified 

examiners, nor how recently the certified examiners had received their certification. Procedures 

for tracking the quality of the assessment process were also not discussed in the report. The 

health outcome assessment methodology used in this study is less than objective; assessments 

rely on substantial interaction between the infant and the examiner. Procedures for ensuring that 

the examiners were blinded to the participants’ exposures were not discussed in the article. 

However, authors did include examiner as an indicator variable in statistical analyses to control 

for slight differences in examiner methodology. 
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Moreover, the behavioral assessment was administered before hospital discharge only on a 

subset of children in the cohort (n =311/404). The BNBAS was not administered if the infant 

was admitted to the NICU (n = 21); if the infant was delivered and discharged over a weekend (n 

= 43); if the parent refused (n = 5); if the infant was not testable (n = 2); or if study personnel 

were unavailable (n = 22). Factors related to weekend delivery (e.g., fewer inductions) would be 

underrepresented among the tested subjects. This missing data has the potential to result in a 

selection bias, reduce the degree of precision with which associations were estimated, and limit 

the generalizability of the study findings. However, the authors’ explanation for the incomplete 

ascertainment is presented and seems appropriate and largely logistical in nature. 

The authors employed a reasonable set of exclusion criteria that likely resulted in recruitment of 

a study population that was relatively homogeneous with respect to confounders of the in utero

pesticide exposure-neurodevelopment relationship (compared to the general population). The 

potential for confounding was thus limited by design (restriction). Bias due to confounding was 

also controlled in the analysis, by assessing and adjusting for risk factors for neuromuscular 

functioning that are potentially associated with pesticide exposure, including maternal age, race, 

marital status, education, cesarean delivery, delivery anesthesia, infant age at examination, infant 

gender, infant jaundice, and smoking, alcohol consumption, caffeine consumption, or illicit drug 

use during pregnancy, and the examiner conducting the BNBAS. 

The use of an automated statistical procedure (backward elimination and change of effect 

measure) as a means to eliminate potential covariates is discouraged in some epidemiologic 

circles on the basis that assessment of confounding is generally not considered to be a statistical 

endeavor, although they continue to be used by others, largely on the grounds that it is 

‘objective’. The authors used a change-in-estimate approach, rather than a reliance on the 

statistical significance (p-value) associated with a covariate; their variable selection rule has been 

shown to produce more reliable models than variable selection methods based on statistical 

significance (Greenland, 1989). Because they used a backward elimination procedure, they 

considered confounding by a given risk factor in the context of adjustment for other covariates. 

The approach to covariate selection is a minor consideration in this study, and different 

approaches are not likely to qualitatively change the study findings or conclusions. A robust 

discussion of covariate selection concepts can be found in Chapter 15 of Modern Epidemiology 

(Kenneth J. Rothman, Sander Greenland, Timothy L. Lash). 

The authors noted that post-entry exclusions/loss-to follow-up in the study may have biased their 

findings if they were associated with both organophosphate pesticide levels and neonatal 

behavior. Missing data has the potential to induce a selection bias if the reasons for missingness 

are determined by exposure (or a predictor of exposure) and are also not independent of 

neurodevelopment. Most of the excluded women were those who moved out of the study area, 

were lost-to-follow up, or lacked prenatal biologic specimens, which largely reflect 

socioeconomic condition. Participants included and excluded differed slighted with regard to 

maternal age and race; however, these factors were not associated with number of abnormal 

reflexes in this study. These considerations may, however, limit the generalizability of the study 

findings. 

The statistical analysis used in the study was appropriate, although several subtleties in the 
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analytic procedures are worthy of mention. The main finding of the study was a statistical 

interaction between biomarker levels of organophosphate pesticides and paraoxon activity. It was 

not clear whether this effect modification was consideration a priori. Also, the authors imputed 

missing data, and this may 1) affect the precision of association estimates and 2) result in 

attenuated effect estimates as a result of exposure measurement error. The description of the 

imputation procedure used was insufficient for drawing strong conclusions regarding its validity 

with respect to the magnitude of measurement error/bias, and its effect on estimates of precision. 

The generalizability of the study may be limited as consequence of the particulars of the study 

population assessed and the numerous exclusion criteria applied. This study population consisted 

of primiparous women, recruited early on in their pregnancies from the prenatal clinic and two 

private practices at Mount Sinai Hospital in New York from March 1998 to March 2002, 

consisted predominantly of young Hispanic women with low education. Sixty-eight percent were 

less than 25 years old. Just over half were Hispanic (51.1%), although substantial numbers of 

White (20.3%) and African-American (27.3%) women also participated. Just under half (47.6%) 

classified themselves as being single. The exclusion criteria applied to potential study 

participants further focused the population assessed, relative to the general population of 

pregnant women. In the likely situation that the prevalence of important modifiers of the 

exposure-fetal development association in this focused study population is different from that in 

other populations of concern, generalizability will be limited. Important modifiers to consider 

may include time spent in the home, patterns of food preparation and consumption, health 

consciousness, the baseline health status of the mother (and father), and a host of socioeconomic 

determinants including access to health care. The investigators finding of effect modification of 

the biomarker-fetal growth relationship by PON1 activity is illustrative of limits on the 

generalizability of the study; populations who have high levels of paraoxonase activity may vary 

by genetotypic and phenotypic determinants. These factors were not discussed in the report, but 

it has been noted previously that there is 10- to 40-fold inter-individual variability in rates of 

paraoxon hydrolysis (Playfer et al 1976). 

The primary finding was an interaction between high paraoxonase 1expression levels and 

organophosphate pesticide exposure, whereby fast metabolizers had lower risk of abnormalities 

associated with prenatal pesticide metabolite levels in maternal urine samples. As with their 

previous report, bias due to confounding, and selection of study participants is unlikely to have 

large and qualitative impact on the association observed. To the extent that prenatal chlorpyrifos 

is the true exposure of interest, the assessment of non-specific organophosphate pesticide 

metabolites in a single sample of prenatal urine is a primary potential source of exposure 

measurement error. Because of the prospective design, information bias is expected to result in 

attenuation of the true (unobserved) associations, toward (observed) associations that are 

relatively smaller, or null. 

 
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Works Cited 

Berkowitz GS, Wetmur JG, Birman-Deych E, Obel J, Lapinski RH, Godbold JH, Holzman IR, 

Wolff MS. In Utero Pesticide Exposure, Maternal Paraoxonase Activity, and Head 

Circumference. Environmental Health Perspectives. 2004;112:388–391. 

Breslow NE, Day NE. Statistical Methods in Cancer Research: Volume II: The Design and 

Analysis of Cohort Studies. Lyon: IARC, 1987. 

Engel SM, Wetmur J, Chen J, Zhu C, Barr DB, Canfield RL, Wolff MS. Prenatal Exposure to 

Organophosphates, Paraoxonase 1, and Cognitive Development in Childhood. Environmental 

Health Perspectives 2011;119:1182-1188 

Greenland, S. Modeling and Variable Selection in Epidemiologic Analysis. American Journal of 

Public Health. 1989; 79:340-349. 

Miettinen OS. Stratification by a multivariate confounder score. American Journal of 

Epidemiology 1976;104:609- 620. 

Playfer J, Eze LC, Bullen MF, Evans DAP. Genetic polymorphism and inter-ethnic variability of 

plasma paraoxonase activity. J Med Genet. 1976;13:337–342. 

Rothman KJ, Greenland S, Lash TL. Modern Epidemiology. Third Edition. Lippincott, Williams, 

& Wilkins, 2008. 
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Article 6. Young et al. (2005) 

Association Between In Utero Organophosphate Pesticide Exposure and Abnormal 

Reflexes in Neonates. Jessica G. Young, Brenda Eskenazi, Eleanor A. Gladstone, Asa 

Bradman, Lesley Pedersen, Caroline Johnson, Dana B. Barr, Clement E. Furlong, Nina T. 

Holland. NeuroToxicology. 26:199–209 (2005). 

Study Summary 

Young et al. assessed the relationship between in utero and early postnatal organophosphate 

pesticide exposure and neonatal neurobehavior in the Center for the Health Assessment of 

Mothers and Children of Salinas (CHAMACOS) cohort. Urinary levels of dialkylphosphate 

(DAP) metabolites, including dimethyl and diethylphosphate metabolites, measured at 

approximately fourteen and twenty-six weeks gestation, and once post-delivery, measured at 

approximately seven days postpartum were assessed as markers of exposure to organophosphate 

pesticides. Cognitive development was assessed using the Brazelton Neonatal Behavioral 

Assessment Scale (BNBAS). 

The CHAMACOS study is a longitudinal birth cohort study of environmental exposures and the 

health of pregnant women and their children living in the Salinas Valley, California. The Salinas 

Valley is a major center of agricultural production in the United States with approximately 

500,000 pounds of organophosphate pesticides applied annually, according to the CDC. 

(California EPA. Pesticide Use Reporting 2001 Summary Data, 2002. 

(www.cdpr.ca.gov/docs/pur/pur01rep/01_pur.htm) Pregnant women initiating prenatal care at 

the Natividad Medical Center, a county hospital located in the city of Salinas, were recruited to 

participate in the Center for the Health Assessment of Mothers and Children of Salinas 

(CHAMACOS) project. Both the source and study populations consisted largely of low-income, 

Latina agricultural workers. Women considered eligible for the study were less than 20 weeks 

gestation, aged 18 years or older, Medi-Cal eligible, fluent in English and/or Spanish, and 

planning to deliver at Natividad Medical Center. A total of 601 women were enrolled between 

October, 1999 and October, 2000, with 528 followed through delivery of a live born infant. The 

BNBAS was performed on 421 infants. Eight twins and 27 preterm infants were excluded from 

analysis. 

Exposure was assessed by measurement of organophosphate dialkylphosphate (DAP) 

metabolites in maternal urine samples collected between 5 and 27 weeks gestation and again 

between 18 and 39 weeks. Maternal urine specimens were generally collected at the time of the 

prenatal and post-delivery interviews. The post-delivery urines were collected within 1 week of 

delivery for 73% of the sample, with the remainder obtained up to 176 days afterwards. 

Quantification of metabolites was conducted by Centers for Disease Control and Prevention 

(CDC) labs. Total DAP metabolite level was defined as the sum of the molar concentrations of 

the six DAP metabolites, dimethylphosphate (DMP) metabolite level as the sum of the molar 

concentrations of the three dimethylphosphate metabolites only, and diethylphosphate (DEP) 

metabolite level as the sum of the molar concentrations of the three diethylphosphate 

metabolites. Total DAP, DMPT, and DEP levels were determined for each participant for each of 

the two pregnancy urine samples and for the post-delivery urine sample. Individual metabolite 

levels below the limit of detection (LOD) were assigned a value of the LOD divided by the 
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square root of two, and this value was included in each sum. A small number of missing 

metabolite values were imputed using regression to predict the missing metabolite level as a 

function of the other known metabolites for given participant at a given time point. The total 

DAP, DMP, and DEP metabolite levels were then log10 transformed. 

For 30% of participating infants, developmental outcomes were assessed prior to discharge from 

the hospital; the remainder was assessed at the CHAMACOS research office or in the 

participant’s home. Assessments were performed away from the mother in a private room with 

low-level light and noise control. The BNBAS, consists of 28 behavioral measurements scored 

on a nine-point scale and 18 reflex items scored on a four-point scale. These scores were 

classified into seven clusters: habituation, orientation, motor performance, range of state, 

regulation of state, autonomic stability, and reflex. Higher scores on the behavioral clusters 

indicate more optimal functioning while higher scores on the reflex cluster indicate less optimal 

functioning. Missing individual BNBAS items for the six behavioral clusters were imputed as the 

median value of known scores within the cluster associated with the missing item. If all items in 

a particular cluster were missing, then the cluster score was considered missing. Missing 

individual reflex items comprising the reflex cluster were not imputed. 

Separate linear regression models were fit for six of the seven clusters of the BNBAS. Poisson 

regression was used to model the association between urinary metabolite levels and number of 

abnormal reflexes. Independent variables included the average of prenatal metabolite levels, the 

postnatal level, and relevant covariates. Models relating metabolite levels to each cluster score 

were initially fit including the postnatal metabolite level and the average of the two prenatal 

metabolite levels. For eighteen mothers missing one of the two pregnancy urine samples, the one 

remaining was used to represent the average. All models adjusted for covariates, and were rerun 

with creatinine-adjusted metabolite levels. Analyses were performed for the entire study sample 

and stratified by the median age at BNBAS assessment (3 days). 

Of the 378 infants included in this analysis, 47.5% were male. The mean maternal age was 26±5 

years. Most mothers lived at or below the poverty line (64%), were born in Mexico (85%), had 

previous live births (68%), were married (81%), and had not completed high school (81%). Few 

women smoked (5%) or drank alcohol (16%) during pregnancy, although most reported caffeine 

use (75%). Only 1% of the sample reported any illicit drug use during pregnancy. 

The authors observed a statistically significant association between organophosphate pesticide 

exposure and the reflex cluster of the BNBAS, both for the entire sample and for infants assessed 

at greater than 3 days of age. Among the >3 day old infants, increasing average prenatal urinary 

metabolite levels were associated with both an increase in number of abnormal reflexes (total 

DAP: β = 0.53, 95% CI = 0.23, 0.82; DMP: β = 0.41, 95% CI = 0.12, 0.69; DEP: β = 0.37, 95% 

CI = 0.09, 0.64), and the proportion of infants with more than three abnormal reflexes (total 

DAP: OR = 4.9, 95% CI = 1.5, 16.1; DMP: OR = 3.2, 95% CI = 1.1, 9.8; DEP: OR = 3.4, 95% 

CI = 1.2, 9.9). No other associations between either prenatal or postnatal DAPs with any other 

developmental outcomes were observed. 

Study Review 
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Overall, authors performed a well-conducted study of pre- and post-natal pesticide exposure and 

adverse neurodevelopmental outcomes. The prospective nature of the study, use of multiple 

biomarkers of exposure, and extensive data collection allowing for adjustment of potentially 

confounding variables are key strengths of this work. For the purpose of informing the nature of 

the relation between chlorpyrifos per se and these outcomes, use of non-specific OP metabolites 

is a disadvantage. Other limitations of this work are also noted. 

The ascertainment and analysis of urinary organophosphate pesticide markers was appropriate. 

Quantification was conducted at the CDC, using published methods. DAP metabolites are 

sensitive albeit non-specific markers of organophosphate pesticide exposure. They are likely 

more accurate and objective indicators of organophosphate pesticide exposure, relative to other 

ascertainment methods such as self-report. In their discussion, the authors note that, “presently, 

there are no better methods of exposure assessment to capture exposure to multiple 

organophosphate compounds.” It is difficult to infer chlorpyrifos effects specifically using 

metabolite levels, as they also indicate exposure to other organophosphates. The study 

population was likely sustained many other potentially relevant exposures to multiple chemicals 

and other pesticides which may affect development. It is not possible to disentangle the various 

effects from these potential co-exposures and chlorpyrifos in this study. Moreover, the degree to 

which metabolites in prenatal maternal urine samples reflect ‘usual’ levels, and thus usual 

exposure, is uncertain. The authors’ observation of large within-mother variability in pesticide 

marker levels between the two prenatal sampling periods is evidence that a single sample may 

not be representative of longer-term levels in this population, particularly when exposures are 

transient and highly variable. Averaging of the two prenatal exposure measures may ameliorate 

this issue somewhat. 

Metabolite levels in postnatal urines likely reflect early postnatal, rather than in utero exposure, 

given the short half-life of organophosphate pesticides. This is particularly likely for the 27% of 

postnatal samples collected greater than one week after delivery. The effect of errors in the 

assessment of organophosphate pesticide exposure, whether due to mismeasurement of the 

metabolites, imperfect correlation between metabolite levels and pesticide exposure, or 

imputation of missing data, is likely to be an attenuation of the association (a bias toward no 

association) in this prospective study. 

The BNBAS was appropriately administered by one of four examiners trained by a BNBAS 

certified trainer. BNBAS is considered to provide an appropriate evaluation of newborn 

development when administered up to the second month of life, and is considered a valid tool, 

when properly conducted. The cluster approach to classification of BNBAS scores has 

precedence in the literature. Missing BNBAS data points were imputed, if possible. Indeed, there 

was a substantial amount of missing outcome data, due largely to the particular requirements of 

the assessment (e.g. that the infant be asleep immediately prior to the assessment of habituation 

and that the infant by crying prior to assessment of consolability and self-quieting). For example, 

fifty-four percent of the sample was missing all habituation items and an additional 22% required 

at least one imputation. Forty percent of the sample required at least one imputation of the four 

items comprising the regulation of state cluster, with 13, 25, and 0.5% of the sample requiring 

one, two, and three items imputed, respectively. Individual items with the most missing values 

for the regulation of state cluster were consolability and self-quieting. Two infants were missing 
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all items comprising the orientation cluster score. The number of abnormal reflexes was 

calculated based on non-missing values only. Reflexes with the most missing values included 

incurvation (9%), nystagmus (7%), and tonic neck reflex (7%). Missing values did not exceed 

4% for the remaining reflex items. Again, the effect of errors in the ascertainment of 

developmental outcomes is likely to result in attenuation of observed associations as it is unlikely 

that missingness in outcome measure is related to pesticide exposure, as examiners were blind to 

the infant’s exposure status and the imputation procedures were unlikely to induce bias. 

The analyses presented were appropriate to the data collected and the research question posed. 

The authors averaged participants’ pesticide marker levels from the two prenatal urine samples 

(if available), and, as a separate variable, the postnatal metabolite level. Sixteen mothers were 

missing the post-delivery urine. In order not to exclude those with missing post-delivery urines 

from the statistical models, results were re-examined with the variable representing post-delivery 

metabolite level excluded from all models. The effect of the missing exposure data is a decrease 

in the precision, and also likely attenuation of observed associations due to measurement error. 

Potential confounders considered in this investigation included primarily predictors of BNBAS 

performance, including: maternal age, pre-pregnancy body mass index, self-reported smoking, 

alcohol, caffeine, and illicit drug use during pregnancy, vitamin use during pregnancy, 

gestational age at which prenatal care was initiated, total number of prenatal care visits, average 

blood pressure during pregnancy, parity, method of delivery, general anesthesia use during 

delivery, breastfeeding initiated after delivery, and poverty level. Other covariates included 

infant sex, age in days at BNBAS assessment, minutes since last fed at BNBAS, and BNBAS 

examiner. Missing covariate values were imputed by random selection of an individual among 

all participants with known values for the covariate and assigning the missing value the selected 

participant’s value. 

Covariates were selected for inclusion in final models if they were statistically significantly 

associated with each of the seven cluster scores in univariate models, (alpha = 0.15). The 

authors used a less conservative alpha level as a cut-off for selection of covariates, a somewhat 

more conservative approach to covariate selection, relative to use of the conventional alpha level 

of 0.05. However, the use of a statistical test as a means to select covariates from a larger set of 

potential confounders is discouraged in some epidemiologic circles (Miettinen 1976, Breslow 

and Day 1987, Greenland 1989). The assessment of confounding is generally not considered to 

be a statistical endeavor, in part because the association between the confounder and the outcome 

is just one factor that determines the degree of confounding, and also because the statistical 

significance of an association is a function not only of the magnitude of the association but also 

of other factors (e.g., sample size, and sample variability) which are irrelevant to bias due to 

confounding. Moreover, the use of significance testing in the selection of confounders treats 

false negative errors (i.e. the deleting a confounder) as secondary to false positive error 

(inclusion of a non-confounder). This implicit ranking of error is backward, as deleting a 

confounder introduces bias and is justified only if the bias induced by its exclusion is tolerably 

small or deemed to be worth the precision gain, whereas including a non-confounder only 

(potentially) reduces precision by increasing the size of the model. This is a small consideration 

in this study, and different approaches are not likely to qualitatively change the study findings or 

conclusions. Birth weight and gestational age were considered potential causal intermediates in 
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this relation, and not confounders. 

Regardless of the confounder evaluation conducted by the investigators, residual confounding 

due to poorly measured or unmeasured confounders is likely to have influenced the observed 

associations. Missing covariate data were imputed. The resulting analysis is relatively more 

precise, owing to the larger sample size analyzed, but this statistical efficiency comes at the trade 

off of bias due to residual confounding by imperfectly measured covariates. Socioeconomic 

conditions and other difficult to measure determinants of early life development may further 

confound the reported associations in the direction of more deleterious effects associated with 

the organophosphate metabolites and with unknown magnitude. The potential for this bias may 

be mitigated somewhat due to restriction in the variability of these confounding factors in this 

relatively homogeneous study population. 

The generalizability of the findings to other populations may be limited, due to the particular 

nature of this study population with respect to factors which may modify the association between 

organophosphate exposures and infant development. 

The results of this investigation are suggestive of a detrimental association between prenatal 

organophosphate exposure, as measured by maternal urinary metabolite levels, and abnormal 

reflexes, as measured by the BNBAS, particularly in infants assessed after the first 3 days of life. 

This study may be limited in its direct contribution to the literature base regarding the specific 

association between chlorpyrifos and developmental outcomes; although approximately 500,000 

pounds of organophosphate pesticides are applied annually in Salinas Valley, chlorpyrifos 

constitutes only a small percentage of the pesticides applied. Although the prospective study 

design and reasonable methods protect from certain false-positive inducing biases, there remains 

the potential of residual confounding in the direction of an adverse effect on development by 

unmeasured factors. 
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Article 7. Rauh et al. (2006) 

Impact of Prenatal Chlorpyrifos Exposure on Neurodevelopment in the First 3 Years of 

Life Among Inner-City Children. Virginia A. Rauh, Robin Garfinkel, Frederica P. Perera, 

Howard F. Andrews, Lori Hoepner, Dana B. Barr, Ralph Whitehead, Deliang Tang, Robin 

W. Whyatt. Pediatrics. 18(6): 1835-1859. 

Study Summary 

To evaluate the affect/impact of prenatal exposure to ambient and indoor pollutants including 

pesticides, the investigators conducted a prospective cohort study among inner-city (NYC) 

mothers and their babies born between February 1998 and May 2002. A major aspect of the 

protocol was to assess pesticide exposure, specifically chlorpyrifos, and its impact on pregnancy 

outcome, neurodevelopment, and behavior. Previous publications presented outcomes of this 

cohort such as birth weight and length findings (Whyatt, et al, 2004 and Perera et al, 2003.) This 

publication presents findings focused on the neurodevelopmental assessments made at the 12, 24, 

and 36 month time points. 

The women enrolled into the study were identified during their pregnancy, through the New 

York Presbyterian Medical Center and Harlem Hospital. To be eligible for the study they were 

required to be between the ages of 18 and 35 years, non-smokers (later verified by blood cotinine 

levels,) and without a history of diabetes, hypertension, HIV infection, or drug abuse. They must 

have sought prenatal care by the 20th week of pregnancy and self-identified as African American 

or Dominican. At the time of this report, the authors explain that out of the original 648 pregnant 

women consented, 536 (83%) of them and 254 children were still actively enrolled at the 3 year 

milestone. Of these children, 228 completed evaluations at 12 months of age, 227 at 24 months, 

and 228 at 36 months, with 189 children having full data at all 3 time points. 

The primary source of chlorpyrifos measurements were samples of cord blood collected at 

delivery as well as samples of maternal blood collected shortly after (within 2 days of) delivery. 

These serum levels were supplemented by maternal self-report of residential pesticide use during 

the third trimester of pregnancy. Using findings from previous analyses of chlorpyrifos effects 

on birth weight (Whyatt, et al, 2004 and Perera et al, 2003,) the investigators chose a cord blood 

chlorpyrifos limit of 6.17pg/g to define the exposure groups. Children were categorized as 

having either high (>6.17pg/g) or low (<6.17pg/g) prenatal exposure. 

Two instruments were used to evaluate neurodevelopment and child behavior. The Bayley 

Scales of Infant Development II (BSID-II) was used to generate a Mental Development Index 

(MDI) and a Psychomotor Development Index (PDI) at 12, 24, and 36 months of age. Second, 

the behavior of the children was described by the mother using the 99-item Child Behavior 

Checklist (CBCL) and the resulting syndrome scale scores. Finally, the Home Observation for 

Measurement of the Environment (HOME) instrument was used to assess the quality of the 

child-care environment. This instrument collects data regarding the physical and interactive 

qualities of the child’s home as a measure of mental stimulation and interactions. 

The maternal and cord blood samples also provided cotinine levels to confirm maternal report of 

environmental tobacco smoke (ETS) exposures and lead levels to understand the role lead 
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exposure may play interacting with the outcomes of measure. During their third trimester of 

pregnancy, the women completed a 45-minute questionnaire to collect demographics, education 

and occupational history, income, active and passive smoking, alcohol and drug use during 

pregnancy, and residential pesticide use. From this self-report questionnaire and additional 

analyte measurement in biological sample, authors obtained information regarding potentially 

confounding variables. 

Multivariate linear regression and logistic regression were used to estimate associations between 

prenatal chlorpyrifos exposure and developmental outcomes. General linear modeling of the 

repeated measures (12, 24, 36 month evaluations) was also used to estimate the effect over time 

that chlorpyrifos had on neurodevelopment. All models included prenatal ETS exposure, gender, 

ethnicity, gestational age at birth, quality of home care-taking environment, maternal education, 

and maternal IQ. Other measured co-variables including blood lead levels were evaluated as to 

the potential confounding influence on the main effect, and were not included in final models 

due to lack of statistical or biological evidence of confounding bias. 

The descriptive statistics present the cohort as relatively homogenous with respect to 

socioeconomic variables but showing a wide range of chlorpyrifos exposure. Chlorpyrifos levels 

ranged from undetectable to 63 pg/g with 20.6% of the cord blood results falling into the high 

exposure category. Significant differences in chlorpyrifos exposure were seen across ethnic 

categories with 24.2 % of African American participants and 14.9% of Dominican participants 

classified as highly exposed (p=.02). Birth weight and prenatal exposure to ETS were also 

significantly associated with the exposure categories with infants in the high exposure group 

weighing approximately 211g less than babies in the low exposure group (p <.05) and 56% of 

those in the high exposure group reporting prenatal ETS versus 32% in the low exposure group 

(p = .002). 

When the BSID-II results were analyzed in terms of exposure group, no significant differences 

were seen at the 12 and 24 month time points. At the 36-month milestone, although MDI scores 

did not show a significant difference across the exposure groups, PDI scores were significantly 

lower among children in the highly exposed group (95.69 vs. 101.63, p = .006). Statistically 

significant differences were also seen at the 36 month milestone, in mental (cognitive) and 

psychomotor delays (p = .048 and .002 respectively.) 

When analyzing the 36-month child behavior checklist (CBCL) (behavioral) scores, results from 

the 12 and 24 month milestones were not significantly associated with exposure status. At 36 

months, significant differences were observed in the general category of attention-problems 

(p=.010) and in the more specific DSM-IV scale for ADHD problems (p = .018). 

The multivariate regression models included gender, ethnicity, gestational age at birth, home 

environment, prenatal ETS exposure, maternal education, and maternal intelligence. The authors 

reported that the difference in MDI scores was “marginally significant” (p = .06) with the 

exposed group scoring an average of 3.3 points lower. Among the significant covariants were 

race/ethnicity (at 24 and 36 months), gender of infant (at 12, 24, and 36 months), gestational age 

(at 24 and 36 months), low maternal education (at 36 months), and HOME score (at 36 months) 

with Dominicans, male children, low gestational age, low maternal education (< high school,) 
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and low HOME score all linked to lower MDI scores. It was also reported that environmental 

exposures (chlorpyrifos and ETS) and sociodemographic covariables accounted for 25% of the 

variance seen in the 36-month MDI scores. When the same multivariate regression models were 

calculated regarding the PDI scores, none of the 12 or 24 month PDI scores showed significant 

effects. The 36 month scores did show significant effects for chlorpyrifos exposure, race, and 

gestational age. 

Next, investigators calculated estimates of adjusted risk for developmental delays (MDI and 

PDI) related to chlorpyrifos exposure. Adjustments were made for gestational age, gender, 

ethnicity, maternal education, maternal intelligence, and home environment. The findings 

showed that before 36 months of age, delays were no more likely in the highly exposed group. 

But, at the 36 month milestone, the likelihood of highly exposed children developing mental 

delays were 2.4 times greater (95% CI: 1.12-5.08, p = .02) and motor delays were 4.9 times 

greater (95% CI: 1.78-13.72; p = .002) than those with lower prenatal exposure. 

General linear models (GLM) were fit to describe longitudinal developmental trajectories for 

high and low chlorpyrifos exposure groups, adjusting for race/ethnicity, gender, gestational age, 

maternal educational level, HOME score, and ETS exposure. The GLM results were consistent 

with the 12, 24, and 36 month linear regression analyses indicating that associations between 

developmental scores and chlorpyrifos were most pronounced at 3 years of age. The authors 

describe an initial decrease in scores in both groups between the ages of 12 and 24 months, but 

remark that such decreases are commonly observed among low-income groups followed by a 

rebound in scores after 24 months of age. The rate of increase in the MDI scores after 24 months 

was greater among the low exposure group than the high exposure group but was not 

significantly greater (p = .23). The GLM analysis for PDI scores showed a significant effect of 

chlorpyrifos exposure over time with an estimated deficit of approximately 7 points by age 36 

months (p = .01). 

 

The authors summarize three main findings from the study: 1) by age 3, the more highly 

exposed children demonstrated mental and motor delays; 2) the observed developmental 

trajectories for PDI and MDI scores confirmed that the adverse impact on cognitive and motor 

development increased over time; and 3) by age 3, highly exposed children were more likely to 

demonstrate clinically significant attention problems. 

Study Review 

The investigators conducted a well-designed prospective cohort study and retained a high 

percentage (83%) of their participating mothers over the three year follow-up period. With very 

narrow inclusion criteria, the investigators selected a homogenous cohort of minority, inner-city 

women, most of whom fall into low income and low education categories. This homogeneity 

serves to equalize the study comparison groups with regard to potential measured and 

unmeasured confounders, but may also limit the generalizability of the findings. 

While the high retention rate of the mothers provides as stable study population, the authors do 

not address that only 53% of the children from these mothers reached the three year milestone 

with study data collected. It is unclear what, if any, percentage of these children did not survive, 
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were lost to follow-up, or were too sick/affected by neurodevelopmental delays to participate. 

Authors did not provide information concerning the degree of missingness by chlorpyrifos 

exposure category in the published analyses, however in supplemental analyses suggested by the 

2008 FIFRA SAP on chlorpyrifos authors did provide this information (See Appendix 4-1). In 

these additional analyses, authors concluded the degree of missingness was relatively equal by 

exposure category and therefore not likely to have imposed a selection bias. 

As discussed by the authors, one weakness of the study is the reliance on a single exposure level 

(prenatal/cord blood.) It is acknowledged that this snapshot of exposure may not accurately 

reflect both pre- and post-natal exposure levels, although in general, for chronic exposures with a 

high prevalence and consistent frequency of use as indicated in this study population, a single 

measure may be sufficient to accurately rank participant exposure levels. The authors further 

explain that previous analyses of this cohort showed that exposures to chlorpyrifos did not show 

significant variation throughout pregnancy (Whyatt et al., 2007) and therefore concluded that this 

single measure can accurately represent prenatal exposure. While this may accurately represent 

prenatal exposure, there is no control for exposure over the subsequent 3 years which may be 

critical, especially as the authors note that their GLM analyses show a critical developmental rebound period between 24 and 36 months. From this study, it is unclear if an increase or decrease 

in exposure during the period of early childhood could also influence the neurodevelopment of 

these children. Finally, the creation of a dichotomous exposure variable also brings limitations 

to the interpretation of the study results due to the amount of within-group variation likely 

present using this method of exposure classification. By setting the cut-off at 6.17pg/g, there are 

members of their “low exposure” group who may approximate high exposure levels and vice 

versa, resulting in non-differential measurement error. 

The authors also address limitations to the sensitivity and predictive validity of the 

developmental tests, especially among children less than 3 years of age. Although, these tests 

are considered to be most valid among school-aged children they are also used as indicators for 

early intervention to prevent continued developmental delay. The authors describe a statistically 

significant reduction in scores associated with high exposure levels but they do not discuss 

whether this 7-point deficit is clinically relevant in terms of diagnosing or treating a child at this 

stage of development. 

Also complicating this analysis is the pervasive, non-specific nature of neurological effects and 

the difficulty in attributing causal pathways related to just one environmental exposure. 

Although the authors site previous research linking chlorpyrifos exposure to inhibition of neural 

growth (Dam, 1998) it is still difficult to attribute developmental and behavioral effects to a 

single exposure variable measured at one point in time. In future studies it would be 

advantageous to include other potential covariates such as family history of developmental delay 

or attention disorders (especially among parents and siblings) as well as other co-morbidities not 

adjusted for by design (i.e, restriction using eligibility criteria to exclude co-morbidities). Not 

only is the etiology of attention and related behavioral disorders not well understood but also the 

diagnosis of these disorders is often influenced by complex outside forces. 

This well-designed study provides suggestive findings linking prenatal chlorpyrifos exposure 

with neurodevelopmental deficits at the three years of age. The lack of outcome information for 
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a high percentage of cohort children is a concern which is not directly addressed by the authors. 

The use of a single exposure estimate (which likely led to non-differential exposure 

misclassification bias in this study) and an imprecise outcome characterization (although a 

clinical tool, neurodevelopmental milestones are ‘soft’ outcomes to measure) may have an 

accumulative effect to underestimate the true effect (bias results toward to null value), if a true 

effect exists. As mentioned previously, at the time of this publication, these analyses covered 

only a snapshot of the findings of the ongoing cohort study. The authors allude to the fact that 

they will use the subsequent evaluations, especially the 7-year scores to further describe the 

effects of chlorpyrifos exposure on neurodevelopment and behavior. 
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Works Cited 

Whyatt RM, Rauh V, Barr DB, et al. Prenatal insecticide exposure and birth weight and length 

among an urban minority cohort. Environ Health Perspect. 2004;112:1125–1132 

Perera F, Rauh VA, Tsai WY, et al. Effects of transplacental exposure to environmental 

pollutants on birth outcomes in a multi-ethnic population. Environ Health Perspect. 2003;111: 

201–205 

Whyatt, R. M., Garfinkel, R., Hoepner, L. A., Holmes, D., Borjas, M., Williams, M. K., . . . 

Camann, D. E. (2007). Within- and between-home variability in indoor-air insecticide levels 

during pregnancy among an inner-city cohort from New York City. Environ Health Perspect, 

115(3), 383-389. doi: 10.1289/ehp.9546 

Dam K, Seidler FJ, Slotkin TA. Developmental neurotoxicity of chlorpyrifos: delayed targeting 

of DNA synthesis after repeated administration. Brain Res. 1998;108:39–45
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Article 8. Lovasi et al. (2010) 

Chlorpyrifos Exposure and Urban Residential Environment Characteristics as 

Determinants of Early Childhood Neurodevelopment. Gina S. Lovasi, James W. Quinn, 

Virginia A. Rauh, Frederica P. Perera, Howard F. Andrews, Robin Garfinkel, Lori 

Hoepner, Robin Whyatt, Andrew Rundle. American Journal of Public Health. 101(1):63-70 

(2005). 

Study Summary 

Lovasi et al evaluated associations between neighborhood characteristics and early-life 

neurodevelopment in the Columbia Mother’s and Newborn Study Cohort, and assessed whether 

such characteristics confound the association between exposure to chlorpyrifos and 

neurodevelopment previously reported. US Census data (2000) were used to estimate 

neighborhood-level measures of physical infrastructure, socioeconomic status, crowding, 

demographic composition, and linguistic isolation, augmenting previously reported prenatal 

chlorpyrifos exposure, psychomotor development, and mental development data on a New York 

City birth cohort. 

Study participants were recruited during early pregnancy (≤20th week) among African-American 

and Dominican women age 18-35 years, and registered at New York Presbyterian Medical 

Center and Harlem Hospital, in New York City. Women who smoked, had a history of drug 

abuse, diabetes, hypertension, or HIV infection were excluded from participation in the study, as 

were women who resided in New York City for less than 1 year. 

Cord blood plasma chlorpyrifos levels were assessed, and dichotomized as high (greater than the 

third tertile level of 6.17 pg/g) or low (<6.17pg/g). Maternal blood levels of chlorpyrifos were 

used to estimate cord blood levels for the 12% of infants for whom cord blood was not obtained. 

The Bayley Scales of Infant Intelligence–Revised (BSID-II) were used to assess cognitive and 

psychomotor development at age 36 months. Development was reported using a Psychomotor 

Development Index (PDI) and a Mental Development Index (MDI), which served as the 

continuous outcome measurements in the study. 

Participants’ neighborhoods were defined using a 1-kilometer network buffer. Using 2000 US 

Census data, network buffer characteristics were determined, including: the percentage of 

housing units without complete plumbing, the percentage of vacant housing units, the percentage 

of residents below the federal poverty line, the percentage of residents older than 25 years of age 

who completed high school, the percentage of households receiving public assistance, the 

percentage of housing units with one or more residents per room, racial composition, the 

percentage of residents born outside the United States, the percentage of Spanish-speaking 

residents, and the percentage of residents who were linguistically isolated (all household 

members aged 14 years and older have at least some difficulty with English). 

Indicators of building disrepair were self-reported by mothers during prenatal interviews, and 

summed to create an index of disrepair that included one point for each of the following 

problems: holes in ceilings or walls, peeling or flaking paint, water damage, leaking pipes, and 
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lack of gas or electricity in the prior 6 months. 

Generalized estimating equation models were created for the continuous outcomes (PDI and 

MDI) and Bayley Scores of Intelligence at 36 months. Robust standard errors were used to 

correct for possible autocorrelation within community district areas. All models included the 

following potential confounders: gender, gestational age at birth, Dominican versus African 

American ethnicity, maternal education, maternal intelligence quotient, the presence of 

secondhand smoke in the home during pregnancy, and an index of the quality of the home 

environment with respect to caretaking. Using hierarchal regression techniques which allow for 

control of individual level as well as group-level variables, authors were able to further clarify 

the nature of the association between mental and psychomotor development (Bayley scales of 

infant development) and prenatal chlorpyrifos exposure. Other variables including blood lead, 

other environmental exposures among other factors were not considered confounders in the 

prenatal chlorpyrifos and Bayley score association. 

Of 266 children included as participants, 47% were male, 59% were Dominican, and 41% were 

African American. After adjusting for potentially confounding variables, high chlorpyrifos 

exposure (greater than 6.17 pg/g) was associated with a 6.5-point decrease in PDI and a 3.3-point 

decrease in MDI at age 36 months, as previously reported. These associations remained 

statistically significant and similar in magnitude in models additionally adjusting for dilapidated 

housing and neighborhood characteristics. 

Study Review 

This study helps clarify the nature of the relation between prenatal chlorpyrifos exposure and 

childhood neurodevelopment by controlling for the potential influence of socioeconomic status 

(SES) using advanced statistical techniques. Unique to this research, authors were able to 

measure variability due to SES using several different variables measured at both the individuallevel and the group-level. This work enhances the precision and accuracy of the epidemiologic 

measures previously reported. 

The authors note that participants lost to follow-up were not significantly different from 

continuing participants with respect to maternal age, ethnicity, marital status, education, income, 

or gestational age and birth weight of the newborn; authors cited a retention rate at the 3-year 

follow-up of 83%. How they arrived at that number is unclear. Originally, 648 pregnant women 

consented to participate and 327 children completed a developmental assessment at 

approximately 36 months of age. In this report, analyses were conducted among 266 children 

with available chlorpyrifos exposure data, yielding an effective retained proportion of 41%. This 

leaves open the possibility of selection bias resulting from loss to follow-up; one may only 

speculate as to the magnitude and direction of such bias. 

The direct measurement of cord blood chlorpyrifos in this study is a substantial strength, 

compared to the assessment of non-specific markers as assessed in similar studies of 

organophosphate pesticide and child neurodevelopment. Umbilical cord blood was obtained at 

delivery for 88% of the children in the study. For the remaining 12% for whom no cord blood 

was obtained, cord blood plasma chlorpyrifos was estimated from maternal plasma levels using 
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blood sampled two days prior to delivery. Any chlorpyrifos measurement error resulting from 

this estimation is almost assuredly non-differential with respect to cognitive development 

outcomes. Children excluded for missing chlorpyrifos exposure data were similar to the analytic 

sample except that excluded children had more indicators of building dilapidation, so the 

likelihood of selection bias of large magnitude is unlikely. The decision to dichotomize cord 

blood chlorpyrifos levels using the highest tertile as a cut-point reduces the ability to assess 

exposure-response relationships, and may somewhat decrease statistical efficiency relative to 

using a continuous measure. If a large number of participants had levels of chlorpyrifos below 

the level of detection, however, using a categorical approach dispenses with the need to estimate 

or otherwise define exposure at low levels. The prenatal exposures assessed in this study are 

likely correlated with other prenatal exposures and factors in the postnatal environment which 

themselves predict cognitive development. This presents a challenge for a causal interpretation 

of prenatal chlorpyrifos exposure per se, as the agent and time period responsible for observed 

cognitive deficits. 

The Bayley Scales of Infant Development–Revised (BSID-II) is a widely used norm-referenced 

developmental test for young children, it can be used to diagnose developmental delay, and it is 

known to be sensitive to the effects of toxic exposures such as low-level intrauterine lead. The 

assessment was appropriately administered, with each child being evaluated under controlled 

conditions by a trained bilingual research assistant. Inter-observer reliability was assessed but 

data were not presented in this report. The authors note that, when administered at age 3 years, 

the BSID-II demonstrates only moderate predictive power for subsequent intelligence and school 

performance, but cite previous literature as evidence that the test is nevertheless clinically useful 

for children performing in the subnormal range. 

The statistical analyses were generally appropriate. Missing data on covariates were estimated 

using multiple imputations, and the variance estimates presented appropriately reflect the degree 

of uncertainty caused by missing covariate data. Robust standard errors were estimated to 

account for potential autocorrelation. The authors presented multiple model results, each with a 

different indicator of neighborhood character. Only neighborhood covariates representing 

socioeconomic conditions (i.e. percentage of individuals living in poverty, and percentage with 

high school education) were statistically significant predictors of PDI and MDI. Interestingly, 

and without explanation, the authors did not present the results of a model which simultaneously 

included all of the neighborhood characteristics as covariates. Presumably, this was done for the 

sake of parsimony, as one could argue against the inclusion of covariates considered not to be 

predictors of cognitive development. Alternatively, these factors were, a priori, considered 

potential confounders, and the use of statistical significance tests in the assessment of 

confounding is discouraged in many epidemiologic circles. 

The observation that neighborhood characteristics did not confound the observed association 

between chlorpyrifos levels and cognitive development in this study has several possible 

explanations. This was a relatively homogenous study population living in the New York City 

Area, and it is possible that confounding is limited by the small degree of variability in 

neighborhood characteristics between those with and without high exposure levels of 

chlorpyrifos. It is also possible that the neighborhood characteristics are measured with error, 

such that they fail to capture aspects of socioeconomic condition, neighborhood composition, 
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and neighborhood condition that effect cognitive development, even within this context of 

measurement of several individual-level as well as group-level SES measures. This residual 

bias, if it exists, could (at least partially) explain the decrement in cognitive development 

associated with chlorpyrifos exposure, assuming that poorer neighborhood conditions are both 

predictors of reduced cognitive function and comparatively more prevalent among those with 

high chlorpyrifos exposure. The first of these two explanations has no effect on internal validity; 

under the assumption of no other bias, the observed association is unbiased, but external validity 

may be limited due to the restricted study population. The investigators’ methods to assess 

neighborhood characteristics were well conceived and well implemented, and reflective of 

commonly utilized variables to measure important aspects of the multi-dimensional characteristic 

of socio-economic status. However, such exposures are notoriously difficult to measure well 

even using these advanced statistical methods and approaches. 

The authors did not assess effect modification of the association between prenatal chlorpyrifos 

exposure and cognitive development by neighborhood conditions, which is an equally, if not 

more, interesting area of inquiry than assessment of confounding. The present study simply does 

not have the sample size/statistical power to address these effects, but, in the discussion, the 

authors speculate that large studies that collect both neighborhood context and exposure data 

could reveal patterns of effect modification. The relative homogeneity of the study population 

may have precluded statistically robust examination of effect modification by factors related to 

SES in this study. 

The setting of the investigation in a sample drawn from low-income African American and 

Dominican communities is both a strength and a limitation of the study. The population is likely 

at high risk of developmental deficits, which increases the power of the study relative to a similar 

study set in a population at low risk. The assessment of confounding by demographic and 

neighborhood characteristics is limited by the restriction of the study sample to low-income 

communities (a weakness), but so too is the bias induced by these potential confounders (a 

strength). The generalizability of the study results may also be limited because participants were 

recruited from a restricted range of neighborhoods in New York City (a weakness), but the study 

population is likely among the most highly exposed and also at risk for cognitive impairment 

(Landrigan, 1990a, 1990b; Landrigan et al., 1999). 

This study represents an extension of previously published work with additional adjustment for 

multiple neighborhood characteristics. The work was well conceived and conducted, 

representing perhaps the best assessment of the association between chlorpyrifos and cognitive 

development in the epidemiologic literature. Chief among its strengths is the direct assessment of 

cord blood plasma chlorpyrifos, the prospective design, and the assessment and control of 

potential confounders. Using the methods of data collection, statistical analyses, and enhanced 

methods to evaluate the role of SES as a potentially confounding variables, authors report no 

substantial difference in the reported association between prenatal chlorpyrifos exposure and 

child neurodevelopment as previously reported (Rauh et al. 2006). 

 
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Works Cited: 

Landrigan, P. J. (1990a). Health effects of environmental toxins in deficient housing. Bull N Y 

Acad Med, 66(5), 491-499. 

Landrigan, P. J. (1990b). Housing and health: conclusions and challenges for the future. Bull N Y 

Acad Med, 66(5), 587-591. 

Landrigan, P. J., Claudio, L., Markowitz, S. B., Berkowitz, G. S., Brenner, B. L., Romero, H., . . 

. Wolff, M. S. (1999). Pesticides and inner-city children: exposures, risks, and prevention. 

Environ Health Perspect, 107 Suppl 3, 431-437. 

 
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Article 9. Engel et al. (2011) 

Prenatal Exposure to Organophosphates, Paraoxonase 1, and Cognitive Development in 

Childhood. Stephanie M. Engel, James Wetmur, Jia Chen, Chenbo Zhu, Dana Boyd Barr, 

Richard L. Canfield, and Mary S. Wolff. Environmental Health Perspectives 2011; 

119:1182-1188 

Study Summary 

This is the third of three published analyses of prenatal pesticide exposure and child growth and 

development in the Mount Sinai Hospital Children’s Environmental Health Study. The first 

(Berkowitz et al 2004) reported on assessments of birth weight, length and head circumference. 

The second (Engel et al 2007) reported on assessments of infant neurocognitive development and 

reflexes shortly after birth. In this study, Engel and colleagues extended their 2007 to include 

analysis of PON1 activity, PON1 polymorphisms, and cognitive development at ages 12 and 24 

months and 6–9 years. 

Of the 404 women originally enrolled between May, 1998 and July, 2001 (n = 404), a subset (n = 

360) had organophosphate metabolite levels quantified in urine samples collected during the 

third trimester. Prenatal maternal blood and cord blood samples were analyzed for PON1 activity 

and genotype. Children of these mothers returned for neurodevelopment assessments at ages 12 

months (n = 200), 24 months (n = 276), and 6–9 (n = 169) years of age. Participants in this study 

were predominantly young, unmarried, Hispanic women with low educational attainment. Most 

women delivered term infants of normal birth weight, as prematurity and very low birth weight 

were exclusion criteria for study enrollment. 

The study population was drawn primarily from East Harlem and consisted largely of young 

Hispanic women (predominantly Puerto Rican), but also included African-American and 

Caucasian women. Mothers were enrolled during early pregnancy from the Prenatal Clinic and 

two private practices at Mount Sinai Hospital from May 1998 to July 2001. The study included 

only primiparous with singleton births. Mothers were excluded if they had any of the following: 

an initial prenatal visit after 26 weeks of gestation, serious chronic diseases, a serious pregnancy 

complication that could affect fetal growth and development; alcohol consumed greater than two 

alcoholic beverages per day, illicit drug use. Mothers and infants were also excluded if the child 

was born with a congenital malformation or severe prematurity. A total of 479 prenatal patients 

were recruited; 75 were excluded because of medical complications, premature birth, an infant 

with birth defects, inability to collect biologic specimens before birth, change of hospital or 

residence outside New York City, or refusal to continue to participate. 

A questionnaire was administered to participants during their third trimester of pregnancy to 

obtain information on environmental exposures, sociodemographic characteristics, obstetrical 

and medical history, and lifestyle factors. 

Generalized linear models were used to analyze the relationship between total diethylphosphates 

(DEPs), total dimethylphosphates (DMPs), total dialkylphosphate metabolites (DAPs), and 

subsequent cognitive development evaluated at age 12 and 24 months using the Bayley Scales of 

Infant Development, 2nd edition (BSID-II) and using the Wechsler Preschool and Primary Scale 
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of Intelligence, 3rd edition (WPPSI-III) at approximately age 7 years. The authors also assessed 

modification of pesticide metabolite - cognitive development associations by maternal and child 

PON 1 activity and PON 1 polymorphisms. 

The authors presented their results stratified by level of race/ethnicity and PON1 genotype, 

having observed apparent effect modification by these variables. The authors found that prenatal 

total dialkylphosphate metabolite level was associated with a decrement in mental development 

at 12 months among blacks and Hispanics. The associations appeared to be strongest among 

children of mothers who carried the PON1 Q192R QR/RR genotype. In later childhood, 

increasing prenatal total DAP and DMP metabolites were associated with decrements in 

perceptual reasoning in the maternal PON1 Q192R QQ genotype (imparting slow catalytic 

activity for chlorpyrifos oxon), with a monotonic trend consistent with greater decrements with 

increasing prenatal exposure. The authors concluded that their findings are suggestive of an 

association between prenatal exposure to organophosphates and decrements in cognitive 

development, particularly perceptual reasoning, with evidence of effects beginning at 12 months 

and continuing through early childhood, with PON1 being a potentially important potential 

susceptibility factor for these deleterious effects. The authors did not observe any effect 

modification by PON1 enzyme activity. 

The authors noted that the associations they observed between the organophosphate pesticide 

metabolites and cognitive development are similar to those reported in a similar prospective 

cohort study set in an agricultural area of California (the Center for the Health Assessment of 

Mothers and Children of Salinas Study, Eskenazi et al. 2010). 

Urine samples obtained during the 3rd trimester prenatal visit were provided to the CDC to 

quantify levels of six dialkylphosphate metabolites. In cases where individual dialkylphosphate 

metabolite levels were missing because of analytic interference, metabolite levels were imputed 

using regression analysis to predict the missing metabolite on the basis of the other non-missing 

metabolites measured for that woman within the group of correlated metabolites (Eskenazi et al. 

Environ Health Perspect, 2004). Samples below the limit of detection (LOD) were defined as 

LOD/√–2. Diethyl- and dimethylphosphate metabolites were then summed on a molar basis (as 

nm/liter) to obtain total diethylphosphates (DEPs) and total dimethylphosphates (DMPs), 

respectively, and together to obtain total dialkylphosphates (DAP) levels. Twenty-six very dilute 

urine samples with < 20 μg/dL creatinine were excluded from organophosphate metabolite 

analyses. Approximately 97%, 89%, and 90% of the cohort had detectable levels of DAP, DEP, 

and DMP metabolites, respectively. Prenatal organophosphate marker levels were dichotomized 

using median exposure as the cut-off. 

Plasma was separated from samples of prenatal maternal peripheral blood and cord blood 

and used to measure PON1 activity. PON1 polymorphisms in both maternal and child DNA 

were also assessed using the blood samples provided by participants. Prenatal and cord blood 

PON1 activity levels were classified in tertiles for analysis of effect modification of the 

relationship between pesticide marker levels and subsequent cognitive development. 

The Bayley Scales of Infant Development, 2nd edition (BSID-II), was administered to 

participating children at 12 and 24 months of age, approximately, to evaluate mental and 
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psychomotor development. The Mental Development Index (MDI) portion of the assessment 

rates a child’s cognitive ability (i.e. memory, habituation, problem solving, early number 

concepts, generalization, classification, vocalizations, language, and social skills). The 

Psychomotor Development Index (PDI) rates fine and gross motor coordination. Between the 

ages of 6 and 9 years of age, a subset of participating children was again assessed using 

psychometric intelligence tests. Before age 7, children were administered Block Design, 

Information, Matrix Reasoning, Vocabulary, Picture Concepts, Symbol Search, Word 

Reasoning, and Coding subtests of the Wechsler Preschool and Primary Scale of Intelligence, 

3rd edition (WPPSI-III). Composite Verbal, Performance, Processing speed, and Full-Scale IQ 

(FSIQ) scores were derived using age-standardized WPPSI-III norms. Children between the ages 

of 7 and 9 years were administered the Block Design, Similarities, Digit Span, Picture Concepts, 

Coding, Vocabulary, Letter-Number Sequence, Matrix Reasoning, Comprehension, and Symbol 

Search subtests of the Wechsler Intelligence Scale for Children, 4th edition (WISC-IV). Working 

Memory, Processing Speed, and FSIQ scores were derived using age-standardized WISC-IV 

norms. 

The BSID-II was administered at Mount Sinai Hospital. Interviews and examinations were 

conducted in English or Spanish. The WPPSI-III was administered in English or Spanish by one 

of four examiners, as was the WISC-IV. by one of four examiners Both the WPPSI-III and the 

WISC-IV were administered in a private room, without the parent being present. 

Of the 200 children who were administered the BSID-II at approximately 12 months of age 

(mean ± SD, 13.1 ± 1.6 months), one was excluded from analyses because they refused to 

compete a substantial portion of the examination; two children were excluded from the 12-month 

analysis because their parent reported a diagnosis of pervasive developmental disorder and 20 

children were excluded because their corresponding maternal urine samples were deemed to 

dilute to analyze. Of the 177 eligible children who completed 12-month exams, 174 had 

organophosphate metabolites measured in prenatal urine. At the 24-month BSID-II (mean ± SD, 

27.4 ± 4.5 months), 276 children completed the exam. Two were excluded from the 24-month 

analysis because the parent reported a diagnosis of pervasive developmental disorder. Children 

of mothers who submitted very dilute samples of urine (less than 20 μg/dL creatinine) were also 

excluded from analysis (n = 23). Of the remaining 251 eligible observations, 247 had 

organophosphate metabolites measured in prenatal urine. MDI scores for 10 children were not 

usable due to extensive refusals; they were included in the PDI analyses because their scores 

were valid. The authors reported an association between prenatal total dialkylphosphate 

metabolite level and a decrement in mental development at 12 months among blacks and 

Hispanics. These associations appeared to be enhanced among children of mothers who carried 

the PON1 Q192R QR/RR genotype. In later childhood, increasing prenatal total dialkyl- and 

dimethylphosphate metabolites were associated with decrements in perceptual reasoning among 

those with maternal PON1 Q192R QQ genotype, which imparts slow catalytic activity for 

chlorpyrifos oxon, with a monotonic trend consistent with greater decrements with increasing 

prenatal exposure. 

Study Review 

This was a well conducted prospective study conducted in a young, predominantly minority 
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population. The study design, analytic approach, and statistical analyses were appropriate. 

Differential measurement error of exposure and outcomes is likely to be minimal, although other 

sources of bias may have influenced the study findings. 

The dramatic loss-to-follow-up over time is a possible cause of selection bias in this study. The 

authors noted that mothers returning for follow-up assessments tended to be older, with a 

disproportionately low fraction of women in the youngest age category; they also had 

disproportionately higher level of education, on average, relative to the original cohort. 

However, the subset of participants who returned for follow-up visits did not differ substantially 

from the originally enrolled cohort with respect to racial/ethnic composition, breast-feeding 

behaviors, and self-reported alcohol use during pregnancy. 

The authors employed a reasonable set of exclusion criteria that likely resulted in recruitment of 

a study population that was relatively homogeneous with respect to confounders of the in utero

pesticide exposure - cognitive development relationship (compared to the general population), 

and the potential for confounding was thus limited by design (restriction). Bias due to 

confounding was also controlled in the analysis, by assessing and adjusting for known risk 

factors for cognitive development that are potentially associated with pesticide exposure, 

including maternal age, race/ethnicity, marital status, education, breast-feeding, child sex, 

alcohol, smoking, or drug use during pregnancy, maternal IQ, a score based on assessment of the 

home environment (HOME), season of urine collection, language spoken in the home, age at 

testing, examiner and urinary creatinine level. The authors considered gestational age at delivery 

and birth weight, not as confounders, but as potential causal intermediates. They used a changein-estimate approach to covariate selection, beginning with a full model, which allowed them to 

consider confounding by a given risk factor in the context of adjustment for other covariates. 

Given the limited sample size in this study, a full model with many covariates may be unstable, 

although it is unclear from the article whether such instability was observed. 

Residual confounding by unmeasured or poorly measured confounders is likely in this study. 

Socioeconomic determinants, which are notoriously difficult to measure well, are likely 

predictive of pesticide exposure, and risk factors for poor cognitive development. The authors 

adjusted for some factors which may be surrogates for socioeconomic status (e.g., race/ethnicity, 

marital status, maternal education), but adjustment for these factors is unlikely to have fully 

accounted for confounding by socioeconomic status. If such residual confounding exists, it is 

likely to result in effect estimates which overestimate the effect of organophosphate pesticide 

exposure. However, the extent of this confounding was likely limited by the recruitment of a 

relatively homogenous study population, vis-à-vis socioeconomic conditions. 

Presence of environmental contaminants in the home, either during pregnancy or after birth, may 

confound the relationship under study. The authors do not discuss co-contaminants present in the 

home during the pregnancy; such factors would likely result in the observation of a greater than 

effect of pesticide exposure on cognitive development if they are positively associated with 

pesticide exposure and predictive of deficits in cognitive development. The authors stated that 

they considered a “Home Observation for Measurement of the Environment” (HOME) score as a 

potential confounder, and, in fact, they included this score in their multivariate models. The 

degree to which this “HOME” score is a good proxy for relevant co-exposures is unclear, and 
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leaves open the possibility of residual confounding by environmental contaminants in the home. 

The authors observed apparent effect modification by maternal race/ethnicity. For example, 

among non-whites, increasing DAP and DMP tertiles of exposure were associated with a 

decrease in the MDI. However, among whites, the opposite was observed; higher metabolite 

levels were associated with an increase in MDI scores. Such an observation may be indicative of 

a true interaction between race/ethnicity and prenatal pesticide exposure, but it may also be 

explained by residual and differential confounding by co-contaminants that are correlated with 

pesticide exposure and race. Indeed, in this study population, race/ethnicity was strongly 

associated with housing type (i.e. public versus private housing), and the authors did, indeed, 

observe similar trends in the effect estimates when stratifying by housing type rather than 

race/ethnicity. The authors speculated that this observed heterogeneity may indicate that whites, 

or those living in private or owner-occupied housing, may have experienced a different source of 

exposure to pesticides or their metabolites that may have contributed substantially to their 

urinary concentrations, and identify pesticide residues from fresh fruit and vegetable 

consumption as one such source. The authors adjusted for seasonal variation in exposure, 

although they note that other time-related variability may influence exposure assessment. 

Although they observed heterogeneity by PON1 genotype, the authors did not observe any effect 

modification by PON1 enzyme activity in blood sampled during the third trimester, as they had 

in their earlier assessments of pesticide metabolites and cognitive development assessed shortly 

after birth. They speculated that this was because genotype is a more stable, long-term predictor 

of metabolism potential, relative to prenatal phenotype as measured by PON1 activity. 

The metabolites assessed in this study are sensitive, but non-specific, markers for chlorpyrifos. 

The analytic methods appear sound. However, they assessed these markers using only a single 

maternal urine specimen taken during the third trimester – a critical period for 

neurodevelopment. If sources and patterns of exposure remain static, a single measurement 

would be a good indicator of pesticide exposure during pregnancy. If, however, sources and 

exposure patterns change over short time-spans, perhaps on the order of days to weeks, such an 

exposure assessment would not necessarily provide a good indicator of the relevant exposure. 

Like the many other sources of error possible in this study, this, too is likely to result in a nondifferential misclassification of the exposure, with respect to the cognitive outcome. Serial 

measurements of urinary biomarkers of pesticide exposure in the third trimester would shed light 

on these patterns, although the benefits of such information are offset by the added cost, 

logistical complexity expense, and burden on the study participants. 

The instruments used to assess neurodevelopment in this study (i.e., the Bayley Scales of Infant 

Development, 2nd edition (BSID-II), the Wechsler Preschool and Primary Scale of Intelligence, 

3rd edition (WPPSI-III), and the Wechsler Intelligence Scale for Children, 4th edition (WISCIV)) are commonly used in both a clinical and research setting and are widely accepted tools. 

In this study, prenatal maternal DEP urinary metabolite concentrations were associated with 

slight decrements in FSIQ, Perceptual Reasoning, and Working Memory between the ages of 6 

and 9 years. Among children of QQ mothers, DAP and DMP urinary metabolite concentrations 

were associated with poorer scores on Perceptual Reasoning and FSIQ. The authors reported 
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stronger Q192R interactions for DAP and DMP urinary metabolites compared to that for the 

DEP metabolites, although the explanation is unclear and may have been influenced by small 

sample size. The authors note that PON1 Q192R exhibits substrate specificity, but the strongest 

interactions were observed for DMP, not DEP (into which chlorpyrifos and diazinon both 

metabolize). Interestingly, researchers state that malathion spraying for mosquitoes carrying 

West Nile virus was a well-publicized source of exposure during the period of enrollment which 

may have increased the levels of DMP in this population. 

The statistical analyses used in the study appear to have been appropriate, although they are not 

explained in detail. The authors stratified their findings by both race/ethnicity and PON 1

genotype, although it is unlikely that the study was sufficiently powered to provide optimal 

statistical power to assess pesticide effects in the presence of such effect modification. To 

maximize the number of participants included in the analysis, the investigators defined a 

combined population of children who came for at least one of the Wechsler psychometric 

intelligence exams. The WISC-IV composite scores were included preferentially, and WPPSI-III 

composite scores substituted if the child did not return for the later exam. Regarding the use of a 

composite population for the outcome evaluations at 6-9 years, the authors report ‘convergent 

validity’ between the WISC-IV and WPPSI-III. In a previous report, the correlation between 

WPPSI-III and WISC-IV FSIQ scores was 0.89; WPPSI-III Verbal IQ and WISC-IV Verbal 

Comprehension, 0.83; and WPPSI-III Performance IQ and WISC-IV Perceptual Reasoning, 

0.79. In the present study, correlations between the composite scores on the WPPSI-III and 

WISC-IV were likewise strong: FSIQ = 0.83, Verbal IQ/Verbal Comprehension = 0.84, and 

Performance IQ/Perceptual Reasoning = 0.78. In their statistical modeling, the authors included a 

variable to indicate whether the WISC-IV and WPPSI-III was used. 

Regarding the measurement of developmental outcomes using the Bayley Score, in general, 

associations observed during the 12-month follow-up were either attenuated or non-existent at 

the 24-month visit, including the observation of race-based heterogeneity. The authors 

speculated that, although exposures likely decreased over time, the 0- to 12-month exposure 

window may have included less hand-to-mouth childhood exposure (from crawling exposure to 

house dust, and/or fresh fruit and vegetable consumption) than in the 12- to 24-month window; 

thus, more childhood exposure in the 12- to 24-month window may modify the effect of prenatal 

exposure. 

The prospective nature of the study is a primary strength. Pesticide exposure indicators and 

outcomes metrics are likely to have been measured with some error, but these errors are likely to 

be non-differential. On average, such non-differential measurement error results in attenuation of 

the measures of association, and are thus unlikely to result in false-positive findings. A further 

strength attributable in part to the prospective design was the investigators’ ability to consider 

potential confounders and effect-modifiers a priori, measure them by design, and adjust 

for/assess their influence in the analysis. 

Importantly, this study was conducted at a time during which important regulatory changes in 

residential use of chlorpyrifos were being implemented (occurring approximately midway 

through the study). As a result, the participating children’s exposures to chlorpyrifos may have 

decreased over time. The investigators reported that they did not observe any time-period 
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interactions, but they did not present data on the presence or absence of temporal changes in 

participants’ pesticide metabolite levels (i.e. comparing those that were enrolled earlier versus 

later in time). In the discussion, they did mention that it was likely that childhood exposure to 

chlorpyrifos and diazinon was lower post-2000 (following the voluntary cancellation of indoor 

residential chlorpyrifos use). To the extent that post-natal exposures were largely reduced, the 

associations observed can be more definitively linked to prenatal exposures. If however, 

pesticide exposures persisted into childhood, it would be less clear whether deficits in cognitive 

functioning observed in this study were caused specifically by pre-natal exposures, rather than 

subsequent exposure early in life, assuming that pesticide exposure is causally associated with 

such decrements. 

In this third study of organophosphate pesticide markers in maternal urine samples collected 

during the third trimester and subsequent cognitive development in children participating in the 

“Mt. Sinai Child Growth and Development Study”, the authors report on findings out to age 

nine. The specter of selection bias induced by loss-to-follow-up is a marked weakness in an 

otherwise well conducted study. Unfortunately, it is difficult to assess the magnitude and 

direction of such bias. Confounding by unmeasured or poorly measured factors is yet another 

limitation, specifically with reference to the measurement of socio-economic factors and other 

environmental co-exposures. Non-specific biomarkers of exposure, as were assessed in this 

study, are likely more objective, sensitive, and likely also more accurate and reliable indicators 

of pesticide exposure, relative to exposure assessment methodologies that rely on self-report. 

However, to the extent that prenatal chlorpyrifos is the true exposure of interest, the assessment 

of non-specific organophosphate pesticide metabolites in a single sample of prenatal urine is a 

primary potential source of exposure measurement error. Because of the prospective design, 

information bias is expected to result in attenuation of the true (unobserved) associations, toward 

(observed) associations that are relatively smaller, or null. The finding of apparent effect 

modification by maternal race/ethnicity in this study could result from differential loss to followup, measurement error in the assessment of actual pesticide exposure, and/or differential residual 

confounding. 

 
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Works Cited 

Berkowitz GS, Wetmur JG, Birman-Deych E, Obel J, Lapinski RH, Godbold JH, Holzman IR, 

Wolff MS. In Utero Pesticide Exposure, Maternal Paraoxonase Activity, and Head 

Circumference. Environmental Health Perspectives. 2004;112:388–391. 

Engel SM, Berkowitz GS, Barr DB, Teitelbaum SL, Siskind J, Meisel SJ, Wetmur JG, Wolff 

MS. Prenatal Organophosphate Metabolite and Organochlorine Levels and Performance on the 

Brazelton Neonatal Behavioral Assessment Scale in a Multiethnic Pregnancy Cohort. American 

Journal of Epidemiology. 2007;165:1397-1404. 

Eskenazi B, Huen K, Marks A, Harley KG, Bradman A, Barr DB, Holland N. PON1 and 

neurodevelopment in children from the CHAMACOS study exposed to organophosphate 

pesticides in utero. Environmental Health Perspectives 2010;118:1775–1781. 
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Article 10. Eskenazi et al. (2007) 

Organophosphate Pesticide Exposure and Neurodevelopment in Young Mexican-American 

Children. Brenda Eskenazi, Amy R. Marks, Asa Bradman, Kim Harley, Dana B. Barr, 

Caroline Johnson, Norma Morga, Nicholas P. Jewell. Environmental Health Perspectives. 

115:792–798 (2007).

Study Summary 

In this article by Eskenazi et al, the authors report on the relationship between prenatal and child 

urinary organophosphate metabolite levels and child neurodevelopment up to two years of age in 

the CHAMACOS cohort. 

Enrollment for the CHAMACOS took place from 1999–2000 at community clinics, and included 

women who were ≥ 18 years old, pregnant at < 20 weeks gestation, Spanish- or Englishspeaking, eligible for low-income health insurance, and planning to deliver at the local public 

hospital. Researcher followed 531 women to delivery of a live-born, surviving, neonate. 

Excluded from the analysis were children that did not have a neurodevelopmental assessment, 

(n=71), who did not have prenatal and relevant concurrent DAP metabolite measurements (n = 

3), were not singletons (n=8), had a medical condition that could affect performance on the 

neurobehavioral assessment (n = 3; deafness, Down syndrome, hydrocephalus). Also excluded 

from analysis were Bayley results with raw scores which were too low for standardization (n=3, 

1, and 1, for the 6-month, 12-month, and 24-month assessments, respectively). 

Maternal/fetal exposure was assessed by measurement of six non-specific organophosphate 

dialkylphosphate (DAP) metabolites in maternal urine samples collected between 5 and 27 

weeks gestation and again between 18 and 39 weeks. Maternal urine was also analyzed for 3,5,6-

trichloro-2-pyridinol (TCPy), a metabolite specific to chlorpyrifos. Post-natal early childhood 

urine samples were also collected, and DAP metabolites quantified, for the participating children 

at the time of the neurodevelopmental assessments. Quantification of organophosphate 

metabolites using gas chromatography-tandem mass spectrometry and isotope dilution 

calibration was conducted by the Centers for Disease Control and Prevention (CDC) labs. 

To asses exposure, investigators measured six nonspecific organophosphate DAP metabolites in 

maternal and child urine: three dimethyl (DMP) phosphate metabolites (dimethylphosphate, 

dimethylthiophosphate, dimethyldithiophosphate); and three diethyl (DEP) phosphate 

metabolites (diethylphosphate, diethylthiophosphate, and diethyldithiophosphate) (Bradman et 

al. 2005). These six metabolites represent the by-products of approximately 80% of OPs used in 

the Salinas Valley. Values below the limit of detection (LOD) were assigned a value of 

LOD/(√2). The urine analysis was completed by the CDC. Maternal prenatal urine was also 

analyzed for specific metabolites including TCPy, a metabolite specific chlorpyrifos. Because a 

large proportion of women had non-detectable levels of TCPy, the chlorpyrifos-specific 

metabolite was categorized into three groups: TCPy less than LOD for both pregnancy 

measurements, and, for those with at least one detectable level, subdivided below and above the 

median of the average pregnancy level. 

The outcome, child neurodevelopment, was assessed using the Bayley Scales of Infant 
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Development (Mental Development (MDI) and Psychomotor Development (PDI) Indices) 

(Bayley, 1993) and the Child Behavior Checklist (CBCL). The Bayley Scales of Infant 

Development were completed at the 6 months (n = 396), 12 months (n = 395), and 24 months (n 

= 372) of age. Both scales were administered in Spanish and/or English by psychometricians 

blinded to exposure. Psychometricians were trained using these standardized protocols and were 

supervised for quality assurance by a clinical neuropsychologist. Assessments were performed in 

a private room at the CHAMACOS research office or in a recreation vehicle (RV) modified to be 

a mobile testing facility. Scores > 1 SD below the mean (i.e., < 85) indicate possible 

developmental delay. 

Mother’s completed the Child Behavior Checklist (CBCL) at the 24 month visit (n = 356). From 

the CBCL report, the authors assessed three scales as indicators of child neurodevelopment: The 

Attention Problems syndrome scale, which includes such items as “can’t concentrate” and “can’t 

sit still”; the DSM-oriented Attention-Deficit/Hyperactivity Disorder (ADHD) scale, which 

additionally includes such items as “gets into everything”; and the DSM-oriented Pervasive 

Developmental Disorder (PDD) scale, which includes such items as “avoids eye contact,” “rocks 

head, body,” and “unresponsive to affection”. Experts consider a score greater than the 98th 

percentile of a national normative sample of clinical significance; they consider a score greater 

than the 93rd percentile of borderline clinical significance. 

Many confounders were ascertained including, intelligence of the mother, maternal depression, a 

measure of how stimulating the child’s environment is, and prenatal exposure to some known or 

suspected neurotoxins. Maternal intelligence was assessed via the Peabody Picture Vocabulary 

Test (PPVT). Maternal depression was assessed using the Center for Epidemiologic Studies 

Depression Scale (CES-D). To measure the quality and extent of stimulation available to a child 

in the home environment, the Infant-Toddler HOME (Home Observation for Measurement of the 

Environment) inventory was completed at the 6-month, 12-month, and partially at the 24-month 

visit. The following known or suspected neurotoxicants were measured: polybrominated 

diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), p,p’-dichlorodiphenyltrichlorethane 

(DDT), p,p’- dichlorodiphenyltrichlorethylene (DDE), and lead. Lead was measured in cord 

blood. PBDEs, PCBs, DDT, and DDE were measured in maternal serum samples collected at 26 

weeks gestation. 

Statistical analyses of the relationship between metabolite levels and Bayley performance were 

conducted using multiple linear regression models and generalized estimating equations (i.e., 

longitudinal models) for MDI and PDI were developed for each of the three assessment time 

points (i.e., 6, 12, and 24 months). Neurodevelopmental scores were modeled as continuous 

variables. Covariates in the models included indicators for the psychometrician conducting the 

assessment and the location of assessment, age at assessment, sex, duration of breast-feeding, 

HOME score, and household income, parity and indicator of maternal intelligence (PPVT score). 

Other covariates that were considered but not included in the analysis were: maternal age, 

education, depressive symptoms, active/passive smoking exposure during pregnancy, regular 

alcohol use during pregnancy, marital status, father’s presence in home, housing density, 

maternal work status, ≥ 15 hours out-of-home childcare/week, birth weight, gestational age, 

abnormal reflexes, PCBs, lead, DDT, β-hexachlorocyclohexane, and hexachlorobenzene. 
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Furthermore, the authors examined interactions between child DAPs and child sex and, because 

they previously observed an association with maternal DDT and 24-month MDI, between 

maternal DAPs and DDT. Finally, authors also reran the models using log-transformed 

creatinine-adjusted metabolites for comparative purposes. 

Child behavior checklist score at the 24-month assessment was modeled using separate 

multivariate logistic regression models for the five child behavior checklist indices, 

dichotomized at the clinical significance level and at the borderline clinical significance level. 

The same covariates that were included in the final Bayley models were used for the CBCL 

models with three exceptions: maternal depression was added, and psychometrician and 

assessment location were dropped, because scores were based on maternal report. 

Of the 348 infants included in this analysis, 49.4% were male. Most mothers were less than 30 

years old at baseline (75.6), born in Mexico and Spanish-speaking (89%), had household income 

below the poverty line (62%). Many of the participating mothers worked in agriculture during 

pregnancy (43%) and had symptoms of depression (51%). Most of the women were married, 

non-smokers, and did not drink alcohol during pregnancy. 

In this study population, prenatal DAP levels were adversely associated with MDI, while early 

life DAP levels were positively associated with MDI. At 24 months of age, these associations 

reached statistical significance (per 10-fold increase in prenatal DAPs: β = –3.5 points; 95% CI: 

–6.6 to –0.5; child DAPs: β = 2.4 points; 95% CI: 0.5 to 4.2). Neither prenatal nor child DAPs 

were associated with PDI or child behavior checklist attention problems. Both prenatal and 

postnatal DAPs were associated with risk of pervasive developmental disorder (per 10-fold 

increase in prenatal DAPs: OR = 2.3, p = 0.05; child DAPs OR = 1.7, p = 0.04). TCPy was not 

associated with any neurodevelopment outcome in this study. 

Study Review 

This study tested whether in utero or postnatal exposures to organophosphate pesticides have a 

detrimental impact on neurodevelopment, as assessed by the Bayley Scales of Infant 

Development and the child behavior checklist (CBCL). The study population was appropriate to 

the hypothesis, given that there were a range of exposure levels to OPs and there was some 

homogeneity in the population, e.g., mostly low SES, Hispanic, agricultural workers. The 

homogeneity of the population restricted confounding by design. For example, another paper 

reported that smoking, alcohol use, and illicit drug use were rare the CHAMACOS cohort. The 

homogeneous population also increased the relative statistical efficiency of the study because it 

is likely at higher risk of deficits in neurodevelopment than the general population (Landrigan, 

1990; Landrigan et al., 1999). However, the exclusion of children who lacked a 

neurodevelopmental assessment (n = 71, approximately 13% of the study population), has the 

potential to induce a selection bias if the reasons for the missing data are determined by exposure 

(or a predictor of exposure) and are also not independent of neurodevelopment. For example, 

participants with high pesticide exposures could have missed neurodevelopmental assessments 

for reasons related to uncontrolled determinants of exposure which are also related to 

neurodevelopment. Comparisons of maternal DAP metabolite levels between those included in 

this analysis and those not included in the analysis were not presented. 
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The exposure assessment methods in this study were good. Quantification of the metabolite 

levels was conducted at the CDC, using published methods. The study measured the 

organophosphate biomarker, DAP metabolites, in maternal and child spot urine samples, and the 

chlorpyrifos specific biomarker, TCPy, in maternal urine only. Measurement of biomarkers in 

urine is a more objective and likely more accurate indicator of organophosphate pesticide 

exposure than other ascertainment methods such as self-report. However, these biomarkers do 

present a few limitations, because study participants could be exposed to less toxic preformed 

DAP or TCPy metabolites (Lu et al. 2005; Wilson et al. 2003; Morgan et al. 2004), and exposure 

to pesticides varies over time and the half-life of chlorpyrifos in the body is short. However, the 

prenatal exposure measures were, with some exceptions, the average of two measurements, and 

thus may better reflect chronic exposure during pregnancy than in studies where only a single 

spot urine sample was collected. Under an assumption of no other biases, the negative findings 

could have resulted from exposure measurement error. 

The outcome measurements, the Bayley Scales of Infant Development and the CBCL, were also 

appropriate. The Bayley Scales of Infant Development is considered a gold standard assessment. 

The assessments were reportedly appropriately administered. Both the MDI and PDI scales were 

administered in Spanish and/or English by psychometricians masked to exposure status of the 

children. Psychometricians were trained using standardized protocols and were supervised for 

quality assurance by a clinical neuropsychologist. Assessments were performed in a private 

room. The influence of inter-observer reliability was controlled by inclusion for an indicator for 

the assessed but data were not presented in this report. 

The Child Behavior Checklist (CBCL) has been widely used in cross-cultural research and 

collects data on a range of behavior problems, yielding scores for several syndrome scales and 

five scales designed to be consistent with Diagnostic and Statistical Manual of Mental Disorders 

(DSM) diagnoses. The three scales used in this assessment were selected by the investigators a 

priori because the effects they measured were similar to effects seen in relation to DAP exposure 

in animal models. The child behavior checklist is limited in that it is reliant on maternal report 

and the authors note the findings from the child behavior checklist are not directly equivalent to a 

DSM diagnosis. 

The statistical analysis used to assess the associations between the markers of exposure and 

neurodevelopment were appropriate. However, the authors did not present nor discuss regression 

diagnostics to assess the degree to which their models met or violated the assumptions implicit in 

linear models (i.e. homoscedasticity, normality of error term distribution, independence of error 

terms, and linearity of the exposure-response relationship), other than to state that they log 

transformed DAP levels. The authors also conducted a host of sensitivity analyses to assess 

influence of modeling decisions on their findings, and generally found their results to be robust. 

For example, they controlled for birth weight, gestational age, and abnormal reflexes which were 

considered causal intermediates, re-ran models excluding low birth weight and preterm infants, 

and ran models with and without adjustment of biomarkers for creatinine levels. The authors also 

assessed effect modification by gender and DDT exposure because population of primarily 

immigrant women who may have been exposed to DDT and other U.S. banned substances in 

country of origin; associations between pesticide markers and the indicators of 
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neurodevelopment did not differ by either of these co-variables. 

To increase the statistical efficiency of the study by preserving the size of the analytic 

population, missing covariate values were imputed by randomly selecting a value from 

participants with non-missing values. Maternal depression had the largest percentage of values 

requiring imputation (5%). Of remaining covariates, between 0% and 1.8% of values were 

imputed. Although a reasonable approach, poorly measured confounders (i.e. residual 

confounding) may also be influenced by imputed covariate data, although perhaps to a lesser 

degree because imputed data may still be preferable to no data at all. 

Bias due to confounding was also controlled in the analysis, by assessing and adjusting for risk 

factors for neurodevelopment that are potentially associated with pesticide exposure, including 

age at assessment, sex, duration of breast-feeding, HOME score, and household income, parity 

and indicator of maternal intelligence. In addition to the variables included in the final models, 

the authors examined potential confounding by several other variables suggested by the literature 

(i.e., maternal age, education, depressive symptoms, active/passive smoking exposure during 

pregnancy, regular alcohol use during pregnancy, marital status, father’s presence in home, 

housing density, maternal work status, childcare), but they did not markedly alter the observed 

associations. Covariates were selected for inclusion in the final analyses if they were related to 

conditions of testing, related to neurodevelopment in the literature and associated (p < 0.10) with 

most outcomes or consistently related to neurodevelopment in the literature, even if not in the 

data. This is a reasonable approach to assessment of confounding. 

The authors’ quantification of environmental co-exposures (DDT, DDE, βhexachlorocyclohexane, hexachlorobenzene, PCBs, and lead) is a significant strength of this 

study. Children and mothers, especially those from economically impoverished environments 

and émigrés from home countries in which these compounds were frequently use in the recent 

past, may be exposed to other environmental agents that may affect neurodevelopment. Potential 

confounding by these exposures, as well as by indicators of fetal development (shortened 

gestation and poorer reflexes) was assessed, and observed associations in these models were 

consistent with the primary findings. 

This study has apparent inconsistencies in the findings which are worth noting. First, while 

DAPs were associated with an increased risk of PDD in this population, post-natal DAPs were 

positively associated with MDI. No explanation was presented by the authors. One possible 

explanation is that children with higher cognitive functioning may be more interactive with their 

environment, leading to higher exposure to pesticide residues, an example of “reverse 

causation”, whereby the temporal order of the exposure and outcome are reversed. The 

measurements of prenatal pesticide markers do not suffer from the same issues of unknown 

temporal order; they clearly preceded the measures of post-natal child development. Another 

possible explanation suggested by the authors is confounding by diet. Children who eat more 

fruits and vegetables may have higher DAP levels, but because of better diets have higher 

functioning. Second, although the investigators observed associations between 

neurodevelopment and the non-specific metabolite markers of organophosphate pesticides 

(DAPs), no such associations were observed for metabolites specific to chlorpyrifos (TCPy), nor 

for a malathion-specific metabolite (MDA). The organophosphate metabolites (DAPs) cannot be 
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traced back to individual pesticides, and the observed associations with DAPs may be attributed 

to other pesticides, oxydemeton-methyl for example, for which no specific metabolite is 

measureable, or may be due to the short-half life and lower storage stability of the pesticide 

specific metabolites as compared to the non-specific metabolites. 

The study has numerous strengths, most notably the prospective design the longitudinal 

assessment of both exposure and outcomes. Strengths in the study design also include the 

quantification of non-specific (DAPs), chlorpyrifos-specific (TCPy) metabolites, and other 

environmental co-exposures. The results of this investigation by Eskenazi et al in the 

CHAMACOS cohort are suggestive of a detrimental association between prenatal 

organophosphate exposure, as measured by maternal urinary metabolite levels and 

neurodevelopment up to 24 months of age. Although the prospective study design and reasonable 

methods protect from certain false-positive inducing biases, there remains the potential of 

residual confounding in the direction of an adverse effect on development by unmeasured factors 

leading to the observation of a false-positive association. The authors reasonably suggest that 

their results should be interpreted with caution because, although they observed adverse 

associations between prenatal DAPs and mental development and pervasive developmental 

problems at 24 months of age, they also observed a positive relationship between 

neurodevelopment and postnatal DAPs. 
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Article 11. Eskenazi et al. (2010) 

PON1 and Neurodevelopment in Children from the CHAMACOS Study Exposed to 

Organophosphate Pesticides in utero. Brenda Eskenazi, Karen Huen, Amy Marks, Kim G. 

Harley, Asa Bradman, Dana Boyd Barr, and Nina Holland. Environmental Health 

Perspectives. 118:1775-1781 (2010). 

In this article by Eskenazi et al. (2010), the authors report on the relationship between PON1

genotype and PON1 activity level and child neurodevelopment, and the potential effect 

modifying role of PON1 status in the relation between prenatal and child urinary 

organophosphate metabolite levels and child neurodevelopment up to two years of age in the 

CHAMACOS cohort. 

Methods and analyses employed in this investigation were similar to Eskenazi et al. 2007, with 

the exception of the measurement of PON1 status in association with this outcome, and 

assessment of interaction between PON1 and DAPs with child neurodevelopment. Enrollment 

for the CHAMACOS took place from 1999–2000 at community clinics, and included women 

who were ≥ 18 years old, pregnant at < 20 weeks gestation, Spanish- or English-speaking, 

eligible for low-income health insurance, and planning to deliver at the local public hospital. 

Researcher followed 528 women to delivery of a live-born, surviving, neonate. Excluded from 

the analysis were children that did not have a neurodevelopmental assessment, (n=71), who did 

not have prenatal and relevant concurrent DAP metabolite measurements (n = 3), were not 

singletons (n=8), had a medical condition that could affect performance on the neurobehavioral 

assessment (n = 3; deafness, Down syndrome, hydrocephalus). Also excluded from analysis were 

Bayley results with raw scores which were too low for standardization (n=3, 1, and 1, for the 6-

month, 12-month, and 24-month assessments, respectively), and participants for whom PON1 

information was not available. The study included 371 mother-infant pairs. 

Maternal/fetal exposure was assessed by measurement of six non-specific organophosphate 

dialkylphosphate (DAP) metabolites in maternal urine samples collected between 5 and 27 

weeks gestation and again between 18 and 39 weeks. Specifically, investigators measured six 

nonspecific organophosphate DAP metabolites in maternal and child urine: three dimethyl 

(DMP) phosphate metabolites (dimethylphosphate, dimethylthiophosphate, 

dimethyldithiophosphate); and three diethyl (DEP) phosphate metabolites (diethylphosphate, 

diethylthiophosphate, and diethyldithiophosphate) (Bradman et al. 2005). These six metabolites 

represent the by-products of approximately 80% of OPs used in the Salinas Valley. Chlorpyrifos 

and diazinon were the most commonly used organophosphates in the region that metabolize to 

the diethyl phosphate metabolite. Values below the limit of detection (LOD) were assigned a 

value of LOD/(√2). Quantification of organophosphate metabolites using gas chromatographytandem mass spectrometry and isotope dilution calibration was conducted by the Centers for 

Disease Control and Prevention (CDC) labs using published, validated, and accepted methods. 

Maternal and umbilical cord blood samples were collected at the time of delivery. Genotyping 

of the PON1-108 and PON1192 SNPs was performed using genomic DNA extracted from blood 

clots and Taqman PCR methods. Two PON1 enzyme activity assays (arylesterase and 

paraoxonase) were performed using spectrophotometric methods. Arylesterase activity measures 

the rate of hydrolysis of the substrate phenyl acetate, and reflects the quantity of PON1 enzyme. 
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Paroaxonase activity measures the rate of hydrolysis of paraoxon, and thus reflects a 

combination of the catalytic efficiency and quantity of the PON1 enzyme. Using triplicate 

samples, quality assurances methods illustrated the coefficient of variation was within the 

acceptable range (<10%). 

The outcome, child neurodevelopment, was assessed using the Bayley Scales of Infant 

Development (Mental Development (MDI) and Psychomotor Development (PDI) Indices) 

(Bayley, 1993) and the Child Behavior Checklist (CBCL). The Bayley Scales of Infant 

Development were completed at the 6 months (n = 396), 12 months (n = 395), and 24 months (n 

= 372) of age. Both scales were administered in Spanish and/or English by psychometricians 

blinded to exposure. Psychometricians were trained using these standardized protocols and were 

supervised for quality assurance by a clinical neuropsychologist. Assessments were performed in 

a private room at the CHAMACOS research office or in a recreation vehicle (RV) modified to be 

a mobile testing facility. Scores > 1 SD below the mean (i.e., < 85) indicate possible 

developmental delay. 

Mother’s completed the Child Behavior Checklist (CBCL) at the 24 month visit (n = 356). From 

the CBCL report, the authors assessed three scales as indicators of child neurodevelopment: The 

Attention Problems syndrome scale, which includes such items as “can’t concentrate” and “can’t 

sit still”; the DSM-oriented Attention-Deficit/Hyperactivity Disorder (ADHD) scale, which 

additionally includes such items as “gets into everything”; and the DSM-oriented Pervasive 

Developmental Disorder (PDD) scale, which includes such items as “avoids eye contact,” “rocks 

head, body,” and “unresponsive to affection”. Experts consider a score greater than the 98th 

percentile of a national normative sample of clinical significance; they consider a score greater 

than the 93rd percentile of borderline clinical significance. 

Many confounders were ascertained including, intelligence of the mother, maternal depression, a 

measure of how stimulating the child’s environment is, and prenatal exposure to some known or 

suspected neurotoxins. Maternal intelligence was assessed via the Peabody Picture Vocabulary 

Test (PPVT). Maternal depression was assessed using the Center for Epidemiologic Studies 

Depression Scale (CES-D). To measure the quality and extent of stimulation available to a child 

in the home environment, the Infant-Toddler HOME (Home Observation for Measurement of the 

Environment) inventory was completed at the 6-month, 12-month, and partially at the 24-month 

visit. The following known or suspected neurotoxicants were measured: polybrominated 

diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), p,p’-dichlorodiphenyltrichlorethane 

(DDT), p,p’- dichlorodiphenyltrichlorethylene (DDE), and lead. Lead was measured in cord 

blood. PBDEs, PCBs, DDT, and DDE were measured in maternal serum samples collected at 26 

weeks gestation. However, these variables were not included in final models. The following 

variables were adjusted in the interaction analysis: age at assessment, sex, parity, breast-feeding 

duration, HOME score, maternal PPVT, and household poverty status. For the MDI and PDI 

models an indicator variable for the psychometrician was included, and maternal depression 

included in the model for PDD. 

Statistical analyses of the relationship between PON1 status and Bayley performance were 

conducted using multiple logistic regression models; interaction terms and stratified analyses 

were presented to evaluate the potential effect modifying role of PON1 status in the DAPs and 
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child neurodevelopment association. Neurodevelopmental scores were modeled as continuous 

variables. PON1 genotype was modeled as a three-level variable, using the wild type as the 

referent. PON1 enzyme levels were categorized as tertiles. Authors log10 transformed DAP 

levels, and averaged the two measures of urinary DAPs taken during pregnancy in the analyses, 

when available (n=22 for which only one sample available). Covariates in the models included 

indicators for the psychometrician conducting the assessment and the location of assessment, age 

at assessment, sex, duration of breast-feeding, HOME score, and household income, parity and 

indicator of maternal intelligence (PPVT score). Authors imputed missing values to preserve 

sample size, particularly in the stratified analyses. Statistical significance of the main effect was 

considered a p-value of 0.05, and significance of an interaction term was considered a p-value of 

0.15. 

Among the selected participants, mothers were generally young age (average 26.4 years, sd 5.2), 

born in Mexico, were predominantly married or living as married, and were poor, 60% living at 

or below the poverty line. Forty-four percent of the women reported working in agriculture 

during pregnancy, 82% resided with a household member who works in agriculture, and 23% 

live near an agricultural operation. Most mothers reported breast feeding, and the duration of 

breast feeding was on average 6 months. Authors detected dimethyl phosphates more frequently 

than diethyl phospahtes. The average score for the MDI was 85 and PDI 98; 14% of children 

evaluated exceeded the concern level of the CBCL. 

Overall, infants with the variant PON1108TT allele who produced lower amounts of PON1 enzyme 

had reduced scores on the MDI and PDI, and increased risk of PDD using the CBCL. Authors 

observed no association with PON1192QQ genotypes in the relation with MDI, PDI or PDD. 

Similarly, authors observed no association between enzyme levels and any measure of child 

neurodevelopment in this study. The interaction between maternal DAP and PON1 genotype 

with the Bayley assessed outcomes was non-significant (p=0.98), however researchers observed 

a trend of lower scores by the number of PON1108 variants alleles. For each 10-fold increase in 

total DAPs, the MDI score reduced by 3.2 PON1108CC, 3.7 PON1108CT, and 5.5 PON1108TT (pinteraction 0.98). A similar trend was observed for the diethyl phosphates, but neither strataspecific estimates nor trend tests were significant. No similar trend was apparent across the 

number of variant alleles at the PON1192 genomic locus. No associations were observed between 

DAPs and PDI by level of the PON1108 genotype. However, there was limited evidence of 

heterogeneity by PON1192 genotype in the relation between DAPs and PDI, but results were not 

significant. 

Study Review

Overall, researchers offer some evidence of a link between infant PON1 status and child 

neurodevelopment state, and authors’ state they cannot conclude the study provides evidence of 

effect modification by PON1 genotype or phenotype status in the relation between DAPs and 

MDI, PDI or PDD in this study. Similarly to the accompanying investigation (Eskenazi et al. 

2007), the study population was appropriate to the hypothesis, given that there were a range of 

exposure levels to OPs and there was some homogeneity in the population, e.g., mostly low SES, 

Hispanic, agricultural workers. The homogeneity of the population restricted confounding by 

design. For example, another paper reported that smoking, alcohol use, and illicit drug use were 
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rare the CHAMACOS cohort. The homogeneous population also increased the relative statistical 

efficiency of the study because it is likely at higher risk of deficits in neurodevelopment than the 

general population (Landrigan, 1990; Landrigan et al., 1999). Authors did not share whether 

DAP levels were similar in mothers of infants for whom the MDI and PDI scores were not 

available; the potential for selection bias therefore cannot be assessed. The measurement of both 

urinary DAPs and PON1 status were well conducted, however limitations previously noted 

regarding the non-specific nature of a DAP measurement to the question of chlorpyrifos toxicity 

and the use of one- or two measures during the latter part of pregnancy as an estimate for 

gestational exposure apply equally to this investigation. 

The outcome measurements, the Bayley Scales of Infant Development and the CBCL, were also 

appropriate. The Bayley Scales of Infant Development is considered a gold standard assessment. 

The assessments were reportedly appropriately administered. Both the MDI and PDI scales were 

administered in Spanish and/or English by psychometricians masked to exposure status of the 

children. Psychometricians were trained using standardized protocols and were supervised for 

quality assurance by a clinical neuropsychologist. Assessments were performed in a private 

room. The influence of inter-observer variability was controlled by inclusion of an indicator 

variable for the psychomatrician. 

The Child Behavior Checklist (CBCL) has been widely used in cross-cultural research and 

collects data on a range of behavior problems, yielding scores for several syndrome scales and 

five scales designed to be consistent with Diagnostic and Statistical Manual of Mental Disorders 

(DSM) diagnoses. The three scales used in this assessment were selected by the investigators a 

priori because the effects they measured were similar to effects seen in relation to DAP exposure 

in animal models. The child behavior checklist is limited in that it is reliant on maternal report, 

and the authors note the findings from the child behavior checklist are not directly equivalent to a 

DSM diagnosis. 

Again, the statistical analysis used to assess the associations between the markers of exposure 

and neurodevelopment were appropriate. However, the authors did not present nor discuss 

regression diagnostics to assess the degree to which their models met or violated the assumptions 

implicit in linear models (i.e. homoscedasticity, normality of error term distribution, 

independence of error terms, and linearity of the exposure-response relationship), other than to 

state that they log transformed DAP levels. The authors also conducted a host of sensitivity 

analyses to assess influence of modeling decisions on their findings, and generally found their 

results to be robust. For example, investigators controlled for maternal depression in the relation 

between pesticide exposure and PDD, and ran models with and without adjustment of 

biomarkers for creatinine levels. 

To increase the statistical efficiency of the study by preserving the size of the analytic 

population, missing covariate values were imputed by randomly selecting a value from 

participants with non-missing values. Maternal depression had the largest percentage of values 

requiring imputation (5%). Of remaining covariates, between 0% and 1.8% of values were 

imputed. Although a reasonable approach, poorly measured confounders (i.e. residual 

confounding) may also be influenced by imputed covariate data, although perhaps to a lesser 

degree because imputed data may still be preferable to no data at all. 
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Bias due to confounding was also controlled in the analysis, by assessing and adjusting for risk 

factors for neurodevelopment that are potentially associated with pesticide exposure, including 

age at assessment, sex, duration of breast-feeding, HOME score, and household income, parity 

and indicator of maternal intelligence. In addition to the variables included in the final models, 

the authors examined potential confounding by several other variables suggested by the literature 

(i.e., maternal age, education, depressive symptoms, active/passive smoking exposure during 

pregnancy, regular alcohol use during pregnancy, marital status, father’s presence in home, 

housing density, maternal work status, childcare), but they did not markedly alter the observed 

associations. Covariates were selected for inclusion in the final analyses if they were related to 

conditions of testing, related to neurodevelopment in the literature and associated (p < 0.10) with 

most outcomes or consistently related to neurodevelopment in the literature, even if not in the 

data. This is a reasonable approach to assessment of confounding. 

The authors’ quantification of environmental co-exposures (DDT, DDE, βhexachlorocyclohexane, hexachlorobenzene, PCBs, and lead) is a significant strength of this 

study. Children and mothers, especially those from economically impoverished environments 

and émigrés from home countries in which these compounds were frequently use in the recent 

past, may be exposed to other environmental agents that may affect neurodevelopment. Potential 

confounding by these exposures, as well as by indicators of fetal development (shortened 

gestation and poorer reflexes) was assessed, and observed associations in these models were 

consistent with the primary findings. 

Similarly to the previous investigation by Eskenazi et al. (2007), the study has numerous 

strengths, most notably the prospective design the longitudinal assessment of both exposure and 

outcomes. Strengths in the study design also include the quantification of non-specific (DAPs), 

PON1 status, and other environmental co-exposures. The results of this investigation by 

Eskenazi et al. (2007) in the CHAMACOS cohort were suggestive of a detrimental association 

between prenatal organophosphate exposure, as measured by maternal urinary metabolite levels 

and neurodevelopment up to 24 months of age, and this investigation presents some additional, 

albeit limited evidence of potential effect modification by PON1 status. While results were not 

significant, sample size within strata of PON1 genotype and question as to the relevance of 

measurement of just PON1 among the suite of enzymes involved in organophosphate 

metabolism remain. Although the prospective study design and reasonable methods protect from 

certain false-positive inducing biases, there remains the potential of residual confounding in the 

direction of an adverse effect on development by unmeasured factors leading to the observation 

of a false-positive association, however less so with respect to PON1 status as these factors 

would not likely be correlated. The authors reasonably suggest that their results should be 

interpreted with caution as the evidence for true effect modification in this relation is weak. 

 
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Article 12. Marks et al. (2010) 

Organophosphate Pesticide Exposure and Attention in Young Mexican-American 

Children: The CHAMACOS Study. Amy R. Marks, Kim Harley, Asa Bradman, Katherine 

Kogut, Dana Boyd Barr, Caroline Johnson, Norma Calderon, and Brenda Eskenazi. 

Environmental Health Perspectives. 118:1768–1774 (2010). 

Study Summary 

Using data collected as part of the CHAMACOS Study, Marks et al conducted a study to 

investigate the association between urinary dialkyl phosphate (DAP) metabolites in pregnant 

women and their children, as a marker of organophosphate exposure, and attention-related 

outcomes among Mexican-American children living Salinas Valley, an agricultural region of 

California. 

Enrollment for the CHAMACOS took place from 1999–2000 at community clinics, and included 

women who were ≥ 18 years old, pregnant at < 20 weeks gestation, Spanish- or Englishspeaking, eligible for low-income health insurance, and planning to deliver at the local public 

hospital. The investigators followed 526 women to delivery of a live born, surviving singleton. 

Excluded from analyses were children who did not have a prenatal DAP metabolite measurement 

(n = 2), had a medical condition that could affect performance on the neurobehavioral 

assessment (n = 3; deafness, Down syndrome, hydrocephalus), were lost to follow-up, or did not 

participate at the 3.5- or 5-year study visit (n = 173) were excluded from the study. The study 

population consisted of the remaining 348 children who had available data at 3.5 and/or 5 years. 

Exposures were assessed by measurement of organophosphate dialkylphosphate (DAP) 

metabolites in maternal and child urine samples. Maternal urine was collected between 5 and 27 

weeks gestation and again between 18 and 39 weeks. The post-delivery urines were collected 

within 1 week of delivery for 73% of the sample, with the remainder obtained up to 176 days 

afterwards. Urine samples were also collected from participating children at the 3.5 year and 5 

year visits. Quantification of organophosphate metabolites was conducted by Centers for Disease 

Control and Prevention (CDC) labs. Total DAP metabolite level was defined as the sum of the 

molar concentrations of the six DAP metabolites, dimethylphosphate (DMP) metabolite level as 

the sum of the molar concentrations of the three dimethylphosphate metabolites only, and 

diethylphosphate (DEP) metabolite level as the sum of the molar concentrations of the three 

diethylphosphate metabolites. Total DAP, DMP, and DEP levels were determined for each 

participant for each of the two pregnancy urine samples and for the children’s urine samples. 

Individual metabolite levels below the limit of detection (LOD) were assigned a value of the 

LOD divided by the square root of two, and this value was included in each sum. Total DAP, 

DMP, and DEP metabolite levels were log10 transformed. “Pregnancy” DAP, DMP, and DEP 

values were created by averaging the two log-transformed pregnancy measures. For 19 women 

with only one DAP measurement in pregnancy, the single measure was used. Several children 

were missing DAP measures at 3.5 years (n = 58) and 5 years (n = 14). 

Attention-related outcomes were assessed in three ways: 1) maternal report of child behavior at 

3.5 and 5 years of age; 2) direct assessment of the child at 3.5 and 5 years; 3) and by a 

psychometrician’s report of the behavior of the child during testing at 5 years. 
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Mothers completed the Child Behavior Checklist (CBCL) for 1.5–5 years of age as part of the 

maternal interview at the 3.5- and 5-year visits to assess emotional/behavioral problems and 

competencies of the children. Attention was evaluated as continuous raw scores on these scales, 

and by the proportion above a cutoff score greater than the 93rd percentile (a borderline clinical 

range). 

Children completed a battery of neurodevelopmental assessments at each visit: The visual 

attention subtest of the NEPSY-II at the 3.5-year visit, and the Conners’ Kiddie Continuous 

Performance Test (K-CPT), during the 5-year neurodevelopmental assessment. The NEPSY-II 

was administered to children by trained psychometricians. Children were asked to circle specific 

images on a page of pictures that included distracters. For data analysis, the authors used 

continuous scores scaled to a normative sample of U.S. children; the age-standardized mean ± 

SD for this subtest is 10 ± 3. The K-CPT is a 7-min computerized test that assesses reaction time, 

accuracy, and impulse control. Briefly, children were instructed to press the space bar when they 

saw any image on the computer screen except a ball. The computer program yields T-scores agestandardized to a general U.S. population (mean ± SD = 50 ± 10) for errors of commission (i.e., 

the child responds when he or she should not), errors of omission (i.e., the child fails to respond 

when he or she should), and hit reaction time. The authors examined T-scores continuously and 

categorically using the cutoff of T-score > 65, which is reportedly considered markedly atypical. 

The program used also combined measures to generate a clinical ADHD Confidence Index score 

(range, 0–100). For statistical analyses, the authors used continuous Confidence Index 

percentiles and selected a cut-point of > 70th percentile (meaning that 70% of children 

performing similarly on the test could be correctly classified as having clinical ADHD). 

Following the 5-year neurodevelopmental assessment, psychometricians blinded to exposure 

status answered several subjective questions evaluating the behavior of the child during the 2-hr 

visit, including four questions derived from the seven-item Hillside Behavior Rating Scale. The 

authors summed responses to two questions assessing motor activity and distractibility to create 

an adapted ADHD symptoms scale. The Hillside Scale is reportedly associated with parent and 

teacher ratings and has been found to add significantly to the clinical prediction of ADHD. For 

analysis the authors created a dichotomized Hillside outcome variable with scores ≥ 7 of 12 

possible points (representing < 10% of children) to flag children displaying a higher degree of 

attention problems based on psychometricians’ observation. 

Statistical analyses were conducted using multivariate linear and logistic regression models to 

evaluate the associations between the markers of organophosphate pesticide exposure and the 

indicators of attention deficits summarized above. Separate models were run with maternal 

DAPs only, child DAPs only, and the two together in the same model. Odds ratios greater than 

one and β-estimates greater than zero indicate positive associations between exposure and 

increased risk of adverse outcome, except for the NEPSY visual attention subtest, where a βestimate greater than zero indicates better performance. All models adjusted for the following 

covariates: an indicator of the evaluating psychometrician (not included in CBCL models), age at 

assessment, sex, maternal education, depressive symptoms, maternal intelligence, out-of-home 

child care, and breast feeding duration. The covariates maternal age, parity, marital status, 

active/passive smoking exposure and regular alcohol use during pregnancy, presence of father in 
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home, maternal work status, and household income were also considered but not included in the 

final models because they did not markedly alter the observed associations and were therefore 

not used in final models. To preserve the size of the analytic population, remaining missing 

covariate values (six instances for maternal depression and three for maternal PPVT) were 

imputed by simple random selection of a value from participants with non-missing values. 

The authors also conducted a number of sensitivity analyses. They reran all models using 

creatinine-adjusted DAP metabolite concentrations. In addition, they added to final models some 

factors potentially on the causal pathway (i.e., birth weight, gestational duration). The authors 

also considered whether controlling for lead (log10-transformed), a known neurotoxicant, altered 

results for DAP concentrations in the subsamples with cord (n = 229) or 2-year (n = 296) lead 

values. Finally, because rates of ADHD vary considerably by sex in the general population 

(Pastor and Reuben 2008) and effects of other toxicants have been found to vary by sex 

(Delaney-Black et al. 2004), the authors tested for interactions between DAP concentrations and 

child sex, using p < 0.15 for the interaction term to determine whether associations of DAP 

concentrations with measures of attention differed for boys and girls. 

Of the 378 infants included in this analysis, 47.5% were male. The mean maternal age was 

26.5±5.2 years. Most mothers were born in Mexico (86%), spoke only Spanish in the home 

(90%), had low income (97% within 200% of the federal threshold for poverty; 63% within 

100%), had not completed high school (79%), were nonsmokers (96%), had previous live births 

(66%), and were married (82%). Many of the participating mothers had symptoms of depression 

(44%). 

In this study population, higher concentrations of organophosphate metabolites in the urine of 

pregnant women were associated with increased odds of attention problems and poorer attention 

scores in their children at age 5 years. Prenatal DAPs were non-significantly associated with 

maternal report of attention problems and ADHD at age 3.5 years but were significantly related 

at age 5 years (CBCL attention problems: β = 0.7 points; 95% CI: 0.2–1.2; ADHD: β = 1.3; 95% 

CI: 0.4–2.1). Prenatal DAPs were associated with scores on the K-CPT ADHD Confidence Index 

> 70th percentile (OR = 5.1; 95% CI: 1.7–15.7 and with a composite ADHD indicator of the 

various measures (OR = 3.5; 95% CI: 1.1–10.7). Some outcomes exhibited evidence of effect 

modification by sex, with associations found only among boys. Children’s concurrent total DAP 

and DMP metabolite levels at 3.5 years and 5 years were unrelated to attention outcomes, and 

but child DEP concentrations at 5 years were adversely associated with the composite measure of 

attention (OR = 2.0; 95% CI:1.1–3.6).

Study Review 

This study tested whether in utero or postnatal exposures to organophosphate pesticides have a 

detrimental impact on attention. The study population was appropriate to the hypothesis, given 

that there were a range of exposure levels to OPs and there was some homogeneity in the 

population, e.g., mostly low SES, Hispanic, agricultural workers. The homogeneity of the 

population restricted confounding by design. For example, another paper reported that maternal 

smoking, alcohol use, and illicit drug use were rare this cohort. The homogeneous population 

also increased the relative statistical efficiency of the study because it is likely at higher risk of 
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deficits in neurodevelopment than the general population. The exclusion of children who did not 

complete the 3.5- or 5-year assessment (n = 173), has the potential to induce a selection bias if 

the reasons for the missing data are determined by exposure (or a predictor of exposure) and are 

also not independent of the development of attention disorders. However, the authors stated that 

the levels of DAPs were similar in the mothers of the children which were not included relative 

to those included in the analysis. The authors also report that distributions of other covariates 

measured before 3.5 years were also similar, except that more boys than girls dropped from the 

study. This provides some indication that selection bias may not be a large factor in this analysis. 

The ascertainment and analysis of urinary organophosphate pesticide markers was appropriate. 

Quantification was conducted at the CDC, using published methods. DAP metabolites are likely 

more accurate and objective indicators of organophosphate pesticide exposure, relative to other 

ascertainment methods such as self-report. Although they are sensitive indicators of exposure, it 

is difficult to infer chlorpyrifos effects specifically from the metabolite levels, as they also 

indicate exposure to other organophosphates. The study population likely sustained many other 

potentially relevant exposures to multiple chemicals and other pesticides which may affect 

development. The six organophosphate metabolites cannot be traced back to individual 

pesticides but, according to the authors, they represent the breakdown products of approximately 

80% of the total organophosphate pesticides used in the Salinas Valley. It is not possible to 

disentangle the various effects from these potential co-exposures and chlorpyrifos in this study. 

Because organophosphate pesticides have short half-lives, the degree to which metabolites in 

urine samples reflect ‘usual’ levels, and thus usual exposure, is uncertain. The prenatal exposure 

measures were, with some exceptions, the average of two measurements, and thus may better 

reflect chronic exposure during the pregnancy. The absence of findings associated with child 

levels may be due, in part, to the fact that the child’s levels are poor indicators of exposure 

during critical windows of exposure with respect to development of attentional difficulties. 

Errors in the assignment of exposure in this prospective study will likely have resulted in 

attenuation of observed associations. The likelihood of these errors is high, and the magnitude of 

the bias induced difficult to assess. Under an assumption of no other biases, the negative 

findings, those for the association between children’s concurrent DAP levels and attention 

outcomes, for example, could have resulted from exposure measurement error. 

The outcome in this study is very difficult to measure. Instead of opting for a single best 

instrument/indicator of attention deficits, the investigators chose to use multiple instruments, and 

also to combine the results of these various instruments to form a composite indicator of 

attentional difficulties. There is no evidence presented in the report that would suggest that the 

outcome assessments were improperly conducted. Nevertheless, outcome measurement error is 

likely. As with the exposure assessment, errors in the assignment of attention deficits in this 

prospective study will likely have resulted in attenuation of observed associations. Again, the 

likelihood of these errors is high, and the magnitude of the bias induced difficult to assess. The 

authors did not report on an assessment of changes in attentional difficulties between the 3.5 and 

5 year visits. If such changes did occur, it would have been interesting to the reader to present 

results of a longitudinal analysis between this change in the outcome, and the child DAP levels 

over time. 
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The statistical analysis used to assess the associations between the markers of exposure and 

attention deficits were appropriate. Model diagnostics were performed and, according to the 

authors, found acceptable. The results of the model diagnostics were not presented. The authors 

also conducted a host of sensitivity analyses to assess influence of modeling decisions on their 

findings, and generally found their results to be robust. 

In addition to those included in final models, the following covariates were considered, but not 

included in multivariate models: maternal age, parity, marital status, active/passive smoking 

exposure, regular alcohol use during pregnancy, presence of father in the home, maternal work 

status, and household income at the time of assessment. Confounding by blood lead levels was 

also assessed in a subset of children with measurements of cord blood (n = 229) and 2-year (n = 

296) lead levels, and found not to influence the DAP and attentional outcomes. 

Covariates were selected for these analyses if they were related to conditions of testing or related 

to attention deficits in the literature and/or associated with more than one outcome (p < 0.10 or 

changes to the main effect coefficient by ≥ 10%). The authors used a less conservative alpha 

level as a cut-off for selection of covariates, a somewhat more conservative approach to 

covariate selection, relative to use of the conventional alpha level of 0.05. However, the use of a 

statistical test as a means to select covariates from a larger set of potential confounders is 

discouraged in some epidemiologic circles (Miettinen 1976, Breslow and Day 1980, Greenland 

1989). The assessment of confounding is generally not considered to be a statistical endeavor, in 

part because the association between the confounder and the outcome is just one factor that 

determines the degree of confounding, and also because the statistical significance of an 

association is a function not only of the magnitude of the association but also of other factors 

(e.g., sample size, and sample variability) which are irrelevant to bias due to confounding. 

Moreover, the use of significance testing in the selection of confounders treats false negative 

errors (i.e. the deleting a confounder) as secondary to false positive error (inclusion of a nonconfounder). This implicit ranking of error is backward, as deleting a confounder introduces bias 

and is justified only if the bias induced by its exclusion is tolerably small or deemed to be worth 

the precision gain, whereas including a non-confounder only (potentially) reduces precision by 

increasing the size of the model. This is a small consideration in this study, and different 

approaches are not likely to qualitatively change the study findings or conclusions. Despite the 

confounder evaluation conducted by the investigators, residual confounding due to mismeasured 

or unmeasured confounders is likely to have somewhat biased the observed associations. 

To preserve the sample size the authors appropriately imputed or replace data for missing 

covariates. Data on maternal depression were missing for 5% of observations and maternal 

intelligence scores for less than 1%. Values of maternal depression measured when the child was 

1 year old were available and substituted for 13 instances where maternal depression when the 

child was 3.5 years was missing. Other missing covariate values (six instances for maternal 

depression and three for maternal PPVT) were imputed by simple random selection of a value 

from participants with non-missing values. The resulting analysis is relatively more precise, 

owing to the larger sample size analyzed, but this statistical efficiency comes at the tradeoff of 

bias due to residual confounding by imperfectly measured covariates. Socioeconomic conditions 

and other difficult to measure determinants of early life development may further confound the 
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reported associations in the direction of more deleterious effects associated with the 

organophosphate metabolites and with unknown magnitude. The potential for this bias may be 

mitigated somewhat due to restriction in the variability of these confounding factors in this 

relatively homogeneous study population. 

The generalizability of the findings to other populations may be limited, due to the particular 

nature of this study population with respect to factors which may modify the association between 

organophosphate exposures and infant development. The organophosphate pesticide exposure 

markers and the prevalence of attention deficits in this low-income Mexican-American 

population sampled from an agricultural area are almost certainly not generalizable to the U.S. 

population, although exposure levels are within a comparable range to the U.S. general 

population (see Section 5.0). 

The study has numerous strengths, most notably the prospective design and the longitudinal 

assessment of both exposure and outcomes. The positive association between organophosphate 

metabolites in the urine of pregnant women and increased odds of attention problems and poorer 

attention scores in their offspring was relatively consistent using different assessments of 

attentional difficulties (i.e. by maternal report, psychometrician observation, and 

neuropsychological testing). Associations were stronger at age 5 years compared to age 3.5 

years, and there was evidence of a stronger association among boys, compared to girls. The 

results of this investigation by Marks et al in the CHAMACOS cohort are suggestive of a 

detrimental association between prenatal organophosphate exposure, as measured by maternal 

urinary metabolite levels, and attentional difficulties at age 5 years. This study may be limited in 

its direct contribution to the literature base regarding the specific association between 

chlorpyrifos and developmental outcomes because the assessment of non-specific 

organophosphate pesticide metabolites (DAPs) is a primary potential source of exposure 

measurement error. Because of the prospective design, information bias is expected to result in 

attenuation of the true (unobserved) associations, toward (observed) associations that are 

relatively smaller, or null. 

 
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Article 13. Rauh et al. (2011) 

Seven-Year Neurodevelopmental Scores and Prenatal Exposure to Chlorpyrifos, a 

Common Agricultural Pesticide. Virginia Rauh, Srikesh Arunajadai, Megan Horton, 

Frederica Perera, Lori Hoepner, Dana B. Barr, and Robin Whyatt. Environmental Health 

Perspectives. 119:1196–1201 (2011). 

Study Summary 

Having previously reported an association between prenatal exposure to chlorpyrifos and 

neurodevelopmental problems at three years of age in the Columbia Center for Children’s 

Environmental Health, Rauh et al now evaluate the relationship between prenatal chlorpyrifos 

exposure and neurodevelopment among cohort children at age seven. 

Study participants were recruited during early pregnancy (≤20th week) among African-American 

and Dominican women age 18-35 years, and registered Hospital, in New York at New York 

Presbyterian Medical Center and Harlem City. Women who smoked, had a history of drug abuse, 

diabetes, hypertension, or HIV infection were excluded from participation in the study, as were 

women who resided in New York City for less than 1 year. The study sample presented in this 

report was recruited between 1998 and 2002, a period which straddles the January, 2001 

implementation of a voluntary cancellation by registrants of indoor residential chlorpyrifos use. 

Of originally 725 consenting women, 535 were active participants in the ongoing cohort study at 

the time of this report. This report uses data from 265 of their children who had reached the age 

of 7 years and had a complete set of data including prenatal maternal interview data, prenatal 

chlorpyrifos marker levels from maternal and/or cord blood samples at delivery, postnatal 

covariates, and neurodevelopmental outcome data. 

Chlorpyrifos levels in umbilical cord blood samples were available for 256 newborns, sampled 

as close to the time of delivery as possible, and within 2 days post-partum. In cases where the 

umbilical cord blood sample was not collected (12% of subjects), cord blood chlorpyrifos levels 

were imputed from mothers’ values. Quantification of chlorpyrifos levels in plasma were 

conducted by the Centers for Disease Control and Prevention (CDC). 

Neurodevelopment at age 7 years was assessed using the Wechsler Intelligence Scale for 

Children, 4th edition (WISC-IV). The instrument measures four areas of mental functioning: the 

Verbal Comprehension Index, the Perceptual Reasoning Index, the Working Memory Index, and 

the Processing Speed Index. These indices are associated with, but distinct from, overall 

intelligence quotient (IQ) and are sensitive to cognitive deficits related to learning and working 

memory, which have been linked to chlorpyrifos exposure in rodent studies. A Full-Scale IQ 

score combines the four composite indices. A General Ability Index score is a summary score of 

general intelligence, similar to Full-Scale IQ, but excludes contributions from both Working 

Memory Index and Processing Speed Index. 

Multivariate linear regression models were used to estimate associations. Chlorpyrifos exposure 

level (picograms per gram) was entered as a continuous variable. The WISC-IV Composite 

Index scores were natural log (ln) transformed to stabilize their variances and to improve the 

linear model fit, based on regression diagnostics. Linear and non-linear functional relationships 
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between chlorpyrifos exposure and each of the log-transformed WISC-IV indices were assessed 

and compared. 

Participating mothers were predominantly from low income households, with 31% failing to 

complete high school by child’s age 7 years, and 66% were never married. The children in the 

study were largely full term and included very few low-birth-weight infants through study 

design. Chlorpyrifos exposure levels as measured in cord blood ranged from non-detectable to 

63 pg/g. 

Unadjusted correlations showed significant inverse associations between chlorpyrifos levels and 

Working Memory (r=-0.21, p=<0.0001); and Full-scale IQ (r=-0.13, p=0.02). Because cord lead 

levels were not significantly correlated with chlorpyrifos levels, and in an effort to avoid 

excluding those with missing lead levels, lead was not included in the regression models. ETS 

was significantly correlated with chlorpyrifos levels (Spearman coefficient 0.113, p=0.01) but 

not with any of the WISC-IV indices. In addition, birth weight was not significantly associated 

with any of the WISC scales and not included in the final models. 

The investigators used both fully-adjusted linear regression modeling and the Least Absolute 

Shrinkage and Selection Operator (LASSO) technique, described as a more parsimonious 

selection of covariates that addresses unstable estimates while avoiding overfitting (Houwelingen 

2001; Tibshirani, 1996.) Both methods yielded very similar estimates, finding associations 

between chlorpyrifos exposure and Full-Scale IQ as well as the Working Memory Index. For 

Full-Scale IQ both techniques generated a coefficient (B) of -0.003 with matching CIs of -0.006, 

0.001 but the P-values were calculated as 0.064 for the LASSO and 0.048 for the fully-adjusted 

model. The fact that both CI’s contain zero while the fully-adjusted model’s P-value is lower, 

suggests that the LASSO technique does in fact shrink unstable estimates towards zero. For the 

Working Memory Index, both techniques generated a coefficient (B) of -0.006 with the LASSO 

calculating a CI of -0.009 to -0.002, p<0.001 and the fully-adjusted model calculating a CI of -

0.010 to -0.002, p=0.003. The investigators articulated these results as showing that a 1 pg/g 

increase in chlorpyrifos exposure was associated with a 0.006 point decrease in the logtransformed Working Memory score and a 0.003 point decrease in the log-transformed FullScale IQ score. In order to understand the magnitude of these effects, the investigators 

calculated the neurodevelopmental deficit associated with the increase in exposure and 

concluded that for each standard deviation increase in exposure (4.61pg/g) there is a 1.4% 

reduction in Full-Scale IQ and a 2.8% reduction in Working Memory. Authors explain that their 

full model results did not show any significant interactions between ETS or polycyclic aromatic 

hydrocarbons (PAH) with measures of prenatal chlorpyrifos exposure during the prenatal period 

and intelligence measures in this study. 

The investigators conducted a sensitivity analysis to determine if the association between 

chlorpyrifos levels and Working Memory Index was partially due to the effect on general 

intelligence. To do this, they looked at the General Ability Index of the WISC-IV, a general 

intelligence scale calculated without the inclusion of the items included in the Working Memory 

Index or Processing Speed Index. When the General Ability Index was added to the linear 

regression model, the estimated chlorpyrifos effect did not change and they did not observe an 

interaction between chlorpyrifos and General Ability Index. From this they concluded that the 
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Working Memory Index is a targeted measure, independent of general intelligence. 

The investigators further explored their findings by conducting a supplemental analysis to 

evaluate whether the observed associations between chlorpyrifos and the Working Memory 

Index were related to underlying behavioral problems. They used the results of the Child 

Behavior Checklist and its clinically-oriented behavioral scales and concluded that there was no 

evidence of mediation caused by behavioral issues. 

Lastly the investigators addressed the possible influence of their method and frequency of use of 

LOD imputations. To do this they recalculated all of the association estimates using only those 

records that had detectable chlorpyrifos levels and explained that this method would provide an 

unbiased estimate of the association (Little, 1992.) They did not find any consistent differences 

in estimates between the full sample (including imputed exposure levels) versus those calculated 

without those imputed levels. 

Study Review 

This was a very well conducted study with numerous strengths including selection of a relevant 

and appropriate cohort, the prospective study design, direct measurement of chlorpyrifos, 

comprehensive consideration of confounding, and analytic design choices which maximize 

statistical efficiency. However, some limitations are also noted. 

The direct measurement of chlorpyrifos in cord blood and personal air samples, rather than selfreported indirect exposure assessment or use of biomarkers of organophosphate pesticide 

exposure that are not specific to chlorpyrifos exposure is a notable strength of the Columbia 

Mother’s and Newborn Study cohort. Unlike previous analyses among this cohort that used a 

dichotomous variable (low vs. high exposure) to classify exposure status, these findings employ 

a continuous chlorpyrifos level, which provides a more meaningful look at the data including the 

ability to assess potential threshold effects and linear and non-linear dose-response trends. The 

investigators rigorously evaluated their methods for imputing values for undetectable 

chlorpyrifos levels; analyses illustrated imputation methods were valid. However, regarding the 

LOD estimations, the authors did not describe the methodology used (e.g. bootstrap estimation), 

if any, to incorporate the added uncertainty (i.e. added imprecision to estimates) inherent in the 

use of imputed data into the estimation of the corresponding variance estimates. However, errors 

in estimation of cord blood chlorpyrifos levels were likely to have been non-differential with 

respect to the indicators of childhood neurodevelopment and intelligence. 

A limitation of the specific biomarker exposure indicators in this study is the single sampling 

period (at delivery). It is not clear to what extent this single measurement reflects exposures over 

critical windows during pregnancy. If the exposure is chronic, a biomarker measured at a single 

time point can provide a representative dosimeter, even if the toxicant has a short half-life, as is 

the case for chlorpyrifos. However, if pesticide exposures are sporadic or otherwise vary over 

short time scales, the biomarker measurement will not be representative of “usual” exposure, or 

of the exposure during critical periods of fetal neurodevelopment. In previous publications from 

this same cohort, the investigators explain that analyses were performed showing that, among 

these women, exposures to chlorpyrifos did not show significant variation throughout pregnancy 
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(Whyatt et al, 2007) and therefore concluded that this single measure can accurately represent 

prenatal exposure. This assumption is further assessed in subsequent validation studies 

conducted by Whyatt et al (2007 and 2009) within this cohort. 

Whether or not these cord blood measurements accurately represent prenatal exposure, there is 

no control for exposure over the subsequent 7 years which may be critical, especially as the 

process of neurocognitive development is fluid and rapid during these early childhood years. 

From this study, it is unclear if an increase or decrease in exposure during this influential 

postnatal period, caused by age-related or seasonal dietary changes or changes in pesticide 

application, could also impact the neurodevelopment of these children. Because this imprecise 

measure of exposure is unlikely to vary differentially, this error would bias towards the null. 

Another influence unaccounted in the analysis is the effect of increased awareness of the risks of 

pesticide exposures over the time period of cohort follow-up in this study (1998-2004) and the 

potential for differential exposure or outcome (CBCL) measures. It is feasible to assume that 

those parents of children with suspected or diagnosed neurodevelopmental issues in early 

childhood would be more aware of issues concerning organophosphate exposure, and thus more 

likely to subsequently reduce their child’s exposures, especially through a reduction in pesticide 

or dietary sources in childhood. If this is true, and post-natal exposures play a role in childhood 

neurodevelopment measured in this study, those children with neurodevelopmental deficits may 

be less likely to experience post-natal exposures than those not diagnosed, thus making the prenatal effects appear weaker. Similarly, mothers of infants in the high exposure category may 

differentially report behavioral issues on the maternal-reported child behavior checklist . Either 

type of differential measurement error may lead to information bias, with unknown 

consequences on the estimate of risk in these studies. It would be meaningful to assess the 

mother’s awareness of the effects of organophosphate exposures as well as details regarding 

dietary exposures and how they may have changed over the 7-year period. 

The prospective nature of the study limits the influence of errors in the exposure assessment on 

the association observed, such that mismeasurement of chlorpyrifos exposure would likely be 

non-differential with respect to the indicators of neurodevelopment and intelligence at age 7 

years. The voluntary cancellation of the residential use of chlorpyrifos introduces exogenous 

variation in the exposure of interest. After the cancellation, levels of chlorpyrifos in personal 

and indoor air samples in this cohort decreased by more than 65%, and plasma blood levels 

dropped by more than 80%. 

The Wechsler Intelligence Scale for Children (WISC-IV) instrument measures four areas of 

mental functioning: the Verbal Comprehension Index is a measure of verbal concept formation, a 

good predictor of school readiness; the Perceptual Reasoning Index measures nonverbal and 

fluid reasoning; the Working Memory Index assesses children’s ability to memorize new 

information, hold it in short-term memory, concentrate, and manipulate information; and the 

Processing Speed Index assesses ability to focus attention and quickly scan, discriminate, and 

sequentially order visual information. The authors explain that the sensitivity of the WISC-IV 

among 6 to 7.5 year old children has been validated in previous neurodevelopmental studies of 

environmental lead exposure, even at low-dose levels (Chiodo et al. 2004). However, WISC-IV 

scores are also known to be influenced by socioeconomic background and/or child behavior 
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problems, particularly those related to anxiety (Wechsler, 2003). The narrowly restricted cohort 

and evaluation of behavioral issues, which may in part reflect anxiety issues, within this study 

somewhat limits this potential bias of the outcome assessment tool in this study. 

One potential weakness is that the investigators do not give the details of the administration of 

the WISC-IV; specifically the credentials of the interviewers, whether or not they were blinded 

to the child’s exposure status, any trainings or certifications, and whether or not intra- or interinterviewer reliability or quality assurance was conducted. These types of administration issues 

can result in less-sensitive measures and misclassification, but there is no reason to believe that 

there would be a differential effect based on exposure level. 

Also complicating this analysis is the pervasive, non-specific nature of neurological effects and 

the difficulty in attributing causal pathways to one agent. In future studies it would be 

meaningful to include other potential co-variants such as family history (especially among 

parents and siblings) of developmental delay or attention disorders as well as other comorbidities. Not only is the etiology of attention and related behavioral disorders not well 

understood but also the diagnosis of these disorders is often influenced by complicated outside 

forces. Because the study population is relatively homogenous with respect to socio-economic 

variables, it is unlikely that differences in co-morbidities or family history would be associated 

with exposure status, thus biasing the association towards the null. 

The authors describe an elegant and methodologically sound statistical analysis, addressing many 

of the potential shortcomings of their exposure data and co-variables. Out of the five WISC-IV 

scores, two were described as being significantly influenced by chlorpyrifos exposure. After 

they conducted both full-adjusted linear regression and LASSO techniques, they concluded that 

both Full Scale IQ and Working Memory index were affected by prenatal chlorpyrifos exposure. 

For each standard deviation increase in exposure (4.61pg/g), there was a 1.4% reduction in FullScale IQ and a 2.8% reduction in Working Memory. The Working Memory Index conclusions 

seem reasonable, but the associations for Full Scale IQ were not statistically significant (LASSO: 

-0.006, 0.001, p=0.064; fully-adjusted: -0.006, 0.001, p=0.048.) In addition, the authors do not 

address the clinical relevance of the 1.4% and 2.8% reductions and how this may or may not 

influence a child or his/her psychological or educational success. 

In their confirmatory analyses they used the General Ability Index (the intelligence score 

calculated after removing Working Memory and Processing Speed items) to verify that the 

association seen between chlorpyrifos exposure and Working Memory was not an artifact of the 

association with Full Scale IQ. With the General Ability Index included in the linear regression 

model, the estimated chlorpyrifos effect did not change and they did not observe an interaction 

between chlorpyrifos and General Ability Index. Subsequently they concluded that the deficits 

in Working Memory Index were associated with chlorpyrifos exposure, independent of general 

intelligence but analyses could have also taken an additional step. These results could also be 

used to conclude that since the General Ability Index was not found to be affected by 

chlorpyrifos exposure, the Full Scale IQ findings were caused by the influence of the Working 

Memory module of the instrument. The influence of chlorpyrifos exposure on the working 

memory aspect of the Full Scale IQ score was strong enough to overcome the non-significant 

results from the other modules of the instrument. 
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Confounding by maternal smoking behaviors was controlled by restriction (i.e., excluding 

smokers from the study sample). Confounding by unmeasured factors may have been similarly 

controlled by design, this being a relatively homogeneous population with respect to 

determinants of neurodevelopment, compared to the general population. Authors evaluated 

several potentially confounding variables including exposure to other environmental chemicals 

including blood lead, and only included those variables which displayed evidence of 

confounding. 

While controlling confounders by restriction, the choice to study low income, urban dwellers 

likely resulted in a relative increase in statistical efficiency (i.e. increased power) because this 

population likely experiences a greater variability of pesticide exposure and is at higher risk of 

neurodevelopmental deficits, relative to the general population. 

Although blood lead levels were not included as covariates in the multivariate models, the 

authors reported that umbilical cord lead was not significantly correlated with chlorpyrifos level 

or WISC-IV scores among the subgroup of children in this population for whom blood lead was 

assessed (n=89). Polyaromatic hydrocarbons were weakly correlated with chlorpyrifos in this 

cohort but were not significantly associated with WISC-IV scores. Birth weight was not 

significantly associated with any of the WISC-IV indices and was not included in the final 

models. 

 

Bias due to confounding by unmeasured determinants of neurodevelopment is possible in this 

observational study, for example, confounding by factors related to socioeconomic condition. 

The urban dwelling, minority children assessed in this study may experience other unmeasured 

exposures or underlying health problems that could potentially confound (or modify) 

associations with pesticide exposure. However, both adjustment through restriction of study 

population, as well as control for several measures of SES in multi-variable models, as well as 

additional methodological analyses of the role of SES in this association (Lovasi et al. 2011), 

does not suggest a significant additional role of unmeasured confounding due to variation in SES 

within this study population. Effect modification by all of the covariates was assessed, including 

for environmental tobacco smoke and polycyclic aromatic hydrocarbons, but no interactions 

were observed. 

The authors state that the retention rate for the full cohort was 82% at the 7-year follow-up, and 

reported that there were no significant sociodemographic differences between subjects retained 

in the study and those lost to follow-up, although these data were not provided. However, the 

265 children included in this analysis were born to a 36.6% subsample of the 725 women who 

originally consented to participate in the study. The resulting large proportion of missing data 

has the potential to induce a selection bias if the reasons for missingness of outcome data are 

determined by exposure (or a predictor of exposure) and are also not independent of 

neurodevelopment. For example, participants with high pesticide exposures could have missed 

neurodevelopmental assessments for reasons related to uncontrolled determinants of exposure 

which are also related to neurodevelopment. This was not discussed. 

A comprehensive set of sensitivity analyses and regression diagnostics were conducted, 
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including examination of residuals for normality and homoscedasticity, influential observations, 

and the linearity of the exposure-response relationship, and assessment of the influence of design 

choices (e.g. imputation) on observed results. Models were determined to be consistent with 

modeling assumptions. 

Non-linearity of the exposure response curves were assessed by superimposing smoothed cubic 

spline regression curves over scatterplots of the data. The log-transformed Working Memory 

Index and Full-Scale IQ were approximately linear, whereas the other functions demonstrated 

some curvature across exposure levels, with sparse observations at the highest exposures. 

However, formal goodness of fit testing did not demonstrate that non-linear models provided a 

better fit to the data, compared to models that assume a linear exposure-response relationship 

(i.e. no observed threshold effect). 

Results of this study showed that higher prenatal chlorpyrifos exposure, as measured in umbilical 

cord blood plasma, was associated with decreases in cognitive functioning on two different 

WISC-IV indices, in this cohort of urban minority children at 7 years of age. For each SD 

increase in exposure (4.61 pg/g), Full-Scale IQ declined, on average, by 1.4% (0.94–1.8 points) 

and Working Memory Index scores declined by 2.8% (1.6–3.7 points). These results are 

consistent with earlier findings of an inverse association between chlorpyrifos levels in umbilical 

cord plasma and neurodevelopment in the same cohort. The study represents perhaps the most 

rigorous study design and analysis of the studies reviewed. 

 
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Works Cited

Bradley RH and Corwyn RF. Socioeconomic Status and Child Development. Annual Review of 

Psychology. 53:371-399 (2002). 

Chiodo LM, Jacobson SW, Jacobson JL. 2004. Neurodevelopmental effects of postnatal lead 

exposure at very low levels. Neurotoxicol and Teratol, 26:359-371. 

Houwelingen JCv. 2001. Shrinkage and penalized likelihood as methods to improve predictive 

accuracy. Stat Neerlandica. 55(1):17-34. 

Lovasi, G. S., Quinn, J. W., Rauh, V. A., Perera, F. P., Andrews, H. F., Garfinkel, R., . . . 

Rundle, A. (2011). Chlorpyrifos exposure and urban residential environment characteristics as 

determinants of early childhood neurodevelopment. Am J Public Health, 101(1), 63-70. doi: 

10.2105/ajph.2009.168419 

Little RJA. 1992. Regression with missing X’s: a review. J Amer Stat Assoc. 87:1227–1237. 

Wechsler D. Wechsler Intelligence Scale for Children. 4rd Edition. San Antonio, Texas: 

Psychological Corporation. 2003. 

Whyatt RM, Rauh V, Barr DB, et al. Prenatal insecticide exposure and birth weight and length 

among an urban minority cohort. Environ Health Perspect. 2004;112:1125–1132 

Whyatt RM, Garfinkel RS, Hoepner LA, Borjas M, Camann DE. Within and between home 

variability in indoor-air insecticide levels during pregnancy [abstract W2A–3]. Presented at the 

15th Annual Conference of the International Society of Exposure Analysis; October 30–

November 3, 2005; Tucson, AZ 

 
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Article 14. Bouchard et al. (2011) 

Prenatal Exposure to Organophosphate Pesticides and IQ in 7-Year-Old 

Children. Maryse F. Bouchard, Jonathan Chevrier, Kim G. Harley, Katherine Kogut, 

Michelle Vedar, Norma Calderon, Celina Trujillo, Caroline Johnson, Asa Bradman, Dana 

Boyd Barr, Brenda Eskenazi. Environmental Health Perspectives. 119:1189–1195 (2011) 

Study Summary 

Researchers with the Center for the Health Assessment of Mothers and Children of Salinas study 

(CHAMACOS study) published an analysis which investigated the relationship between prenatal 

and early life exposures to organophosphate pesticides (OPs) and cognitive development. The 

CHAMACOS study is a prospective birth cohort study of environmental exposures and the 

health of pregnant women and their children living in the Salinas Valley, an agricultural area of 

California. 

A 2007 report from this cohort suggested that prenatal, but not postnatal, exposure to OPs was 

associated with increased odds of pervasive developmental disorder and lower scores of mental 

development at two years (Eskenazi B 2007). Additionally, a 2010 paper reported an association 

between organophosphate pesticide exposure and poorer attention skills as well as hyperactive 

behaviors at 5 years (Marks AR 2010). This 2011 study by Bouchard et al was conducted to 

determine whether cognitive deficits associated with prenatal exposure to organophosphate 

pesticides are persistent, since previous cohort studies have not followed children to school-age, 

when cognitive deficits may have greater implications for school performance. This paper 

reports the association between prenatal and postnatal exposure to organophosphate pesticides, 

indicated by urinary dialkyl phosphate (DAP) metabolite concentrations, and cognitive abilities 

of 7-year olds. 

Enrollment took place from 1999–2000 at community clinics, and included women who were ≥

18 years old, pregnant at < 20 weeks gestation, Spanish- or English-speaking, eligible for lowincome health insurance, and planning to deliver at the local public hospital. The initial 601 

women who were enrolled into the study delivered 526 infants. From this analysis, 2 participants 

with missing DAP concentration measurements were excluded, 4 children with a medical 

condition that would affect the assessment, children who were lost to follow-up or who did not 

participant in the 7-year visit were excluded (72 moved, 59 refused, 24 unable to trace, 21 unable 

to schedule, 2 deceased), and children missing the cognitive assessment at the 7-year visit (13) 

were also excluded. 

Prenatal urine was collected between 5 and 27 weeks gestation and again between 18 and 39 

weeks. Urine was collected from the children at 6 months and 1, 2, 3 1⁄2 and 5 years of age. Six 

nonspecific organophosphate DAP metabolites (3 DEP metabolites and 3 DMP metabolites) 

were measured in maternal and child urine which represent the breakdown products of about 

80% of the total organophosphate pesticides used in the Salinas Valley (CDC 2009). The most 

commonly used organophosphate pesticides in the Salinas Valley include chlorpyrifos and 

diazinon (which are metabolized to DEP) as well as malathion and oxydemeton-methyl (which 

devolve to DMP). Concentrations below the limit of detection (LOD) were randomly imputed 

based on a log-normal probability distribution whose parameters were estimated using maximum 
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likelihood estimation. The DAP metabolite concentrations were expressed on a molar basis and 

summed to yield total DEP, DMP, and DAP concentrations. 

Cognitive abilities were measured at the 7-year study visit using the Wechsler Intelligence Scale 

for Children - Fourth Edition (WISC-IV) (Wechsler 2003). Scores for four domains were 

calculated based on the following subtests: Verbal Comprehension (comprised of the Vocabulary 

and Similarities subtests), Perceptual Reasoning (Block Design and Matrix Reasoning subtests), 

Working Memory (Digit Span and Letter-Number Sequencing subtests), and Processing Speed 

(Coding and Symbol Search subtests). All subtests were administered in the dominant language 

of the child, which was determined through administration of the Oral Vocabulary subtest of the 

Woodcock-Johnson/Woodcock-Muñoz Tests of Cognitive Ability in both English and Spanish 

(Woodcock and Johnson 1990). Ultimately, 67% of children were tested in Spanish, and 33% in 

English. WISC-IV scores are standardized against U.S. population-based norms for English and 

Spanish-speaking children. 

Many confounders were measured, including intelligence of the mother, measures of how 

stimulating the environment is, and known or suspected neurotoxins were measured prenatally. 

Maternal intelligence was assessed via the Peabody Picture Vocabulary Test (PPVT). To 

measure the quality and extent of stimulation available to a child in the home environment, the 

Infant-Toddler HOME (Home Observation for Measurement of the Environment) inventory was 

completed at the 6-month, 1, 2, 3.5, 5, and 7 year visits. The following known or suspected 

neurotoxicants were measured: polybrominated diphenyl ethers (PBDEs), polychlorinated 

biphenyls (PCBs), p,p’-dichlorodiphenyltrichlorethane (DDT), p,p’- 

dichlorodiphenyltrichlorethylene (DDE), and lead. Lead was measured in maternal blood at 26 

weeks gestation, in cord blood for a subset of participants, and children’s blood a 2 years of age. 

PBDEs, PCBs, DDT, and DDE were measured in maternal serum samples collected at 26 weeks 

gestation. 

Nonspecific total DAP, DEP, and DMP metabolites (nmol/L) were log10-transformed. The 

authors examined the association between non-creatinine adjusted urinary DAP concentrations 

and cognitive scores using multiple linear regression, with point estimates representing the 

change in cognitive scores for each 10-fold increase in DAP concentrations. For prenatal 

exposure, they examined associations with the DAP concentrations measured separately for urine 

collected during the 1st and 2nd half of pregnancy (≤20 vs. >20 weeks gestation). However, 

since they found similar relations between cognitive scores and DAPs measured earlier and later 

than 20 weeks gestation, they averaged the two DAP measures for further analyses. For 20 

children (6%) only one prenatal measure was available for the analyses. Because DMP and DEP 

metabolites might have different relationships to the outcomes, the authors examined them 

separately. For postnatal exposure, they examined the cross-sectional association of cognitive 

scores with DAP concentrations measured in children’s urine collected at different ages in 

separate models. 

They also calculated the cumulative DAP level between 6 and 60 months using the area under 

the curve (AUC), calculated using the trapezoidal method. For 46 children with 1 missing DAP 

measurement at 1, 2, or 31⁄2 years, they imputed the mean of the 2 measures closest in time for 

the AUC calculation. Forty-nine children who were missing DAP measures at either the 6-month 
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or 5-year visit, or missing more than 1 DAP measure from the 3 other time points were excluded 

from the AUC analysis. For comparison with prenatal exposure, they calculated the mean urinary 

DAP concentrations measured during childhood for children with at least 3 out of 5 measures 

(taken at 6 months, 1, 2, 3 1⁄2, and 5 years); this excluded 20 children. 

The authors compared effect estimates for urinary DAPs measured in early vs. late pregnancy 

and in the prenatal vs. postnatal periods using seemingly unrelated estimation and they used the 

mean postnatal DAP concentrations (as opposed to AUC) for these analyses in order to compare 

metrics with similar units. They used generalized additive models with 3-degree-of-freedom 

cubic splines to evaluate the shape of dose-response curves, test the linearity assumption, and 

investigate potential thresholds while controlling for covariates. They did not observe evidence 

of departure from linearity, or threshold for effect, thus, they retained the simpler models based 

on linear regression. For illustration purposes, they grouped DAP concentrations into quintiles, 

entered this categorical variable in the multiple regression model with the same covariates 

described above, and obtained the mean IQ score for each quintile. 

The authors explored potential confounding effects for the following variables: maternal 

intellectual abilities, maternal education, HOME score at 6 months, 1, 2, 3 1⁄2, and 5 years, 

breastfeeding duration, maternal age, birth order, poverty category, marital status, children’s age 

at WISC testing, and maternal levels of PBDEs, PCBs, DDE, DDT, and lead during pregnancy. 

They added each of these variables added individually to the final model. However only 

maternal intellectual abilities (PPVT score, continuous), maternal education (3 categories), and 

continuous HOME score at 6 months were retained in the final models. Other variables were 

dropped from the final models because they did not change the magnitude of the coefficient for 

urinary DAP concentrations by >10%. 

In separate analyses, the authors also investigated potential confounding and effect modification 

by variables possibly on the causal pathway (i.e. birth weight and gestational age, assessed 

continuously). Because the majority of children (67%) were tested in Spanish, the authors reran 

the analyses restricted to this subset. The authors also examined the interaction between sex and 

DAP concentrations, based on previous findings in this cohort (Marks AR, 2010). 

To explore possible synergistic effects between pre- and postnatal DAP concentrations, the 

authors included an interaction term for mean prenatal DAP concentrations×AUC. However, 

since this term was not statistically significant (p > .15) they did not include it in the final 

models. 

The authors observed evidence of an association between prenatal exposures to organophosphate 

pesticides as measured by urinary DAP metabolites in women during pregnancy, and decreased 

cognitive functioning in children at age 7. This finding was seen for total DAP, DMP, and DEP 

metabolites. Children in the highest quintile had of maternal DAP concentrations had an average 

deficit of 7.0 IQ points compared with those in the lowest quintile. The associations were linear 

with no threshold. Urinary DAP concentrations in childhood were not associated with cognitive 

scores in this cohort of children. 

Study Review 
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This study tested whether in utero or postnatal exposures to organophosphate pesticides have a 

detrimental impact on cognitive abilities at age seven years. The study population was 

appropriate to the hypothesis, given that there were a range of exposure levels to OPs and there 

was some homogeneity in the population, e.g., mostly low SES, Hispanic, agricultural workers. 

The homogeneity of the population restricted confounding by design. For example, another paper 

reported that maternal smoking, alcohol use, and illicit drug use were rare this cohort. The 

recruitment of an inner-city cohort may also have increased the relative statistical efficiency of 

the study if the study population is at higher risk of neurodevelopmental deficits compared to the 

general population. Selection bias may have been a factor in this analysis as only 329 families 

out of 601 who were originally enrolled into the study were included. The authors state that the 

families included in this analysis did not differ on most attributes, including urinary DAP 

concentrations during pregnancy, maternal measures of cognitive ability, maternal education, 

marital status, poverty category, and child’s birth weight. However, mothers of children included 

in the present study were slightly older and breastfed longer than the initial cohort. Differences 

between maternal blood lead and other environmental exposures which are potential confounders 

were not discussed. 

The ascertainment and analysis of urinary organophosphate pesticide markers was appropriate. 

Quantification was conducted at the CDC, using published methods. DAP metabolites are likely 

more accurate and objective indicators of organophosphate pesticide exposure, relative to other 

ascertainment methods such as self-report. Although they are sensitive indicators of exposure, it 

is difficult to infer chlorpyrifos effects specifically from the metabolite levels, as they also 

indicate exposure to other organophosphates. The study population likely sustained many other 

potentially relevant exposures to multiple chemicals and other pesticides which may affect 

development. The six organophosphate metabolites cannot be traced back to individual 

pesticides but, according to the authors, they represent the breakdown products of approximately 

80% of the total organophosphate pesticides used in the Salinas Valley. It is not possible to 

disentangle the various effects from these potential co-exposures and chlorpyrifos in this study. 

The IQ test used the WISC-IV is very widely utilized in both clinical and research setting, and 

has a long history of use, and was appropriately administered by psychometrician. Additionally, 

the test was administered at age 7 years, which is above the floor for administration (age 6) 

(Wechsler 2003). However, IQ tests are notoriously susceptible to possible biases due to 

differences in cultural and/or ethnic composition of the population, although the homogeneity of 

the population may ameliorate the effect of this potential bias. The authors did perform the 

analysis both by looking at only the tests that were administered in Spanish and with the Spanish 

and English included. However, language may not have controlled for all potential biases which 

are inherent in IQ testing, e.g., degree of acculturation. 

The statistical models used are appropriate and well powered. For exposure measures that were 

less than the laboratory analytic limit of detection, the measures were imputed. Imputation is a 

better method than substitution of the LOD/√2 or LOD/2, which is the method used in many 

studies. It appears that the investigators selected parsimonious models by only including terms 

which changed the outcome measure and were statistically significant. Additionally, the authors 

tested the linearity assumption which is inherent in linear regression using cubic splines. 
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Residual confounding by unmeasured or poorly measured confounders is possible in this study. 

The authors did measure a large number of potential confounders, but not all were evaluated 

notably, childhood exposure to neurotoxins, and maternal drug used during pregnancy. A 

number of environmental chemicals were considered, although only measurements of exposure 

during pregnancy were included in the analysis. Additionally, exposure to carbamates, which are 

also potentially neurotoxic, was not included in the analysis. The authors also did not consider 

maternal alcohol, tobacco, or drug use in this analysis. However, another study with this cohort 

reported that these confounders are very rare in this study population. 

The authors did not discuss paternal intelligence or intelligence of other caregivers. This is 

probably because only the mothers and children were enrolled into the study, and it is difficult to 

enroll fathers and other caregivers into birth cohort studies. Although it is understandable that 

these measures were not addressed, it seems highly possible that intelligence of the fathers and 

other caregivers could be associated with the exposure, organophosphate pesticides, and the 

disease, cognitive impairment of the child. Thus these factors could have confounded the study 

results, and it is a limitation that they were not included in the measurement or analysis. 

A potential limitation with respect to generalizability is the high level of exposures observed in 

the study cohort, compared to the US population. The mean level of urinary DAP metabolites in 

this cohort is higher than the mean which has been seen among women of child bearing years in 

NHANES. However, the investigators report that 25% of the US population have urinary DAP 

levels exceeding the median in this study. So, the concentrations in this cohort are not out of the 

range of normal for the US as a whole. Generalizing beyond the range of exposure observed in 

this study requires an assumption that the linear exposure-outcome relationship observed in the 

study is applicable to populations with different levels of exposure. 

This study has many strengths, the longitudinal design, the measurement of urinary DAP at many 

time points and following children to age seven when tests of cognitive function are reportedly 

more reliable. The authors were able to adjust for or consider many factors related to cognitive 

function, such as prenatal exposure to other environmental agents, socioeconomic indicators, 

maternal intelligence and education, and child stimulation. The cohort had a relatively 

homogenous socioeconomic profile, reducing the potential for uncontrolled confounding. 

Additionally, the authors noted their findings were consistent with findings from previous reports 

from this cohort (Eskenazi, 2007) and other investigations of adverse associations between 

prenatal exposure to organophosphate pesticides and cognition (Rauh, 2006). They also noted 

that although they did not see an association between childhood exposures to organophosphate 

pesticides, the other studies that have differed in the measurement of exposure and/or the specific 

outcomes found to be associated with exposure to organophosphate pesticides (Lizardi, 2008, 

Ruckart, 2004). The many advantages of this study, the plausibility of the findings, and that 

similar association have been seen in other studies support the hypothesis that prenatal exposure 

to OPs reduces cognitive function of children. However, to the extent that prenatal chlorpyrifos 

is the true exposure of interest, the assessment of non-specific organophosphate pesticide 

metabolites is a primary potential source of exposure measurement error. Because of the 

prospective design, information bias is expected to result in attenuation of the true (unobserved) 

associations, toward (observed) associations that are relatively smaller, or null. 
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Works Cited 

Bradman A, Barr DB, Claus Henn BG, Drumheller T, Curry C, Eskenazi B. "Measurement of 

pesticides and other toxicants in amniotic fluid as a potential biomarker of prenatal exposure: a 

validation study." Environ Health Perspect, 2003: 1779-1782. 

Bradway DE, Shafik TM, Lores EM. "Comparison of cholinesterase activity, residue levels, and 

urinary metabolite excretion of rats exposed to organophosphorus pesticides." J Agric Food 

Chem, 1977: 1353-1358. 

CDC. "Fourth National Report on Human Exposure to Environmental Chemicals. Chemical 

Information. Organophosphorus Insecticides: Dialkyl Phosphate Metabolites." 2009. 

http://www.cdc.gov/exposurereport/data_tables/OP-DPM_ChemicalInformation.html (accessed 

March 29, 2011). 

Eskenazi B, Marks AR, Bradman A, Harley K, Barr DB, Johnson C, et al. "Organophosphate 

pesticide exposure and neurodevelopment in young Mexican-American." Environ Health 

Perspect, 2007: 792-798. 

Lizardi PS, O'Rourke MK, Morris RJ. "The effects of organophosphate pesticide exposure on 

Hispanic children's cognitive and behavioral functioning." J Pediatr Psychol, 2008: 91-101. 

Lu C, Kedan G, Fisker-Andersen J, Kissel JC, Fenske RA. "Multipathway organophosphorus 

pesticide exposures of preschool children living in agricultural and nonagricultural 

communities." Environ Res, 2004: 283-289. 

Marks AR, Harley K, Bradman A, Kogut K, Barr DB, Johnson C, et al. "Organophosphate 

pesticide exposure and attention in young Mexican-American children." Environ Health 

Perspect, 2010: 1768-1774. 

Muto MA, Lobell F Jr, Bidanset JH, Wurpel JN. "Embyrotoxicity and neurotoxicity in rats 

associated with prenatal exposure to DURS-BAN." Vet Hum Toxicology, 1992: 498-501. 

Rauh VA, Garfinkel R, Perera FP, Andrews H, Barr D, Whitehead D, Tang D, Whyatt RM. 

"Impact of prenatal chlorpyrifos exposure on neurodevelopment in the first three years of life 

among inner-city children." Pediatrics, 2006: 1845-1859. 

Ruckart PZ, Kakolewski K, Bove FJ, Kaye WE. "Long-term neurobehavioral health effects of 

methyl parathion exposure in children in Mississippi and Ohio." Environ Health Perspect, 2004: 

46-51. 

Wechsler, D. Wechsler Intelligence Scale for Children-IV: Administration and Scoring Manual. 

San Antonio, TX: Harcourt Assessment Inc, 2003. 

Whyatt RM, Garfinkel R, Hoepner LA, Andrews H, Holmes D, Williams MK, et al. "A 

biomarker validation study of prenatal chlorpyrifos exposure within an inner-city cohort during 

pregnancy." Environ Health Perspect, 2009: 559-567. 
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Article 15. Whyatt et al. (2007) 

Within- and Between-Home Variability in Indoor-Air Insecticide Levels during Pregnancy 

among an Inner-City Cohort from New York City. Robin M. Whyatt, Robin Garfinkel, 

Lori A. Hoepner, Darrell Holmes, Mejico Borjas, Megan K. Williams, Andria Reyes, 

Virginia Rauh, Frederica P. Perera, and David E. Camann. Environmental Health 

Perspectives. 115:383–389 (2007). 

Study Summary 

Whyatt et al conducted a study to assess within- and between-home variability in indoor-air 

insecticides over the final 2 months of pregnancy among a subset of participants in the Columbia 

Mother’s and Newborn Study, a cohort of women from New York City. Participants (n = 102) 

assessed in this investigation were enrolled between February, 2001 and May, 2004, after the 

January, 2001 implementation of a voluntary cancellation by registrants of indoor residential 

chlorpyrifos use. Expectant mothers were recruited during early pregnancy (≤20th week) among 

African-American and Dominican women age 18-35 years, and registered at New York 

Presbyterian Medical Center and Harlem Hospital, in New York City. Women who smoked, had 

a history of drug abuse, diabetes, hypertension, or HIV infection were excluded from 

participation in the study, as were women who resided in New York City for less than 1 year or, 

for this validation study, who were employed outside the home at the time of enrollment. Most 

of the mothers (96%) in the present study lived in residential apartment buildings; the remaining 

lived in multifamily housing (n = 3) or in combined residential/commercial buildings (n = 1). 

Nine insecticides (the organophosphates chlorpyrifos, diazinon, malathion, and methyl parathion; 

the carbamates propoxur, bendiocarb, and carbofuran; and the pyrethorids cis and transpermethrin) and piperonyl butoxide, an adjuvant that is frequently added to pyrethroid 

formulations, were measured in 48-hr personal air samples and 2-week integrated indoor air 

samples collected sequentially for 7.0 ± 2.3 weeks (n = 337 air samples). Indoor air monitoring 

occurred around the 32nd week of pregnancy, and air samples were collected every 2 weeks 

throughout the monitoring period. Personal air samples were obtained for mothers over a 48 hour 

period during the 32nd week of pregnancy. 

Arithmetic means ± SDs were calculated for pesticides detected in greater than 45% of samples. 

Before statistical analyses, pesticide values were log-transformed to normalize their distributions. 

Values below the level of detection (LOD) were assigned a value of half of the LOD. Pearson 

correlation coefficients or Spearman’s correlation coefficients, as appropriate, were calculated to 

assess the relationship between pesticide levels in indoor air and personal air samples. Analysis 

of variance (ANOVA) was used to test whether pesticides levels varied significantly by season 

and year of monitoring and among the following groups: women not using any pest control 

methods; women using “nonspray” methods only; and women using higher-toxicity pesticides. 

If levels differed significantly among the groups, the least significant difference test was used to 

determine which groups varied significantly. T-tests were employed to compare differences in 

mean pesticide levels by ethnicity. Chi-square analyses were used to test whether the proportion 

of women using pest control measures over the final 2 months of pregnancy was related to pest 

sightings in the home. Finally, a mixed-effects model was used to assess within-home and 

between-home variability in repeat indoor air samples, after controlling for season and year of 
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delivery. Except for the Spearman’s rank and chi-square analyses, all statistical testing was 

conducted on log-transformed data. 

Sixty-one percent of the participating women reported using pest control during the air 

samplings. Chlorpyrifos, diazinon, and propoxur were detected in 99–100% of personal and 

indoor samples (range, 0.4–641 ng/m3

). Piperonyl butoxide (a pyrethroid adjuvant) was detected 

in 45.5–68.5% (0.2–608 ng/m3

). There was little within-home variability and no significant 

difference in air concentrations within homes over time (p ≥ 0.2); between-home variability 

accounted for 88% of the variance in the indoor air levels of propoxur, 92% in chlorpyrifos, 94% 

in diazinon, and 62% in piperonyl butoxide (p < 0.001). Indoor and maternal personal air 

insecticide levels were highly correlated (r = 0.7–0.9, p < 0.001). Diazinon and chlorpyrifos 

levels declined 5-fold between 2001 and 2004 but were detected in all homes at 1.5 and 2.5 

years, respectively, after implementation of a voluntary cancellation by registrants of indoor 

residential chlorpyrifos use. No previously unreported associations between chlorpyriphos and 

health outcomes were presented in this report. The authors concluded that the insecticides were 

persistent in the home with little variability in air concentrations over the 2 months and likely 

contributed to chronic maternal inhalation exposures during pregnancy. 

Study Review 

Although the Columbia Mother’s and Newborn Study enrolled participants in years prior to the 

implementation of the 2001 voluntary cancellation by registrants of indoor residential 

chlorpyrifos use, only data for participants recruited in the post-cancellation period were 

considered in this validation study analysis. Explanation for this design choice was not provided, 

but is likely that appropriate repeated sampling was not conducted during earlier study periods. 

The authors noted that the mothers in the present study were comparable with women in the 

larger cohort in terms of maternal age, ethnicity, marital status, annual household income, and 

reported use of pest control methods during pregnancy. However, a greater proportion of women 

in the present study compared with those in the larger cohort reported having less than a high 

school education, which could be due to the fact that, for these analyses, the investigators 

excluded women who worked outside the home. 

The exposure assessment in this study was well conducted using appropriate and previously 

published methodology. The semivolatile insecticides were readily detected in indoor air after 

residential use. Indoor air levels of these chlorpyrifos and the other organophosphate pesticides 

in the 2-week integrated indoor air samples remained remarkably stable within most homes over 

6–8 weeks of sequential sampling. The two-week integrated sampling methodology was 

targeted towards evaluation of chronic exposure during the latter pregnancy period, and thus 

precludes assessment of changes in concentrations in indoor air at times scales shorter than twoweeks (e.g. day-to-day changes). 

In this study, pesticide levels in indoor air were remarkably stable. Correspondingly, almost all 

(90%) of the total variance in the indoor air levels was explained by between-home variability. 

The degree to which this finding in the post-cancellation period informs variability in airborne 

pesticide levels prior to the cancellation was beyond the scope of the investigation. 
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The authors did not observe an association between indoor air levels of chlorpyrifos and 

maternal self-reported pesticide use, a finding which indicates that most of the chlorpyrifos 

concentrations detected in the indoor air samples likely resulted from prior, rather than current, 

pesticide use. However, that chlorpyrifos was detected in indoor air samples from all the homes 

assessed, including those evaluated 2.5 years after implementation of the voluntary cancellation 

of indoor residential chlorpyrifos use, demonstrates the persistence of the compound in certain 

indoor environments. 

No associations between pesticide exposures and health outcomes were assessed in this 

validation study. 
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Article 16. Whyatt et al. (2009) 

A Biomarker Validation Study of Prenatal Chlorpyrifos Exposure within an Inner-City 

Cohort during Pregnancy. Robin M. Whyatt, Robin Garfinkel, Lori A. Hoepner, Howard 

Andrews, Darrell Holmes, Megan K. Williams, Andria Reyes, Diurka Diaz, Frederica P. 

Perera,1 David E. Camann, and Dana B. Barr. Environal Health Perspectives. 117:559–567 

(2009). 

Study Summary 

Whyatt et al conducted a biomarker validation study within the Columbia Mother’s and 

Newborn Study to evaluate trends over time in multiple biomarkers of prenatal chlorpyrifos 

exposure. The group previously documented significant decreases in chlorpyrifos concentrations 

in maternal personal and indoor air samples among pregnant African-American and Dominican 

women from New York City after the 2001 voluntary cancellation by registrants of indoor 

residential chlorpyrifos use. Participants (n = 102) were enrolled between February, 2001 and 

May, 2004. 3,5,6-trichloro-2-pyridinol (TCPy) was measured in newborn meconium (n = 83), 

repeat prenatal maternal urine samples (n = 253), and postnatal urine from mothers (n = 73) and 

newborns (n = 59). Chlorpyrifos was directly measured in postnatal maternal (n = 92) and 

umbilical cord (n = 65) blood plasma and in personal and indoor air samples. No previously 

unreported associations between chlorpyriphos and health outcomes were presented in this 

report. 

Study participants were recruited during early pregnancy (≤20th week) among African-American 

and Dominican women age 18-35 years, and registered at New York Presbyterian Medical 

Center and Harlem Hospital, in New York City. Women who smoked, had a history of drug 

abuse, diabetes, hypertension, or HIV infection were excluded from participation in the study, as 

were women who resided in New York City for less than 1 year or who were employed outside 

the home at the time of enrollment (for the purposes of this validation study). The study sample 

presented in this report was recruited between 2001 and 2004, after the January, 2001 

implementation of a voluntary cancellation by registrants of indoor residential chlorpyrifos use. 

Most of the mothers (96%) in the present study lived in residential apartment buildings; the 

remaining lived in multifamily housing (n = 3) or in combined residential/commercial buildings 

(n = 1). 

Indoor air monitoring occurred around the 32nd week of pregnancy using 2-week integrated 

indoor air samples collected every 2 weeks throughout the monitoring period. Personal air 

samples were obtained for mothers over a 48 hour period during the 32nd week of pregnancy. 

Repeat prenatal maternal urine samples were collected from mothers at the end of each 2-week 

indoor air sampling period, beginning at the 34th week of pregnancy and continuing until 

delivery. Women who went into labor before the end of the first 2 weeks of indoor air sampling 

did not provide urine samples. Urine samples were collected from 97 of 102 women in the study; 

more than half of the women provided three or more urine samples, 29 of 97 (30%) two samples, 

and 15 of 97 (15%) provided one sample. An additional urine sample was obtained on the day 

after delivery for 73 women and 59 newborns. 
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An umbilical cord blood sample was collected from 65 newborns after delivery. A sample of 

maternal blood was obtained from 92 mothers within 2 days postpartum. Paired maternal and 

cord blood samples were available for 64 of the mothers and newborns. 

Meconium samples were collected from 83 newborns during the postpartum hospital stay. In 53 

(64%) of the births, the meconium samples were collected within 1 day of delivery. 

Biologic samples were analyzed for organophosphate pesticide markers by the CDC using 

previously established methods using both positive and negative controls. The limit of detection 

(LOD) of chlorpyrifos in blood samples was 0.5–1 pg/g plasma. The LOD of TCPy in urine 

samples was 0.26 ng/mL urine. The LOD for TCPy in meconium was 0.2 ng based on a sample 

weighing 0.5 g. 

Exposure marker levels below the LOD were given a value of half the level of detection, and 

were then log10 transformed. Nonparametric statistics were used when distributions remained 

non-Gaussian. Spearman rank correlation coefficients were used to assess relationships between 

levels of chlorpyrifos or TCPy in biological samples and to examine correlations between levels 

of chlorpyrifos or TCPy in the biological samples and chlorpyrifos levels in air samples. 

Multiple linear regression was employed to evaluate the amount of variance in prenatal maternal 

urine TCPy levels explained by indoor air chlorpyrifos levels among subjects enrolled in 2001–

2002. 

Analysis of variance (ANOVA) or Kruskal–Wallis tests with post-hoc testing as appropriate, 

were used to assess whether pesticide levels varied by year of birth (between 2001 and 2004), or 

among the following groups: women not using any pest control methods; women using 

“nonspray” pest abatement methods only; and women using can sprays, pest bombs, or sprays by 

an exterminator, with or without nonspray methods. Authors performed t-tests corresponding 

nonparametric Mann–Whitney U-test were used to assess whether pesticide levels differed by 

ethnicity. Chi-square analyses were used to test whether the frequencies of detection of 

chlorpyrifos and TCPy in biological samples varied by year of assessment as well as to compare 

detection frequencies of chlorpyrifos in maternal and cord blood samples. A mixed effects model 

was used to evaluate the within- and between-subject variability in TCPy levels in the repeat 

maternal urine samples collected during pregnancy, and to calculate an intraclass correlation 

coefficient (ICC) for women enrolled in 2001–2002 who had three repeat urine samples (TCPy 

was detected in only one-third of samples among subjects enrolled after 2002). The ICC is a 

measure of reliability of repeated measures over time, defined as the ratio of between-subject 

variance to total variance. 

The authors did not detect TCPy in infant urine, but all other biomarkers showed a highly 

significant decrease in detection frequencies (χ2 = 7.8–34.0, p ≤ 0.005) and mean ranks (p ≤

0.006) among subjects enrolled in 2003–2004 compared with those enrolled in 2001–2002. 

Chlorpyrifos in maternal personal and indoor air declined 2- to 3-fold over the same period (p < 

0.05). In 2001–2002 samples, TCPy levels in repeat prenatal urine samples were positively 

correlated (r = 0.23–0.56), although within-subject variability exceeded between-subject 

variability (intraclass correlation coefficient = 0.43); indoor air levels explained 19% of the 

variance in prenatal urine TCPy (p = 0.001). Meconium TCPy concentrations were positively 
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correlated with chlorpyrifos in maternal and cord blood (r = 0.25–0.33, p < 0.05) and with TCPy 

in maternal urine (r = 0.31, p < 0.01). 

Study Review 

Although the Columbia Mother’s and Newborn Study enrolled participants in years prior to the 

implementation of the 2001 voluntary cancellation by registrants of indoor residential 

chlorpyrifos use, only data for participants recruited in the post-cancellation period were 

considered in this analysis. Explanation for this design choice was not provided, but is likely that 

appropriate repeated sampling was not conducted during earlier study periods. The authors 

noted that the mothers in the present study were comparable with women in the larger cohort in 

terms of maternal age, ethnicity, marital status, annual household income, and reported use of 

pest control methods during pregnancy. However, a greater proportion of women in the present 

study compared with those in the larger cohort reported having less than a high school education, 

which could be due to the fact that, for these analyses, the investigators excluded women who 

worked outside the home. 

The investigators’ use of nonparametric, rank-based statistics is appropriate for analysis of nonnormally distributed data. However, the large number of ties among marker levels due to 

observations below the level of detection receiving equal rank, may be problematic. When 

ranked values have the same numeric number (e.g., half the level of detection), the rank is 0.5 

times the number of ties. Whether, and at what point a large proportion of ties begins to limit the 

inferential power of rank based statistics is unclear, although at some point, a non-continuous 

classification of the data (e.g., classification of observations into bins at a cut-point equal to the 

level of detection) seems to make more sense if the number of below the level of detection 

observations is large. 

The exposure assessment conducted in this investigation is the most comprehensive of any 

epidemiologic study of chlorpyrifos to date. Strengths of the exposure assessment include the 

quantification of actual blood chlorpyrifos levels in addition to sensitive, but non-specific 

markers of organophosphate pesticide exposure in urine, the repeated sampling of urinary 

markers, measures in meconium and the environmental sampling. The exposure assessments 

were appropriately timed with respect to critical window of gestation for fetal growth and 

neurodevelopment outcomes which likely occurs, in part, during the third trimester of pregnancy, 

and appear to have been well conducted. Although inhalation exposures are likely a major route 

of exposure, no direct dietary assessment was included in this investigation. 

The study was set during a period of rapidly declining use of chlorpyrifos, and the findings are 

demonstrative of decreasing exposure over time. The investigators observed a highly significant 

decrease in the detection frequency and mean ranks of all biomarkers among subjects enrolled 

during years 2001–2004, with the exception of TCPy in newborn urine, which was not detected. 

Maternal and umbilical cord blood plasma chlorpyrifos levels were below the LOD in all 

samples collected after 2002. The results suggest that chlorpyrifos from residential use was the 

main contributor to the chlorpyrifos levels detected in both the maternal and umbilical cord 

blood among study subjects in the larger cohort (1998-2004, both before and after voluntary 
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cancellation of chlorpyrifos containing compounds). 

While no association was observed between maternal and umbilical blood chlorpyrifos levels 

measured at delivery and TCPy in maternal urine samples collected through the latter third 

trimester, TCPy levels in meconium (an integrative measure week 13-delivery) were 

significantly correlated with chlorpyrifos levels in both maternal and umbilical cord blood 

measured at delivery, and with TCPy levels in maternal urine samples collected in third 

trimester. These results suggest that TCPy in meconium may provide a valid biomarker of 

prenatal exposure, possibly both prior to and subsequent to the cancellation period. 

The finding that within-subject variability exceeded between-subject variability in maternal urine 

TCPy (intraclass correlation coefficient, ICC= 0.43), again emphasizes the point that a single 

prenatal urine sample is likely insufficient to capture ‘usual’ exposure. The authors noted that 

despite the significant within-subject variability seen in their study, the ICC observed was higher 

than that observed in another population (Meeker et al., 2005). 

The authors concluded that their results suggest that pesticide biomarkers utilized in these studies 

are reliable dosimeters to differentiate between groups with prenatal chlorpyrifos exposures 

varying by a factor of 2 or more, and that cord blood measures are reasonable well correlated 

with exposure measures taken throughout pregnancy (meconium correlated with cord blood 

(delivery) and maternal urine (3rd trimester). Authors also support that these data illustrate the 

efficacy of the voluntary cancellation of indoor residential chlorpyrifos use to reduce the internal 

dose during pregnancy. 

 
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Works Cited 

Meeker, J. D., Barr, D. B., Ryan, L., Herrick, R. F., Bennett, D. H., Bravo, R., & Hauser, R. 

(2005). Temporal variability of urinary levels of nonpersistent insecticides in adult men. J Expo 

Anal Environ Epidemiol, 15(3), 271-281. doi: 7500402 [pii] 

10.1038/sj.jea.7500402 

 
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Article 17. Rauh et al. (2012) 

Rauh, V. A., Perera, F. P., Horton, M. K., Whyatt, R. M., Bansal, R., Hao, X., . . . Peterson, 

B. S. (2012). 

Brain anomalies in children exposed prenatally to a common organophosphate pesticide. 

Proc 

Natl Acad Sci U S A, 109(20), 7871-7876. 

Study Summary

In this study, authors with the Columbia Center for Children’s Environmental Health (CCCEH) 

birth cohort study evaluated whether there were areas of morphological change in the pediatric 

brain in regions of the brain known to be associated with learning, cognition and social behavior 

in association with prenatal chlorpyrifos (CPF) exposure (V. A. Rauh et al., 2012). These are 

functional neurodevelopmental outcomes that have been adversely associated with chlorpyrifos 

exposure in previous observational research (V. A. Rauh et al., 2006; V. Rauh et al., 2011), as 

well as some experimental studies, as noted by the authors. Specifically, in this study, authors 

evaluated 1) whether morphological changes in the brain including cortical thickness and 

differences in the cerebral brain surface area and volume were related to prenatal chlorpyrifos 

exposure (the main effect), and, 2) whether the relation between the exposure and morphological 

changes in the brain were modified by either full scale intelligence quotient (FSIQ) score 

(evidence of effect modification by IQ) and, 3) in preliminary analyses, whether changes in the 

normal sexual differentiation of the brain exist by exposure-level (V. A. Rauh et al., 2012). 

Authors concluded that while overall brain size did not differ by prenatal chlorpyrifos exposure, 

certain areas of the brain were enlarged among those more highly exposed to chlorpyrifos 

including superior temporal, posterior middle temporal, and inferior post-central gyri, and 

superior frontal gyrus, gyrus rectus, cuneus, and pre-cuneus in the mesial views of the right 

hemisphere of the cerebrum. Further, they noted a difference exposure-response relation between 

cerebral surface distances and CPF exposure, by FSIQ wherein the relation between brain area 

enlargements and FSIQ were positively correlated in the low CPF group (greater regional brain 

size correlated with greater IQ), but an inverse relation among the high CPF group. In 

supplemental analyses, authors illustrated that areas of regional brain enlargement among those 

more highly exposed to chlorpyrifos were due to underlying white matter enlargements. 

Researchers also noted sporadic differences in cortical thickness wherein higher CPF exposure 

was related to reduced cortical thickness. Preliminary analyses also displayed a disruption of 

normal male-larger-than-female areas as well as female-larger-than-male in certain areas of the 

brain known to differ by child sex in normal brain. Overall, researchers concluded that 

differences in brain structure (regional cerebral size and thickness) exist between CPF exposure 

groups, and the differences (high>low CPF) in regional brain size may be due to enlargement of 

underlying white matter. However, authors did not speculate as to the underlying biological 

explanation of the observed qualitative interaction between CPF exposure and FSIQ with 

morphological changes in key areas of the brain linked with learning, cognition and social 

behavior. 

Design and Methods 

Children selected for this MRI study (n=40) were participants in the larger Columbia Center for 

Children’s Environmental Health (CCCEH) birth cohort study (n=369). The study was initiated 

in the late 1990’s to evaluate the relation between air pollution and pesticide exposure and 
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neurodevelopmental effects in children. The cohort is comprised of a low income, inner-city 

population primarily of Dominican and African American ethnicity. Mothers were recruited 

among those who sought prenatal care by 20 weeks of pregnancy at one of the participating 

study hospitals. Participants were non-smoking women, ages 18-35, with no other co-morbidities 

including diabetes, hypertension, known HIV infection, and no documented drug use. Therefore, 

the cohort represents a population of women at low-risk for adverse neurodevelopmental 

outcomes among their children. Participants were residents of NYC for at least one year prior to 

enrollment in the research study. EPA’s review of all the epidemiological studies from the 

CCCEH (the Columbia studies) is contained in a recent presentation to the Federal Insecticide, 

Fungicide, and Rodenticide Act (FIFRA) Science Advisory Panel (FIFRA SAP), April 2012 

(FIFRA Scientific Advisory Panel, 2012). 

Authors measured chlorpyrifos and other organophosphate pesticides, cotinine, lead and mercury 

in umbilical cord blood samples taken at delivery using CDC analytic methods with published 

quality assurance and control procedures. Prenatal environmental tobacco smoke (ETS) was 

measured as maternal self-report, and verified through cotinine levels (≤15 ng/mL). Polycyclic 

aromatic hydrocarbon (PAH), another environmental chemical possibly related to adverse 

neurodevelopmental outcomes, was measured through personal air monitoring in the third 

trimester of pregnancy. In the current (MRI) study, high chlorpyrifos exposure was defined as 

the upper tertile of the exposed group, ≥4.39 pg/g, and low chlorpyrifos exposure was defined as 

values below the upper tertile, <4.39 pg/g, including those not exposed to CPF. 

To measure intelligence among school aged children, authors used the Wechsler Intelligence 

Scale for Children, 4th edition (WISC-IV). The instrument measures four areas of mental 

functioning: the Verbal Comprehension Index, the Perceptual Reasoning Index, the Working 

Memory Index, and the Processing Speed Index. These indices are associated with, but distinct 

from, overall intelligence quotient (IQ) and are sensitive to cognitive deficits related to learning 

and working memory. A Full-Scale IQ score combines the four composite indices. A General 

Ability Index score is a summary score of general intelligence, similar to Full-Scale IQ, but 

excludes contributions from both Working Memory Index and Processing Speed Index. WISCIV scores are standardized against U.S. population-based norms for English and Spanishspeaking children. 

MRI 

This section provides some broad detail as to the method of MRI data collection employed in this 

research paper; this is not a critical review of methods. Health outcomes of interest in this study 

were morphological changes in the brain in regions of the pediatric brain that are related to 

learning, cognition and social behavior, as noted in the traditional epidemiology studies. 

Researchers performed brain imaging studies using Magnetic Resonance Imaging (MRI) 

technology among a sub-sample of the cohort, and measured and compared morphological 

characteristics by chlorpyrifos exposure status. Details concerning the MRI imaging procedures, 

processing and pre-processing image modifications, methods to measure cortical thickness and 

methods used to identify the “template” brain against which individual child participant brain 

images were compared are presented in the Supplemental Information. 

Briefly, researchers used a high-resolution MRI, whole body scanner, with 8-channel head 

scanner for this study. Authors processed MRI images using biomedical imaging software 

(ANALYZE 8.0) while technicians were blinded to participant characteristics, and whether left 

or right hemisphere of the brain was imaged. Before morphological analyses were performed, 
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images in the dataset were re-sliced to correct for residual head rotation, tilt, and 

flexion/extension by participants during imaging. MRI images were pre-processed to correct for 

large-scale variation in color intensity. Extra-cerebral tissues captured in the MRI brain image 

were manually removed from the cerebral image before analyzing differences in brain 

morphology among participants by chlorpyrifos exposure; authors hypothesized difference in 

regional brain areas of the cerebrum only, other parts of the brain were therefore excised from 

the image before processing. Other similar modifications to the brain image were made to 

enhance measurement of key/pre-identified endpoints (cerebral surface morphology and cortex 

thickness) (See Supplemental Information). Variation in cortical grey matter was assigned by 

authors as four categories of color changes across images; authors selected color categories that 

adequately grouped the participants as to degree of cortical grey matter (i.e., creation of fourlevel categorical variable to measure cortical thickness) and to allow for a robust statistical 

analysis, particularly considering the small study sample (n=40). 

To measure morphological characteristics within cerebral areas associated with learning, 

cognition and social behavior, authors identified a “template” brain image against which all 

participant images were compared and measured. Using a two-step process, authors initially 

identified the individual in the study sample that most closely approximated average 

demographic features (child age, height and weight). Using this brain image, all other participant 

brain image studies were co-registered, i.e., similar points on the cerebral surface were matched 

according to the International Consortium on Brain Imaging. Researchers then measured 

distances of correlated points on the cerebral surface between the “template” brain image and all 

participant images. Using these measured distances, authors then identified a final “template” 

brain based upon the brain image series which most closely matched the average distances 

among cortical surface measures. Authors advocated that use of one “template” brain as opposed 

to an amalgamated “average” brain is superior because of the enhanced clarity and more well 

defined tissue interface with which to make the measurements for the study. All sets of brain 

image studies were then re-calibrated to the final “template” brain image for statistical analyses. 

Statistical Analyses 

Regarding the statistical analyses employed in this paper, the analyses involved comparison of 

the Euclidean distances measured between correlated points on the cerebral surface or cortical 

thickness by chlorpyrifos (high/low) exposure level, adjusting for age and sex. Supplemental 

information reported that thousands of statistical tests were performed. Authors used generalized 

linear equations and linear regression modeling to estimate average differences in morphological 

characteristics by change in chlorpyrifos exposure level (high/low). Because authors performed 

multiple statistical tests, they utilized the False Discovery Rate (FDR) methods to adjust the pvalue against Type I error. Authors tested the statistical significance of interaction terms of 

cerebral surface measures and FSIQ, and by child sex at p<0.05, adjusted for multiple testing. 

Study Results & Authors’ Interpretation 

For this study, authors selected child participants who had complete measures for CPF, PAH, 

ETS, and who completed the WISC IV testing. There were 70 participants who met these criteria 

and who also had cord blood levels in the upper tertile of 4.39 pg/g (High CPF exposure), and 

among these 28 met the additional criteria of having low or no exposure to PAH and ETS. 

Twenty of these participants completed the MRI series (20 of 28 eligible participants enrolled in 
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study). Similarly, there were 99 participants who met the enrollment criteria (completed the 

environmental exposure and IQ testing battery) and were in the lower exposure group (<4.39 

pg/g) and had no or low ETS and PAH. Among this group of low CPF exposed participants, 38 

were randomly selected to participate in the MRI study, and 20 participants completed the series 

(20 of 38 eligible participants enrolled in study). 

Authors reported no significant differences between those in the high and low chlorpyrifos 

exposure group, or among the larger cohort according the common socioeconomic indicators 

including maternal income and education, maternal IQ, race, gestational age and birth weight of 

child, and blood lead measure (See Table 1, main text). The correlation between chlorpyrifos and 

blood lead was low (ρ<0.20) and non-significant (p>0.20). 

Authors concluded that while overall brain size did not differ by prenatal chlorpyrifos exposure, 

certain areas of the brain were enlarged among those more highly exposed to chlorpyrifos 

including superior temporal, posterior middle temporal, and inferior post-central gyri, and 

superior frontal gyrus, gyrus rectus, cuneus, and pre-cuneus in the mesial views of the right 

hemisphere of the cerebrum. Further, they noted interaction, or a different exposure response 

relation between cerebral surface distances and FSIQ, by CPF exposure wherein the relation 

between brain area enlargements and FSIQ were positively correlated in the low CPF group 

(greater regional brain size equated to greater IQ), but an inverse relation among the high CPF 

group (greater regional brain size related to lower FSIQ scores). Authors noted statistical 

interactions in the superior temporal, inferior frontal, inferior pre-central, and inferior post 

central gyri bilaterally, and the pre-cuneus of the left hemisphere. 

In supplemental analyses, authors illustrated that areas of regional brain enlargement among 

those more highly exposed to chlorpyrifos were due to underlying white matter enlargements. 

This conclusion was supported by the observation of sporadic differences in cortical thickness 

wherein higher CPF exposure was related to reduced cortical thickness, or thinning of the cortex 

in certain areas. The qualitative interaction between CPF exposure and FSIQ with cerebral brain 

region enlargements may be explained by size of underlying white matter of the cerebrum, but 

authors did not speculate as to a biological explanation of the observed qualitative interaction. 

Preliminary analyses also displayed a disruption of normal male-larger-than-female areas as well 

as female-larger-than-male in certain areas of the brain known to differ by child sex in normal 

brain. 

Overall, researchers conclude that differences in brain structure (regional cerebral size and 

thickness) exist between CPF exposure groups, and the differences (high>low CPF) in regional 

brain size is likely due to enlargement of underlying white matter. Authors also observed an 

inverse relation between regional brain enlargements and IQ among those highly exposed to 

CPF, and positive association between regional brain size and IQ among those in the low/no 

exposed group. While authors do not speculate as to a possibly biological explanation of this 

observed statistical interaction, EPA notes that given that total brain size did not differ by 

exposure group, the enlargement of the white matter in certain areas could have been at the 

decrement of the size, quality or other morphological changes in the grey matter in other areas; 

neurons in grey matter of cerebrum affect learning and cognition. Observed inward deformations 

(reduced brain area) in the dorsal and mesial surfaces of the left superior frontal gyrus as also 
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reported the authors suggest may also be part of the explanation of the qualitative interaction. 

However, the authors indicate that additional study is needed to elucidate these possibilities. 

EPA Review 

This study by Rauh et al. 2012 follows upon both previous epidemiological research and 

experimental toxicology studies that suggest an association between chlorpyrifos exposure in the 

pre-natal and early life periods with adverse neurodevelopmental outcomes observed in children 

through school age, including intelligence deficits. Within this CCCEH cohort, authors reported 

links between prenatal chlorpyrifos exposure (concentration of CPF in cord blood at delivery 

(pg/g)) with attentional difficulties, behavioral problems and measures of intelligence 

particularly working memory (V. A. Rauh et al., 2006; V. Rauh et al., 2011). EPA’s review of 

these previous epidemiology studies from the CCCEH cohort were presented to the FIFRA SAP 

in April 2012 (FIFRA Scientific Advisory Panel, 2012). Regarding this study, EPA notes a 

limited and somewhat unbalanced depiction of the available rodent experimental data was 

presented in the paper. A brief review of references seems to provide some support for the 

authors’ assertion that the epidemiological findings are consistent with those in the experimental 

literature; however, a fuller evaluation of the data concerning animal to human concordance is 

needed. For example, the authors’ stated support for their finding regarding enlargements in 

underlying white matter is not strongly supported by existing experimental data concerning CPF 

induced glial scarring. 

In this study, authors evaluated morphological characteristics of cortical brain regions which 

have been linked to disrupted learning, cognition and social behavior in previous epidemiologic 

and experimental studies. The cerebrum has been described as the “seat of intelligence” and 

provides the ability to read, write, and speak; to make calculations ,and to compose music; and to 

remember the past, to plan for the future, and to imagine things that have never existed before 

(Tortora & Grabowski, 2003). However, while the cerebrum’s influence upon human 

intelligence is generally supported, the study lacked specific hypotheses regarding particular 

areas of the cerebrum affected (the largest of the four components of the brain). Therefore, as 

noted in supplemental information, many thousands of statistical tests were performed evaluating 

areas of the cerebrum that have been mapped. Authors appropriately employed methods to 

control for Type I error, however in many ways this research requires replication, as noted by the 

authors. Therefore, use of these data to inform specific underlying biological mechanisms or 

pathways of the potential toxic action of CPF is limited. 

EPA is seeking external peer review of this specific aspect of the current study to verify that the 

MRI methods are sound and appropriate. Briefly, however, the main text and supplemental 

information states that technicians (MRI image readers) were blinded as to exposure status, as 

well as right or left hemisphere. This design feature adds to the validity and reliability of the 

study. However, EPA notes authors do not state whether multiple MRI image readers were 

employed in identifying and measuring cortical surface thickness and cerebral surface area, etc. 

of interest in this study, and whether quality control statistics such as the inter-rater reliability 

coefficient were calculated and considered. Supplemental information notes that validation 

studies were performed, however does not describe the aspects of methods that were validated. 

EPA particularly seeks input from experts in this area as to whether these methods and validation 

studies, if known, are sound. 
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Authors appropriately described the inclusion and exclusion criteria used in this MRI study. 

Authors excluded participants for who measureable levels of both PAH and ETS were reported 

in the total cohort study. In this way, by design, authors eliminated the chance of confounding by 

some other environmental chemicals known to effect neurodevelopment in the relation between 

chlorpyrifos and brain morphology. Authors also included only those participants who provided 

full information regarding prenatal chlorpyrifos exposure, other environmental chemical 

exposure and who completed the WISC intelligence testing. Given the relatively small sample 

size (n=40) for this relatively expensive imaging study these decisions eliminated the issue of 

missing data, and the need to add degrees of freedom to statistical models for control of other 

measured environmental chemicals. These decisions added to the validity and statistical power of 

the study. Although inclusion and exclusion criteria were somewhat rigorous in this study, 

authors still achieved a 60% participation rate among those eligible to participant (HIGH: 20/28, 

LOW: 20/38). Further, Table 1 indicates adequate comparability between the exposure groups 

and the total cohort, although those in the high exposed group were slightly albeit not statistically 

significantly more likely to be in families in which maternal education and income were slightly 

greater than the low exposure group. Therefore, in general, EPA believes that authors were able 

to identify an unbiased sample of cohort, representative of population sample of low income, 

inner city kids. However, given the rigorous enrollment criteria, the external validity of the study 

findings may be limited if the organophosphate pesticide exposure-brain morphology association 

is modified by factors that are more, or less, prevalent in the study populations relative to the 

population(s) to which inference is being made, or to populations with a substantially different 

exposure range. 

In the larger cohort, authors measured concentration of chlorpyrifos parent compound in the cord 

blood of infants at delivery. Generally, biomarkers are considered superior measures than 

questionnaire based self report or environmental monitoring. The assays used in the study were 

highly sensitive allowing detection of very low levels of chlorpyrifos, in the pg/g range (CDC 

published methods). However, these measures are comparable to those in other samples of the 

general population, as noted by authors. Uncertainties remain however, as to the extent the use of 

one biomarker measurement reflects exposure(s) over critical windows of development during 

pregnancy, the temporality or periodicity of exposure, over the relevant time period of gestation. 

These cord blood measures have been used in several studies to estimate pre-natal exposure to 

chlorpyrifos, i.e., during the entire period of gestation. Validations studies performed within the 

CCCEH cohort support that use of a one-time measure is an accurate and reliable measure with 

which to rank participants (high/low). In this study, authors categorized chlorpyrifos exposure as 

high (greater than or equal to the upper tertile of 4.39 pg/g) and low (all values below 4.39 pg/g 

including those values below the LOD) which exposure methodology can accurately and reliably 

support. PAH in the ambient air was measured using stationary air monitors and ETS was 

measured through maternal self-report, validated by cotinine level. A noted limitation in the 

measurement of pesticide exposure in these studies is the lack of measurement during the postnatal period through the time period of the epidemiologic evaluations, i.e., school age. Chemical 

exposure in the post-natal period may confound or alter the relation between prenatal 

chlorpyrifos and neurodevelopmental outcomes, as noted in EPA’s evaluation (FIFRA Scientific 

Advisory Panel, 2012). In this study, child age and sex were the only co-variables considered; 

these variables are simple to accurately and reliably measure using linkage with birth/medical 
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records. 

The selection of few co-variables for the statistical models used to evaluate the relation between 

prenatal chlorpyrifos exposure and brain morphology changes is appropriate. For another 

variable to be a confounder in this relation, the other variable would have to be related to both 

chlorpyrifos exposure and (causally) related to brain morphology (e.g., surface area, cortical 

thickness, gyri dimensionality). Child age and sex are appropriate confounders as these factors 

would influence brain size, growth and differentiation. While some research has noted a link 

between social characteristics and brain development and possibly morphological characteristics, 

the authors’ decision to restrict CCCEH participation to those with very similar SES 

characteristics reduces that possibility of confounding the association of interest in this MRI 

study by SES. Further, the small sample size supports the use of few additional co-variables in 

the statistical model to maximize statistical power. Similar, within the MRI study, authors 

restricted participation to those without other co-exposures such as PAH and ETS thus 

eliminating the need to consider co-adjustment for these variables. Authors additionally 

measured blood lead in this sample and the overall, total cohort and observe the correlation 

between chlorpyrifos and blood lead (necessary factor for positive confounding bias to occur) 

was very low (ρ<0.20) and not statistically significant (p>0.20). 

Other biases that may be present in epidemiology studies include selection and information 

biases. Selection bias in prospective cohort studies may occur if participants drop out of a study 

and the decision to drop out was related to either the CPF exposure or neurodevelopmental 

outcome. In the CCCEH study the retention rate is high for a birth cohort study. The 

participation rate in the MRI study was good (60%). Authors illustrated that those included and 

excluded were comparable, and the sample and total cohort were also comparable. This 

ameliorates the concern. Temporal bias, a question as to whether exposure preceded disease, is 

not a large concern in a prospective study. The prospective nature of the cohort study also 

reduces that chance of differential information bias or the misclassification of exposure, 

confounder, or outcome information by key study factors. Non-differential exposure 

measurement error may have occurred: 1) the single measurement chlorpyrifos during the third 

trimester, 2) error arising from differences between measured biomarker levels and actual 

chlorpyrifos at the one-time point, 3) unmeasured time- and space- dependent patterns of 

chlorpyrifos exposure, 4) uncertainty regarding the critical period for chlorpyrifos effects on 

development, 5) missing exposure data, 6) laboratory errors, and 7) imputation of missing 

exposure levels. Measurement errors in the ascertainment of chlorpyrifos exposure are likely to 

have occurred. However, because of the prospective study design employed, the errors are 

unlikely to result in falsely positive findings, because the probability and magnitude of these 

errors are likely to be independent of the outcome status of participants (MRI characteristics). 

The degree to which measurement error in the imaging studies performed using MRI technology 

are of keen interest to EPA. 

Authors employed generalized linear models to estimate the association between CPF exposure 

(high/low) and “voxels” or three-dimensional data reflecting morphological characteristics of the 

cerebral cortex, specifically cortical thickness, distances on the cerebrum, size and dimension of 

key gyri important to learning, cognition, and social behavioral, adjusting for child age and sex. 

Use of a two-level exposure variable is preferable as it conserves study power, and is supported 
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by the one-time measure of CPF at delivery (cord blood CPF concentration pg/g). Researchers 

utilized a method to correct for multiple statistical tests (FDR) which was reasonable and 

appropriate in this instance. Authors acknowledged low statistical power to evaluate statistical 

interaction was likely including gene by environment interactions. Linear modeling is a preferred 

method when sample size is small as the method conserves statistical power. Authors did not 

present a table of model results, only graphical depictions of areas of morphological differences 

of the cerebrum by CPF exposure, color coded to reflect the statistical significance of the 

differences in specific, region brain size or volume or area. Additional information reflecting 

model fit statistics, as well as display of evidence of effect modification in both exposure strata 

(high and low CPF exposure) would enhance clarity and presentation of the research results. 

While authors appropriately characterized the evaluation of the interaction with child sex and 

CPF and brain morphology as preliminary, given the number of statistical tests performed and 

lack of hypotheses regarding particular areas of the cerebrum and cortical surface, results require 

replication before firm conclusions regarding underlying biological mechanisms at play can be 

posited. 

Authors concluded that the evidence from the study illustrated changes in brain morphology in 

association with higher CPF exposure, and that changes observed were in areas of the brain that 

sub-serve those learning, cognition and social behavioral, supported by previous observational 

and experimental literature. 

 
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References 

FIFRA Scientific Advisory Panel. (2012). Draft Issue Paper Scientific Issues Concerning the 

Health Effects of Chlorpyrifos. http://www.regulations.gov/#!documentDetail;D=EPAHQ-OPP-2012-0040-0002. 

Rauh, V. A., Garfinkel, R., Perera, F. P., Andrews, H. F., Hoepner, L., Barr, D. B., . . . Whyatt, 

R. W. (2006). Impact of prenatal chlorpyrifos exposure on neurodevelopment in the first 

3 years of life among inner-city children. Pediatrics, 118(6), e1845-1859. doi: 

10.1542/peds.2006-0338 

Rauh, V. A., Perera, F. P., Horton, M. K., Whyatt, R. M., Bansal, R., Hao, X., . . . Peterson, B. S. 

(2012). Brain anomalies in children exposed prenatally to a common organophosphate 

pesticide. Proc Natl Acad Sci U S A, 109(20), 7871-7876. doi: 1203396109 [pii] 

10.1073/pnas.1203396109 

Rauh, V., Arunajadai, S., Horton, M., Perera, F., Hoepner, L., Barr, D. B., & Whyatt, R. (2011). 

Seven-year neurodevelopmental scores and prenatal exposure to chlorpyrifos, a common 

agricultural pesticide. Environ Health Perspect, 119(8), 1196-1201. doi: 

10.1289/ehp.1003160 

Tortora, Gerard J., & Grabowski, Sandra Reynolds. (2003). Principles of Anatomy and 

Physiology (pp. page 467). United States of America: John Wiley & Sons. 
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Appendix 4. Detailed Summary Tables of Children’s Environmental Health Epidemiology 

Author, Year, 

Sample Size 

Outcome 

Assessed 

Exposure 

Measurement 

Potential confounders 

considered 

Primary Result Conclusions/Uncertainties 

Article 1: Whyatt et al. (2004) Columbia U. (N=314) 

Birth length, Birth weight, head circumference 

Four cord plasma chlorpyrifos exposure groups and four chlorpyrifos and diazinon exposure groups. Chlorpyrifos only categories, Group 1: levels below LOD (32% of participants); Group 2: lowest 1/3 of detectable levels (20 %); Group 3: middle 1/3 (24%), Group 4: highest 1/3 (25%.) Chlorpyrifos and diazinon together: Group 1: 26%, Group 2: 22 %, Group 3: 26%, Group 4: 26%. 

Gestational age, maternal prepregnancy weight, maternal net pregnancy weight gain, gender of newborn, parity, race/ethnicity, ETS in home, season, cesarean section 

For each log unit increase in cord plasma chlorpyrifos levels, birth weight decreased by 42.6 g (95% CI: –81.8 to –3.8) and birth length decreased by 0.24 cm (95% CI: –0.47 to –0.01). Birth weight averaged 186.3 g less (95% CI: –375.2 to –45.5) among newborns with the highest compared with lowest 26% of exposure levels (p = 0.01). 

Associations between birth weight and length and cord plasma chlorpyrifos were statistically significant (p ≤

0.007) among newborns born before the January 2001 policy change. Among newborns born after January 2001, exposure levels were substantially lower, and no associations with fetal growth outcomes were observed (p > 0.8). 

Article 2: Berkowitz et al.(2004) Mt. Sinai (N=404) 

Birth length, birth weight, head circumference, gestational age 

LOD: 11 ug/L (57% <LOD TCPy) 

Race/ethnicity, infant sex, and gestational age. The authors also controlled for birth weight or birth length in their assessment of head circumference and pesticide exposure. 

Mean levels of birth weight, length, head circumference, and gestational age did not differ between those with urinary pesticide metabolite levels below and above the level of detection. Similarly, no statistically significant associations were observed between reported pesticide exposure and mean indices of fetal 

PON1 activity also predictor of smaller head circumference; creatinine corrected 
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growth and gestational age. 

Article 3: Eskenazi et al. (2004) CHAMACOS (N=488) 

Birth length, birth weight, head circumference, Gestation Length, Ponderal index 

Total DAPs: median 136 nmol/L (range: 10–6,854); DEP: median 22 nmol/L 

Gestational age, gestational age squared, maternal age, pregnancy weight gain, week of initiating prenatal care, parity, infant sex, mother's country of 

Decreases in gestational duration associated with two measures of in utero pesticide exposure: levels of metabolites of dimethyl 

Maternal urine collection averaged wks.14, 26, not creatinine-corrected 
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(range: 2–680 nmol/L); TCPy: median 3.3 nmol/L (range: 0.2–56.1nmol/L) (76% >LOD) 

birth, body mass index, family income, poverty level, smoking, alcohol, illicit drug use, environmental tobacco smoke, caffeine, history of low birth weight, and history of pre-term delivery. 

phosphate pesticide compounds and whole blood ChE. 

Article 4: Harley et al. (2011) CHAMACOS (N=329) 

Birth length, birth weight, head circumference, gestational age 

The geometric mean for the DAP concentrations during pregnancy (for the average of the two sampling periods) was 146 nmol/L (95% CI: 133, 160); of this, a larger proportion was DMP metabolites (GM = 109 nmol/L; 95% CI =98, 120) than DEPs (GM= 23 nmol/L; 95% CI = 21, 25). Allele 

Maternal intelligence (Peabody Picture Vocabulary Test (PPVT)), measures of how stimulating the environment is, and known or suspected neurotoxins were measured prenatally. To measure the quality and extent of stimulation available to a child in the home environment, the Infant-Toddler HOME (Home Observation for Measurement of the Environment) inventory was completed at the 6-month, 1, 2, 3.5, 5, and 7 year visits; known or suspected neurotoxicants, polybrominated diphenyl ethers 

The authors observed evidence of an association between prenatal exposure to OP pesticides as measured by urinary DAP metabolites in women during pregnancy, is associated with decreased cognitive functioning in children at age 7. 

Infants whose PON1 genotype and enzyme activity levels suggested that they might be more susceptible tothe effects of OP pesticide exposure had decreased fetal growth and length of gestation. PON1 may be a contributing factor to preterm or low birth weight birth. 
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Primary Result Conclusions/Uncertainties 

frequencies: PON1 192 Q allele= 50%; PON1 -108 T allele= 46%.Mean arylesterase activity: For infants: 33.6 U/mL (SD = 16) For mothers: 136.6 U/mL (SD = 44). Mean paraoxonase activity: For infants: 256.6 U/L (SD = 165); For mothers: 989.0 U/L (SD = 616). 

(PBDEs), polychlorinated biphenyls (PCBs), p,p’-dichlorodiphenyltrichlorethane (DDT), p,p’- dichlorodiphenyltrichlorethylene (DDE), and lead. 

Article 5: Engel et al. (2007) Mt. Sinai (N=311) 

Brazelton Neonatal Behavioral Assessment Scale (BNBAS) , primitive reflexes (neurological integrity) measured before hospital discharge. 

Diethylphosphates (DEP): 24.7 nmol/L; Total DAP: 82 nmol/L 

Maternal age, race, marital status, education, cesarean delivery, delivery anesthesia, infant age at examination, infant gender, infant jaundice, smoking (yes/no), alcohol consumption, caffeine consumption, illicit drug use during pregnancy, and the examiner. 

No adverse associations were found for DAPs and any measured behavior. Relative to the first quartile, quartiles 2–4 of total DEPs, DMPs, and DAPs were associated with an increased proportion of abnormal reflexes, although the associations did not increase monotonically and varied in their strength and precision. 

Used non-specific biomarker DEP/DAP 
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Primary Result Conclusions/Uncertainties 

Article 6: Young et al. (2005) CHAMACOS (N=381) 

Neurodevelopment, Brazelton Neonatal Behavioral AssessmentScale (BNBAS), abnormal reflexes 

DAP (ave. during pregnancy): median 222nmol/L (range: 7–21,867 nmol/L); DEP (ave. pregnancy): median 21nmol/L (range: 2–680 nmol/L) 

Maternal age, BMI, any smoking/alcohol/drug use during pregnancy, gestational age at which prenatal care was initiated, total number of prenatal care visits, mean pregnancy blood pressure, parity, method of delivery, general anesthesia used during delivery, breastfeeding initiated after delivery, poverty level, infant sex, age in days at BNBAS, minutes since last feed at BNBAS, and BNBAS examiner. 

Among the >3 day old infants, increasing average prenatal urinary metabolite levels were associated with both an increase in number of abnormal reflexes (total DAP: adjusted beta = 0.53, 95% CI = 0.23, 0.82; dimethyls: adjusted beta = 0.41, 95% CI = 0.12, 0.69; diethyls: adjusted beta = 0.37, 95% CI = 0.09, 0.64), and the proportion of infants with more than three abnormal reflexes (total DAP: adjusted OR = 4.9, 95% CI = 1.5, 16.1; dimethyls: adjusted OR = 3.2, 95% CI = 1.1, 9.8; diethyls: adjusted OR = 3.4, 95% CI = 1.2, 9.9). 

Associations seen pre-natal OP, not post-natal OP exposure, Maternal urine collection averaged wks.14, 26 

Article 7: Rauh et al. (2006) Columbia U. (N=254) 

Neurodevelopment: The Bayley Scales of Infant Development II 

Exposure levels were categorized as low (< 6.17 

pg/g) or high 

Data were collected regarding lead exposure, demographics, education and occupational history, income, active and 

At the 36 month milestone, the likelihood of highly exposed children developing mental delays 

The authors summarize three main findings: 1) by age 3, the more highly exposed children 
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Primary Result Conclusions/Uncertainties 

(BSID-II), Mental Development Index (MDI) and Psychomotor Development Index (PDI) at 12, 24, and 36 months of age. • Behavior: Child Behavior Checklist (CBCL) at 12, 24, and 36 months . • Quality of the child-care environment: The Home Observation for Measurement of the Environment (HOME) 

(>6.17 pg/g) passive smoking, alcohol and 

drug use during pregnancy, and residential pesticide use. Final models included prenatal environmental tobacco smoke (ETS) exposure, gender, ethnicity, gestational age at birth, quality of home caretaking environment, maternal education, and maternal IQ. 

were 2.4 times greater (95% CI: 1.12-5.08, p = .02) and motor delays were 4.9 greater (95% CI: 1.78-13.72; p = .002) than those with lower prenatal exposure. The GLM analysis for PDI scores showed a significant effect of chlorpyrifos exposure 

over time with an estimated deficit of approximately 7 points by age 36 months (p = .01). 

demonstrated mental and motor delays; 2) the observed developmental trajectories for PDI and MDI scores confirmed that the adverse impact on cognitive and motor development increased over time; and 3) by age 3, highly exposed children were more likely to demonstrate clinically significant attention problems. 

Article 8: Lovasi et al. 2010 Columbai U. (N=266) 

Bayley scores (MDI/PDI) 12 mo., 24 mo., 36 mo. 

N/A Neighborhood characteristics: 

The percentage of housing units without complete plumbing, the percentage of vacant housing units, the percentage of residents below the federal poverty line, the percentage of residents older than 25 years of age who completed high school, the percentage of households receiving public assistance, the percentage of housing units with one or more residents per room, racial composition, the percentage of residents born outside the United States, the percentage of Spanish-speaking residents, and the percentage of residents who were linguistically isolated 

Neighborhood characteristics did not confound the observed association between chlorpyrifos levels and cognitive development. 

Hierarchal regression analysis of potential confounding by SES 
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considered 

Primary Result Conclusions/Uncertainties 

Article 9: Engel et al. (2011) Mt. Sinai (N=276) 

Bayley scores (MDI/PDI) at 12 mo., 24 mo. 

DEP: 24.7 nmol/L; Total DAP: 82 nmol/L (same as Engel 2007) 

Maternal age, race/ethnicity, marital status, education, breastfeeding, child sex, alcohol, smoking, or drug use during pregnancy, maternal IQ, a score based on assessment of the home environment (HOME), season of urine collection, language spoken in the home, age at testing, examiner, and urinary creatinine level. 

An observed association between prenatal total dialkylphosphate metabolite level and a decrement in mental development at 12 months among blacks and Hispanics. 

Used non-specific biomarker DEP/DAP; some evidence of effect modification by PON1 genotype 

Article 10: Eskenazi et al. (2007) 

Neurodevelopment, Bayley Index (MDI, PDI), 

DEP: geom. Mean in mother 18.1 nmol/L (95% CI 

Psychometrician, location of assessment, exact age at assessment, sex, breast-feeding 

DAP metabolite levels during pregnancy, particularly from dimethyl 

No strong associations identified with DE or TCPy, Maternal urine collection 
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considered 

Primary Result Conclusions/Uncertainties 

CHAMACOS (N=372) 

Maternal behavior checklist at 6, 12, and 24 months 

16.7–19.7); DEP geom mean in child at 24 months 10.5 nmol/L (95% CI 8.8–12.6); TCPy median 3.54 ug/l 

duration (months), HOME score, and household income, parity, maternal PPVT, maternal age, education, depressive symptoms, active/passive smoking exposure during pregnancy, regular alcohol use during pregnancy, marital status, father’s presence in home, housing density, maternal work status, ≥ 15 hours out-of-home 

childcare/week, birth weight, gestational age, abnormal reflexes, PCBs, lead, DDT, βhexachlorocyclohexane, and 

hexa-chlorobenzene 

phosphate pesticides, may be negatively associated at 24 months with mental development (MDI) on the Bayley Scales and an increase in risk of maternally reported PDD. 

averaged wks.14, 26 

Article 11: Eskenazi et al. (2010) CHAMACOS (N=371) 

Neurodevelopment, Bayley Index (MDI, PDI), Maternal behavior checklist at 6, 12, and 24 months, PON1 gene and enzyme levels 

The geometric mean for the DAP concentrations during pregnancy (for the average of the two sampling periods) was 146 nmol/L (95% CI: 133, 160); of this, a larger proportion was DMP metabolites (GM = 109 nmol/L; 95% CI =98, 120) than DEPs (GM= 23 nmol/L; 95% CI = 21, 25). Allele frequencies: PON1 192 Q allele= 50%; PON1 -108 T allele= 46%.Mean arylesterase activity: For infants: 33.6 U/mL (SD = 16) For mothers: 136.6 

Psychometrician, location of assessment, exact age at assessment, sex, breast-feeding duration (months), HOME score, and household income, parity, maternal PPVT, maternal age, education, depressive symptoms, active/passive smoking exposure during pregnancy, regular alcohol use during pregnancy, marital status, father’s presence in home, housing density, maternal work status, ≥ 15 hours out-of-home 

childcare/week, birth weight, gestational age, abnormal reflexes, PCBs, lead, DDT, βhexachlorocyclohexane, and 

hexa-chlorobenzene 

Decrease MDI (24 mo.) PON1108TT –5.7 (–9.0 to –

2.5) 9=0.01; Decrease PDI (24 mo.) PON1108TT –2.8 (–

5.7 to 0.2) p=0.07; increased odds PDD 2.0 (0.8 to 5.1) p=0.14; no association PON1192; no 

association PON1 activity measured newborn, 2 years, maternal and MDI, PDI, PDD. Evidence of decreasing MDI score by number of PON1108 variant 

alleles: PON1108CC –3.2 (–

9.8 to 3.5), CT –3.7 (–8.0 to 0.6), TT –5.5 (–11.1 to 0.1), p-interaction 0.98. 

In this study population, evidence PON1 may influence MDI score, but not PDI or PDD risk at twoyears. Non-significant evidence of decreasing MDI score by increasing DAP levels across strata of the number of PON1108 variant 

alleles, interaction nonsignificant. Similar trend with prenatal DEP levels and MDI, PDI by PON108 alleles, but less pronounced. Overall, limited, nondefinitive evidence of effect modification by PON1 status in the relation between mental and psychomotor effects and prenatal DAPs. 
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Primary Result Conclusions/Uncertainties 

U/mL (SD = 44). Mean paraoxonase activity: For infants: 256.6 U/L (SD = 165); For mothers: 989.0 U/L (SD = 616). 

Article 12: Marks et al. (2010) CHAMACOS (N=348) 

CBCL; K-CPT; ADHD confidence index; Hillside behavioral rating scale; composite ADHD indicator 

DAP (geom mean) pregnancy 109.0 nmol/L; DEP 17.7 nmol/L 

Psychometrician, exact age at assessment, sex, maternal education, depressive symptoms, PPVT (continuous), ≥ 15 hr out-of-home child care/week, breast feeding duration (months), maternal age, parity, marital status, active/passive smoking exposure and regular alcohol use during pregnancy, presence of father in home, maternal work status, and household income 

Prenatal DAPs were nonsignificantly associated with maternal report of attention problems and ADHD at age 3.5 years, but were significantly related at age 5 years [CBCL attention problems: β = 0.7 

points; 95% confidence interval (CI), 0.2-1.2; ADHD: β = 1.3; 95% CI, 

0.4-2.1]. 

Marked effect modification by gender: 11-fold increase ADHD composite indicator in boys, less than 2-fold in girls, however unstable estimates; weak evidence of association DAPs at 3.5, 5 yrs. and attention 

Article 13: Rauh et al. (2011) Columbia U. (N=265) 

• Wechsler Scales of Intelligence for Children (WISCIV) • Child Behavior Checklist (CBCL). 

• Chlorpyrifos levels in umbilical cord blood samples, N=256 newborns• If no cord blood (12% of subjects), levels were imputed from mothers’ values. • Values for samples with nondetectable chlorpyrifos levels (N=115, 43%) were imputed by 

Data were collected regarding lead exposure, demographics, education and occupational history, income, active and passive smoking, alcohol and drug use during pregnancy, and residential pesticide use. Final models included prenatal environmental tobacco smoke (ETS) exposure, gender, ethnicity, gestational age at birth, quality of home caretaking environment, maternal education, and maternal IQ. 

Full-Scale IQ: (B) of -0.003, CI -0.006, 0.001, p= 0.064 Working Memory Index: (B) of -0.006, CI -0.009, -0.002, p<0.001. The investigators articulated these results as showing that a 1 pg/g increase in chlorpyrifos exposure was associated with a 0.006 point decrease in the log-transformed Working Memory score and a 0.003 point decrease in the log-transformed FullFor each standard deviation increase in exposure (4.61pg/g) there is a 1.4% reduction in Full-Scale IQ and a 2.8% reduction in Working Memory. 
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Primary Result Conclusions/Uncertainties 

using assayspecific limit of detection (LOD) values to impute an approximate level. 

Scale IQ score. The investigators concluded that for each standard deviation increase in exposure (4.61pg/g) there is a 1.4% reduction in Full-Scale IQ and a 2.8% reduction in Working Memory. 
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considered 

Primary Result Conclusions/Uncertainties 

Article 9: Engel et al. (2011) Mt. Sinai (N=169) 

Wechsler Preschool and Primary Scale of Intelligence, 3rd edition (WPPSIIII) at ages < 7 years; Wechsler-IV Intelligence Scale (verbal comprehension; perceptual reasoning, working memory, processing speed, full scale intelligence) at age 7-9 years 

DEP: 24.7 nmol/L; Total DAP: 82 nmol/L (same as Engel 2007) 

Maternal age, race/ethnicity, marital status, education, breastfeeding, child sex, alcohol, smoking, or drug use during pregnancy, maternal IQ, a score based on assessment of the home environment (HOME), season of urine collection, language spoken in the home, age at testing, examiner and urinary creatinine level. 

At age 6-9 years, nonstatistically significant reductions in full scale IQ, perceptual reasoning, verbal comprehension, working memory and processing speed with increasing DAP, more profound with DEP than DMP; 

Used non-specific biomarker DEP/DAP; some evidence of effect modification by PON1 genotype 

Article 14: Bouchard et al. (2011) CHAMACOS (N=329) 

Wechsler-IV Intelligence Scale (verbal comprehension; perceptual reasoning, working memory, 

Total DAPs (quintiles): Q1 (39 nmol/L); Q2 75 nmol/L; Q3 126 nmol/L; Q4 221 nmol/L; Q5 508 nmol/L. Geometric 

Maternal intelligence, measures of how stimulating the environment is, and known or suspected neurotoxins were measured prenatally. Maternal intelligence was assessed via the Peabody Picture Vocabulary 

The authors observed evidence of an association between prenatal exposures to OP pesticides as measured by urinary DAP metabolites in women during pregnancy, and 

Prenatal measures taken later half of pregnancy more significantly associated intelligence than early; little evidence postnatal OP exposure associated with intelligence; 
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Primary Result Conclusions/Uncertainties 

processing speed, full scale intelligence) measured at age 7 years 

mean DAP 131 nmol/L 

Test (PPVT). To measure the quality and extent of stimulation available to a child in the home environment, the Infant-Toddler HOME (Home Observation for Measurement of the Environment) inventory was completed at the 6-month, 1, 2, 3.5, 5, and 7 year visits; known or suspected neurotoxicants, polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), p,p’-dichlorodiphenyltrichlorethane (DDT), p,p’- dichlorodiphenyltrichlorethylene (DDE), and lead. 

decreased cognitive functioning in children at age 7. 

7 point reduction in full scale intelligence DAP Q5/Q1 (SS) 

Article 15: Whyatt et al. (2007) Columbia U. (N=102) 

None Geometric mean, 

6.9 ± 17.0 ng/m3; 

range < 0.4–171 ng/m3. Personal air 

monitor: median 2.8 ng/m3, mean 

6.2 ± 11.1 ng/m3, 

range < 0.4–83.4 ng/m3

N/A There was little withinhome variability and no 

significant difference in air concentrations within homes over time (p ≥ 0.2); between-home variability accounted for 88% of the variance in the indoor air levels of propoxur, 92% in chlorpyrifos, 94% in diazinon, and 62% in piperonyl butoxide (p < 0.001). Indoor and maternal personal air insecticide levels were highly correlated (r = 0.7–0.9, p < 0.001). 

Indoor and maternal personal air insecticide levels were highly correlated (r = 0.7–0.9, p < 0.001). 

Article 16: Whyatt et al. (2009) Columbia U. (N=102) 

None The limit of 

detection (LOD) of chlorpyrifos in blood samples was 0.5–1 pg/g plasma. The LOD of TCPy in urine samples was 0.26 ng/mL urine. The LOD for TCPy in meconium was 0.2 

N/A Meconium TCPy 

concentrations were positively correlated with chlorpyrifos in maternal and cord blood (r = 0.25–0.33, p < 0.05) and with TCPy in maternal urine (r = 0.31, p < 0.01). 

TCPy in maternal urine samples was not reliable, but the maternal and cord blood chlorpyrifos as well as the TCPy levels in meconium were reliable measures of exposure 
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Primary Result Conclusions/Uncertainties 

ng based on a sample weighing 0.5 g. Exposure marker levels below the LOD were given a value of half the level of detection, and were then log10 transformed. 

Rauh (2012), (n=40) 

Morphologicalchange in the

pediatric brain in

regions of the

brain known to be

associated with

learning, cognition

and social behavior

Tertile 3 ( ≥4.39pg/g), compared

to Tertiles 0, 1, 2

(<4.39 pg/g,including those not

exposed to CPF)

Age, sex Authors report differences

in brain structure (regional

cerebral size and thickness)

byCPF exposure groups, and

the differences (high>low

CPF) in regional brain size is

likelydue to enlargement of

underlying white matter.Statistical interaction by

gender reported.

Authors concluded that the

evidence from the study

illustrated changes in brain

morphology in associationwith higher CPF exposure,

and that changes observed

were in areas of the brain

that subserve those

learning, cognition andsocial behavioral,

supported by previous

observational andexperimental literature.
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Appendix 5. Summary of OPP’s ChE Policy & Use of BMD Modeling 

OPP’s ChE policy (USEPA, 200074) describes the manner in which ChE data are used in human 

health risk assessment. The following text provides a brief summary of that document to provide 

context to points of departure selected. 

AChE inhibition can be inhibited in the central or peripheral nervous tissue. Measurements of 

AChE or cholinesterase (ChE) inhibition in peripheral tissues (e.g., liver, diaphragm, heart, lung 

etc) are rare. As such, experimental laboratory studies generally measure brain (central) and 

blood (plasma and red blood cell, RBC) ChE. Blood measures do not represent the target tissue, 

per se, but are instead used as surrogate measures for peripheral toxicity in studies with 

laboratory animals or for peripheral and/or central toxicity in humans. In addition, RBC 

measures represent AChE, whereas plasma measures are predominately BuChE. Thus, RBC 

AChE data may provide a better representation of the inhibition in target tissues. As part of the 

dose response assessment, evaluations of neurobehavior and clinical signs are performed to 

consider the dose response linkage between AChE inhibition and apical outcomes. 

Refinements to OPP’s use of ChE data have come in the implementation of BMD approaches in 

dose response assessment. Beginning with the OP CRA, OPP has increased its use of BMD 

modeling to derive PoDs for AChE inhibiting compounds. Most often the decreasing 

exponential empirical model has been used. 

OPP does not have a defined benchmark response (BMR) for OPs. However, the 10% level has 

been used in the majority of dose response analyses conducted to date. This 10% level 

represents a 10% reduction in AChE activity (i.e., inhibition) compared to background (i.e., 

controls). Specifically, the BMD10 is the estimated dose where ChE is inhibited by 10% 

compared to background. The BMDL10 is the lower confidence bound on the BMD10. 

The use of the 10% BMR is derived from a combination of statistical and biological 

considerations. A power analysis was conducted by the Office of Research and Development 

(ORD) on over 100 brain AChE datasets across more than 25 OPs as part of the OP CRA 

(USEPA, 2002). This analysis demonstrated that 10% is a level that can be reliably measured in 

the majority of rat toxicity studies. In addition, the 10% level is generally at or near the limit of 

sensitivity for discerning a statistically significant decrease in ChE activity in the brain 

compartment and is a response level close to the background brain ChE level. With respect to 

biological considerations, a change in 10% brain AChE inhibition is protective for downstream 

cholinergic clinical signs and apical neurotoxic outcomes. With respect to RBC AChE 

inhibition, these data tend to be more variable than brain AChE data. OPP begins its BMD 

analyses using the 10% BMR for RBC AChE inhibition but BMRs up to 20% could be 

considered on a case by case basis as long as such PoDs are protective for brain AChE inhibition, 

potential peripheral inhibition, and clinical signs of cholinergic toxicity.

 

74 USEPA (2000) Office of Pesticide Programs, US Environmental Protection Agency, Washington DC 20460. 

August 18, 2000 Office of Pesticide Programs Science Policy of The Use of Data on Cholinesterase Inhibition for 

Risk Assessments of Organophosphorous and Carbamate Pesticides.
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Appendix 6. Columbia Center for Children’s Environmental Health 

(CCCEH) Epidemiology Data Acquisition “Raw Data” Request 

I. ACTION REQUESTED 

 To fulfill identified information needs for the purposes of incorporating the Columbia Center for 

Children’s Environmental Health (CCCEH) epidemiology data into the Human Health Risk 

Assessment (HHRA) for chlorpyrifos, the agency sought to obtain certain “raw data” from 

CCCEH researchers. Specifically, EPA requested the original analytic data file used to support 

analyses presented in the peer-reviewed, published epidemiology studies concerning in utero 

chlorpyrifos exposure (V. Rauh et al., 2011; V. A. Rauh et al., 2006; Whyatt et al., 2004). 

CCCEH researchers did not agree to provide these data, however, the researchers met with EPA 

and discussed the agency’s questions about the data to help determine whether further review of 

the raw data might assist EPA in resolving uncertainties. As a result of new information gathered 

through an on-site meeting and other sources, EPA is no longer pursuing the request for the 

original analytic data file from CCCEH researchers. This memorandum details the new 

information gained that resolves or renders unobtainable the previously identified information 

needs. 

II. BACKGROUND 

EPA considers many different types of scientific information when performing a human health 

risk assessment (HHRA) of pesticide exposure in the human population. Traditionally, EPA uses 

toxicology, product and residue chemistry, and industrial hygiene studies as well as measured 

and modeled human and environmental exposure information to support assessment of 

environmental risks. In its preparation of the HHRA for chlorpyrifos, the agency has evaluated 

environmental epidemiology studies of the potential risk of long-term neurodevelopmental 

effects such as delayed motor skill acquisition or reduced intelligence quotient (IQ) measures 

among children who experienced pesticide exposure during gestational development. There are 

three prospective birth cohort studies in the U.S. that examine pesticide exposure (as well as 

other environmental toxicants) to the pregnant mother and fetus, and then measure neurological 

and neurodevelopmental performance in children as they grow older. EPA has provided some of 

the funding support for each of these studies. Authors hypothesize that in utero and early life 
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exposure may influence brain development and effect neurological functioning in children. 

These studies include the CHAMACOS study in the Salinas Valley, CA, the Mt. Sinai children’s 

environmental health study (Mt. Sinai study), and the Columbia Center for Children’s 

Environmental Health (CCCEH). 

The CCCEH study is the only one of the three studies that measures maternal and fetal exposure 

to chlorpyrifos specifically; the other two cohorts measure exposure to organophosphate 

pesticides generally. Authors with the CCCEH study reported reduced birth weight and birth 

length among neonates more highly exposed to chlorpyrifos during gestation (as measured by 

cord blood concentration of chlorpyrifos) (Whyatt et al., 2004). Similarly, authors observed 

slower motor skill acquisition and reduced mental capacity among infants who were more highly 

exposed to the chemical in utero (V. A. Rauh et al., 2006). In 2011, authors from all three birth 

cohort studies concurrently reported evidence of reduced measures of intelligence (Wechslar 

intelligence scale scores) by increasing in utero chlorpyrifos and/or organophosphate exposure 

(M. F. Bouchard et al., 2011; Engel et al., 2011; V. Rauh et al., 2011). 

Given the value of this information to the agency’s HHRA for chlorpyrifos, EPA requested the 

FIFRA SAP to provide external peer review of the strengths and limitations of the epidemiology 

data for use in the chlorpyrifos HHRA (FIFRA SAP September 2008 and April 2012). The 

agency identified two major areas in which additional information was needed to fully 

incorporate these data into the HHRA: additional measures of environmental exposure to 

chlorpyrifos in the CCCEH cohort to discern whether acetyl cholinesterase inhibition was likely 

to have occurred in connection with reported adverse outcomes, and also the role of other 

environmental chemicals (lead, polycyclic aromatic hydrocarbon (PAH), other organophosphate 

pesticides) in the observed adverse neurological effects reported in relation to in utero 

chlorpyrifos exposure. 

To fulfill these information needs for the purposes of incorporating the epidemiology data into 

the chlorpyrifos HHRA, the agency sought to obtain certain “raw data” from the Columbia 

Center for Children’s Environmental Health (CCCEH) study. Specifically, EPA requested the 

original analytic data file used to support analyses presented in the peer-reviewed, published 
Chlorpyrifos Human Health Risk Assessment D424485 

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epidemiology studies concerning in utero chlorpyrifos exposure (V. Rauh et al., 2011; V. A. 

Rauh et al., 2006; Whyatt et al., 2004). CCCEH did not agree to provide the data based upon 

these initial inquiries and they asserted that because EPA did not fund the pesticide exposure 

component of their cohort study EPA was not legally entitled to review their underlying data. 

CCCEH did agree, however, to meet and discuss EPA’s questions about the data to help 

determine whether further review of the raw data might assist EPA in resolving uncertainties. As 

a result on April 15th, 2013, EPA scientists and CCCEH researchers held an all-day meeting at 

the CCCEH data center (Mailman School of Public Health, New York City, NY) to discuss 

EPA’s information needs and whether acquisition of the full analytic data would be necessary or 

valuable to EPA’s assessment. Addendum 1 delineates the questions EPA posed to CCCEH 

study staff at this all-day meeting. 

III. RESOLUTION OF INFORMATION NEEDS 

A. EPIDEMIOLOGY STUDY EXPOSURE CHARACTERIZATION 

The primary rationale supporting EPA’s request for “raw data” from the CCCEH researchers 

relates to the agency’s need to determine whether the levels of chlorpyrifos exposure in the 

environment (apartments, apartment building or other outdoor environment, or dietary exposure) 

of CCCEH study participants were above or below levels that may elicit a greater than 10% 

inhibition of acetylcholinesterase enzyme levels, the current regulatory endpoint. During the 

April 2013 meeting, EPA learned that this type of information is neither available nor obtainable. 

CCCEH researchers estimated relative pesticide exposure using several different exposure 

methods including 48-hour air sampling with personal monitor, 2-week integrated stationary air 

monitoring, maternal urinary concentration of TCPy (urinary metabolite of chlopryrifos) during 

the last trimester of pregnancy, maternal urinary concentration of TCPy at delivery, and 

umbilical cord blood and meconium at delivery. To determine whether a significant change in 

acetyl cholinesterase levels may have occurred as a result of actual environmental exposure, 

temporal concordance between pesticide use and the chlorpyrifos measurement is needed, i.e., 

exposure estimation at the time of pesticide application is optimal. The CCCEH study design did 

not incorporate pre- and post-pesticide use/exposure measurement in the study protocol. 

Therefore, this information was not collected and is not retrospectively obtainable. 
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In addition, EPA requested any additional information obtained by researchers as to specific 

pesticide products used to better understand the pattern and frequency of organophosphate 

pesticide use among cohort participants. This information was solicited from participants in a 

written questionnaire administered during a follow-up period (unpublished copy of questionnaire 

obtained by EPA Oct. 2012). In response to the EPA inquiry, researchers recalled that the 

Whyatt (2002) publication described the challenges of collecting pesticide product information 

in etiologic epidemiology studies, and in the on-site meeting in April 2013 confirmed that the 

information quality in the CCCEH written questionnaire responses is very low. This information 

was deemed of such poor quality by CCCEH data analysts that the data were not coded or 

entered into the analytic data file. Therefore, EPA learned that this specific request for “raw 

data” concerning pesticide product use is not available. 

As a surrogate for this information, CCCEH researchers suggested EPA contact the New York 

City Department of Health to obtain a linked dataset of CCCEH study participant residential 

address and public housing pesticide usage. The linked dataset provides aggregated pesticide 

usage data at the cohort participant building-level only. EPA has obtained and reviewed these 

data (June 2013) and determined that pursuing a data reconstruction exercise is the most 

appropriate way to estimate environmental pesticide exposure that would have to occur among 

CCCEH study participants. EPA has conducted such analysis and included it in the revised 

human health risk assessment. 

B. CO-EXPOSURE TO OTHER ENVIRONMENTAL CONTAMINENTS 

A second major concern raised by EPA, FIFRA SAP peer reviewers, and public commenters is 

the ability of the CCCEH study authors to accurately measure and statistically model the 

relationship between other environmental chemicals (lead and PAH, specifically) or other 

pesticides (diazinon, propoxur) that may influence fetal brain development and childhood 

neurodevelopmental performance, and also be related to chlorpyrifos exposure (these are 

“potentially confounding” exposures). EPA’s concern stems from the understanding that if these 

other exposures are not sufficiently considered in the epidemiological analysis, then an incorrect 
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inference and conclusion may result (i.e., a potential false positive association). For example, 

prenatal and early life exposure to lead in the environment has been causally linked to adverse 

neurodevelopmental outcomes similar to those measured in the CCCEH cohort study including 

intelligence measures. EPA was concerned about the potential error in the CCCEH study if lead 

levels were not appropriately considered, i.e., the apparent chlorpyrifos effect on 

neurodevelopment observed in the study may have been due to the lead exposure. 

However, EPA has confirmed with study authors that lead levels and chlorpyrifos levels in cord 

blood are not statistically associated in this population. Plotting blood lead levels against cord 

blood chlorpyrifos levels illustrates that the two exposures are extremely weakly (linearly) 

correlated in this cohort (ρ<1%) (V. A. Rauh et al., 2006). Further, EPA learned from 

unpublished, supplemental analyses performed by CCCEH researchers upon EPA request that 

postnatal blood lead levels and prenatal chlorpyrifos levels are also not strongly statistically 

associated (Andrews, January 21, 2013). This is plausible because of intensive lead abatement 

programs on-going in New York City during the time period of this study. According to the New 

York City Department of Health, the number of children with elevated blood lead levels declined 

92% between 1995 and 2008.75 Therefore, because the two exposures are not related, it is not 

likely that pre- or postnatal blood lead exposure could explain the observed association with 

chlorpyrifos. 

Furthermore, during the April 2013 meeting CCCEH researchers pointed out that based upon 

available information it appears that lead and chlorpyrifos may affect the brain differently. It is 

well understood that lead affects the neurodevelopmental sub-domain leading to outward 

motivation and aggression; while research within the CCCEH cohort indicates chlorpyrifos may 

affect inward motivation, information processing and organization (V. Rauh et al., 2011; V. A. 

Rauh et al., 2006; Wright et al., 2008). Additionally, MRI imaging studies of lead affected 

persons and preliminary brain imaging studies of chlorpyrifos affected persons show different 

MRI patterns, grey matter as opposed to white matter compositional patterns, respectively 

(Brubaker, Dietrich, Lanphear, & Cecil, 2010; Brubaker et al., 2009; Cecil et al., 2008; Cecil et 

al., 2011; V. A. Rauh et al., 2012). Therefore, given that neither pre- nor postnatal lead levels 

 

75 http://www.nyc.gov/html/doh/html/data/stats-childlead.shtml 
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and chlorpyrifos levels are not statistically associated with one another in the CCCEH study, and 

the different ways through which lead and chlorpyrifos appear to influence neurodevelopmental 

domains EPA concludes that lead exposure did not likely confound (bias or render incorrect) the 

observed association between chlorpyrifos exposure and neurodevelopment in this study 

population. 

Peer review panelists participating on the April 2012 FIFRA SAP panel identified the concern 

that authors had not fully considered the long-term effects of polycyclic aromatic hydrocarbon 

(PAH) exposure, a ubiquitous air pollutant in inner-city areas such as NYC, in the observed 

association between chlorpyrifos and neurodevelopmental outcomes. Specifically, panelist 

argued that ‘a shift in environmental exposures over time’ such that postnatal PAH exposure may 

have combined with the measured in utero pesticide exposure to result in the observed ND 

outcomes. During the April 2013 meeting, authors clarified that the study design did not include 

a repeat measure of exposures over time, so an analysis of postnatal PAH exposures is not 

possible. In the published studies, authors were able to control for the effect of prenatal PAH 

through statistical adjustment. In addition, authors examined the possible modifying role of 

prenatal PAH in this epidemiological association and did not observe any evidence of a different 

risk estimate between chlorpyrifos and ND among those more highly exposed to PAH. 

Concerning the role of postnatal environmental exposures, CCCEH researchers also stated their 

belief that their overall study results illustrate that it is gestational exposure, and not early life 

exposure, that influences neurodevelopment in the study population. They state that the 

longitudinal analyses of infant and child neurodevelopment in relation to in utero chlorpyrifos 

exposure illustrates a persistent effect of the prenatal environment (M. Bouchard et al., 2003; M. 

F. Bouchard et al., 2011; Engel et al., 2007; Engel et al., 2011; Eskenazi et al., 2004; Eskenazi et 

al., 2007; V. Rauh et al., 2011; V. A. Rauh et al., 2006; Whyatt et al., 2004). EPA concluded that 

CCCEH researchers utilized best practices in statistical analysis of epidemiological data 

concerning the role of prenatal PAH in neurodevelopmental outcomes, and that a study of 

repeated, postnatal PAH exposure was beyond the scope of the current CCCEH study, and would 

require a follow-up study not yet undertaken. 

EPA was also interested to learn more about the co-exposure to other organophosphate pesticides 
Chlorpyrifos Human Health Risk Assessment D424485 

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among CCCEH study participants. Specifically, EPA as well as external peer review panelists 

noted the uncertainty as to the degree to which exposure to multiple acetyl cholinesterase 

inhibiting pesticides exposures over time and/or concurrent in time may have influenced study 

results. CCCEH researchers agreed that a more clear understanding of the role of mixtures – 

exposure to multiple OP pesticides overall or concurrent in time – on these neurodevelopmental 

outcomes is desirable; however they also recognized that the current sample size is too small to 

perform this type of analysis. To better understand the role of exposure to a mixture of OP 

pesticides a new cohort study with a larger sample size and different design is required. 

Therefore, EPA concluded that co-exposure to multiple organophosphate mixtures is not 

currently obtainable. 

For risk characterization purposes, EPA was also interested in understanding the relative 

contributions of various environmental exposures on ND outcomes, (e.g., PAH, environmental 

tobacco smoke, chlorpyrifos). Researchers noted that a preliminary indication of the relative 

contribution of various risk factors for intelligence measures in these cohorts can be seen through 

examination of supplemental tables published by CCCEH researchers, i.e., the beta-coefficients 

provided in published supplemental tables provide an indication of the relative contribution of 

each risk factor (V. Rauh et al., 2011). However, CCCEH researchers indicated that to gain a 

true reflection the causal model in the population a series of studies in other study populations is 

needed. EPA and CCCEH researchers agreed that these studies will likely accumulate over time, 

however they are not currently available. 

IV. CONCLUSIONS

In the past, EPA sought to obtain the original analytic data file used to support certain 

epidemiological analysis of in utero exposure to chlorpyrifos and subsequent adverse 

neurodevelopmental health outcomes in children generated by the Columbia Center for 

Children’s Environmental Health (CCCEH) to support the Human Health Risk Assessment 

(HHRA) of chlorpyrifos. EPA believed these data were important to both clarify the exposureresponse relationship observed in the epidemiology study relative to the current regulatory 

endpoint (acetylcholinesterase inhibition), and also to resolve uncertainties regarding study 
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participants co-exposure to other environmental contaminants, among other areas of 

uncertainties. CCCEH researchers did not agree to provide these data, however, the researchers 

met with EPA and discussed the agency’s questions about the data to help determine whether 

further review of the raw data might assist EPA in resolving uncertainties. As a result of this 

meeting and additional discussions with CCCEH staff, EPA concluded that access to the raw 

data would either not provide answers to EPA’s questions or that the information EPA sought 

could be obtained without analyzing the raw data. Indeed, based on discussions in that meeting 

as well as further work conducted by agency staff, EPA has gained additional information to 

better clarify and characterize the major issue areas identified as uncertainties. For these reasons, 

EPA decided that it would not further pursue its request for the analytic data file from the 

CCCEH researchers. 
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Works Cited 

Andrews, H. F. (January 21, 2013). [Clarification of Relation between Blood Lead and Cord Blood 

Levels of Chlorpyrifos in the Columbia Center for Children's Environmental Health (CCCEH) 

Studies (Electronic mail communication)]. 

Bouchard, M., Gosselin, N. H., Brunet, R. C., Samuel, O., Dumoulin, M. J., & Carrier, G. (2003). A 

toxicokinetic model of malathion and its metabolites as a tool to assess human exposure and risk 

through measurements of urinary biomarkers. Toxicol Sci, 73(1), 182-194. doi: 

10.1093/toxsci/kfg061 

Bouchard, M. F., Chevrier, J., Harley, K. G., Kogut, K., Vedar, M., Calderon, N., . . . Eskenazi, B. (2011). 

Prenatal exposure to organophosphate pesticides and IQ in 7-year-old children. Environ Health 

Perspect, 119(8), 1189-1195. doi: 10.1289/ehp.1003185 

Brubaker, C. J., Dietrich, K. N., Lanphear, B. P., & Cecil, K. M. (2010). The influence of age of lead 

exposure on adult gray matter volume. Neurotoxicology, 31(3), 259-266. doi: 

10.1016/j.neuro.2010.03.004 

Brubaker, C. J., Schmithorst, V. J., Haynes, E. N., Dietrich, K. N., Egelhoff, J. C., Lindquist, D. M., . . . 

Cecil, K. M. (2009). Altered myelination and axonal integrity in adults with childhood lead 

exposure: a diffusion tensor imaging study. Neurotoxicology, 30(6), 867-875. doi: 

10.1016/j.neuro.2009.07.007 

Cecil, K. M., Brubaker, C. J., Adler, C. M., Dietrich, K. N., Altaye, M., Egelhoff, J. C., . . . Lanphear, B. 

P. (2008). Decreased brain volume in adults with childhood lead exposure. PLoS Med, 5(5), e112. 

doi: 10.1371/journal.pmed.0050112 

Cecil, K. M., Dietrich, K. N., Altaye, M., Egelhoff, J. C., Lindquist, D. M., Brubaker, C. J., & Lanphear, 

B. P. (2011). Proton magnetic resonance spectroscopy in adults with childhood lead exposure. 

Environ Health Perspect, 119(3), 403-408. doi: 10.1289/ehp.1002176 

Engel, S. M., Berkowitz, G. S., Barr, D. B., Teitelbaum, S. L., Siskind, J., Meisel, S. J., . . . Wolff, M. S. 

(2007). Prenatal organophosphate metabolite and organochlorine levels and performance on the 

Brazelton Neonatal Behavioral Assessment Scale in a multiethnic pregnancy cohort. Am J 

Epidemiol, 165(12), 1397-1404. doi: 10.1093/aje/kwm029 

Engel, S. M., Wetmur, J., Chen, J., Zhu, C., Barr, D. B., Canfield, R. L., & Wolff, M. S. (2011). Prenatal 

exposure to organophosphates, paraoxonase 1, and cognitive development in childhood. Environ 

Health Perspect, 119(8), 1182-1188. doi: 10.1289/ehp.1003183 

Eskenazi, B., Harley, K., Bradman, A., Weltzien, E., Jewell, N. A., Barr, D. B., . . . Holland, N. T. (2004). 

Association of in utero organophosphate pesticide exposure and fetal growth and length of 

gestation in an agricultural population. Environmental Health Perspectives, 112(10), 1116-1124. 

doi: 10.1289/ehp.6789 

Eskenazi, B., Marks, A. R., Bradman, A., Harley, K., Barr, D. B., Johnson, C., . . . Jewell, N. P. (2007). 

Organophosphate pesticide exposure and neurodevelopment in young Mexican-American 

children. Environ Health Perspect, 115(5), 792-798. doi: 10.1289/ehp.9828 

Rauh, V., Arunajadai, S., Horton, M., Perera, F., Hoepner, L., Barr, D. B., & Whyatt, R. (2011). Sevenyear neurodevelopmental scores and prenatal exposure to chlorpyrifos, a common agricultural 

pesticide. Environ Health Perspect, 119(8), 1196-1201. doi: 10.1289/ehp.1003160 
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Rauh, V. A., Garfinkel, R., Perera, F. P., Andrews, H. F., Hoepner, L., Barr, D. B., . . . Whyatt, R. W. 

(2006). Impact of prenatal chlorpyrifos exposure on neurodevelopment in the first 3 years of life 

among inner-city children. Pediatrics, 118(6), e1845-1859. doi: 10.1542/peds.2006-0338 

Rauh, V. A., Perera, F. P., Horton, M. K., Whyatt, R. M., Bansal, R., Hao, X. J., . . . Peterson, B. S. 

(2012). Brain anomalies in children exposed prenatally to a common organophosphate pesticide. 

Proceedings of the National Academy of Sciences of the United States of America, 109(20), 7871-

7876. doi: 10.1073/pnas.1203396109 

Whyatt, R. M., Camann, D. E., Kinney, P. L., Reyes, A., Ramirez, J., Dietrich, J., . . . Perera, F. P. (2002). 

Residential pesticide use during pregnancy among a cohort of urban minority women. Environ 

Health Perspect, 110(5), 507-514. 

Whyatt, R. M., Rauh, V., Barr, D. B., Camann, D. E., Andrews, H. F., Garfinkel, R., . . . Perera, F. P. 

(2004). Prenatal insecticide exposures and birth weight and length among an urban minority 

cohort. Environ Health Perspect, 112(10), 1125-1132. 

Wright, J. P., Dietrich, K. N., Ris, M. D., Hornung, R. W., Wessel, S. D., Lanphear, B. P., . . . Rae, M. N. 

(2008). Association of prenatal and childhood blood lead concentrations with criminal arrests in 

early adulthood. PLoS Med, 5(5), e101. doi: 10.1371/journal.pmed.0050101 
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Appendix 6. Addendum 1: 

Columbia University Epidemiology Studies 

The agency is obligated to review and address peer review comments in support of 

regulatory decisions. The following is a list of key issues about the epidemiological 

studies carried out by researchers at Columbia University that were raised in peer 

review comments. These issues require EPA to have access to the raw data for 

additional analyses by the agency. 

1) Further analysis of other chemical exposures (e.g., lead, PAHs, other pesticides) 

to address, if possible, their impact or contribution as modulating factors on the 

measured outcomes 

 2012 SAP -- “it should be noted that it cannot be stated that chlorpyrifos is the 

sole contributor to the observed outcomes.” 

 2012 SAP -- “In an earlier examination of the same cohort, Perera et al.(2009) 

reported an association between a decrease in full-scale IQ and verbal IQ in 

5year-olds with prenatal polycyclic aromatic hydrocarbons (PAH) exposure 

rather than chlorpyrifos, thus, raising an issue of the shift in chemical exposure 

association with increase in age. In each of these analyses, statistical modeling 

showed that the exposures were independently associated with IQ, and no 

significant interaction was observed with the other chemical. While this is a 

statistically sound approach to determine independent responses, panel members 

noted that it is very difficult to identify the independent physiological effects of 

a single chemical in this type of multi-chemical exposure scenario.” 

 2012 Federal Peer Review -- “even low levels of lead can impact 

neurodevelopment, and even that the observed neurobehavioral deficits are 

more pronounced at lower blood lead levels when compared with higher blood 

lead levels”. 

 2008 SAP -- “In order to eliminate the possible causes of neurodevelopmental 

effects by other pesticides in the Columbia study, it is suggested that EPA 

should repeat the pre-post residential cancellation analysis done for chlorpyrifos 

using other pesticide measurements, such as malathion diacid (MDA), a specific 

metabolite of malathion. The outcomes from those additional analyses will 

either confirm or reject EPA’s preliminary conclusion that chlorpyrifos is likely 

to play a role in the neurodevelopmental outcomes.” 

 2008 SAP -- ““It would be useful to examine the results of a statistical analysis 

that includes all three AChE-inhibiting insecticides in the analysis model as 

dichotomous variables (above or below LOD) in combination with continuous 

measurements for these variables. This type of analysis would likely not change 
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the results, but it could be helpful in illustrating threshold or dose response 

effects.” 

2) Further analysis and information to address and, if possible, better characterize 

uncertainty around outcome measures on learning/memory/IQ 

 2012 SAP-- Alternative considerations for non-quantified samples: “little use 

was made of techniques to integrate non-quantified samples into the statistical 

test.... Various methods were reviewed by the July 2010 SAP that can be applied 

to either normally or lognormally distributed data that include a significant (even 

a majority) of non-detectable sample . . . . Specifically, the use of ’probability 

plots’ was described that can yield an estimate of the geometric mean of the 

distribution [GM], the geometric standard deviation [GSD], and corresponding 

percentiles.” 

 Federal Peer Review -- “There is a scatterplot showing the raw scores for overall 

IQ and for each of the subtests, but it is not possible to obtain the necessary 

information to compare the distributions of these scores with the norms for the 

test or with any other study sample. Ideally, the means and standard deviations 

for these scores should be presented for either a non-exposed or a non-exposed 

combined with low exposed group and these should be compared to a moderate or 

high-exposed group as was done for the BSID-II in the Rauh et al., 2006 paper. 

Here the uncertainties stem from the assumptions that are made when regression 

analyses are performed. The main issue here is that outliers can greatly influence 

the slope of the function.” 

 Federal Peer Review--A between group analysis using inferential statistics, as 

was done for the Bayley Scales of Infant Development II in the Rauh et al., 

2006 paper, should be performed on each variable in both studies (i.e., the Child 

Behavior Checklist in Rauh et al., 2006, and the full scale IQ and subscales for 

the WISC-IV in the Rauh et al., 2011 study). This would be the most direct and 

least problematic method for determining whether exposure to chlorpyrifos 

resulted in significant decreases in IQ or significant increases in behavioral 

problems “..... no information was provided regarding the qualifications of the 

individuals who administered and scored the tests. “ 

3) Further analysis to assess, if possible, whether individual cohort members had 

the potential for exposure to chlorpyrifos and/or other acetylcholinesterase 

(AChE) inhibiting pesticides (e.g., diazinon, propoxur), prenatally and /or 

postnatally, at levels leading to greater than 10% AChE inhibition (the level 

used to derive the regulatory point of departure). 
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 2012 SAP-- recommended conducting a dose reconstruction analysis—“data on 

the concentration of chlorpyrifos in various media (i.e. house dust, air and 

water) while market basket data exists on the concentration of chlorpyrifos on 

food. These data provide the main tools for developing an effective exposure 

assessment and a subsequent reconstruction of potential dose.” The agency has 

begun such analysis but the current draft analysis is limited without data on the 

exposure information relevant to individual women such that environmental 

chlorpyrifos exposure can then be linked to measures of blood chlorpyrifos. 

 2012 SAP-- recommended the agency consider issues related to multiple 

chemical exposure (i.e., mixtures) to chlorpyrifos and other key AChE 

inhibiting pesticides identified by the Columbia University studies (diazinon, 

propoxur). Assumptions of co-exposure will likely be grossly overestimated 

without access to the raw data; such raw data may enable the agency to evaluate 

actual co-exposure information for individuals from air monitoring samples and 

blood samples. 
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Appendix 7. Physical/Chemical Properties 

Physical/Chemical Properties of Chlorpyrifos. 

Parameter Value Reference 

Melting point/range 41.5-42.5 oC Chlorpyrifos 

pH NR IRED 

Density (21oC) 1.51 g/mL 

Water solubility (25°C) 1.05 mg/L 

Solvent solubility (20°C) Acetone >400 g/L 

Dichloromethane >400 g/L 

Methanol 250 g/L 

Ethyl acetate >400 g/L 

Toluene >400 g/L

 

n-hexane >400 g/L 

Vapor pressure, (25°C) 

1.87x10-5 torr1

Dissociation constant, pKa NR 

Octanol/water partition 

coefficient, Log(KOW) 

4.7 

UV/visible absorption 

spectrum 

NR 

NR – not reported. 

1 R. Bohaty, June 2011, D368388 and D389480, Chlorpyrifos Drinking Water Assessment for Registration Review (CRF 

assessment, Oct. 16, 2009 product chemistry BC 2062713)
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Appendix 8. Current U.S. Tolerances and International Residue Limits 

Chlorpyrifos (059101) 

Summary of US and International Tolerances and Maximum Residue Limits 

Residue Definition: 

US Canada Mexico2 Codex3 

40CFR180.342 

chlorpyrifos per se ( O,O -

diethyl O -(3,5,6-trichloro2-pyridyl) phosphorothioate 

O,O-diethyl-O-(3,5,6-trichloro-2-

pyridyl) phosphorothioate 

(apples, grapes, tomatoes) 

O,O-diethyl-O-(3,5,6- trichloro2-pyridyl) phosphorothioate, 

including the metabolite 3,5,6-

trichloro-2-pyridinol 

(citrus fruits; fat, kidney, and 

liver of cattle; kiwifruit; peppers; 

rutabagas; meat and meat 

byproducts of cattle (calculated 

on the fat content)) 

 Chlorpyrifos. The 

residue is fat 

soluble. 

Commodity1, Tolerance (ppm) /Maximum Residue Limit (mg/kg) 

US Canada Mexico2 Codex3 

Alfalfa, forage 3.0 

Alfalfa, hay 13 5 alfalfa fodder 

Almond 0.2 0.05 

Almond, hulls 12 

Apple 0.01 0.01 1 pome fruits 

Apple, wet pomace 0.02 

Banana 0.1 2 

Beet, sugar, dried pulp 5.0 

Beet, sugar, molasses 15 

Beet, sugar, roots 1.0 0.05 

Beet, sugar, tops 8.0 

Cattle, fat 0.3 1.0 

Cattle, meat 0.05 1.0 1 (fat) 

Cattle, meat byproducts 0.05 1.0 0.01 cattle, 

kidney and liver 

Cherry, sweet 1.0 

Cherry, tart 1.0 

Citrus, dried pulp 5.0 

Citrus, oil 20 

Corn, field, forage 8.0 

Corn, field, grain 0.05 0.05 0.05 maize 

Corn, field, refined oil 0.25 0.2 maize oil, 

edible 

Corn, field, stover 8.0 10 maize fodder 

(dry) 

Corn, sweet, forage 8.0 

Corn, sweet, kernel plus 

cob with husk removed 

0.05 0.05 0.01 sweet corn 

(corn-on-the-cob) 

Corn, sweet, stover 8.0 
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Summary of US and International Tolerances and Maximum Residue Limits 

Residue Definition: 

US Canada Mexico2 Codex3 

Cotton, undelinted seed 0.2 0.3 cotton seed 

Cranberry 1.0 1 

Cucumber 0.05 0.05 

Egg 0.01 0.01 (*) 

Fig 0.01 

Fruit, citrus, group 10 1.0 1.0 1 

Goat, fat 0.2 

Goat, meat 0.05 

Goat, meat byproducts 0.05 

Hazelnut 0.2 

Hog, fat 0.2 

Hog, meat 0.05 0.02 (fat) 

Hog, meat byproducts 0.05 0.01 (*) pig, 

edible offal 

Horse, fat 0.25 

Horse, meat 0.25 

Horse, meat byproducts 0.25 

Kiwifruit 2.0 2.0 

Milk, fat (Reflecting 0.01 

ppm in whole milk) 

0.25 0.02 milk 

Nectarine 0.05 0.05 

Onion, bulb 0.5 0.2 

Peach 0.05 0.05 0.5 

Peanut 0.2 

Peanut, refined oil 0.2 

Pear 0.05 1 pome fruits 

Pecan 0.2 0.05 (*) 

Pepper 1.0 

1.0 

2 peppers sweet 

including pimento 

or pimiento); 20 

peppers chili, 

dried 

Peppermint, tops 0.8 

Peppermint, oil 8.0 

Plum, prune, fresh 0.05 0.5 plums 

(including prunes)

Poultry, fat 0.1 

Poultry, meat 0.1 0.01 (fat) 

Poultry, meat byproducts 0.1 0.01 (*) poultry, 

edible offal 

Pumpkin 0.05 

Radish 2.0 

Rutabaga 0.5 0.5 

Sheep, fat 0.2 

Sheep, meat 0.05 1 (fat) 
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Summary of US and International Tolerances and Maximum Residue Limits 

Residue Definition: 

US Canada Mexico2 Codex3 

Sheep, meat byproducts 0.05 0.01 sheep, edible 

offal 

Spearmint, tops 0.8 

Spearmint, oil 8.0 

Sorghum, grain, forage 0.5 

Sorghum, grain, grain 0.5 0.5 

Sorghum, grain, stover 2.0 2 sorghum straw 

and fodder, dry 

Soybean, seed 0.3 0.1 soya bean 

(dry) 

Strawberry 0.2 0.3 

Sunflower, seed 0.1 0.1 

Sweet potato, roots 0.05 

Turnip, roots 1.0 

Turnip, tops 0.3 

Vegetable, brassica, leafy, 

group 5 

1.0 

 

2 Broccoli 

1 Cabbages, head 

0.05 Cauliflower 

1 Chinese 

cabbage (type petsai) 

Vegetable, legume, group 6 

except soybean 

0.05 0.05 lentils 0.01 common 

bean (pods and/or 

immature seeds); 

peas (pods and 

succulent=immat

ure seeds) 

Walnut 0.2 0.05 (*) 

Wheat, forage 3.0 

Wheat, grain 0.5 0.5 

Wheat, straw 6.0 5 wheat straw and 

fodder, dry 

MRLs with No US Equivalents 

Grapes 0.01 0.5 

Tomatoes 0.01 

Carrot 0.1 

Coffee beans 0.05 

Cotton seed oil, crude 0.05 (*) 

Cotton seed oil, edible 0.05 (*) 

Dried grapes (=currants, 

raisins and sultanas) 0.1 

Potato 2 

Rice 0.5 

Soya bean oil, refined 0.03 

Tea, green, black (black, 

fermented and dried) 2 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 401 of 531

Summary of US and International Tolerances and Maximum Residue Limits 

Residue Definition: 

US Canada Mexico2 Codex3 

Wheat flour 0.1 1 Includes commodities listed in the CFR as of 4/12/11. The 40CFR 180.342 (a) (3) also stipulates that “a tolerance of 0.1 part

per million is established for residues of chlorpyrifos, per se, in or on food commodities (other than those already covered by a 

higher tolerance as a result of use on growing crops) in food service establishments where food and food products are prepared 

and served, as a result of the application of chlorpyrifos in microencapsulated form.” 2 Mexico adopts US tolerances and/or Codex MRLs for its export purposes. 

3 * = absent at the limit of quantitation; Po = postharvest treatment, such as treatment of stored grains. PoP = processed 

postharvest treated commodity, such as processing of treated stored wheat. (fat) = to be measured on the fat portion of the 

sample. MRLs indicated as proposed have not been finalized by the CCPR and the CAC. 

(c) Tolerances with regional registrations. Tolerances with regional registration, as defined in 

180.1(m), are established for residues of the pesticide chlorpyrifos per se (O,O -diethyl- O -

(3,5,6-trichloro-2-pyridyl) phosphorothioate) in or on the following food commodities: 

Commodity Parts per million Canada Codex 

Asparagus 5.0 

Grape 0.01 0.01 0.5 

In addition, the following tolerances for chlorpyrifos are recommended under registration 

review: 

Table 8A. Recommended Tolerances for Chlorpyrifos 

Commodity Established Tolerance 

(ppm) 

Recommended 

Tolerance (ppm) 

Comments 

Correct Commodity Definition 

Aspirated grain fractions NA 22 

Cotton, gin byproducts NA 15 

Corn, milled byproducts NA 0.1 

Wheat, milled byproducts NA 1.5 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 402 of 531

Appendix 9. Master Use Summary Document 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 403 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

AGRICULTURAL FARM PREMISES Livestock housing and holding areas (such as hog barns, empty chicken houses, dairy areas, milkrooms, calf hutches, calving pens and parlors). 



Indoor 

general 

surface spray 

backpack 

sprayer; high 

and low sprayer 

(pressure or 

volume) 

0.075 lb a.i./ 

1000 ft sq 

1.2 

EC, ME 

[14.4] 

NS NA 12 NA NA NS 

ALFALFA 

 At plant groundboom 1.0 G 1.0 1.0 [1] NS 1 21 24  Foliar aerial or ground/ 1.0 EC [4.0] NS 4.0 [4] NS 4 21 24 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 404 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

broadcast, 

chemigation 

Total 1.0 5.0 5.0 [5] NS 5 21 24 

ALMOND 

 dormant/ 

delayed aircraft, airblast 2.0 WDG, WP 2.0 NA 1 NA NA 24 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 405 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

dormant; 

broadcast 

 foliar; 

broadcast aircraft, airblast 2.0 WDG,WP 6.0 NA 3 NA 14 



 pre-plant, 

foliar; 

trunk 

spray/drenc

h or preplant dip

handheld, 

backpack, 

drench/dip, 

handgun, and 

low pressure 

hand wand 

2.5 

(3.0/100 gal) 

WDG 

2.5 NA 1 NA 14 



 Dormant/ 

delayed 

dormant; 

foliar; 

orchard 

floors 

broadcast 

ground boom, 

handgun, 

chemigation 

4.0 

EC* 4.0 NA 2 NA 14 

Total -- 4.0 14.5 NA 7 NA 14 

APPLE 



dormant/ 

delayed 

dormant; 

broadcast 

aircraft, airblast 

2.0 

EC 

2.0 

WDG 

1.5 

WP 

2 2.0 1 1 NA 24/ 4 d 

 pre-plant, 

foliar; 

handheld, 

backpack, 

1.5 

(1.5 lb ai/100 1.5 NA 1 1 28 4d 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 406 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

trunk 

spray/drenc

h or preplant dip; 

ground 

drench/dip, 

handgun, and 

low pressure 

hand wand 

gal) 

WDG 

 

Total 2.0 3.5 2 

ASPARAGUS 



Foliar, preharvest; 

broadcast 

aircraft, ground 

boom 

1.0 

EC, WDG 1.0 1.0 1 1 1 24 

 Postharvest, 

broadcast 

aircraft, ground 

boom 

1.0 

EC, WDG 2.0 2.0 2 1 1 24 

granular soil 

band treatment 

ground boom 

1.5 

G 3.0 3.0 2 2 180 24 

Total 1.5 G 3.0 G 2.0 3.0 G 2.0 3 3 1 24 

BEANS 

 Preplant; 

Seed Seed Treatment 0.016-0.348 0.000798 lb NS [0.348] NS NS [1] NS NS NS 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 407 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

treatment ai/lb seed 

ME 

0.013-0.272 

0.000625 lb 

ai/lb seed 

WP 

0.012-0.253 

0.00058 lb 

ai/lb seed 

EC 

BEEF/RANGE/ FEEDER CATTLE (MEAT)/DAIRY CATTLE (NONLACTATING) 

Summer, late 

fall, spring; 

impregnated 

collar/tag 

Animal 

treatment (ear 

tag) 

0.0066 

lb/animal 

[0.0099

] 

NS 

NA 3 NA NS NS 

BEETS (UNSPECIFIED; TABLE OR SUGAR) 

“grown for seed” 



At plant, soil 

band 

treatment 

Ground boom 1.0 EC NS 1 NS 1 24 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 408 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 



Preplant, soil 

incorporated 

treatment

Broadcast/ 

ground boom 

1.9 

EC 

NS 

(2.8 ID) NS 1 NS 

Total 1.9 NS NS NS NS 24 

SUGAR BEETS 



Preplant, soil 

incorporated 

treatment 

Broadcast/ 

ground boom

1.0 

EC 

2.0 

G 

3.0 2.0 1 1 NA 24 



At plant, soil 

band 

treatment 

Broadcast/ 

ground boom

1.0 

 EC, WDG 

2.0 

G 

3.0 2.0 1 1 30 24 

 Postplant, 

soil band 

Broadcast/ 

ground boom 

2.0 

G 3.0 2.0 1 1 30 24 



 Postemergence 

band 

treatment; 

broadcast 

Broadcast/ 

ground boom

1.0 

EC, WDG 3.0 1.0 3 1 30 24 

 broadcast Aircraft, ground boom, 1.0 EC, WDG 3.0 1.0 3 1 30 24 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 409 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

chemigation 

Total 

1.0 

EC 

2.0 

G 

4.0 [4.0] NS 3 [3] NS 30 24 

CARROT Grown for Seed (INCLUDING TOPS) 



Foliar prebloom 

broadcast 

aircraft, ground 

boom

0.94 

EC 0.94 1 1 1 7 24 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 410 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

CHERRIES 



 dormant/ 

delayed 

dormant; 

broadcast 

aircraft, airblast 

2.0 

WDG, EC 

1.5 

WP 

2.0 NA 1 NA NS 24 



foliar; 

broadcast 

airblast 4.0 EC 10.0 NA 5 NA 14 24 

aircraft 2.0 



Foliar, post 

harvest; 

trunk 

spray/drenc

h 

handheld, 

backpack, 

drench/dip, 

handgun, and 

low pressure 

hand wand 

2.5 

(3.0/100 gal) 

WDG, EC 

2.5 NA 1 NA 2 24 

Total 

-- 

4.0 

4.5 

(sweet) 

14.5 

(tart 

only) 

6 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 411 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

CHRISTMAS TREE PLANTATIONS 

 foliar; 

broadcast

helicopter, 

orchard blast

1.0 

EC, WDG, WP 3.0 NA 3 NA [0] NS 

24 



post harvest; 

Stump 

Treatment 

handheld, 

backpack, 

drench/dip, 

handgun, and 

low pressure 

hand wand

2.5 

(3.0/100 gal) 

EC, WDG

2.5 NA 1 NA NA 

Total 2.5 5.5 4 

CITRUS 

 foliar; 

broadcast 

airblast, ground 

boom 

6.0 

WP, WSP, EC 7.5 NA 2 NA 

35 

(21 

for 

low 

rate

s) 

5d 

 aircraft 2.3 WP, WSP, EC 21 5 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 412 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 



foliar; 

orchard 

floors 

broadcast

ground boom, 

chemigation, 

handheld, 

backpack, 

drench/dip, 

handgun, and 

low pressure 

hand wand 

1.0 

G*, WSP, EC 3.0 NA 3 NA 28 24/ 5 d 

Total -- 6.0 10.5 5 

CLOVER (GROWN FOR SEED) 

 Preplant Ground boom 1.9 EC 1.9 1.9 1 1 NS 24  Post-Plant 

Foliar 

aircraft and 

ground boom 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 413 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

COLE CROPS (EXCLUDES CAULIFLOWER AND 



Preplant, soil 

incorporated 

treatment 

Ground boom 

2.0 

EC, WDG, G 4.0 2.0 2 

1 

30 

24 

BRUSSELS SPROUTS)



At plant, soil 

band 

treatment 

Ground boom 1 

 Post plant Ground boom 1 

 Foliar 

Established 

Plantings, 

soil sidedress 

treatment 

Ground boom 1 

 Foliar, 

broadcast 

Aircraft, ground 

boom, 

chemigation

1.0 

EC, WDG, WP 4.0 3.0 4 3 21 

Total 8.0 5 6 4 

BRUSSELS 

 At plant, soil Ground boom 2.0 2.0 [2.0] 2 1 21 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 414 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

SPROUTS band 

treatment

EC; G NS 

24 



 Preplant, soil 

incorporated 

treatment 

Ground boom 



 Postplant, 

soil 

application 

Ground boom 2.25 EC, G 2.25 [2.25] NS 

 Foliar 

broadcast 

Aircraft, 

Ground boom 

1.0 

EC 

[5.3] 

NS 3.0 NS 3 

Total 2.3 5.3 NS 21 24 

CAULIFLOWER 

 At plant, soil 

band Ground boom 2.0 EC 2.0 EC NS [1] NS 1 21 3d 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 415 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

treatment 2.3 

G 

2.25 

G 



 Preplant, soil 

incorporated 

treatment 

Ground boom 2.3 

G 

2.0 

EC 

2.3 NS [1] NS 1 

30, 

EC, 

21 

G 



 Postplant, 

soil 

application 

Ground boom 

 Foliar 

broadcast 

aircraft, ground 

boom 

1.0 

EC 

[5.3] 

NS 3.0 NS 3 21 

Total 2.3 5.3 [5.3] NS NS [4] NS 21 24 

COMMERCIAL/INSTITUTIONAL/ INDUSTRIAL PREMISES/ EQUIP. (INDOOR) Non-food areas of manufacturing, industrial, and food processing plants; 

Broadcast Product Container 

0.4373 lb 

a.i./100 sq ft 

190.5 

G 

NS NA NS NA NA NS 

Crack and 

Crevice/Void 

Sprayer/ 

Injection 

0.0625 lb 

a.i./1000 sq ft 

2.7 

ME 

NS NA NS NA NA NS 

 Crack and 

Crevice/Spot 

Sprayer/ 

Injection 

0.0424 lb/gal 

ME NS NA NS NA NA NS 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 416 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

warehouses; ship holds; railroad boxcars.COMMERCIAL/INSTITUTIONAL /INDUSTRIAL PREMISES/EQUIP. (OUTDOOR)

Outdoor commercial use around non-food areas of manufacturing, industrial, and food processing plants; warehouses; ship holds; railroad boxcars

Soil 

broadcast 

Low and High 

Pressure, 

Backpack, 

Handgun 

Sprayers 

0.0247 lb 

a.i./1000 sq ft 

1.1 

ME 

NS NA NS NA NA NS 

Directed 

spray 

0.1132 lb 

a.i./1000 sq ft 

4.9 

ME 

NS NA NS NA NA NS 

 Crack and 

Crevice/void/

general 

outdoor 

0.0424 lb/gal 

ME NS NA NS NA NA NS 

CONIFERS AND DECIDUOUS TREES; 

 ? foliar; broadcast Ground boom 1.0 EC 3 NA 6 NA 7 24 

PLANTATION, NURSERY

 ? foliar; stump treatment

backpack, 

drencher, low 

pressure hand 

wand 

0.3 

EC 0.3 NA 1 NA 7 24 

Total 1.0 3 NA 6 NA 7 24 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 417 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

CORN (ALL) 

 Preplant 

ground/ soil 

incorporated

conservation 

tillage, in 

furrow, 

broadcast, 

chemigation, 

soil band 

3.0 

EC 

2.0 

G 

3.0 3.0 NS 3 NA 

24/ 5 EC 

soil 

incorporated

aerial 

conservation 

tillage 

2.0 

EC, G 



ground/ 

conservation 

tillage, in 

furrow, 

broadcast, 

1.0 

EC 

2.0 

G 

3.0 3.0 NS 3 21 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 418 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

chemigation, 

soil band 



Storage or 

preplant seed 

treatment 

Seed treatment 

0.001-0.021 

0.000625 lb 

a.i./ lb seed 

WP 

0.1-1.9 

0.058 lb a.i./ lb 

seed 

FC 

[?] 

NS 

[1.9] 

NS 

[?] 

NS 1 NS NS 

 At plant 

soil 

incorporated, 

conservation 

tillage 

2.0 

G 

[?] 

NS 3.0 [?] NS 3 21 24 

 Post 

emergence 

Aerial or 

ground, 

broadcast, 

chemigation 

1.5 

EC 

1.0 

WDG 

NS 3.0 NS 3 21 

24/ 5d (EC 

 Foliar 

Aerial or 

ground/ 

broadcast, 

granule, seed 

and 

chemigation 

1.5 

EC 3.0 3.0 NS 3 21 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 419 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

Total 3.0 8.1 8.1 NS 4 21 

COTTON 



Storage or 

preplant seed 

treatment 

Seed treatment 

0.8-2.2 

0.00116 lb/lb 

seed 

EC 

[2.2] 

NS 

[2.2] 

NS 

[1] 

NS 1 NS NS 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 420 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

 Foliar 

aerial, 

chemigation, 

ground boom 

1.0 

EC, WDGP 3 3.0 3 3 14 24 

Total 1.0 3.2 3.2 3 3 14 24 

CRANBERRY 

 Foliar 

aircraft, ground 

boom/ 

broadcast and 

chemigation 

1.5 

EC, WDG 3.0 NA 2 NA 60 24 

CUCUMBER 



Storage or 

preplant seed 

treatment 

Commercial 

seed treatment 

0.4 

0.00058 lb/lb 

seed 

EC 

NS 0.1 2 1 NS NS 

FIGS 



dormant/ 

delayed 

dormant; 

soil 

application 

ground boom 2.0 WDG, EC 2.0 NA 1 NA 217 4 d 

FILBERTS/ 

 dormant/ aircraft, airblast 2.0 2.0 NA 1 NA 14 24 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 421 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

HAZELNUT delayed 

dormant; 

broadcast 

WP 

 foliar; 

broadcast aircraft, airblast 2.0 WDG, WP, EC 6.0 NA 3 NA 14 

Total 2.0 6.0 NS 3.0 NA 14 24 

FOOD PROCESSING PLANT PREMISES (NONFOOD CONTACT) 

When 

needed, crack 

and crevice 

treatment, 

spot 

treatment 

0.0424 lb/ gal 

ME NS NA NS NA NA NS 

FOREST PLANTINGS (REFORESTATION PROGRAMS) (TREE FARMS, TREE 

 Foliar, 

broadcast ground boom 1.0 EC 6.0 NA 6 NA 

24 

PLANTATION, ETC.)

 Foliar, stump 

treatment 

direct spray, 

drencher 

0.34 

EC 6.0 NA [18] NS NA 

FOREST 

 Foliar, ground boom, 0.61 3.6 NA NS NA 24 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 422 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

TREES (SOFTWOODS, CONIFERS) 

broadcast drencher EC 

 Foliar, stump 

treatment direct spray 

[3.6] 

2.4 lb a.i./100 

gal 

EC 

3.6 NA NS NA 

FRUITS & NUTS Non-bearing (not to bear fruit within 1 year) fruit trees in nurseries (includes: almonds, citrus, filbert, apple, cherry, nectarine, peach, pear, plum, prune). 



Foliar-Nonbearing 

nursery 

broadcast 

High/low 

volume spay/ 

hand held 

sprayer/power 

sprayer 

4.0 

EC 4.0 NA NS NA 14 NS 



Foliar-Nonbearing 

nursery trunk 

drench 

drencher, high 

and low 

pressure sprayer 

2.0 

WDG 2.0 NA NS 1 14 

Total 4.0 6.0 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 423 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

GINSENG (MEDCINAL) 



Preplant, 

postemergence Ground, soil broadcast 2.0 G 2.0 NA 1 NA 365 24 

GOLF COURSE TURF 

When 

needed, soil 

broadcast/ 

spot 

treatment 

Ground, low 

pressure 

1.0 

EC 2.0 NA 2 NA 24 

Foliar, 

broadcast, 

Ground boom, 

handgun, low 

1.0 

EC, G, B 2.0 NA 2 NA 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 424 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

pressure and 

backpack 

Tractor drawn 

spreader, push 

type spreader, 

belly grinder 

1.0 

G 

[24

] 

NS 

Mound 

treatment 

Granule 

applicator 

1.0 

G 2.0 NS 2 NS NS 

Total 2.0 2.0 NA 2 NA NS 

GRAPES 



Dormant/ 

Delayed 

Dormant 

(pre-bloom) 

Ground boom, 

broadcast, 

drench 

high/low spray 

volume 

1.0 

WDG, EC 1.0 1 1 NA 35 

24 

 2.0 

EC 2.0 1 1 NA 35 

 Foliar 

Ground/ 

broadcast, basal 

spray and 

drench (soil 

treatment) 

2.25 

EC 2.25 1 1 NA 35 

 1.0 

EC 3.0 3 3 NA 35 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 425 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 



 Postharvest, 

dormant/ 

delayed 

dormant 

Ground boom, 

broadcast 

2.0 

EC 2.0 1 1 NA NS 

Total 2.25 2.25 1 35 24 

2.0 5.0 4 NS 

GRASS FORAGE/ FODDER/HAY 

 Foliar, 

broadcast 

Aircraft, ground 

boom, 

chemigation 

1.0 

EC 3.0 NA 3 NA NS 24 

GREENHOUSE 



early 

evening, 

aerosol, fog 

or fumigation 

Total release 

fogger 

0.029 

0.0066 lb 

a.i./1000 sq. ft 

PL 

NS NA NS NA NS NS 

HOUSEHOLD/ DOMESTIC DWELLINGS INDOOR PREMISES

 When needed Bait station 0.0003 lb/bait station NS NA NS NA NA NS 

HYBRID COTTONWOOD/ POPLAR PLANTATIONS 



Foliar, 

dormant, 

delayed 

dormant; 

broadcast 

High volume 

(dilute) 

Low volume 

(concentrate) 

1.9 

EC 

[2.0] 

NS 6.0 [1] NS 3 24 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 426 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

LEGUME VEGETABLES 

 Preplant, soil 

treatment Ground boom 1.0 EC, WDG 1.0 NA 1 NA NS 24 

 At planting, 

soil treatment Ground boom 1.0 EC, WDG 1.0 NA 1 NA NS 

Total 1.0 1.0 NA 1 NA NS 24 

MINT/ PEPPERMINT/ SPEARMINT 

 Preplant soil 

incorporated 

Aerial or 

ground/ 

broadcast 

2.0 

EC, WDG 

[2.0] 

NS 2.0 [1] NS 1 90 24 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 427 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 



Postemergence, 

Postharvest, 

Foliar 

Chemigation, 

ground/ airblast 

2.0 

EC 2.0 2.0 [1] NS 2 90 

Total 2.0 4.0 4.0 2.0 3 90 24 

MOSQUITO CONTROL; HOUSEHOLD/ DOMESTIC DWELLINGS OUTDOOR PREMISES; RECREATIONAL AREAS 



When 

needed; 

broadcast 

Ultra low 

volume air and 

ground 

0.01 

EC 0.26 NA 26 NS NA NS 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 428 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

NECTARINE 



dormant/ 

delayed 

dormant

broadcast 

airblast, 

handgun 

3.0 

WDG, EC 

3.0 NA 1 NA NS 

24/

4d 

Aircraft 2.0 WDG, EC 



pre-plant, 

foliar; 

trunk 

spray/drenc

h or preplant dip 

Handgun, low 

pressure 

backpack, dip 

2.5 

(3.0/100 gal) 

WDG, EC 

2.5 NA 1 NA 14 

Total 3.0 5.5 NA 2 NA 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 429 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

NONAGRICULTURAL OUTDOOR BUILDINGS/STRUCTURES

to and around outside surfaces of nonresidential buildings and structures. Permitted areas of use include fences, preconstruction foundations, refuse dumps, outside of walls, and other areas where pests congregate or have been seen 

Outdoor 

general 

surface/ Band 

(may be 

better if 

called 

perimeter) 

Ground sprayer/ 

band sprayer 

1.0 

EC NS NA NS NA NA NS 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 430 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

NURSERYSTOCK: : Ornamental nursery stock annuals, perennials and woody plants being grown in the field, in ball and burlap or in containers outdoor and in greenhouses

Dormant/ 

Delayed 

Dormant 

high spray 3.0 EC 3.0 NA 1 NA 24 

 Preplant 

Ground boom, 

soil 

incorporated 

4.0 

EC, WP NS NA NS NA 

foliar, soil 

directed 

Tractor drawn 

spreader, push 

type spreader, 

belly grinder, 

gravity fed 

backpack, 

spoon 

1.1 

G 

Total 4.0 CBD 3 

ONIONS 



Post plant 

(seeding) 

Broadcast 

Ground boom 1.0 EC 1.0 

NS 

2 

NS 60 24 



At plant, soil 

drench or 

basal spray 

Ground boom 1.0 EC, WDG, G 1.0 1 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 431 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

Total 2.0 2.0 2 60 24 

ORNAMENTAL AND/OR SHADE TREES, HERBACEOUS PLANTS 

 Foliar 

broadcast 

Ground boom, 

air blast, 

handgun, low 

and high 

pressure hand 

wands 

2.0 

EC, WP 

1.0 

G, B 

2.0 NA [2] NS NA NS 

24 



Dormant 

/Delayed 

Dormant 

Handgun, low 

pressure and 

backpack 

3.0 

EC 3.0 NA 1 NA NS 

ORNAMENTAL LAWNS AND TURF, SOD FARMS (TURF) 



When 

needed, 

broadcast, 

soil or spot 

treatment 

ground boom 

(WP only), high 

pressure hand 

wand 

3.76 

EC, WP 7.52 NA 2 NA NS 24 

 NS 

Tractor drawn 

spreader, push 

type spreader, 

belly grinder 

1.0 

B 2.0 NA 2 NA NS 24 

ORNAMENTAL NONFLOWERING PLANTS 



Foliar, 

broadcast, 

soil drench 

Chemigation, 

ground boom, 

low and high 

pressure 

handwand, 

handgun, 

backpack 

sprayer, 

sprinkling can 

0.007/gal 

ME NS NA 12 NA NA 24 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 432 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

ORNAMENTAL WOODY SHRUBS AND VINES 

Foliar 

broadcast 

Ground boom, 

air blast, 

handgun, low 

and high 

pressure 

sprayer, 

backpack 

2.0 

EC, WDG 

0.01 lb/gal 

EC 

2.0 

0.01 

lb/gal 

NA [1] NS NA NS 24 

Dormant/ 

delayed 

dormant 

1.0 

EC 

0.005 lb/gal 

EC 

1.0 NA [1] NS NA 

 Preharvest 

Tractor drawn 

spreader, push 

type spreader, 

belly grinder 

6.0 

G 6.0 NA [1] NS NA 

Preplant, 

potted, 

bailed-and 

burlapped, 

containerized 

groundboom, 

handgun, low 

and high 

pressure 

sprayer, 

backpack, 

drench 

1.0 

EC NS 1 NS 1 

 Pretransplant groundboom 4.0 WP [48.0] NS 4 12 4 

Total 

6.0 

G 

4.0 

CBD CBD 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 433 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

 WP 

PEACH 



dormant/ 

delayed 

dormant

broadcast 

airblast 

3.0 

EC 

2.0 

WDG 3.0 NA 1 NA 10 

24/

4d 

aircraft, 

2.0 

EC 

2.0 

WDG 

 Post-harvest 

broadcast

airblast 

2.5 

(3.0/100 gal) 

EC 

2.5 

NA 1 NA NA 

aircraft 

2.0 

(3.0/100 gal) 

EC 

2.0 



 pre-plant, 

foliar; 

trunk 

spray/drenc

h or preplant dip; 

ground 

handheld, 

backpack, 

drench/dip, 

handgun, and 

low pressure 

hand wand 

2.5 

(3.0/100 gal) 

WDG 

2.5 NA 1 NA 14 5 

Total 

3.0 5.5 NA 3 NA NA 24 3.0 8.0 NA 3 NA NA 24 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 434 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

PEANUT 

 Preplant 

Aerial or 

ground/ 

broadcast 

2.0 

EC, WDG 

[4.0] 

NS 4.0 [2] NS 2 NA 24 

 At plant, 

postplant 

4.0 

G 

[4.0] 

NS 4.0 2 2 21 24 

 At pegging 

2.0 

G 

EC, WDG 

[4.0] 

NS 4.0 2 [2] NS 21 24 

Total 

4.0 

G 

2.0 

EC, WDG 

4.0 4.0 2 2 10 24 

PEAR 



dormant/ 

delayed 

dormant

broadcast 

aircraft, airblast 2.0 WDG, EC 2.0 NA 1 NA NA 24 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 435 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

 Post-harvest 

broadcast aircraft, airblast 

2.0 

WDG, EC 2.0 NA 1 NA NA 24 

Total 2.0 WDG, EC 4.0 NA 2 NA NA 24 

PEAS 

 Preplant Seed 

treatment Seed Treatment 

0.30 

0.000625 lb/lb 

seed 

WP 

0.28 

0.00058 lb/lb 

seed 

NS NS NS NS NS NS 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 436 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

EC 

PECANS 



dormant/ 

delayed 

dormant

broadcast 

aircraft, airblast 2.0 EC, WDG 2.0 NA 1 NA 14 

24 

 foliar; 

broadcast 

airblast 4.3 EC, WDG 

6.3 NA 3 NA 14 

aircraft 2.0 EC, WDG 



foliar; 

orchard 

floors 

broadcast 

Ground boom, 

chemigation 

4.3 

EC, WDG 4.3 NA 2 NA 14 

Total 4.3 12.6 NA 6 NA 14 24 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 437 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

PEPPER 

 Foliar Ground broadcast 1.0 WDG [8] NS 8.0 [8] NS 8 7 24 

PINEAPPLE 

 Postplant Ground boom, broadcast 2.0 EC 6.0 6.0 3 NA 365 24 

PLUM/ PRUNE



dormant/ 

delayed 

dormant; 

broadcast 

Aircraft, 

airblast 

2.0 

EC, WDG 2.0 NA 1 NA NA 

24/

4d 



foliar; 

trunk 

spray/drenc

h

handheld, 

backpack, 

drench/dip, 

handgun, and 

low pressure 

hand wand 

2.5 

3.0/100 gal 

WDG 

2.5 NA 1 NA NA 

Total 2.5 4.5 NA 2 NA 

POULTRY 

 When Sprayer 0.07126 NS NA NS NA NA 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 438 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

LITTER needed, 

animal 

bedding/litter 

treatment. 

a.i./1000 sq ft 

3.1 

ME 

PUMPKIN 

 Preplant Seed 

treatment Seed treatment 

0.3 

0.00058 lb /lb 

seed 

WP 

[0.3] 

NS 

[1] 

NS 

[1] 

NS 1 NS NS 

RADISH 

 Foliar Broadcast ground 1.0 EC NS 1 NS 1 NS 24  Preplant 

Soil 

incorporation 

ground 

3.0 

EC 12.0 3 4 1 NS NS 

 At plant/postplant In furrow drench/ 3.0 EC [15.0] NS 3 [5] NS 1 30, EC, 24 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 439 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

treatment 2.8 

G 7, 

G 

Total 3.0 [22.0] NS 2 [9] NS 

RIGHTS OF WAY, ROAD MEDIANS 

When 

needed, soil 

broadcast 

Granular or low 

pressure wand 

1.0 

EC, G, Bait 

[2.0] 

NS NA 2 NA NA NS 

RUTABAGA 

 Preplant 

Chemigation, 

Groundboom 

2.4 

EC, WDG [4.8] 

NS 

2.4 

[2] 

NS 1 30 24 

Aerial 2.0 EC, WDG 2.0 

 At plant/postplant In furrow drench/ treatment 2.4 EC, G WDG 4.8 2.4 [2] NS 1 7 24 

Total 2.4 [9.6] NS 4.8 [4] NS 2 24 

SEWER MANHOLE COVERS AND WALLS 

When needed Low pressure 

0.31 

lb/manhole 

RTU 

NS NA NS NA NA NA 

SEED ORCHARD TREES 

 foliar; 

broadcast Ground boom 1.0 EC 3.0 3.0 NS NA 30 24 

 High volume 2.5 2.5 NS [1] NA 30 24 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 440 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

sprayer 0.01 

a.i./tree 

0.02 EC 

NS 

 foliar; stump 

treatment

backpack, 

drencher, low 

pressure hand 

wand, 

0.3 

EC 0.3 1.0 NS NA 30 24 

Total 1.0 5.8 3 NS NA 30 24 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 441 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

SORGHUM GRAIN 

 Seed 

Treatment Seed treatment 

[0.0009] 

0.01- 

0.0024 lb ai/ 

100 lbs seed 

EC 

0.01 0.01 [1] NS 1 NA NS 

 Preplant Soil 

Directed 

Ground 

Spreader/T 

Band 

1.5 

G 1.5 1.5 [1] NS 1 60 24 

 Foliar/Post 

emergent 

Ground, Aerial, 

Chemigation 

1.0 

EC, WDG 1.5 [1.5] NS [1] NS 3 30 24 

Total 

3.3 

G 

1.0 

EC, WDG 

3.01 3.01 [3] CBD 3 30 24 

SOYBEAN 



foliar , postemergence 

soil broadcast 

broadcast 

ground, aerial, 

chemigation 

1.0 

EC, WDG 3.0 3.0 3 3 28 24 



At plant/post 

plant 

treatment; 

soil band 

ground boom 

2.2 

G 

1.0 EC 

3.0 3.0 1 (G), 3 (EC) 1 (G), 3 (EC) 28 24 

Total 

1.0 

EC, WDG 

2.2 

G 

3.0 3.0 3 3 

STRAWBERRIES 

 Pre-plant 

Aerial or 

ground/ 

broadcast 

2.0 

EC 2.0 NS 1 NS NA 24 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 442 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

 Foliar 

Aerial or 

ground/ 

broadcast, foliar 

spray 

1.0 

EC, WDG 2.0 NS 2 NS 21 24 

 Post harvest Ground directed 

spray 

1.0 

EC, WDG 2.0 NS 2 NS 21 

Total 2.0 4.0 

3

SUNFLOWER 

 At plant 

Aerial/ground 

2.0 

G 3.0 3.0 [1] NS 1 42 

24 

 Preplant 2.0 EC, WDG 3.0 3.0 [1] NS 1 42 

 Post 

emergent or 

foliar 

1.5 

EC, WDG 3.0 3.0 [2] NS 2 42 

Total 2.0 5.0 5.0 3 3 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 443 of 531

Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

SWEET POTATO 

 Preplant, soil 

broadcast 

Aircraft, ground 

boom

2.1 

G, EC, WDG 

2.1 NS 1 1 125 24 

Aircraft 2.0 G, EC, WDG 

TOBACCO 

 Preplant Aircraft, ground boom 2.0 EC, G, WDG 2.0 NS 1 1 7 24 

TRITICALE 



Storage 

Commercial 

Slurry Seed 

Treatment 

Seed treatment 

0.003 

0.0024 lb ai/ 

100 lbs seed 

EC 

[0.003] 

NS 

[1] 

NS 

[1] 

NS 

[1] 

NS NA 

[10] NS 

TURNIP 

 Preplant 

soil 

incorporation/ 

ground boom, 

handgun 

2.3 

G, WDG 

[4.6] 

NS 2.3 [2] NS 1 30 24 

 Postplant 

Soil 

incorporation/ 

ground boom, 

handgun 

2.3 

G, WDGP 

[4.6] 

NS 2.3 [2] NS 1 30 24 

Total 2.3 4.6 2.3 2 1 30 24 
Chlorpyrifos Human Health Risk Assessment D424485 

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Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

UTILITIES For use in and around telecommunications, power, utilities and railroad systems equipment: Buried cables, cable television pedestals, cables, pad-mounted electric power transformers, telephone cables, underground vaults, telecommunications equipment, power and utilities equipment 

When 

needed, 

broadcast 

Product 

container 

190.5 

G 

0.44 lba.i./100 

sq ft 

(see 

comments) 

NS NS NS NS NS NS 

WALNUTS 



dormant/ 

delayed 

dormant; 

broadcast 

Aircraft, 

airblast 

2.0 

EC, WDG 2.0 NA 1 NA 14 

24 

 foliar; 

broadcast 

aircraft, 

airblast, 

chemigation 

2.0 

EC, WDG 4.0 NA 2 NA 14 

 foliar; Ground boom, 4.0 4.0 NA 1 NA 14 
Chlorpyrifos Human Health Risk Assessment D424485 

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Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

orchard 

floors 

broadcast

chemigation EC, WDG 

Total 4.0 10.0 4 

WIDE AREA/ GENERAL OUTDOOR TREATMENT For ants and other misc. pests.





when needed, 

Broadcast Ground sprayer 

0.5084 lb 

ai/100 gal 

EC 

[1.02] 

NS NA 2 NA NA 

NS 

when needed, 

Drench Drench 

1 NS NA NS NA NA 

[1] 

8.2 lb a.i/100 

gal EC 

NS NA NS NA NA 

Total [1] NS NA NS NA NA 

WHEAT 

 Slurry Seed 

Treatment Seed treatment 

0.003 

0.0024 lb ai/ 

100 lbs seed 

EC 

[0.006] 

NS 1 [2] NS 1 NA NA 

 Foliar, soil 

treatment 

Ground, 

broadcast 

0.5 

EC 

[8.0] 

NS 4.0 [2] NS 1 14/ 28 24 
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Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 



Postemergence 

foliar 

Ground, Aerial, 

Chemigation 

1.0 

EC 

[4.0] 

NS 2.0 [4] NS 2 14/ 28 

Total 

[1] 

4.0 

EC 

[12.006

] 

[6.003] 

5.0

[8] 

NS 

[4] 

2 

WOOD PROTECTION TREATMENT TO BUILDINGS/ PRODUCTS OUTDOOR

When 

needed, 

Wood 

surface 

treatment 

Low pressure 

handwand, 

backback 

sprayer, 

paintbrush 

16.65 

lb/10,000 sq ft 

0.17 lb a.i./gal 

EC

NS NA NS NA NS NS 

0.08 lb ai/gal 

EC, RTU EC, 

ME 

NS NA NS NA NS NS 
Chlorpyrifos Human Health Risk Assessment D424485 

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Table 9.1. Summary of Current Chlorpyrifos Usage

Crop/Site 

Residential 

Agricultural 

Forestry Timing; Application Type Method/ Equipment 

Maximum 

Single 

Application 

Rate by 

Formulation1 

(lb a.i./A) 

Maximum 

Application Rate 

Maximum 

Application 

Number 

PHI (days)3 

REI (hours)3 

Per Year lb a.i./A Per CC2 lb a.i./A Per Year Per CC2 

1. EC - emulsifiable concentrate; WDG – water dispersible granular in water soluble packet; WP – wettable power in water soluble packet; B 

microencapsulated; RTU – ready to use. 

2. Reported as per crop cycle or per season 

3. PHI – Preharvest interval; REI – reentry interval; MRI – Minimum retreatment interval 

4. Becker, J.; Ratnayake, S. Acres Planted per Day and Seeding Rates of Crops Grown in the United States, U.S. EPA OPP/BEAD, 2011; ex

Beans: 0.00058 lb a.i./lb seed / 960 seeds/lb seed x 418,176 seeds/A [pgs. 19, 81 (beans, succulent)] Corn: 0.000625 lb a.i./lb seed / 1,800 seeds/lb seed x 59,739 seeds/A [pgs. 24, 81 (corn, sweet)] Cotton: 0.00116 lb a.i./lb seed / 4,500 seeds/lb seed x 85,00 seeds/A [pgs. 13, 81] Cucumber: 0.00058 lb a.i./lb seed / 12,000 seeds/lb seed x 80,418 seeds/A [pgs. 25, 81] 

Peas: 0.000625 lb a.i./lb seed / 1,361 seeds/lb seed x 653,400 seeds/A [pgs. 34, 82] Pumpkin: 0.00058 lb a.i./lb seed / 1,600 seeds/lb seed x 7,260 seeds/A [pgs. 37, 82] Sorghum: 0.001 lb a.i./lb seed / 11,000 seeds/lb seed x 100,000 seeds/A [pgs. 16, 39] Triticale: 0.003 lb a.i./100 lb seed / 109 lb seed/A [pg.16] Wheat: 0.003 lb a.i./100 lb seed /116 lb seed/A [pg. 16] [ ] indicate assumptions that are made when the information is not specified but can be inferred 
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Appendix 10. Dose Reconstruction Analysis 

Introduction 

In order to better understand the possible exposure patterns in the Columbia University study 

cohort and their influence on the health outcomes identified in the research, an analysis was 

conducted to supplement the exposure metrics reported by the Columbia investigators. The goal 

was to predict potential exposures which could have occurred to pregnant women and young 

children who may have been exposed to chlorpyrifos through other pathways not addressed in 

the research then determine their potential for associated cholinesterase inhibition. 

Chlorpyrifos, at the time, was one of the most widely used insecticides for indoor pest control so 

there are many possible ways which exposures could have occurred as a result of these types of 

uses (e.g., use of a total release fogger, aerosol can sprays, or crack and crevice treatment by a 

pest control operator). This analysis was based on the Agency’s SOPs for Residential Exposure 

Assessment76 which describe, on a scenario basis, specific algorithms and data used to predict the 

potential exposures which could have occurred to individuals in the cohort. Once complete, the 

resulting dose estimates were used to evaluate the potential level of cholinesterase inhibition 

which could be attributed to them using the PBPK-PD model. The scenarios which were 

considered include: 

 Pregnant mothers who may have purchased a consumer aerosol can product and applied 

it in their homes; 

 Exposures to pregnant mothers who may have had contact with residues in their homes 

after a previous treatment which leads to exposure; and 

 Exposures to young children (aged 1 to 2 years old) who may have contact with residues 

in their homes after a previous treatment which leads to exposure. 

This appendix describes the details of those calculations. 

Use Profile 

In order to evaluate the chlorpyrifos exposure potential for the Columbia study cohort, it was 

first necessary to define what chlorpyrifos products would have been in use during the time of 

study conduct which spanned from 1998 to 2004. In 1997, the registrant, Dow AgroSciences, 

voluntarily agreed to cancel chlorpyrifos registrations for indoor broadcast use and direct pet 

treatments, except pet collars. In December 2001, the majority of the remaining chlorpyrifos 

residential products -- except for fully contained ant baits in child resistant packaging and limited 

public health uses -- were subject to voluntary phase out/cancellation. This analysis 

reconstructed the potential exposures for those in the cohort between the years 1997-2001 based 

on the chlorpyrifos uses that remained after the voluntary cancellation in 1997 and the phase out 

in 2001. The exposure questionnaire-based reported use from the Columbia publications was 

also used to inform this process. Per the publications, pest control measures were used by 92% 

of respondents, whether by housing superintendent, pest control operator (PCO), or by 

themselves. Of these, respondents reported that 39% were by PCO, 26% by spray can, and 5% 

foggers/bombs. The majority reported regular use of a pesticide product, at least once per 

 

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month. Unfortunately, while these data help inform the types of products used, they do not allow 

for the determination of what pesticide active ingredient(s) were used. Multiple active 

ingredients were in regular use during this time period including, chlorpyrifos, diazinon, and 

propoxur, among others. For the purpose of the dose reconstruction exercise, it was assumed 

that only chlorpyrifos applications occurred. Considering all the available information, the 

potential for chlorpyrifos exposures from use of the following chlorpyrifos product types were 

assessed: broadcast ready to use (RTU) sprays applied by individual themselves and not 

professional applicators; surface sprays applied by individuals; crack and crevice sprays applied 

by individuals themselves or professional pest control applicators; and foggers/bombs applied by 

individuals. These chlorpyrifos residential products contained either 0.5% or 1% active 

ingredient. The latter was assumed to ensure an assessment reflective of the highest potential 

exposures, or those most likely to result in the greatest % RBC AChE inhibition. 

Exposure Assessment Methods 

With the products and application rates defined, the 2012 Standard Operating Procedures for 

Residential Pesticide Exposure Assessment77 were used to assess the exposures that may have 

occurred from these indoor residential chlorpyrifos uses. Based on the use patterns of the 

defined products, it is expected that exposures occurred either from the application (handling) of 

products by those in the cohort or after applications from being in contact with previously treated 

indoor environments. 

A series of assumptions and exposure factors serve as the basis for completing residential 

handler and post-application exposure assessment: 

 maximum application rates allowed by labels in its risk assessments are assumed; 

 the use of personal protective equipment is not considered in residential risk 

assessments; 

 residential assessments are based on the assumption that individuals are wearing shorts, 

short-sleeved shirts, socks, and shoes; 

 adults are assumed to perform all pesticide applications; 

 the mean female bodyweight reflective of all trimesters of pregnancy, 75 kg, was 

assumed for pregnant women to reflect the population of interest from the Columbia 

cohort being evaluated which was derived from the EPA Exposure Factors Handbook 

2011 Edition78 (adult and adult female: Tables 8-3 through 8-5; body weight of pregnant 

women: Table 8-29). 

 the most protective index lifestage which serves as the basis for evaluating young 

children’s indoor exposures are children 1 to < 2 years old as described in in the 2012 

Residential SOPs; 

 the body weight used to evaluate exposures to children 1 to < 2 years old is 11 kilograms. 

For residential handler exposure assessment, the following assumptions and exposure factors 

apply: 

 a broadcast application of an aerosol can is considered and the entire contents of the can 

(16 oz.) are applied at once, half the contents of the spray can are applied for crack and 

crevice applications, and a quarter of the can contents for a space spray application. 

 

77 http://www.epa.gov/pesticides/science/USEPA-OPP-HED_Residential%20SOPs_Oct2012.pdf 78 http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=236252
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For residential post-application exposure assessment, the following assumptions and exposure 

factors apply: 

 inhalation exposures are expected to occur up to 2 hours following application by which 

time the aerosolized particulate is assumed to have settled and, as a result, after that time 

frame exposures are assumed to be negligible; 

Use of the PBPK-PD Model 

Once complete, the PBPK-PD model was used to estimate the potential level of cholinesterase 

inhibition (% RBC AChE) attributable to the daily dose estimates that result in the highest 

exposure potential. In order to model the potential health outcomes in a manner reflective of 

these exposures, a series of inputs were used that correspond to those used to quantify the daily 

dose estimates. The following exposure characteristics were used: number of days exposed; 

body weights which vary by lifestage; daily exposure duration (hours per day, days per week); 

and the fraction of the dermal surface area exposed. 

The dose reconstruction exercise was modeled for a subsequent 14 day post-application duration. 

The daily dose estimated with the 2012 Standard Operating Procedures for Residential Pesticide 

Exposure Assessment for the day of application was inputted into the PBPK-PD model. Dermal 

exposure was modeled assuming that chlorpyrifos residues dissipated at a rate of 10% per day. 

A 14 day period was used as the basis of the PBPK-PD modeling to ensure it adequately 

addressed the time it takes for the ACHE enzyme inhibition to reach a steady state. 

For the adult and children 1 to < 2 years old age groups, the appropriate body weights were 

obtained for modeling purposes from the Exposure Factors Handbook79 (USEPA, 2011). The 

mean adult female bodyweight reflective of all trimesters of pregnancy, 75 kg, was assumed for 

pregnant women to reflect the population of interest from the Columbia cohort being evaluated 

(Exposure Factors Handbook: adult and adult female: Tables 8-3 through 8-5; body weight of 

pregnant women: Table 8-29). For children 1 to < 2 years old, the body weight was set to 11 kg 

(Exposure Factors Handbook, Table 8-3, mean body weight for the 1 to < 2 year old age group). 

For the evaluation of adult handler exposures (dermal and inhalation) it was assumed that the 

application event continued for 1 hour daily and that it occurred each day of the 14 day exposure 

period. The 2012 Residential SOPs makes no recommendation regarding the daily exposure 

duration for handlers. It was assumed that 1 hour is a high end estimate of the time required to 

make an aerosol can application. For the evaluation of adults and children 1 < 2 years old postapplication exposures (i.e., adults: dermal; children: dermal and incidental oral), the daily 

exposure duration was assumed to be 2 hours daily and that it occurs each day in the 14 day 

exposure period. This exposure duration is consistent with that recommended in the 2012 

Residential SOPs for exposures on hard surfaces. For adults and children 1 to < 2 years old 

inhalation exposures, it was assumed that the exposure duration was 2 hours on the day of 

application (Day 0) and was not repeated because the exposure source (i.e., aerosols from an 

application event) occurred only once on the first day of the exposure period. This is consistent 

with the available monitoring data and consistent with the 2012 Residential SOPs, the Day 0 air 

monitoring measures were followed by a marked drop to negligible levels by the following day. 

For all dose reconstruction dermal exposures to chlorpyrifos, the fraction of skin in contact with 

 

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chlorpyrifos was 50% which reflected uncovered skin areas for children wearing shorts and a tee 

shirt. No shower (i.e., washing off the chlorpyrifos) was assumed to occur for the 14 day 

exposure duration modeled. 

Residential Handler Exposure Scenarios 

The residential handling of chlorpyrifos residential products are expected to have occurred for 

some percentage of the Columbia cohort population, these are anticipated to have resulted in 

dermal and inhalation exposures. Dermal and inhalation residential handler exposures are 

predicted by use of a factor known as the unit exposure. Unit exposure is the ratio, for a given 

formulation and application equipment, of an individual’s exposure the amount of active 

ingredient handled (AaiH), expressed as mass active ingredient exposure per mass active 

ingredient handled (e.g., mg/lb ai). For the dose reconstruction assessment, unit exposure data 

were used as recommended in the 2012 Residential SOPs for ready-to-use (RTU) aerosol cans 

(dermal, 370 mg/ lb ai; inhalation, 3.0 mg/lb ai). Residential handler exposures from application 

of fogger formulations were not assessed which is consistent with the 2012 Residential SOPs. 

Fogger product labels typically direct the user to activate the device and evacuate the treated area 

and not to re-enter for several hours. Therefore, it is assumed that handler exposure from fogger 

application is not expected. 

The exposure assessment for chlorpyrifos Columbia cohort handler exposures is based on the 

following exposure scenarios: 

 Adult dermal exposure from broadcast application of a RTU spray can; 

 Adult inhalation exposure from broadcast application of a RTU spray can; 

 Adult dermal exposure from crack and crevice application of a RTU spray can; 

 Adult inhalation exposure from crack and crevice application of a RTU spray can; 

 Adult dermal exposure from space spray application of a RTU spray can; 

 Adult inhalation exposure from space spray application of a RTU spray can. 

Handler Exposure Algorithm 

Daily dermal and inhalation exposure (mg/day) for residential pesticide handlers, for a given 

formulation-application method combination, is estimated by multiplying the formulationapplication method-specific unit exposure by an estimate of the amount of active ingredient 

handled in a day, using the equation below: 

E = UE * AR * A 

where: 

E = exposure (mg/day); 

UE = unit exposure (dermal, 370 mg/ lb ai; inhalation, 3.0 mb/lb ai); 

AR = application rate (e.g., lb ai/can); and 

A = area treated or amount handled (broadcast, 1 can/day; crack and crevice, 0.5 can/day; 

space spray, 0.25 can/day). 

Residential Handler Exposure/Cholinesterase Inhibition Summary 
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Worst-case results of this analysis are presented below representing pregnant women who 

potentially made a broadcast application of an aerosol spray can (Table 10.1). Peak % RBC 

AChE inhibition estimated for pregnant women from combined dermal and inhalation exposures 

occurring from product application is 0.0012%. 

Table 10.1. Residential Handler (Pregnant Women in the Columbia Cohort) Estimated Exposures and 

Predicted % ChE Inhibition (Route-specific and Combined) 

Exposure 

Scenario Formulation Amount Handled 

Dermal Dose 

(mg/kg/day) 

Dermal: 

Peak 

% ChE 

Inhibition 

Inhalation 

Dose 

(mg/kg/day) 

Inhalation: 

Peak 

% ChE 

Inhibition 

Combined 

Peak 

% ChE 

Inhibition 

Broadcast 

Ready to Use: 

1 % Aerosol 

Spray Can 

1 can 0.049 0.0007% 0.030 0.0008% 0.0012% 

a. Dermal Exposure = Dermal Unit Exposure (370 mg/lb ai) × Application Rate (0.010 lb ai/can) × Area Treated or Amount 

Handled (cans/day) 

b. Inhalation Exposure = Inhalation Unit Exposure (3.0 mg/lb ai) × Application Rate (0.010 lb ai/can) × Area Treated or Amount 

Handled (cans/day) 

Residential Post-application Exposure Scenarios 

The use of indoor residential chlorpyrifos products are anticipated to have resulted in the 

potential for post-application exposures from public housing residents in the Columbia study 

cohort being in a previously treated environment. Based on the product use profile, residents 

may have been exposed via inhalation to the aerosols resulting from application or by means of 

contact following settling of product contents following settling on indoor surfaces or flooring. 

The exposure assessment for chlorpyrifos Columbia cohort post-application exposures is based 

on the following exposure scenarios: 

 Adult dermal contact with residues deposited on flooring (carpeted and hard) from 

broadcast application of RTU spray can; 

 Adult inhalation of aerosolized particulates following broadcast application of RTU spray 

can; 

 Adult dermal contact with residues deposited on flooring (carpeted and hard) from space 

spray application of RTU spray can; 

 Adult inhalation of aerosolized particulates following space spray application of RTU 

spray can; 

 Adult dermal contact with residues deposited on flooring (carpeted and hard) from crack 

and crevice RTU spray can or PCO application; 

 Adult inhalation of aerosolized particulates following crack and crevice application of 

RTU spray can; 

 Children 1 to < 2 years old dermal contact with residues deposited on flooring (carpeted 

and hard) from broadcast application of RTU spray can; 

 Children 1 to < 2 years old incidental oral (hand-to-mouth) ingestion of residues 

deposited on flooring (carpeted and hard) from broadcast application of RTU spray can 

that have transferred to children’s hands; 

 Children 1 to < 2 years old incidental oral (object-to-mouth) ingestion of residues 

deposited on toys in contact with flooring (carpeted and hard) treated by broadcast 

application of RTU spray can; 

 Children 1 to < 2 years old inhalation of aerosolized particulate following broadcast 
Chlorpyrifos Human Health Risk Assessment D424485 

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application of RTU spray can; 

 Children 1 to < 2 years old dermal contact with residues deposited on flooring (carpeted 

and hard) from space spray application of RTU spray can; 

 Children 1 to < 2 years old incidental oral (hand-to-mouth) ingestion of residues 

deposited on flooring (carpeted and hard) from space spray application of RTU spray can 

that have transferred to children’s hands; 

 Children 1 to < 2 years old incidental oral (object-to-mouth) ingestion of residues 

deposited on toys in contact with flooring (carpeted and hard) treated by space spray 

application of RTU spray can; 

 Children 1 to < 2 years old inhalation of aerosolized particulate following space spray 

application of RTU spray can; 

 Children 1 to < 2 years old dermal contact with residues deposited on flooring (carpeted 

and hard) from crack and crevice RTU spray can or PCO applications; 

 Children 1 to < 2 years old incidental oral (hand-to-mouth) ingestion of residues 

deposited on flooring (carpeted and hard) from crack and crevice RTU spray can or PCO 

applications that have transferred to children’s hands; 

 Children 1 to < 2 years old incidental oral (object-to-mouth) ingestion of residues 

deposited on toys in contact with flooring (carpeted and hard) treated by crack and 

crevice RTU spray can or PCO applications; 

Since all exposure routes (dermal, inhalation, and incidental oral) share a common toxicological 

endpoint, RBC AChE inhibition, risk estimates should been combined. The incidental oral 

scenarios (i.e., hand-to-mouth and object-to-mouth) should be considered inter-related and it is 

likely that they occur interspersed amongst each other across time. Combining these scenarios 

with the dermal exposure scenario would be unrealistic because of the conservative nature of 

each individual assessment. Therefore, the post-application exposure scenarios that were 

combined for children 1 < 2 years old are the dermal and hand-to-mouth scenarios. This 

combination should be considered a protective estimate of children’s exposure to pesticides used 

indoors. 

The lifestages selected for each post-application scenario are based on an analysis provided as an 

Appendix in the 2012 Residential SOPs. While not the only lifestage potentially exposed for 

these post-application scenarios, the lifestage that is included in the quantitative assessment is 

health protective for the exposures and risk estimates for any other potentially exposed lifestage. 

Post-application Exposure Data 

The 2012 Residential SOPs relied on exposure data from Agency submitted studies in order to 

establish many of the inputs necessary for estimation of post-application indoor exposures. 

Where chemical-specific exposure data are available, these data were relied upon as a more 

accurate measure of exposures from chlorpyrifos and are described below. In the absence of 

these data, exposure inputs are based on the use of available exposure data as recommended in 

the 2012 Residential SOPs. 

Exposure Data Used for Dermal and Incidental Oral Exposure Assessment: No chemicalspecific deposition data were available for the indoor chlorpyrifos products assessed. Therefore, 

as recommended by the 2012 Residential SOPs, default deposited residue values were used 
Chlorpyrifos Human Health Risk Assessment D424485 

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based on the type of application to be made whether it be a broadcast, crack and crevice, or space 

spay application. For broadcast applications only, residue values were influenced by the percent 

spray applied (i.e., the higher the percent spray, the higher the residue values). The higher, 1% 

chlorpyrifos spray formulation was assumed for post-application assessment of broadcast 

exposures. The default values used are based on an analysis of available residue deposition data 

from Agency submitted studies and literature studies. A summary of the recommended values 

are presented in the 2012 Residential SOPs. 

The transfer coefficent (TC) input used for calculation of post-application exposures is a measure 

of surface-to-skin residue transfer derived from concurrent measures of exposure and surface 

residue. Per the 2012 Residential SOPs, no studies were available that measure both exposure 

and surface residue with subjects performed typical indoor activities. Therefore, the transfer 

coefficients used for indoor scenarios are derived from information provided in three different 

studies: 1) two studies which measured exposure and surface residues while subjects performed a 

Jazzercise routine (Krieger, 2000 and Selim, 2004) and 2) a study which measured 

biomonitoring doses which adults performed scripted activities for 4 hours on carpets (Vaccaro, 

1991). Of these studies, the Krieger study was conducted using chlorpyrifos. 

As for the fraction of residue available for transfer input, a complete dataset was compiled for 

chlorpyrifos based on available chemical-specific data (Beamer et al., 2009). Therefore, the 

chlorpyrifos-specific residue fractions anticipated to transfer for both carpets and hard surfaces 

were used for estimation of post-application dermal and incidental oral exposures. 

Exposure Data Used for Inhalation Exposure Assessment: In order to best represent the potential 

for inhalation exposures following use of the indoor chlorpyrifos products assessed, a review of 

available literature studies and registrant-submitted exposure data was conducted. The following 

exposure data sources were identified and used: 

Indoor broadcast applications – 

 Fenske, R., et al (1990). Potential Exposure and Health Risks of Infants 

following Indoor Residential Pesticide Applications. American Journal of Public 

Health. 80 (6): 689-693. 

 Lu, C. and Fenske, R (1998). Air and Surface Chlorpyrifos Residues following 

Residential Broadcast and Aerosol Pesticide Applications. Environmental Science 

and Technology. 32 (10): 1386 -1390. 

 EPA MRID: 42887201. Contardi, J. (1993). An Evaluation of the Appropriate 

Drying Time via Air Monitoring, Dislodgable Residue Determination, and Carpet 

Weight Loss, After Applying Dursban LO Insecticide to a Carpeted Surface: 

Unpublished study prepared by Dow Chemical Co., Health and Environmental 

Sciences. 29 pp. 

 Indoor crack and crevice applications – 

 Byrne, S.L., Shurdut, B.A. and Saunders, D.G. (1998). Potential Chlorpyrifos 

Exposure to Residents following Standard Crack and Crevice Treatment. 106 

(11): 725 -731. 

 Hore, P. et al. (2005). Children’s Residential Exposures to Chlorpyrifos: 

Application of CPPAES Field Measurements of Chlorpyrifos and TCPy within 
Chlorpyrifos Human Health Risk Assessment D424485 

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MENTOR/SHEDS – Pesticides Model. Science of the Total Environment. 336 (2 

-3): 525-537. 

 Stout II, D.M. and Mason, M.A. (2003). The Distribution of Chlorpyrifos 

following a Crack and Crevice Type Application in the US EPA Indoor Air 

Quality Research House. Atmospheric Environment. 37 (39 -40): 5539 -5549. 

 EPA MRID 44458201: Byrne, S.; Saunders, D.; Cook, W. et al. (1998) 

Residential Exposure to Chlorpyrifos from Reentry to Structures Treated with 

Crack and Crevice and Spot Applications of Dursban Pro. Unpublished study 

prepared by Dow AgroSciences. 133 pp. 

Based on a review of the available data and consistent with the 2012 Residential SOPs, the day 

of application (Day 0) air monitoring measures were significant, followed by a marked drop to 

negligible levels by the following day. Therefore, HED made use only of day of application air 

monitoring measures for purpose of post-application exposure assessment. All studies 

referenced were based on 0.5% chlorpyrifos formulated products. In order to address the 

potential for use of the 1% chlorpyrifos product formulations available at the time, all air 

monitoring measures were doubled. The broadcast exposure data are intended for use in 

assessing the potential inhalation exposures/doses anticipated to occur from both broadcast and 

space spray applications; the crack and crevice exposure data were used only for that application 

type. The resulting mean day of application air monitoring measures are presented in Table 10.2 

below. 

Table 10.2. Airborne Concentrations of Chlorpyrifos from Open Literature and Registrant-submitted 

Studies Resulting from Indoor Broadcast and Crack and Crevice Applications 

Study Chlorpryrifos Concentration (mg/m3) 

Broadcast 

Fenske, R., et al (1990) 0.18 

Lu, C. and Fenske, R (1998) 0.088 

EPA MRID: 42887201 0.031 

Average (All Studies) 0.092 

Crack and Crevice 

Byrne, S.L., Shurdut, B.A. and Saunders, D.G. (1998) 0.0010 

Hore, P. et al. (2005). 0.00057 

Stout II, D.M. and Mason, M.A. (2003) 0.00086 

EPA MRID 44458201 0.0011 

Average (All Studies) 0.00089 

Post-Application Algorithms for All Scenarios 

Post-Application Dermal Exposure Algorithm (hard surfaces and carpets) 

The algorithm to calculate exposure is as follows: 

1ܨܥ ∗ ܶܧ ∗ ܥܶ ∗ ܴܶ ൌ ܧ

where: 

E = exposure (mg/day); 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 456 of 531

TR = indoor surface transferable residue (μg/cm2

); 

TC = transfer coefficient (cm2

/hr); 

ET = exposure time (hr/day); and 

CF1 = conversion factor (0.001 mg/μg). 

If chemical-specific TR data are available, this is preferred and should be used to calculate 

exposure. However, if chemical-specific TR data are not available, then TR can be calculated 

using the following formula: 

௜௔ܨ ∗ ܴ݌݁ܦ ൌ ܴܶ

 

where: 

TR = indoor surface transferable residue (μg/cm2

); 

DepR = deposited residue (g/cm2), based on (in order of preference): 

 (1) Chemical-specific residue deposition data (g/cm2

), 

 (2) Application rate (lb ai/area), or 

 (3) Default residue based on type of application (g/cm2); and 

Fai = fraction of ai available for transfer from carpet or hard surface (unitless). 

Absorbed dermal dose, normalized to body weight, are calculated as: 

ൌ ܦ

ܨܣ ∗ ܧ

 ܹܤ

where: 

D = dose (mg/kg-day); 

E = exposure (mg/day); 

AF = absorption factor; and 

BW = body weight (kg). 

Table 10.3. Indoor Environments (Hard Surfaces and Carpets) – Inputs for Residential Post-application 

Dermal Exposure 

Algorithm 

Notation 

Exposure Factor 

(units) Point Estimate(s) 

TR Transferable residue 

(g/cm2

) Estimated: DepR * Fai 

DepR Deposited residue 

(g/cm2

) 

Estimated based on default residue 

related to type of application 

Fai

Fraction of DepR as TR 

following application 

Carpets 0.020a

Hard surfaces 0.13a

TC Transfer Coefficient 

(cm2

/hr) 

Adult 6,800 

Children 1 < 2 years old 1,800 

ET Exposure Time (hrs/day) Adults 

Carpets 8 

Hard 

Surfaces 2 

Children 1 < 2 Carpets 4 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 457 of 531

Table 10.3. Indoor Environments (Hard Surfaces and Carpets) – Inputs for Residential Post-application 

Dermal Exposure 

Algorithm 

Notation 

Exposure Factor 

(units) Point Estimate(s) 

years old Hard 

Surfaces 2 

BW Body weight (kg) Adult 80 

Children 1 < 2 years old 11 

a. Chlopyrifos-specific as identified (Table 7-8) in the 2012 Residential SOPs. 

Post-application Hand-to-Mouth Exposure Algorithm 

Exposure from hand-to-mouth activity is calculated as follows (based on algorithm utilized in 

SHEDS-Multimedia):

ܧ ൌ ቈܪܴ 	∗ ሺܨெ ∗ 	 ܵܣுሻ ∗	 ሺܧܶ ∗ ܰ_ܴ݁݌݈݁݊ሻ ∗ ቆ1 െ ሺ1 െ ܵܧሻ

ி௥௘௤_ு௧ெ

ே_ோ௘௣௟௘௡ ቇ቉

 

where: 

E = exposure (mg/day); 

HR = hand residue loading (mg/cm2); 

FM = fraction hand surface area mouthed / event (fraction/event); 

ET = exposure time (hr/day); 

SAH = surface area of one hand (cm2); 

N_Replen = number of replenishment intervals per hour (intervals/hour); 

SE = saliva extraction factor (i.e., mouthing removal efficiency); and 

Freq_HtM = number of hand-to-mouth contacts events per hour (events/hour). 

and 

ൌ ܴܪ

ܧܦ ∗ ௗ௦௛௔௡ܽ݅ܨ

2 ∗ ுܣܵ

where: 

 HR = hand residue loading (mg/cm2

); 

 Faihands = fraction ai on hands compared to total surface residue from jazzercise 

study (unitless); 

 DE = dermal exposure (mg); and 

SAH = typical surface area of one hand (cm2

). 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 458 of 531

Table 10.4. Indoor Environments – Inputs for Residential Post-application Hand-to-Mouth 

Exposure 

Algorithm 

Notation 

Exposure Factor 

(units) Point Estimate(s) 

Faihands

Fraction of ai on hands from jazzercise study 

(unitless) 0.15 

DE 

Dermal exposure calculated in Section Error! Reference source 

not found.

(mg) 

Calculated 

HR Residue available on the hands 

(mg/cm2

) Calculated 

SAH

Surface area of one hand 

(cm2

) Children 1 < 2 years old 150 

AR Application rate 

(mass active ingredient per unit area) 1% formulation 

FM

Fraction of hand mouthed per event 

(fraction/event) 0.13 

N_Replen Replenishment intervals per hour 

(intervals/hr) 4 

ET Exposure time 

(hours per day) 

Children 1 < 2 

years old 

Carpets 4 

Hard Surfaces 2 

SE Saliva extraction factor 

(fraction) 0.48 

Freq_HtM 

Hand-to-mouth events 

per hour 

(events/hr) 

Children 1 < 2 years old 20 

BW Body Weight 

(kg) Children 1 < 2 years old 11 

Post-application Object-to-Mouth Exposure Algorithm 

Exposure from object-to-mouth activity is calculated as follows (based on algorithm utilized in 

SHEDS-Multimedia):

ܧ ൌ ܱܴ ∗ ܨܥ1 	∗ ܵܯܣை ∗ 	 ሺܧܶ ∗ ܰ_ܴ݁݌݈݁݊ሻ ∗ ቆ1 െ ሺ1 െ ܵܧሻ

ி௥௘௤_ை௧ெ

ே_ோ௘௣௟௘௡ ቇ

where: 

E = exposure (mg/day); 

OR = chemical residue loading on an object (g/cm2

); 

CF1 = weight unit conversion factor (0.001 mg/g); 

SAMO = area of the object surface that is mouthed (cm2

/event); 

ET = exposure time (hr/day); 

N_Replen = number of replenishment intervals per hour (intervals/hour); 

SE = saliva extraction factor (i.e., mouthing removal efficiency); and 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 459 of 531

Freq_OtM = number of object-to-mouth contact events per hour (events/hour). 

and 

ைܨ ∗ ܴ݌݁ܦ ൌ ܱܴ

 

where: 

 

OR = chemical residue loading on the object (μg/cm2); 

 DepR = deposited residue (μg/cm2); and 

 FO = fraction of residue transferred to an object (unitless). 

Table 10.5. Indoor Environments – Inputs for Residential Post-application Object-to-Mouth 

Exposure 

Algorithm 

Notation 

Exposure Factor 

(units) Point Estimate(s) 

AR Application rate 

(mass active ingredient per unit area) [input] 

FO

Fraction of residue 

transferred to an 

object 

Carpets 0.020a

Hard surfaces 0.13a

SAMO

Surface area of object mouthed 

(cm2

/event) 10 

N_Replen Replenishment intervals per hour 

(intervals/hour) 4 

SEO Saliva extraction factor 0.48 

ET Exposure Time 

(hours per day) 

Children 1 < 

2 years old 

Carpets 4 

Hard Surfaces 2 

Freq_OtM 

Object-to-mouth events 

per hour 

(events/hour) 

Children 1 < 2 years old 14 

BW Body Weight 

(kg) Children 1 < 2 years old 11 

a. Chlopyrifos-specific as identified (Table 7-8) in the 2012 Residential SOPs. 

Residential Post-Application Exposure/Cholinesterase Inhibition Summary 

The results of this analysis are presented below for pregnant who were potentially exposed from 

contact to residues which occur in previously treated areas such as their homes (Table 10.6). 

Results for young children (aged 1 to < 2 years old) who are also exposed to residues which 

occur in previously treated areas such as their homes are presented in Table 10.7. Peak 

cholinesterase inhibition for pregnant women resulting from combined dermal and inhalation 

exposures occurring from contact with previously treated indoor areas is 0.45%. For children 1 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 460 of 531

to < 2 years old, peak cholinesterase inhibition associated with combined dermal, incidental oral, 

and inhalation exposures from being in previously treated areas is 2.7%. 

 

Table 10.6. Residential Post-application To Pregnant Women in the Columbia Cohort Estimated Dermal and 

Inhalation Exposures and Predicted % ChE Inhibition (Route-specific and Combined) 

Exposure 

Scenario Formulation Dermal Dose (mg/kg/day) 

Dermal: 

 Peak 

% ChE 

Inhibition 

Airborne 

Concentration of 

Chlorpyrifos 

(mg/m3) 

Inhalation: 

Peak 

% ChE 

Inhibition 

Combined: 

Peak 

% ChE 

Inhibition 

Broadcast 

(Hard 

Surfaces) 

1% PCO 

Application 

or RTU 1 16 

oz can 

0.71 0.45% 0.092 0.0049% 0.45% 

* See algorithms and inputs above used to estimate post-application dermal exposures. 

Table 10.7. Residential Post-application (Children 1 to < 2 Years Old in the Columbia Cohort) Estimated Dermal 

and Inhalation Exposures and Predicted % RBC AChE Inhibition (Route-specific and Combined)

Exposure 

Scenario Formulation 

Dermal 

Dose 

(mg/kg/day) 

Dermal: 

Peak 

% ChE 

Inhibition 

HTM 

Dose 

(mg/kg/day) 

HTM Peak 

% ChE 

Inhibition 

(mg/day) 

Airborne 

Concn. of 

Chlorpyrifos 

(mg/m3) 

Inhalation: 

Peak 

% ChE 

Inhibition 

Combined: 

Peak 

% ChE 

Inhibition 

Broadcast 

(Hard 

Surfaces) 

1% PCO 

Application 

or RTU 1 16 

oz can 

1.3 0.14% 0.10 2.2% 0.092 0.014% 2.7% 

* See algorithms and inputs above used to estimate post-application dermal, (HTM) hand-to-mouth, and (OTM) object-to-mouth 

exposures/doses. 

Appendix 11. New Literature on Chlorpyrifos since the 2012 FIFRA SAP 

Meeting 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 461 of 531

Appendix 11.

Experimental toxicology

Literature search for

chlorpyrifos

Revised

HHRA, 2014

Comments Authors

PubDate

(Year)

PubDate

(Month) Full Citation

Acute effects

Acker CI, Souza AC, Dos Santos

MP, Mazzanti CM, Nogueira CW 2012 Sep

Acker CI, Souza AC, Dos Santo

CW.Diphenyl diselenide atten

toxicity induced by chlorpyrifo

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Cardona D, LÃ3pez‐Granero C,

CaÃ±adas F, Llorens J, Flores P,

Pancetti F, SÃ¡nchez‐Santed F 2013

Cardona D, LÃ3pez‐Granero C, 

Pancetti F, SÃ¡nchez‐Santed F

brain acetylcholinesterase and 

inhibition without cell death a

administration. J Toxicol Sci. 2

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Cardona D, LÃ3pez‐Granero C,CaÃ±adas F, Llorens J, Flores P,Pancetti F, SÃ¡nchez‐Santed F 2013

Cardona D, LÃ3pez‐Granero C, Pancetti F, SÃ¡nchez‐Santed Fbrain acetylcholinesterase and inhibition without cell death aadministration. J Toxicol Sci. 2

Acute effects

Jayawardane P, Senanayake N,

Buckley NA, Dawson AH 2012 Apr

Jayawardane P, Senanayake N

AH.Electrophysiological correl

acute organophosphate poiso

roles of muscarinic and nicotin

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Li B, Eyer P, Eddleston M, Jiang

W, Schopfer LM, Lockridge

O 2013 Jun

Li B, Eyer P, Eddleston M, Jian

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chlorpyrifos. Toxicol Appl Pha

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Acute effects Liu J, Parsons L, Pope

C 2013 Nov

Liu J, Parsons L, Pope C.Compa

chlorpyrifos on extracellular e

hippocampus: influence on ch

Pharmacol. 2013 Nov 1; 272(3
Chlorpyrifos Human Health Risk Assessment D424485 

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Acute effects

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Cardona D, Yu Y, Gimenez E,

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Sanchez‐Santed, F. 2013 Jan

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Lozano R, Avila DS, Aschner M

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are they mediated by analogo

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J, Ye X, Yang

X 2013 Aug

Ma P, Wu Y, Zeng Q, Gan Y, Ch

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Montes de Oca L, Moreno M,

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Santed F 2013 Feb

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Park JH, Lee JE, Shin IC, Koh H

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Acute effects/Notrelated toneurodevelopmentaleffects

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Gullapalli RP.Delayed hippoca

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Ki YW, Park JH, Lee JE, Shin IC,

Koh HC 2013 Apr

Ki YW, Park JH, Lee JE, Shin IC, 

regulate oxidative stress and t

chlorpyrifos‐induced apoptosi

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Added to Appendix Ojha A, Srivastava

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pesticides induced oxidative D

lymphocytes. Mutat Res Gene2014 Feb; 761:10‐7.

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M, Athanaselis S.A simple and 

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Appendix 1

Chen XP, Chen WZ, Wang FS, Liu

JX 2012 Sep

Chen XP, Chen WZ, Wang FS, L

impairments are related to se

prefrontal cortex deficits after 

exposure. Brain Res. 2012 Sep 

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Chiapella G, Flores‐MartÃn J, de Potas G, Panzetta‐Dutari Gorganophosphate chlorpyrifos triggers antioxidant defense mPlacenta. 2013 Sep; 34(9):792

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Cole TB, Fisher JC, Burbacher

TM, Costa LG, Furlong CE 2012 May‐Jun

Cole TB, Fisher JC, Burbacher T

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Appendix 1 Lee JE, Park JH, Shin IC, Koh HC 2012 Sep

Lee JE, Park JH, Shin IC, Koh Hregulated mitochondria‐mediaexposed to chlorpyrifos. Toxic263(2):148‐62.

Appendix 1

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Bartels MJ, Beck MJ, Juberg DR 2012 Jul

Marty MS, Andrus AK, Bell MP

KA, Bartels MJ, Beck MJ, Juber

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Appendix 1

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M 2013 Jan

Ohishi T, Wang L, Akane H, ItaA, Mitsumori K, Shibutani M.Rexposure to chlorpyrifos on thprogenitors in the hippocampoffspring. Reprod Toxicol. 201

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Reiss R, Neal B, Lamb JC 4th,Juberg DR 2012 Jun

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Vatanparast J, Naseh M, Bania

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differentially affect passive avoxide synthase‐containing neucomplex of the amygdala. Bra

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Appendix 1/No body of

evidence; Small

number of studies

without mechanistic

hypothesis to outcome Basha M, Poojary

A 2012 May

Basha M, Poojary A.Cold stres

chlorpyrifos toxicity in aging ra

Toxicol Int. 2012 May; 19(2):1

Appendix 1/No body of

evidence; Small

number of studies

without mechanistic

hypothesis to outcome Basha PM, Poojary

A 2012 Feb

Basha PM, Poojary A.Oxidative 

in the rat central nervous syst

experimentally co‐induced ch

comparative assessment in ag

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evidence; Small

number of studies

without mechanistic

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Poojary A, Basha PM.Cold stre

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assessment in rat cerebral cor

50(2):110‐6.

Appendix 1/ReviewArticle

Carr RL, Adams AL, Kepler DR,

Ward AB, Ross MK 2013 Sep

Carr RL, Adams AL, Kepler DR, 

of endocannabinoid levels in j

developmental chlorpyrifos ex

135(1):193‐201.

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Flaskos J.The developmental norganophosphorus insecticidemetabolites. Toxicol Lett. 201

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Poet, Torka S., Timchalk, C.,Hotchkiss, Jon A., Bartels, M. J. 2014 April

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Pezzotti G, Cano J, Manfredonia

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Smith JN, Wang J, Lin Y, Klohe

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Chaou CH, Lin CC, Chen HY, Lee

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Cumulative risk

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Wason SC, Smith TJ, Perry MJ,

Levy JI 2012 May

Wason SC, Smith TJ, Perry MJ, 

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Editorial Biello

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Biello D.Bad for bugs and brai

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Juberg DR.Differentiating expe

human exposures to chlorpyri

2012 Aug 14; 109(33):E2195; a

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Thivakaran T, Gamage R, Guna

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Fenske RA, Farahat FM, Galvin 

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Galvin

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Freire C, Koifman S.Pesticides, 

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Thomas K, Hines CJ, Weichent

Alavanja M, Beane Freeman L

organophosphorous insecticid

applicators in the Agricultural 

Environ Epidemiol. 2012 Nov; 

Epidemiology/Exposure

Horton MK, Kahn LG, Perera F,

Barr DB, Rauh

V 2012 Sep‐Oct

Horton MK, Kahn LG, Perera F

home environment and the se

adverse effects of prenatal ex

working memory? Neurotoxic

34(5):534‐41.

Epidemiology/Exposure

Huen K, Bradman A, Harley K,

Yousefi P, Boyd Barr D, Eskenazi

B, Holland

N 2012 Aug

Huen K, Bradman A, Harley K, 

Eskenazi B, Holland N.Organo

blood and urine of women an

agricultural community. Enviro
Chlorpyrifos Human Health Risk Assessment D424485 

Page 470 of 531

Epidemiology/Exposure

Karunanayake CP, Spinelli JJ,

McLaughlin JR, Dosman JA,

Pahwa P, McDuffie HH 2012 Jan

Karunanayake CP, Spinelli JJ, M

Pahwa P, McDuffie HH.Hodgk

exposure in men: a Canadian c

Agromedicine. 2012 Jan; 17(1

Epidemiology/Exposure

Khan K, Ismail AA, Abdel Rasoul

G, Bonner MR, Lasarev MR,

Hendy O, Al‐Batanony M, Crane

AL, Singleton ST, Olson JR,

Rohlman DS 2014 Mar

Khan K, Ismail AA, Abdel Raso

Hendy O, Al‐Batanony M, Cran

Rohlman DS.Longitudinal asse

exposure and self‐reported ne

adolescent pesticide applicato

4(3):e004177.

Epidemiology/Exposure

Kim HH, Lim YW, Yang JY, Shin

DC, Ham HS, Choi BS, Lee JY 2013 Feb

Kim HH, Lim YW, Yang JY, Shin 

JY.Health risk assessment of e

dichlorvos in children at childc

2013 Feb 1; 444:441‐50.

Epidemiology/Exposure

Lein PJ, Bonner MR, Farahat FM,

Olson JR, Rohlman DS, Fenske

RA, Lattal KM, Lasarev MR,

Galvin K, Farahat TM, Anger WK 2012 Aug

Lein PJ, Bonner MR, Farahat F

Fenske RA, Lattal KM, Lasarev 

Anger WK.Experimental strate

organophosphorus pesticide n

world occupational exposures 

Neurotoxicology. 2012 Aug; 3

Epidemiology/Exposure Lurati AR 2013 Jun

Lurati AR.Organophosphate ex

pseudocholinesterase deficien

2013 Jun; 61(6):243‐5.

Epidemiology/Exposure

Ostrea EM Jr, Reyes A,

Villanueva‐Uy E, Pacifico R,

Benitez B, Ramos E, Bernardo

RC, Bielawski DM, Delaney‐Black

V, Chiodo L, Janisse JJ, Ager JW 2012 Aug

Ostrea EM Jr, Reyes A, Villanu

Ramos E, Bernardo RC, Bielaw

Chiodo L, Janisse JJ, Ager JW.F

abnormal child neurodevelop

Neurotoxicology. 2012 Aug; 3

Epidemiology/Exposure

Phung DT, Connell D, Yu Q, Chu

C 2013 Sep

Phung DT, Connell D, Yu Q, Ch

of chlorpyrifos using epidemio

probabilistic techniques: a cas

Vietnam. Risk Anal. 2013 Sep; 
Chlorpyrifos Human Health Risk Assessment D424485 

Page 471 of 531

Epidemiology/Exposure

QuirÃ3s‐AlcalÃ¡ L, Bradman A,

Smith K, Weerasekera G,

Odetokun M, Barr DB, Nishioka

M, Castorina R, Hubbard AE,

Nicas M, Hammond SK, McKone

TE, Eskenazi

B 2012 Nov

QuirÃ3s‐AlcalÃ¡ L, Bradman A, 

Odetokun M, Barr DB, Nishiok

Nicas M, Hammond SK, McKo

B.Organophosphorous pestici

house dust and children's urin

2012 Nov; 22(6):559‐68.

Epidemiology/Exposure

Rauh VA, Perera FP, Horton MK,Whyatt RM, Bansal R, Hao X, LiuJ, Barr DB, Slotkin TA, PetersonBS 2012 May

Rauh VA, Perera FP, Horton MLiu J, Barr DB, Slotkin TA, Petechildren exposed prenatally to pesticide. Proc Natl Acad Sci

U 

109(20):7871‐6.

Epidemiology/Exposure

Wang N, Yi L, Shi L, Kong D, Cai

D, Wang D, Shan

Z 2012

Wang N, Yi L, Shi L, Kong D, Ca

level and human health risk as

and polychlorinated biphenyls 

China. J Environ Sci (China). 20

Epidemiology/Exposure Wu N, Hao F, Yu

X 2012 Jul

Wu N, Hao F, Yu X.Peripheral 

associated with working in a S

cases. Clin Toxicol (Phila). 201

Epidemiology/Exposure

Costa LG, Giordano G, Cole TB,

Marsillach J, Furlong CE 2013 May

Costa LG, Giordano G, Cole TB

CE.Paraoxonase 1 (PON1) as a 

susceptibility to organophosp

May 10; 307:115‐22.

Exposure Abdulra'uf LB, Tan GH 2013 Dec

Abdulra'uf LB, Tan GH.Multiva

the determination of pesticide 

headspace solid phase microe

chromatography‐mass spectro

factorial design. Food Chem. 2

Exposure Affam AC, Chaudhuri

M 2013 Nov

Affam AC, Chaudhuri M.Degra

chlorpyrifos, cypermethrin an

solution by TiO2 photocatalys

30; 130:160‐5.
Chlorpyrifos Human Health Risk Assessment D424485 

Page 472 of 531

Exposure

Agbo SO, KeinÃ¤nen M, Keski‐

Saari S, Lemmetyinen J, Akkanen

J, LeppÃ¤nen MT, Mayer P,

Kukkonen JV 2013 Sep

Agbo SO, KeinÃ¤nen M, Keski‐

Akkanen J, LeppÃ¤nen MT, M

Lumbriculus variegatus metab

to benzo(a)pyrene, chlorpyrifoconsequences on biotransform

Sep; 93(2):302‐10.

Exposure

Agostini MG, Kacoliris F,

Demetrio P, Natale GS, Bonetto

C, Ronco AE 2013 May

Agostini MG, Kacoliris F, Deme

Ronco AE.Abnormalities in am

agroecosystems in NortheasteArgentina. Dis Aquat Organ. 2

Exposure

Agudelo

C RM, Jaramillo ML,

PeÃ±uela

G 2012 Aug

Agudelo

C RM, Jaramillo ML, P

removal of chlorpyrifos and di

horizontal sub‐surface and su

Environ. 2012 Aug 1; 431:271‐

Exposure

Aguilera‐Luiz MM, Romero‐GonzÃ¡lez R, Plaza‐BolaÃ±os P,

MartÃnez Vidal JL, Garrido

Frenich

A 2013 Aug

Aguilera‐Luiz MM, Romero‐Go

MartÃnez Vidal JL, Garrido Fre

veterinary drug and pesticide 

liquid chromatography couple

mass spectrometry. Anal Bioa

405(20):6543‐53.

Exposure Akkad R, Schwack

W 2012 Sep‐Oct

Akkad R, Schwack W.Determin

and carbamate insecticides in 

high‐performance thin‐layer c

inhibition assay. J AOAC Int. 20

Exposure

AlcÃ¡ntara‐ConcepciÃ3n V, Cram

S, Gibson R, Ponce de LeÃ3n C,

Mazari‐Hiriart

M 2013 Jul‐Aug

AlcÃ¡ntara‐ConcepciÃ3n V, Cra

C, Mazari‐Hiriart M.Method d

the simultaneous determinati

organophosphorus pesticides 

J AOAC Int. 2013 Jul‐Aug; 96(4
Chlorpyrifos Human Health Risk Assessment D424485 

Page 473 of 531

Exposure

Alves AC, GonÃ§alves MM,

Bernardo MM, Mendes BS 2012 Oct

Alves AC, GonÃ§alves MM, Be

BS.Dispersive liquid‐liquid mic

organophosphorous pesticide

solvents. J Sep Sci. 2012 Oct; 3

Exposure

Anderson B, Phillips B, Hunt J,

Siegler K, Voorhees J, Smalling K,

Kuivila K, Hamilton M,

Ranasinghe JA, Tjeerdema

R 2014 Mar

Anderson B, Phillips B, Hunt J, 

K, Kuivila K, Hamilton M, Rana

R.Impacts of pesticides in a Ce

Environ Monit Assess. 2014 M

Exposure

Armstrong JL, Fenske RA, Yost

MG, Tchong‐French M, Yu J 2013 Jul

Armstrong JL, Fenske RA, Yost 

J.Comparison of polyurethane 

matrices to measure airborne 

and their oxygen analogs in an 

Chemosphere. 2013 Jul; 92(4)

Exposure

Arora S, Mukherjee I, Kumar A,

Garg DK 2014 Jan

Arora S, Mukherjee I, Kumar A

assessment of pesticide residu

from IPM and non‐IPM trials o

Assess. 2014 Jan; 186(1):361‐6

Exposure

Babina K, Dollard M, Pilotto L,

Edwards JW 2012 Nov

Babina K, Dollard M, Pilotto L, 

exposure to organophosphoru

South Australian preschool ch

Environ Int. 2012 Nov 1; 48:10

Exposure

Bagheri H, Es'haghi A, Es‐haghi

A, Mesbahi

N 2012 Aug

Bagheri H, Es'haghi A, Es‐hagh

throughput approach for the d

residues in cucumber samples 

microextraction on 96‐well pla

31; 740:36‐42.

Exposure

Baldim IM, Souza MC, Souza JC,

Figueiredo EC, Martins I 2012 Oct

Baldim IM, Souza MC, Souza J

I.Application of the molecular

extraction to the organophosp

in strawberries. Anal Bioanal C

66.
Chlorpyrifos Human Health Risk Assessment D424485 

Page 474 of 531

Exposure

Beamer PI, Canales RA, Ferguson

AC, Leckie JO, Bradman

A 2012 Jan

Beamer PI, Canales RA, Fergus

A.Relative pesticide and expos

aggregate and cumulative dos

children. Int J Environ Res Pub

96.

Exposure

Bedi JS, Gill JP, Aulakh RS, Kaur

P, Sharma A, Pooni PA 2013 Oct

Bedi JS, Gill JP, Aulakh RS, Kau

PA.Pesticide residues in huma

for infants from Punjab, India. 

463‐464:720‐6.

Exposure

Bhattacharjee S, Fakhruddin AN,

Chowdhury MA, Rahman MA,

Alam MK 2012 Aug

Bhattacharjee S, Fakhruddin A

MA, Alam MK.Monitoring of s

levels in water samples of pad

cypermethrin and chlorpyrifos 

bran. Bull Environ Contam Tox

Exposure Bootharaju MS, Pradeep

T 2012 Feb

Bootharaju MS, Pradeep T.Un

pathway of the pesticide, chlo

nanoparticles. Langmuir. 2012 

Exposure

Brun EM, Puchades R, Maquieira

Ã 2013 Apr

Brun EM, Puchades R, Maquie

aluminum low‐reflectivity com

platforms. Anal Chem. 2013 A

Exposure

Cai D, Wang L, Zhang G, Zhang X,

Wu

Z 2013 Sep

Cai D, Wang L, Zhang G, Zhang 

loss by natural porous micro/n

based biochar and biosilica. A

Sep 25; 5(18):9212‐6.

Exposure

Carlson JC, Challis JK, Hanson

ML, Wong CS 2013 Feb

Carlson JC, Challis JK, Hanson 

pharmaceuticals and other po

on polar organic chemical inte

phase extraction cartridges. E

32(2):337‐44.
Chlorpyrifos Human Health Risk Assessment D424485 

Page 475 of 531

Exposure

Catalina Rodriguez D, Carvajal S,

Penuela

G 2013 Jul

Catalina Rodriguez D, Carvajal 

chlorpyrifos on the inhibition 

acetylcholinesterase by cross‐

and milk from dairy cattle. Tal

Exposure

Cavalcante RM, Lima DM,

Fernandes GM, DuavÃ WC 2012 May

Cavalcante RM, Lima DM, Fern

WC.Relation factor: a new stra

determination of pesticides in 

matrices. Talanta. 2012 May 1

Exposure

Cavari Y, Landau D, Sofer S,Leibson T, Lazar I 2013 May

Cavari Y, Landau D, Sofer S, Le

I.Organophosphate poisoning

Pediatr Emerg Care. 2013 May

Exposure

Chai LK, Wong MH, Bruun

Hansen HC 2013 Aug

Chai LK, Wong MH, Bruun Han

chlorpyrifos in humid tropical 

Aug 15; 125:28‐32.

Exposure

Chen C, Qian Y, Liu X, Tao C,

Liang Y, Li

Y 2012 Feb

Chen C, Qian Y, Liu X, Tao C, Li

chlorpyrifos on rice and cabba

Pharmacol. 2012 Feb; 62(1):12

Exposure

Chishti Z, Hussain S, Arshad KR,

Khalid A, Arshad

M 2013 Jan

Chishti Z, Hussain S, Arshad KR

degradation of chlorpyrifos in 

Manage. 2013 Jan 15; 114:372

Exposure

Chowdhury MA, Banik S, UddinB, Moniruzzaman M, Karim N,

Gan SH 2012 Sep

Chowdhury MA, Banik S, Uddi

N, Gan SH.Organophosphorus 

residues detected in water sam

and vegetable fields of the Sav

Bangladesh. Int J Environ Res 

9(9):3318‐29.

Exposure

Clifton MS, Wargo JP, Weathers

WS, ColÃ3n M, Bennett DH,

Tulve NS 2013 Jan

Clifton MS, Wargo JP, Weathe

Tulve NS.Quantitative analysis pyrethroid insecticides, pyreth

polybrominated diphenyl ethe

residential surface wipe samp

18; 1273:1‐11.
Chlorpyrifos Human Health Risk Assessment D424485 

Page 476 of 531

Exposure Dabrowski JM, Balderacchi

M 2013 Nov

Dabrowski JM, Balderacchi M.validation of an indicator to as

risk of pesticides in the Louren

Africa. Chemosphere. 2013 No

Exposure

Deme P, Azmeera T, Prabhavathi

Devi BL, Jonnalagadda PR,

Prasad RB, Vijaya Sarathi UV 2014 Jan

Deme P, Azmeera T, Prabhava

Prasad RB, Vijaya Sarathi UV.A

phase extraction clean‐up me

chromatography‐negative chespectrometric determination o

in edible oils. Food Chem. 201

Exposure

Du D, Wang J, Wang L, Lu D, Lin

Y 2012 Feb

Du D, Wang J, Wang L, Lu D, L

strip with electrochemical sen

phosphorylated cholinesteras

organophosphorus agents. An

84(3):1380‐5.

Exposure Dubey KK, Fulekar MH 2012 Apr

Dubey KK, Fulekar MH.Chlorpy

Pennisetum rhizosphere by a 

Stenotrophomonas maltophili

Microbiol Biotechnol. 2012 Ap

Exposure

Duffner A, Ingwersen J,

Hugenschmidt C, Streck

T 2012 Jul‐Aug

Duffner A, Ingwersen J, Hugen

transport pathways from a slo

adjacent tropical stream as ide

separation. J Environ Qual. 20

Exposure

EstÃ©vez E, Cabrera Mdel C,

Molina‐DÃaz A, Robles‐Molina J,

Palacios‐DÃaz Mdel P 2012 Sep

EstÃ©vez E, Cabrera Mdel C, M

J, Palacios‐DÃaz Mdel P.Scree

and priority substances (2008/

irrigation and groundwater in 

Canaria, Canary Islands, Spain

433:538‐46.
Chlorpyrifos Human Health Risk Assessment D424485 

Page 477 of 531

Exposure

Fan S, Deng K, Yu C, Zhao P, Bai

A, Li Y, Pan C, Li

X 2013 Sep

Fan S, Deng K, Yu C, Zhao P, Ba

of different planting seasons o

residues of five pesticides. J A

61(38):9036‐44.

Exposure

Fan S, Zhang F, Deng K, Yu C, Liu

S, Zhao P, Pan

C 2013 Mar

Fan S, Zhang F, Deng K, Yu C, L

amaranth contains highest res

butyl, chlorpyrifos, and lambd

vegetables upon open field ap

2013 Mar 6; 61(9):2039‐44.

Exposure

Femia J, Mariani M, Zalazar C,

Tiscornia I 2013

Femia J, Mariani M, Zalazar C, 

of chlorpyrifos in water by UV

evaluation. Water Sci Technol

Exposure

Fuentes MS, BriceÃ±o GE, Saez

JM, Benimeli CS, Diez MC,

Amoroso MJ 2013

Fuentes MS, BriceÃ±o GE, Sae

Amoroso MJ.Enhanced remov

single cultures and consortia o

Streptomyces strains. Biomed 

Exposure

Garrison VH, Majewski MS,

Foreman WT, Genualdi SA,

Mohammed A, Massey Simonich

SL 2014 Jan

Garrison VH, Majewski MS, Fo

Mohammed A, Massey Simon

contaminants in Saharan dust 

Cape Verde and the eastern C

2014 Jan 15; 468‐469:530‐43.

Exposure

Gebremariam SY, Beutel MW,

Flury M, Harsh JB, Yonge DR 2012 Jan

Gebremariam SY, Beutel MW, 

DR.Nonsingular adsorption/de

and sediments: experimental 

Sci Technol. 2012 Jan 17; 46(2

Exposure

Gebremariam SY, Beutel MW,

Yonge DR, Flury M, Harsh JB 2012

Gebremariam SY, Beutel MW, 

JB.Adsorption and desorption 

sediments. Rev Environ Conta

Exposure

Ghoshdastidar AJ, Saunders JE,

Brown KH, Tong AZ 2012

Ghoshdastidar AJ, Saunders JE

AZ.Membrane bioreactor treaorganophosphate pesticides. J 

47(7):742‐50.
Chlorpyrifos Human Health Risk Assessment D424485 

Page 478 of 531

Exposure

GonzÃ¡lez‐Curbelo MÃ ,

HernÃ¡ndez‐Borges J, Borges‐Miquel TM, RodrÃguez‐Delgado

MÃ 2012

GonzÃ¡lez‐Curbelo MÃ , Hern

Miquel TM, RodrÃguez‐Delgad

pesticides and their metabolit

Food Addit Contam Part

A Che

Assess. 2012; 29(1):104‐16.

Exposure

GonzÃ¡lez‐Curbelo MÃ ,

Herrera‐Herrera AV,

HernÃ¡ndez‐Borges J, RodrÃ‐guez‐Delgado MÃ 2013 Feb

GonzÃ¡lez‐Curbelo MÃ , Herr

Borges J, RodrÃguez‐Delgado 

residues in environmental wat

carbon nanotubes dispersive s

Sci. 2013 Feb; 36(3):556‐63.

Exposure

Gu S, Lu Y, Ding Y, Li L, Zhang F,

Wu

Q 2013 Sep

Gu S, Lu Y, Ding Y, Li L, Zhang F

microfluidics for dose‐respons

by electrochemical method. A

796:68‐74.

Exposure Hall L Jr, Anderson RD 2012

Hall L Jr, Anderson RD.Historic

2009 toxicity and pesticide da

J Environ Sci Health

A Tox Haz

47(6):801‐11.

Exposure

Harnpicharnchai K, Chaiear N,

Charerntanyarak L 2013 Nov

Harnpicharnchai K, Chaiear N, 

organophosphate pesticides u

ambient air, surface water and 

Subdistrict, Khon Kaen, Thaila

Public Health. 2013 Nov; 44(6

Exposure

Harwood AD, Landrum PF,

Weston DP, Lydy MJ 2013 Feb

Harwood AD, Landrum PF, We

fibers and Tenax to predict the 

and chlorpyrifos in field sedim

173:47‐51.
Chlorpyrifos Human Health Risk Assessment D424485 

Page 479 of 531

Exposure

Hayward DG, Wong JW, Shi F,

Zhang K, Lee NS, DiBenedetto

AL, Hengel MJ 2013 May

Hayward DG, Wong JW, Shi F, 

AL, Hengel MJ.Multiresidue pe

dietary supplements using sal

solid‐phase extraction cleanup chromatography‐triple quadru

Chem. 2013 May 7; 85(9):468

Exposure

Ismail M, Khan HM, Sayed M,

Cooper WJ 2013 Oct

Ismail M, Khan HM, Sayed M, 

for the treatment of chlorpyri

Chemosphere. 2013 Oct; 93(4

Exposure

Jyot G, Mandal K, Battu RS, Singh

B 2013 Jul

Jyot G, Mandal K, Battu RS, Sin

chlorpyriphos and cypermeth

annuum L.) by gas‐liquid chro

Assess. 2013 Jul; 185(7):5703‐

Exposure

KÃ¶ck‐Schulmeyer M, Olmos M,

LÃ3pez de Alda M, BarcelÃ3

D 2013 Aug

KÃ¶ck‐Schulmeyer M, Olmos 

D.Development of a multiresid

pesticides in sediments based 

chromatography‐electrosprayChromatogr A. 2013 Aug 30; 1

Exposure

Karanasios EC, Tsiropoulos NG,

Karpouzas DG 2013 Sep

Karanasios EC, Tsiropoulos NG

and qualitative differences in biobed substrates and soil. Ch

93(1):20‐8.

Exposure

Karpuzcu ME, Sedlak DL,

Stringfellow WT 2013 Jan

Karpuzcu ME, Sedlak DL, Strin

of chlorpyrifos in riparian wet

watersheds: implications for w

Mater. 2013 Jan 15; 244‐245:1

Exposure

Kong Z, Shan W, Dong F, Liu X,

Xu J, Li M, Zheng

Y 2012 Aug

Kong Z, Shan W, Dong F, Liu X, 

home processing on the distri

pesticide residues in apples. F

Anal Control Expo Risk Assess
Chlorpyrifos Human Health Risk Assessment D424485 

Page 480 of 531

Exposure

Korba K, Pelit L, Pelit FO,

Ozdokur KV, ErtaÅŸ H, EroÄŸlu

AE, ErtaÅŸ FN 2013 Jun

Korba K, Pelit L, Pelit FO, Ozdo

ErtaÅŸ FN.Preparation and ch

dodecyl sulfate doped polypyr

extraction fiber and its applica

pesticide analysis. J Chromato

Life Sci. 2013 Jun 15; 929:90‐6

Exposure

Kouzayha A, Al Ashi A, Al Akoum

R, Al Iskandarani M, Budzinski H,

Jaber F 2013 Nov

Kouzayha A, Al Ashi A, Al Akou

Budzinski H, Jaber F.Occurren

Lebanon's water resources. Bu

2013 Nov; 91(5):503‐9.

Exposure

Kuang Y, Qiu F, Kong W, Luo J,

Cheng H, Yang

M 2013 Nov

Kuang Y, Qiu F, Kong W, Luo J, 

M.Simultaneous quantificatio

residues in ginseng with one‐s

performance liquid chromatog

tandem mass spectrometry. J 

Biomed Life Sci. 2013 Nov 15; 

Exposure

Kumar A, Nayak AK, Shukla AK,

Panda BB, Raja R, Shahid M,

Tripathi R, Mohanty S, Rath PC 2012 Apr

Kumar A, Nayak AK, Shukla AK

Tripathi R, Mohanty S, Rath PC

mineralization in agricultural s

addition. Bull Environ Contam 

42.

Exposure

Kusvuran E, Yildirim D, Mavruk F,

Ceyhan

M 2012 Nov

Kusvuran E, Yildirim D, Mavruchloropyrifos ethyl, tetradifon 

residues from citrus by using o

Nov 30; 241‐242:287‐300.

Exposure

Lari SZ, Khan NA, Gandhi KN,

Meshram TS, Thacker NP 2014 Jan

Lari SZ, Khan NA, Gandhi KN, M

NP.Comparison of pesticide re

ground water of agriculture in

Sci Eng. 2014 Jan 7; 12(1):11.
Chlorpyrifos Human Health Risk Assessment D424485 

Page 481 of 531

Exposure

Lavin KS, Hageman KJ, Marx SK,

Dillingham PW, Kamber BS 2012 Jan

Lavin KS, Hageman KJ, Marx S

BS.Using trace elements in pa

sources of semivolatile organi

alpine site. Environ Sci Techno

Exposure Lee KG, Lee SK 2012 Dec

Lee KG, Lee SK.Monitoring and 

residues in yuza fruits (Citrus j

tea samples produced in Kore

135(4):2930‐3.

Exposure Li H, Wei Y, Lydy MJ, You J 2014 Jul

Li H, Wei Y, Lydy MJ, You J.Inte

organophosphate and pyrethr

implications for a regional risk 

2014 Jul; 190:19‐26.

Exposure

Linhares AG, Assis CR, Siqueira

MT, Bezerra RS, Carvalho LB Jr 2013 Aug

Linhares AG, Assis CR, Siqueira 

Jr.Development of a method f

human erythrocyte acetylchol

inhibition. Hum Exp Toxicol. 2

Exposure

Lu D, Qiu X, Feng C, Jin Y, Lin Y,

Xiong L, Wen Y, Wang D, Wang

G 2012 May

Lu D, Qiu X, Feng C, Jin Y, Lin Y

Wang G.Simultaneous determ

and vegetable using an improv

line gel permeation chromato

chromatography/mass spectro

Technol Biomed Life Sci. 2012 

Exposure

Lu HY, Shen Y, Sun X, Zhu H, Liu

XJ 2013 Sep

Lu HY, Shen Y, Sun X, Zhu H, Li

limonene on pesticide residue

Agric. 2013 Sep; 93(12):2917‐

Exposure

Maliyekkal SM, Sreeprasad TS,

Krishnan D, Kouser S, Mishra AK,

Waghmare UV, Pradeep

T 2013 Jan

Maliyekkal SM, Sreeprasad TS

AK, Waghmare UV, Pradeep T

substrate for unprecedented a

2013 Jan 28; 9(2):273‐83.

Exposure Mast MA, Alvarez DA, Zaugg SD 2012 Mar

Mast MA, Alvarez DA, Zaugg S

of airborne organic contamina

California. Environ Toxicol Che
Chlorpyrifos Human Health Risk Assessment D424485 

Page 482 of 531

Exposure

Mishra RK, Istamboulie G, Bhand

S, Marty JL 2012 Oct

Mishra RK, Istamboulie G, Bha

organophosphate residues us

their evaluation using flow ba

2012 Oct 1; 745:64‐9.

Exposure

Moreno‐GonzÃ¡lez R, Campillo

JA, GarcÃa V, LeÃ3n VM 2013 Jul

Moreno‐GonzÃ¡lez R, Campillo 

VM.Seasonal input of regulate

pollutants through surface wa

coastal lagoon. Chemosphere

Exposure

Moreno‐GonzÃ¡lez R, Campillo

JA, LeÃ3n VM 2013 Dec

Moreno‐GonzÃ¡lez R, Campillo 

intensive agricultural drainage 

distribution of organic polluta

Mediterranean coastal lagoon 

Pollut Bull. 2013 Dec 15; 77(1‐

Exposure

Moscoso F, Teijiz I, Deive FJ,

SanromÃ¡n MA 2013 Sep

Moscoso F, Teijiz I, Deive FJ, S

chlorpyrifos bioelimination at 

Bioprocess Biosyst Eng. 2013 S

Exposure

Moussaoui Y, Tuduri L, Kerchich

Y, Meklati BY, Eppe

G 2012 Jul

Moussaoui Y, Tuduri L, Kerchic

G.Atmospheric concentrations 

pesticides in northern Algeria 

Chemosphere. 2012 Jul; 88(3)

Exposure

Mugni H, Demetrio P,

Paracampo A, Pardi M, Bulus G,

Bonetto

C 2012 Jul

Mugni H, Demetrio P, Paracam

Bonetto C.Toxicity persistence 

experimental soybean plots fo

application. Bull Environ Conta

12.

Exposure

Muhammad F, Awais MM,

Akhtar M, Anwar MI 2013 Jan

Muhammad F, Awais MM, Ak

structure activity relationship 

pesticides in the goat milk. Ira

2013 Jan 4; 10(1):4.
Chlorpyrifos Human Health Risk Assessment D424485 

Page 483 of 531

Exposure

Munoz‐Quezada MT, Iglesias V,

Lucero B, Steenland K, Barr DB,

Levy K, Ryan PB, Alvarado S,

Concha

C 2012 Oct

Munoz‐Quezada MT, Iglesias V

DB, Levy K, Ryan PB, Alvarado 

exposure to organophosphate 

the Province of Talca, Chile. En

36.

Exposure

Nagarajan G, Khan ZS, Utture SC,

Dasgupta S, Banerjee

K 2013 Nov

Nagarajan G, Khan ZS, Utture 

K.Ensuring selectivity and sens

selective reaction monitoring 

tandem mass spectrometric d

Chromatogr A. 2013 Nov 29; 1

Exposure

Navarro P, Morais S, GabaldÃ3n

JA, PÃ©rez AJ, Puchades R,

Maquieira

A 2013 Jun

Navarro P, Morais S, GabaldÃ3

Maquieira A.Arrays on disc for 

of pollutants. Anal Chim Acta. 

Exposure

Navarro P, PÃ©rez AJ,

GabaldÃ3n JA, NÃoÃ±ez‐Delicado

E, Puchades R, Maquieira A,

Morais S 2013 Nov

Navarro P, PÃ©rez AJ, Gabald

Puchades R, Maquieira A, Mor

residues in tangerine juices by 

Talanta. 2013 Nov 15; 116:33‐

Exposure

NougadÃ ̈re A, Sirot V, Kadar A,

Fastier A, Truchot E, Vergnet C,

Hommet F, BaylÃ© J, Gros P,

Leblanc JC 2012 Sep

NougadÃ ̈re A, Sirot V, Kadar A

C, Hommet F, BaylÃ© J, Gros 

on pesticide residues in Franc

and chronic dietary risk to con

15; 45:135‐50.

Exposure Odabasi M, Cetin

B 2012 Dec

Odabasi M, Cetin B.Determina

coefficients of organochlorine 

of temperature: application to 

Manage. 2012 Dec 30; 113:43

Exposure Oellig C, Schwack

W 2012 Oct

Oellig C, Schwack W.Planar so

for pesticide residue analysis i

chromatography‐mass spectro

Oct 19; 1260:42‐53.
Chlorpyrifos Human Health Risk Assessment D424485 

Page 484 of 531

Exposure

Oliver DP, Pan YF, Anderson JS,

Lin TF, Kookana RS, Douglas GB,

Wendling LA 2013 Jan

Oliver DP, Pan YF, Anderson JS

GB, Wendling LA.Sorption of p

mining by‐product, neutralise

Environ. 2013 Jan 1; 442:255‐

Exposure

Omeroglu PY, Ambrus A,

Boyacioglu

D 2013

Omeroglu PY, Ambrus A, Boya

uncertainties of extraction an

residue analysis of plant comm

Part

A Chem Anal Control Exp

20.

Exposure

Osman R, Saim N, Juahir H,

Abdullah MP 2012 Jan

Osman R, Saim N, Juahir H, Ab

application in identifying sour

Langat river basin. Environ Mo

184(2):1001‐14.

Exposure

Otieno PO, Owuor PO, Lalah JO,

Pfister G, Schramm KW 2013 Mar

Otieno PO, Owuor PO, Lalah J

KW.Impacts of climate‐induce

of pesticides residues in wate

Naivasha, Kenya. Environ Mon

185(3):2723‐33.

Exposure

Otieno PO, Owuor PO, Lalah JO,

Pfister G, Schramm KW 2013 Dec

Otieno PO, Owuor PO, Lalah J

KW.Comparative evaluation o

the determination of chlorpyr

sediments. Talanta. 2013 Dec 

Exposure

Otieno PO, Schramm KW, Pfister

G, Lalah JO, Ojwach SO, Virani

M 2012 Apr

Otieno PO, Schramm KW, Pfis

Virani M.Spatial distribution a

concentration of carbofuran, d

residues in sediment and wate

Environ Contam Toxicol. 2012 

Exposure

Pelit FO, ErtaÅŸ H, Seyrani I,

ErtaÅŸ FN 2013 May

Pelit FO, ErtaÅŸ H, Seyrani I, E

S19 method for the determina

disruptor pesticides in wine sa

the uncertainty of the analytic

May 1; 138(1):54‐61.
Chlorpyrifos Human Health Risk Assessment D424485 

Page 485 of 531

Exposure

Pelit FO, Pelit L, ErtaÅŸ H, Nil

ErtaÅŸ F 2012 Sep

Pelit FO, Pelit L, ErtaÅŸ H, Nil E

chromatographic method for 

Chlorpyrifos and its metabolit

matrix. J Chromatogr

B Analyt 

Sep 1; 904:35‐41.

Exposure Pelit L, DizdaÅŸ TN 2013 Oct

Pelit L, DizdaÅŸ TN.Preparatio

polythiophene solid‐phase mi

determination of endocrine‐d

waters. J Sep Sci. 2013 Oct; 36

Exposure

Phung DT, Connell D, Miller G,

Chu

C 2012 Jul

Phung DT, Connell D, Miller G, 

assessment of chlorpyrifos ex

Nam. J Expo Sci Environ Epide

Exposure

Phung DT, Connell D, Miller G,

Hodge M, Patel R, Cheng R,

Abeyewardene M, Chu

C 2012 Apr

Phung DT, Connell D, Miller G, 

Abeyewardene M, Chu C.Biolo

chlorpyrifos exposure to rice f

Chemosphere. 2012 Apr; 87(4

Exposure

Querejeta GA, Ramos LM, FloresAP, Hughes EA, Zalts A,

Montserrat JM 2012 Apr

Querejeta GA, Ramos LM, Flor

Montserrat JM.Environmenta

horticultural and floricultural 

Chemosphere. 2012 Apr; 87(5

Exposure Rani M, Saini S, Kumari

B 2013 Sep

Rani M, Saini S, Kumari B.Pers

processing on chlorpyriphos r

(Lycopersicon esculantum Mil

Sep; 95:247‐52.

Exposure Rani M, Saini S, Kumari

B 2014 Jan

Rani M, Saini S, Kumari B.Leac

chlorpyriphos and cypermeth

Monit Assess. 2014 Jan; 186(1
Chlorpyrifos Human Health Risk Assessment D424485 

Page 486 of 531

Exposure

Robles‐Molina J, MartÃn de

Vidales MJ, GarcÃa‐Reyes JF,

CaÃ±izares P, SÃ¡ez C, Rodrigo

MA, Molina‐DÃaz

A 2012 Nov

Robles‐Molina J, MartÃn de V

CaÃ±izares P, SÃ¡ez C, Rodrigo 

A.Conductive‐diamond electro

chlorpyrifos in wastewater an

degradation products by LC‐TO

Nov; 89(10):1169‐76.

Exposure

Roca M, Miralles‐Marco A,FerrÃ© J, PÃ©rez R, YusÃ

V 2014 May

Roca M, Miralles‐Marco A, Fe

YusÃ V.Biomonitoring exposu

contemporary pesticides in a s

Spain. Environ Res. 2014 May; 

Exposure

RodrÃguez T, van Wendel deJoode B, Lindh CH, Rojas M,

Lundberg I, Wesseling

C 2012 Feb

Rodriguez T, van Wendel de Jo

Lundberg I, Wesseling C.Asses

pesticide exposure among rur

Occup Environ Med. 2012 Feb

Exposure

Rojas R, Morillo J, Usero J,

Delgado‐Moreno L, Gan J 2013 Aug

Rojas R, Morillo J, Usero J, Del

J.Enhancing soil sorption capa

addition of three different org

2013 Aug 1; 458‐460:614‐23.

Exposure Rotariu L, Zamfir LG, Bala

C 2012 Oct

Rotariu L, Zamfir LG, Bala C.A 

multiwalled carbon nanotube

tetracyanoquinodimethan sen

acetylcholinesterase inhibitor

748:81‐8.

Exposure Samadi S, Sereshti H, Assadi

Y 2012 Jan

Samadi S, Sereshti H, Assadi Y

determination of thirteen org

water samples using solid‐pha

dispersive liquid‐liquid microe

chromatography with flame p

Chromatogr A. 2012 Jan 6; 12
Chlorpyrifos Human Health Risk Assessment D424485 

Page 487 of 531

Exposure

Sangchan W, Bannwarth M,Ingwersen J, Hugenschmidt C,

Schwadorf K, Thavornyutikarn P,

Pansombat K, Streck

T 2014 Feb

Sangchan W, Bannwarth M, In

Schwadorf K, Thavornyutikarn 

T.Monitoring and risk assessm

river of an agricultural waters

Environ Monit Assess. 2014 Fe

Exposure

Sarkouhi M, Shamsipur M,

Hassan J 2012 Dec

Sarkouhi M, Shamsipur M, Ha

organophosphorus pesticides 

promoted hydrolysis. Environ 

184(12):7383‐93.

Exposure

Sasikala C, Jiwal S, Rout P,

Ramya

M 2012 Mar

Sasikala C, Jiwal S, Rout P, Ram

chlorpyrifos by bacterial conso

agriculture soil. World J Micro

28(3):1301‐8.

Exposure

Seebunrueng K,

Santaladchaiyakit Y, Srijaranai S 2012 Sep

Seebunrueng K, Santaladchaiy

effect of chain‐length compat

surfactants on the cloud‐point 

organophosphorus pesticides

404(5):1539‐48.

Exposure

Shahpoury P, Hageman KJ,

Matthaei CD, Magbanua FS 2013 Oct

Shahpoury P, Hageman KJ, Ma

FS.Chlorinated pesticides in st

integrated and conventional f

181:219‐25.

Exposure Sharma I, Bhardwaj R, Pati PK 2012 Nov

Sharma I, Bhardwaj R, Pati PK

of chlorpyrifos by 24‐epibrass

markers in a rice variety Pusa 

Saf. 2012 Nov; 85:72‐81.

Exposure Shi H, Zhao G, Liu M, Fan L, Cao

T 2013 Sep

Shi H, Zhao G, Liu M, Fan L, Ca

colorimetric sensing of acetam

sensitivity, selectivity and mec

Sep 15; 260:754‐61.
Chlorpyrifos Human Health Risk Assessment D424485 

Page 488 of 531

Exposure

Shin HM, McKone TE, Tulve NS,

Clifton MS, Bennett DH 2013 Jan

Shin HM, McKone TE, Tulve N

DH.Indoor residence times of 

compounds: model estimation 

Sci Technol. 2013 Jan 15; 47(2

Exposure Shing WL, Heng LY, Surif S 2013 May

Shing WL, Heng LY, Surif S.Per

whole cell‐based fluorescence 

pesticide detection. Sensors (B

13(5):6394‐404.

Exposure

Silva E, Pereira AC, Estalagem SP,

Moreira‐Santos M, Ribeiro R,

Cerejeira MJ 2012 Sep‐Oct

Silva E, Pereira AC, Estalagem 

R, Cerejeira MJ.Assessing the 

protected area within the Tag

Portugal). J Environ Qual. 201

Exposure

Smalling KL, Kuivila KM, Orlando

JL, Phillips BM, Anderson BS,

Siegler K, Hunt JW, Hamilton

M 2013 Aug

Smalling KL, Kuivila KM, Orlan

Siegler K, Hunt JW, Hamilton M

fungicides and other current‐u

California estuary. Mar Pollut 

53.

Exposure

Sreeprasad TS, Gupta SS,

Maliyekkal SM, Pradeep

T 2013 Feb

Sreeprasad TS, Gupta SS, Mali

T.Immobilized graphene‐base

facile synthesis and applicatio

Hazard Mater. 2013 Feb 15; 2

Exposure

Srinivas P S, Banerjee K, Jadhav

MR, Ghaste MS, Lawande KE 2012

Srinivas P S, Banerjee K, Jadha

KE.Bioefficacy, dissipation kine

selected insecticides in Allium 

2012; 47(7):700‐9.

Exposure Starner K, Goh KS 2013 Sep

Starner K, Goh KS.Chlorpyrifos

surface water contamination i

California, USA. Bull Environ C

91(3):287‐91.

Exposure

Suciu NA, Ferrari F, Vasileiadis S,

Merli A, Capri E, Trevisan

M 2013

Suciu NA, Ferrari F, Vasileiadis 

M.Pesticides water decontam

conditions. J Environ Sci Healt
Chlorpyrifos Human Health Risk Assessment D424485 

Page 489 of 531

Exposure

Sun X, Cao Y, Gong Z, Wang X,

Zhang Y, Gao J 2012 Dec

Sun X, Cao Y, Gong Z, Wang X, 

amperometric immunosensor 

nanotubes‐thionine‐chitosan 

chlorpyrifos detection. Sensor

12(12):17247‐61.

Exposure Sun X, Zhai C, Wang

X 2013 Mar

Sun X, Zhai C, Wang X.A novel 

cholinesterase biosensor mod

nanospheres. Bioprocess Bios

83.

Exposure Swarnam TP, Velmurugan

A 2013 Jul

Swarnam TP, Velmurugan A.P

samples from the Andaman Is

Assess. 2013 Jul; 185(7):6119‐

Exposure Tiwari MK, Guha S 2013 Oct

Tiwari MK, Guha S.Simultaneo

chlorpyrifos, and their metabo

samples using gas chromatogr

spectrometry. Environ Monit A

185(10):8451‐63.

Exposure

Tortella GR, Rubilar O, Castillo

Md, Cea M, Mella‐Herrera R,

Diez MC 2012 Jun

Tortella GR, Rubilar O, Castillo 

Diez MC.Chlorpyrifos degrada

at different maturity stages. C

88(2):224‐8.

Exposure

Urrutia C, Rubilar O, Tortella GR,

Diez MC 2013 Aug

Urrutia C, Rubilar O, Tortella G

pesticide mixture on modified 

system with alternatives lignoChemosphere. 2013 Aug; 92(1

Exposure

van Wendel de Joode B, Barraza

D, Ruepert C, Mora AM,

CÃ3rdoba L, Oberg M, Wesseling

C, Mergler D, Lindh CH 2012 Aug

van Wendel de Joode B, Barra

CÃ3rdoba L, Oberg M, Wesseli

CH.Indigenous children living 

chlorpyrifos‐treated bags have 

pyridinol (TCPy) urinary conce

Aug; 117:17‐26.
Chlorpyrifos Human Health Risk Assessment D424485 

Page 490 of 531

Exposure

Vogt R, Bennett D, Cassady D,

Frost J, Ritz B, Hertz‐Picciotto I 2012 Nov

Vogt R, Bennett D, Cassady D, 

I.Cancer and non‐cancer healt

contaminant exposures for ch

a risk assessment. Environ Hea

Exposure Walorczyk S, DroÅ1⁄4dÅ1⁄4yÅ„ski

D 2012 Aug

Walorczyk S, DroÅ1⁄4dÅ1⁄4yÅ„sk

to new analytes of a multi‐res

determination of pesticides in 

using gas chromatography‐tan

spectrometry revisited. J Chro

1251:219‐31.

Exposure

Wang C, Zhou Q, Zhang L, Zhang

Y, Xiao E, Wu

Z 2013

Wang C, Zhou Q, Zhang L, Zha

characteristics of chlorpyrifos hydroponic system. Int J Phyto

60.

Exposure

Wang P, Dai W, Ge L, Yan M, Ge

S, Yu J 2013 Feb

Wang P, Dai W, Ge L, Yan M, G

photoelectrochemical sensor 

molecularly imprinted poly(o‐TiO2 nanotubes for specific an

chlorpyrifos. Analyst. 2013 Fe

Exposure

Wang Q, Yang J, Li C, Xiao B, Que

X 2013

Wang Q, Yang J, Li C, Xiao B, Q

pesticide concentrations in wa

removal by Iris pseudacorus. W

67(9):1908‐15.

Exposure

Wang S, Wang Z, Zhang Y, Wang

J, Guo

R 2013 Jun

Wang S, Wang Z, Zhang Y, Wa

in market foods in Shaanxi Pro

Chem. 2013 Jun 1; 138(2‐3):20

Exposure Wang Z, Huang J, Chen J, Li F 2013 Aug

Wang Z, Huang J, Chen J, Li F.E

liquids in removing chlorothal

from cherry tomatoes. Chemo

8.
Chlorpyrifos Human Health Risk Assessment D424485 

Page 491 of 531

Exposure Wang ZW, Huang J, Chen JY, Li FL 2013 Jul

Wang ZW, Huang J, Chen JY, L

movement and distribution of 

in tomatoes. Ecotoxicol Enviro

Exposure

Wason SC, Julien R, Perry MJ,

Smith TJ, Levy JI 2013 Jul

Wason SC, Julien R, Perry MJ, 

exposures to organophosphat

living in an urban low‐income 

2013 Jul; 124:13‐22.

Exposure

Wei W, Zong X, Wang X, Yin L, Pu

Y, Liu S 2012 Dec

Wei W, Zong X, Wang X, Yin L, 

amperometric immunosensor 

on immunogen/platinum dop

screen‐printed carbon electro

135(3):888‐92.

Exposure Weschler CJ, Nazaroff WW 2012 Oct

Weschler CJ, Nazaroff WW.SV

look at dermal pathways. Indo

Exposure

Weston DP, Ding Y, Zhang M,

Lydy MJ 2013 Jan

Weston DP, Ding Y, Zhang M, 

of sediment toxicity in agricult

pyrethroids and nine seldom‐

pesticides. Chemosphere. 201

Exposure

Wofford P, Segawa R, Schreider

J, Federighi V, Neal R, Brattesani

M 2014 Mar

Wofford P, Segawa R, Schreid

Brattesani M.Community air m

3: using health‐based screenin

collected for a year. Environ M

186(3):1355‐70.

Exposure Yang L, Wang G, Liu Y, Wang

M 2013 Sep

Yang L, Wang G, Liu Y, Wang M

based on immobilization of ac

nanoparticles‐carboxylic graph

for detection of pesticides. Ta

41.

Exposure

Yang L, Wang GC, Liu YJ, An JJ,

Wang

M 2013 Mar

Yang L, Wang GC, Liu YJ, An JJ, 

stable biosensor based on a Si

glassy carbon electrode for se

Anal Bioanal Chem. 2013 Mar
Chlorpyrifos Human Health Risk Assessment D424485 

Page 492 of 531

Exposure

Yao GH, Liang RP, Huang CF,

Wang Y, Qiu JD 2013 Dec

Yao GH, Liang RP, Huang CF, W

resonance sensor based on m

polymers amplification for pes

2013 Dec 17; 85(24):11944‐51

Exposure

Zhai C, Sun X, Zhao W, Gong Z,

Wang

X 2013 Apr

Zhai C, Sun X, Zhao W, Gong Z

biosensor based on chitosan/p

nanotubes/hollow gold nanos

one‐step electrodeposition. B

15; 42:124‐30.

Exposure Zhan Y, Zhang

M 2012 Aug

Zhan Y, Zhang M.PURE: a web

to evaluate pesticide environm

management practices in Calif

2012 Aug; 82:104‐13.

Exposure Zhang Q, Wang B, Cao Z, Yu

Y 2012 Jun

Zhang Q, Wang B, Cao Z, Yu Y.

bioaugmentation for the degr

Hazard Mater. 2012 Jun 30; 22

Exposure

Zhang W, Tang Y, Du D, Smith J,

Timchalk C, Liu D, Lin

Y 2013 Sep

Zhang W, Tang Y, Du D, Smith 

Y.Direct analysis of trichloropy

an Au nanoparticles‐based im

strip for biomonitoring of exp

2013 Sep 30; 114:261‐7.

Exposure Zhang X, Shen Y, Yu XY, Liu XJ 2012 Apr

Zhang X, Shen Y, Yu XY, Liu XJ.

residue analysis in rice, soil an

conditions. Ecotoxicol Environ 

Exposure Zhang X, Starner K, Spurlock F 2012 Nov

Zhang X, Starner K, Spurlock F

agricultural use in regions of f

detections in California, USA. 

2012 Nov; 89(5):978‐84.
Chlorpyrifos Human Health Risk Assessment D424485 

Page 493 of 531

Exposure

Zhang Y, An J, Ye W, Yang G,

Qian ZG, Chen HF, Cui L, Feng

Y 2012 Sep

Zhang Y, An J, Ye W, Yang G, Q

Y.Enhancing the promiscuous 

a thermostable lactonase (Gka

degradation of organophosph

Microbiol. 2012 Sep; 78(18):6

Exposure

Zhao X, Wu C, Wang Y, Cang T,

Chen L, Yu R, Wang

Q 2012 Feb

Zhao X, Wu C, Wang Y, Cang T

Q.Assessment of toxicity risk o

ecosystem on Trichogramma j

rice lepidopterans. J Econ Ento

Exposure

Zhao Y, Wang C, Wendling LA,

Pei

Y 2013 Aug

Zhao Y, Wang C, Wendling LA, 

drinking water treatment resid

adsorbent. J Agric Food Chem

Exposure

Zhong G, Xie Z, Cai M, MÃ¶ller A,

Sturm R, Tang J, Zhang G, He J,

Ebinghaus

R 2012 Jan

Zhong G, Xie Z, Cai M, MÃ¶lle

He J, Ebinghaus R.Distribution 

current‐use pesticides (CUPs) 

Arctic Ocean. Environ Sci Tech

Exposure

Zhou J, Lu Q, Tong Y, Wei W, Liu

S 2012 Sep

Zhou J, Lu Q, Tong Y, Wei W, L

by using hairpin molecular bea

oxide. Talanta. 2012 Sep 15; 9

Exposure

GonzÃ¡lez‐Curbelo MA, Asensio‐

Ramos M, Herrera‐Herrera AV,

HernÃ¡ndez‐Borges J 2012 Jul

GonzÃ¡lez‐Curbelo MA, Asens

AV, HernÃ¡ndez‐Borges J.Pest

based baby foods using multi‐

dispersive solid‐phase extract

Jul; 404(1):183‐96.

Exposure

Lin YZ, Zeng GM, Zhang Y, Chen

M, Jiang M, Zhang JC, Lu LH, Liu

LF 2012 Mar

Lin YZ, Zeng GM, Zhang Y, Che

Liu LF.[Biodegradation mecha

using molecular simulation]. H33(3):1015‐9.

Exposure/Biomarkers

Adolfsson‐Erici M, Ã...Kerman G,

McLachlan MS 2012 Jun

Adolfsson‐Erici M, Ã...Kerman 

passive sampling to measure e

bioaccumulation tests. Chemo
Chlorpyrifos Human Health Risk Assessment D424485 

Page 494 of 531

Exposure/Biomarkers

Ding Y, Landrum PF, You J,

Harwood AD, Lydy MJ 2012 Sep

Ding Y, Landrum PF, You J, Ha

phase microextraction to estim

concentrations to toxicity‐‐pa

Sep; 31(9):2159‐67.

Exposure/Biomarkers

Sun H, Si C, Bian Q, Chen X, Chen

L, Wang

X 2012 Aug

Sun H, Si C, Bian Q, Chen X, Ch

vitro reporter gene assays to a

activities of chemicals frequen

J Appl Toxicol. 2012 Aug; 32(8

High Dose Nutter TJ, Cooper BY 2014 Jun

Nutter TJ, Cooper BY.Persisten

following chronic exposure to 

pyridostigmine bromide. Toxic

15; 277(3):298‐309.

High Dose

Yildirim, E., Baydan E, Kanbur M,

Kul O, CÄ±nar M, Ekici H, Atmaca

N 2013

Yildirim, E., Baydan E, Kanbur 

Atmaca N.The effect of chlorp

aorta in rats. Biomed Res Int. 

High Dose/Mixture Nutter TJ, Jiang N, Cooper BY 2013 Dec

Nutter TJ, Jiang N, Cooper BY.

dysfunctions after chronic exp

pyridostigmine bromide. Neur

83.

High Dose/Mixture Ojha A, Srivastava

N 2012 Jan

Ojha A, Srivastava N.Redox im

with organophosphate pestici

of antioxidant vitamins. Ecoto

75(1):230‐41.

HighDoses/Acute/Adults

Sandhu MA, Saeed AA, Khilji MS,

Ahmed A, Latif MS, Khalid

N 2013

Sandhu MA, Saeed AA, Khilji M

N.Genotoxicity evaluation of c

approach in regular toxicity te

38(2):237‐44.
Chlorpyrifos Human Health Risk Assessment D424485 

Page 495 of 531

Methods Paper

(Biosensor and

Biomonitor Approach)

Camann DE, Schultz ST, Yau AY,

Heilbrun LP, Zuniga MM, Palmer

RF, Miller CS 2013 Mar

Camann DE, Schultz ST, Yau AY

Palmer RF, Miller CS.Acetamin

diethylhexyl phthalate metabo

acids in deciduous molars: pot

exposure. J Expo Sci Environ E

6.

Methods Paper

(Biosensor and

Biomonitor Approach)

Henderson JD, Glucksman G,

Leong B, Tigyi A, Ankirskaia A,

Siddique I, Lam H, DePeters E,

Wilson BW 2012 Jan

Henderson JD, Glucksman G, L

Siddique I, Lam H, DePeters E, 

bromide protection against ac

by pesticides. J Biochem Mol T

Methods Paper

(Biosensor and

Biomonitor Approach) Jiang W, Duysen EG, Lockridge

O 2012 May

Jiang W, Duysen EG, Lockridge 

nontoxic dose of chlorpyrifos 

diethoxyphosphotyrosine labe

days post exposure, detected 

Toxicology. 2012 May 16; 295

Methods Paper

(Biosensor and

Biomonitor Approach)

Joly C, Gay‐QuÃ©heillard J,

LÃ©kÃ© A, Chardon K,

Delanaud S, Bach V, Khorsi‐

Cauet

H 2013 May

Joly C, Gay‐QuÃ©heillard J, LÃ

S, Bach V, Khorsi‐Cauet H.Imp

doses of chlorpyrifos on the in

Simulator of the Human Intest(SHIME) and in the rat. Enviro

20(5):2726‐34.

Methods Paper

(Biosensor and

Biomonitor Approach)

Jones OA, Swain SC, Svendsen C,

Griffin JL, Sturzenbaum SR,

Spurgeon DJ 2012 Feb

Jones OA, Swain SC, Svendsen 

Spurgeon DJ.Potential new me

using metabolomics and Caen

Res. 2012 Feb 3; 11(2):1446‐5

Methods Paper

(Biosensor and

Biomonitor Approach)

Maravgakis G, Tzatzarakis MN,

Alegakis AK, Stivaktakis PD,

Tsatsakis AM 2012 May

Maravgakis G, Tzatzarakis MN

Tsatsakis AM.Diethyl phospha

hair as an indicator of long ter

chlorpyrifos. Forensic Sci Int. 2
Chlorpyrifos Human Health Risk Assessment D424485 

Page 496 of 531

Methods Paper

(Biosensor and

Biomonitor Approach)

Multari RA, Cremers DA, Scott T,Kendrick P 2013 Mar

Multari RA, Cremers DA, Scott 

pesticides and dioxins in tissue 

laser‐induced breakdown spec

Chem. 2013 Mar 13; 61(10):23

Methods Paper

(Biosensor and

Biomonitor Approach)

Papoutsis I, Mendonis M,

Nikolaou P, Athanaselis S, Pistos

C, Maravelias C, Spiliopoulou

C 2012 May

Papoutsis I, Mendonis M, Niko

Maravelias C, Spiliopoulou C.D

a simple GC‐MS method for th

of 11 anticholinesterase pestic

forensic toxicology application

57(3):806‐12.

Methods Paper

(Biosensor and

Biomonitor Approach) Schopfer LM, Lockridge

O 2012 Mar‐Apr

Schopfer LM, Lockridge O.Ana

monitoring exposure to organ

agents through analysis of pro

2012 Mar‐Apr; 4(3‐4):246‐61.

Methods Paper for

Dermal Absorption

Mircioiu C, Voicu VA, Ionescu M,

Miron DS, Radulescu FS,

Nicolescu AC 2013 May

Mircioiu C, Voicu VA, Ionescu 

Nicolescu AC.Evaluation of in v

decontamination and desorpt

compounds from skin and syn

Lett. 2013 May 23; 219(2):99‐

MethodsPaper/Alternatives forneurodevelopment

Visan A, Hayess K, Sittner D, PohlEE, Riebeling C, Slawik B, GulichK, OelgeschlÃ¤ger M, Luch A,Seiler AE 2012 Oct

Visan A, Hayess K, Sittner D, PGulich K, OelgeschlÃ¤ger M, Ldifferentiation of mouse embassess developmental neurotoNeurotoxicology. 2012 Oct; 33

MethodsPaper/Alternatives forneurodevelopment/Notready to establish AOP

Amaroli A, Aluigi MG, Falugi C,Chessa MG 2013 Feb

Amaroli A, Aluigi MG, Falugi Cneurotoxic thionophosphate pdifferentiating alternative mo90(7):2115‐22.
Chlorpyrifos Human Health Risk Assessment D424485 

Page 497 of 531

MethodsPaper/Alternatives forneurodevelopment/Notready to establish AOP

Culbreth ME, Harrill JA,Freudenrich TM, Mundy WR,Shafer TJ 2012 Dec

Culbreth ME, Harrill JA, FreudTJ.Comparison of chemical‐indand apoptosis in human and mNeurotoxicology. 2012 Dec; 33

MethodsPaper/Alternatives forneurodevelopment/Notready to establish AOP

Estevan C, Vilanova E, SogorbMA 2013 Feb

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MethodsPaper/Alternatives forneurodevelopment/Notready to establish AOP

GuiÃ±azÃo N, Rena V, Genti‐Raimondi S, Rivero V, MagnarelliG 2012 Apr

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Microbial

Abraham J, Shanker A,

Silambarasan S 2013 Dec

Abraham J, Shanker A, Silamb

JAAS1 in biodegradation of ch

metabolite 3,5,6‐trichloro‐2‐p

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Mixtures

Cole, T.B. , Jansen, Karen, Park,

Sarah , Li, Wan‐Fen, Furlong,

Clement E. , Costa, Lucio G.

Cole, T.B. , Jansen, Karen, Park

Clement E. , Costa, Lucio G.Th

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on neurodifferentiation are al

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Small number of

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Small number of

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Vera, Berta , Santa Cruz, Silvia 

cholinesterase and carboxyles

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Å ukaszewicz‐Hussain

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Å ukaszewicz‐Hussain A.[Para

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Juberg DR, Gehen SC, Coady KK,

LeBaron MJ, Kramer VJ, Lu H,

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activities in chlorpyrifos insect

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Noro Y, Tomizawa M, Ito Y,

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Svensson RU, Bannick NL, Marin

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Tripathi S, Suzuki N, Srivastav 

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endocrine glands (calcitonin c

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Å ozowicka

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Ahmad F, Iqbal S, Anwar S, Afz

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Alvi AH, Sayyed AH, Naeem M,

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risk of butene‐fipronil using D

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Bajracharya NS, Opit GP, Talley J,

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Bajracharya NS, Opit GP, Talle

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Ban L, Zhang S, Huang Z, He Y,

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Barata C, FernÃ¡ndez‐San Juan

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Bhinder P, Chaudhry A.Mutag

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Bhinder P, Chaudhry A.Evalua

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Boatti L, Robotti E, Marengo E,

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Carrasco‐Letelier L, Mendoza‐

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Carrillo D, Crane JH, PeÃ±a JE.

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Chatterjee S, Basak P, Chaklader

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Elsharkawy EE, Yahia D, El‐Nisr

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Elsharkawy EE, Yahia D, El‐Nis

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Park IK.Insecticidal activity of 

Piper nigrum against adult of t

pipiens pallens and Aedes aeg

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Not related toneurodevelopmentaleffects Prabhaker N, Gispert C, Castle SJ 2012 Aug

Prabhaker N, Gispert C, Castle 

Planococcus ficus (Hemiptera

California to select insecticide

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Not related toneurodevelopmentaleffects

Qin G, Jia M, Liu T, Zhang X, Guo

Y, Zhu KY, Ma E, Zhang J 2013

Qin G, Jia M, Liu T, Zhang X, G

J.Characterization and functio

S‐transferases from the migra

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Not related toneurodevelopmentaleffects

Quesada‐Moraga E, Yousef M,

Ortiz A, RuÃz‐Torres M, Garrido‐

Jurado I, EstÃ©vez

A 2013 Aug

Quesada‐Moraga E, Yousef M

Garrido‐Jurado I, EstÃ©vez A.

(Ascomycota: Hypocreales) wo

Euzophera pinguis (Lepidopte

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Not related toneurodevelopmentaleffects Ramu S, Seetharaman

B 2014

Ramu S, Seetharaman B.Biode

methamidophos by a soil bact

aeruginosa strain Is‐6. J Enviro

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Not related toneurodevelopmentaleffects

RodrÃguez‐Vivas RI, Miller RJ,

Ojeda‐Chi MM, Rosado‐Aguilar

JA, Trinidad‐MartÃnez IC,

PÃ©rez de LeÃ3n AA 2014 Feb

RodrÃguez‐Vivas RI, Miller RJ, 

JA, Trinidad‐MartÃnez IC, PÃ©

ivermectin resistance in a field 

microplus (Acari: Ixodidae) co

elaphus) in the Mexican tropic

200(1‐2):179‐88.

Not related toneurodevelopmentaleffects

Rouimi P, Zucchini‐Pascal N,

Dupont G, Razpotnik A,

FouchÃ© E, De Sousa G,

Rahmani

R 2012 Aug

Rouimi P, Zucchini‐Pascal N, D

FouchÃ© E, De Sousa G, Rahm

pesticide mixtures on liver cel

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Not related toneurodevelopmentaleffects

Rubach MN, Baird DJ,

Boerwinkel MC, Maund SJ,

Roessink I, Van den Brink PJ 2012 Oct

Rubach MN, Baird DJ, Boerwin

Van den Brink PJ.Species traits 

sensitivity of aquatic inverteb

chlorpyrifos. Ecotoxicology. 20

Not related toneurodevelopmentaleffects

Sehgal B, Subramanyam B,

Arthur FH, Gill BS 2013 Aug

Sehgal B, Subramanyam B, Art

susceptibility of field strains o

species to spinosad and chlorpdeltamethrin on hard red wint

2013 Aug; 106(4):1911‐9.

Not related toneurodevelopmentaleffects

Shetty NJ, Hariprasad TP, Sanil D,

Zin

T 2013 Nov

Shetty NJ, Hariprasad TP, Sani

inversions among insecticide‐

stephensi Liston, a malaria mo

Nov; 112(11):3851‐7.

Not related toneurodevelopmentaleffects

Singh AK, Parashar A, Singh AK,

Singh

R 2013 Apr‐Jun

Singh AK, Parashar A, Singh AK

chlorpyrifos exposure associat

adulthood in Swiss albino mic

Jun; 10(2):141‐9.

Not related toneurodevelopmentaleffects

Speed HE, Blaiss CA, Kim A, Haws

ME, Melvin NR, Jennings M,

Eisch AJ, Powell CM 2012 Jan

Speed HE, Blaiss CA, Kim A, Ha

M, Eisch AJ, Powell CM.Delaye

synaptic transmission and spin

repeated subclinical doses of o

adult mice. Toxicol Sci. 2012 J

Not related toneurodevelopmentaleffects

Speed HE, Blaiss CA, Kim A, Haws

ME, Melvin NR, Jennings M,

Eisch AJ, Powell CM 2012 Jan

Speed HE, Blaiss CA, Kim A, Ha

M, Eisch AJ, Powell CM.Delaye

synaptic transmission and spin

repeated subclinical doses of o

adult mice. Toxicol Sci. 2012 J

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Srinivasulu M, Jaffer MohiddinG, Subramanyam K,

Rangaswamy

V 2012 Jun

Srinivasulu M, Jaffer Mohiddin 

Rangaswamy V.Effect of insec

combination with fungicides o

activity in two groundnut (Ara

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Not related toneurodevelopmentaleffects

Srivastava PK, Singh VP, Prasad

SM 2012 Sep

Srivastava PK, Singh VP, Prasa

ascorbate‐glutathione cycle e

against low and high UV‐B exp

exposed to low and high dose

Environ Saf. 2012 Sep; 83:79‐8

Not related toneurodevelopmentaleffects Stoner KA, Eitzer BD 2013

Stoner KA, Eitzer BD.Using a h

pesticide residues detected in 

bees (Apis mellifera) in Conne

8(10):e77550.

Not related toneurodevelopmentaleffects Su J, Zhang Z, Wu M, Gao

C 2014 Feb

Su J, Zhang Z, Wu M, Gao C.Ch

of the rice striped stem borer 

Econ Entomol. 2014 Feb; 107(

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Terry AV Jr, Beck WD, Warner S,Vandenhuerk L, Callahan PM 2012 Jan‐Feb

Terry AV Jr, Beck WD, Warner PM.Chronic impairments in sprats previously exposed to chldiisopropylfluorophosphate. NFeb; 34(1):1‐8.

Not related toneurodevelopmentaleffects Tirello P, Pozzebon A, Duso

C 2013 Oct

Tirello P, Pozzebon A, Duso C.T

the non‐target predatory mite 

laboratory studies. Chemosph

Not related toneurodevelopmentaleffects Tirello P, Pozzebon A, Duso

C 2012 Jan

Tirello P, Pozzebon A, Duso C.

the predatory mite Kampimod

Acarol. 2012 Jan; 56(1):1‐8.

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Tiwari S, Killiny N, Mann RS,

Wenninger EJ, Stelinski LL 2013 Apr

Tiwari S, Killiny N, Mann RS, W

LL.Abdominal color of the Asia

citri, is associated with suscep

Pest Manag Sci. 2013 Apr; 69(

Not related toneurodevelopmentaleffects Uzun FG, Kalender

Y 2013 May

Uzun FG, Kalender Y.Chlorpyri

hematologic changes in rats: t

catechin. Food Chem Toxicol. 
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Ventura C, Nunez M, Miret N,

Martinel Lamas D, Randi A,

Venturino A, Rivera E, Cocca

C 2012 Sep

Ventura C, NÃoÃ±ez M, Miret 

Venturino A, Rivera E, Cocca C

action are involved in chlorpy

dependent or ‐independent b

low or high concentrations of 

2012 Sep 3; 213(2):184‐93.

Not related toneurodevelopmentaleffects Wang C, Lu G, Cui J 2012 Jan

Wang C, Lu G, Cui J.Responses 

insecticide coexposure in Cara

2012 Jan; 27(1):50‐7.

Not related toneurodevelopmentaleffects

Wang JJ, Wei D, Dou W, Hu F, Liu

WF, Wang JJ 2013 Apr

Wang JJ, Wei D, Dou W, Hu F, 

synergistic effects of several in

fruit fly (Diptera: Tephritidae)

106(2):970‐8.

Not related toneurodevelopmentaleffects Wang S, Zhang C, Yan

Y 2012 Feb

Wang S, Zhang C, Yan Y.Biode

and p‐nitrophenol by a newly strain Yw12. Biodegradation. 2

Not related toneurodevelopmentaleffects

Wang SY, Zhou XH, Zhang AS, Li

LL, Men XY, Zhang SC, Liu YJ, Yu

Y 2012 Jul

Wang SY, Zhou XH, Zhang AS, 

Yu Y.[Resistance mechanisms 

phoxim‐resistant Frankliniella 

population]. Ying Yong Sheng 

23(7):1933‐9.

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Williamson SM, Moffat C,

Gomersall MA, Saranzewa N,

Connolly CN, Wright GA 2013

Williamson SM, Moffat C, Gom

Connolly CN, Wright GA.Expos

inhibitors alters the physiolog

honeybees. Front Physiol. 201

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Xia X, Zheng D, Zhong H, Qin B,

Gurr GM, Vasseur L, Lin H, Bai J,

He W, You

M 2013

Xia X, Zheng D, Zhong H, Qin B

J, He W, You M.DNA sequenci

microbiota of diamondback m

a possible relationship with in

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Chlorpyrifos Human Health Risk Assessment D424485 

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Not related toneurodevelopmentaleffects

Xiang Y, Wang N, Song J, Cai D,

Wu

Z 2013 Jun

Xiang Y, Wang N, Song J, Cai D

fabricated by high‐energy elec

structure for controlling pestic

2013 Jun 5; 61(22):5215‐9.

Not related toneurodevelopmentaleffects Xie H, Li Q, Wang M, Zhao L 2013 Jun

Xie H, Li Q, Wang M, Zhao L.Pr

laccase from Pichia pastoris an

chlorpyrifos in a laccase/vanil

Biotechnol. 2013 Jun 28; 23(6

Not related toneurodevelopmentaleffects Xie J, Zhao Y, Zhang H, Liu Z, Lu

Z 2014 Jan

Xie J, Zhao Y, Zhang H, Liu Z, L

hydrolase to enhance its chlor

Lett Appl Microbiol. 2014 Jan; 

Not related toneurodevelopmentaleffects

Yan C, Jiao L, Zhao J, Yang H,Peng S 2012 Jul

Yan C, Jiao L, Zhao J, Yang H, Pchlorpyrifos lead to spatial memotor activity alteration. Neu34(4):442‐9.

Not related toneurodevelopmentaleffects Yang X, Li X, Zhang

Y 2013 Dec

Yang X, Li X, Zhang Y.Molecula

CYP9A61: a chlorpyrifos‐ethyl 

inducible cytochrome P450 cD

Mol Sci. 2013 Dec 13; 14(12):2

Not related toneurodevelopmentaleffects

Zein MA, McElmurry SP, Kashian

DR, Savolainen PT, Pitts DK 2014 Jan

Zein MA, McElmurry SP, Kashi

DK.Optical bioassay for measu

insecticides in Daphnia pulex. 

Jan; 33(1):144‐51.

Not related toneurodevelopmentaleffects

Zhang J, Li D, Ge P, Yang M, Guo

Y, Zhu KY, Ma E, Zhang J 2013 Oct

Zhang J, Li D, Ge P, Yang M, Gu

J.RNA interference revealed th

carboxylesterase genes in inse

Locusta migratoria. Chemosph

Not related toneurodevelopmentaleffects

Zhang NN, Liu CF, Yang F, Dong

SL, Han ZJ 2012

Zhang NN, Liu CF, Yang F, Don

mechanisms to chlorpyrifos anfrequencies in the acetylcholin

populations of the tobacco wh

J Insect Sci. 2012; 12:41.
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Not related toneurodevelopmentaleffects Zhang SK, Ren XB, Wang YC, Su J 2014 Apr

Zhang SK, Ren XB, Wang YC, S

Cnaphalocrocis medinalis (Lep

chemistry insecticides. J Econ 

107(2):815‐20.

Not related toneurodevelopmentaleffects

Zhang X, Wallace AD, Du P,

Kibbe WA, Jafari N, Xie H, Lin S,

Baccarelli A, Soares MB, Hou L 2012 Aug

Zhang X, Wallace AD, Du P, Kib

Baccarelli A, Soares MB, Hou L

in response to pesticide expos

Mutagen. 2012 Aug; 53(7):542

Not related toneurodevelopmentaleffects Zhao L, Teng S, Liu

Y 2012 Apr

Zhao L, Teng S, Liu Y.Characte

rhizospheric organism from cu

Ochrobactrum haematophilum

52(2):232‐44.

Not related toneurodevelopmentaleffects

Zheng Y, Zhao JW, He YX, Huang

J, Weng QY 2012 Jan

Zheng Y, Zhao JW, He YX, Hua

of insecticide resistance and it

population of Bemisia tabaci i

Ying Yong Sheng Tai Xue Bao. 

Not related toneurodevelopmentaleffects

Zhou WW, Li XW, Quan YH,

Cheng J, Zhang CX, Gurr G, Zhu

ZR 2012 Sep

Zhou WW, Li XW, Quan YH, Ch

ZR.Identification and expressio

S‐transferase genes from the 

sucker and virus vector Laode

(Hemiptera: Delphacidae). Pes

68(9):1296‐305.

Not related toneurodevelopmentaleffects

Zhou WW, Liang QM, Xu Y, Gurr

GM, Bao YY, Zhou XP, Zhang CX,

Cheng J, Zhu ZR 2013

Zhou WW, Liang QM, Xu Y, Gu

CX, Cheng J, Zhu ZR.Genomic i

transferase gene family of two 

Nilaparvata lugens (StÃ¥l) and 

(Hemiptera: Delphacidae). PLo

Not related toneurodevelopmentaleffects/Mixtures

Ojha A, Yaduvanshi SK, Pant SC,

Lomash V, Srivastava

N 2013 Oct

Ojha A, Yaduvanshi SK, Pant S

N.Evaluation of DNA damage 

three commonly used organo

individually and in mixture, in 

2013 Oct; 28(10):543‐52.
Chlorpyrifos Human Health Risk Assessment D424485 

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Not related toneurodevelopmentaleffects/Mixtures

Rajpoot DS, Prakash A, Mandil R,

Rahal A, Garg SK 2013

Rajpoot DS, Prakash A, Mandi

SK.Differential modulation of 

enzymes in rats following sing

chlorpyrifos, arsenic, and asco

Health A. 2013; 76(24):1354‐6

Not relevant

Slotkin TA, Card J, Infante A,Seidler FJ 2013 May‐Jun

Slotkin TA, Card J, Infante A, Spentabromodiphenyl ether) suneurotransmitter phenotypes Teratol. 2013 May‐Jun; 37:13‐

Not Relevant /Exposure

Khairy MA, Kolb M, Mostafa AR,

El‐Fiky A, Bahadir

M 2012 Mar

Khairy MA, Kolb M, Mostafa A

posed by chlorinated organic 

East Alexandria, Egypt. Enviro

19(3):794‐811.

Not relevant/Acute

Duysen EG, Cashman JR,

Schopfer LM, Nachon F, Masson

P, Lockridge

O 2012 Feb

Duysen EG, Cashman JR, Scho

Lockridge O.Differential sensit

carboxylesterase‐null mice to 

chlorpyrifos oxon, but not to d

diisopropylfluorophosphate, c

cyclosarin thiocholine, tabun t

Chem Biol Interact. 2012 Feb 5

Not Relevant/Acute

Mechanisms

Li T, Zhao H, Hung GC, Han J, Tsai

S, Li B, Zhang J, Puri RK, Lo SC 2012 Dec

Li T, Zhao H, Hung GC, Han J, T

SC.Differentially expressed ge

organophosphates in human n

Med (Maywood). 2012 Dec; 2

Not Relevant/Acute

Mechanisms

SchÃ¤fer M, Koppe F, Stenger B,

Brochhausen C, Schmidt A,

Steinritz D, Thiermann H,

Kirkpatrick CJ, Pohl

C 2013 Dec

Schafer M, Koppe F, Stenger B

Steinritz D, Thiermann H, Kirk

organophosphate poisoning o

Biol Interact. 2013 Dec 5; 206(

Notrelevant/Acute/LowerTaxa

Buono S, Manzo S, Maria G,

Sansone

G 2012 Apr

Buono S, Manzo S, Maria G, Sa

pentachlorophenol, azinphos‐

development of Paracentrotu

Ecotoxicology. 2012 Apr; 21(3
Chlorpyrifos Human Health Risk Assessment D424485 

Page 518 of 531

Not relevant/In vitro

study, chlorpyrifos usedas positive control with

no new information on

effects in this cell line

Slotkin TA, Card J, Infante A,

Seidler FJ 2013 May‐Jun

Slotkin TA, Card J, Infante A, S

dexamethasone augments the 

neurotoxicity of chlorpyrifos: 

after pharmacotherapy for pre

Teratol. 2013 May‐Jun; 37:1‐1

Not relevant/Mixtures

Chen L, Qu G, Sun X, Zhang S,

Wang L, Sang N, Du Y, Liu J, Liu S 2013

Chen L, Qu G, Sun X, Zhang S, 

S.Characterization of the inter

chlorpyrifos with integrative t

synergistic hepatoxicity. PLoS 

Not Relevant:Ecological Effects

Alexander AC, Luis AT, Culp JM,

Baird DJ, Cessna AJ 2013 Sep

Alexander AC, Luis AT, Culp JM

nutrients mask community res

mixtures? Ecotoxicology. 2013 

Not Relevant:Ecological Effects

Amaral MJ, Sanchez‐Hernandez

JC, Bicho RC, Carretero MA,

Valente R, Faustino AM, Soares

AM, Mann RM 2012 Oct

Amaral MJ, Sanchez‐Hernande

Valente R, Faustino AM, Soare

exposure and effect in a lacert

Seoane) exposed to chlorpyrif

Oct; 31(10):2345‐53.

Not Relevant:Ecological Effects

Ashauer R, Hintermeister A,

O'Connor I, Elumelu M,

Hollender J, Escher BI 2012 Mar

Ashauer R, Hintermeister A, O

Hollender J, Escher BI.Significa

for bioaccumulation kinetics o

Gammarus pulex. Environ Sci 

46(6):3498‐508.

Not Relevant:Ecological Effects

Assis CR, Linhares AG, Oliveira

VM, FranÃ§a RC, Carvalho EV,

Bezerra RS, de Carvalho LB Jr 2012 Dec

Assis CR, Linhares AG, Oliveira 

Bezerra RS, de Carvalho LB Jr.C

pesticides on brain acetylchol

Total Environ. 2012 Dec 15; 44

Not Relevant:Ecological Effects

Baylay AJ, Spurgeon DJ,

Svendsen C, Griffin JL, Swain SC,

Sturzenbaum SR, Jones OA 2012 Jul

Baylay AJ, Spurgeon DJ, Svend

Sturzenbaum SR, Jones OA.A m

independent action and conce

earthworm Lumbricus rubellu

21(5):1436‐47.
Chlorpyrifos Human Health Risk Assessment D424485 

Page 519 of 531

Not Relevant:Ecological Effects

BottÃ© ES, Jerry DR, Codi King S,

Smith‐Keune C, Negri AP 2012

BottÃ© ES, Jerry DR, Codi King 

AP.Effects of chlorpyrifos on c

stress markers in the tropical 

polyacanthus. Mar Pollut Bull. 

Not Relevant:Ecological Effects

Cacciatore LC, Guerrero NV,

CochÃ3n AC 2013 Mar

Cacciatore LC, Guerrero NV, C

carboxylesterase inhibition in 

to binary mixtures of azinphos

Aquat Toxicol. 2013 Mar 15; 1

Not Relevant:Ecological Effects

Cacciatore LC, Kristoff G,

Verrengia Guerrero NR, CochÃ3n

AC 2012 Jul

Cacciatore LC, Kristoff G, Verre

AC.Binary mixtures of azinpho

oxon produce in vitro synergis

Planorbarius corneus. Chemos

Not Relevant:Ecological Effects

Campillo JA, Albentosa M,

ValdÃ©s NJ, Moreno‐GonzÃ¡lez

R, LeÃ3n VM 2013 Oct

Campillo JA, Albentosa M, Val

LeÃ3n VM.Impact assessment 

Mediterranean coastal lagoon 

transplanted clams (Ruditapes 

and physiological responses. A

142‐143:365‐79.

Not Relevant:Ecological Effects

Carvalho RA, Omoto C, Field LM,

Williamson MS, Bass

C 2013

Carvalho RA, Omoto C, Field L

C.Investigating the molecular organophosphate and pyrethr

armyworm Spodoptera frugip

8(4):e62268.

Not Relevant:Ecological Effects

Chen S, Liu C, Peng C, Liu H, Hu

M, Zhong

G 2012

Chen S, Liu C, Peng C, Liu H, H

of chlorpyrifos and its hydroly

pyridinol by a new fungal stra

cladosporioides Hu‐01. PLoS O

Not Relevant:Ecological Effects Chen X, Song M, Qi S, Wang

C 2013 Feb

Chen X, Song M, Qi S, Wang C

insecticides to Trichogramma 

Trichogrammatidae). J Econ E

41.
Chlorpyrifos Human Health Risk Assessment D424485 

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Ciliberti A, Martin S, Ferrandez E,

Belluco S, Rannou B, Dussart C,

Berny P, de Buffrenil

V 2013 May

Ciliberti A, Martin S, Ferrande

Dussart C, Berny P, de Buffren

juvenile savannah monitors (V

environmentally relevant mixt

effects and accumulation in ti

Int. 2013 May; 20(5):3107‐14.

Not Relevant:Ecological Effects

Dasgupta R, Chakravorty PP,

Kaviraj

A 2012

Dasgupta R, Chakravorty PP, K

chlorpyrifos and endosulfan o

respiration of tropical epigeic 

excavatus (Perrier). J Environ S

103.

Not Relevant:Ecological Effects

Davidson C, Stanley K, Simonich

SM 2012 Aug

Davidson C, Stanley K, Simonic

and declines of the Cascades f

California Cascades, USA. Envi

31(8):1895‐902.

Not Relevant:Ecological Effects

de Solla SR, Palonen KE, Martin

PA 2014 Jan

de Solla SR, Palonen KE, Marti

associated with potato produc

to snapping turtle eggs (Chely

Toxicol Chem. 2014 Jan; 33(1)

Not Relevant:Ecological Effects

Demetrio PM, Bulus Rossini GD,

Bonetto CA, Ronco AE 2012 Jan

Demetrio PM, Bulus Rossini G

AE.Effects of pesticide formula

on the coelenterate Hydra att

Environ Contam Toxicol. 2012 

Not Relevant:Ecological Effects

DemirdaÄŸ R, Yerlikaya E,

Aksakal E, KÃ1⁄4frevioÄŸlu OI,

Ekinci

D 2012 Sep

DemirdaÄŸ R, Yerlikaya E, Aks

D.Influence of pesticides on th

carbonic anhydrase, from Euro

erythrocytes. Environ Toxicol 

34(2):218‐22.

Not Relevant:Ecological Effects

Diepens NJ, Pfennig S, Van den

Brink PJ, Gunnarsson JS, Ruepert

C, Castillo LE 2014 Jan

Diepens NJ, Pfennig S, Van de

Ruepert C, Castillo LE.Effect of 

pineapple plantations on aqua

Environ Biol. 2014 Jan; 35(1):7
Chlorpyrifos Human Health Risk Assessment D424485 

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Not Relevant:Ecological Effects

Dinh Van K, Janssens L, Debecker

S, Stoks

R 2014 Jul

Dinh Van K, Janssens L, Debec

increases chlorpyrifos effects 

predator behaviours. Aquat To

Not Relevant:Ecological Effects

Dzul‐Caamal R, DomÃnguez‐

LÃ3pez ML, GarcÃa‐Latorre E,

Vega‐LÃ3pez

A 2012 Oct

Dzul‐Caamal R, DomÃnguez‐LÃ

Vega‐LÃ3pez A.Implications of aryl‐esterase and oxonase actacetylcholinesterase of Chiros

phosphorothionate pesticides

Oct; 84:199‐206.

Not Relevant:Ecological Effects

El‐Amrani S, Pena‐Abaurrea M,

Sanz‐Landaluze J, Ramos L,

Guinea J, CÃ¡mara

C 2012 May

El‐Amrani S, Pena‐Abaurrea M

Guinea J, CÃ¡mara C.Bioconce

zebrafish eleutheroembryos (2012 May 15; 425:184‐90.

Not Relevant:Ecological Effects

Enis Yonar M, Yonar SM, Ural

MÅž, Silici S, DÃ1⁄4ÅŸÃ1⁄4kcan

M 2012 Aug

Enis Yonar M, Yonar SM, Ural 

M.Protective role of propolis i

in the haematological parame

oxidative/antioxidative status 

Chem Toxicol. 2012 Aug; 50(8

Not Relevant:Ecological Effects

Fu Y, Li M, Liu C, Qu JP, Zhu WJ,

Xing HJ, Xu SW, Li S 2013 Aug

Fu Y, Li M, Liu C, Qu JP, Zhu W

atrazine and chlorpyrifos expo

contents and enzyme activitie

Ecotoxicol Environ Saf. 2013 A

Not Relevant:Ecological Effects

George T, Beevi SN, Xavier G,

Kumar NP, George J 2013 Jun

George T, Beevi SN, Xavier G, 

kinetics and assessment of pro

and lambda‐cyhalothrin in car

2013 Jun; 185(6):5277‐84.

Not Relevant:Ecological Effects Guo W, Yan X, Zhao C, Han

R 2013 Jun

Guo W, Yan X, Zhao C, Han R.E

Steinernema and Heterorhabdgrubs (Coleoptera: Scarabaeid

Entomol. 2013 Jun; 106(3):111
Chlorpyrifos Human Health Risk Assessment D424485 

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Not Relevant:Ecological Effects

Hodgins SM, Kasten SA, Harrison

J, Otto TC, Oliver ZP, Rezk P,

Reeves TE, Chilukuri N, Cerasoli

DM 2013 Mar

Hodgins SM, Kasten SA, Harris

Reeves TE, Chilukuri N, Ceraso

against OP pesticides and nerv

HuPON1 purified from Trichop

adenoviral infection. Chem Bio

203(1):177‐80.

Not Relevant:Ecological Effects

Hua J, Cothran R, Stoler A,

Relyea

R 2013 Apr

Hua J, Cothran R, Stoler A, Rel

amphibians: wood frog morta

insecticides with a common m

Chem. 2013 Apr; 32(4):932‐6.

Not Relevant:Ecological Effects Huynh HP, Nugegoda

D 2012 Jan

Huynh HP, Nugegoda D.Effect

growth and food utilization in 

tandanus. Bull Environ Contam 

Not Relevant:Ecological Effects Janssens L, Dinh Van K, Stoks

R 2014 Mar

Janssens L, Dinh Van K, Stoks R

adult stage shape delayed effe

comparison between latitudes

148:74‐82.

Not Relevant:Ecological Effects

Key PB, Simonik E, Kish N, Chung

KW, Fulton MH 2013

Key PB, Simonik E, Kish N, Chu

in response of two model estu

and sublethal exposures to ch

B. 2013; 48(11):967‐73.

Not Relevant:Ecological Effects

Khalil F, Kang IJ, Undap S, Tasmin

R, Qiu X, Shimasaki Y, Oshima

Y 2013 Jun

Khalil F, Kang IJ, Undap S, Tasm

Oshima Y.Alterations in social 

(Oryzias latipes) in response to 

exposure. Chemosphere. 2013 

Not Relevant:Ecological Effects

LÃ3pez‐RoldÃ¡n R, Jubany I,

MartÃ V, GonzÃ¡lez S, Cortina JL 2013 Dec

LÃ3pez‐RoldÃ¡n R, Jubany I, M

JL.Ecological screening indicat

Llobregat river water. J Hazard 

1:239‐47.

Not Relevant:Ecological Effects Lee KY, Strand SE, Doty SL 2012 Jan

Lee KY, Strand SE, Doty SL.Phy

by Populus and Salix. Int J Phy

14(1):48‐61.
Chlorpyrifos Human Health Risk Assessment D424485 

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Not Relevant:Ecological Effects

Li X, Liu L, Zhang Y, Fang Q, Li Y,

Li

Y 2013 Sep

Li X, Liu L, Zhang Y, Fang Q, Li Y

chlorpyrifos on lysozyme activ

complement C3 and IgM, and 

expressions in common carp (

Chemosphere. 2013 Sep; 93(2

Not Relevant:Ecological Effects

Lu P, Li Q, Liu H, Feng Z, Yan X,

Hong Q, Li S 2013 Jan

Lu P, Li Q, Liu H, Feng Z, Yan X

of chlorpyrifos and 3,5,6‐trich

sp. DT‐1. Bioresour Technol. 2

Not Relevant:Ecological Effects

MartÃnez Morcillo S, Yela JL,

Capowiez Y, Mazzia C, Rault M,

Sanchez‐Hernandez JC 2013 May

MartÃnez Morcillo S, Yela JL, C

Sanchez‐Hernandez JC.Avoida

esterase inhibition in the eart

after exposure to chlorpyrifos

22(4):597‐607.

Not Relevant:Ecological Effects

Maryoung LA, Lavado R, Schlenk

D 2014 Jul

Maryoung LA, Lavado R, Schle

acclimation on the acute toxic

chlorpyrifos to salmonids. Aqu

90.

Not Relevant:Ecological Effects

Maya K, Upadhyay SN, Singh RS,

Dubey SK 2012 Dec

Maya K, Upadhyay SN, Singh R

kinetics of chlorpyrifos and 3,5

by fungal communities. Biores

126:216‐23.

Not Relevant:Ecological Effects

Munaretto JS, Ferronato G,

Ribeiro LC, Martins ML, AdaimeMB, Zanella

R 2013 Nov

Munaretto JS, Ferronato G, Ri

MB, Zanella R.Development o

determination of endocrine d

chromatography‐triple quadruspectrometry. Talanta. 2013 N

Not Relevant:Ecological Effects

Narra MR, Begum G, Rajender K,

Rao JV 2012 May

Narra MR, Begum G, Rajender 

organophosphate insecticides 

a food fish and assessment of 

Health. 2012 May; 28(4):343‐5
Chlorpyrifos Human Health Risk Assessment D424485 

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Not Relevant:Ecological Effects

Narra MR, Rajendera K, Rao JV,

Begum

G 2013 Jul‐Aug

Narra MR, Rajendera K, Rao JV

biochemical stress response to 

edible crab Barytelphusa guer

improves the nutritional value

Aug; 68(7‐8):318‐26.

Not Relevant:Ecological Effects

Negro CL, Senkman LE, Marino F,

Lorenzatti E, Collins P 2014 Jun

Negro CL, Senkman LE, Marino 

P.Effects of chlorpyrifos and e

stages of the freshwater burro

collastinensis P.: protective ro

Contam Toxicol. 2014 Jun; 92(

Not Relevant:Ecological Effects Oruc E 2012 May

Oruc E.Oxidative stress respon

the liver of Oreochromis niloti

ethyl. Bull Environ Contam To

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PÃ©rez J, Domingues I,

Monteiro M, Soares AM,

Loureiro S 2013 Jul

Perez J, Domingues I, Monteir

S.Synergistic effects caused by 

on chlorpyrifos toxicity to earl

Danio rerio. Environ Sci Pollut 

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Pal S, Kokushi E, Koyama J, Uno

S, Ghosh AR 2012

Pal S, Kokushi E, Koyama J, Un

AR.Histopathological alteratio

common carp exposed to chlo

2012; 47(3):180‐95.

Not Relevant:Ecological Effects

Palumbo AJ, Tenbrook PL, FojutTL, Faria IR, Tjeerdema RS 2012

Palumbo AJ, Tenbrook PL, Foju

RS.Aquatic life water quality c

method: I. Organophosphate 

Contam Toxicol. 2012; 216:1‐4

Not Relevant:Ecological Effects

Patetsini E, Dimitriadis VK,Kaloyianni

M 2013 Jan

Patetsini E, Dimitriadis VK, Kal

marine mussels, Mytilus gallo

environmentally relevant leve

chlorpyrifos and penoxsulam. 

126:338‐45.
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Phillips BM, Anderson BS, Hunt

JW, Siegler K, Voorhees JP,

Tjeerdema RS, McNeill

K 2012 Jul

Phillips BM, Anderson BS, Hun

Tjeerdema RS, McNeill K.Pyret

pesticide‐associated toxicity in 

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Pinnock Branford MV, de la Cruz

E, Solano K, RamÃrez

O 2014 Jan

Pinnock Branford MV, de la Cr

O.Pesticide exposure on sloth

Choloepus hoffmanni) in an ag

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Poletika NN, Teply M,

Dominguez LG, Cramer SP,

Schocken MJ, Habig C, Kern M,

Ochoa‐AcuÃ±a H, Mitchell GC 2012 Apr

Poletika NN, Teply M, Doming

MJ, Habig C, Kern M, Ochoa‐A

spatially and temporally explic

from a prey base exposed to a

Environ Assess Manag. 2012 A

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Rhee JS, Kim BM, Jeong CB, Park

HG, Leung KM, Lee YM, Lee JS 2013 Nov

Rhee JS, Kim BM, Jeong CB, Pa

JS.Effect of pharmaceuticals e

acetylcholinesterase (AchE) ac

AchE gene in the monogonont 

Comp Biochem Physiol

C Toxic

158(4):216‐24.

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Richendrfer H, Pelkowski SD,Colwill RM, CrÃ©ton

R 2012 Jul

Richendrfer H, Pelkowski SD, C

R.Developmental sub‐chronic reduces anxiety‐related behav

Neurotoxicol Teratol. 2012 Jul

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Rivadeneira PR, Agrelo M, Otero

S, Kristoff

G 2013 Apr

Rivadeneira PR, Agrelo M, Ote

effects of subchronic exposure 

organophosphate insecticide c

gastropod. Ecotoxicol Environ 
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Ruan QL, Ju JJ, Li YH, Li XB, Liu R,

Liang GY, Zhang J, Pu YP, Wang

DY, Yin LH 2012 Jul

Ruan QL, Ju JJ, Li YH, Li XB, Liu 

Wang DY, Yin LH.Chlorpyrifos 

capacity owing to a damaging 

nematode Caenorhabditis ele32(7):527‐35.

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Ruiz de Arcaute C, Salgado Costa

C, Demetrio PM, Natale GS,Ronco AE 2012 Nov

Ruiz de Arcaute C, Salgado Co

Ronco AE.Influence of existing 

sensitivity of Rhinella fernand

tadpoles to LorsbanÂ®48E for

Ecotoxicology. 2012 Nov; 21(8

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San Segundo L, Martini F, Pablos

MV 2013 Sep

San Segundo L, Martini F, Pab

responses for detecting sublet

whole effluents on a Xenopus 

Toxicol Chem. 2013 Sep; 32(9)

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Santos MJ, Ferreira MF, Cachada

A, Duarte AC, Sousa JP 2012 Nov

Santos MJ, Ferreira MF, Cacha

JP.Pesticide application to agr

reproduction and avoidance b

and Eisenia andrei. Ecotoxicol

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Serrano R, PortolÃ©s T, Blanes

MA, HernÃ¡ndez F, Navarro JC,

VarÃ3 I, Amat F 2012 Sep

Serrano R, PortolÃ©s T, Blane

JC, VarÃ3 I, Amat F.Characteriz

contamination pattern of a hy

screening using gas chromato

resolution time‐of‐flight mass Environ. 2012 Sep 1; 433:161‐

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Silva KC, Assis CR, Oliveira VM,

Carvalho LB Jr, Bezerra RS 2013 Jan

Silva KC, Assis CR, Oliveira VM

RS.Kinetic and physicochemica

acetylcholinesterase from the 

and in vitro effect of pesticide

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Sotomayor V, Lascano C, de

D'Angelo AM, Venturino

A 2012 Sep

Sotomayor V, Lascano C, de D

A.Developmental and polyam

Rhinella arenarum embryos ex

organophosphate chlorpyrifos

Sep; 31(9):2052‐8.

Not Relevant:Ecological Effects

Uggini GK, Patel PV,

Balakrishnan S 2012 Mar

Uggini GK, Patel PV, Balakrishn

teratogenic effects of pesticid

comparative study using two c

Environ Toxicol. 2012 Mar; 27

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Ural MÅž.Chlorpyrifos‐induced oxidant/antioxidant status and 

of Cyprinus carpio carpio: ame

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Vera‐Candioti J, Soloneski S,

Larramendy ML 2013 Dec

Vera‐Candioti J, Soloneski S, L

electrophoresis assay in the te

Cnesterodon decemmaculatus 

agrochemical‐induced genoto

2013 Dec; 98:368‐73.

Not Relevant:Ecological Effects

Vernon RS, Van Herk WG,

Clodius M, Harding

C 2013 Apr

Vernon RS, Van Herk WG, Clod

studies on wireworm manage

protection versus wireworm m

Entomol. 2013 Apr; 106(2):78

Not Relevant:Ecological Effects Wan ZJ, Chen ZD, Luan X, Liang P 2012 Feb

Wan ZJ, Chen ZD, Luan X, Lian

and its degradation dynamics 

tuberosum) plant and soil]. Yin

2012 Feb; 23(2):525‐30.

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Wang JH, Zhu LS, Liu W, Wang J,

Xie

H 2012 Apr

Wang JH, Zhu LS, Liu W, Wang 

of earthworm (Eisenia foetida

and fenvalerate. Toxicol Mech 

22(3):236‐41.
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Wang LL, Liu T, Wang C, Zhao

FQ, Zhang ZW, Yao HD, Xing HJ,

Xu SW 2013 Jul

Wang LL, Liu T, Wang C, Zhao 

Xu SW.Effects of atrazine and 

of nitric oxide and expression 

synthase in the brain of comm

Ecotoxicol Environ Saf. 2013 J

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Weston DP, Asbell AM,

Lesmeister SA, Teh SJ, Lydy MJ 2014 Apr

Weston DP, Asbell AM, Lesme

and agricultural pesticide inpu

threatened delta smelt (Hypo

Toxicol Chem. 2014 Apr; 33(4)

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Xing H, Li S, Wang X, Gao X, Xu S,

Wang

X 2013 Jan

Xing H, Li S, Wang X, Gao X, Xu 

and chlorpyrifos on the mRNA 

the liver, brain, kidney and gil

carpio L.). Chemosphere. 2013 

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Xing H, Li S, Wang Z, Gao X, Xu S,

Wang

X 2012 Jul

Xing H, Li S, Wang Z, Gao X, Xu 

changes and antioxidant respo

common carp exposed to atra

Chemosphere. 2012 Jul; 88(4)

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Xing H, Wang X, Sun G, Gao X, Xu

S, Wang

X 2012 Mar

Xing H, Wang X, Sun G, Gao X, 

atrazine and chlorpyrifos on a

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Xing H, Wu H, Sun G, Zhang Z, Xu

S, Li S 2013 Jan

Xing H, Wu H, Sun G, Zhang Z, 

and mRNA expression of acety

kidney and gill of common car

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Yoo DH, Shin EH, Lee DK, Ahn YJ,

Chang KS, Kim HK, Kim SY, Park

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Yoo DH, Shin EH, Lee DK, Ahn 

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Abass K, Pelkonen O.The inhib

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Crane AL, Klein K, Olson JR.Bio

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Crane AL, Klein K, Zanger UM, 

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Crow JA, Bittles V, Herring KL, 

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paraoxon and methyl paraoxo

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Han XL, Tian FF, Ge YS, Jiang FL,

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Leoni C, Balduzzi M, Buratti FM,

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Leoni C, Balduzzi M, Buratti FM

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physiologically‐based pharma

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support an AOP

Naseh M, Vatanparast J,

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Naseh M, Vatanparast J, Bania

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Wang L, Ohishi T, Akane H,

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Wang L, Ohishi T, Akane H, Sh

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Slotkin TA, Card J, Seidler FJ.Cneurotoxicity: interaction with Neurotoxicol Teratol. 2012 Se

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Li AA, Lowe KA, McIntosh LJ,

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Li AA, Lowe KA, McIntosh LJ, M

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Mascarelli A.Growing up with 

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