Review Of Worker Exposure 

Assessment Methods

Presented Jointly To The FIFRA Scientific Advisory Panel By:

U.S. Environmental Protection Agency 

Office Of Pesticide Programs 

Health Effects Division 

Health Canada 

Pest Management Regulatory Agency 

California Environmental Protection Agency 

California Department of Pesticide Regulation 

Worker Health and Safety Branch 

Presented On:

January 9 - 12, 2007 

Page 1 of 114 
Table of Contents 

1. Introduction ................................................................................................................ 4 

2. Background Information ........................................................................................... 9 

2.1 Historical Perspective......................................................................................... 9

2.2 Exposure Assessment Approach .....................................................................14

2.2.1 Handler Exposure Scenarios................................................................... 14 

2.1.2 Algorithms Used For Risk Calculations................................................. 19 

2.3 Occupational Handler Unit Exposure Case Study ........................................ 22

3.1. Relative Predictive Capability of Passive Dosimetry and Biological 

Monitoring..................................................................................................................... 36

3.1.1. Passive Dosimetry..................................................................................... 38 

3.1.2 Biological Monitoring............................................................................... 41 

3.1.3 Comparison of Passive Dosimetry and Biomonitoring Techniques.... 43 

3.2 Systematic Evaluation of Hand Sampling Methods ...................................... 61

3.2.1: Mechanistic Hand Methods Analysis...................................................... 62 

3.2.2: Hand Method Field Performance Data .................................................. 76 

3.3: Summary of Methods Analysis ........................................................................... 81

4. Unit Exposure & Applicability Across Anticipated Working Conditions.......... 83 

4.1 Proportionality between Exposure and Amount Active Ingredient Handled

 83

4.1.1 Proportionality Investigation using Dermal Exposure ......................... 84 

4.1.2 Constructing Proportionality Plots......................................................... 85 

4.1.3 Results........................................................................................................ 86 

4.1.4 Conclusion and Discussion....................................................................... 98 

5. Scope of Research Plan .......................................................................................... 100 

5.1 Within-worker and Between-worker Variability ........................................ 104

6. Bibliography............................................................................................................ 106 

Page 2 of 114 
Exhibit A: PHED Case Study Spreadsheet 

Exhibit B: PHED Hand Exposure Methods Analysis Case Study Spreadsheets 

Exhibit C: Proportionality Data Analysis Spreadsheets 

Page 3 of 114 
1. Introduction 

 In this meeting of the Scientific Advisory Panel (SAP) the Agency is seeking review and 

comments on its evaluation of currently available exposure data and on proposed exposure 

measurement techniques to collect data that will be used in the future to assess the exposure to 

those involved in pesticide application activities. The data under consideration include the data 

available in the Pesticide Handlers Exposure Database (PHED). PHED is a database consisting 

of dermal and inhalation exposure measurements from a wide variety of scenario specific 

pesticide handler exposure studies (e.g., mixing and loading liquid formulations of pesticides into 

tractor mounted spray tanks). While this database has been a valuable tool used by North 

American regulatory agencies since the early 1990s, it is not without limitations. Therefore, over 

the years there has been an increased desire by various stakeholders to replace it. In 2001, a 

group of pesticide industry companies formed the Agricultural Handlers Exposure Task Force 

(AHETF) to develop a new database; the Agricultural Handlers Exposure Database (AHED). 

In June 2006, the AHETF submitted 5 protocols for field trials for an ethics review by the 

Human Subjects Review Board (HSRB) pursuant to 40 CFR 26, subpart K. The protocols 

described 5 field trials that were part of their 2006 data collection efforts. These trials are part of 

a multi-year effort to develop data to populate AHED. In their review of the five protocols, the 

HSRB identified a number of issues including dermal exposure collection methodologies 

proposed by the AHETF and a lack of information on how an adequate sample size was derived. 

In addition, the HSRB asserted that the Agency had not demonstrated the need for these data in 

light of the current PHED database and consequently questioned whether additional testing 

involving intentional exposure to workers was justified. The HSRB report from that meeting is 

available at (http://www.epa.gov/osa/hsrb/files/june2006mtgreportfinal100606.pdf). Since the 

HSRB identified key scientific issues impacting many data generating efforts outside of the 

AHETF, such as the Antimicrobial Task Force (AEATF) which is planning to update exposure 

data to individuals involved in the handling of antimicrobial pesticides, the Agency believes the 

timing is appropriate for a scientific review of the methods and data analysis approaches that are 

to be used for the next generation of exposure data. 

 

 The broad purposes of this SAP review are:

1) To obtain comments on the adequacy of existing data (the PHED dataset) to support more 

refined worker exposure assessments (there is a case study of six example scenarios presented in 

the background document);

2) To obtain advice on how best to ensure that when new data are generated (whether for AHED 

or other efforts), such data are collected in manner that produces accurate information, to the 

extent reasonably possible, without, resulting in systematic underestimation of exposure; and 

3) To obtain comment on the AHETF and AEATF documents: Agricultural Handlers Exposure 

Task Force (AHETF) Technical Summary Document For a Multi-Year Pesticide Handler Worker 

Exposure Monitoring Program (AHETFa, 2006), Procedures for Determining the Required 

Number of Clusters and Monitoring Units per Cluster to Achieve Benchmark Adequacy 

(AHETFb, 2006) and American Chemistry Council-Antimicrobials Exposure Assessment Task Force II 

(AEATF II) Background and Scoping Summary (ACCa, 2006). To obtain comment on the AHETF 

Page 4 of 114 
document: Procedures for Determining the Required Number of Clusters and Monitoring Units 

per Cluster to Achieve Benchmark Adequacy. 

 The Agency has collaborated very closely with the Pest Management Regulatory Agency 

of Health Canada (PMRA) as well as the Worker Health and Safety Branch of the California 

Department of Pesticide Regulation (DPR) in preparing for this meeting and, in general, over 

several years regarding occupational exposure assessment issues. This meeting represents a joint 

effort by all three agencies. In addition, these agencies have worked closely with the regulated 

industry (mainly through task forces) to ensure that the study designs and methodologies 

collected comprehensive, reliable data on exposures to workers. 

The current state of the science as well as the development of more refined analytical tools 

necessitates the need to obtain more refined data with which to conduct occupational exposure 

assessments. Recognizing this and the Agency’s desire to conduct more refined occupational 

handler exposure assessments, the affected industry has also pursued development of monitoring 

data that could be used for these purposes (Stasikowski, 2001). This industry effort essentially 

mirrors the development of the Pesticide Handlers Exposure Database (PHED) in which the 

industry collaborated with the Agency as well as PMRA, DPR and various European entities 

(e.g., Pesticide Safety Directorate in the UK). The ongoing development and implementation of 

the research by AHETF and AEATF have been closely monitored by the Agency, PMRA and 

DPR. 

In 2006, the Agency established the Human Studies Review Board (herein referred to as 

the HSRB) that is charged with the evaluation of studies that involve intentional exposure of 

human subjects, from both a scientific and ethical perspective. HSRB review of new data is 

required prior to the Agency relying on the information in its regulatory decisions under current 

pesticide laws. More information regarding the HSRB is available at the following website 

(http://www.epa.gov/OSA/hsrb/index.htm ). The AHETF has conducted a number of worker 

exposure field studies to date and had proposed several more for the 2006 growing season and 

beyond. The protocols for the studies to be conducted in 2006 were reviewed in June by the 

HSRB. Some of the major issues identified by the HSRB included: 

• Study design issues 

o Limitations and utility of existing data need clarification; 

o Use of new data needs clarification (e.g., as a replacement or combined with 

existing data); 

o Statistical considerations of the research plan for conducting new studies needs 

further development; 

o Sample size issues related to the utility of the data need explored/clarified; 

o Baseline biomedical and/or biological monitoring data need to be considered. 

• Methodological issues 

o Systematic bias in the sampling methods should be clarified; 

o Hand sampling methods (gloves or washes) need examination; 

o Dermal sampling methods (swiping, patch, or whole-body dosimeter) need 

examination; 

o Dosimeter performance under field conditions needs examination (e.g., losses, 

breakthrough, etc.). 

Page 5 of 114 
The HSRB established criteria that it will use as the basis for the scientific evaluation of data. 

The Agency has attempted to address these scientific review criteria in this document in order to 

ensure that the SAP review of the occupational exposure assessment methods can also be used in 

any upcoming meetings of the HSRB. Because the issues to be considered in this meeting are 

interconnected to and associated with the research plans of the AHETF and AEATF, many 

analyses completed by these groups will be referenced in this document along with a discussion 

of the Agency’s consideration of those analyses. 

In addition to the AHETF which was formed to generate exposure data for conventional 

agricultural pesticides, a task force (the AEATF) was also formed to generate exposure data for 

antimicrobial pesticides. Many of the same issues and concerns regarding the methodologies and 

data generation that the HSRB raised in its review of the AHETF protocols also are relevant for 

the AEATF. The Agency believes the timing is appropriate for a scientific review of the methods 

and data analysis approaches that are to be used for the next generation of exposure data. 

Specifically, the exigencies giving rise to this effort include: 

• the Agency’s desire to develop a broader and more current database of 

occupational exposure data for completing risk assessments, formation of the 

AHETF and AEATF, and the scientific issues and concerns expressed by the 

HSRB in its review of the proposed 2007 AHETF protocols; 

• the Agency’s desire to incorporate more sophisticated data analysis tools such as 

distributional and probabilistic treatments into occupational exposure assessments 

for both agricultural and antimicrobial pesticides; 

• to address the HSRB’s questions pertaining to the need for additional data which 

would require developing and implementing an approach for examining the 

currently available data and then providing a justification for collecting additional 

information in a robust manner to estimate occupational exposures; 

• the completion of the Food Quality Protection Act analysis of all food tolerance 

chemicals and an Agency emphasis on refining its tools (e.g., exposure assessment 

guidelines such as Series 875, Group B described below) for analyses related to 

occupational handler exposure assessment that are to be completed under PRIA 

(Pesticide Registration Improvement Act) registration and reregistration review; 

• the development of updated requirements for occupational handler exposure 

monitoring data as illustrated in proposed updates to 40CFR158 which include 

codified requirements for occupational handler exposure data; and 

• additional programmatic emphasis on worker exposure issues related to the results 

of studies such as the Agricultural Health Study and surveillance monitoring 

programs such as that conducted by the Washington state government. 

 The purpose of this document is to provide general background information for the SAP 

pertaining to how the Agency completes exposure assessments for occupationally exposed 

agricultural or antimicrobial pesticide handlers and to present the scientific issues to be 

Page 6 of 114 
considered in that context. The issues which have been addressed in this document are described 

in the following sections: 

• Section 2: Background Information - An overview of the data and 

methodologies typically used by the Agency for completing occupational handler 

risk assessments is contained in this section to provide context for the deliberations 

of the panel. Information includes a history of critical events that pertain to 

occupational handler exposure assessment by the Agency, and identification of the 

types of exposure scenarios considered by the Agency and the calculations used. 

Finally, this section contains a case study that presents the data and detailed 

methods used in PHED to calculate exposures for 6 common occupational 

pesticide handler tasks. [Note: Information pertaining to the data used to assess 

antimicrobial chemicals has also been included in this section. However, the 

detailed case study and follow-up analyses have been developed using only the 

agriculturally-based data.] The case study has been used as the basis for the 

analyses presented below in order to illustrate the approaches used by the Agency 

to examine the need for additional data and the potential refinements that such data 

could support. 

• Section 3: Monitoring Methods - The performance of current recommended 

passive dosimetry exposure monitoring methods will be examined with a focus on 

the potential for inherent bias compared to biological monitoring estimates. The 

possibility of bias associated with hand monitoring techniques will also be 

examined since hands tend to be a large contributor during many activities. Other 

issues will also be addressed including the possibility for breakthrough of residues 

in dosimeters and dermal absorption when using removal methods such as hand 

washing. The information presented herein will also provide additional context to 

the analyses presented below. 

• Section 4: Unit Exposure & Applicability Across Anticipated Working 

Conditions – Unit exposures, as currently used by the Agency, represent exposure 

normalized by the amount of active ingredient handled (e.g., mg/lb ai) that is based 

on the assumption that the two variables are proportional (i.e., if one doubles the 

amount of pesticide handled or applied, the resultant exposure will be doubled as 

well). The case study data has been analyzed to evaluate the extent to that the 

underlying default assumption of proportionality between exposure and amount of 

active ingredient handled for the Agency’s unit exposure estimates is demonstrated 

and applicable across anticipated working conditions within our current database. 

Other possible normalization factors and the applicability of this approach for 

other possible use practices are discussed to the extent possible. The limitations of 

the existing data, as they pertain to this analysis, have been described as well as 

how additional data could be used to refine the Agency's understanding of the 

normalization issue.

Page 7 of 114 
• Section 5: Scope of Research Plan – The intent of this section is to 

describe/summarize the basic research plans that have been developed by both the 

AHETF and AEATF, as well as to provide the Agency's thoughts on the nature of 

these plans. Additionally, the methods that have been used by these groups to 

define the scope of their approaches (e.g., numbers of measurements) will be 

addressed. The size of the exposed population is an integral part of these analyses, 

especially related to the number of required datapoints. The Agency has provided 

background information of this issue as well in this section in order to provide a 

context for the analysis on the AHETF and AEATF proposals. 

In addition, Section 6: Bibliography provides the citations used in the development of this 

document. 

 

Page 8 of 114 
2. Background Information

 This section provides the general background information on how the Agency currently 

conducts its occupational exposure assessments that will be helpful to the SAP as it considers the 

issues raised by the charge questions. The processes that lead to the development of current 

Agency guidance on exposure monitoring have been described below in Section 2.1: Historical 

Perspective. Additionally, Section 2.2: Exposure Assessment Approach provides a brief 

overview of how exposures are calculated in order to provide context for the deliberations of the 

panel. In order to illustrate the concepts associated with the data currently used in typical Agency 

exposure assessments and the issues and limitations those data, a case study has been developed 

that is presented in Section 2.3: Occupational Handler Unit Exposure Case Study. 

The Agency has been actively involved over a number of years in the development and 

upgrading of testing guidelines which inform the development of the current protocols used for 

exposure monitoring purposes. Sound risk assessment practice dictates that appropriate scientific 

developments be integrated into the Agency's risk assessment process to ensure that the best 

available, sound science forms the basis of its regulatory decisions. Additionally, a case study has 

been developed that describes the available information which the Agency currently uses to 

develop its assessments and to illustrate why the Agency would like additional data. The 

considerations regarding how that data might be used in the regulatory process is also presented. 

In practical terms, this will allow the Agency to refine its standard exposure and risk assessment 

methods for use in the future. 

2.1 Historical Perspective 

 Throughout the last 25 or so years, the Agency has been very actively engaged in 

attempting to refine the methodologies for completing occupational exposure and risk 

assessments. There have been many milestones but some of the more critical events are identified 

below: 

• The 1974 President’s Council On Environmental Quality (CEQ) Task Group This 

group focused on occupational exposure to pesticides and identified a need to evaluate 

exposures from plant surfaces with a focus on heavy contact field operations (e.g., 

picking, thinning) in order to establish restricted entry intervals. Key recommendations 

included requiring monitoring data, evaluating geographical and other factors which 

influence exposures, establishment of a surveillance system, and to develop lower risk 

alternatives. The general guidance from this working group stimulated Agency activity in 

the area of occupational exposure assessment over the next decade or so, which lead to the 

development of the Agency’s first guidelines related to the assessment of occupational 

exposures. 

Page 9 of 114 
• 1984 Pesticide Assessment Guidelines, Subdivision K – Exposure: Reentry Protection

[NTIS Document Number PB85-120962 (October, 1984).] Much of the seminal research 

related to exposure monitoring was conducted between 1974 and 1984. The Agency 

synthesized this information (a summary of key studies is included in preamble) and 

established the testing guidelines for monitoring post-application worker exposures based 

on this information (USEPA, 1984).

• 1986 Pesticide Assessment Guidelines, Subdivision U – Applicator Exposure 

Monitoring [NTIS Document Number PB87-133286 (October, 1986).] When first 

engaged in developing occupational testing guidelines the Agency focused on postapplication worker exposure issues and after completion of Subdivision K, completed 

similar guidelines for occupational handlers of pesticides (USEPA, 1986b). 

• 1986 FIFRA Science Advisory Panel (SAP) Review Of Subdivision U This effort 

represents the first SAP review of the Agency’s exposure monitoring guidelines. Key 

recommendations from this review that are germane to the current SAP evaluation 

include: the use of concurrent dosimetry and biological monitoring should be encouraged; 

the use of either handwashes or lightweight gloves was recommended for monitoring hand 

exposures with some chemicals needing special consideration because of rapid absorption, 

persistence in the skin, etc.; and the utility of exposure monitoring data in a generic sense 

was agreed upon although factors that affect bioavailability should be considered when the 

approach is used (USEPA, 1986a) 

• 1989 Good Laboratory Practices (40CFR160) This document provides generalized 

guidance related to ensuring the quality control and quality assurance elements of data 

generated for pesticide registration purposes. Key elements related to exposure 

monitoring include recordkeeping, field calibration, laboratory quality control, and sample 

integrity (USEPA, 1989). 

• 1992 Pesticide Handlers Exposure Database (PHED) This database assembled the 

available data for pesticide handlers into a system that is still used today to generically 

address the exposures of that population (Hackathorn and Eberhart, 1985). This system 

established many precedents related to the use of data in a generic manner (e.g., correction 

for quality control, data grouping based on exposures, and monitoring refinements). 

Many of the studies conducted by the pesticide registrants were generated during the 1986 

through 1992 timeframe which necessitated that Agency resources be devoted to protocol 

development and data review activities. PHED is an important tool because it allowed the 

Agency to begin to compile data for similar scenarios and to begin to elucidate trends in 

the data as well as limitations. The case study to be presented in this document is based 

on PHED data. [Note: The case study data are provided in Exhibit A. Also see Reference 

Manual and Surrogate Guide below – USEPA 1995a & USEPA, 1998b, respectively.] 

• 1992 Chemical Manufacturers Association (CMA) Exposure Study On 4 March 1987, 

a Data Call-In Notice was issued for submission of data for antimicrobial pesticide active 

ingredients. In response, the Chemical Manufacturers Association (CMA) developed a 

Page 10 of 114 
generic biocide exposure assessment study, Chemical Manufacturers Association 

Antimicrobial Exposure Assessment Study. In total, 88 separate samples were obtained for 

six end-use settings and nine application methods to assess both dermal and inhalation 

exposures. Based on the considerable limitations of this study, industry has formed the 

AEATF to replace these data. 

• 1993 Pesticide Rejection Rate Analysis: Occupational and Residential Exposure: 

[EPA Document 738-R-93-008, September, 1993] In many scientific disciplines, there 

was a high rejection rate among studies submitted to the Agency for pesticide registration 

purposes. Therefore, in 1991 the Agency decided to evaluate the factors that most 

frequently caused studies to be rejected in order to assess the adequacy of EPA’s guidance 

documents and to determine if rejection issues were avoidable. The key factors related to 

worker exposure monitoring were identified as inadequate quality assurance/quality 

control and inadequate sampling for residue decay studies (USEPA, 1993).

• 1994 & 1997 Workshops On Revisions To Agency Guidelines For Exposure 

Assessment: After the release of the rejection rate analysis for exposure data and the 

experience gained during the development and review of many studies, it was clear that 

the current Agency guidelines for worker exposure required updating and that additional 

data were required to address various exposure issues. As such, the Agency along with 

other regulatory bodies (i.e., Health Canada, California Department Of Pesticide 

Regulation, and the Organization For Economic Cooperation and Development) 

sponsored workshops to determine how the current guidelines could be refined. Both 

whole-body dosimetry and various hand exposure monitoring methods were considered as 

appropriate at those meetings (PMRA, 1994 & USEPA, 1997).

• 1995 Pesticide Handlers Exposure Database (PHED) V1.1 The second release of 

PHED occurred in February 1995. PHED V1.1 is the current version in use today. 

Updates to the original release from 1992 included additional field exposure studies, 

additional application method codes, and additional studies with whole body dosimeters 

(USEPA 1995a). 

• 1995 Data Call-In For Post-Application Worker Exposure Data: This action by the 

Agency required pesticide registrants with the potential for post-application worker 

exposure associated with it (e.g., pickers, thinners) to generate data which could be used to 

quantify these exposures. This effort initiated the current model for developing large 

datasets of non-dietary exposure data in a systematic manner (USEPA, 1995b). [Note: 

The results of this group are undergoing Agency review and revised policies related to its 

efforts will be developed thereafter. More information can be found about this group at 

the following website (http://www.exposuretf.com/artf/index.htm).] 

• 1997 Organization for Economic Co-operation and Development (OECD) Guidance 

Document for the Conduct of Studies of Occupational Exposure to Pesticides During 

Agricultural Application The origin of this document was conceived at the International 

Workshop on Risk Assessment for Worker Exposure to Agricultural Pesticides held in the 

Hague, The Netherlands, in 1992, and was the focus of the Workshop on Methods of 

Pesticide Exposure Assessment held in Ottawa, Canada, in 1993. The OECD document 

Page 11 of 114 
was developed by Health Canada and the North Atlantic Treaty Organization (NATO) and 

supported by USEPA. This document was developed to review and standardize the 

available exposure monitoring methodologies. The document also provides a detailed 

outline of the tier approach to pesticide risk assessments (OECD 1997). 

• 1998 Draft Series 875 – Occupational and Residential Exposure Test Guidelines, 

Group B – Postapplication Exposure Monitoring Test Guidelines: This draft 

guideline document reflected the state of the science for conducting exposure monitoring 

studies at that time. This document also describes the uncertainties associated with 

monitoring approaches as well as issues that should be considered by investigators related 

to logistics and data quality (USEPA, 1998a). These guidelines were taken to the SAP in 

1998 and are available at http://www.epa.gov/scipoly/sap/1998/march/contents.htm . 

[Note: This document will also undergo final review in the next year or so and will 

incorporate many of the results of this SAP meeting since the charge for this meeting 

addresses generally applicable sampling methods issues and study/research plan design 

issues.] 

Page 12 of 114 
• 2001 Formation of Agricultural Handlers Exposure Task Force (AHETF): The 

AHETF was formed to address industry concerns over the nature of the data being used in 

Agency assessments for occupational handlers of pesticides. [Note: See the following 

website for more information: http://www.exposuretf.com/ahetf/index.htm .] The Agency 

expects this effort will improve the breadth, depth, and quality of the data currently in use 

for these purposes. Therefore; the AHETF will be an integral part of the discussions at the 

upcoming SAP meeting. The preparatory materials for SAP review related to each of the 

charge questions have referenced AHETF produced materials as appropriate. Agency 

responses to the content of these materials have also been provided as appropriate. 

• 2003 Formation of Antimicrobial Exposure Assessment Task Force (AEATF): The 

AEATF was formed to address industry concerns regarding the data being used in Agency 

assessments for occupational handlers of antimicrobial products. The Agency expects this 

effort will improve the breadth, depth, and quality of the data currently in use for these 

purposes. The AEATF will be an integral part of the discussions at the upcoming SAP 

meeting. The preparatory materials for SAP review related to each of the charge 

questions have referenced AEATF produced materials as appropriate (ACCa, 2006 & 

ACCb, 2006). Agency responses to the content of these materials have also been provided 

as appropriate. [Note: The case study included in this background document and much of 

the discussions are focused on the AHETF since it is further along with the development 

of protocols and a research plan than AEATF. However, many of the issues to be 

discussed are also applicable to the goals of AEATF. Specific references to AEATF are 

included in this document to delineate differences as appropriate. Additionally, separate 

presentation materials during the meeting will also be developed related to AEATF as 

appropriate to address the scope of the effort and any substantive differences with 

AHETF.] 

The above information has attempted to summarize much of the level of effort by the 

Agency related to worker exposure and risk assessment over the last 25 years in order to provide 

a level of background knowledge for the panel discussions. Much of this effort has been focused 

on two areas including (1) conducting exposure and risk assessments under the timelines 

specified by the reregistration process and the tolerance reassessment timelines in the Food 

Quality Protection Act and (2) coordination with Health Canada and the California Department of 

Pesticide Regulation related to the data development efforts of the ARTF, AHETF, and AEATF. 

At this point in time, many of these efforts are either nearing completion (e.g., ARTF) or are 

formative (e.g., AHETF) and the Agency desires to move forward by again evaluating the current 

state of the science with regard to exposure monitoring methodologies. Since the AHETF and 

AEATF are in their formative stages of development of its research goals and objectives the 

Agency believes that the timing of this SAP review is appropriate. 

Page 13 of 114 
2.2 Exposure Assessment Approach 

 The Agency uses the term “Handlers” to describe those individuals who are involved in 

the pesticide application process. The Agency believes that there are distinct job functions or 

tasks related to applications and that exposures can vary depending on the specifics of each task. 

Job requirements (e.g., amount of chemical to be used in an application), the kinds of equipment 

used, the crop or target being treated, and the circumstances of the user (e.g., the level of 

protection used by a handler) can cause exposure levels to differ in a manner specific to each 

application event. The scenarios that serve as the basis for the risk assessment are described in 

Section 2.2.1: Handler Exposure Scenarios. Section 2.2.2: Algorithms Used For Risk 

Calculations describes the actual mathematical procedures used to calculate exposures for 

toxicological endpoints that are based on both non-cancer and cancer effects. 

2.2.1 Handler Exposure Scenarios

 Exposure scenarios have been developed to categorize and group the kinds of exposures 

that occur related to the use of a chemical. The use of scenarios in exposure assessment is very 

common as described in the U.S. EPA Guidelines For Exposure Assessment (U.S. EPA; FR Vol. 

57, No. 104; May 29, 1992; http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=15263 ). 

The purpose of this section is to describe the exposure scenarios that are typically used by the 

Agency in occupational handler exposure assessments and to explain how the scenarios were 

defined. 

 The first step in the handler exposure assessment process is to identify the kinds of 

workers that are likely to be exposed during the application process. In order to do this in a 

consistent manner, the Agency has developed a series of general descriptions for tasks that are 

associated with pesticide applications. Common tasks (as an example) can include: preparation 

of dilute, water-based spray solutions for application; transferring or loading dilute spray 

solutions into sprayers for application; and making applications with specific types of equipment 

such as a groundboom or airblast sprayer. Tasks associated with occupational pesticide use (i.e., 

for “handlers”) can generally be categorized using one of the following terms: 

• Occupational Mixer/loaders: these individuals perform tasks in preparation for 

an application using concentrated end-use products. For example, they would 

prepare dilute spray solutions and/or load/transfer solid materials (e.g., granulars) 

or dilute spray solutions into application equipment such as a groundboom tractor 

or planter prior to application. 

• Occupational Applicators: these individuals operate application equipment 

during the release of a pesticide product into the environment. These individuals 

can make applications using equipment such as groundboom sprayers with dilute 

sprays or tractor-drawn spreaders for concentrated granular materials. 

Page 14 of 114 
• Occupational Mixer/loader/applicators: these individuals are involved in the 

entire pesticide application process (i.e., they do all job functions related to a 

pesticide application event). These individuals would use concentrate materials to 

prepare a dilute spray solution and then also apply the solution or use concentrate 

solids. The Agency always considers some exposures to be 

mixer/loader/applicator exposures because of the equipment used and the logistics 

associated with such applications. For example, if one uses a small handheld 

device such as a 1 gallon low pressure handwand sprayer it is anticipated that one 

individual will mix a spray solution and then apply the solution because of labor 

and logistical considerations. 

• Occupational Flaggers: these individuals guide aerial applicators during the 

release of a pesticide product onto an intended target. Most flaggers are primarily 

exposed to dilute sprays. 

Next, assessors must understand how exposures occur (i.e., frequency and duration) and 

how the patterns of these occurrences can cause result in different toxicological effects. 

Wherever possible, use and usage data determine the appropriateness of certain types of risk 

assessments (e.g., a chronic risk assessment for occupational handlers is generally not warranted 

for many pesticide uses because chronic duration exposure patterns do not generally occur due to 

the seasonal uses of most pesticides). Other parameters are also determined from use and usage 

data such as application rates and application frequency. The Agency always conducts risk 

assessments using maximum application rates for each scenario because what is possible under 

the label (the legal means of controlling pesticide use) must be evaluated, for complete 

stewardship, in order to ensure there are no concerns for each specific use. Additionally, 

whenever the Agency has additional information such as typical application rates for some crops, 

it uses the information to evaluate the overall risks associated with the use of the chemical in 

order to allow for a more informed risk management decision. 

A chemical can produce different toxic effects based on the duration of a person's 

exposure, how frequently exposures occur, and the intensity of exposure. It is likely that 

exposures for most chemicals can occur in a variety of patterns. The Agency believes that 

occupational exposures for most chemicals can occur over a single day or up to weeks at a time 

even though each crop or application target is generally treated only a few times per season. 

Intermittent exposures over several weeks are also anticipated. Some applicators may apply 

pesticides over a period of weeks because they need to cover large acreages. They may be 

custom or professional applicators who are completing a number of applications within a region, 

or they may be applying over a period of several days. In order to better describe different 

exposure patterns, a series of generalized time-based categories have been developed. For 

example, the Agency classifies exposures up to 30 days as short-term and exposures greater than 

30 days up to several months as intermediate-term. The Agency completes both short- and 

intermediate-term assessments for occupational scenarios in essentially all cases because these 

kinds of exposures are likely and acceptable use and usage data are not available to justify 

omitting intermediate-term scenarios. 

Page 15 of 114 
The toxicity of chemicals can also vary based on the route of exposure or how a chemical 

enters the body. For example, exposures via the skin can result in a different toxic effect and/or 

severity of reaction than exposures via inhalation. The effects of a chemical can also vary for 

different durations of exposure. The toxicology database for many chemicals indicates that the 

Agency consider exposures to the skin combined with exposures via inhalation because the 

effects which occur are the same regardless of whether it is deposited on the skin or it is inhaled 

(e.g., cholinesterase inhibition is an example of a common effect where this type of analysis is 

completed). 

EPA's Occupational handler exposure assessments also reflect that a worker may use 

different levels of personal protection. The Agency typically evaluates all exposures with a tiered 

approach. The lowest tier is represented by the baseline exposure scenario followed by increasing 

levels of personal protective equipment (PPE) such as gloves, extra clothing, and respirators or 

the use of engineering controls (e.g., closed cabs and closed loading systems). The Agency 

conducts assessments of exposure with differing levels of PPE in order to be able to define label 

language using a risk-based approach and not based on generic requirements for label language as 

outlined in the Agency’s Worker Protection Standard (40CFR170). [Note: All AHETF protocols 

at this point require single layer clothing, chemical-resistant gloves, and no respirator. This 

represents the current labels for the chemicals which have been selected for use by AHETF.] In 

addition, the minimal level of adequate protection for a chemical is generally considered by the 

Agency to be the most practical option for risk reduction (i.e., over-burdensome risk mitigation 

measures are not considered a practical alternative). The levels of protection described below 

form the basis for calculations completed in most Agency assessments and they include: 

• Baseline: Represents typical work clothing or a long-sleeved shirt and long pants 

with no respiratory protection. No chemical-resistant gloves are included in this 

scenario. 

• Minimum Personal Protective Equipment (PPE): Represents the baseline 

scenario with the use of chemical-resistant gloves and a dust/mist respirator with a 

protection factor of 5 which is commonly used for exposure assessment purposes 

in different settings (NIOSH, 2004). 

• Maximum Personal Protective Equipment (PPE): Represents the baseline 

scenario with the use of an additional layer of clothing (e.g., a pair of coveralls), 

chemical-resistant gloves, and an air purifying respirator with a protection factor of 

10 which is commonly used for exposure assessment purposes in different settings 

(NIOSH, 2004). 

• Engineering Controls: Represents the use of an appropriate engineering control 

such as a closed tractor cab or closed loading system for granulars or liquids. 

Engineering controls are generally not applicable to handheld application methods 

which have no known devices that can be used to routinely lower the exposures for 

these methods. 

Page 16 of 114 
It has been determined that exposure to pesticide handlers is likely during the occupational 

use of various pesticides. These uses can occur in a variety of environments including 

agriculture, commercial/industrial premises, and in residential environments. After examining the 

anticipated use patterns and current labeling for many pesticides, EPA identified 37 major 

agricultural exposure scenarios for workers. These scenarios reflect different activities and the 

types of equipment and techniques used in agriculture. Quantitative exposure assessments are 

typically developed for occupational handlers based on these scenarios but not limited by them 

(e.g., seed treatment is addressed using a different source of data but with a similar analytical 

approach). [Note: The scenario numbers correspond to the PHED surrogate exposure guide that 

is used by the Agency. The results and values which are applicable to these scenarios are 

summarized in the PHED Surrogate Exposure Guide (USEPA, 1998b). It should also be noted 

there are exposure estimates for differing levels of PPE associated with each scenario.] The 

scenarios in this guide include: 

Mixing/Loading

(1) Dry Flowable mixing & loading; 

(2) Granular mixing & loading; 

(3) Liquids mixing & loading; 

(4) Wettable Powder; 

(5) Wettable Powder (or any formulation) in water soluble packets; and 

(6) Liquids in closed loading systems. 

Applicator:

(7) Aerial fixed wing aircraft liquid applications; 

(8) Aerial fixed wing aircraft granular applications; 

(9) Helicopter closed cockpit applications; 

(10) Aerosol can applications; 

(11) Airblast open cab tractor applications; 

(12) Airblast closed cab tractor applications; 

(13) Groundboom open cab applications; 

(14) Groundboom closed cab applications; 

(15) Solid broadcast spreader open cab applications for agricultural settings; 

(16) Solid broadcast spreader closed cab applications for agricultural settings; 

(17) Granular bait dispersed by hand; 

(18) Low pressure handwand applications; 

(19) High pressure handwand applications; 

(20) Backpack applications; 

(21) Handgun (i.e., turfgun) applications; 

(22) Paintbrush applications; 

(23) Airless sprayer applications; and 

(24) Rights-of-way sprayer application. 

Flaggers:

(25) Flagging for liquid spray applications; and 

(26) Flagging for granular applications. 

Page 17 of 114 
Mixer/Loader/Applicators:

(27) Wettable powder/liquid open mixing/loading with open cab tractor applications; 

(28) Groundboom liquid open mixing/loading with open cab tractor applications; 

(29) Groundboom liquid open mixing/loading with closed cab tractor applications; 

(30) Belly grinder applications; 

(31) Push-type granular spreader applications; 

(32) Liquid open mixing/loading low pressure handwand applications; 

(33) Wettable powder open mixing/loading low pressure handwand applications; 

(34) Liquid open mixing/loading backpack sprayer applications; 

(35) Liquid open mixing/loading high pressure handwand applications; 

(36) Liquid open mixing/loading garden hose-end sprayer applications; and 

(37) Liquid open mixing/loading termiticide injection applications. 

The scenarios from the PHED Surrogate Exposure Guide presented above represent those 

commonly used in Agency assessments. The case study the Agency is using in its analyses is a 

subset of these scenarios as described below. The same methods could be applied to each 

scenario to examine the entire database if so desired. The AHETF research plan will address 

some, but not all, of these scenarios for a variety of reasons which are discussed in Section 6 

below as well as the research plan itself. 

 The above are scenarios mostly for conventional agricultural pesticides. Similar types of 

scenarios would apply to the use of antimicrobial chemicals. The antimicrobial scenarios used by 

the Agency include data from both PHED and the CMA study (i.e., CMA = Chemical 

Manufacturers Association). The common handler exposure scenarios used in antimicrobial 

assessments have been organized in a slightly different manner by Use Site Categories and 

Application Methods. These are presented below: 

 

(1) Agricultural Premises and Equipment (pump, pour liquid, aerosol spray, spray, mop, 

wipe, fog, soak/immerse); 

(2) Food Handling/Storage Establishments Premises and Equipment (pump, pour liquid, 

aerosol spray, spray, mop, wipe, fog, soak/immerse); 

(3) Commercial, Institutional & Industrial Premises and Equipment (pump, pour liquid, 

aerosol spray, spray, mop, wipe, fog, soak/immerse); 

(4) Residential and Public Access Premises (pump, pour liquid, aerosol spray, spray, mop, 

wipe, fog, soak/immerse); 

(5) Medical Premises and Equipment (aerosol spray, spray, mop, wipe, fog, 

soak/immerse); 

(6) Human Drinking Water Systems (pump); 

(7) Industrial Process Water Systems (pump, pour liquid); 

(8) Material Preservatives (pump, pour liquid, pour solid, place solid, spray, 

soak/immersion, airless spray, brush/roll); 

(9) Antifoulant Coatings (airless spray, brush/roll); 

(10) Wood Preservatives (pressure treatment, soak/immersion, brush/roll, spray); 

(11) Swimming Pools (pump, pour liquid, pour solid, place solid); and 

(12) Aquatic Areas (pump, pour liquid, pour solid, place solid). 

Page 18 of 114 
2.1.2 Algorithms Used For Risk Calculations

The occupational handler exposure and risk calculations are presented in this section. 

Noncancer risks are calculated using the Margin of Exposure (MOE) which is a ratio of the 

toxicological endpoint of concern to dose. Dose values are calculated by first calculating 

exposures by considering application parameters (i.e., rate and area treated) along with unit 

exposure levels (i.e., the amount of exposure per pound of active ingredient handled for a specific 

job task using specific equipment types and personal protective equipment). Exposure is 

normalized by body weight and adjusted for absorption factors as appropriate to calculate dose 

(i.e., body burden). Finally, MOE values are calculated to represent the magnitude of the risk. 

For cancer risk calculations, the next step is to develop lifetime average daily dose estimates 

(LADD) which are then compared to a cancer slope factor (i.e., Q1*) in order to calculate 

population-based risk estimates. 

Daily Exposure: The daily exposure, daily dose and hence the risks, to handlers are calculated as 

described below. The first step is to calculate daily exposure (dermal or inhalation) using the 

following formula: 

Daily Exposure (mg ai/day) = 

Unit Exposure (mg ai/lb ai) x Application Rate (lb ai/A) x Daily Area Treated (A/day) 

Where: 

Daily Exposure = Amount deposited on the surface of the skin that is available for dermal absorption or 

amount that is inhaled, also referred to as potential dose (mg ai/day); 

Unit Exposure = Normalized exposure value derived from August 1998 PHED Surrogate Exposure Guide 

and various referenced exposure studies noted above (mg ai/lb ai); 

Application Rate = Normalized application rate based on a logical unit treatment such as acres or gallons, 

maximum and typical values are generally used (lb ai/A); and 

Daily Area Treated = Normalized application area based on a logical unit treatment such as acres (A/day) 

or gallons per day can be substituted (gal/day), such as common with antimicrobials. 

The Agency uses a concept known as unit exposure as the basis for the scenarios used to 

assess handler exposures to pesticides (Hackathorn and Eberhart, 1985). Unit exposures

numerically represent the exposures one would receive related to an application. They are 

generally presented as (mg active ingredient exposure/pounds of active ingredient handled). The 

Agency has developed unique unit exposures for each scenario typically considered in our 

assessments (i.e., there are different unit exposures for different types of application equipment; 

formulation type, job functions; and levels of protection). These are considered generic and used 

regardless of chemical identity. The Agency only uses these to address exposures to pesticides 

which are considered semi-volatile or non-volatile. More volatile materials such as fumigants are 

not addressed with generic data. Typically, chemical-specific data are required for these 

assessments. Chemical-specific and/or scenario-specific data can also be used if available for any 

assessment if it is determined that the specific data are more applicable to the scenario being 

evaluated. 

Page 19 of 114 
The generic unit exposure concept is generally accepted in the scientific literature 

(Hackathorn and Eberhart, 1985) and also through various exposure monitoring guidelines 

published by the U.S. EPA and international organizations such as Health Canada and OECD 

(Organization For Economic Cooperation and Development). The concept of generic unit 

exposures can be illustrated by the following example. If an individual makes an application 

using a groundboom sprayer with either 10 pounds of chemical A or 10 pounds of chemical B 

using the same application equipment and protective measures, the exposures to chemicals A and 

B would be similar. The unit exposure in both cases would be 1/10th of the total exposure 

(measured in milligrams) received during the application of either chemical A or chemical B (i.e., 

milligrams on the skin after applying 10 pounds of active ingredient divided by 10 pounds of 

active ingredient applied). Likewise, if 5 pounds of chemical were applied, instead of 10 pounds, 

then the unit exposures would be 1/5th of the total exposure received. [Note: The underlying 

premise for unit exposures is examined below.] 

PHED is the source of the generic unit exposure estimates that are used by the Agency as 

the basis for its exposure assessments for conventional pesticides and some antimicrobial 

pesticide uses. PHED was designed by a task force of representatives from the U.S. EPA, Health 

Canada, the California Department of Pesticide Regulation, and member companies of the 

American Crop Protection Association. PHED is a software system consisting of two parts -- a 

database of measured exposure values for workers involved in the handling of pesticides under 

actual field conditions and a set of computer algorithms used to segment and statistically 

summarize the selected data. Currently, the database contains values for over 1,700 monitored 

events. Once the data for a given exposure scenario have been selected, the data are typically 

normalized (i.e., divided by) by the amount of pesticide handled then adjusted by the dermal 

surface area for the region of the body resulting in standard unit exposures (milligrams of 

exposure per pound of active ingredient handled). Following this, the data from various body 

parts are summarized. The distribution of exposure values for each body part (e.g., chest upper 

arm) is categorized as normal, log-normal, or “other” (i.e., neither normal nor log-normal). A 

central tendency value is then selected from the distribution of the exposure values for each body 

part. These values are the arithmetic mean for normal distributions, the geometric mean for lognormal distributions, and the median for all “other” distributions. Once selected, the central 

tendency values for each body part are summed into a "central tendency" value representing the 

entire body. A complete description of the system is included in the PHED Reference Manual

(USEPA, 1995a). This document describes all algorithms and analysis approaches included in 

PHED. It also contains an explanation of the data coding system that is included in PHED that is 

critical to the interpretation of the raw data as presented. [Note: As indicated above, the Agency 

is seeking guidance on many factors related to occupational exposure monitoring, study design, 

and the analysis of data. It was also noted that the industry groups AHETF and AEATF are in the 

formative stages of generating substantive databases that will likely be used for a number of years 

in order to address occupational exposures. The specific product of AHETF and AEATF will be 

databases of generic unit exposure estimates for a variety of agricultural and biocidal use 

patterns. How these data will be used in Agency assessments is pending but likely scenarios 

include replacing PHED estimates because they are of better quality, being combined with PHED 

estimates in order to enhance the number of available measurements, or addressing scenarios not 

adequately determined in PHED and/or CMA. Since the AHETF and AEATF products are 

integral to the Agency’s plan for refining its assessments over the next several years, many 

AHETF and AEATF produced analyses have been referenced in this document. Similar findings 

Page 20 of 114 
of the SAP that pertain to AHETF will be considered in the process for evaluating AEATF 

progress.] 

Daily Dose: After daily exposure is calculated, daily dose (inhalation or dermal) is calculated by 

normalizing the daily dermal exposure value by body weight and accounting for dermal 

absorption if appropriate. If a dermal administration toxicity study is used to calculate risks for 

dermal exposure, the term to adjust for dermal absorption is dropped from the calculation. It 

should also be noted that there are generally no specific inhalation absorption factors available so 

a factor of 100 percent is typically used for all calculations. Daily dose is calculated using the 

following formula: 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

Body (kg) Weight

AbsorptionFactor(%/100) x 

day

ai mg Daily = Exposure kg/dayai mg DoseDaily Average

Where: 

Average Daily Dose = the amount as absorbed dose received from exposure to a pesticide in a given 

scenario (mg pesticide active ingredient/kg body weight/day, also referred to as ADD); 

Daily Exposure = Amount deposited on the surface of the skin that is available for dermal absorption or 

amount that is inhaled, also referred to as potential dose (mg ai/day); 

Absorption Factor = A measure of the flux or amount of chemical that crosses a biological boundary such 

as the skin (% of the total available absorbed); and 

Body Weight = Body weight determined to represent the population of interest in a risk assessment (kg).

Margins of Exposure: Once daily dose estimates are defined, they are compared to the 

appropriate point of departure (i.e., NOAEL or LOAEL) to assess noncancer risks (or threshold 

cancer effects) to handlers for each exposure route within each scenario and suite of personal 

protective equipment. MOEs for all applicable exposure durations are also calculated for each of 

the scenarios. MOEs are calculated using the formula below: 

⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛

⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛

kg/day

ai mg DoseDaily Average

kg/day

ai mg NOAELorLOA EL

 = MOE

Where: 

MOE = Margin of exposure, value used by the Agency to represent risk or how close a chemical exposure 

is to being a concern (unitless); 

ADD = (Average Daily Dose) or the amount as absorbed dose received from exposure to a pesticide in a 

given scenario (mg pesticide active ingredient/kg body weight/day); and 

NOAEL or LOAEL = Dose level in a toxicity study, where no observed adverse effects occurred (NOAEL) 

in the study or the lowest dose level where an adverse effect occurred (LOAEL) in the study (mg pesticide 

active ingredient/kg body weight/day).

Page 21 of 114 
[Note: A risk is typically considered a concern if the MOE is <100 for occupational populations 

(10x for intraspecies variation and 10x for interspecies extrapolation). This can vary based on 

different factors related to the breadth and quality of the hazard database upon which the 

assessment is based.] 

It is important to present risk values for each route of exposure (i.e., dermal or inhalation) in each 

scenario because it makes determining appropriate risk mitigation measures easier. For example, 

if overall risks are driven by dermal exposures and not inhalation, it would not be advisable to 

require respirators as they may marginally reduce overall risks. It is also important to present 

overall risk estimates for each scenario considered by calculating total MOEs. Total MOEs can 

be calculated if toxicity endpoints are similar between routes of exposure (e.g., cholinesterase 

inhibition associated with carbamate pesticides). The following formula is used to calculate total 

MOE values by combining the route-specific MOEs: 

MOE total = 1/((1/MOE dermal) + (1/MOE inhalation)) 

Cancer Risks: Cancer risk calculations build on the above daily dose estimates which are used 

to calculate a lifetime average daily dose (LADD). LADD values are multiplied by an estimate of 

the carcinogenic potency (cancer slope factor or Q1*) to calculate a population-based cancer risk. 

LADD values are calculated by amortizing Average Daily Dose estimates based on the amount of 

years worked over a lifetime (i.e., typically 30 out of an average 70 year lifetime) and also the 

annual frequency of use (e.g., in many cases 30 working days per year with a particular 

chemical). To calculate a population-based cancer risk estimate, LADD values are multiplied by 

the slope factor. 

 The algorithms that are typically used by the Agency for exposure assessment purposes 

have been presented above in order to put the theme of this SAP meeting into context (i.e., how 

unit exposure estimates are generated including the factors that should be considered and the 

methods used). Other factors of equal import in the calculation of risks include inputs such as 

acres treated, application rates, or the selection of toxicological endpoints and associated dose 

estimates for risk assessment. These additional factors have not been included in the charge to 

the panel for this meeting as the Agency desires to focus on the issues related to the generation of 

unit exposure estimates. 

2.3 Occupational Handler Unit Exposure Case Study 

The intent of this section is to describe the data and methods the Agency uses to conduct 

its occupational pesticide handler exposure assessments and to discuss, in general terms, the 

limitations that are associated with the Agency's current data and approaches. This has been done 

in order to provide context to the analyses of the case study data below. PHED is a database 

system that the Agency uses to store occupational handler exposure data and also develop 

exposure estimates essentially from skin loading rates presented as either (μg/cm2 of skin surface 

area or residue loading across an entire body). For convenience, a subset of the entire PHED 

database (i.e., approximately 25 % of all PHED data) was selected that reflects very common job 

tasks that occur in agriculture. The analyses of the data also illustrates why the Agency needs 

additional occupational exposure monitoring data if EPA is to be able to perform more refined 

assessments. For example, EPA would like to describe what “more refined” assessments could 

Page 22 of 114 
include probabilistic treatments of data and the ability to define higher percentiles of exposure 

from a distribution. 

Exhibit A provides the detailed data from the scenarios selected for the case study. The 

PHED Reference Manual and the PHED Surrogate Exposure Guide include the approaches used 

to calculate unit exposure estimates from these data (and also specifies the coding processes for 

the data) and the unit exposures which have been calculated by the Agency based on these data 

which are commonly used. [Note: The report of the June 2006 HSRB indicated that the Agency 

modify its nomenclature with regard to individuals who participate in exposure monitoring 

studies. The surrogate guide and reference manual documents were developed approximately 10 

years ago and have been in constant use since then. In these documents, the term "replicate" has 

been used to represent a dataset generated by collecting dosimeters from an individual after one 

monitoring event. The Agency will modify this nomenclature (the term monitoring unit is under 

consideration). It should also be noted that the term "replicate" has not been revised in the 

historical documents. This issue will be addressed in the future, especially related to the 

development of any new databases and guidance documents.] 

 As described above in Section 2.2.1: Handler Exposure Scenarios, unit exposure 

estimates have been developed for a total of 37 agricultural pesticide handler scenarios as 

described in the PHED Surrogate Exposure Guide. This case study presents the data from 6 of 

these scenarios. The data contained in the selected scenarios range from extremely sparse (yet the 

best available information for the particular scenarios) to more complete datasets that tend to be 

of higher quality. The scenarios upon which the case study is based include: 

Mixing/Loading

(1) Dry Flowable mixing & loading 

(2) Granular mixing & loading 

(3) Liquids mixing & loading 

Applicator:

(11) Airblast open cab tractor applications 

(12) Airblast closed cab tractor applications 

(15) Solid broadcast spreader open cab applications for agricultural settings 

More specifics regarding the data used to develop each scenario are included in Table 2-1. 

Table 2-1: Summary of Studies And Monitoring Events Used In PHED Case Study 

# Scenario 

Description 

# 

Studies 

Timespan For Data 

Collection 

Locations Where Collected # Subjects # Monitoring 

Events 

# Subjects With >1 

Event 

(range of events per 

each of these 

subjects) 

1 Dry Flowable 

Mixer/Loader 

6 1985 to 1991 CA, GA, IN, ND, MO, TX 

Canada, Australia 

28 50 9 (2 to 5) 

2 Granular 

Mixer/Loader 

8 1977 to 1993 Il, MN, ND, AK, MO, NE, IA, 

NC, GA, Canada 

48 96 15 (2 to 5) 

3 Liquid 

Mixer/Loader 

43 1977 to 1994 SC, IA, WI, FL, CA, NJ, IL, NE, 

MN, CO, ND, PA, VA, NC, OH, 

IN, AK, OR, MISS, AZ, MD, 

GA, MI, England, Sri Lanka, 

Malaysia 

146 271 54 (2 to 15) 

11 Airblast Open 

Cab 

14 1977 to 1992 CA, NC, NY, OH, PA, VA, WA 39 91 19 (2 to 7) 

Page 23 of 114 
Page 24 of 114 

Application 

12 Airblast Closed 

Application 

4 1987 TO 1991 CA, OR, GA 19 32 3 (4 to 7) 

15 Solid Broadcast 

Spreader Open 

Cab 

Application 

2 1979 & 1990 ND & Manitoba 2 5 1 (4) 

Based on the information presented in Table 2-1 it is clear that there are great differences in the 

amount of data which are available for varying scenarios. Other differences exist with respect to 

the quality and the manner in which the data were collected (e.g., measurements of intra- and 

inter-personal variability within each set of data). 

 Additionally, the types of collection media used to generate these data need to be 

considered. As described in the Agency guidelines for conducting exposure studies (USEPA 

1984; USEPA, 1986 and http://www.epa.gov/scipoly/sap/1998/march/contents.htm ) there are 

varying media and approaches that can be used to collect similar types of information. For 

example, dermal exposure data (not on the hands) have typically been collected using either 

patches, made of alpha-cellulose or gauze, or whole body dosimeters (Durham & Wolfe, 1962 

and WHO 1985). Other devices have sometimes been used such as cotton socks covering the 

forearm or swabbing methods. Additionally, differences exist in the configurations of the 

monitors (e.g., different investigators could place patches at different body locations). Hand 

exposures have also been collected using a variety of media and approaches but generally involve 

the use of cotton gloves or some type of hand washing method. In this case study, 8 different 

media were used to quantify hand exposures (i.e., ethanol, acetone, methanol, cotton gloves, 

Tyvek© gloves, isopropanol, gauze patches, soapy water). Figures 2-1 through 2-3 depict the 

variety of sampling methods used to generate the data for three of the scenarios in the case study. 
Figure 2-1: Sampling Regimen Associated With Data From PHED Scenario 1. 

Page 25 of 114 
 Figure 2-2: Sampling Regimen Associated With Data From PHED Scenario 2. 

W

PHED Scenario 2: Open M/L Gran

= patch(transparent on back)

WBD = whole body dosimeterS = socks

0422 (N=2) Gauze Patches,

Gloves, PSP

0425 (N=3) Gauze Patches,

Cotton gloves, PSP

0448 (N=8) G

Han

H = HandwashW= wipe or swipeG = Gloves

G-p G-p

0427 (N=1) Gauze Patches, 

Cotton Gloves, PSP

G-b G-b G-p G-p H-b 

1003 (N=22) Upper WBDs,

Detergent HW, Socks, PSP

1004 (N=15) Upper & Lower WBD,

Detergent HW, PSP

1011 (N=30) Union Suit WBDs sectioned into 

parts, Ethanol HW, Ethanol Wipes, PSP

1027 (N=15) Unio

Detergent HW

WBD

S S

H-p H-p

WBDWBD

H-p H-p

WBDWBD

H-p H-p

H-p 

Samplers under long-sleeves/pants or more unless noted. always unprotected unless noted. Green – combined sampFor hand exposure; p = wore protective gloves & b = bareha

Page 26 of 114 
Page 27 of 114 

Samplers under long-sleeves/pants or more unless noted. Heaalways unprotected unless noted. Green – combined samples, For hand exposure; p = wore protective gloves & b = barehand

PHED Scenario 12 – Closed Cab Airb

Figure 2-3: Sampling Regimen Associated With Data From PHED Scenario 12 

WBD WB

WB

W W

1000 (N=7) Gauze Patches, Forearm swipes

Detergent HW, Urine collection, PSP

= patch(transparent on back)

WBD = whole body dosimeterS = socks

H = HandwashW= wipe or swipeG = Gloves

0523 (N=16) Cellulose Patches,

10% IPA HW, PSP

0460 (N=4) Gauze Patches,

Ethanol HW, PSP

1007 (N=5

separates WB

hand sa

H-p H-p H-p H-p H-p H-p
PHED contains a large amount of information about each monitoring event used to 

develop exposure estimates (these are referred to as a “replicate” in PHED). The PHED 

Reference Manual (USEPA, 1995a) describes all of the different data fields in detail captured in 

PHED. Figure 2-4 reproduces a form on which EPA recorded some of the data which were 

collected for applicators when developing the system (i.e., it is an image of an actual data entry 

sheet). Similar data would be collected under current Agency guidelines. [See the reference 

manual appendices for complete data entry forms (U.S. EPA, 1995a).] 

Page 28 of 114 
The Agency has characterized estimates calculated using PHED by considering associated 

exposure factor information such as the amounts handled, field conditions and various sampling 

parameters such as the limit of quantification for specific sampling media. These factors help 

determine the applicability of exposure data for relating to various agricultural use patterns where 

they could be used to calculate exposures. They also help in determining how skin loading and 

other exposure information can be segregated based on application techniques and other factors. 

Exhibit A contains all of the raw data fields that represent the 6 scenarios selected for the case 

study including various exposure factors data. An example of the type of information that can be 

identified from the data within this case study is presented in Table 2-2 for PHED Scenario 1 – 

Open Mixing/Loading of Dry Flowables. For example, note that in 45 monitoring events the 

container size ranged from 1 to 5 pounds and that the overall mean was approximately 2.1 

pounds. This indicates that the use of mini-bulk or bulk packaging systems (which contain larger 

quantities of materials) containing dry flowable formulations was not evaluated in the monitoring 

data available for this scenario. The meteorological data, likewise, indicate that conditions were 

typical for agricultural pesticide applications particularly since temperatures were not extremely 

low during the monitoring events. 

 

Table 2-2: Example Summary Of Exposure Factors For PHED Scenario 1 – Open Mixing/Loading Of Dry Flowables 

Mixing/Loading Parameters

Container Size (lb) % AI [mix conc. (lb/gal)] Total Gal. Mixed # Tank loads Total ai Mixed 

N 45 50 49 49 45 50 

Average 2.133 55.900 0.046 1594.255 3.800 55.893 

Min. 1.000 25.000 0.004 242.000 1.000 2.340 

Max. 5.000 85.000 0.670 4229.000 9.000 440.000 

Weather Conditions

High % RH Low % RH High Temp Low Temp Tank Cap (gal) Wind speed (mph) 

N 45 45 45 45 36 NA 

Average 61.511 40.822 80.511 58.600 398.333 6 to 10 MPH 

Min. 30.000 16.000 48.000 43.000 130.000 0 to 5 MPH 

Max. 93.000 85.000 96.000 82.000 900.000 >10 MPH 

Sampling Conditions

Air Time (min.) Air LOQ (ug) Air Vol (L) Dermal Time (hr) Der. LOQ(ug/cm2) Hand LOQ (ug) 

N 42 42 42 50 48 44 

Average 158.071 0.094 283.895 2.884 0.013 23.097 

Min. 6.000 0.025 9.300 0.100 0.0001 0.0300 

Max. 321.000 0.400 578.000 8.125 0.075 60.000 

PHED contains a series of calculation functions which develop unit exposure estimates 

based on skin loading rates. As illustrated by the breadth and depth of the information available 

in PHED (see Table 2-2, Figure 2-5and USEPA, 1995a) the Agency opted to approach 

occupational handler exposure estimates using a tiered approach that was developed in an 

international effort under the auspices of the Organization For Economic Cooperation and 

Development (OECD, 1997). The first part of this tiered approach is to utilize data in a 

systematic way that does not require and does not require active ingredient-specific data. This 

approach resulted in the development of the PHED Surrogate Exposure Guide (USEPA, 1998b). 

This approach also allows for a more consistent risk assessment process within the Agency since 

the analyses of the data are standardized in the first tier of the process. Further analyses could be 

required depending upon the nature of individual assessments (e.g., if only large acreage 

equipment for a specific chemical use pattern might be typical). 

Page 29 of 114 
A major step in interpreting any PHED-based assessment is determining which data are to 

be included in the calculations. EPA thinks it is preferable that the best quality information be 

used. The Agency defines high quality data as data derived from studies which were well 

conducted and have adequate analytical quality control data. The Agency approach has always 

been to address exposure scenarios with the highest quality data available but if sufficiently high 

quality data are not available the Agency would always use the best available data even if there 

could be quality issues associated with it (e.g., there may be a low number of monitoring events 

such as for Scenario 15 in Table 2-1 but these data are the only available for that scenario. In 

cases such as this, the data would be used in an assessment but appropriate language would also 

accompany the assessment to characterize the results). In those cases, however, the Agency 

would typically require that additional confirmatory information be collected to support any final 

regulatory decision as the Agency has done for a number of pesticides during reregistration. The 

criteria in the PHED Surrogate Exposure Guide that the Agency uses to grade data (i.e., describe 

the data quality based on analytical methods performance) are summarized in Table 2-3. 

Page 30 of 114 
Table 2-3: PHED Grading Criteria 

Data Grade % Lab 

Recovery 

CV* for 

Lab recovery 

% Field 

Recovery 

% Storage 

Stability 

Data Corrected 

For:*** 

A 90-110 ≤15 70-120 ** Field Recovery 

B 80-110 ≤25 50-120 ** Field Recovery 

C 70-120 

70-120 

≤33 

≤33 

30-120 

or 

missing 

** 

50-120 

Field Recovery 

D 60-120 ≤33 ** ** Field Recovery 

if available; if 

not then storage 

stability, if not 

then lab 

recovery 

E Does not meet above criteria 

* CV = Coefficient of Variation 

** Does not matter if available or missing 

*** If a field recovery of 90% or greater is obtained, no correction of the data is necessary 

 

 As described above, PHED contains many fields and codes that are intended to capture 

raw data on field conditions, exposure factors, exposure monitoring methods, and exposure 

monitoring results. Table 2-4 below presents an example of the actual exposure monitoring 

results used to calculate the unit exposure estimates for Case Study Scenario 1 which is open 

mixing/loading of dry flowable formulations. These data differ from above in that they reflect the 

actual skin loading measurements which have been collected and not the ancillary exposure 

factors data described above which characterize the conditions under which they were collected. 

Table 2-4: Example Summary Of Raw Exposure Data Used To Calculate Unit Exposure Values For PHED 

Scenario 1 – Open Mixing/Loading Of Dry Flowables 

[Note: “0”s indicate no samples taken for that location. All residues are ug/cm2 and under clothing or protective gloves unless noted with the term 

“out” which indicates bare skin; -1.000 = sample residues were not detected so 1/2 LOQ used for the corresponding calculation of exposure.] 

Air (ug) Head Out Neck Out 

Both Hands 

(ug) 

Both Hands 

[Without 

protective 

glove] 

(ug) R-Hand (ug) L-Hand (ug) 

N 42 32 10 12 7 26 26 

#NDs 0 3 0 2 0 16 16 

Min. 0.027 -1.000 0.006 -1.000 58.400 -1.000 -1.000 

Max. 258.000 25.686 0.656 1200.000 930.300 399.000 339.000 

R-FrtArm L-FrtArm R-Shoulder L-Shoulder R-Chest L-Chest R-Back 

N 38 38 0 0 32 48 48 

#NDs 17 17 0 0 5 17 20 

Min. -1.000 -1.000 0.000 0.000 -1.000 -1.000 -1.000 

Max. 0.815 0.815 0.000 0.000 0.815 0.815 0.815 

R-Thigh L-Thigh R-Shin L-Shin R-Calf L-Calf R-Ankle 

N 48 38 38 28 12 12 22 

#NDs 13 17 17 16 5 5 6 

Min. -1.0000 -1.0000 -1.0000 -1.0000 -1.0000 -1.0000 -1.0000 

Max. 12.085 0.512 1.458 0.151 0.104 0.104 0.104 

Page 31 of 114 
 As indicated above, PHED is a database which is used to store raw data records such as 

those illustrated in Tables 2-2 and 2-4. Its second major function is as a calculator that has been 

used to develop unit exposure estimates which are based on segmenting the data to reflect a 

specific exposure scenario (this process is referred to as subsetting in the PHED Reference 

Manual; USEPA, 1995a). For example, one could determine the unit exposure to the bare hands 

for all mixer/loaders regardless of formulation type (e.g., solids of varied types along with 

liquids) or the data can be segmented based on specific solid type such as a dust or dry flowable 

(which is how the Agency uses the data). Tables 2-5 and 2-6 below are intended to provide an 

example of this calculation process using the dry flowable scenario from the case study. Table 2-

5 is an actual output from PHED where the data have been subsetted to reflect exposures of 

mixer/loaders using a dry flowable formulation in an open mixing process. In this evaluation, 

individuals are wearing typical work clothing (i.e., long pants and long-sleeved shirts) along with 

some sort of protective gloves (i.e., they were not barehanded). In the dataset upon which this 

calculation is based, there were also a sufficient number of monitoring events to develop unit 

exposure estimates based on better quality data (i.e., AB Grades - see Table 2-3 above). It should 

be noted that in Table 2-4 as many as 48 monitoring events are available for the scenario. 

However, when only "AB" grade data are selected the maximum number of records for body 

location specified in the Table 2-5 output is 26 monitoring events. This result means that 22 

records had data of lesser quality (i.e., grades "C", "D" or "E") and excluded from this analysis. 

Once records are identified the selected data are used to populate arrays based on differing body 

locations. PHED then uses a statistical test (i.e., Kolomogorov-Smirnov) to determine the 

distribution which best reflects the data in that array. Most have been log-normally distributed. 

PHED then calculates unit estimates by combining the univariate statistic that best reflects the 

measure of central tendency for that body location. For example, a geometric mean is used for 

log-normal distributions while the arithmetic mean is used when data are normally distributed. 

Table 2-5: PHED Output, Summary Statistics for Calculated Dermal Unit Exposures 

(Dry Flowable, Open Mixing and Loading, Long Pants, Long Sleeves, Gloves - Dermal/Hand AB Grade) 

Unit Exposure (ug/lb ai handled) Patch 

Location Distribution Median Mean Coefficient of 

Variation 

Geo. 

Mean 

Observations 

Head (All) Log-normal 13.65 30.50 125 11.99* 19 

Neck. Front Log-normal 8.06 13.91 103 5.84* 16 

Neck. Back Log-normal 0.47 3.29 170 0.75* 19 

Head/Neck Unit Exposure = 18.6 ug/lb ai handled 

Upper Arms Log-normal 5.82 8.35 99 6.59* 23 

Chest Log-normal 7.10 8.93 67 6.79* 26 

Back Other 6.04* 6.79 73 4.88 26 

Forearms Log-normal 2.42 3.71 66 3.10* 23 

Thighs Log-normal 8.79 90.27 297 11.36* 26 

Lower Legs Log-normal 4.40 17.83 299 4.29* 26 

Upper and Lower Arm, Chest, Back, Thigh and Lower Leg Unit Exposure = 38.2 ug/lb ai handled 

Hands Normal 10.67 9.74* 53 7.13 21 

Hands Unit Exposure = 9.7 ug/lb ai handled 

*The numbers in bold font are those used for each unit exposure and are based upon the data distribution for each 

body part. The geometric mean is used when the data distribution is log-normal, the mean is used when the 

distribution is normal and the median is used when the distribution is other. 

Page 32 of 114 
 Above, the Agency described its tiered approach for calculating occupational exposure 

estimates (OECD, 1997). In order to systematically accomplish typical Tier 1 assessments, the 

Agency developed the PHED Surrogate Exposure Guide (USEPA, 1998b). The reference values 

commonly used for open mixing dry flowables are presented in Table 2-6. The results from the 

PHED output described in Table 2-5 above have been used to populate the guide presented in 

Table 2-6. Results from the above analysis represent individuals wearing normal work clothing 

so the estimate for dermal exposure (excluding head, neck and hands) is reported below as 

0.0382 mg/lb active ingredient handled which corresponds to the above PHED output for that 

scenario. Likewise the head/neck and hand estimates (for individuals wearing protective gloves) 

also correspond. It should also be noted that the number of observations are consistent in that 16 

to 26 monitoring events were used to calculate the dermal (nonhand) exposures and 21 

monitoring events were used to calculated hand exposures. The Agency has completed this sort 

of calculation for all of the 37 scenarios listed above the agricultural handler scenarios it routinely 

considers in its Tier 1 assessments but wanted to present this one to illustrate how PHED is used 

to develop unit exposure estimates. The full details of the "calculator" capability of PHED and of 

the processes used in the system can be found in the Reference Manual (USEPA, 1995a).

Table 2-6: Example PHED Surrogate Guide Excerpt For Scenario 1, Open Mixing/Loading Of Dry 

Flowables 

SCENARIO 1. DRY FLOWABLE, OPEN MIXING and LOADING (MLOD) 

 The use of PHED, as well as other datasets, allow the Agency to develop exposure 

assessments in a more systematic and consistent process. However, the Agency (as well as its 

other regulatory partners such as PMRA and DPR) and industry, realized that there are a number 

of possible refinements that could be implemented with additional data and development of 

Page 33 of 114 
updated analytical methodologies. As a result, there has been a significant level of interest over 

the last few years in generating additional occupational exposure monitoring data. The goal in 

these efforts has been to eliminate many of the limitations inherent in much of the data which is 

currently used for regulatory purposes. Some of the key limitations which have been identified as 

well as the Agency's proposed approach for addressing them are summarized in Table 2-7. 

Table 2-7: Summary of Key PHED Limitations & Agency Proposals For Addressing Them 

Limitation Proposed Approach To Address Limitation 

As illustrated above, a variety of data are available for different 

scenarios (e.g., Scenario 3 has 43 studies while Scenario 15 has 

2 studies) which could limit the ability to accurately extrapolate 

these data across many situations in agriculture (e.g., from small 

family farms to large commercial operations) 

The impact of modern agricultural practices and 

equipment would be evaluated and a range of situations 

would be monitored 

It is not possible to equally evaluate inter- and intra-personal 

variability since varying numbers of individuals were used in 

the sample collection process 

Statistical approaches would used to ensure that the scope 

of any research plan would provide sufficient information 

to address occupational exposure assessment issues. 

The studies used to develop unit exposure estimates include a 

number of varied designs as illustrated in Figures 2-1 through 2-

4 (e.g., patches or whole-body dosimetry, combining or not 

samples from similar body locations) - also see the 

"Observations" column in Table 2-5 that presents the number of 

samples for each body part used in the calculation [Note: This 

disparity among combined data is sometimes referred to as the 

"Frankenstein" approach which limits how the data can be 

utilized - e.g., a distributional assessment would not be 

appropriate in many cases.]; 

Similar sampling regimens be used which would 

eliminate possible uncertainties related to combining data 

from disparate study designs; 

The studies used to develop unit exposure estimates include a 

number of varied sample collection media as illustrated in 

Figures 2-1 through 2-4 (e.g., patches or whole-body dosimetry 

of varied material or different hand monitoring methods 

including soap and alcohol washes or the use of cotton gloves) 

which may introduce uncertainties in the calculated unit 

exposures as the sampling media were treated equally when 

combined 

Monitors would be consistent in newer studies to 

eliminate the uncertainty of combining results using 

several different sampling media (e.g., AHETF uses 

whole-body dosimetry with aqueous surfactant hand 

washing) 

Patch data are used to calculate dermal exposures by adjusting 

dermal loading rates using standard skin surface areas while 

dosimetry reflects the size and body shape of those individuals 

who were monitored 

Whole-body dosimeters would be used. Additionally, 

monitoring methods would be standardized to allow 

better comparisons between newer studies 

Some scenarios, especially when higher levels of personal 

protective equipment or engineering controls are used, could be 

biased because of the limits of quantification used to analyze the 

samples (e.g., many would be considered relatively high by 

modern analytical chemistry standards) 

Modern analytical techniques would be used to ensure 

that appropriate limits of detection and quantification 

were used 

Modern agricultural practices and equipment may not be 

adequately reflected in the current data - e.g., systems such as 

induction bowls, bulk or mini-bulk transfer are represented in 

limited fashion or not at all in the current data 

The impact of modern agricultural practices and 

equipment would be evaluated 

Note: The AHETF has developed criteria for accepting existing data into its AHED database that is under development. 

These criteria are outlined in Appendix A of their multi-year research proposal (AHETFa, 2006). Similarly, the AEATF has 

developed criteria for accepting existing data in its effort. These criteria are outlined in their acceptance criteria document 

(AEATFb, 2006). 

Page 34 of 114 
3. Monitoring Methods 

 This section of the document provides the background information needed for the panel 

deliberations related to the sampling methods recommended by the Agency for exposure 

monitoring (see guidelines described above with the latest thinking outlined in the document from 

the 1998 SAP review available at http://www.epa.gov/scipoly/sap/1998/march/contents.htm). 

 There are two basic approaches of either using passive dosimetry which employs some 

sort of physical monitor that traps residues from the surface of the skin (i.e., they absorb or 

remove - such as a dosimeter or a wash) to determine dermal exposure (i.e., also referred to as a 

potential dose) or biological monitoring which is usually employed to quantify absorbed dose 

(i.e., also referred to as body burden). Outside of these, there appear to be limited other 

techniques for calculating risks from dermal exposure that can provide similar types of results 

(e.g., first-principles models are very limited for this purpose). Thus, as the Agency moves 

toward the development of new, more refined data it is important to examine how the 

performance of these two basic approaches compares. If there is a systematic bias between the 

two methods, the Agency wishes to address possible approaches for compensating in order to 

account for that bias. Over time, the Agency has conducted several analyses of the comparability 

of exposure estimates using passive dosimetry and biological monitoring. But EPA is revisiting 

this issue now because more data are available compared to the previous examinations and the 

Agency and other affected stakeholders are prepared to proceed with the generation of a large 

amount of additional data. As a result, the Agency desires that these issues be addressed to 

reduce uncertainties related to methodological issues before the next major phase of data 

development begins. 

 The passive dosimetry methods (e.g., patches, gloves, dosimeters or washes) that are 

included in current Agency guidelines were described in the scientific literature as early as the 

1950s but seminal work includes publications by Durham and Wolfe (1962), the World Health 

Organization (1982), and other investigators (e.g., Fenske and Popendorf). The Agency also 

accepts monitoring data based on the collection of biological media such as urine or blood. 

Biomarker data can also be used for predicting exposures. In addition to biological sampling 

media, the Agency also requires that additional supporting pharmacokinetics and/or 

pharmacodynamic information be submitted that can be used to develop exposure and/or risk 

estimates. 

 Even though the Agency had developed guidance for collecting dermal exposure data, a 

number of passive dosimetry techniques have been historically used to quantify dermal exposures 

by field investigators in studies which have been submitted to the Agency for regulatory 

purposes. For example, EPA's PHED database contains large numbers of studies in which dermal 

exposure was estimated using patches but many other studies used whole-body dosimeters. Even 

though a technique such as a patch or whole-body dosimeter is used, there may still be inherent 

bias in results due to the nuances of how that technique was employed. For example, a dermal 

monitoring patch may be constructed of gauze or alpha-cellulose which may trap and retain 

residues at different rates. Also, hand monitoring could be accomplished using cotton or Tyvek® 

gloves or varying wash media that have ranged from acetone to various alcohols or soap 

solutions. The use of a 100 percent shaking approach in a hand sampling event as opposed to 

allowing for mechanical agitation like normal hand washing may also impact results. Clearly, 

Page 35 of 114 
solubility of the residues being quantified, polarity of solutions, glove material and the use of 

mechanical agitation could impact the residues that are collected. 

 Biological monitoring results can also be used for quantifying exposures given that 

appropriate samples are collected, the time-course of the metabolites being screened for is 

reflected in the sampling regimen, and there are not significant interferences from exposure to 

other chemical agents (e.g., some pesticides have metabolites that are ubiquitous in the 

environment). The rate of transformation and kinetics of dissipation and/or excretion must also 

be well characterized for these types of data to also be used in a quantitative manner. 

 Based on discussions with stakeholders and scientific experts over time, the Agency has 

identified several key issues related to the ruggedness and performance of the available exposure 

monitoring methods. These include: 

• Examination of the potential for a systematic bias between biological monitoring and 

passive dosimetry methods; 

• Whether dermal absorption could cause a significant underestimate of exposure when 

residue removal methods are used (e.g., swiping or washing techniques); 

• Whether residue breakthrough from whole-body dosimeters could cause a significant 

underestimate of exposure; and 

• Examination of the hand monitoring methods to determine if there is a systematic bias due 

to whether the method is based on residue trapping (e.g., gloves) or residue removal (e.g., 

washing) or the nature of the media used (e.g., alcohol or solvents and aqueous soap 

solutions). 

The Agency believes that when results based on passive dosimetry and biological monitoring are 

compared that the first three of the above issues can be addressed. Discussion pertaining to these topics 

is included in Section 3.1: Relative Predictive Capability Of Passive Dosimetry and Biological 

Monitoring. The remaining hand monitoring issue is addressed separately in Section 3.2: Systematic 

Evaluation of Hand Sampling Methods. Hand exposure monitoring is addressed separately because 

hands can be major contributors to overall exposure estimates for many scenarios and the related data and 

mechanistic issues differ from the analysis in Section 3.1. 

3.1. Relative Predictive Capability of Passive Dosimetry and Biological Monitoring 

 Risk from exposure to pesticides can result in toxicity when sufficient dose is absorbed 

and transmitted to a target tissue in the body where an adverse effect can occur. In order to more 

accurately predict these types of risks, concentrations of the toxicologically active compound 

(which could be the pesticide active ingredient or one its metabolites) at the target organ, tissue, 

cell, or cellular component need to be quantified. In most cases, however, the information 

required for this type of approach is not available. The mode of action of a specific pesticide at 

the cellular level may not even be known. 

Page 36 of 114 
 Even if such mechanistic information is known, the concentration of the active compound 

at a target site may be difficult or impossible to quantify. In lieu of concentrations of active 

compounds at target sites, risk assessment for pesticides often relies on data that are used to 

quantify overall body burdens which are then compared to toxic endpoints that have been derived 

on a similar basis (e.g., mg/kg/body weight). As described above in Section 2.1.2 Algorithms 

Used For Risk Calculations and in Section 3: Monitoring Methods either passive dosimetry or 

biological monitoring methods are used for this purpose. It should be noted that, in theory, the 

results based on these methods could also be used to develop target tissue concentrations but the 

data are generally lacking to develop target tissue estimates from body burdens. Agency 

guidelines allow for the collection of the following kinds of data to quantify exposure: 

• Passive dosimetry measures the amount of a chemical impinging on the surface of the 

skin or the amount of the chemical available for inhalation through the use of 

appropriate trapping devices. 

• Biological monitoring measures body burden in selected tissues or fluids or the 

amount of pesticide or its metabolites eliminated from the body. 

Figure 3-1 below provides an illustration of the relationship between these methods. 

Figure 3-1: Overview of Relationship Available Exposure Monitoring Methods a

Pesticide 

residue 

accumulates 

on body and 

is absorbed. 

Passive 

Dosimetry:

Amount of 

pesticide trapped 

on materials 

outside the body 

is quantified. 

Biological 

Monitoring of 

Urine: Amount 

of pesticide or a 

metabolite is 

quantified in 

urine samples. 

Active 

compound at 

target 

protein, 

Absorbed pesticide is 

metabolized and distributed 

throughout the body 

a

 Text in star represents information needed to most accurately predict toxicity from pesticide. Text in boxes 

represents techniques used to collect data used in estimating exposure to a pesticide. Human figure from 

Cull (1989). 

Page 37 of 114 
 Passive dosimetry and biological exposure monitoring techniques are evaluated and 

compared in this document, and the usefulness of both techniques in monitoring exposures is 

discussed. Most exposure data currently available for estimating handler and reentry exposure are 

come from passive dosimetry-based studies. It is important to ascertain, given the quantity of 

data now available, how well this approach supports accurate estimates of exposure. The purpose 

of this section is to compare the performance of passive dosimetry as a predictive tool for 

exposure assessment purposes with biological monitoring data. Comparisons among data sets 

generated using these diverse techniques provide some support for estimates based on both 

techniques. Section 3.1.1: Passive Dosimetry describes the attributes and limitations of that 

technique while Section 3.1.2: Biological Monitoring does the same for that method. [Note: 

These are summary in nature and more information can be in the Agency guideline documents 

referenced above.] Section 3.1.3 Comparison of Passive Dosimetry and Biomonitoring 

Techniques provides the results of the comparisons of the two methods completed by the Agency. 

The AHETF also developed an analysis for this purpose which is summarized along with the 

Agency response to this document. Section 3.1.4: Conclusions describes the Agency's summary 

interpretation and conclusions based on the analyses contained in this section. 

3.1.1. Passive Dosimetry 

 Dermal penetration and inhalation are the major routes of occupational pesticide exposure. 

Multiple studies with several different pesticides (outside of highly volatile chemicals such as 

fumigants) have shown that inhalation generally contributes much less to exposure than the 

dermal route (Ware et al., 1973; Popendorf et al., 1979; Zweig et al., 1984; Zweig et al., 1985). 

[Note: Based on these results and the relative uncertainties associated with dermal monitoring 

methods that are not associated with inhalation monitoring techniques, this analysis is focused on 

the limitations and uncertainties related to dermal monitoring.] Dermal exposure is defined by 

U.S. EPA (1996b) as the process of pesticide residue deposition on the skin, as well as the 

measurement of the deposited residue. Dermal exposure to the clothed portions of the body is 

usually monitored either with absorbent pads (patches) placed on the body at fixed locations, or 

by whole-body dosimeters (WBD). Both techniques have been reviewed by numerous authors, 

including Durham and Wolfe (1962), Chester (1993), Fenske (1993), and Soutar et al. (2000). 

Exposure to hands is typically measured by either hand washes or glove dosimeters. 

 Assumptions of Passive Dosimetry: Passive dosimetry relies on several assumptions. A 

key assumption is that residues removed from the skin, extracted from dosimeters next to skin, 

and trapped in breathing zone are available for absorption into the body. Another assumption, 

important in cases where monitoring intervals differ from exposure intervals of interest, is that 

exposure is constant over each exposure interval. That is, workers who handled 10 pounds of 

active ingredient would be assumed to accumulate about twice as much pesticide as a worker 

handled 5 pounds of active ingredient. To the extent this assumption is true, durations of 

exposure monitoring intervals would not affect results. However, studies in which exposure 

monitoring intervals varied suggest that monitoring intervals affect results. For example, in a 

study of peach harvesters exposed to azinphos-methyl, normalized (by hours worked in this case) 

dermal exposure estimates were proportionately higher after 2-hour monitoring intervals than 

after 3- to 7-hour intervals, suggesting that extrapolation of short monitoring intervals to longer 

workdays can result in overestimating exposure (Spencer et al., 1995). It is assumed that the 

Page 38 of 114 
amount of a dose penetrating skin can be extrapolated from laboratory studies, and that this 

amount is consistent between individuals. Laboratory studies of dermal penetration may rely on 

human, non-human primate, or rodent subjects. When patches are used to monitor dermal 

exposure, it is assumed that exposure over the body is uniform and can be represented by the 

small area sampled by each patch. 

 Advantages of Passive Dosimetry: U.S. EPA (1996a) identified the major advantages of 

passive dosimetry as “the ability to differentiate exposure received during discrete work activities 

within a work day and in differentiating the relative contributions of the dermal and respiratory 

exposure routes for each separate work activity.” These are advantageous because they allow 

evaluation of measures intended to reduce exposure. Another advantage identified by U.S. EPA 

(1996a) is that “the study participants are typically under the supervision of the investigator 

during the entire period when exposure data are being collected.” Passive dosimetry also allows 

creation of generic exposure databases, under the assumption that in many cases deposition of 

residues on the skin is essentially independent of chemical properties of the pesticide (U.S. EPA, 

1996a). Such databases contain data collected under known conditions, and allow a limited 

number of studies to support exposure estimates for a wide range of pesticides. 

 Fenske (1993) argued that it is necessary to estimate dermal exposure using data from 

passive dosimetry rather than biological monitoring under the following circumstances: 

• When there are no major target analytes available in an accessible biological medium; 

• When adequate human pharmacokinetic data are lacking; or 

• When there are obstacles to complete sampling of exposed individuals; for example, when 

workers refuse cooperation, the sampling schedule is too complex to be feasible, or when 

analytical costs are too high. 

 Disadvantages of Passive Dosimetry: Perhaps the most important disadvantage of 

passive dosimetry is that absorption of the pesticide into the body must be estimated which would 

then be compared to an endpoint derived from an oral administration toxicity study. Voluntary 

human in vivo dermal penetration studies yield the most comparable results; however, these 

studies are not always feasible, especially for highly toxic chemicals. When human in vivo data 

are unavailable, surrogate in vitro or animal in vivo data may be used, but these data introduce 

additional uncertainties. [Note: EPA tends not to use in vitro data.] Relationships between in 

vivo and in vitro test results, or between laboratory animal and human results, have not been 

reliably established for many classes of compounds, and have been shown to vary for compounds 

that have been tested (Franklin et al., 1989; U.S. EPA, 1992; Wester and Maibach, 2000). 

 Another way in which dermal absorption affects estimates of absorbed dose is that for 

several pesticides percent dermal absorption has been shown to vary inversely to concentration of 

the pesticide on dosed skin (Thongsinthusak et al., 1999). While the percentage absorption drops 

as the amount of pesticide loaded onto skin increases, the total amount of pesticide absorbed 

continues to increase with increasing concentration on the skin. Applying a fixed percentage for 

dermal absorption for any amount of pesticide likely may overstate the absorbed dose for higher 

concentrations of pesticide. Conversely, fixed dermal absorption estimates may be underestimated at lower dermal loading concentrations. However, as the significance of this effect 

Page 39 of 114 
cannot be quantitatively ascertained under field conditions, it adds to the uncertainty of estimating 

the magnitude of dermally absorbed doses. 

 In addition to the issues attendant to dermal absorption, another disadvantage of passive 

dosimetry is the uncertainty that results from the various methods of measuring dermal 

absorption. Patch dosimetry could over- or underestimate exposure due to extrapolation of 

residues from the relatively small surface area of the patches to much larger body surface areas. 

Splashes or placement of a patch in a relatively high-exposure location on the body can lead to 

overestimated exposure; conversely, placement of a patch in a relatively low-exposure location on 

the body can lead to underestimated exposure (U.S. EPA, 1996a). While placement of patches 

has been standardized by U.S. EPA (1996a), available data show that exposure patterns differ by 

activities, suggesting that extrapolations from patch dosimetry might have varying effects on 

exposure estimates. Also, many investigators use non-standard patch construction and placement, 

confounding comparisons between studies. 

 Materials used in patches or WBDs can result in different exposure monitoring results, as 

pesticide residues on dosimeters can be unstable, and be lost through evaporation or binding to 

dosimeter materials (Serat et al., 1982; Wester et al., 1994). Use of field-fortified dosimeter 

samples is an essential quality control requirement. The results of these samples are essential 

components of the PHED grading criteria presented above. 

 Hand washes and gloves can overestimate or underestimate dermal exposure on the hands. 

Several studies provide evidence that exposure estimates based on gloves are often higher than 

estimates based on hand washes. Although some authors (e.g., U.S. EPA, 1996a) interpret such 

differences as evidence that gloves overestimate exposure, data are lacking to support the 

accuracy of one method above the other. Several studies have been published in the literature or 

submitted to DPR in which cotton gloves were compared to hand washes for monitoring hand 

exposure. While gloves gave higher results than hand washes in most studies (e.g., Davis et al., 

1983; Fenske et al., 1999), at least over short sampling intervals, that is not consistently true (e.g., 

Fenske et al., 1989; Meikle, 1991). As pointed out by Fenske et al. (1999), the accuracy of either 

method has never been determined. In one of the few documented validation studies available, 

Fenske et al. (1999) used a laboratory removal efficiency study with a surrogate chemical - and a 

different hand wash solution - to develop a value with which they corrected hand wash samples. 

The removal efficiency study (Fenske et al., 1998) used an isopropanol solution rather than the 

surfactant solution used by Fenske et al. (1999). [Note: There is a more detailed discussion of 

this issue below in Section 3.2: Systematic Evaluation of Hand Sampling Methods.] 

 It has been argued that cloth gloves provide a more absorptive matrix for pesticides than 

skin. While a layer of cloth may hold more pesticide than an equivalent layer of skin, a glove 

does not have blood circulation that can carry away absorbed residues. Hand washes supposedly 

measure the amount of pesticide loaded onto the skin of a worker during a given period of time. 

This is an operational definition in that only the amount of pesticide that can be washed off the 

skin is considered for exposure calculations. However, radiolabelled pesticide, applied 

experimentally to hands that were then washed at various times, could not be fully recovered even 

after a few seconds of time (Wester et al., 1992). Thus, hand washing may underestimate dermal 

exposure, as not all of the pesticide loaded onto hands during occupational activities can be 

recovered by hand washes. Conversely, in at least one study (Zweig et al., 1984), the early 

Page 40 of 114 
morning dew absorbed by gloves decreased the amount of pesticide taken up by the gloves, 

potentially leading to an underestimate of exposure. 

 

 3.1.2 Biological Monitoring 

 For practical reasons, biological monitoring (“biomonitoring”) to estimate worker 

exposure to pesticides in the field typically uses compounds measured in urine samples collected 

from the workers (U.S. EPA, 1996a). Although monitoring of blood (plasma or red blood cell) 

acetylcholinesterase levels in workers exposed to organophosphate and carbamate pesticides is a 

common assay, because of individual variability in enzyme activities it provides at best only a 

qualitative estimate of exposure. And while target compounds could be monitored in blood or 

other tissues or fluids, urine collection is less invasive than blood sample collection and urine is a 

major excretion route for metabolites of many pesticides, making it a logical choice for 

monitoring. Biomonitoring procedures have been reviewed by many authors, including Franklin 

et al. (1986), Woollen (1993), Maroni et al. (2000), and Cocker et al. (2002). U.S. EPA (1996c) 

recommends collecting a series of 24-hour samples, starting with one 24-hour sample before the 

exposure period begins, and continuing as long as indicated by the excretion profile from a 

properly conducted human pharmacokinetic study. 

 Biological Monitoring Assumptions: Perhaps the most important assumption of 

biological monitoring is that collecting urine for analysis is the best sampling approach because 

urine is typically a major elimination route for a pesticide of interest or its metabolite and it can 

refine passive dosimetry estimates. Fortunately, this has been shown to be the case for many 

pesticides, including OPs (Nutley and Cocker, 1993), pyrethroids (Kuhn et al., 1999), and many 

other classes of pesticides (Kolmodin-Hedman et al., 1983; Libich et al., 1984). The 

pharmacokinetics relating absorbed dose to compounds recovered from urine can be extrapolated 

from laboratory studies but it can have an associated uncertainty depending upon the quality of 

the data used to develop the relationship. However, such studies tend to be conducted in humans 

only for compounds with anticipated low toxicities (Harris et al., 2001). 

 Another assumption is that residues recovered from urine are entirely (or at least 

substantially) due to pesticides absorbed from exposures occurring during shifts of interest. This 

assumption is verified with the required baseline pre-exposure samples to check for presence of 

monitored compound (U.S. EPA, 1996c). For compounds that are metabolites of multiple 

pesticides (e.g., alkylphosphates), it is also assumed that the occupational exposures result in 

substantially greater excretion than background amounts due to other sources such as diet. For 

alkylphosphates, this assumption is supported by multiple studies (e.g., Nutley and Cocker, 1993). 

 Creatinine corrections assume a constant excretion of creatinine (Boeninger et al., 1993). 

Urine output is assumed to have little effect on concentrations of pesticides and metabolites 

(Boeninger et al., 1993). 

 Biological Monitoring Advantages: If sufficient information is available about the 

pesticide and if biomonitoring is conducted properly, a quantitative estimate of absorbed dose 

may be obtained. By definition, these resulting dose estimates integrate exposure across all 

routes. Such an estimate of absorbed dose, which avoids potential confounding from assumptions 

Page 41 of 114 
of dermal penetration or inhalation retention, may be more useful in assessing risk than routespecific doses estimated from passive dosimetry. 

 The calculation of absorbed dose from amount of pesticide or metabolite recovered from 

urine is straightforward. For a metabolite, a simple multiplication of the amount recovered from 

urine times the ratio of pesticide to metabolite molecular weights and adjusting for other 

appropriate pharmacokinetic factors (e.g., percentage of total parent equivalents excreted which 

could be difficult to define) gives an estimate of absorbed dose of pesticide. 

 Biological Monitoring Disadvantages: A thorough understanding of pharmacokinetics 

of the pesticide of interest is required before biomonitoring can provide adequate estimates of 

absorbed dose (Woollen, 1993; Wessels et al., 2003). Thus, a prerequisite for any biomonitoring 

study is a pharmacokinetic study. If the dermally applied dose used is too great, then absorption 

processes may be saturated (e.g., Meuling et al., 2005), potentially giving misleading results. In 

other words, the prerequisite studies are subject to the same pitfalls as the dermal penetration 

studies that are used with results of passive dosimetry to estimate exposure. 

 Inherent variability in pharmacokinetics contributes to uncertainty in biomonitoring 

results. Metabolism of a pesticide, its distribution in the body, and rate of excretion can vary 

considerably among individuals for many reasons. Factors affecting variability include exposure 

route (e.g., inhalation or ingestion versus dermal); body composition (relative amounts of water 

and fat in the body); individual differences in enzyme activities; and environmental factors such 

as concurrent exposure to other chemicals (Silvaggio and Mattison, 1994; Kuhn et al., 1999; 

Garfitt et al., 2002). Data from well-conducted pharmacokinetic studies are essential to 

extrapolating absorbed doses from compounds analyzed in urine samples, yet as acknowledged 

by Woollen (1993), even when these data are available they can at best provide an “indication of 

the extent of this variability.” Pharmacokinetic studies are often done with only four or six 

human subjects, giving little chance of capturing the potential range of variability. Such 

variability may result in over- or underestimation of exposure. 

 Biological monitoring generally requires a greater degree of cooperation from study 

participants in the collection of samples than passive dosimetry, because biomonitoring samples 

are often collected outside the workday. Participants may need to collect, store, and transport 24-

hour urine samples before, during, and after the exposure period (U.S. EPA, 1996a). While 

passive dosimetry can be supervised through the entire monitoring period, biomonitoring often 

requires unsupervised collection of samples. Compliance in 24-hour sample collections may be 

difficult for some test subjects, and some samples may be omitted, spilled, or improperly 

collected through misunderstanding of instructions (Greenberg et al., 1989). The completeness of 

sampling is typically estimated by measuring creatinine levels in urine samples, with abnormally 

low levels suggesting that sampling was incomplete. Incomplete sampling can result in 

underestimation of exposure; attempts to correct results for sample incompleteness can result in 

over- or underestimation. 

 For chemicals with a long half-life relative to the sampling period, biomonitoring results 

can be affected by absorbed material not yet excreted (resulting in underestimated exposure) or by 

previous exposures (resulting in overestimated exposure). In particular, the interval in which 

sequential 24-hour urine samples are collected must be long enough in order for the human body 

Page 42 of 114 
to excrete nearly all of the chemical being monitored. Even in cases where the target analyte 

urinary excretion half-life is less than 24 hours, the biomonitoring interval may have to be longer 

than 4 or 5 days as it takes longer for a dermal dose than an oral dose (used to estimate the halflife) to be absorbed into the body and hence to be excreted from the body. The total absorbed 

dose would be underestimated if it were based on incomplete urinary excretion data. Alternately, 

pre-exposure samples would sometimes show higher results than samples collected during and 

following exposure (e.g., Honeycut and DeGeare, 1993). When the pre-exposure concentration is 

subtracted from such samples, a negative exposure results (Geer et al., 2004). This necessitates 

either use of a default result in place of the sample result, or the loss of the sample for purposes of 

estimating exposure. It should also be noted that peak exposures can be of concern depending 

upon the nature of the toxicological effect. For example, with carbaryl cholinesterase inhibition is 

rapidly reversible which made peak exposures more of a concern than total integrated exposure 

over time. More information regarding this issue can be found at: 

(http://www.epa.gov/scipoly/sap/meetings/2004/december2/finalreportdec22004saprev1.pdf). 

 The correct choice of compound to monitor is key for biomonitoring; the compound must 

be one for which a valid analytical method exists, and it should also be a major metabolite of the 

pesticide to minimize errors of extrapolation. Candidate compounds for monitoring must be 

obtainable in pure form for analysis, and must be stable (Cruz Marquez et al., 2001). If minor 

metabolites, accounting for 10 percent or less of the total dose, are used as target analytes in 

biomonitoring, then errors of extrapolation are magnified when the dose is back-calculated from 

the amount of metabolite found. Exposures could be under- or overestimated due to such 

extrapolation errors. 

 Many metabolites are common to more than one chemical, and some metabolites are 

common to non-pesticide chemicals as well as pesticides. Furthermore, some pesticide 

metabolites are widely used chemicals (e.g., the parathion metabolite, 4-nitrophenol). Assuming 

that the entire amount of these chemicals recovered in urine is due to metabolism of a specific 

pesticide would result in exposures being overestimated (Krieger et al., 2003). Additionally, it 

should be noted that if biomonitoring data are to be used generically, a calculation that develops 

skin loading estimates from absorbed dose levels would require adjusting these levels by an 

inverse of dermal absorption estimates which could create a level of uncertainty based on the 

potential differences in dermal absorption rates. 

 

3.1.3 Comparison of Passive Dosimetry and Biomonitoring Techniques 

 Both passive dosimetry and biomonitoring have advantages and limitations. Fenske and 

Day (2005) suggested that, as regulatory agencies receive studies conducted with both techniques 

concurrently, the concordance between results within these studies can be examined in more 

detail. The Agency desires to evaluate the possible concordance between these two methods in 

order to assess the level of confidence associated with the use of either technique for regulatory 

purposes. Any conclusions would also be developed in the context of the typical uncertainty and 

variability that is associated with such data. 

Page 43 of 114 
 The analysis below presents that examination. Several investigators have conducted 

comparisons of exposure estimates based on biomonitoring and estimates based on passive 

dosimetry. These results are summarized below along with similar analyses completed by the 

Agency and AHETF. These types of comparisons can take one of two approaches that include: 

• Concurrent Analysis: A comparison of concurrent monitoring results in which subjects 

wear dosimeters of some sort and also provide biological specimens related to the same 

monitored activity (i.e., reflects results for the same person at the same time). Typically, 

absorbed dose estimates would be used for comparison necessitating use of dermal 

absorption factors together with results based on passive dosimetry. Similarly, adequate 

pharmacokinetics information would be required to estimate absorbed dose from the 

biological monitoring results. Both of these additional factors can introduce uncertainty 

into the results (e.g., depending upon skin loading concentration dermal absorption can 

vary, for chlorpyrifos 1% is supported by Griffin et al, 1999; 3% is supported by Nolan et 

al, 1984; and 10% is supported by Krieger et al, 1995 – US EPA used 3% in its 

assessments, DPR uses 9.6% in its assessments). U.S. EPA (1996a) also acknowledged 

that studies of this type can confound results because passive dosimeters may intercept a 

portion of the residues that otherwise would reach the skin and thus may negatively bias 

biological monitoring results. However, studies having this design can inform 

assumptions about the potential for passive dosimetry residue breakthrough under field 

sampling conditions. For example, if biological monitoring does not quantify measurable 

residues, then it likely demonstrates that there was no or minimal breakthrough to the skin 

in a study. Conversely, if biological monitoring measures residues were much higher than 

predicted using passive dosimetry it could be a sign that residue interception or removal 

was incomplete (e.g., high absorption into hands before handwash samples collected). 

The AHETF completed a similar analysis (AHETFc et al, 2006). The Agency essentially 

concurs with their results but a more detailed analysis of this document is included below. 

• Retrospective Analysis: Another approach for comparing biological monitoring and 

passive dosimetry is to complete a retrospective analysis in which absorbed dose estimates 

generated using biological monitoring would be compared to passive dosimetry results 

that have been separately generated (e.g., Spencer et al., 1993). One approach would be 

to apply PHED and exposure factors to which the results are to be compared to generate 

estimates of exposure for the conditions under which from the biological monitoring was 

performed (e.g., for a similar scenario and activity, acres treated and application rate). 

Alternatively, researchers could conduct a separate passive dosimetry duplicative study of 

the conditions of the biological monitoring. U.S. EPA (1996a) noted that this latter 

approach would eliminate the possibility of negatively biasing biological monitoring 

results. Comparison of biomonitoring results for workers with and without concurrent 

dermal monitoring should give identical results if both sets of workers did approximately 

the same activities under the same conditions, and if the dosimeters did not interfere with 

pesticide residues reaching the skin. However, as will be described below, there can be 

intra-personal variability which has been noted in various monitored test subjects which 

could lead to limitations in the conclusions that could be drawn on this approach. The 

AHETF completed a similar analysis (AHETFd, 2006). The Agency essentially concurs 

with their results but a more detailed analysis of this document is included below. 

Page 44 of 114 
 Prior to using the results from studies measuring absorbed dose from biological 

monitoring to review the concordance with passive dosimetry monitoring methods, the accuracy 

of the biological monitoring itself needs to be properly determined. However, because the review 

provided in this section is based on existing studies not specifically designed to compare 

monitoring methods, highlighting the limitations of study design discussed in Sections 3.1.1 and 

3.1.2 above for both biological and passive dosimetry monitoring methods is important. 

Uncertainties in both methods highlighted below may explain some of the variability of the 

results presented in this section. 

 The absorbed dose measured from biological monitoring methods is dependent upon the 

accuracy of the pharmacokinetics of the chemical monitored as discussed in Section 3.1.2. 

Assuming accurate pharmacokinetics, some of the factors that may affect the absorbed dose 

monitored in the studies presented in this section include: 

• Incomplete urine collection from test subjects, previous exposure to the same chemical, 

and use of concurrent passive dosimetry may intercept potential exposure. 

• In the comparisons that are presented below from non concurrent analysis of biological 

and passive dosimetry estimates, minor differences in study design parameters (e.g., PPE, 

personal hygiene, equipment types and configuration, nozzle pressures, product and dilute 

concentrations, etc.) will influence the potential exposure. 

Estimating total absorbed dose from passive dosimetry monitoring methods is also dependent on 

many factors that affect the comparison of methods in this section. For inhalation, nearly all of 

the monitoring techniques presented are based on personal air sampling using filter cassettes. 

Inhalation exposures are based on all of the particulates collected on the filters, assuming all 

particulates are either inhalable or respirable. Pump flow rates and breathing rates are 

standardized among most of the studies. The dermal portion of the total absorbed dose introduces 

even more variability. As discussed in Section 3.1.1 above, some of the confounding factors 

associated with the dermal passive dosimetry in the comparison presented below are attributed to 

the following: 

• Inconsistent dermal monitoring techniques are used in some of the studies presented 

below including whole body dosimeters versus patches and hand washing 

techniques/solvents versus cotton gloves. Additionally, for some of the chlorpyrifos 

studies, the dermal estimates are based on measured clothing penetration factors that also 

introduce uncertainties. 

• Chlorpyrifos is used in many of the examples presented below and a 3 percent dermal 

absorption is used (Nolan et al 1984) while the range of dermal absorption for chlorpyrifos 

has been reported in the literature from 1 percent (Griffin et al 1999) to 10 percent 

(Krieger 1995). 

• A single dermal absorption value is used to estimate absorbed dose from dermal 

measurements when the actual dermal absorption over various loading concentrations is 

not expected to be constant (Zendzian 2000). 

The following describes many of the analyses that have been completed for the purposes of 

comparing passive dosimetry and biological monitoring results. It should be noted that although 

Page 45 of 114 
some of these results are inconclusive, they were included to show the Agency’s current in-house 

data base. 

Comparisons Involving Chlorpyrifos: Chlorpyrifos has been the compound of choice in 

numerous studies featuring biomonitoring. The metabolite, 3,5,6-trichloropyridinol (TCP) 

is specific to chlorpyrifos; furthermore, it represents a majority of the absorbed 

chlorpyrifos dose. Nolan et al. (1984) recovered 70% ± 11% of the applied dose four days 

following a 0.5 mg/kg oral dose of chlorpyrifos to six volunteers (range: 49-79%). Eight 

days following a 5.0 mg/kg dermal application of chlorpyrifos to five volunteers, Nolan et 

al. (1984) recovered 1.28% ± 0.83% of the applied dose (range: 0.50-2.60%); the lower 

recovery of the dermal dose is consistent with a dermal absorption of 3%. This study 

provides the basis for biomonitoring of chlorpyrifos, with urinary TCP considered to 

represent 70% of the absorbed chlorpyrifos and an elimination half-life of 26.9 hours. A 

more recent study suggested that the half-life of dermally applied chlorpyrifos yields an 

elimination half-life of about 41 hours, indicating that full urine clearance following 

dermal exposure could take a week or more (Meuling et al., 2005). 

Comparisons between passive dosimetry and biomonitoring among multiple studies with 

chlorpyrifos show that both methods generally give similar results. For example, Fenske 

and Day (2005) compared multiple handler studies using both passive dosimetry and 

urinary biomonitoring, which had been submitted to U.S. EPA in support of the 

reregistration of chlorpyrifos (Leighton, 2000). Figure 3-2 summarizes geometric mean 

internal doses reported by Fenske and Day (2005), from exposure estimates calculated by 

Leighton (2000). Eight studies were used in the comparison. In the eight studies 

summarized in Figure 3-2, the ratios of mean estimates derived from passive dosimetry vs. 

biomonitoring range between approximately 0.3 and 3.3, and the greatest difference 

between mean exposures was about 3-fold. This suggests that the differences in exposures 

estimated by the two methods at the means were not very large, nor did one method 

consistently result in greater estimates than the other. At higher percentiles of exposure, 

these differences could vary which is difficult to predict because of the limited utility of 

PHED for developing distributional estimates of exposure. 

Page 46 of 114 
Figure 3-2. Comparison of Concurrent Biomonitoring and Passive Dosimetry Data 

from Selected Studies Monitoring Handler Exposure to Chlorpyrifos a

-20

-10

0

10

20

30

40

50

60

1234567

Handler Description

Total Absorbed Chlorpyrifos 

(g/kg Active Ingredient Handled)

8

Dosimetry Urine Monitoring

a

 Each point represents the geometric mean of absorbed dose (ug/kg active ingredient handled) calculated from 

either passive dosimetry (closed circles and solid lines) or biomonitoring (open circles and dotted lines), as 

reported by Fenske and Day (2005) from calculations made by Leighton (2000). Numbers across the 

horizontal axis represent groups of handlers performing similar activities within a study. 1: 

Mixer/loader/applicator (M/L/A) handling a granular formulation (N=12; Bischoff, 1998). 2: M/L/A 

applying a microencapsulated formulation with a high-pressure handwand (N=13; Contardi et al., 1993). 3: 

Groundboom applicator (N=9; Shurdut et al., 1993). 4: Airblast applicator (N=15; Honeycutt and DeGeare, 

1994). 5: Mixer/loader (M/L) handling an emulsifiable concentrate (EC) formulation (N=13; Knuteson et 

al., 1999). 6: M/L handling an EC formulation (N=15; Honeycutt and DeGeare, 1994). 7: M/L handling an 

EC formulation (N=3; Shurdut et al., 1993). 8: M/L handling a wettable powder formulation (N=6; Shurdut 

et al., 1993). 

Honeycutt et al. (2000) compared multiple handler and reentry worker studies using both 

passive dosimetry and urinary biomonitoring of chlorpyrifos, and summarized results of 

the comparison with both arithmetic and geometric mean estimates of exposure for each 

study. All of the handler studies evaluated by Honeycutt et al. (2000) were also reviewed 

by Fenske and Day (2005), and are included in Figure 3-2. In addition to the studies 

involving handlers, Honeycutt et al. (2000) also reviewed two studies involving reentry 

workers (Honeycutt and DeGeare, 1993; Shurdut et al., 1993). Figure 3-3 summarizes 

results from these reentry studies. 

Page 47 of 114 
Figure 3-3. Comparison of Concurrent Biomonitoring and Passive Dosimetry Data 

from Selected Studies Monitoring Reentry Exposure to Chlorpyrifos a

-2

-1

0

1

2

3

4

5

6

7

8

1234

Reentry Worker Description

Total Absorbed Chlorpyrifos 

(g/kg Active Ingredient Handled)

Dosimetry Urine Monitoring

a

 Each point represents the geometric mean of absorbed dose (ug/kg active ingredient handled) calculated from 

either passive dosimetry (closed circles and solid lines) or biomonitoring (open circles and dotted lines) in 

studies reported by Honeycutt et al. (2000). Numbers across the horizontal axis represent groups of reentry 

workers performing similar activities in treated crops within a study. 1: Scouts in cover crops, small trees, 

tomatoes, and cauliflower (N=10; Shurdut et al., 1993). 2: Citrus harvesters (N=5; Honeycutt and DeGeare, 

1993). 3: Citrus pruners, wet weather (N=5; Honeycutt and DeGeare, 1993). 4: Citrus pruners, dry weather 

(N=5; Honeycutt and DeGeare, 1993). 

As with the doses absorbed by handlers summarized in Figure 3-2, the absorbed doses 

estimated by passive dosimetry and biomonitoring for reentry workers differ at most by 

about 3-fold. This is a small difference considering the variability that is typical in such 

studies. Honeycutt et al. (2000) plotted geometric and arithmetic means of passive 

dosimetry vs. biomonitoring data, and found a good correlation (r2

 = 0.79 and r2 = 0.86, 

respectively). 

Geer et al. (2004) examined exposure monitoring data from five studies, four of which 

were also reviewed by Fenske and Day (2005), Honeycutt et al. (2000), or both. These 

four studies included Contardi et al. (1993), Shurdut et al. (1993), Bischoff (1998), and 

Knuteson et al. (1999). The fifth study monitored five M/L/A doing termiticide 

applications with a handheld spraygun (Barnekow and Shurdut, 1998). Using a simple 

regression model of urinary TCP against the absorbed dose estimated from passive 

dosimetry on 71 monitoring events from the five studies, Geer et al. (2004) found a 

positive association between these two parameters and that 29% of the variability in 

urinary TCP levels could be explained by the simple regression model. It should also be 

noted that Geer et al (2004) considered the impact of the possible values related to the 

dermal absorption of chlorpyrifos which could impact any comparison of results by as 

much as a factor of 10 since absorption estimates range from 1 to 10 percent. 

Page 48 of 114 
Comparisons Involving Other Organophosphorus Pesticides (OPs): Several OPs in 

addition to chlorpyrifos have specific metabolites that have been quantified in urine. 

These include chlorfenvinphos, diazinon, fenitrothion, malathion, methyl-parathion and 

parathion (Maroni et al., 2000; Wong and Anderson, 2000; Tuomainen et al., 2002). 

Alternately, dialkylphosphate metabolites, which are common to multiple OPs, can be 

monitored (Griffin et al., 1999). Several studies have been done in which exposure 

estimates based on passive dosimetry have been correlated with metabolites excreted in 

urine. Although these did not feature comparisons of absorbed dose estimates, as the 

correlations involving a range of exposures they provide support for the idea that the two 

methods both give reasonable estimates of absorbed dose. 

For example, Aprea et al. (2004) monitored respiratory and dermal exposures to 

dimethoate of eight applicators spraying olive trees. Absorbed doses were calculated from 

these exposures assuming 10% dermal absorption and 100% inhalation absorption. 

Applicators also provided urine samples for 24 hours before and 24 hours following 

exposure; the alkylphosphates dimethyldithiophosphate (DMDTP), dimethylthiophosphate 

(DMTP), and dimethylphosphate (DMP) were quantified in these samples. Linear 

regression showed moderate correlation between estimated absorbed dose and excreted 

alkylphosphates (r2

 = 0.60; p = 0.024). 

Spencer et al. (1993) monitored harvester exposure in peach and apple orchards treated 

with azinphosmethyl and phosmet. Dermal exposure was monitored with clothing 

dosimeters (long-sleeved t-shirts and socks), hand wipes followed by hand washes, and 

face/neck wipes; azinphosmethyl and phosmet were quantified in all samples. 24-hour 

urine samples were collected and analyzed for DMDTP, DMTP, and DMP. Passive 

dosimetry and biomonitoring were done concurrently in 48 harvesters at five study sites; 

an additional 22 harvesters participated in urine monitoring without passive dosimetry. 

Excretion of alkylphosphates into urine did not differ significantly between harvesters 

subject to dermal exposure monitoring and those who were not. A linear regression of 

mean potential dermal exposure against mean excreted alkylphosphates at the five sites 

resulted in a moderate correlation (r2

 = 0.64; p < 0.10). 

In addition to the numerous studies where passive dosimetry data correlated well with 

biomonitoring results, several studies have been published in which estimated absorbed 

dose did not correlate with biomonitoring. Often in these studies, correlations were 

adversely affected by factors including small sample sizes (as few as three monitoring 

events; e.g., Tuomainen et al., 2002); history of exposure to a pesticide prior to the 

exposure monitoring period (e.g., Grover et al., 1986); incomplete passive dosimetry (e.g., 

no monitoring of hand exposures; Krieger and Dinoff, 2000); or short urine sampling 

intervals (as short as 2 hours; e.g., Krieger and Dinoff, 2000). Comparison of partial 

samples with full 24-hour urine collections suggests that sampling for shorter intervals 

yields unreliable results for at least some OPs (Franklin et al., 1981). 

In a preliminary study, Tuomainen et al. (2002) monitored malathion exposure of three 

applicators in greenhouses using handheld lance sprayers, using the specific metabolite, 

monocarboxylic acid (MMA). Dermal exposure was monitored by analysis of patches cut 

from the cotton coverall each applicator wore, with patches cut in locations dictated by 

OECD guidelines. Hand exposure was estimated with outer glove washes and by shaking 

Page 49 of 114 
the gloves with ethanol. Respiratory exposure was not monitored; however, full face 

respirators were worn by all three applicators, which would be anticipated to substantially 

decrease exposure. A regression of excreted MMA against potential hand exposure to 

malathion (residues extracted from gloves) was highly correlated (R2 = 0.82), while a 

regression of excreted MMA against potential total dermal exposure to malathion was (R2

= 0.44) was poor. Tuomainen et al. (2002) suggested that the poor correlation of potential 

total dermal exposure with excreted MMA was possibly due to the small sample size. 

In some cases, even when urine samples were collected over a short interval, pesticide 

metabolites in urine may correlate with exposure. For example, Weisskopf et al. (1988) 

monitored exposure of 15 handlers applying a granular formulation of diazinon to planters 

and shrub areas. Dermal exposure was monitored with patches and hand rinses; a collar 

patch was used to estimate head exposure. Dermal samples were collected during lunch 

(morning shift) and at the end of the workday (afternoon shift). Urine samples were 

collected at the beginning and the end of the workday and analyzed for the metabolite, 

diethylthiophosphate (DETP). A log-log plot of morning shift dermal exposures 

correlated moderately well (R2 = 0.65) with afternoon DETP levels in urine samples 

(Weisskopf et al., 1988). 

Comparisons Involving Non-OP Pesticides: Several biomonitoring studies and 

preliminary investigations have been reported on pesticides other than OPs. Typically, 

urine is analyzed for a specific metabolite or occasionally the unchanged pesticide; nonchemical-specific metabolites analogous to alkylphosphates are rarely used. However, 

difficulties are frequently encountered in finding metabolites that account for at least 30% 

of the absorbed dose, confounding such studies. For example, biomonitoring studies with 

captan generally rely on the metabolite, tetrahydrophthalimide (THPI), and some 

conducted concurrent passive dosimetry (Winterlin et al., 1986; de Cock et al., 1995; de 

Cock et al., 1998). Yet pharmacokinetic studies done in the rat suggest that just 2% of an 

absorbed captan dose is excreted as THPI (van Welie et al., 1991), limiting its ability to 

provide quantitative estimates of captan exposure. However, de Cock et al. (1995) 

reported that while THPI recovered from the urine of growers applying captan did not 

correlate well with total exposure estimates, there were correlations with estimates of 

exposure to exposed skin, as well as use of protective equipment. 

Some non-OP pesticides have major metabolites that are excreted into urine. For 

example, Chester and Hart (1986) monitored a group of thirteen mixer/loader/applicators 

(M/L/As) who applied the herbicide, fluazifop-butyl, using vehicle-mounted sprayers. 

Each M/L/A was monitored during two separate applications; first, with biomonitoring 

(24-hour urine samples for two days pre-exposure and 7 days post-exposure); then, after 

urine collections were complete, a second application was monitored with WBDs 

(synthetic coveralls, cotton gloves and face masks). Dosimeters were analyzed for 

fluazifop-butyl and urine for the metabolite, fluazifop. This study was unusual in that it 

did not feature concurrent passive dosimetry and biomonitoring. It is perhaps not 

surprising that no correlation was found between the estimated dermal exposure of each 

M/L/A to fluazifop-butyl and the fluazifop residues recovered from the urine (R2

 = 0.009). 

Figure 3-4 shows the results of biomonitoring vs. estimated dermal exposure; Chester and 

Hart (1986) adjusted the dermal exposure estimates to account for differences in amounts 

handled between the first and second applications (amounts handled were not reported). 

Page 50 of 114 
However, Woollen (1993) reported on subsequent laboratory studies suggesting that 

dermal absorption was inversely related to dermal exposure at applied doses 

corresponding to the range estimated by Chester and Hart (1986). Depending on the 

magnitude of the effect, it could confound estimates of absorbed dose from passive 

dosimetry. Conversely, a correlation might be apparent if absorbed doses were 

normalized by amount of active ingredient handled. More information is needed about 

amounts of active ingredient handled by each replicate, and about residues recovered from 

dosimeter sections, before the effectiveness of the study design can be evaluated. 

Figure 3-4. Comparison of Results of Sequential Biomonitoring and Passive 

Dosimetry of Mixer/Loader/Applicators Handling Fluazifop-Butyl a

0

0.5

1

1.5

2

2.5

3

3.5

0 50 100 150 200 250 300 350 400

Estimated Dermal Exposure to Fluazifop-Butyl (mg)

Fluazifop Recovered from Urine (mg)

a

 Each point represents one replicate doing two applications; the first application was monitored with 24-hour 

urine samples collected for 7 days and the second application was monitored with coveralls, cotton gloves 

and disposable face masks (Chester and Hart, 1986). Dosimeters were analyzed for fluazifop-butyl and 

urine for the metabolite, fluazifop. Chester and Hart (1986) adjusted the dermal exposure estimates to 

account for differences in amounts handled between the first and second applications (amounts handled were 

not reported).

Cowell et al. (1991) monitored exposure of M/L/As handling dithiopyr during 

applications to turfgrass at sites in three cities: Atlanta, Georgia, Cincinnati, Ohio, and 

Cleveland, Ohio. Concurrent passive dosimetry and biomonitoring were conducted with 

six handlers at each site, using cotton gauze patches, hand washes, personal air monitors, 

and 24-hour urine samples collected for 72 hours post-application. Absorbed dose was 

estimated from passive dosimetry data, assuming a dermal absorption of 0.08% from an 

earlier study using rhesus monkeys. Data from that study also supported biomonitoring, 

by showing that rhesus monkeys dosed dermally with dithiopyr excreted an average of 

64.5% of the absorbed dose in the urine as the metabolite, dicarbothioic acid (DCTA). 

During analysis of urine samples, DCTA was methylated to dithiopyr and quantified; 

however, none of the samples had dithiopyr residues above the limit of quantification of 

5.4 ppb. About two-thirds of the samples had dithiopyr residues above the limit of 

detection (LOD); the LOD varied from day to day, but averaged 0.32 ppb. Absorbed 

Page 51 of 114 
doses estimated from biomonitoring were based on results above the LOD, with 1⁄2 LOD 

substituted for non-detects. Estimates of absorbed dose calculated from passive dosimetry 

data averaged 0.0809 + 0.0575 μg/kg body weight/lb active ingredient handled, nearly 

twice the average absorbed dose based on biomonitoring, 0.0460 + 0.0238 μg/kg body 

weight/lb active ingredient handled. 

Additional Concurrent Analysis: In addition to the analyses prepared above, the 

Agency evaluated all of the data in PHED and its risk assessments and not just for the 

scenarios in the case study in order to identify additional studies that used a concurrent 

biomonitoring and passive dosimetry approach. This included a review of the PHED data 

and of results for risk assessments completed over several years by the Agency. Much of 

the data which were identified have already been addressed in the analyses described 

above. Data which are not redundant have been included below (Table 3-1 & Figure 3-5). 

A ratio that compares mean dose estimates from each study has been calculated. If the 

values exceed 1 then it illustrates that absorbed dose estimates based on passive dosimetry 

exceed those calculated using concurrent biomonitoring. 

Table 3-1: Summary of Concurrent Biomonitoring & Passive Dosimetry Results Based On PHED & Agency Risk Assessments 

Chemical Study 

Reference 

Scenario/ 

Equipment 

(Scenario #) 

mean lb 

ai/ sample 

size 

Clothing/PPE Dosimeters 

Passive Dosimetry 

Dose (Dermal and 

Inhalation) 

Biomonitoring Dose 

Ratio Mean 

Passive 

Dosimetry/ 

Biomonitoring 

mixer/loader/ 

applicator 

liquids: 

Backpack 

sprayer 

(1) 

0.13 lb ai / 

N=2 

0.07 ug/kg and 0.09 

ug/kg 

(3% dermal abs, 100% 

inh) 

0.6 ug/kg and 0.1 

ug/kg 0.51 

mixer/loader/ 

applicator 

liquids: low 

pressure 

handwand 

(2) 

0.06 lb ai / 

N=1 

0.13 ug/kg 

(3% dermal abs, 100% 

inh) 

Chlorpyrifos 0.1 ug/kg 1.30 

Contardi et al. 

1993. 

43027901 as 

cited in 

USEPA 2000 

(RED ORE 

chpt, June 19, 

T. Leighton) mixer/loader/ 

applicator 

liquids: high 

pressure 

handwand 

(3) 

0.47 lb ai 

(mean) / 

N=13 

Coveralls represented 

single layer of normal 

work clothing (also used 

for dosimetry) 

Coveralls torso sections 

used in conjunction with 

briefs/T-shirts to estimate 

penetration factor. Arm and 

leg sections of coveralls 

adjusted for penetration 

factor to determine dermal 

exposure values. Total 

dermal exposure calculated 

as sum of arm/leg adjusted 

coverall values, t-shirt/brief 

values (torso), head 

patch/headband/neckband 

(dependent on worker), and 

hand rinse. 

3.85 ± 7.7 ug/kg (A.M.) 

range: 0.21-28 ug/kg 

(3% dermal abs, 100% 

inh) 

1.15 ug/kg ±1.13 

(A.M.) 

range: 0.1- 4.2 ug/kg 

3.35 

open 

mixer/loader, 

wettable 

powder 

(4) 

50 WP 

formulation

; 32.6 lb ai; 

N=6 

32.5 ± 24.2 ug/kg (A.M) 

range: 3.8-61.7 ug/kg 

(3% dermal abs; 100% 

inh) 

11.7 ± 6.9 ug/kg 

(A.M.) 

range: 4.2-22 ug/kg 

2.78 

Chlorpyrifos 

Shurdut et al. 

1993, 

42974501 as 

cited in 

USEPA 2000 

(RED ORE 

chpt, June 19, 

T. Leighton) 

open 

mixer/loader - 

liquids 

(5) 

4E 

formulation

; 107 lb ai; 

N=3 

9.6 ± 16 ug/kg (A..M) 

 

range: 0.2 - 28 ug/kg 

(3% dermal abs, 100% 

inh) 

7.8 ± 10 ug/kg (A.M) 

range: 2.1 - 20 ug/kg 

1.23 

Chlorpyrifos 

Shurdut et al. 

1993. 

42974501 as 

cited in 

USEPA 2000 

(RED ORE 

chpt, June 20, 

D. Smegal) 

groundboom 

applicator--

open cab 

liquid 

application 

(6) 

27-330 lb 

ai; n=9 

Coveralls represented 

single layer of normal 

work clothing (also used 

for dosimetry). Workers 

also wore 1⁄2 face 

respirator. 

Coveralls torso sections 

used in conjunction with 

briefs/T-shirts to estimate 

penetration factor. Arm, leg 

and torso sections of 

coveralls adjusted for 

penetration factor to 

determine dermal exposure 

values. Total dermal 

exposure calculated as sum 

of arm/leg/torso adjusted 

coverall values, head patch, 

and hand rinse. 

Dermal: 0.96 ug/kg 

Inhalation: 1.7 ug/kg; 

Total absorbed dose = 

1.63 ug/kg 

9.8 ug/kg 0.17 

Page 52 of 114 
Table 3-1: Summary of Concurrent Biomonitoring & Passive Dosimetry Results Based On PHED & Agency Risk Assessments 

Chemical Study 

Reference 

Scenario/ 

Equipment 

(Scenario #) 

mean lb 

ai/ sample 

size 

Clothing/PPE Dosimeters 

Passive Dosimetry 

Dose (Dermal and 

Inhalation) 

Ratio Mean 

Passive Biomonitoring Dose Dosimetry/ 

Biomonitoring 

mixer/loader 

(open mixing) 

(7) 

78 lb ai; 

N=15 

6.2 ± 6.2 ug/kg (A.M.) 

range: 0.67-26 ug/kg 

(3% dermal abs, 90% PF 

for inh) 

10 ± 21 ug/kg (A.M) 

 

range: 1-85 ug/kg 

0.62 

Chlorpyrifos 

Honeycutt 

and Day 

1994. 

43138102 as 

cited in 

USEPA 2000 

(RED ORE 

chpt, June 19, 

T. Leighton) airblast 

applicator--

open cab 

(8) 

78 lb ai; 

N=15 

Coverall (also used for 

dosimetry), short sleeve 

shirt, pants, socks, work 

boots, chemical resistant 

gloves, helmet, goggles, 

and chemical respirator. 

The short sleeve shirt 

and pants were designed 

to be an intermediate 

layer of clothing which 

is required to be worn 

under coveralls as a 

protective garment in 

addition to coveralls by 

the State of California. 

Inner body dosimeters: 

100% cotton T-Shirts and 

briefs. Outer whole body 

dosimeters: coveralls. 

Coveralls torso sections 

used in conjunction with 

briefs/T-shirts to estimate 

penetration factor. Arm, leg 

and torso sections of 

coveralls adjusted for 

penetration factor to 

determine dermal exposure 

values. Total dermal 

exposure calculated as sum 

of arm/leg/torso adjusted 

coverall values, head patch, 

and hand rinse. 

5.9 ± 6 (A.M.) 

range: 0.73-20 ug/kg 

(3% dermal abs and 

90% PF for inh) 

13 ± 24 ug/kg (A.M.) 

range: 1.4 - 95 ug/kg 

0.45 

Chlorpyrifos 

Murphy 1998 

(Bischoff 

1998 in ORE 

chpt), 

44483501 as 

cited in 

USEPA 2000 

(RED ORE 

chpt, June 19, 

T. Leighton) 

granular--

loading/ 

applying 

(enclosed cab) 

(9) 

77.8 lb ai; 

n=16 

dosimetry; 

n=12 

biomonitori

ng 

Cotton coverall 

represented single layer 

or work clothing (also 

used as dosimeter), 

socks and a hat 

Coveralls torso sections 

used in conjunction with 

briefs/T-shirts to estimate 

penetration factor. Arm and 

leg sections of coveralls 

adjusted for penetration 

factor to determine dermal 

exposure values. Total 

dermal exposure calculated 

as sum of arm/leg adjusted 

coverall values, t-shirt/brief 

values (torso), head patch, 

and hand rinse. 

0.3 ± 0.36 ug/kg (A.M) 

range: 0.056-1.4 ug/kg 

(3% dermal abs and 

100% inh) 

0.73 ±0.33 ug/kg 

(A.M) 

range: 0.13-1.39 

ug/kg 

0.41 

Chlorpyrifos 

Knuteson et 

al. 1999; 

44739302 as 

cited in 

USEPA 2000 

(RED ORE 

chpt, June 19, 

T. Leighton, 

and Study 

DER) 

mixing/ 

loading 

emulsifiable 

concentrate 

for aerial 

application 

(10) 

n=15 

dosimetry; 

n=13 

biomonitori

ng (avg 89 

min 

exposure) 

Cotton coverall 

represented single layer 

or work clothing (also 

used as dosimeter), 

socks and a hat 

Coveralls torso sections 

used in conjunction with 

briefs/T-shirts to estimate 

penetration factor. Arm and 

leg sections of coveralls 

adjusted for penetration 

factor to determine dermal 

exposure values. Total 

dermal exposure calculated 

as sum of arm/leg adjusted 

coverall values, t-shirt/brief 

values (torso), head patch, 

and hand rinse. 

1.13 ug/kg ± 0.66 (A.M) 

range: 0.66-2.54 ug/kg 

(3% dermal abs and 

100% inh) 

3.61 ug/kg (A.M.) 

1.32 ug/kg (G.M) 

range: 0-32 ug/kg 

0.31 

Chlorpyrifos 

Barnekow 

and Shurdut 

1998; 

44729401 as 

cited in 

USEPA 2000 

(RED ORE 

chpt, June 20, 

D. Smegal) 

hose-end 

sprayer; 

applicator 

(11) 

2.17 lb; avg 

1.5 hr turf 

treatment; 

n=15 

Cotton coverall 

represented single layer 

or work clothing (also 

used as dosimeter), 

socks and a hat 

Coveralls torso sections 

used in conjunction with 

briefs/T-shirts to estimate 

penetration factor. Arm and 

leg sections of coveralls 

adjusted for penetration 

factor to determine dermal 

exposure values. Total 

dermal exposure calculated 

as sum of arm/leg adjusted 

coverall values, t-shirt/brief 

values (torso), head patch, 

and hand rinse. 

0.88 ± 0.62 ug/kg (A.M) 

range: 0.21-2.24 ug/kg 

0.65 ± 1.43 ug/kg 

(A.M.) (n=15) 

Range: 0 - 4.84 ug/kg 

0.4 ug/kg (G.M) 

(n=8) 

 (7 workers had 

exposure less than 

background) 

1.35 

Chlorpyrifos 

Barnekow et 

al. 1999; 

44739301 as 

cited in 

USEPA 2000 

(RED ORE 

chpt, June 20, 

D. Smegal) 

screw top 

bottle 

(12) 

0.00073 lb 

ai; n=15 

Cotton coverall with the 

sleeves cut off to 

simulate a short sleeve 

shirt and a hat. 

Coveralls torso sections 

used in conjunction with 

briefs/T-shirts to estimate 

penetration factor. Leg 

sections of coveralls 

adjusted for penetration 

factor to determine dermal 

exposure value. Exposure 

to the forearms determined 

using a 2.5 cm wide arm 

band around each forearm. 

Total dermal exposure 

calculated as sum of arm 

bands, leg adjusted coverall 

values, t-shirt/brief values 

(torso), head patch and 

hand rinse. 

0.25 ug/kg (A.M.) 

range: 0.03-0.86 ug/kg 

0.49 ug/kg (A.M.) 

0.24 ug/kg (G.M.) 

Range: 0-1.9 ug/kg 

0.51 

Page 53 of 114 
Table 3-1: Summary of Concurrent Biomonitoring & Passive Dosimetry Results Based On PHED & Agency Risk Assessments 

Chemical Study 

Reference 

Scenario/ 

Equipment 

(Scenario #) 

mean lb 

ai/ sample 

size 

Clothing/PPE Dosimeters 

Passive Dosimetry 

Dose (Dermal and 

Inhalation) 

Ratio Mean 

Passive Biomonitoring Dose Dosimetry/ 

Biomonitoring 

Chlorpyrifos 

Barnekow 

and Shurdut, 

1998.4472940

2 as cited in 

USEPA 2000 

(RED ORE 

chpt, June 20, 

D. Smegal) 

mixer/loader/ 

applicator--

hand held 

sprayer or 

injection rod 

(13) 

10.72 lb ai; 

n=15 

dosimetry 

and n=5 

biomonitori

ng 

Cotton coverall 

represented single layer 

or work clothing (also 

used as dosimeter), 

socks and a hat 

Cotton long underwear (for 

non-biomonitoring 

replicates) or cotton briefs 

and T-shirt (for 

biomonitoring replicates). 

Coveralls torso sections 

used in conjunction with 

briefs/T-shirts to estimate 

penetration factor. Arm and 

leg sections of coveralls 

adjusted for penetration 

factor to determine dermal 

exposure values. Total 

dermal exposure calculated 

as sum of arm/leg adjusted 

coverall values (or long 

underwear values), tshirt/brief values (torso), 

head patch, and hand rinse. 

2.5 ug/kg (G.M for 15 

replicates) (3% dermal 

abs) and 0.91 ug/kg 

inhalation (G.M.) (no 

protection) 

total dosimetry = 3.24 

ug/kg (based on n=5 

individuals used in 

biomonitoring) 

4.3 ug/kg 

(n=5) 0.75 

Mixing/ 

Loading 

Using Probe 

Extractors 

(14) 

Mixer/Loaders = 2.1 

ug/kg ai/day 

Mixer/Loaders = 55 

ug/kg ai/day 0.04 

Propanil 

Honeycutt, 

2003, 

46075501; as 

cited in 

USEPA 2004 

Study 

Review, DP 

293906 

Aerial 

Application 

(15) 

710 lb ai. 

N=30 

Mixer/loaders: long 

sleeved shirts (100% 

cotton), long pants 

(100% cotton), t-shirt 

(100% cotton), briefs 

(100% cotton), socks, 

full rubber boots over 

socks, a baseball type 

cap, chemical resistant 

apron, goggles and a 

pair of chemical 

resistant gloves. Pilots: 

long sleeved shirts 

(100% cotton), long 

pants (100% cotton), tshirt (100% cotton), 

briefs (100% cotton), 

socks, tennis shoes, 

helmet with visor and 

chemical resistant 

gloves worn when 

outside of the airplane 

(removed inside the 

plane). 

Inner dosimeter: tshirt/briefs. Outer 

dosimeter: long sleeved 

shirt and long pants. Outer 

dosimeter torso sections 

used in conjunction with 

inner dosimeter to estimate 

penetration factor. Arm, leg 

and torso sections of outer 

dosimeter adjusted for 

penetration factor to 

determine dermal exposure 

values. Total dermal 

exposure calculated as sum 

of arm/leg/torso adjusted 

values, head patch, and 

hand rinse. 

Pilots = 0.48 ug/kg 

ai/day 

Pilots 35.5 ug/kg 

ai/day 0.01 

ready to use 

hose end 

sprayer-liquid 

applicator 

(16) 

0.5 lb ai 

(18-122 

minutes) 

(Dosimetry 

estimates 

for 0.5 ac, 

biomonitori

ng 

estimates as 

in study, 

5,000 ft2 or 

0.1 ac) 

Dermal: 3.6 ug/kg 

(8% dermal abs) 

Inhalation 1.31 ug/kg 

(G.M.) 

(n=11) 

0.61 ug/kg (G.M) 

range: 0.08-3.11 

(n=15) 

5.73 

conventional 

hose end 

sprayer 

(17) 

0.5 lb ai 

(18-122 

minutes) 

(Dosimetry 

estimates 

for 0.5 ac, 

biomonitori

ng 

estimates as 

in study, 

5,000 ft2 or 

0.1 ac) 

Dermal: 5.36 ug/kg 

(4% dermal abs 

Inhalation = 0.33 ug/kg 

(G.M.) 

(n=12) 

0.96 ug/kg (G.M) 

range: 0.03-10.9 

(n=14) 

5.70 

Diazinon (a) 

Rosenheck 

2000, 

45184305; as 

cited in 

USEPA 2000 

Study Review 

DP 268247, 

And 

November 

2000 ORE 

chpt for 

dosimetry 

dose 

estimates 

(D.Smegal) 

handpump 

sprayer 

(18) 

0.021 lb ai 

(or 4 

gallons 

diluted 

product) 

Assumed 

1000 ft2 

treated 

shorts, short-sleeve shirt, 

shoes, socks. 

Long underwear worn 

under shorts and shirt, 

face/neck wipes, and 

handwashes used to 

calculate dermal exposure. 

Dermal = 2.24 ug/kg 

(14% dermal abs) 

Inhalation = 0.21 ug/kg 

(n=10) 

0.75 ug/kg (A.M--

normally dist) 

range: 0.09-2.46 

(n=13) 

3.27 

Page 54 of 114 
Table 3-1: Summary of Concurrent Biomonitoring & Passive Dosimetry Results Based On PHED & Agency Risk Assessments 

Chemical Study 

Reference 

Scenario/ 

Equipment 

(Scenario #) 

mean lb 

ai/ sample 

size 

Clothing/PPE Dosimeters 

Passive Dosimetry 

Dose (Dermal and 

Inhalation) 

Ratio Mean 

Passive Biomonitoring Dose Dosimetry/ 

Biomonitoring 

Applicatorsgroundboom 

(19) 

148-3450 lb 

atrazine 

over 2-3 

days; n=14 

(7 workers 

for 2 days) 

7.71E-4 mg/lb ai (GM) 

range: 2.1E-2 to 6.4E-5 

mg/lb ai 

6.05E-4 mg/lb ai 

(GM) 

range: 7.87E-3 to 

8.61E-3 mg/lb ai 

1.27 

Mixerloader/truck 

tenders—

groundboom 

(20) 

148-3450 lb 

atrazine 

over 2-3 

days; n=14 

(7 M/L 

closed 

system for 

2 day) 

7.34E-4 mg/lb ai (GM) 

3.77E-4 mg/lb ai 

(GM) 

range: 7.87E-3 to 

8.61E-3 mg/lb ai 

1.95 

Atrazine (b) 

Honeycutt et 

al. 1996 

MRID 

43934417 

(interim); 

Selman 1996, 

MRID 

43934418 

(interim); 

Selman and 

Rosenheck 

1996, MRID 

44152109 

(Final); 

Honeycutt et 

al. 1996, 

44152111 

(Final) 

44315403 

(amendment) 

as cited in 

USEPA 2002 

Revised ORE 

Chpt (April 

2002, G. 

Bangs). 

mixer-loader/ 

applicators—

groundboom 

(21) 

148-3450 lb 

atrazine 

over 2-3 

days; n=6 

(3 closed 

mixing and 

closed cab 

and 1 open 

mixing/clos

ed cab for 2 

days) 

sweatshirt, long sleeved 

shirt and long pants (for 

cold weather reps; no 

sweatshirt for warm 

weather reps), socks, 

leather work boots, 

chemical resistant 

gloves, goggles (mixerloaders and truck tenders 

only) and a belt. 

From 43934417 and 

44152109 -- Inner body 

dosimeter: t-shirt and 

briefs. Outer body 

dosimeter: sweatshirt 

and/or long sleeved shirt 

and long pants (cold 

weather; no sweatshirt in 

warm weather). When 

sweatshirt worn, used as 

outer dosimeter and long 

sleeved shirt used as inner 

dosimeter. Penetration 

factors determined from 

outer and inner torso 

dosimeter samples—used to 

calculate arm/leg values if 

needed. Total dermal 

exposure = adjusted arm/leg 

values, inner dosimeter 

torso value, hand rinse, 

head patch 

1.29E-4 to 1.03E-3 

mg/lb ai (GM) 

range: 1.55E-2 to 1.68E5 mg/lb ai 

Range: 1.03E-3 to 

4.59E-3 mg/lb ai 0.13-0.22 

(a) Since a dermal toxicity endpoint was used in the risk assessment for diazinon, a dermal absorption factor was not necessary. Although, the Agency 

HIARC report recommended a dermal absorption factor of 100% based on comparative toxicity studies, the Data Evaluation Record (DER) for this study 

estimated dermal absorption factors that range from 4% to 14%. The study specific dermal absorption factors were used to estimate the dermally absorbed 

dose. 

(b) A total of 3 chlorotriazine degradates that comprise 12% was used for biomonitoring. Unable to verify complete urine volume. No creatinine. Very 

variable field recoveries for fortified matrices (22-230%). In all cases but atrazine, dose is reported as ug/kg BW. For atrazine, unit exposure estimates 

(mg/lb ai handled) were presented in the risk assessment which allowed for a similar comparison of the efficiency of passive dosimetry and biological 

monitoring.

Figure 3-5. Ratios of Mean Passive Dosimetry versus Biomonitoring 

for Concurrent Agency Analysis a

0.01

0.1

1

10

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Handler Description

Ratio of Passive Dosimetry versus 

Biomonitoring

a

 Each point represents the ratio of the passive dosimetry dose or unit exposure versus the biomonitoring dose or unit exposure. Numbers across the 

horizontal axis represent different studies for comparison (numbers correspond to scenario numbers in Table 3-1). 

Page 55 of 114 
Additional Retrospective Analysis: In addition to the analyses prepared above, the 

Agency evaluated all of the data in PHED and its risk assessments and not just for the 

scenarios in the case study in order to identify additional studies that could be used for a 

retrospective biomonitoring and passive dosimetry analysis. Essentially, this type of 

analysis involve a similar comparison as above except body burdens based on biological 

monitoring have been compared to estimates for the same scenarios which were developed 

using the exposure factors obtained from the biological monitoring data. For example, if 

in a biological monitoring study 20 acres were treated with an open cab groundboom 

applicator at a rate of 1 lb active ingredient per acre then the passive dosimetry estimate to 

which it was compared would be based on PHED and similar inputs. Data which are not 

redundant to that described above have been included below (Table 3-2 & Figure 3-6). 

The ratio that compares mean dose estimates from each study has been calculated. If a 

unit exposure was provided in the biomonitoring study (rather than a dose), then the 

appropriate PHED unit exposures were identified (according to the application equipment 

and PPE identified in the study), were totaled (dermal plus inhalation), and were then 

compared to the biomonitoring unit exposure, to obtain a ratio of mean passive dosimetry 

to biomonitoring. If the values exceed 1 then it illustrates that absorbed dose estimates 

based on passive dosimetry exceed those calculated using concurrent biomonitoring. It 

should be noted that the range of dermal absorption estimates has been considered in this 

analysis in order to illustrate the sensitivity of the results to that parameter. 

Table 3-2: Summary of Retrospective Biomonitoring & Passive Dosimetry Analyses Based On PHED & Agency Risk Assessments 

Study Citation Chemical Application rate 

from study 

Application 

equipment 

Dermal 

absorption PPE worn Ratio Passive Dosimetry (dermal + inhalation) / Biomonitoring 

(1)a 2% dermal absorption: 0.11 Scott, R.C.; Chester, G.; 

Hart, T.B.; Woolen, B.H.; 

Ward, R.J.; Laird, W.J.D. 

(1983). Fluazifop-butyl: a 

spray trial to assess 

knapsack spraying. 

fluazifopbutyl 

5 g/L = 0.042 lb 

ai/gal and 21.38 

gal/A = 0.898 lb 

ai/A 

knapsack 

sprayer 

2% (high 

exposure) and 9% 

(low exposure) 

from D316892 

T-shirts, shorts and shoes 

(2) 9% dermal absorption: 0.42 

(3) SL w/gloves: 0.03 

Findlay, M.L. (1997). 

Molinate; Biological 

Monitoring of Workers 

During Loading of 

Arrosolo 3-3E into 

Airplane Hoppers 

molinate 

states max rate as 

4 lb ai/A for 

product; average 

gallons handled 

provided for each 

worker level 

M/L for aerial 

application 40% 

All mixer loaders wore chemical 

resistant gloves, half-face 

respirator and chemical resistant 

footwear. Additional protection 

consisted of one of the following: 

(1) Level 1: Activated carbon suit 

worn underneath 'Kleenguard' 

coveralls; (2) Level 2: 

'Kleenguard' coveralls worn over 

normal work clothing and (3) 

Level 3: Normal work clothing, 

recommended as long sleeved 

shirt and long trousers. 

(4) DL w/gloves: 0.01 

Barney, W.P. (2001). 

Occupational Exposure 

Monitoring of Aerial 

Mixing/Loading of 

PENNCAP M® Utilizing 

Biological Monitoring 

methyl 

parathion 1 lb ai/A aerial 6% 

M/L: Long sleeved shirt, long 

pants, coveralls, socks, rubber 

boots, goggles, dust/mist 

respirator, and neoprene gloves 

(5) Ratio of unit exposures 

(dermal + inhalation PHED 

and 90th percentile 

biomonitoring UE): 

19.5 

Page 56 of 114 
Table 3-2: Summary of Retrospective Biomonitoring & Passive Dosimetry Analyses Based On PHED & Agency Risk Assessments 

Application rate Application Dermal Ratio Passive Dosimetry (dermal + inhalation) / Study Citation Chemical PPE worn from study equipment absorption Biomonitoring 

(6) Ratio of unit exposures 

assuming groundboom 

equipment (dermal + 

inhalation PHED and geomean 

biomonitoring UE): 

Siemer, S.R. (1995). The 3.02 

Evaluation of Worker 

Exposure During Loading 

and Spray Application of 

Dormex to Dormant 

Grapevines 

hydrogen 

cyanamide 

biomonitoring 

study rate= 8.7 to 

21.6 lb ai/A; 

application rates 

used in risk 

assessment for 

hydrogen 

cyanamide taken 

from labels: 

peach=12.9 lb ai; 

grape=17.2 lb ai; 

apple=34.4 lb ai 

closed-cab 

over-the-row 

sprayer 

11% (from 

D306179) 

Applicators only: chemical 

resistant rainsuits, gloves, rubber 

boots, long sleeve-shirt, long 

pants, respirators (7) Ratio of unit exposures 

assuming airblast equipment 

(dermal + inhalation PHED 

and geomean biomonitoring 

UE): 

17.6 

Hicks, S.C. (1998). The 

Evaluation of Spray 

Applicator Exposure 

During Airblast Spray 

Application of Dormex® 

to Dormant Fruit Trees 

hydrogen 

Cyanamide 

biomonitoring 

study rate= 11.2 

to 26.6 lb ai/A 

application rates 

used in risk 

assessment for 

hydrogen 

cyanamide taken 

from labels: 

peach=12.9 lb ai; 

grape=17.2 lb ai; 

apple=34.4 lb ai 

open-cab 

airblast 

11% (from 

D306179) 

Applicators only: chemical 

resistant rainsuits, gloves, rubber 

boots, long sleeve-shirt, long 

pants, respirators 

(8) Ratio of unit exposures 

(dermal + inhalation PHED 

and geomean biomonitoring 

UE): 

16.4 

Siemer, S.R. (1990). The 

Determination of Nacetylcyanamide in Urine 

of Workers Exposed to 

Dormex7, as a Measure of 

Exposure 

hydrogen 

cyanamide 

biomonitoring 

study rate= 17.2 -

22.4 lbai/A 

application rates 

used in risk 

assessment for 

hydrogen 

cyanamide taken 

from labels: 

peach=12.9 lb ai; 

grape=17.2 lb ai; 

apple=34.4 lb ai 

closed-cab 

airblast 

11% (from 

D306179) 

Applicators only: chemical 

resistant rainsuits, gloves, rubber 

boots, long sleeve-shirt, long 

pants, respirators 

(9) Ratio of unit exposures 

(dermal + inhalation PHED 

and average biomonitoring 

UE) : 

1.95 

Belcher, T. (2001). 

Biomonitoring 

Assessment of Worker 

Exposure to Methyl 

Parathion During 

Application to Potatoes 

Using Penncap-M 

Microencapsulated 

Insecticide: Final Study 

Report: Lab Project 

Number: KP-2001-02: 

ERS21007. Unpublished 

study prepared by 

methyl 

parathion 1.5 lbs ai/A ground spray 

applications 100% 

coveralls over long-sleeved shirt 

and long pants; chemical resistant 

gloves, socks, and footwear; 

protective eyewear; chemicalheadgear for overhead exposure; 

and dust/mist filtering half-face 

respirator (OSHA/NIOSH 

approval number prefix TC-21C). 

(10) Ratio of unit exposures 

(dermal + inhalation PHED 

and 90th percentile 

biomonitoring UE): 

25.3 

Willard, T.R. (2001). 

Occupational Exposure 

Monitoring of 

Mixing/Loading 

Activities for Aerial 

Application of PENNCAP 

M® Microencapsulated 

Insecticide Utilizing 

Biological Monitoring 

methyl 

parathion 1.0 lbs ai/A aerial spray 

applications 100% 

cotton coveralls over long-sleeved 

shirt, undershirt, and long pants; 

chemical resistant boots; long 

nitrile gloves; full-face shield; 

overhead exposure; dust/mist 

filtering half-face respirator 

(OSHA/NIOSH approval number 

prefix TC-21C); chemical 

resistant nitrile apron; and 

Tyvek® hat. 

(11) Ratio of unit exposures 

(dermal + inhalation PHED 

and 90th percentile 

biomonitoring UE): 

19.3 

Page 57 of 114 
Table 3-2: Summary of Retrospective Biomonitoring & Passive Dosimetry Analyses Based On PHED & Agency Risk Assessments 

Application rate Application Dermal Ratio Passive Dosimetry (dermal + inhalation) / Study Citation Chemical PPE worn from study equipment absorption Biomonitoring 

Merricks, D.L. (2001). 

Biological Monitoring of 

Workers Mixing and 

Loading a 4 lb/gallon 

Emulsifiable Concentrate 

Formulation of Methyl 

Parathion for Aerial 

Application (Using a 

MICRO MATIC ‘DV’ 

Liquid Transfer Valve 

System) 

methyl 

parathion 0.75 lbs a.i./Acre aerial mixer/loaders 100% 

coveralls over long-sleeved shirt 

and long pants; chemical resistant 

gloves; chemical resistant 

footwear and socks; protective 

eyewear; chemical-resistant 

apron; and dust/mist filtering halfface respirator (OSHA/NIOSH 

approval number prefix TC-21C) 

(12) Ratio of unit exposures 

(dermal + inhalation PHED 

and 90th percentile 

biomonitoring UE): 

56.9 

(13) Mixer/loader: 1.06 

Rosenheck, L. (2004). 

Determination of 

Exposure During the 

Mixing, Loading and 

Application of Curacron 

8E (Profenofos) by Air 

Through the Use of 

Biological Monitoring: 

Final Report. Project 

Number: 184/99. 

Unpublished study 

prepared by Syngenta 

Crop Prot 

profenofos 1 lb ai/acre 

aerial 

mixer/loaders 

and aerial 

applicators 

50% 

mixer/loaders: long-sleeved shirt, 

long pants, shoes and socks, a 

chemical-resistant apron, and 

chemical- resistant gloves; 

applicators: long-sleeved shirt, 

long pants, shoes, and socks 

(14) Applicator: 3.49 

(15) Mix/Load/Apply Open 

Cab Airblast 9.75 

Krolski, M. (2004). 

Azinphos-Methyl - 

Biomonitoring of 

Applicators Following 

Airblast Treatment of 

Orchard Crops using 

Open and Closed Cab 

Equipment: Final Report. 

Project Number: 201054, 

GU264704, GU264705. 

Unpublished study 

prepared by Bayer 

Cropscience 

AZM 

1.5 lb ai/A used 

for comparison; 

from study, 

application rates 

were 0.534 to 

1.098 for open 

cab and 0.62 to 

2.052 for closed 

cab 

airblast 42% 

For open cab one- or two-piece 

chemical-resistant coveralls made 

from TYVEK, SARANEX, PVC 

or similar material , long-sleeved 

shirt and long pants, chemicalresistant footwear plus socks, 

chemical-resistant gloves, 

chemical-resistant headgear, and 

protective eyewear. In addition, 

workers wore a respirator with 

either an organic vapor removing 

cartridge with a prefilter approved 

for pesticides. For closed cab 

field trials, the Study Report states 

that workers wore long-sleeved 

shirts, long pants, and shoes plus 

socks 

(16) Mix/Load/Apply Closed 

Cab Airblast 4.92 

(17) 

2% dermal 

absorption: 

0.057 

Mixer/loader 

(18) 

9% dermal 

absorption: 

0.209 

(19) 

2% dermal 

absorption: 

0.044 

Chester, G.; Hart, T.B.; 

Sabapathy, N.N.; Woolen, 

B.H.; Atreya, N. (1985). 

Fluazifop-butyl: spray 

operator exposure on 

Malaysian plantation. 

fluazifopbutyl 1.5 lb ai applied knapsack 

(backpack) 

2% (high 

exposure) and 9% 

(low exposure) 

from D316892 

Workers wear shorts, T-shirts, 

plimsolls 

Applicator: (20) 

9% dermal 

absorption: 

0.16 

a Numbers in parentheses correspond to handler description numbers in Figure 3-6. 

Page 58 of 114 
Figure 3-6. Ratios of Mean Passive Dosimetry versus Biomonitoring for Retrospective 

Agency Analysis a

0.01

0.1

1

10

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Handler Description

Ratio of Passive Dosimetry vs. Biomonitoring

a

 Each point represents the ratio of the passive dosimetry dose or unit exposure versus the biomonitoring dose or unit exposure. Numbers across the 

horizontal axis represent different studies for comparison (numbers correspond to scenario numbers in Table 3-2). 

 Although many studies of passive dosimetry and biological monitoring are available to 

draw generalized conclusions, none of the studies were designed with the sole purpose of 

“validating” passive dosimetry. Nor is a ratio of 1 expected (i.e., exact duplicative results from 

both methods). Although the confounding factors discussed in the beginning of this section 

prevent any conclusive statements, the following observations were noted in the comparison of 

passive dosimetry to biological monitoring: 

• For the chlorpyrifos examples, when comparing biological monitoring to passive 

dosimetry one method did not consistently result in greater absorbed dose estimates over 

the other when assuming 3 percent dermal absorption. A lower dermal absorption of 1 

percent as suggested by Griffin et al (1999) would result in passive dosimetry estimates 

lower than biological monitoring results. Conversely, assuming a 10 percent dermal 

absorption suggested by Krieger (1995) would result in passive dosimetry overestimating 

the absorbed dose from the biological monitoring data. 

• When comparing biological monitoring to passive dosimetry for other organophosphorus 

pesticides, numerous studies showed that the passive dosimetry data correlated well with 

the biomonitoring results. However, several other studies showed that the absorbed dose 

did not correlate with biomonitoring. The poor correlation may be due to small sample 

sizes, short sampling intervals, prior exposure to pesticides, or incomplete measuring of 

passive dosimetry. 

• Although some studies show that the passive dosimetry data support biomonitoring results 

for non-organophosphorus pesticides, several factors make it difficult to identify any 

Page 59 of 114 
definitive trends among these studies. Identifying a metabolite that accounts for at least 

30% of the absorbed dose may be difficult. Furthermore, the dermal absorption factors 

selected could confound estimates of absorbed dose from the passive dosimetry since 

absorption may be inversely correlated to dermal dose. 

• The concurrent passive dosimetry to biological monitoring comparison presented in Table 

3-1 indicates that the ratio of passive to biological monitoring methods ranged from 0.01 

to 5.73 with a ratio below 1 for 10 scenarios (indicating passive dosimetry under 

predicting the biological absorbed dose) and a ratio greater than 1 for 10 scenarios 

(indicating passive dosimetry over predicting the biological absorbed dose). Of the 20 

scenarios, 90 percent of the ratios were within 1 order of magnitude of a ratio of 1. For 

the scenarios that relied on individual replicate clothing penetration factors, 10 scenarios 

have ratios that are below 1 and seven scenarios have ratios that are greater than 1. For 

the chlorpyrifos examples, eight scenarios have ratios below 1 and five scenarios with 

ratios greater than 1. For the non chlorpyrifos examples, two scenarios have ratios below 

1 and five scenarios greater than 1. 

• The retrospective analysis presented in Table 3-2 indicates that eight of the scenarios have 

ratios below 1 and 12 scenarios have ratios greater than 1. The ratios for 10 of the 20 

scenarios presented are within an order of magnitude of 1. The ratios ranged from 0.01 to 

56.9. 

In conclusion, the passive dosimetry to biological monitoring ratios for the 40 scenarios used 

in EPA risk assessments (i.e., Tables 3-1 and 3-2) indicate that 70% of the scenarios were within 

an order of magnitude of the ratio of 1. The range of the ratios is from 0.01 to 56.9; based on the 

limitations of the data presented, to conclude anything more than most of the data are within an 

order of magnitude of 1 or most of the data range within 2 orders of magnitude, specific studies 

designed to “validate” the accuracy of passive dosimetry would be needed. Also of note is the 

fact that using biological monitoring data to develop a surrogate data base would introduce 

additional uncertainties because of the need for accurate dermal absorption data for backcalculating a measured absorbed dose to estimate the chemical contact with the skin (and the need 

for the determination of the inhalation route) along with the need for accurate dermal absorption 

data for the chemical to be assessed using the surrogate data. 

AHETF Concurrent & Retrospective Analysis: In preparation for this meeting the 

AHETF prepared a similar analysis in order to compare in a concurrent and retrospective 

approach both biological monitoring and passive dosimetry estimates of exposure 

(AHETFc et al, 2006 & AHETFd, 2006). In large part, the Agency concurs with the 

approaches and data utilized by AHETF. Here are the critical issues and considerations 

that the Agency identified in the review of these documents. These include: 

Page 60 of 114 
• The uncertainties associated with the AHETF analysis are similar to those inherent to 

the analyses completed above. 

• In many cases, the same data were used by both the Agency and AHETF yet the 

numerical results varied slightly in many cases. This is due to varied inputs being 

used such as for defining pharmacokinetics or dermal absorption. 

• The results for the AHETF analysis appear to be consistent with those developed by 

the Agency. 

• It is believed that biological monitoring should be included in any sampling plan of 

this nature because it can provide information to characterize dosimeter performance 

and quantify exposures that have not been captured using other methods (e.g., 

compare biomonitoring to wipe, inhalation, and handwash results). 

3.2 Systematic Evaluation of Hand Sampling Methods

 Historically, hand sampling methods have included two basic approaches that involve 

quantifying residues from the surface of the skin as described in the guideline documents 

referenced above. In some cases, monitors provide an absorptive layer on the surface of the skin 

that traps impinging residues (e.g., cotton gloves removed and analyzed for residues) or a removal 

method is used that typically involves some sort of handwash or swipe method. Different 

investigators over time have used various materials as monitors such as cotton or Tyvek© gloves 

and many types of wash solutions that have included acetone, a variety of alcohols or aqueous 

soap solutions. 

 The use of washing (i.e., removal) methods to estimate hand exposure from pesticides has 

been an industry standard since the early 1990’s. The shift from trapping media (e.g., gloves) 

occurred because of the general belief that the use of gloves tended to over-estimate residue levels 

on the hands because cotton gloves trapped more residues than would normally be retained on the 

skin. The use of removal methods such as washes of different types, however, has been subject to 

criticism because of its potential, conversely, to underestimate exposure to the hands. Studies 

conducted by Fenske and colleagues are often cited as evidence of the limited recovery of 

residues from hands by the hand rinse method (see below for further information). There are a 

number of factors that may influence the interpretation of data using this method. These include 

the physical-chemical properties of the chemical being measured and the type of solvent used for 

the hand rinse. Other factors include the duration of the exposure monitoring event (one hour or 

several hours), the length of time the hand rinse was performed after the monitoring event, the 

nature of the chemical being monitored (concentrate, dilute spray, field residue/soil/plant material 

matrix), the concentration of the chemical on the skin (μg/cm2

), and the level of mechanical 

agitation that is used during sampling (e.g., in some cases subjects shook their individual hands in 

soap solutions while in other cases subjects vigorously washed their hands together). 

 If there is a systematic bias associated with hand sampling methods, the Agency wishes to 

address possible approaches for compensating in order to account for that bias. The Agency also 

desires to better understand the ruggedness of the available methods for application to settings 

Page 61 of 114 
where concentrated liquids, dilute sprays, or solid materials may reflect the physical nature of the 

pesticide being monitored. Two approaches have been used in the evaluation of hand sampling 

methods. These include: 

• Mechanistic Analysis: Based on a variety of data, the Agency has examined how 

factors such as solubility, Kow, molecular weight, and other physical-chemical factors 

can influence hand exposure results. The techniques used by investigators have also 

been considered in the interpretation of the results. 

• Field Performance Analysis: Another approach for completing a comparison 

between various hand monitoring techniques is to evaluate the relative performance of 

different methods under actual field conditions. In this case, hand monitoring results 

from the case study (see Section 2 above) were used to examine how factors such as 

sampling media or nature of the contaminant being screened for can influence results. 

Section 3.2.1: Mechanistic Hand Methods Analysis describes the available data which has been 

used to illustrate the mechanistic factors that can influence hand exposure levels. In Section 

3.2.2: Hand Method Field Performance Data the Agency has examined how various methods 

have performed under field conditions based on the case study data described above. Section 

3.2.3: Conclusions describes the Agency's summary interpretation and conclusions based on the 

analyses contained in this section. 

3.2.1: Mechanistic Hand Methods Analysis 

 The Agency is unaware of any comprehensive study that compares the performance of 

hand rinses and absorbent gloves for estimating true hand exposure. However, articles have 

appeared in the published literature that address key factors the Agency believes can impact the 

interpretation of exposure data relying on hand rinses. The seminal study for this discussion is 

Davis et al., 1983 where the authors reported results of a study comparing two methods for 

measuring hand exposure; hand rinses and gloves (cotton and nylon). 

Potential Exposure of Apple Thinners to Azinphos-methyl and Comparison of Two 

Methods for Assessment of Hand Exposure: James E. Davis, Edwin R. Stevens, and 

Donald C. Staiff. Bull. Environ. Contam. Toxicology, 31: 631-638, 1983. 

 

The study involved apple thinners working in orchards treated with azinphos-methyl. 

Davis suggested that the hand rinse method described in Durham and Wolfe (1962) had 

limitations despite being a recognized standard. The limitations include identifying 

solvents that remove residues without injuring the skin, the potential for causing 

interferences in laboratory analysis and that residues that penetrate the skin during the 

exposure period would not be included in the measurement. 

Mean hand measurements using cotton gloves were reported to be 4.7 to 5.5 times higher 

than ethanol based hand rinses for workers thinning apples in azinphos-methyl (azm) 

treated orchards. Two separate monitoring periods were evaluated for a duration of two 

hours. One monitoring period was two days after an application of azm and another 9 

days after the application. Unfortunately, a description or discussion of the absorptive 

Page 62 of 114 
capacity of the type of cotton gloves used in the study was not provided. Nor did the 

authors attempt to discern what percent of the hand rinse represented true hand exposure. 

Methods for Assessing Fieldworker Hand Exposure to Pesticides during Peach 

Harvesting: R.A. Fenske, S.G. Birnbaum, M.M. Methner and R. Soto. Bull Environ. 

Contam, Toxicology, 43:805-813, 1989. 

In this study, Fenske and colleagues explored the impact of exposure duration (hours) on 

the differences between the use of hand rinses and cotton gloves in workers harvesting 

peaches in captan treated orchards. They found that the absorptive capacity of the gloves 

was greater for short exposure durations (0.5 and 1 hour sampling periods) than the longer 

(1.5 and 3 hour) exposure durations. Results from the shorter exposure durations 

demonstrated that gloves produced significantly higher measurements than hand rinses. 

However, comparisons of the hand rinse and glove measurements at the 1.5 and 3 hour 

time periods, showed no significant differences between the methods. 

 

The authors considered glove loading (defined as total mass of captan deposited on the 

glove) as a possible explanation for the lack of significant differences in the hand 

rinse/glove comparison for the longer exposure durations. They also hypothesized that 

decreases in the absorptive capacity of the gloves may have been due to moisture, soil and 

or sweat combined with captan residues. The authors also noted captan residues were 

recovered beneath gloved hands indicating breakthrough. Summary statistics from this 

study are shown in Table 3-3. 

Table 3-3: Glove and handwash exposure rates by sampling time 

Time Glove exposure rate (mg/hr) Hand exposure rate (mg/hr) 

(hours) Mean Median Std Dev Mean Median Std Dev 

Glove/Hand 

Ratio 

0.5 43.6 43 28 18 17 9 2.4 

1.0 32.5 31 18 15.5 15 9 2.1 

1.5 23.2 25 8 16.6 16 8 1.4 

3.0 21 19 9 14.7 13 9 1.4 

n=8 for all results 

The results from this study indicated glove measurements (short exposure durations) were 

1.5 to 2.5 fold higher than hand rinses while Davis et al., 1983 observed an approximate 5 

fold difference. In the Fenske study, light weight (11.6 mg/cm2

) cotton gloves (marketed 

for use in photographic darkrooms) were used while it was speculated that heavier weight 

gloves may have been used in the Davis study. Heavier weight cotton gloves are 

assumed to be more absorptive. 

Page 63 of 114 
Comparison of Three Methods for Assessment of Hand Exposure to AzinphosMethyl (Guthion) During Apple Thinning: Richard A. Fenske, Nancy J. Simcox, 

Janice E. Camp, and Cynthia J. Hines. Applied Occupational and Environmental Hygiene 

Volume 14(9): 618-623, 1999. 

Hand measurement techniques used in this study consisted of cotton gloves, hand rinses 

(washing hands in a polyethylene bag containing a surfactant/distilled water solution) and 

hand wipes (surgical gauze wipes sprayed with the surfactant/distilled water solution). 

The study subjects performed apple thinning in azinphos-methyl treated orchards for a 

duration of 2 hours. The results showed that glove exposure (6.48 mg/hr) was 3.5 times 

higher than the hand rinse rate (1.38 mg/hr) which was 6.4 times higher than the hand 

wipe rate (0.28 mg/hr). In conclusion the authors asserted that gloves resulted in a 2.4 

fold overestimate while hand wipes indicated a 10 fold underestimate. In this study the 

authors proposed adjusting hand rinse (wash) measurements by using a correction factor 

from a laboratory study discussed in Fenske et al., 1998 (Table 3-4). 

Table 3-4: Dislodgeable foliar residues (DFR) data collected by Fenske 

Sample 

day(s) 

Days post 

application N 

Mean foliar 

residue 

(μg/cm2) 

CV (%) 

Hand 

sampling 

method 

Day 1 4 7 1.05 26 Glove 

Day 2 5 4 0.98 21 Wash, wipe 

Day 3 6 4 1.48 43 Wash, wipe 

Day 4 9 8 1.38 22 Glove 

1 and 4 -- 15 1.22A 27 Glove 

2 and 3 -- 8 1.23A 42 Wash, wipe A Authors determined that mean foliar residues for days 1 and 4 compared to days 2 and 3 were not 

different (using a t-test). 

The mean measured exposure rates for the three methods are shown in Table 3-5. 

Table 3-5: Mean Measured Exposure Rates 

Method N Mean measured 

exposure rate 

(mg/hr)A 

CV (%) Percent of 

estimated true 

exposure rate

Glove 15 6.48 28 240 

Wash 12 1.83 27 68B

Wipe 12 0.28 33 10 

A Hourly exposure for two hands. Means were determined to be significantly different 

using ANOVA (p < 0.001). 

B

 Assumed handwash removal efficiency of 68% from Fenske et al., 1998. See table 12 

below. 

The authors adjusted the hand rinse (wash) data by using the rinse efficiency of captan as 

discussed in Fenske et al., 1998. [A discussion of Fenske 1998 follows.] Although the 

Page 64 of 114 
removal efficiency of azinphos-methyl is unknown, the authors assumed that azm may 

have a rinse efficiency similar to captan because both chemical’s have similar log octanol: 

water partition coefficients (Ko/w). The octanol/water partition coefficient is defined as the 

ratio of a chemical's concentration in the octanol phase to its concentration in the aqueous 

phase of a two-phase octanol/water system. A comparison of parameters for three 

chemicals considered relevant to hand wash removal is presented in Table 3-6. 

 

Table 3-6: Comparison Of Factors Related To Handwash Removal Of Residues 

Chemical log Ko/w Formulation type Removal efficiency 

at 1 hour 

Captan 2.35 Wettable powder 68% 

Azinphos-methyl 2.75 Wettable powder Unknown 

Chlorpyrifos 4.96 Liquid concentrate 22% 

Incomplete Removal of the Pesticide Captan from Skin by Standard Handwash 

Exposure Assessment Procedures. R.A. Fenske, C. Schulter, C. Lu and E.H. Allen. 

Bull. Environ. Contam. Toxicol. (1998) 61:194-201. 

The hand rinse removal efficiency of captan using rinses was investigated by Fenske and 

colleagues in a laboratory study designed to simulate occupational hand exposure. 

Volunteers contacted test tubes spiked with captan (10 times). After grasping the test 

tubes, the participants rubbed their fingers against the palm of the same hand for even 

distribution of the captan. Another group of volunteers performed a similar routine. 

The hand rinses (washes) consisted of two washes using separate polyethylene bags 

containing isopropanol in distilled water. In this mass balance study, captan was 

measured on the hands and the amount that was transferred to the hand was accounted for 

by measuring what was left on the test tubes after the volunteers contacted them (Table 3-

7). In addition, the study design included an ability to explore the impact of the timing of 

hand rinse collection after exposure/contact (immediately and 1 hour after exposure). 

Table 3-7: Summary Data For Captan Removal Using Handwash Methodology 

Handwash removal efficiency 

Time 

(hr) 

N Total 

spike (mg) 

Captan 

transferred 

to hand 

(mg) 

Captan 

removed 

from hand 

(mg) 

2 

washes 

 (%) 

CV 

(%)

First 

wash only 

CV 

(%) 

0 12 7.43 4.37 3.81 90.7 22 78.1 -- 

0 6 5.62 5.25 4.10 77.8 18 67.1 22 

1 12 5.88 5.62 3.85 68.4 7 58.9 -- 

The authors acknowledge that exposure to captan (as reported in Fenske et al., 1989 and 

discussed above) has been reported at rates of 10 to 20 mg/hour with DFR’s of 6.4 

μg/cm2. This study highlights the impact of time with respect to hand wash collection. 

Page 65 of 114 
Determination of Handwash Removal Efficiency: Incomplete Removal of the 

Pesticide Chlorpyrifos from Skin by Standard Handwash Techniques. Richard A. 

Fenske and Chensheng Lu. Am. Ind. Hyg. Assoc. J 55(5):425-432, 1994. 

This study predates Fenske et al., 1998 described above but has a similar mass balance 

study design and investigated the rinse efficiency of chlorpyrifos which has lower 

water/alcohol solubility than captan. The volunteers’ hands were rinsed with either 

ethanol or an isopropanol/water mixture and showed lower removal efficiency than was 

seen with the pesticide captan. The study does not address the impact of long term 

repeated contacts representative of field reentry workers. 

The biggest impact appeared to be the lag (0 or 1 hour) between hand exposure and hand 

rinse collection. Higher efficiencies were observed when hand rinses were performed 

immediately after contact with the test tubes. The hands were exposed for a relatively 

short duration compared to worker reentry tasks conducted for more than 4 hours (Table 

3-8). 

Table 3-8: Summary Data For Chlorpyrifos Removal Using Handwash Methodology 

Time Removal efficiency (%) 

(hr) 

N Test tube 

spike 

level 

(μg) 

Transfer 

efficiency

Skin 

loading 

(μg/cm2)

# washes 

Mean Std. Dev. 

ethanol

0 12 2500 45.5 7.9 2 27.0 4.8 

1 12 2500 54.7 6.2 2 31.3 6.2 

Isopropanol/water 

0 12 2500 64.4 12.3 2 42.6 24.1 

1 12 2500 60.8 11.1 2 22.6 9.0 

0 10 250 52.9 0.97 1 21.2 7.1 

0 12 25 87.6 0.13 1 23.1 7.2 

0 12 2.5 92.9 0.024 1 38.5 4.8 

Acephate Exposure and Decontamination of Tobacco Harvesters’ Hands. Brian D. 

Curwin, Misty J. Hein, Wayne T. Sanderson, Marcia Nishioka and Wayne Buhler. 

Journal of Exposure Analysis and Environmental Epidemiology (2003) 13, 203-210. 

As noted, the efficiency of hand rinses using isopropanol and ethanol were investigated in 

Fenske et al., 1998 and Fenske and Lu, 1994 and indicated a wide range of hand rinse 

efficiencies (23 – 90.7%). Curwin and colleagues studied the effectiveness of soap and 

water washes for removing pesticide residues from field workers’ hands after harvesting 

tobacco treated with the water soluble pesticide acephate. Hand wipes as described in 

Geno et al., 1996) were used to sample the hands. Briefly, the method relies on the use of 

SofWick® dressing sponges moistened with 10 ml of 100% isopropanol. First, the entire 

hand is wiped then, with a second sponge each finger is wiped separately. Both sponges 

are combined to represent one sample. The samples were collected at 4 times during the 

work day; day 1 morning, day 1 afternoon, day 2 morning, day 2 afternoon. The exposure 

duration was approximately 4 hours. The fact that the hands were reportedly heavily 

covered with plant residue and soil removed any doubt among the investigators that 

Page 66 of 114 
potential handedness (left-handedness versus right-handedness) was a factor. Therefore, 

the investigators modified the sampling scheme to account for “inter-worker variability by 

randomizing the workers to have either (i) their right hand samples prewash and then their 

left hand sampled postwash, or (ii) their left hand sampled prewash and then their right 

hand sampled postwash. The workers were instructed to wash their hands as they 

normally would.” Wipes were also collected from leaf surfaces using the aforementioned 

sponges and a template (clamped to leaves) yielding a surface area of 200 cm2

. Upper 

and lower leaf surfaces were wiped for a combined surface area of 400 cm2. Hand rinses 

were converted from μg/sample to μg/cm2

 by assuming a single hand surface area of 420 

cm2

. The authors reported that hand washing reduced the acephate residues on the hands 

by 96%. The high efficiency of the hand wash was thought to be due to the workers 

washing their hands shortly after working and the water soluble nature of acephate. 

Summary statistics are presented in Table 3-9. 

Table 3-9: Summary Data For Acephate Removal From Tobacco Harvester Hands 

Field Sample order N GM (ng/cm2) GSD Range (ng/cm2) 

1 Prewash 18 6.6 4.0 0.6-103 

Postwash 18 0.2 3.2 0.04-1.2 

2 Prewash 12 12.6 4.2 0.5-64.7 

Postwash 12 0.7 2.8 0.07-2.4 

3 Prewash 6 8.1 5.3 0.4-36.6 

Postwash 6 0.4 1.6 0.2-0.68 

4 Prewash 6 59.0 2.3 32.1-257 

Postwash 6 2.3 1.3 1.7-3.7 

5 Prewash 6 6.5 10.3 0.07-46.5 

Postwash 6 0.1 1.7 0.06-02 

In a similar study evaluating nicotine exposure (green tobacco illness) for tobacco 

harvesters, 96% of the nicotine was removed from the harvesters hands after washing 

with soap and water (Curwin et al., 2005). 

Hand Wash and Manual Skin Wipes. Derk H. Brouwer, Mark F. Boeniger and Joop 

van Hemmen. Am. Occup. Hyg. Vol. 44 No. 7, pp. 501-510. 2000. 

In this paper, a discussion of hand wash efficiency results based on different hand rinsing 

procedures for various pesticides (one non-pesticide) is presented. In Table 3-10, results 

of hand rinse efficiency studies conducted by Fenske and investigators in The Netherlands 

are shown. Variables such as residence time and hand loading (μg) are presented. The 

two Fenske studies have been discussed previously. 

In the studies performed in The Netherlands (Brouwer and Marquart) the investigators 

relied on direct spiking of residues onto the hands whereas Fenske and colleagues relied 

on volunteers contacting spiked test tubes. The rinse efficiency results indicate a range of 

22 to 96% with a median of 73%. 

Page 67 of 114 
Table 3-10: Comparison Of Hand Wash Efficiency Studies 

Pesticide Method Loading (μg) Wash 

efficiency (%) 

Mean 

Std 

(%) 

N Reference 

B 1500b 

15000b 

94 

63 

11 

13 

4 

4 

Brouwer et al., 

2000a 

Captan 

B 5620c

 (1 h) 

5250c (0 h) 

68 

78 

 5 

14 

6 

3 

Fenske et al., 

1998 

Carbendazim B 500b 

5000b 

94 

59 

8 

13 

3 

3 

Brouwer et al., 

2000a

Chlorothalonil A 4400b 74 11 4 Brouwer et al., 

2000a

Chlorpyrifos B 

B 

C 

1700c (0 h) 

1570 (1 h) 

1100 (1 h) 

43 

23 

27 

24 

 9 

 5 

6 

6 

6 

Fenske and Lu, 

1994 

Mancozeb A 

B 

D 

2275b 

2275b 

5000; 15,000; 

30,000b

81 

66 

86 

10 

5 

5 

4 

5 

12

Brouwer et al. 

2000a, 1992 

Marquart et al., 

2002 

Methiocarb D 500b 

1800b 

7000b 

77 

84 

84 

3 

3 

6 

4 

4 

4 

Brouwer et al., 

2000a

Methomyl D 300b 

1490b

71 

70 

3 

4 

4 

4 

Brouwer et al., 

2000a

Prochloraz B 500b 

5000b

95 

96 

14 

6 

4 

4 

Brouwer et al., 

2000a

175b 

575b 

1400b

Brouwer et al., 

2000a,b 

Propoxur D 

500, 5000, 

7500b

66 

71 

72 

46 

8 

13 

10 

3 

4 

4 

4 

12 Marquart et al., 

2002 

Vinclozolin D 59.2 

227 

384b 

81 5 3 Brouwer et al., 

2000a,b 

a

 (A) 2-propanol rinsing, 2 hands 500 ml, PE-bag; (B) 2-propanol rinsing, 1 hand 250 ml, 

PE-bag; (C) ethanol rinsing, 1 hand 250 ml, PE-bag; (D) water-soap rinsing, 2 hands, tap 

water; N= number of test subjects per level of loading. 

b

 Direct repeated spiking of 0.5 ml on the hands. c

 Mass balance approach from transfer of a contaminated tube. 

In the paper, existing studies relying on wipe methods were also considered. Generally, it 

was determined that wipe sampling is less efficient than the described hand rinse methods. 

Page 68 of 114 
Comparison of Solvents for Removing Pesticides from Skin Using an In Vitro

Porcine Model. Jerry Campbell, Mary Alice Smith, Mark A. Eiteman, Phillip L. 

Williams, Mark F. Boeniger. AIHAJ (61) January/February 2000, Ms. #906. 

The removal efficiency of four wash solutions were compared in a trial using dorsal 

porcine skin fortified with four different pesticides at varying concentrations (0.5, 2.0 and 

8.0 μg/cm2

). The pesticides were selected on the basis of their solubility in water: 

glyphosate (12,000 mg/L), alachlor (240 mg/L), methyl parathion (55 mg/L) and 

trifluralin (0.3 mg/L). The wash solutions consisted of 1-propanol, 10% soap and water 

(Ivory® Liquid), polyethylene glycol (PEG) and a mixture of surfactants in propylene 

glycol (D-TAM). Wipe samples were collected after the pesticide had dried on the skin. 

The residence time was 90 minutes (Table 3-11). 

The authors concluded that the amount of pesticide applied to the skin (not shown in the 

following table) had a significant effect on the amount of pesticide recovered from the 

skin (wash efficiency). In general higher efficiencies were seen at the higher pesticide 

concentrations (2.0 and 8.0 μg/cm2). According to the authors, “The overall recovery of 

the four pesticides with 1-propanol was significantly higher (p < 0.05) than recoveries 

with all other solvents. Soap and water recovered significantly more (p < 0.05) of the four 

pesticides than either PEG or D-TAM. For glyphosate, alachlor, and methyl parathion, 

there were no significant differences (p < 0.05) among wipe recoveries of the four 

solvents. On average, the greatest amount of trifluralin was recovered using 1-propanol 

moistened wipes. However, the recovery of trifluralin using 1-propanol was not 

significantly different (p < 0.05) than recoveries using soap and water.” 

Table 3-11: Removal Efficiency For Various Solvents Based On Porcine Model 

Pesticide Wash solution Wash efficiency (%) 

Mean Std 

1-propanol 57 13 

PEG 55 8 

Soap and water 52 12 

Alachlor 

D-TAM 51 6 

1-propanol 44 12 

PEG 41 11 

Soap and water 49 14 

Glyphosate 

D-TAM 36 9 

1-propanol 57 17 

PEG 41 18 

Soap and water 50 19 

Methyl parathion 

D-TAM 41 15 

1-propanol 69 10 

PEG 51 15 

Soap and water 56 13 

Trifluralin 

D-TAM 53 14 

Page 69 of 114 
Table 3-11: Removal Efficiency For Various Solvents Based On Porcine Model 

Pesticide Wash solution Wash efficiency (%) 

Mean Std 

Alachlor 1-propanol 57 13 

1-propanol 57 16 

PEG 47 14 

Soap and water 52 14 

Overall recovery 

D-TAM 45 13 

The 90 minute residence time was selected for two reasons: 1) because of the reported 

binding or diffusion of glyphosate (Wester et al., 1991) into the stratum corneum within 

30 to 60 minutes and 2) because 90 minutes appeared to be a reasonable amount of time 

before exposure study investigators could collect hand rinse samples from volunteers. 

The authors also recognize the higher recoveries using hand rinses described in Geno et 

al., 1996, (~90%) were likely due to wiping the hands immediately after loading, that is 

before the substances could be bound to the skin. The authors also recognized that there 

is a potential for alcohol based solvents to increase the absorptive dose of compounds by 

removing surface lipids from the skin. For this reason, some investigators have avoided 

using alcohol based rinses. 

Methods of Assessing Dermal Absorption with Emphasis on Uptake from 

Contaminated Vegetation. P.R. Durkin, L. Rubin, J. Withey, W. Meyland. Toxicology 

and Industrial Health, Vol. 11, No. 1, 1995 pp. 63-79. 

As seen in the previous study discussions and stated in Durkin et al., 1995, “the duration 

of contact between the skin and the contaminant, as well as the interval between the 

exposure event and washing, are important parameters that affect the uptake of the 

contaminant.” In an exercise to develop a model that predicts uptake from spills and 

contaminated vegetation, the authors identify a variety of factors impacting skin 

contamination. Examples include volatilization, washing, desquamation (shedding of 

skin) or contact with other surfaces. 

A number of the earlier agricultural reentry studies reported in the literature were 

evaluated in this paper. These studies covered a wide variety of reentry activities and 

pesticides and are shown in Table 3-12. 

Table 3-12: Activities Addressed By Durkin et al 

Chemical Vegetation type Activity 

Duration of 

exposure (h) References 

Abamectin Roses Cutting and 

packing 

1-1.5 Brouwer et al., 

1992 

Azodrin Cotton Inspect and 

sample 

2-4 Ware et al., 

1975 

Benomyl Strawberries Picking 8 Zweig et al., 

1983 

Burpirimate Roses Cutting -1 Brouwer et al., 

Page 70 of 114 
Table 3-12: Activities Addressed By Durkin et al 

Chemical Vegetation type Activity 

Duration of 

exposure (h) References 

1992 

Captan Picking and 

weeding 

8 

Carbaryl 

Strawberries 

Picking 4 

Zweig et al., 

1985 

Chlorobenzilate Oranges Picking 8 Nigg et al., 

1984 

Stamper et al., 

1986 

Dodemorph Roses Bundling -1-1.5 Brouwer et al., 

1992 

Azinphosmethyl Peaches Picking 2.5 Popendorf et 

al., 1979 

Methiocarb Blueberries Picking 8 Zweig et al., 

1985 

Methyl 

parathion 

2-4 

Methyl 

paraoxon 

Parathion 

Paraoxon 

Cotton Inspect and 

sample 

0.5-4 

Ware et al., 

1973,1974,1975

Vinclozolin Strawberries Picking 4 Zweig et al., 

1985 

Zolone Peaches Picking ~2.5 Popendorf et 

al., 1979. 

Recognizing the differences in study designs among the above referenced studies (e.g., 

placement of patches inside or outside of clothing), the authors focused on the hand 

measurements collected either as solvent washes or gloves. The hand measurements were 

converted from μg/hr to μg/(cm2

 × hr) by using a surface area of the 840 cm2 (both 

hands). These hand conversions were then plotted on a log scale with their corresponding 

dislodgeable foliar residue (DFR) measurements (μg/cm2

). The transfer rate (TR) 

relationship to DFR was expressed as: 

Log TR = 1.09 log DFR + 0.05. 

The reported correlation coefficient was 0.78 with significance at p < 0.00001. The 

authors assert that the “equation indicates that the transfer rate, expressed as μg/(cm2 × 

hr), is approximately equal to the DFR expressed as μg/cm2

...and that the transfer rate 

from the vegetation to the skin surface appears to be solely dependent upon, and directly 

related to, the DFR on the vegetation.” 

The concept of equilibrium being established between hands and treated/contaminated 

surfaces is most likely due to repeated contacts. This phenomenon has been considered 

and evaluated in the context of industrial settings (Brouwer, Kroses, and van Hemmen, 

Page 71 of 114 
1999) as Brouwer and colleagues explored the impact of repeated hand contacts for 

modeling dermal exposure to industrial workplace contaminants. Many agricultural 

reentry tasks also involve repeated contact with treated foliage. Thus, the impact of hand 

loading and its potential for hand concentrations (μg/cm2) to come into equilibrium with 

treated surface concentration (DFR’s - μg/cm2) can impact the interpretation of worker 

dermal exposure studies. In particular, when using dermal exposure studies that have 

been conducted for short durations and using them to extrapolate longer periods of 

exposure. The length of dermal exposure monitoring periods and its impact on the 

development of dermal exposure models (transfer coefficients cm2

/hr) was explored by 

California Department of Pesticide Regulation (then, the California Department of Food 

and Agriculture). 

Long and Short Intervals of Dermal Exposure of Peach Harvesters to Foliar 

Azinphos-Methyl Residues. Janet R. Spencer, James R. Sanborn, Bernardo Z. 

Hernandez, Frank A. Schneider and Sheila S. Margetich. California Department of Food 

and Agriculture, HS-1578. April 22, 1991. 

After considering earlier reentry studies (e.g., Fenske et al., 1989; Davis et al., 1983), 

Spencer and colleagues found that these data suggested that pesticides may not “transfer 

to a worker in a linear manner with time or production, but may load on the sampling 

media during the initial hours.” They hypothesized that: “if monitoring media exhibit 

uneven acquisition or loading effects, then extrapolating from residues transferred over a 

one or two-hour exposure to an eight–hour exposure may over-estimated dermal 

exposure.” Furthermore, “monitoring a worker for eight hours and assuming the resultant 

dermal residues were transferred linearly over the workday may underestimate the 

residues available for dermal absorption during the early portion of the day.” This study 

was also reported in Toxicology Letters under the same title (78 (1995) 17-24). 

In the study, two groups of peach harvesters were evaluated: 29 harvesters in Sutter 

County and 12 in Stanislaus County, California (Table 3-13, Sutter County results). Both 

orchards were treated with azinphos-methyl. The orchard in Sutter County was treated at 

a rate of 1.5 lb ai/acre, 50 days before reentry. The Stanislaus County orchard was treated 

with 0.75 lb ai/acre, 74 days before reentry. The study design included groups of 

harvesters working different durations before hand rinses were performed and dosimetry 

patches removed. On the first day, dosimeters and hand wipes were evaluated for 7 

workers after 1.5 hours, 3 hours, and 5 hours, and 8 workers at 7 hours (entire work day). 

On the second day, workers hand wipes and dosimeters were evaluated after 2, 4 and 5.5 

hours. 

Table 3-13: Summary Of AZM Exposures In Sutter County Workers For Different 

Durations And Days After Treatment 

mg Azinphos-methyl + Oxon/Interval 

N Interval: Outer dosimeter Inner dosimeter Hands 

First Day 

7 1.5 13.3 ± 2.9 5.6 ± 0.5 1.4 ± 0.6 

7 3 20.8 ± 0.5 7.1 ± 1.7 1.7 ± 0.6 

7 5 20.4 ± 7.2 13.9 ± 3.8 2.1 ± 0.6 

Page 72 of 114 
Table 3-13: Summary Of AZM Exposures In Sutter County Workers For Different 

Durations And Days After Treatment 

mg Azinphos-methyl + Oxon/Interval 

N Interval: Outer dosimeter Inner dosimeter Hands 

8 7 21.5 ± 9.7 12.5 ± 2.5 3.5 ± 1.5 

Second Day 

7 2 13.1 ± 2.4 6.2 ± 7.0 2.3 ± 7.3 

7 4 16.0 ± 3.0 6.8 ± 1.1 1.8 ± 6.0 

7 5.5 17.1 ± 7.1 10.6 ± 3.2 1.5 ± 8.2 

The authors suggest that “equilibrium in the rate of residue acquisition over time would be 

indicated by equal means for each of the four monitoring intervals. The mean for the first 

interval, 1.5-2 hours, showed a significantly higher (student’s t-test, p<0.01) dermal 

exposure per bin (2.7 mg) than for any of the succeeding intervals. There was no 

difference among the means for the last three intervals. This 1.5 fold higher exposure for 

a two hour interval compared to longer intervals indicates that sampling media may have a 

higher affinity for azm residues early in the exposure (loading) period and may require up 

to three hours to reach equilibrium. It appears that a sampling interval of three hours 

would give a dermal exposure that could be extrapolated to longer time intervals without 

being skewed by the effects of the residue loading seen in the first two hours. 

Conservatism in Pesticide Exposure Assessment. John H. Ross, Michael H. Dong, and 

Robert I. Krieger, 2000. Regulatory Toxicology and Pharmacology 31: 53-58. 

Ross and colleagues also considered the work of Spencer et al. in developing Figure 3-7 

that shows initial loading and the establishment of equilibrium regardless of a workers 

performance. By citing Spencer, they suggest that estimates for harvesters could be 

overestimated by 60% or more if estimates were made using the data from studies 

conducted for periods of less than 3 hours exposure. 

In Figure 3-7 it appears that hand exposures were similar regardless of numbers of bins 

filled. Perhaps the figure is also illustrative of the residue concentration on peach 

harvester’s hands coming into a steady state with the environment (foliar/fruit surface 

residues). The dotted lines drawn from the two hour monitoring period to the zero 

intercept indicate the period of dosimeter loading considered by Fenske and others in the 

proceeding study synopses. 

Page 73 of 114 
Figure 3-7. Dermal Monitoring of Azinphos-Methyl Residents vs. Daily Peach Harvest Production 

(adapted from Spencer et al. 1995) 

The study synopses presented in this section highlighted factors that can impact the results 

of worker exposure studies relying on hand rinses. They are as follows: 

Physical chemical properties (solubility, octanol/water partition coefficient (Ko/w)); 

Residence time on the skin before rinsing; 

Type of solvent rinse (e.g., alcohol or soap and water); 

Skin concentration (μg/cm2); 

Duration of exposure/monitoring period (hours); and 

The nature of the residues on the hands [Note: pesticide handlers (applicators and 

mixer/loaders) are exposed primarily to the pesticide (dilute or concentrate) while 

reentry workers are exposed to a mixture of pesticide residues, soil, plant 

materials and sweat.] 

The mass balance studies performed by Fenske and colleagues involving test tubes 

allowed for potential comparisons of hand rinse efficiency based on a chemical’s Ko/w. 

Curwin noted that a pesticide’s water solubility may have an impact on rinse removal 

efficiency. Campbell and colleagues looked at the efficiency of the various types of rinse 

mixtures available to investigators and found minor differences based on the type of rinse 

used and that skin concentration appeared to be more critical. That is the higher the 

concentration, the higher the rinse efficiency. Brouwer focused largely on the Fenske data 

(test tubes) and studies in which hands were spiked with dilute spray. In those studies, 

concentration did not appear to play as large a role (e.g., propoxur). In general rinse 

efficiencies were high for all chemicals regardless of concentration and/or physical 

Page 74 of 114 
chemical properties. The exception appears to be the Fenske chlorpyrifos data which 

represents dried concentrates rather than dilute sprays. All of these studies appear to have 

been conducted for short durations (less than three hours). 

The skin concentration may be influenced by the length of the exposure study used to 

model worker exposure. The impact of a chemical binding to the skin at initial loading 

may be mitigated by longer exposure durations as the skin surfaces come into equilibrium 

with the surface residues after repeated contacts. Although limited in scope, Fenske saw 

less of a difference between the performance of gloves and rinses at longer monitoring 

periods and the potential for a steady state relationship with hands and treated surfaces in 

Durkin, Spencer and Ross. In more recent studies, exposure duration tends to be longer 

(~ 4 to 6 hours) than studies performed in the 80s and early 90s. 

Curwin’s team observed the highest efficiency overall which may have been impacted by 

study duration, the nature of the residues (soil/plant material/residue matrix), immediate 

washing after the exposure period and the high water solubility of acephate. 

The Agency is considering adjusting dermal exposure measurements to account for any 

potential residue losses due to incomplete collection using the hand rinse method. The 

AHETF asserts that adjustments are not required based on their comparison of exposures 

made using passive dosimetry and biological monitoring. However, the Agency believes 

it is a prudent to consider such an adjustment when developing a generic database for 

assessing a wide variety of pesticides. For the AHETF database, the Agency believes this 

minor adjustment will have a small impact on the total unit exposures since all of the 

studies will be based on individuals wearing chemical resistant gloves. This adjustment 

may have a larger impact on studies addressing reentry exposure or residential 

applications where the use of protective gloves is not considered. 

The Agency invites the panel to consider options the Agency may consider to adjust 

exposure estimates when relying on studies using hand rinses. Various options include: 

• Consider the slope of a regression equation based in plots of hand rinse efficiency and 

log Ko/w. These can be based on such factors such as skin concentrations, rinse 

solvent, residence time. The adjustment would be identical to the adjustments 

commonly made for laboratory/field fortifications. 

• As part of a field study design, select a surrogate chemical with a known and reliable 

biomarker and add any remaining residues based on what was found in the urine. 

(adjustments must consider the inhalation pathway as well). 

• Consider additional laboratory-based hand-rinse efficiency studies to further evaluate 

residue retention and removal properties. 

• Make no adjustments based on the conclusions of the passive dosimetry/biological 

monitoring comparisons. 

Page 75 of 114 
3.2.2: Hand Method Field Performance Data 

 In addition to the mechanistic analysis above, an additional evaluation of hand sampling 

methods was conducted using the data from the PHED-based case study. In this analysis the unit 

exposures (μg/lb active ingredient handled) were calculated for the combined exposure of both 

hands. Data were then segmented based on the type of sampling media used, the nature of the 

contaminant being screened for in each study (i.e., solid pesticide formulation, dilute liquid sprays 

or liquid concentrates), and whether or not protective (e.g., chemical resistant) gloves were worn. 

[Note: AHETF proposed studies all require the use of protective gloves at this point. As such, 

the focus of this analysis is on that scenario instead of on quantifying residues deposited on bare 

hands which tends to lead to much higher loading rates.] 

 The data which were obtained for this analysis from the PHED case study were from 3 

mixer/loader and 3 applicator exposure scenarios. The data represent 8 different sampling media 

that included trapping approaches (i.e., gloves and patches) and residue removal methods (i.e., 

soap solutions, acetone, and various alcohols). All of the data are included in Exhibit B which 

includes the hand monitoring data as prepared for this analysis. The data have been summarized 

as follows: 

• N (all) = 513 (188 were without protective gloves – i.e., barehanded); 

• N (mixer/loaders) = 402 (150 were without protective gloves); 

• N (applicators) = 111 (38 were without protective gloves); 

• 8 Sampling media used; 

o Removal methods (i.e., washes): ethanol, methanol, acetone, isopropanol, 

soapy water 

o Trapping methods: cotton gloves, Tyvek© gloves, patches 

• Some wore protective (e.g., chemical resistant) gloves, others did not (i.e., they were 

barehanded) but the focus for this analysis will be on use of protective gloves since 

labels tend to require their use more routinely for good hygienic practices 

The available data, segmented by sampling media and whether or not protective gloves were 

worn are presented below (Table 3-14 & Figure 3-8). The data itself are illustrated in Figures 3-9 

and 3-10. Figure 3-9 presents total exposure estimates that have not been normalized by the 

pounds of active ingredient handled while Figure 3-10 presents hand unit exposure estimates 

which have been normalized. [Note: More information is also available in Exhibit B.] 

Page 76 of 114 
Table 3-14: Summary Of PHED Case Study Hand Analysis Data 

Sample Media Protective Glove Use N 

Acetone Yes 12 

No 16 

Aqueous Detergent 

Yes 108 

No 53 

Ethanol 

Yes 75 

No 79 

Cotton Gloves 

Yes 69 

Isopropanol Yes 51 

No 3 

Methanol 

Yes 10 

Patches No 12 

Tyvek Gloves No 25 

Total 513 

PHED Case Study - Hand Sample Methods Analysis

Number of Data Points By Sampler Method

[Incl. All Samplers, ML & APP, All Form., Bare/Prot. Gloves (N=513)]

0

20

40

60

80

100

120

AcetoneProtGLAqDeterBareAqDeterProtGlEthanol-Bare EthanolProtGlCottonGlBareCottonGlProtGlIsopropanolProtGlMethanolBareMethanolProtGlPatches-Bare Tyvek-Bare

Sampling Media & Hand Protection Level

Number of Data Points (N)

Figure 3-8 

Page 77 of 114 
PHED Case Study - Hand Sample Methods Analysis

Total Hand Exposure (ug) vs. Total Handled (lb ai)

[Incl. All Samplers, ML & APP, All Form., Bare/Prot. Gloves (N=513)]

0.010

0.100

1.000

10.000

100.000

1000.000

10000.000

100000.000

1000000.000

10000000.000

0.0 0.0 0.1 1.0 10.0 100.0 1000.0 10000.0

Total (lb ai) Handled

Total Exposure (ug)

Acetone-ProtGl

AqDeter-Bare

AqDeter-ProtGl

Ethanol-Bare

Ethanol-ProtGl

CottonGl-Bare

CottonGl-ProtGl

Isopropanol-ProtGl

Methanol-Bare

Methanol-ProtGl

Patches-Bare

Tyvek-Bare

Figure 3-9 

PHED Case Study - Hand Sample Methods Analysis

Hand Unit Exposure (ug/lb ai handled) vs. Total Handled (lb ai)

[Incl. All Samplers, ML & APP, All Form., Bare/Prot. Gloves (N=513)]

0.001

0.010

0.100

1.000

10.000

100.000

1000.000

10000.000

100000.000

1000000.000

0.0 0.0 0.1 1.0 10.0 100.0 1000.0 10000.0

Total (lb ai) Handled

Unit Exposure (ug/lb ai)

Acetone-ProtGl

AqDeter-Bare

AqDeter-ProtGl

Ethanol-Bare

Ethanol-ProtGl

CottonGl-Bare

CottonGl-ProtGl

Isopropanol-ProtGl

Methanol-Bare

Methanol-ProtGl

Patches-Bare

Tyvek-Bare

Figure 3-10 

Figure 3-11 illustrates how constraining two parameters can decrease the size of the available 

dataset. These data represent a subset of the total data which have been constrained to represent 

only those results for applicators who wore protective gloves. It should be noted that these data 

also include only 4 of the 8 sampling media. 

Page 78 of 114 
PHED Case Study - Hand Sample Methods Analysis

Hand Unit Exposure (ug/lb ai handled) vs. Total Handled (lb ai)

[Incl. All Samplers, Applicators, All Form., Protective Gloves (N=73)]

0.0010

0.0100

0.1000

1.0000

10.0000

100.0000

0.1 1 10 100 1000

Total (lb ai) Handled

Unit Exposure (ug/lb ai)

AqDeter-ProtGl

Ethanol-ProtGl

CottonGl-ProtGl

Isopropanol-ProtGl

Figure 3-11 

 

In order to supplement the literature data available for investigating the efficiency of hand 

wash recoveries, the Agency conducted an exploratory analysis to determine if certain types of 

hand wash methods consistently resulted in better recoveries (i.e. higher unit exposure values). 

The categories of hand wash methods were “cotton” (cotton gloves), “other” (patches & Tyvek 

gloves), “soap” (soap solutions), and “wash” (various alcohols and acetone). Unit exposure data 

were analyzed separately for various scenarios. These scenarios were specified by worker type 

(mixer/loader vs. applicator), formulation type (liquid vs. solid), and glove type (protective vs. 

none). 

An example of the analyses that have been developed for evaluating the performance of 

varying hand monitoring methods is presented below (Figure 3-12). The analyses which have 

been completed for all scenarios are based on the unit exposure estimates calculated in Exhibit B. 

A simple ANOVA model was fit to the unit exposure data to determine if the (logged) 

group means of various wash methods (within a given scenario) were significantly different. The 

results of the ANOVA model and related significance test are based on the standard ANOVA 

assumptions that 1) the data being analyzed are normally distributed, 2) the group variances are 

equal, and 3) samples within a level are independent. 

In order to address the first assumption that the data are normally distributed, the 

logarithms of unit exposure values were analyzed. Probability plots are a useful tool for 

qualitatively assessing whether or not the data are normal. In Figure 3-12, probability plots of the 

logs of the unit exposures for each group of hand wash methods are presented (middle graph with 

“Normal Quantile” on the x-axis). If the data plot along an approximately straight line then they 

are normally distributed (since the logs of the unit exposure values are plot, the log-normality of 

Page 79 of 114 
the unit exposure values is being assessed). For this particular scenario, log transforming the data 

effectively converts the unit exposure data into normal distributions. 

The second assumption that needs to be verified is the equality of the variances. The 

probability plots shown in Figure 3-12 can also be used to qualitatively assess if the group 

variances are equal. If the group variances are equal, then the slopes of lines will be 

approximately parallel. For this particular scenario, the “cotton” and “soap” variances appear to 

be equal, but the “wash” variance is decidedly different. 

The third assumption is that the samples within a hand method are independent. In Figure 

3-12, the scatter plots (the left graph with the green diamonds) use colored symbols to uniquely 

identify samples from the same study. A visual inspection of the scatter plots indicate that the 

samples obtained from the same study tend to “clustered” together. For example, the black, gray, 

and blue circles are clustered together in the cotton glove scatter plot. This clustering of 

observations is indicative of intra-study correlation. The intra-study correlation evidenced by the 

scatter plots implies that the samples are not independent and, as one might guess, unit exposure 

values from the same study tend to be more similar than those from different studies. 

Figure 3-12: Oneway Analysis of log Unit Exposure By Sampler For Applicators Exposed 

To Liquid Sprays Wearing Protective Gloves 

log Unit_Exposure

-3

-2

-1

0

1

2

Cotton Soap Wash

Sampler

Cotton Soap .01 .05.10 .25 .50 .75 .90.95Wash .99

-3 -2 -1 0 1 2 3

Normal Quantile

When evaluating the three underlying assumptions of a simple ANOVA analysis and the 

related significance testing for this exploratory analysis, it was generally found for all scenarios 

that (1) log transforming the unit exposure values effectively converted the data to normal 

distributions, (2) the variance of the unit exposure values from at least one hand wash methods 

was not equal to the other, and (3) the samples from within a study appear to be correlated 

indicating a lack of independence between the samples within a hand wash method. 

Page 80 of 114 
Violation of the variance equality assumption and independence assumption implies that 

one needs to be cautious when interpreting the results of any significance tests based upon the 

ANOVA results. Additionally, the particular type of non-independence observed in the hand 

wash data informs the violation of the variance equality. The non-independence observed in the 

hand wash data suggests that the error structure of the data is not properly modeled by the simple 

ANOVA approach employed by the Agency for exploratory purposes. The more complex error 

component can be better modeled by more sophisticated techniques, such as nested ANOVA or 

mixed linear models. This improper modeling of the error structures in turn affects the modeled 

group variances of the hand wash methods. 

Even given the violation of some basic assumptions, the Agency hoped to gain insight into 

the relationship of recoveries and hand wash methods using a simple ANOVA analysis and 

simple graphical techniques. The results from the analysis of the scenario included in this section 

suggest that the alcohol “wash” hand wash method results in higher unit exposure values (higher 

recoveries) on average. However, this is not a consistent conclusion derived from examining the 

unit exposure data from the other scenarios. In general, no overall conclusions, such as “alcohol 

and acetone washes consistently resulted in higher recoveries for all scenarios” could be drawn 

from the data. What was consistently observed for all scenarios was that the degree of intra-study 

correlation is often too large to be ignored if more refined analyses were to be completed in the 

future. Another consistent, yet preliminary observation of the data was that the study-to-study 

variation within hand wash methods was generally greater than the method-to-method variation. 

This observation implies that even with a more sophisticated analysis that appropriately models 

the nested error structure, much larger differences in unit exposure values between hand wash 

methods would have to be observed to conclude that significant differences exist between 

recoveries. Overall, then, a review of the literature as well as a review of available experimental 

and observational PHED hand wash data suggest that no consistent or reliable conclusions can be 

reached regarding the efficiency of hand wash methods. 

3.3: Summary of Methods Analysis 

 Passive dosimetry and biomonitoring techniques have been used extensively to estimate 

exposure to pesticides. Each technique has advantages and disadvantages, and the choice of 

which to use is determined by how the resulting data will be used. Several studies incorporating 

both methods concurrently have been reviewed in this document. Concurrent passive dosimetry 

and biomonitoring studies often give similar or at least correlated results. Likewise, retrospective 

analyses give similar results. In some cases, insufficient information is available to adequately 

assess the relationship between results obtained using the two types of techniques. 

 Exposure monitoring studies typically yield highly variable results. The high variability is 

inherent in studies conducted in the field, where conditions are less controlled and the natural 

differences between workers are captured in the results. Because of this variability, ratios in 

study means that differ by less than an order of magnitude, for example, may be considered to be 

fairly small. Ratios in mean absorbed doses estimated from passive dosimetry and biomonitoring 

for the most-studies active ingredient, chlorpyrifos, are within 3-fold of one another for nearly all 

handler and reentry studies reviewed. As passive dosimetry and biomonitoring represent very 

diverse approaches, these results tend to support both as providing reasonably consistent 

estimates of exposure. Given this premise, it is unlikely that dermal absorption during sample 

collection or breakthrough through dermal dosimeters likely contributes to a negative bias in any 

Page 81 of 114 
pragmatic application of the results in Agency assessments. The Agency acknowledges that 

certainly from a theoretical and physical perspective that these events likely take place. One 

adjustment that could be made to address the possibility of breakthrough under varied field 

conditions, especially when developing data for a large generic database such as that proposed by 

the AHETF, would be to select pesticide active ingredients that have well characterized 

pharmacokinetics. This would enable biological samples to be collected to characterize sampling 

method performance. 

 Hand exposure monitoring techniques have also been extensively evaluated as described 

above. At this point, both the mechanistic and field performance analyses that have been most 

studied appear to be equivocal with regard to a bias pertaining to the selection of a specific 

method. This should also be considered with the notion that there are a number of uncertainties 

associated with any selection related to hand monitoring methodology.

Page 82 of 114 
4. Unit Exposure & Applicability Across Anticipated Working Conditions

The Agency’s use of unit exposures, defined previously in this document as 

exposure normalized by the amount of active ingredient handled is based historically upon 

the broad assertion that levels of exposure are most dependent upon the physical 

parameters of handling pesticides, not its chemical properties (Severn, 1982; Hackathorn 

and Eberhart, 1985; Reinart and Severn, 1985; Honeycutt, 1985; USEPA, 1986a). More 

specifically, Reinart and Severn (1985), said, “...it is our experience that the proper (and 

logical) conversion factors are amounts of pesticide applied for applicators and the 

quantity of chemical handled by mixer/loaders. Unfortunately, these data are not always 

readily retrievable...In such instances, other factors (e.g., tank concentration for 

applicators or time involved in mixing-loading) must be used.” 

It is the Agency’s intention in this section to examine this claim in terms of the 

specific relationship assumed for our unit exposure metric: proportionality between 

exposure and amount of active ingredient handled. For brevity, the amount of active 

ingredient handled is referred to "AaiH" in this section. 

 

4.1 Proportionality between Exposure and Amount Active Ingredient Handled 

The unit exposure, defined as exposure per AaiH, is based on the assumption that 

the two variables are proportional. That is, if one doubles the amount of pesticide they 

handled or applied, the resultant exposure will be doubled as well. As described above, 

this relationship has historically been the principle assumption since the mid-1980s 

underlying the use of exposure data in the Agency’s pesticide handler exposure 

assessments. This metric is not only practical in terms of risk assessment methodology 

(discussed in Section 2); it is also practical in terms of regulatory efforts (e.g., limiting, in 

various ways, the amount of pesticide an applicator can handle). 

This section of the document discusses the Agency’s exploration of the current 

PHED data with respect to the relationship between AaiH and exposure. This is done for 

each of the six case study scenarios described earlier (note: these are numbered, as in 

previous sections, to correspond to the PHED Surrogate Exposure Guide (USEPA, 

1998b)): 

• Scenario #1: Open Mixing/Loading Dry flowables 

• Scenario #2: Open Loading Granules 

• Scenario #3: Open Mixing/Loading Liquids 

• Scenario #11: Open Air Blast Applications 

• Scenario #12: Closed-cab Airblast Applications 

• Scenario #15: Open-cab Solid Broadcast Spreader Applications 

Importantly, the AHETF is proposing as part of its future studies to investigate 

proportionality as a secondary objective of its study protocols. AHETF proposes to 

incorporate more advanced and appropriate statistical concepts and designs in their 

Page 83 of 114 
analyses to distinguish between complete proportionality and complete independence of 

exposure and AaiH. Additional information on this approach is provided in the AHETF 

document entitled Procedures for Determining the Required Number of Clusters and 

Monitoring Units per Cluster to Achieve Benchmark Adequacy (AHETF, 2006b). 

Because this relationship is referenced both historically and in proposed future 

studies, it is the Agency’s intention to examine, using our current database, the extent to 

which this relationship is demonstrated and applicable across the anticipated pesticide 

handler working conditions. The Agency has presented both a discussion of the 

methodology used to do so using dermal exposure and findings regarding proportionality 

for the six PHED scenarios in our case study. Though not considered for the purposes of 

this exercise, the Agency believes that the methodology presented herein would apply, 

since proportionality between exposure and AaiH is also assumed, for inhalation 

exposure. 

4.1.1 Proportionality Investigation using Dermal Exposure 

Current occupational exposure assessment strategies use PHED data on a body 

part-by-body part basis (discussed in Section 2). This is based on the premise that data 

from various body parts in different studies can be averaged and then combined to yield a 

unit exposure that is the sum of different body parts.1 Ideally a data set in which total 

body dermal exposure was measured would be used to describe the relationship between 

dermal exposure and AaiH. Because not all records in PHED measured total dermal 

exposure the Agency utilized a method similar to that developed during a Probabilistic 

Worker Exposure Assessment Workshop sponsored by the International Life Sciences 

Institute (ILSI) Risk Science Institute (RSI) and described in Investigating Probabilistic 

Methods to Assess Mixer/Loader Exposure to Pesticides (ILSI, 2003)). The authors of 

this document recognized that the studies within PHED did not follow the same design in 

terms of location and number of patch dosimeters used and many records were therefore 

considered incomplete in terms of representing total exposure. As a result, the authors 

found it necessary to subset the PHED records and use only those records which met 

certain minimum criteria for body parts in order to “ensure that the body parts measured 

were consistent across records and that they were the parts likely to be most exposed in 

the scenario queried.” In other words, the ILSI authors recognized the limitations of 

PHED and determined that it was most appropriate to use only those records which, in 

their opinion, were sufficiently complete in terms of critical body parts such that total 

dermal exposures could be reasonably estimated. Following this methodology, the current 

analysis of the relationship between exposure and AaiH use only PHED records using 

WBDs and records using patch dosimetry that have at least the minimum-critical body 

parts determined necessary for the specific scenario of interest2

. 

 

1

 For example, if study A measured exposure to the hands only and study B measured exposure to the body 

only, the records for study A and B could be averaged individually and then added together to yield a central 

tendency unit exposure estimate for the hands and the body. 

2

 While it may seem incorrect or illogical to combine records that correctly measure the body component of 

dermal exposure (e.g., WBD) with those that represent only certain parts of an individual’s body (e.g., patch 

dosimetry), this methodology was considered appropriate considering the data limitations. Without this 

Page 84 of 114 
4.1.2 Constructing Proportionality Plots 

The model the Agency assumes for its unit exposures in pesticide handler exposure 

assessments is a power relationship: 

E = AWx

Where: E = exposure (measured in micrograms) 

 A = constant 

 W = AaiH (measured in pounds active ingredient handled) 

 X = 1 

The Agency used ordinary least squares (OLS) regression techniques in order to 

evaluate the resulting slope of the regression line for the log conversion equation of this 

model: 

log (E) = log (A ) + x(log W) with intercept log (A) and slope x. 

If the relationship between exposure and AaiH is directly proportional, then the 

resultant slope (x) should be 1. A non-zero slope other than 1 would indicate an 

alternative power relationship (e.g., E = AW0.6) and a slope of 0 would suggest 

independence between the variables. 

We recognize that there are a number of statistical limitations and caveats to the 

use of OLS regression with the PHED data – not the least of which is the apparent 

clustering of data points by study and their lack of independence which may severely 

compromise some of the statistical tests performed here. Nevertheless, this exercise and 

the resultant plots are believed to be valuable exploratory tools which graphically illustrate 

the relationship between exposure and AaiH3. 

 

methodology the Agency would have had to plot separately studies utilizing patch dosimetry and studies 

using WBD, thus minimizing the amount of data for this exercise. Additionally, the Agency has, for all 

scenarios within the case study, compared results for total dermal exposure (reported in micrograms) with 

total dermal exposure results normalized for the total body part surface area represented by the 

corresponding exposure measurements (reported as micrograms per square centimeter) to demonstrate how 

the minimum-critical body part methodology is adequate for our purposes. The open mixing/loading dry 

flowables and granules scenarios are used to show this methodology graphically by indicating what body 

parts are represented by each data point (see “LOG-LOG REGRESSION SUMMARY” tabs in Exhibit C). 

This comparison resulted in no discernible difference and provides evidence for the notion that by declaring 

and using the minimum-critical body parts we have captured the majority of exposure. 

3

 More appropriate statistical methods for examining the relationship between these two quantities would 

involved hierarchical linear modeling (aka mixed models) approaches or robust regression techniques which 

specifically account for the clustering (non-independence) of exposure measurements within studies. When 

such clustering by study is present, the observations within the study (cluster) may not be treated as 

independent, but the studies (clusters) themselves are independent. Note that the AHETF recognizes these 

statistical issues and is proposing more appropriate statistical techniques to be used with its data. 

Page 85 of 114 
Plots illustrating the Agency’s examination of proportionality between exposure 

and AaiH were generated using the following subsetting criteria (where applicable) for the 

case study scenarios: 

• PHED data grade (e.g., A, B grade only, A, B, C grades, all grades, etc.); 

• Exposure location 

o Outer Dosimetry 

 Exposure to bare hands 

 Exposure to the head without protective head gear 

 Exposure to the body without clothing or PPE (i.e., representing 

“bare skin”) 

o Inner Dosimetry 

 Exposure to gloved hands 

 Exposure to the head with protective head gear 

 Exposure to the body underneath a single layer of clothing or PPE 

The purpose of this data formatting and presentation was to maximize the 

Agency’s ability to investigate proportionality between exposure and AaiH using PHED. 

The presentation of differing data grades or outer and inner dosimetry is not meant as a 

recommendation for using such data sets in unit exposure determination – it was simply 

meant as a data optimization method for examining proportionality using our current 

database. However, because pesticide handlers may don differing combinations of 

clothing and/or PPE, the separation of the body into different regions (e.g., head, body, 

hands) is practical. Total dermal exposure can then be estimated depending on the 

“clothing” worn (e.g., exposure to the body while wearing a single layer of clothing plus 

hand exposure with or without gloves plus exposure to the head with or without protective 

head gear). 

4.1.3 Results 

As described previously, if the relationship between exposure and AaiH is indeed 

proportional, a regression of the log of exposure as a function of the log of AaiH, should 

result in a regression line with a slope not significantly different than 1. Alternatively, one 

can also see whether the slope of the regression line is significantly different than 0 (i.e., 

whether exposure is independent of AaiH). 

 

Descriptions of all data and graphical representations (Figures 4.1 – 4.8) of results for 

two scenarios within the case study – the open mixing/loading granules and open-cab 

airblast applications – are provided as examples in this section. Results for all six 

scenarios within the case study can be seen in the Proportionality Analysis Spreadsheets 

provided in Exhibit C. 

Descriptions and graphical representations (Figures 4.1 – 4.8) of results for two 

scenarios within the case study – the open mixing/loading granules and open-cab airblast 

applications – are provided as examples in this section. Results for all six scenarios within 

the case study can be seen in the Proportionality Analysis Spreadsheets provided in 

Exhibit C. 

Page 86 of 114 
The log-log regression plots for each case study scenario and corresponding subsets 

(e.g., various body part and clothing combinations) contain: 

• Individual data points of each study: represented by different colors and/or 

characters; 

• Regression line: represented by a solid black line; 

• “Slope = 1” line for comparison: represented by a dashed black line; and, 

• 95% confidence intervals for the slope of the regression line: represented by 

dashed blue lines. 

Both the characteristics and potential limitations of PHED studies (described in Table 

2.7) and the statistical limitations of our methodology (discussed in Section 4.1.2) should 

be noted when reviewing the results and discussing any conclusions of the Agency’s 

examination of proportionality between exposure and AaiH. 

Scenario #1: Open Mixing/Loading Dry Flowables 

Scenario #1 includes individuals exposed during open mixing and loading of 

chemicals formulated as dry flowables (e.g., water-dispersible granules). Shown in Table 

4.1 below are the various subsets used to evaluate proportionality for this scenario. These 

include the relationship between protected hand exposure (e.g., with chemical resistant 

gloves) and AaiH for which there are 37 measurements with data quality grades of A 

through E and the relationship between total dermal exposure (hands plus body) and AaiH 

measured using outer dosimetry (i.e., bare skin) for which there were 25 measurements 

graded A through D and 23 measurements graded A through C. The minimum-critical 

body parts required to represent exposure to the rest of the body (RoB) are listed, as are 

the number of records available having concurrent hand plus RoB exposure representing 

total dermal exposure. 

Table 4.1 Scenario #1: Open Mixing/Loading Dry Flowables 

# Records by PHED Grade, Dosimetry Location, and Body Section 

[Minimum-critical Body Parts representing RoB Exposure: forearms, chest, back, thighs] 

Inner Dosimetry Outer Dosimetry PHED 

Grade Hands RoB Total 

Dermal Hands RoB Total Dermal 

A – E 37 

A – D 25 

A – C 34 38 23 

Not plotted 

Only 7 

records 

available 21 

Not plotted 

No records had 

concurrent 

hand/body 

measurements 

See ExhibitC_I_c_ML_DF_inner.xls See ExhibitC_I_b_ML_DF_outer.xls 

Scenario #2: Open Loading Granules 

Scenario #2 includes individuals exposed during open loading of chemicals 

formulated as granules. Shown in Table 4.2 below are the various subsets used to 

evaluate proportionality for this scenario. These include the relationship between 

unprotected (bare) hand exposure and AaiH for which there are 10 measurements with 

data quality grades of D and E and the relationship between protected hand exposure (e.g., 

Page 87 of 114 
with chemical resistant gloves) and AaiH for which there are 67 measurements graded A 

through C. The minimum-critical body parts required to represent exposure to the rest of 

the body (RoB) are listed, as are the number of records available having concurrent hand 

plus RoB exposure representing total dermal exposure. Plots for these subsets are 

provided below as examples (Figures 4.1 – 4.5). 

Table 4.2 Scenario #2: Open Loading Granules* 

# Records by PHED Grade, Dosimetry Location, and Body Section 

[Minimum-critical Body Parts representing RoB Exposure: forearms, chest, back, thighs] 

PHED Inner Dosimetry Outer Dosimetry 

Grade Hands RoB Total Dermal Hands RoB Total Dermal 

D – E 10 

A – C 67 33 

A – B 42 

A only 45 

Not plotted 

Only 7 

records had 

concurrent 

hand/body 

measurements 

See ExhibitC_II_c_L_Gran_inner.xls See ExhibitC_II_b_L_Gran_outer.xls 

*Study 1027 is not included in this scenario 

Open Loading Granules – Bare Hand Exposure: There were 10 monitoring events, 

where between approximately 20 and 1000 lbs ai were handled, that had bare hand 

exposure measurements, all classified as either D or E grade data. Figure 4.1, below, 

shows that the slope of the regression line is significantly different than 1, and not 

significantly different than 0, suggesting that for bare hand exposure in this scenario, 

exposure is not proportional, and, in fact, may be independent of AaiH.

Page 88 of 114 
Figure 4.1 Scenario #2: Open Loading Granules (PHED Grades D-E)

Outer Dosimetry - Hand Exposure vs. lb ai handled

100

1000

10000

10 100 1000 10000

lbs ai handled

Exposure (ug)

Study 425 Study 448 Regression Line Slope = 1 95% Confidence Limit

Regression Slope = -0.26

95% CIslope = -0.61, 0.09

R2

 = 0.2686

n = 10

Open Loading Granules – Exposure to the Body (excluding hands) Outside 

Clothing/PPE (i.e., “bare”): There were 33 monitoring events, where between 

approximately 5 and 2100 lbs ai were handled, that had at least the minimum-critical body 

parts required to represent dermal body exposure for this subset. These were classified as 

A, B, or C grade data. Figure 4.2, below, shows the slope of the regression line 

significantly different than 1, but also significantly different than 0, suggesting that, 

though it is not independent of AaiH, exposure may not be proportional to AaiH. 

The figure additionally provides information on the body part combinations for each 

study. All monitoring events have at least the minimum-critical body parts representing 

exposure to the body (see Table 4.2), however, additional body parts above and beyond 

the minimum-critical were included when available. Additional analysis for this 

methodology was previously described (e.g., normalizing the sum of exposure by the sum 

of the surface areas of the body parts measured) and is included in the “LOG-LOG 

REGRESSION SUMMARY” tabs in the file ExhibitC_II_b_L_Gran_outer.xls.

Page 89 of 114 
Figure 4.2 Scenario #2: Open Loading Granules (PHED Grades A-C)

Outer Dosimetry - RoB Exposure vs. lb ai handled

1000

10000

100000

1000000

1 10 100 1000 10000

lbs ai handled

Exposure (ug)

Study 422 [min+upparms+lowlegs+head (all patches)] Study 427 [min+upparms (all patches)

Study 448 [min+upparms (all patches)] Study 1003 [min+upparms+lowlegs (True-WBD)]

Regression Line Slope = 1

95% Confidence Limit

Regression Slope = 0.49

95% CIslope = 0.20, 0.78

R2

 = 0.2792

n = 33

*min = forearms, chest, back, thighs

Open Loading Granules – Total Dermal Exposure Outside of Clothing/PPE (i.e., 

“bare” hands plus “bare” RoB): For this scenario only 7 monitoring events had 

concurrent outer dosimetry measurements for hand and body exposure. The Agency did 

not examine proportionality for this subset. 

Open Loading Granules – Gloved Hand Exposure: There were 67 monitoring events, 

where between approximately 5 and 2100 lbs ai were handled, that had gloved hand 

exposure measurements. These were classified as A, B, or C grade data. In Figure 4.3, 

below, the slope of the regression line was significantly different than 1, however also 

significantly different than 0. This suggests that while the relationship may not be 

proportional, exposure is not independent of AaiH. 

Page 90 of 114 
Figure 4.3 Scenario #2: Open Loading Granules (PHED Grades A-C)

Inner Dosimetry - Hand Exposure vs. lb ai handled

0

1

10

100

1000

10000

1 10 100 1000 10000

lbs ai handled

Exposure (ug)

Study 422 Study 427 Study 1003 Study 1004 Study 1011 Regression Line Slope = 1 95% Confidence Limit

Regression Slope = 0.74

95% CIslope = 0.57, 0.91

R2

 = 0.5427

n = 67

Open Loading Granules – Exposure to the Body (excluding hands) underneath a 

Single Layer of Clothing/PPE: There were 45 monitoring events, where between 

approximately 5 and 60 lbs ai were handled, that had at least the minimum-critical body 

parts required to represent dermal body exposure for this subset. These were all classified 

as A grade data. In Figure 4.4, below, the slope of the regression line was significantly 

different than 1, however also significantly different than 0. This suggests that while the 

relationship may not be proportional, exposure is not independent of AaiH.

Page 91 of 114 
Figure 4.4 Scenario #2: Open Loading Granules (PHED Grade A)

Inner Dosimetry - RoB Exposure vs. lb ai handled

1

10

100

1000

10000

1 10

lbs ai handled

Exposure (ug)

100

Study 1004 Study 1011 Regression Line Slope = 1 95% Confidence Limit

Regression Slope = 2.17

95% CIslope = 1.75, 2.60

R2

 = 0.7155

n = 45

Open Loading Granules – Total Dermal Exposure underneath a Single Layer 

Clothing/PPE (gloved hands plus RoB under Single Layer): There were 42 

monitoring events, where between approximately 5 and 60 lbs ai, were handled, that had 

concurrent gloved hand exposure and at least the minimum-critical body parts required to 

represent body exposure underneath a single layer of clothing. These were classified as 

either A or B grade data. In Figure 4.5, below, the slope of the regression line was 

significantly different than 1, however also significantly different than 0. This suggests 

that while the relationship may not be proportional, exposure is not independent of AaiH. 

One can see by comparing the plot below with the previous plots for gloved hand 

exposure and RoB exposure underneath a single layer of clothing (Figures 4.3 and 4.4, 

respectively), that only studies 1004 and 1011 had concurrent hand and body exposure 

measurements. Note that the data points for each of the two studies shifted upward only 

slightly when gloved hand exposure is added to the RoB exposure. Additionally the slope 

of the regression line and additional statistical characteristics only slightly changed. 

These observations suggest that when using gloves, hand exposure becomes a minimal 

driver in relation to the rest of the body. 

Page 92 of 114 
Figure 4.5 Scenario #2: Open Loading Granules (PHED Grades A-B)

Inner Dosimetry - Total Dermal (Hands+Body) Exposure vs. lb ai handled

1

10

100

1000

10000

1 10

lbs ai handled

Exposure (ug)

100

Study 1004 Study 1011 Regression Line Slope = 1 95% Confidence Limit

Regression Slope = 2.21

95% CIslope = 1.78, 2.64

R2

 = 0.7294

n = 42

Scenario #3: Open Mixing/Loading Liquids

Scenario #3 includes individuals exposed during open mixing and loading of 

chemicals formulated as liquids (e.g., soluble concentrates). Shown in Table 4.3 below 

are the various subsets used to evaluate proportionality for this scenario. These include 

the relationship between exposure to unprotected (bare) hands and AaiH for which there 

are 130 measurements with data quality grades of A through E to the relationship between 

total dermal exposure (hands plus body) with protective clothing (e.g., single layer PPE 

with chemical resistant gloves) and AaiH for which there are 50 measurements graded A 

through C. The minimum-critical body parts required to represent exposure to the rest of 

the body (RoB) are listed, as are the number of records available having concurrent hand 

plus RoB exposure representing total dermal exposure. 

Table 4.3 Scenario #3: Open Mixing/Loading Liquids 

# Records by PHED Grade, Dosimetry Location, and Body Section 

[Minimum-critical Body Parts representing RoB Exposure: forearms, chest, back, thighs] 

PHED Inner Dosimetry Outer Dosimetry 

Grade Hands RoB Total Dermal Hands RoB Total Dermal 

A – E 129 130 143 74 

A – C 58 80 50 96 139 58 

A – B 35 63 36 53 75 35 

See ExhibitC_III_c_ML_LIQ_inner.xls See ExhibitC_III_b_ML_LIQ_outer.xls 

Page 93 of 114 
Scenario #11: Open-cab Airblast Applications

Scenario #11 includes individuals exposed during the application of pesticides 

using airblast equipment that are not equipped with an enclosed structure in which the 

applicator would sit (i.e., open-cab). Shown in Table 4.4 below are the various subsets 

used to evaluate proportionality for this scenario. These include the relationship between 

unprotected (bare) head exposure and AaiH for which there are 62 measurements with 

data quality grades of A through E to the relationship between protected body exposure 

and AaiH for which there are 39 measurements graded A through C. The minimumcritical body parts required to represent exposure to the rest of the body (RoB) are listed. 

Plots for these subsets are provided as examples below (Figures 4.6 – 4.8). 

Table 4.4 Scenario #11: Open-cab Airblast Applications 

# Records by PHED Grade, Dosimetry Location, and Body Section 

[Minimum-critical Body Parts representing RoB Exposure: forearms, chest, back, thighs] 

HED Inner Dosimetry Outer Dosimetry 

Grade Head only RoB Head only RoB 

A – D 62 

A – C 39 61 56 

See ExhibitC_IV_c_APP_OpencabAirblast_inner.xls See ExhibitC_IV_b_APP_OpencabAirblast_outer.xls 

Open-cab Airblast Applications – Exposure to the Body (excluding hands) Outside 

Clothing/PPE (i.e., “bare”): There were 56 monitoring events, where between 

approximately 0.8 and 110 lbs ai were handled, that had measurements for at least the 

minimum-critical body parts required to represent dermal body exposure. These were 

classified as either A or C grade data. In Figure 4.6, below, the slope of the regression 

line was not significantly than 1 and also significantly different than 0, suggesting 

proportionality is reasonable.

Page 94 of 114 
Figure 4.6 Scenario #11: Open-Cab Airblast Application (PHED Grades A-C)

Outer Dosimetry - RoB Exposure vs. lb ai handled

100

1000

10000

100000

1000000

0 1 10 100 1000

lbs ai handled

Exposure (ug)

Study 442 Study 449 Study 460 Study 510 Study 517

Study 518 Study 1026 Regression Line Slope = 1 95% Confidence Limit

Regression Slope = 0.79

95% CIslope = 0.54, 1.03

R2

 = 0.4312

n = 56

Open-cab Airblast Applications – Total Dermal Exposure Outside of Clothing/PPE 

(i.e., “bare” hands plus “bare” RoB): There were only 13 monitoring events that had 

concurrent bare hand exposure measurements and exposure measurements for the rest of 

the body. Additionally, in these instances, the total dermal exposure was not significantly 

influenced by the inclusion of the hand exposure measurements. Therefore, the Agency 

opted to only present the RoB exposure (Figure 4.6, above) as an adequate representation 

of total dermal exposure. 

Open-cab Airblast Applications – Exposure to the Head without Protective 

Headgear: In addition to the RoB exposure the Agency examined the relationship 

between exposure to individuals’ heads and AaiH. This was considered applicable 

because head exposure, especially when one is wearing protective clothing over their 

bodies but no protective head gear, is a significant potential exposure driver for open-cab 

airblast applications. 

There were 62 monitoring events, where between approximately 0.8 and 110 lbs ai were 

handled, that had bare head exposure measurements. These were classified as A – D 

grade data (note: there was only one individual with D grade data). In Figure 4.7, below, 

the slope of the regression line was not significantly different than 1 and also significantly 

different than 0, suggesting proportionality is reasonable. 

Page 95 of 114 
Figure 4.7 Scenario #11: Open-Cab Airblast Application (PHED Grades A-D)

Outer Dosimetry - Head Exposure vs. lb ai handled

1

10

100

1000

10000

100000

1000000

0.01 0.10 1.00 10.00 100.00 1000.00

lbs ai handled

Exposure (ug)

Study 433 Study 434 Study 435 Study 442 Study 460

Study 510 Study 517 Study 518 Study 1006 Study 1026

Regression Line Slope = 1 95% Confidence Limit

Regression Slope = 0.80

95% CIslope = 0.54, 1.06

R2

 = 0.3902

n = 62

Open-cab Airblast Applications – Exposure to the Body (excluding hands) 

underneath a Single Layer Clothing/PPE: Because gloved hand exposure would not be 

considered a major driver of total dermal exposure for this subset (as opposed to the 

situation for total dermal exposure representing a single layer of clothing without gloves), 

the Agency examined proportionality for dermal exposure underneath a single layer of 

clothing excluding the hands. Additionally, the data would have been minimized 

considerably having to use only those individuals that had concurrent measurements for 

gloved hands and body exposure underneath a single layer of clothing. 

There were 39 monitoring events, where between approximately 0.8 and 13 lbs ai were 

handled, that had at the least the minimum-critical body parts required to represent RoB 

dermal exposure underneath a single layer of clothing for this scenario. These were 

classified as A, B, or C grade data. In Figure 4.8, below, the slope of the regression line, 

though positive, was significantly different than both 0 and 1, suggesting that while not 

independent, the relationship may also not be proportional. 

Page 96 of 114 
Figure 4.8 Scenario #11: Open-Cab Airblast Application (PHED Grades A-C)

Inner Dosimetry - RoB Exposure vs. lb ai handled

1

10

100

1000

10000

100000

0 1 10 100

lbs ai handled

Exposure (ug)

Study 460 Study 517 Study 518 Study 1000 Study 1006 Regression Line Slope = 1 95% Confidence Limit

Regression Slope = 1.58

95% CIslope = 1.07, 2.10

R2

 = 0.5093

n = 39

Scenario #12: Closed-cab Airblast Applications

Scenario #12 includes individuals exposed during the application of pesticides 

using airblast equipment equipped with an enclosed structure in which the applicator sits 

(i.e., closed-cab). Shown in Table 4.5 below are the various subsets used to evaluate 

proportionality for this scenario. These include the relationship between unprotected body 

exposure (represented by outer dosimetry) and AaiH for which there are 20 measurements 

with data quality grades of A and B to the relationship between protected total dermal 

exposure (hands plus body) and AaiH for which there are 20 measurements graded A and 

B. The minimum-critical body parts required to represent exposure to the rest of the body 

(RoB) are listed, as are the number of records available having concurrent hand plus RoB 

exposure representing total dermal exposure. 

Table 4.5. Scenario #12: Closed-cab Airblast Applications 

# Records by PHED Grade, Dosimetry Location, and Body Section 

[Minimum-critical Body Parts representing RoB Exposure: forearms, chest, back, thighs] 

PHED Inner Dosimetry Outer Dosimetry 

Grade RoB Total Dermal RoB 

A – B 30 20 20 

See ExhibitC_V_c_APP_ClosedcabAirblast_inner.xls See ExhibitC_V_b_APP_ClosedcabAirblast_outer.xls 

Scenario #15: Open-cab Solid Broadcast Spreader Applications

Page 97 of 114 
Scenario #15 includes individuals exposed during the application of solid 

formulation pesticides (e.g., granules) using a tractor-drawn broadcast spreader not 

equipped with an enclosed structure in which the applicator sits (i.e., open-cab). Shown in 

Table 4.6 below is the only subset used to evaluate proportionality for this scenario: 

exposure to the body underneath a single layer of clothing or PPE, with data quality 

grades of A and B. The minimum-critical body parts required to represent exposure to the 

rest of the body (RoB) is listed. 

Table 4.6. Scenario #15: Open-cab Solid Broadcast Spreader Applications 

# Records by PHED Grade, Dosimetry Location, and Body Section 

[Minimum-critical Body Parts representing RoB Exposure: forearms, chest, back, thighs] 

PHED Inner Dosimetry Outer Dosimetry 

Grade RoB RoB 

A – B 4 

See ExhibitC_VI_c_APP_OpencabSolidSpreader_inner.xls No ExhibitC file 

Overall, results of the Agency’s proportionality investigation were mixed. In some 

cases the slope of the regression line did not significantly differ from 1 and was also 

significantly different than 0, which would suggest proportionality is a reasonable 

assumption, while in others the slope of the regression line was significantly different than 

1 but not significantly different than 0, which would suggest independence. The Agency 

is unsure whether the inconsistent results of this investigation were a function of the 

characteristics or limitations of the data itself (e.g., disparate study designs and others 

previously discussed in Section 2 and Table 2.7) or that the assumed proportional 

relationship is, in actuality, not the proper one for certain scenarios.

4.1.4 Conclusion and Discussion 

As shown in the open mixing/loading granules and open-cab airblast applications 

examples above and in the additional case studies in Exhibit C, proportionality between 

exposure and AaiH appears reasonable in some, but not all cases. As stated previously, 

the Agency is unsure whether this is a function of limitations of our database potentially 

solved by additional/replacement data or whether proportionality in some cases is not a 

reasonable assumption. 

In cases where proportionality between exposure and AaiH may not be a 

reasonable assumption, the Agency believes that information on additional ancillary 

variables such as mix concentration, duration of exposure, number of tank mixes, etc., also 

collected during field trials, may assist in characterization of unit exposures. In order to 

evaluate whether these additional parameters might be significant determinants of 

exposure, the Agency proposes to construct various multiple regression models in which 

these parameters are included and also evaluate the resulting models using likelihood ratio 

tests, stepwise regression, and Akaike (AIC) and Bayesian information criteria (BIC) to 

determine how useful these additional parameters might be in improving the prediction 

capability of the model. 

Page 98 of 114 
Proposed study designs by the AHETF for additional data for various pesticide 

handling scenarios will allow for a more robust examination of proportionality in certain 

cases. The methodology for examining proportionality proposed by the AHETF includes 

accounting for the “clustering” effects of studies by using appropriate statistical modeling 

including nested models. 

The Agency believes that, though neither the studies in our current database nor 

the proposed studies by the AHETF were designed for the primary purpose of examining 

proportionality, compared with our current database, the AHETF studies will enable a 

more thorough discussion of proportionality due to their streamlined and consistent study 

design across all proposed scenarios and the possibility for more sophisticated statistical 

analysis. 

Page 99 of 114 
5. Scope of Research Plan 

In previous sections we have seen examples of handler exposure scenario analyses having 

both large and small numbers of samples (N). In all examples, the issue of a nested data structure 

(e.g., worker nested in study which is in turn nested in collection method) combined with a 

variety of other confounding issues appears to be an important factor which can substantially 

complicate the data analyses. Ideally a given handler scenario would have a reasonable number 

of samples with the right mix of representative activities which use as large a range of amount of 

active ingredient handled as possible. These monitored activities would be performed by as many 

different individuals and at as many different locations as possible. In addition, it is desirable to 

have the samples collected in as consistent a manner as possible such that confounding by the use 

of multiple methods is minimized (e.g., patches vs. whole body dosimeters or varied hand 

monitoring techniques). Finally, there is an ethical component, in that the Agency should not 

require additional sampling involving intentional dosing if this additional sampling would not 

significantly contribute to increased confidence in the individual datasets and the predicted 

exposures. 

Historically, the sample size requirements for worker/handler exposure studies have been 

largely driven by cost and logistics. In a previous SAP, (US EPA, 1986b), the Agency proposed 

requiring 9 samples for studies of exposure of aerial pesticide applicators and 15 samples for 

monitoring other handling tasks. Although the proposed sample sizes were acknowledged by the 

Panel as being “somewhat arbitrary”, the SAP agreed with the Agency’s approach of determining 

sample size requirements based on balancing the statistical advantages of large samples and the 

cost and practicality of conducting these kinds of studies. The Panel also found the sample size 

requirements to be too rigid. Alternatively, the SAP suggested that the required sample size be 

based on the variability inherent in the similar data from already existing studies. As a result of a 

need to systematically address this issue, the Agency, along with PMRA, DPR and industry, 

developed PHED which is a database of many exposure studies grouped by scenario as described 

above in Section 2. However over time, the limitations of PHED have been acknowledged, 

examples of which have been illustrated in this document. 

The focus of this section are the documents entitled Agricultural Handlers Exposure Task 

Force (AHETF) Technical Summary Document For a Multi-Year Pesticide Handler Worker 

Exposure Monitoring Program (AHETFa, 2006), Procedures for Determining the Required 

Number of Clusters and Monitoring Units per Cluster to Achieve Benchmark Adequacy 

(AHETFb, 2006) and American Chemistry Council-Antimicrobials Exposure Assessment Task Force II 

(AEATF II) Background and Scoping Summary (ACCa, 2006). However, it is important to 

characterize the number of agricultural pesticide applicators presently in the United States in 

order to provide some context for the discussions related to the overall sampling plan proposed by 

AHETF and AEATF, the proposed sampling intensity, and its representativeness. The Agency 

believes the nature and the size of the exposed population has a direct bearing on the development 

of the research plan including the numbers of samples that would be required to adequately 

predict exposures for that population. 

 

The Agency is not aware of any direct census or survey of agricultural pesticide 

applicators in the United States. However, by using a mixture of agricultural censuses and 

pesticide usage surveys, the Agency has developed an estimate of the approximate number of 

domestic aerial applicators and can identify the number of farms that receive agricultural 

Page 100 of 114 
pesticide applications categorized by pesticide formulation, application method, and type of 

applicator. The Agency estimates that there are approximately 3,500 to 4,000 aerial applicators in 

the United States. Table 5-1 reports the approximate number of farms reporting pesticide 

applications categorized by formulation, application method, and type of applicator. The types of 

application methods and pesticide formulations match fairly well with PHED/AHETF scenarios. 

Table 5-1. Approximate number of farms reporting the application of pesticides, categorized by 

formulation, application method, and type of applicator. Note that a given farm is counted 

multiple times in the column and row totals based on the unique combinations of formulation, 

application methods, and type of applicator reported on that farm. Data represent the annual 

number of farms (5-year average, 2001-2005). 

Formulation Application 

Method 

Commercial 

Applicator 

Private 

Applicator 

Totals 

Dry Flowable/WDG Aerial 14,000 14,000 

Ground 97,000 160,000 300,000 

Spot 1,000 7,000 8,000 

Granular Aerial 2,000 2,000 

Ground 9,000 82,000 98,000 

Spot <1,000 2,000 2,000 

Liquid Aerial 62,000 62,000 

Ground 340,000 600,000 1,000,000 

Spot 8,000 115,000 130,000 

Wettable Powder Aerial 10,000 10,000 

Ground 9,000 43,000 110,000 

Spot <1,000 1,000 2,000 

Totals 550,000 1,000,000 1,800,000 

Note: Values in the above table are rounded and therefore the row and column totals do not sum. Further, the row 

totals include records where the type of applicator is unknown. These unclassified records can have a substantial impact 

on the row totals (e.g., wettable powder applied by ground). Chemigation data are incomplete and are not included. Spot 

includes spot treatment and trunk sprays. Ground includes broadcast, banded, in-row, and airblast methods of 

application. 

The AHETF submitted a proposal for the number of samples to be collected for various 

agricultural handler scenarios as part of developing an exposure database, referred to as 

AHED (Agricultural Handlers Exposure Database). Table 5.2 provides a comparison of 

the AHETF-proposed sample sizes for the AHED scenarios and the samples sizes 

currently available to the Agency from the PHED. The proposed number of samples for 

each scenario will be re-evaluated as the AHETF proceeds with its project. Each scenario 

is meant to be representative of handlers wearing long pants, long-sleeved shirts, and 

chemical resistant gloves. The numerical scenario identifiers are based on the scenario 

numbering scheme presented in the Agency’s PHED Surrogate Exposure Guide (see 

Section 2 above). The range of PHED sample sizes for a given scenario reflects the 

disparate number of body part exposure values available (e.g., for scenario #1 perhaps leg 

patches were only collected for 16 monitoring events, whereas arm patches were collected 

for 21 monitoring events). Unlike PHED, the AHETF-proposed sample sizes represent an 

individual’s entire exposure since all studies to be included in the AHED will follow the 

Page 101 of 114 
same monitoring and sample collection protocol, which include collecting exposure 

measurements for all body parts for each monitoring event. 

Table 5-2: Proposed AHETF Sampling Plan By Scenario And Numbers of Monitoring Events 

Handler Scenario AHETF Proposed (n) PHED (n) 

1: Open mix/load, dry flowables 25 16 - 21 

2: Open mix/load, granules 16 33 - 78 

3: Open mix/load, liquids 45 59 - 72 

4. Open mix/load, wettable powder 25 22 - 45 

5: Water Soluble Packets 16 6 -15 

6: Closed System mix/load, liquids 40 16 – 31 

6a: Closed System, load granules 16 none 

7: Closed Cockpit, fixed-wing aerial application of liquids 

To be combined with Scenario 9 

36 7 - 22 

8: Closed Cockpit, fixed-wing aerial application of granules 

to be combined with Scenario 9a. 

8 0 - 13 

9: Closed Cockpit, rotary-wing aerial application of liquids 

To be combined with Scenario 7 

10 3 

9a: Closed Cockpit, rotary-wing aerial application of granules 

To be combined with Scenario 8 

8 none 

9b: Open Cockpit, rotary-wing aerial application of liquids 15 none 

9c: Open Cockpit, rotary-wing aerial application of granules 15 none 

11: Airblast, open cab 35 18 – 48 

12: Airblast, closed cab 16 20 – 30 

13: Ground boom, open cab, liquids 40 21 – 42 

13a: Ground boom open cab, liquids with soil incorporation 

To be combined with Scenario 13 

5 Included in 

Scenario 13 

14: Ground boom, closed cab, liquids (with and without soil 

incorporation) 

15 12 -31 

15: Ground boom, open cab, granules 

To be combined with Scenario 15b 

11 0 - 5 

15b: Ground application, open cab, granules with soil 

incorporation 

To be combined with Scenario 15 

5 2 – 30 

16: Ground application, closed cab, granules with/without soil 

incorporation 

Rely on Scenario 15 none 

18: Low pressure hand-wand application of liquids 25 4 -13 

19: High pressure hand-wand application of liquids 33 9 - 11 

24: Rights-of-way application of liquids 15 4 - 20 

30: Belly Grinder, mix/load/apply 15 20 – 45 

34: Backpack, mix/load/apply liquids 40 9 – 11 

34a: Backpack, mix/load/apply granules 40 none 

40: Commercial Seed Treatment 60 0 

41: On farm treatment, including planting treated seed 16 0 

42: On farm treatment and planting, liquids 15 0 

43: Planting Treated Seed 0 

Rely on Scenario 41 

0 

44: Chemigation, mix/load 0 0 

Page 102 of 114 
Table 5-2: Proposed AHETF Sampling Plan By Scenario And Numbers of Monitoring Events 

Handler Scenario AHETF Proposed (n) PHED (n) 

Rely on Scenario 6 Rely on 

Scenario 6 

The sample plan submitted by AHETF includes justification for the numbers of samples per 

scenario and an analytical plan for testing the statistical power of each dataset (scenario). The 

AHETF sampling program places greater emphasis on scenarios that represent higher exposures 

or that represent a larger segment of the agrochemical market. Overall, the procedures for 

performing the studies reflect several years of multi- agency meetings with the task force 

representatives. It is anticipated for a variety of reasons that the information produced from these 

studies will be of a much higher quality than the PHED studies currently used. 

The proposed AEATF exposure scenarios and corresponding numbers of monitoring events are 

presented in Table 5-3. The proposed number of samples to be collected for each exposure 

scenario is generally based on Series 875 Group A (i.e., 15 monitoring events). Some of the 

studies include monitoring workers at their place of work with minimal influence on work habits 

other then wearing of dosimeters (e.g., observational studies such as metal working fluid and 

pressure treatment of wood). Other studies will be performed in simulated circumstances (e.g., 

mopping floors, wiping walls at a rented reception hall). While other studies will be performed in 

a simulated room/chamber at a laboratory to control air exchange rates, etc. (e.g., aerosol sprays). 

 

Table 5-3: Proposed AEATF Sampling Plan By Scenario And Numbers of 

Monitoring Events 

Handler Scenario AEATF Proposed (n) Existing Data 

1: Mop Study 15 CMA 

2: Wipe Study 15 CMA 

3: Pour Solid Study 20 CMA 

4. Pour Liquid Study 20 CMA 

5: Aerosol Spray Study 15 PHED 

6: Metal Working Fluid Study 15 Model 

7: Brush/Roller Painting Study 20 PHED 

8: Pump Liquid Study 15 CMA 

9, 10, 11, 12: High/Low Pressure Spray 

(four studies total) 

60 PHED/CMA 

13: Airless Spray Study 15 PHED 

14: Immersion/Dip/Soak Study 15 NA 

15: Pressure Treatment Study (Wood Preservative) 20 Various 

16: Place Solid Study 15 CMA 

17: Fogging Study 15 Model 

Page 103 of 114 
5.1 Within-worker and Between-worker Variability 

To date, all regulatory agencies have assumed that all observed variability is betweenhandler variability. That is, repeated measurements on the same individual performing the same 

task would be considered as separate (different) individuals This a priori assumption has not 

been rigorously addressed in the literature with respect to mixer/loader applicators in agricultural 

settings (e.g., Nigg et al., 1986; Kromhout and Vermeulen, 2001). The studies comprising PHED 

contain exposure measurements for both the same worker performing the same task multiple 

times and for different workers performing the same task one time. Figure 5-1 below exhibits this 

feature of the data set. 

PHED: Open-cab Airblast Application (ABC Grade Data)

RoB Outer Dosimetry

1026*OO

1026*NN

1026*MM

1026*LL

1026*KK

1026*JJ

1026*II

1026*HH

1026*GG

1026*FF

1026*EE

1026*DD

1026*CC

1026*BB

1026*AA

518*SP

517*SP

510*E

460*F

460*E

460*B

460*A

449*C

449*B

442*AC

442*AB

442*AA

10 100 1000 10000 100000

Unit Exposure (ug/lb ai)

Same worker 

performing task 

multiple times 

Different Workers 

performing the same 

task one time 

Figure 5-1 

 

Page 104 of 114 
A corresponding plot(Figure 4.6) using the same color/character combinations in Section 4.1.3 

presents the data only by study code – the number of separate individuals monitored within each 

study is presented here. Each data point on the plot represents one monitoring event whose value 

is the exposure normalized by the AaiH (i.e, . unit exposures). One can see that each monitoring 

event does not necessarily represent a separate individual each time. Currently, the Agency does 

not distinguish between the monitoring events in these studies based on whether they are repeat 

measurements of the same individual or of different individuals (i.e., within PHED each of these 

56 monitoring events would be considered to represent 56 separate individuals). 

The AHETF does not intend to make repeat measurements on individuals to evaluate 

within-worker variations (e.g., from day to day or site-to-site) The AHETF has performed a 

number of simulation exercises and contend that it is more effective from a statistical point of 

view to increase the number of clusters, or studies rather than the number of monitoring units 

within each study. The AHETF further asserts that resources would be better spent monitoring 

single handler-days and increasing the number of studies rather than focusing on intra-individual 

variability. 

 The AHETF submitted two critical documents that define proposed research plan and also 

illustrate the methods that are to be used to develop the scope of the plan. These are referenced in 

Section 6 as AHETFa&b, 2006. The Agency has evaluated these documents and identified a 

number of issues about which it will be requesting the Panel’s assistance in addressing. 

 

Page 105 of 114 
6. Bibliography 

AHETFa, 2006. Agricultural Handlers Exposure Task Force (AHETF) Technical 

Summary Document For a Multi-Year Pesticide Handler Worker Exposure Monitoring 

Program (submitted to docket by Agricultural Handlers Exposure Task Force as 

background document for this meeting). 

AHETFb, 2006. Procedures for Determining the Required Number of Clusters and 

Monitoring Units per Cluster to Achieve Benchmark Adequacy (submitted to docket by 

Agricultural Handlers Exposure Task Force as background document for this meeting). 

AHETFc, 2006. Summary Comparative Evaluation of Absorbed Dose Estimates Derived 

From Passive Dosimetry Measurements With Those Derived From Biological Monitoring: 

Validation of Exposure Monitoring Methodologies (Sponsor AHETF). 

AHETFd, 2006. Passive Dosimetry Data derived from the Outdoor Residential Exposure 

Task Force & Pesticide Handler Exposure Databases - Comparisons to Biomonitoring 

Data (Sponsor: AHETF & ORETF). 

ACCa, 2006. American Chemistry Council-Antimicrobials Exposure Assessment Task Force II 

(AEATF II) Background and Scoping Summary (submitted to docket by American Chemistry 

Council-Antimicrobials Exposure Assessment Task Force II as background document for this 

meeting). 

ACCb, 2006. American Chemistry Council-Antimicrobial Exposure Assessment Task 

Force II, Data Review and Study Acceptance Criteria For Inclusion of Existing 

Antimicrobial Exposure Monitoring Studies In The Antimicrobial Exposure Assessment 

Task Force II (AEATF II) Database (submitted to docket by American Chemistry CouncilAntimicrobials Exposure Assessment Task Force II as background document for this meeting). 

Aprea, C., Terenzoni, B., De Angelis, V., Lunghini, L., Borzacchi, G., Vasconi, D., Fani, 

D., Quercia, A., Salvan, A. and Settimi, L. 2004. Evaluation of skin and respiratory doses 

and urinary excretion of alkylphosphates in workers exposed to dimethoate during 

treatment of olive trees. Archives of Environmental Contamination and Toxicology 

48:127-134. 

Barnekow, D.E. and Shurdut, B.A. 1998. Evaluation of Workers’ Exposure to 

Chlorpyrifos During the Use of Dursban TC Termiticide Concentrate for PostConstruction Termiticide Applications. Unpublished study submitted by Dow 

AgroSciences LLC. Study ID No. HEA97054. DPR Data Volume No. 342-788, Record 

No. 166148. 

Bischoff, R.F. 1998. Evaluation of Chlorpyrifos Exposure to Workers During Loading 

and Application of Lorsban 15% Granular Insecticide During Corn Planting. Unpublished 

study submitted to U.S. EPA in support of product registration. U.S. EPA MRID No. 

444835-01. 

Page 106 of 114 
Brouwer DH, Kroese R, van Hemmen JJ. 1999. Transfer of contaminants from surface to 

hands: experimental assessment of linearity of the exposure process, adherence to the skin, 

and area exposed during fixed pressure and repeated contact with surfaces contaminated 

with a powder. Appl Occup Environ Hyg. 14:231-239. 

Brouwer DH, Boeniger MF, van Hemmen JJ. 2000. Hand wash and manual skin wipes. 

Ann Occup Hyg. 44(7):501-510. 

Campbell JL, Smith MA, Eiteman MA, Williams PL, Boeniger MF. 2000. Comparison 

of solvents for removing pesticides from skin using an in vitro porcine model. AIHAJ. 

61(1):82-88. 

Chester, G. 1993. Evaluation of agricultural worker exposure to, and absorption of, 

pesticides. Annals of Occupational Hygiene 37:509-523. 

Chester, G. and Hart, T.B. 1986. Biological monitoring of a herbicide applied through 

backpack and vehicle sprayers. Toxicology Letters 33:137-149. 

Cocker, J., Mason, H.J., Garfitt, S.J. and Jones, K. 2002. Biological monitoring of 

exposure to organophosphate pesticides. Toxicology Letters 134:97-103. 

Contardi, J.S., Gilbert, J.R., Lambesis, D.A., Murphy, P.G. and Nolan, R.J. 1993. 

Evaluation of Chlorpyrifos Exposures During Mixing/Loading and Application of 

Empire® 20 Insecticide to Ornamental Plants in Greenhouses. Unpublished study 

submitted by DowElanco, Indianapolis, IN. Study ID No. HEH2.1-1-182(130). DPR 

Data Volume No. 342-457, Record No. 127533. U.S. EPA MRID No. 430279-01. 

Cowell, J.E., Lottman, C.M. and Manning, M.J. 1991. Assessment of lawn care worker 

exposure to dithiopyr. Archives of Environmental Contamination and Toxicology 21:195-

201. 

Cruz Marquez, M., Arrebola, F.J., Egea Gonzalez, F.J., Castro Cano, M.L. and Martinez 

Vidal, J.L. 2001. Gas chromatographic-tandem mass spectrometric analytical method for 

the study of inhalation, potential dermal and actual exposure of agricultural workers to the 

pesticide malathion. Journal of Chromatography A 939:79-89. 

Cull, P. 1989. The Sourcebook of Medical Illustration. The Parthenon Publishing Group. 

Website with illustrations accessed December 22, 2005. 

http://www.msjensen.gen.umn.edu/webanatomy/Images/default.html

Curwin BD, Hein MJ, Sanderson WT, Nishioka M, Buhler W. 2003. Acephate exposure 

and decontamination on tobacco harvesters' hands. J Expo Anal Environ Epidemiol. 

13(3):203-210. 

Davis, J.E., Stevens, E.R. and Staiff, D.C. 1983. Potential exposure of apple thinners to 

azinphosmethyl and comparison of two methods for assessment of hand exposure. 

Bulletin of Environmental Contamination and Toxicology 31:631-638. 

Page 107 of 114 
de Cock, J., Heederik, D., Hoek, F., Boleij, J and Kromhout, H. 1995. Urinary excretion 

of tetrahydrophthalimide in fruit growers with dermal exposure to captan. American 

Journal of Industrial Medicine 28:245-256. 

de Cock, J., Heederik, D., Kromhout, H., Boleij, J.S., Hoek, F., Wegh, H. and Tjoe Ny, E. 

1998. Exposure to captan in fruit growing. American Industrial Hygiene Association 

Journal 59:158-165. 

Durham W.F. and Wolfe, H.R. 1962. Measurement of the exposure of workers to 

pesticides. Bulletin of the WHO 26:75-91. 

Fenske, R.A. 1993. Dermal exposure assessment techniques. Annals of Occupational 

Hygiene 37:687-706. 

Fenske, R.A., Birnbaum, S.G., Methner, M.M. and Soto, R. 1989. Methods for assessing 

fieldworker hand exposure to pesticides during peach harvesting. Bulletin of 

Environmental Contamination and Toxicology 43:805-813. 

Fenske RA, Lu C. 1994. Determination of handwash removal efficiency: incomplete 

removal of the pesticide chlorpyrifos from skin by standard handwash techniques. Am 

Ind Hyg Assoc J. 55(5):425-432. 

Fenske, R.A., Schulter, C., Lu, C. and Allen, E.H. 1998. Incomplete removal of the 

pesticide captan from skin by standard handwash exposure assessment procedures. 

Bulletin of Environmental Contamination and Toxicology 61:194-201. 

Fenske, R.A., Simcox, N.J., Camp, J.E. and Hines, C.J. 1999. Comparison of three 

methods for assessment of hand exposure to azinphos-methyl (Guthion) during apple 

thinning. Applied Occupational and Environmental Hygiene 14:618-623. 

Fenske, R.A. and Day, E.W., Jr. 2005. Assessment of exposure for pesticide handlers in 

agricultural, residential and institutional environments. In: Occupational and Residential 

Exposure Assessment for Pesticides. Franklin, C.A. and Worgan, J.P., editors. John 

Wiley & Sons, Ltd., pp. 13-43. 

Franklin, C.A., Fenske, R.A., Greenhalgh, R., Mathieu, L., Denley, H.V., Leffingwell, J.T. 

and Spear, R.C. 1981. Correlation of urinary pesticide metabolite excretion with 

estimated dermal contact in the course of occupational exposure to Guthion. Journal of 

Toxicology and Environmental Health 7:715-731. 

Franklin, C.A., Muir, N.I. and Moody, R.P. 1986. The use of biological monitoring in the 

estimation of exposure during the application of pesticides. Toxicology Letters 33:127-

136. 

Franklin, C.A., Somers, D.A. and Chu, I. 1989. Use of percutaneous absorption data in 

risk assessment. Journal of the American College of Toxicology 8:815-827. 

Page 108 of 114 
Garfitt, S.J., Jones, K., Mason, H.J. and Cocker, J. 2002. Exposure to the 

organophosphate diazinon: data from a human volunteer study with oral and dermal doses. 

Toxicology Letters 134:105-113. 

Geer, L.A., Cardello, N., Dellarco, M.J., Leighton, T.J., Zendzian, R.P., Roberts, J.D. and 

Buckley, T.J. 2004. Comparative analysis of passive dosimetry and biomonitoring for 

assessing chlorpyrifos exposure in pesticide workers. Annals of Occupational Hygiene 

48:683-695. 

Geno PW, Camann DE, Harding HJ, Villalobos K, Lewis RG. 1996. Handwipe sampling 

and analysis procedure for the measurement of dermal contact with pesticides. Arch 

Environ Contam Toxicol. 30(1):132-138. 

Greenberg, G.N. and Levine, R.J. 1989. Urinary creatinine excretion is not stable: a new 

method for assessing urinary toxic substance concentrations. Journal of Occupational 

Medicine 31:832-838. 

Griffin, P., Mason, H., Heywood, K. and Cocker, J. 1999. Oral and dermal absorption of 

chlorpyrifos: a human volunteer study. Occupational and Environmental Medicine 56:10-

13. 

Grover, R., Franklin, C.A., Muir, N.I., Cessna, A.J. and Riedel, D. 1986. Dermal 

exposure and urinary metabolite excretion in farmers repeatedly exposed to 2,4-D amine. 

Toxicology Letters 33:73-83. 

Hackathorn, D.R. and D.C. Eberhart, Data base proposal for use in predicting mixerloader/applicator exposure. American Chemical Society Symposium Series 273, pp. 341–

355 (1985). 

Harris, S.A., Corey, P.N., Sass-Kortsak, A.M. and Purdham, J.T. 2001. The development 

of a new method to estimate total daily dose of pesticides in professional turf applicators 

following multiple and varied exposures in occupational settings. International Archives 

of Occupational and Environmental Health 74:345-358. 

Honeycutt, R.C. and DeGeare, M.A. 1993. Worker Reentry Exposure to Chlorpyrifos in 

Citrus Treated with Lorsban 4E Insecticide. Unpublished study submitted by DowElanco, 

Indianapolis, IN. Study ID No. 91-102HE. DPR Data Volume No. 342-468, Record No. 

128306.U.S. EPA MRID No. 430627-01. 

Honeycutt, R.C. and DeGeare, M.A. 1994. Evaluation of the Potential Exposure of 

Workers to Chlorpyrifos During Mixing and Loading, Spray Application and Clean-Up 

Procedures During the Treatment of Citrus Groves with Lorsban 4E Insecticide. 

Unpublished study submitted by DowElanco, Indianapolis, IN. Study ID No. 91-101HE. 

DPR Data Volume No. 342-753, Record No. 162528. U.S. EPA MRID No. 431381-02. 

Honeycutt, R.C., Honeycutt, M., DeGeare, M., Day, E.W., Houtman, B., Chen, B., 

Shurdut, B.A., Nolan, R.J., Vaccaro, J.R., Murphy, P. and Bartels, M.J. 2000. Use of 

simultaneous biological monitoring and dermal dosimetry techniques to determine the 

Page 109 of 114 
exposure of chlorpyrifos to applicators and reentry workers. In: Worker Exposure to 

Agrochemicals. Honeycutt, H.C. and Day, E.W., editors. ACS Symposium Series. Baton 

Rouge, FL, USA: CRC, Lewis Publishers, pp. 21- 34. 

Honeycutt, R.C. Field Worker Exposure: The Usefulness of Estimates Based on Generic 

Data. American Chemical Society Symposium Series 273, pp. 369–375 (1985). 

Knuteson, J.A., Barnekow, D.E., Cook, W.L., and Meitl, T.J. 1999. Evaluation of 

Potential Exposure to Workers Mixing and Loading Lorsban 4E Insecticide Products for 

Aerial Application. Unpublished study submitted by Dow AgroSciences, LLC, 

Indianapolis, IN. Study ID No. HEA97038. DPR Data Volume No. 342-780, Record No. 

166311. U.S. EPA MRID No. 447393-02. 

Kolmodin-Hedman, B., Hoglund, S. and Akerblom, M. 1983. Studies on phenoxy acid 

herbicides. I. Field study. Occupational exposure to phenoxy acid herbicides (MCPA, 

dichlorprop, mecoprop and 2,4-D) in agriculture. Archives of Toxicology 54:257-265. 

Krieger, R.I. and Dinoff, T.M. 2000. Malathion deposition, metabolite clearance, and 

cholinesterase status of date dusters and harvesters in California. Archives of 

Environmental Contamination and Toxicology 38:546-553. 

Krieger, R., Dinoff, T., Williams, R., Zhang, X., Ross, J., Aston, L., and Myers, G. 2003. 

Preformed biomarkers in produce inflate human organophosphate exposure assessments. 

Environmental Health Perspectives 111: A688-689. 

Kromhout, H. and Vermeulen, R. 2001. Ann. Occup. Hyg. Vol. 45, No.4: 257-273. 

Kuhn, K., Wieseler, B., Leng G and Idel, H. 1999. Toxicokinetics of pyrethroids in 

humans: consequences for biological monitoring. Bulletin of Environmental 

Contamination and Toxicology 62:101-108. 

International Life Sciences Institute (ILSI) Risk Science Institute (RSI). 2003. 

Probabilistic Exposure Assessment Workshop. Investigating Probabilistic Methods to 

Assess Mixer/Loader Exposure to Pesticides. 

Leighton, T. 2000. Agricultural and Occupational Exposure Assessment and 

Recommendations for the Reregistration Eligibility Decision Document for Chlorpyrifos 

DB Barcode D263893, dated June 19. Washington, DC: Office of Prevention, Pesticides 

and Toxic Substances, U.S. Environmental Protection Agency. 

http://www.epa.gov/oppsrrd1/op/chlorpyrifos/agricultural.pdf

Libich, S., To, J.C., Frank, R. and Sirons, G.J. 1984. Occupational exposure of herbicide 

applicators to herbicides used along electric power transmission line right-of-way. 

American Industrial Hygiene Association Journal 45:56-62. 

Marquart H, Brouwer DH, van Hemmen JJ. 2002. Removing pesticides from the hands 

with a simple washing procedure using soap and water. J Occup Environ Med. 

44(11):1075-1082. 

Page 110 of 114 
Meikle, S.B. 1991. Exposure of Mixer/Loaders and Applicators to Decis (Deltamethrin) 

5.0 EC Insecticide Applied by Aerial Equipment in Saskatchewan, Canada, 1990. 

Unpublished study submitted by Hoechst Canada, Inc. Project No. 33689. DPR Data 

Volume No. 50665-59, Record No. 113026. 

Meuling, W.J., Ravensberg, L.C., Roza, L. and van Hemmen, J.J. 2005. Dermal 

absorption of chlorpyrifos in human volunteers. International Archives Occupational 

Environmental Health 78:44-50. 

Maroni, M., Colosio, C., Fait, A., Ferioli, A., and Panzacchi, A. 2000. Biological 

monitoring methods for pesticide exposure evaluation. In: Worker Exposure to 

Agrochemicals. Honeycutt, H.C. and Day, E.W., editors. ACS Symposium Series. Baton 

Rouge, FL, USA: CRC, Lewis Publishers, pp. 1- 20. 

Nigg, H.N., Stamper, J.H., and Queen, R.M. 1986. Dicofol Exposure to Florida Citrus 

Applicators: Effects of Protective Clothing. Arch. Environ. Contam. Toxicol. 15. 121-

134. 

Nolan, R.J., Rick, D.L., Freshour, N.L. and Saunders, J.H. 1984. Chlorpyrifos: 

Pharmacokinetics in human volunteers. Toxicology and Applied Pharmacology 73:8–15. 

Nutley, B.P. and Cocker, J. 1993. Biological monitoring of workers occupationally 

exposed to organophosphorus pesticides. Pesticide Science 38:315-322. 

OECD, 1997 Organization For Economic Cooperation and Development, Environmental 

Health and Safety Publications- Series on Testing and Assessment No. 9; Guidance 

Document For The Conduct of Studies of Occupational Exposure to Pesticides During 

Agricultural Application 

PMRA & U.S. EPA, 1997. Workshop On Postapplication Exposure Assessment – Final 

Report. 

Popendorf, W.J., Spear, R.C., Leffingwell, J.T., Yager, J. and Kahn, E. 1979. Harvester 

exposure to Zolone® (phosalone) residues in peach orchards. Journal of Occupational 

Medicine 21:189-194. 

Reinert, J.C. and D.J. Severn, Dermal Exposure to Pesticides: The Environmental 

Protection Agency’s Viewpoint. American Chemical Society Symposium Series 273, pp. 

357–368 (1985). 

Ross, J. et al, 2000. Conservatism in Pesticide Exposure Assessment. Regulatory 

Toxicology and Pharmacology 31: 53-58. 

 

Serat, W.F., Van Loon, A.J. and Serat, W.H. 1982. Loss of pesticides from patches used 

in the field as pesticide collectors. Archives of Environmental Contamination and 

Toxicology 11:227-234. 

Page 111 of 114 
Severn, D.J. Exposure Assessment for Agricultural Chemicals. In Genetic Toxicology: 

An Agricultural Perspective. Fleck R.A. and Hollaender, A., Eds. Plenum: New York, 

1982; pp. 235-242. 

Shurdut, B.A., Murphy, P.G., Nolan, R.J., and McNett, D.A. 1993. Lorsban 4E and 50W 

Insecticides: Assessment of the Chlorpyrifos Exposures to Applicators, Mixer/Loaders, 

and Reentry Personnel During and Following Application to Low Crops. Unpublished 

study submitted by DowElanco, Indianapolis, IN. Study ID No. HEH2.1-1-182(118/124). 

DPR Data Volume No. 342-451, Record No. 126814. U.S. EPA MRID No. 429745-01. 

Silvaggio, T. and Mattison, D.R. 1994. Setting occupational health standards: 

toxicokinetic differences among and between men and women. Journal of Occupational 

Medicine 36:849-854. 

Soutar, A., Semple, S., Aitken, R.J. and Robertson, A. 2000. Use of patches and whole 

body sampling for the assessment of dermal exposure. Annals of Occupational Hygiene 

44:511-518. 

Spencer, J.R., Hernandez, B.Z., Schneider, F.A., Sanborn, J.R., Margetich, S.S., Begum, 

S. and Wilson, B.W. 1993. Dermal and Urinary Monitoring of Peach and Apple 

Harvesters Exposed to Organophosphate Residues in Sutter, Stanislaus, and Madera 

Counties, 1989 and 1990. Report No. HS-1577. Sacramento, CA: California Department 

of Pesticide Regulation, Worker Health and Safety Branch. 

http://www.cdpr.ca.gov/docs/whs/pdf/hs1577.pdf

Spencer, J.R., Sanborn, J.R., Hernandez, B.Z., Krieger, R.I., Margetich, S.S., and 

Schneider, F.A. 1995. Long vs. short monitoring intervals for peach harvesters exposed 

to azinphos-methyl residues. Toxicology Letters 78:17-24. 

Thongsinthusak, T., Ross, J.H. and Dong, M.H. 1999. Significance of Dermal Dose 

Levels in Dermal Absorption Studies of Pesticides. Report No. HS-1801. Sacramento, 

CA: California Department of Pesticide Regulation, Worker Health and Safety Branch. 

http://www.cdpr.ca.gov/docs/whs/pdf/hs1801.pdf

Tuomainen, A., Kangas, J.A., Meuling, W.J. and Glass, R.C. 2002. Monitoring of 

pesticide applicators for potential dermal exposure to malathion and biomarkers in urine. 

Toxicology Letters 134:125-132. 

U.S. EPA, 1984. Pesticide Assessment Guidelines, Subdivision K – Exposure: Reentry 

Protection [NTIS Document Number PB85-120962] 

U.S.EPA, 1986a. Pesticide Assessment Guidelines, Subdivision U – Applicator Exposure 

Monitoring [NTIS Document Number PB87-133286 (October, 1986).] 

U.S. EPA, 1986b. Transmittal of the Final FIFRA Science Advisory Panel (SAP) Reports 

on the February 11-12, 1986 Meeting 

U.S. EPA, 1989. Good Laboratory Practices (40CFR160) 

Page 112 of 114 
U.S. EPA. 1992. Guidelines for Exposure Assessment. EPA/600Z-92/001. Washington, 

DC: Office of Health and Environmental Assessment, United States Environmental 

Protection Agency. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=15263

U.S. EPA, 1993. Pesticide Rejection Rate Analysis: Occupational and Residential 

Exposure: [EPA Document 738-R-93-008] 

U.S. EPA, 1994 Breakout Summary Report For Workshop On The Revisions To 

Subdivision K Post-Application Exposure Guidelines (April 14-15, 1994) 

U.S. EPA, 1995a. PHED: Pesticide Handlers Exposure Database Reference Manual V1.1 

U.S. EPA, 1995b Data Call-In Notice For Post-Application Exposure Data 

U.S. EPA, 1998a. Draft Series 875 – Occupational and Residential Exposure Test 

Guidelines, Group B – Postapplication Exposure Monitoring Test Guidelines - available at 

http://www.epa.gov/scipoly/sap/1998/march/contents.htm 

U.S.EPA, 1998b PHED Surrogate Exposure Guide – Estimates of Worker Exposure From 

The Pesticide Handlers Exposure Database Version 1.1 (August 1998) 

van Welie, R.T., van Duyn, P., Lamme, E.K., Jager, P., van Baar, B.L. and Vermeulen, 

N.P. 1991. Determination of tetrahydrophtalimide and 2-thiothiazolidine-4-carboxylic 

acid, urinary metabolites of the fungicide captan, in rats and humans. International 

Archives of Occupational and Environmental Health 63:181-186. 

Winterlin, W.L., Kilgore, W.W., Mourer, C.R., Hall, G. and Hodapp, D. 1986. Worker 

reentry into captan-treated grape fields in California. Archives of Environmental 

Contamination and Toxicology 15:301-311. 

Ware G.W., Morgan, D.P., Estesen, B.J., Cahill, W.P. and Whitacre, D.M. 1973. 

Establishment of reentry intervals for organophosphate-treated cotton fields based on 

human data: I. Ethyl- and methyl parathion. Archives of Environmental Contamination 

and Toxicology 1:48-59. 

Weisskopf, C.P., Seiber, J.N., Maizlich, N. and Schenker, M. 1988. Personnel exposure 

to diazinon in a supervised pest eradication program. Archives of Environmental 

Contamination and Toxicology 17:201-212. 

Wessels, D., Barr, D.B. and Mendola, P. 2003. Use of biomarkers to indicate exposure of 

children to organophosphate pesticides: implications for a longitudinal study of children's 

environmental health. Environmental Health Perspectives 111:1939-1946. 

Wester, R.C. and Maibach, H.I. 2000. Understanding percutaneous absorption for 

occupational health and safety. International Journal of Occupational and Environmental 

Health 6:86-92. 

Page 113 of 114 
Wester, R.C., Melendres, J. and Maibach, H.I. 1992. In vivo percutaneous absorption and 

skin decontamination of alachlor in rhesus monkey. Journal of Toxicology and 

Environmental Health 36:1-12. 

Wester, R.C., Melendres, J., Serranzana, S. and Maibach, H.I. 1994. Time-response 

necessary in validation for extraction of pesticides from cloth patches used in field 

exposure studies. Archives of Environmental Contamination and Toxicology 27:276-280. 

Wester RC, Melendres J, Sarason R, McMaster J, Maibach HI. 1991. Glyphosate skin 

binding, absorption, residual tissue distribution, and skin decontamination. Fundam Appl 

Toxicol. 16:725-732. 

Winterlin, W.L., Kilgore, W.W., Mourer, C.R., Hall, G. and Hodapp, D. 1986. Worker 

reentry into captan-treated grape fields in California. Archives of Environmental 

Contamination and Toxicology 15:301-311. 

Wong, A.J. and Anderson, G.D. 2000. A Randomized Double-Blend, Ascending, Acute, 

Oral Dose Study of Diazinon to Determine the No-Effect Level (NOEL) for Plasma and 

RBC Cholinesterase Activity in Normal, Healthy Volunteers. Part C: Analysis of 

Diazinon in Blood and G-27550 in Urine. Unpublished study submitted by Novartis Crop 

Protection, Inc, Greensboro, NC. Covance CRU Study No. NCP-8373 Part C, Novartis 

No. 615-98. DPR Data Volume No. 153-585, Record No. 178237. 

Woollen, B.H. 1993. Biological monitoring for pesticide absorption. Annals of 

Occupational Hygiene 37:525-540. 

Zendzian, R.P. 2000. Dermal absorption of pesticides in the rat. Am Ind Hyg Assoc J. 

61: 473-83. 

Zweig, G., Gao, R.-Y., Witt, J.M., Popendorf, W. and Bogen. K. 1984. Dermal exposure 

to carbaryl by strawberry harvesters. Journal of Agricultural and Food Chemistry 32:1232-

1236. 

Zweig, G., Leffingwell, J.T. and Popendorf, W. 1985. The relationship between dermal 

pesticide exposure by fruit harvesters and dislodgeable foliar residues. Journal of 

Environmental Science and Health B20:27-59. 

Page 114 of 114