# 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 Dosime try 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 Ac ross 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 114Exhibit A: PHED Case Study Spreadsheet

Exhibit B: PHED Hand Exposure Met hods Analysis Case Study Spreadsheets

Exhibit C: Proportionality Data Analysis Spreadsheets

Page 3 of 1141. Introduction

In this meeting of the Scientific Advisory Panel (SAP) the Agency is seeking review and
comments on its evaluation of cu rrently 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 appli cation activities. The data under consideration include the data
available in the Pesticide Handl ers 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 lo ading 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 Agricu ltural Handlers Exposur e 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 includi ng dermal exposure collection methodologies proposed by the AHETF and a lack of informati on on how an adequate sample size was derived. In addition, the HSRB asserted that the Agency ha d not demonstrated the need for these data in light of the current PHED database and cons equently 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/f iles/june2006mtgreportfinal100606.pdf ). Since the HSRB identified key scientific issues impacti ng 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 scientif ic 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 existin g 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 gene rated (whether for AHED or other efforts), such data ar e 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-Antimicrobi als Exposure Assessment Task Force II (AEATF II) Background and Scoping Summary (ACCa, 2006). To obtain comment on the AHETF

Page 4 of 114document: Procedures for Determining the Require d 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 pr eparing for this meeting and, in general, over several years regarding occupationa l exposure assessment issues. Th is meeting represents a joint effort by all three agencies. In addition, these agencies have wo rked 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 da ta with which to conduct occupational exposure assessments. Recognizing this and the Agency’s desire to conduct more refined occupational handler exposure assessments, the affected indus try has also pursued development of monitoring data that could be used for these purposes (Sta sikowski, 2001). This industry effort essentially mirrors the development of the Pesticide Handl ers 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 Stud ies Review Board (her ein 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 inform ation 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 cond ucted 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 utilit y 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 sampli ng 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 cond itions needs examination (e.g., losses, breakthrough, etc.).

Page 5 of 114The HSRB established criteria that it will use as the basis for the sc ientific evaluation of data. The Agency has attempted to address these scientific review criteria in th is 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 re search plans of the AHETF and AEATF, many analyses completed by these groups will be refere nced 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 fo rce (the AEATF) was also formed to generate exposure data for antimicrobial pesticides. Many of the same issu es and concerns regarding the methodologies and data generation that the HSRB rais ed 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 completi ng 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 s ophisticated data analysis tools such as distributional and probabilistic treatments into occupational exposure assessments for both agricultural and an timicrobial 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 providi ng a justification for collecting additional
information in a robust manner to estimate occupational exposures;

• the completion of the Food Quality Protec tion 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 desc ribed 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 requireme nts for occupational handler exposure monitoring data as illustrated in pr oposed updates to 40CFR158 which include codified requirements for occupational handler exposure data; and

• additional programmatic emphasis on worker ex posure issues related to the results of studies such as the Agricultural Health Study and surv eillance 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 expos ure assessments for occupationally exposed agricultural or antimicrobial pe sticide handlers and to present the scientific issues to be

Page 6 of 114considered in that context. The issues which ha ve 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 Agen cy for completing occupational handler
risk assessments is contained in this sec tion to provide context for the deliberations of the panel. Information includes a hist ory 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 a nd the calculations used. Finally, this section contai ns a case study that presents the data and detailed
methods used in PHED to calculate exposures for 6 common occupational pesticide handler tasks. [Note: Informa tion pertaining to the data used to assess
antimicrobial chemicals has also been in cluded 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 a dditional context to the analyses presented below.

• Section 4: Unit Exposure & Applicab ility Across Anticipated Working
Conditions – Unit exposures, as cu rrently used by the Ag ency, represent exposure normalized by the amount of active ingredie nt 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 analy zed to evaluate the extent to that the underlying default assumption of proportiona lity 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 th e 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 plan s 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 nu mber 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 th e AHETF and AEATF proposals.

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

Page 8 of 1142. Background Information

This section provides the general backgr ound 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 quest ions. 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 calc ulated in order to provide cont ext for the deliberations of the panel. In order to illustrate the concepts associat ed with the data currently used in typical Agency exposure assessments and the issues and limitation s 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 w hy the Agency would like additional data. The considerations regarding how that data might be used in the regulat ory process is also presented. In practical terms, this will allo w 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 Ag ency has been very actively engaged in attempting to refine the methodologies for completing occupational exposure and risk assessments. There have been many milestones but so me of the more critical events are identified
below:

• The 1974 President’s Council On Enviro nmental Quality (CEQ) Task Group This group focused on occupational exposure to pes ticides and identified a need to evaluate exposures from plant surfaces with a focu s on heavy contact field operations (e.g., picking, thinning) in order to establish restricted entry intervals. Key recommendations included requiring monitoring data, evalua ting geographical and other factors which influence exposures, establishment of a surv eillance 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, Subdivi sion K – Exposure: Reentry Protection [NTIS Document Number PB85- 120962 (October, 1984).] Much of the seminal research related to exposure monitoring was c onducted between 1974 and 1984. The Agency synthesized this information (a summary of key studies is included in preamble) and established the testing guidel ines for monitoring post-appli cation 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 testi ng guidelines the Agency focused on postapplication worker exposure issues and after completion of Subdivision K, completed similar guidelines for occupational ha ndlers of pesticides (USEPA, 1986b).

• 1986 FIFRA Science Advisory Panel (SAP) Review Of Subdivision U This effort represents the first SAP review of the Ag ency’s exposure monitoring guidelines. Key recommendations from this review that are germane to the current SAP evaluation include: the use of concurrent dosimetry a nd 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 th at affect bioavailability s hould be considered when the approach is used (USEPA, 1986a)

• 1989 Good Laboratory Practices (40CFR160) This document provides generalized guidance related to ensuring the quality contro l and quality assurance elements of data generated for pesticide registration purpos es. Key elements related to exposure monitoring include recordkeeping, field calibra tion, 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 syst em that is still used today to generically address the exposures of that population (Hack athorn and Eberhart, 1985). This system established many precedents relate d to the use of data in a generic manner (e.g., correction for quality control, data grouping based on e xposures, 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 s cenarios 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 submissi on of data for antimicrobial pesticide active ingredients. In response, the Chemical Manufacturers A ssociation (CMA) developed a

Page 10 of 114generic 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 limitatio ns of this study, industry has formed the AEATF to replace these data.

• 1993 Pesticide Rejection Rate Analysis: O ccupational and Residential Exposure: [EPA Document 738-R-93-008, Sept ember, 1993] In many scientific disciplines, there was a high rejection rate among studies submitte d to the Agency for pesticide registration purposes. Therefore, in 1991 the Agency deci ded 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 th e rejection rate analysis for exposure data and the experience gained during the development and review of many studi es, it was clear that the current Agency guidelines for worker e xposure required updating and that additional data were required to addre ss various exposure issues. As such, the Agency along with other regulatory bodies (i.e., Health Cana da, California Department Of Pesticide Regulation, and the Organization For Ec onomic Cooperation and Development) sponsored workshops to determine how the cu rrent 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 rel ease from 1992 included additi onal field exposure studies, additional application method codes, and a dditional studies with whole body dosimeters (USEPA 1995a).

• 1995 Data Call-In For Post-App lication Worker Exposure Data: This action by the Agency required pesticide registrants with the potential for post-application worker exposure associated with it (e.g., pi ckers, thinners) to generate data which could be used to quantify these exposures. This effort init iated 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 po licies 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-operati on and Development (OECD) Guidance
Document for the Conduct of Studies of Occ upational 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 114was developed by Health Canada and the Nort h Atlantic Treaty Orga nization (NATO) and supported by USEPA. This document was de veloped to review and standardize the available exposure monitoring methodologies. The document also provides a detailed outline of the tier approach to pes ticide 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/sa p/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 met hods 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, de pth, 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 material s for SAP review related to each of the charge questions have referenced AHETF pr oduced materials as appropriate. Agency responses to the content of these materials have also been provided as appropriate.

• 2003 Formation of Antimicrobial Expo sure Assessment Task Force (AEATF): The AEATF was formed to address industry concerns regarding the data being used in Agency assessments for occupational handlers of antimic robial 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 pa rt of the discussions at the upcoming SAP meeting. The preparatory materials for SA P 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 incl uded in this background document and much of
the discussions are focused on the AHETF sin ce 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 differenc es as appropriate. Additionally, separate presentation materials during the meeting wi ll also be developed related to AEATF as appropriate to address the sc ope 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 assess ment over the last 25 years in order to provide a level of background knowledge for the panel discu ssions. Much of this effort has been focused on two areas including (1) conducting exposure and risk as sessments under the timelines specified by the reregistration process and the tolerance reassessment timelines in the Food Quality Protection Act and (2) coordination with H ealth Canada and the California Department of Pesticide Regulation related to the data devel opment 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 rega rd to exposure monitoring methodologies. Since the AHETF and AEATF are in their formative stages of devel opment of its research goals and objectives the Agency believes that the timing of this SAP review is appropriate.

Page 13 of 1142.2 Exposure Assessment Approach

The Agency uses the term “Handlers” to de scribe those individuals who are involved in the pesticide application process. The Agency believes that ther e 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 us ed in an application), the kinds of equipment used, the crop or target being treated, and the circumstances of the us er (e.g., the level of protection used by a handler) can cause exposure le vels to differ in a manner specific to each application event. The scenarios that serve as the basis for the ri sk assessment are described in Section 2.2.1: Handler Exposure Scenarios

. Section 2.2.2: Algorithms Used For Risk
Calculations describes the actual mathematical proce dures 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 th e kinds of exposures
that occur related to th e use of a chemical. The use of scen arios 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/r ecordisplay.cfm?deid=15263 ). The purpose of this section is to describe the ex posure scenarios that are typically used by the Agency in occupational handler exposure assess ments 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 e xposed during the application proce ss. In order to do this in a consistent manner, the Agency has developed a seri es 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 categor ized using one of the following terms:

• Occupational Mixer/loaders: these individuals perform tasks in preparation for an application using concentrated end-us e products. For example, they would prepare dilute spray solutions and/or load /transfer solid materials (e.g., granulars) or dilute spray solutions into applicat ion equipment such as a groundboom tractor or planter prior to application.

• Occupational Applicators: these individuals operat e application equipment during the release of a pestic ide product into the envir onment. These individuals can make applications using equipment such as groundboom sprayers with dilute sprays or tractor-drawn spreaders fo r concentrated granular materials.

Page 14 of 114• Occupational Mixer/loader/applicators: these individuals are involved in the entire pesticide applicati on process (i.e., they do all job functions related to a pesticide application event). These individuals would us e concentrate materials to prepare a dilute spray solution and then al so 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. Fo r example, if one uses a small handheld device such as a 1 gallon low pressure ha ndwand 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 aer ial applicators during the release of a pesticide product onto an intende d target. Most fla ggers 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 t oxicological effects. Wherever possible, use and usage data determine the appropriateness of certain types of risk assessments (e.g., a chronic risk assessment for o ccupational 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 pestic ides). Other parameters are also determined from use and usage data such as application rate s and application frequency. Th e Agency always conducts risk assessments using maximum application rates for each scenario because what is possible under the label (the legal means of controlling pest icide 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 su ch as typical application rates for some crops,
it uses the information to evaluate the overall ri sks associated with the use of the chemical in
order to allow for a more informed risk management decision.

A chemical can produce different toxic e ffects 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 vari ety of patterns. The Agency believes that occupational exposures for most chemicals can o ccur 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 al so 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 comp leting 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 tim e-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-t erm. The Agency completes both short- and intermediate-term assessments for occupational s cenarios in essentially all cases because these kinds of exposures are likely and acceptable us e and usage data are not available to justify omitting intermediate-term scenarios.

Page 15 of 114The toxicity of chemicals can also vary base d on the route of exposure or how a chemical enters the body. For example, expos ures 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 ma ny chemicals indicates that the Agency consider exposures to the skin combined with exposur es via inhalation because the effects which occur are the same regardless of wh ether it is deposited on th e skin or it is inhaled (e.g., cholinesterase inhibition is an example of a common effect wh ere this type of analysis is completed).

EPA's Occupational handler exposure assessmen ts also reflect that a worker may use different levels of personal protection. The Agen cy 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 clot hing, 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 appr oach and not based on generic re quirements for label language as outlined in the Agency’s Worker Protection St andard (40CFR170). [Note: All AHETF protocols at this point require single la yer clothing, chemical-resistant glove s, and no respirator. This represents the current labels for the chemicals wh ich 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 alterna tive). 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-sleev ed shirt and long pants with no respiratory protection. No chemi cal-resistant gloves are included in this
scenario.

• Minimum Personal Prot ective Equipment (PPE): Represents the baseline
scenario with the use of chemical-resistan t gloves and a dust/mist respirator with a protection factor of 5 which is commonl y used for exposure assessment purposes in different settings (NIOSH, 2004).

• Maximum Personal Prot ective Equipment (PPE): Represents the baseline
scenario with the use of an additional la yer of clothing (e.g., a pair of coveralls), chemical-resistant gloves, and an air purifying respirator wi th a protection factor of 10 which is commonly used for exposure asse ssment 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 a pplicable to handheld application methods
which have no known devices th at can be used to routinely lower the exposures for
these methods.

Page 16 of 114It has been determined that exposure to pest icide handlers is likely during the occupational use of various pesticides. These uses can o ccur in a variety of environments including agriculture, commercial/indus trial premises, and in residential environments. After examining the anticipated use patterns and current labeli ng for many pesticides, EPA identified 37 major agricultural exposure scenar ios for workers. Thes e scenarios reflect diffe rent activities and the types of equipment and techniques used in agri culture. Quantitative exposure assessments are typically developed for occupati onal 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 corres pond 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 formulati on) in water soluble packets; and (6) Liquids in closed loading systems.

Applicator: (7) Aerial fixed wing airc raft liquid applications; (8) Aerial fixed wing aircra ft 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 114Mixer/Loader/Applicators: (27) Wettable powder/liquid open mixing/load ing with open cab tractor applications; (28) Groundboom liquid open mi xing/loading with open cab tractor applications; (29) Groundboom liquid open mi xing/loading with closed cab tractor applications; (30) Belly grinder applications; (31) Push-type granular spreader applications; (32) Liquid open mixing/loading lo w 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 st udy 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 resear ch plan will address some, but not all, of these scenarios for a vari ety of reasons which are discussed in Section 6 below as well as the research plan itself.

The above are scenarios mostly for conventiona l 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 (p ump, 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, aeros ol 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 li quid, pour solid, place solid); and (12) Aquatic Areas (pump, pour liquid, pour solid, place solid).

Page 18 of 1142.1.2 Algorithms Used For Risk Calculations

The occupational handler exposur e and risk calculations are presented in this section.
Noncancer risks are calculated using the Margin of Exposure (M OE) which is a ratio of the toxicological endpoint of concer n 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 pe rsonal protective equipment). Exposure is normalized by body weight and adjusted for absorp tion factors as appropri ate to calculate dose (i.e., body burden). Finally, MOE values are calcul ated to represent the ma gnitude 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., Q 1 *) 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 logi cal 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 logi cal unit treatment such as acres (A/day) or gallons per day can be substituted (gal/d ay), 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 pesticid es (Hackathorn and Eberhart, 1985). Unit exposures numerically represent the exposur es one would receive related to an application. They are generally presented as (mg active ingredient expo sure/pounds of active ingr edient handled). The Agency has developed unique unit exposures fo r each scenario typically considered in our assessments (i.e., there are differe nt unit exposures for different types of application equipment; formulation type, job functions; a nd levels of protection). These are considered generic and used regardless of chemical identity. The Agency only uses these to address e xposures to pesticides
which are considered semi-volatile or non-volatile. More volatile materials such as fumigants are not addressed with generic data. Typically, ch emical-specific data are required for these assessments. Chemical-specific and/or scenario-speci fic 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 114The generic unit exposure concept is generally accepted in the scientific literature (Hackathorn and Eberhart, 1985) and also through various ex posure monitoring guidelines published by the U.S. EPA and international orga nizations such as Health Canada and OECD (Organization For Economic Cooperation and De velopment). The concept of generic unit exposures can be illustrated by the following exampl e. 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 protec tive 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). Li kewise, 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 exposur e estimates that are used by the Agency as the basis for its exposure assessments for conve ntional pesticides a nd some antimicrobial pesticide uses. PHED was designed by a task for ce of representatives from the U.S. EPA, Health Canada, the California Department of Pestic ide 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 work ers 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 da tabase contains values for over 1,700 monitored events. Once the data for a gi ven exposure scenario have been selected, the data are typically normalized (i.e., divided by) by the amount of pe sticide handled then ad justed 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 expo sure 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 th e distribution of the exposure values for each body part. These values are the arithmetic mean fo r normal distributions, the geometric mean for lognormal distributions, and the median for all “oth er” 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 al gorithms and analysis approaches included in PHED. It also contains an explan ation 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 fact ors related to occupational exposure monitoring, study design, and the analysis of data. It was also noted th at the industry groups AHETF and AEATF are in the formative stages of generating subs tantive databases that will likely be used for a number of years
in order to address o ccupational 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, bein g combined with PHED
estimates in order to enhance the number of av ailable measurements, or addressing scenarios not adequately determined in PHED and/or CM A. Since the AHETF and AEATF products are integral to the Agency’s plan for refining its assessments over the ne xt several years, many AHETF and AEATF produced analyses have been re ferenced in this document. Similar findings

Page 20 of 114of the SAP that pertain to AHETF will be c onsidered in the process for evaluating AEATF progress.]

Daily Dose: After daily exposure is calculated, daily dos e (inhalation or dermal) is calculated by normalizing the daily dermal exposure valu e by body weight and accounting for dermal absorption if appropriate. If a dermal administra tion 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 absorpti on factors available so a factor of 100 percent is typically used for all calculations. Daily dose is calculated using the following formula:

⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

(kg) Weight Body 00) Factor(%/1 Absorption

x day ai mg Exposure Daily = kg/day ai mg DoseDaily Average

Where:

Average Daily Dose = the amount as absorbed dose received fr om exposure to a pesticide in a given scenario (mg pesticide active ingredient/kg b ody 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 po pulation 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 asse ss noncancer risks (or threshold cancer effects) to handlers for each exposure rout e 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 EL NOAELorLOA

= 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 advers e 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 concer n if the MOE is <100 for occupational populations (10x for intraspecies variation and 10x for inters pecies 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 rout e 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 woul d not be advisable to require respirators as they may ma rginally reduce overall risks. It is also important to present
overall risk estimates for each scenario consid ered by calculating total MOEs. Total MOEs can be calculated if toxicity endpoi nts 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 th e 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 calc ulate 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 th eme of this SAP meeting into context (i.e., how unit exposure estimates are generated including th e 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 toxicologi cal endpoints and associated dose
estimates for risk assessment. These additional f actors have not been incl uded 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 th e data and methods the Agency uses to conduct its occupational pesticide handler exposure assessments and to di scuss, 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 occupa tional handler exposure data and also develop exposure estimates essentially from skin loading rates presente d as either (μg/cm 2 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 da ta) 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 mon itoring data if EPA is to be able to perform more refined assessments. For example, EPA would like to de scribe what “more refined” assessments could

Page 22 of 114include probabilistic treatments of data and the ability to defi ne higher percentiles of exposure
from a distribution.

Exhibit A provides the detailed data from th e 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 da ta (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 indi viduals who participate in exposure monitoring studies. The surrogate guide and reference ma nual documents were developed approximately 10 years ago and have been in consta nt use since then. In these documents, the term "replicate" has been used to represent a datase t generated by collecti ng dosimeters from an individual after one monitoring event. The Agency w ill modify this nomenclature (t he term monitoring unit is under consideration). It shou ld also be noted that the term "rep licate" has not been revised in the
historical documents. This issue will be addr essed 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 select ed 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 de velop 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 114Page 24 of 114 Application

12 Air blast 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 di fferences exist with respect to the quality and the manner in which the data we re collected (e.g., measur ements of intra- and inter-personal variability within each set of data).

Additionally, the types of co llection media used to generate these data need to be considered. As described in the Agency gui delines for conducting exposure studies (USEPA 1984; USEPA, 1986 and http://www.epa.gov/scipoly/sa p/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, diffe rences exist in the c onfigurations of the monitors (e.g., different invest igators could place patches at different body locations). Hand exposures have also been collected using a variet y 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, me thanol, cotton gloves, Tyvek© gloves, isopropanol, gauze patches, soa py water). Figures 2-1 through 2-3 depict the variety of sampling methods used to generate the data for three of the s cenarios in the case study.Figure 2-1: Sampling Regimen Associated With Data From PHED Scenario 1.Page 25 of 114Figure 2-2: Sampling Regimen Associated With Data From PHED Scenario 2.WWPHED Scenario 2: Open M/L Granules= patch(transparent on back)WBD = whole body dosimeterS = socks0422 (N=2) Gauze Patches,Gloves, PSP0425 (N=3) Gauze Patches,Cotton gloves, PSP0448 (N=8) Gauze Patches, EthanolHandwash, PSPH = HandwashW= wipe or swipeG = GlovesG-pG-p0427 (N=1) Gauze Patches,Cotton Gloves, PSPG-p G-pH-bH-bG-bG-b1003 (N=22) Upper WBDs,Detergent HW, Socks, PSP1004 (N=15) Upper & Lower WBD,Detergent HW, PSP1011 (N=30) Union Suit WBDs sectioned intoparts, Ethanol HW, Ethanol Wipes, PSP1027 (N=15) Union suit WBDs sectioned intoparts,Detergent HW, Detergent Wipes, PSPWBDS SH-pH-pWBDWBDH-p H-pWBDWBDH-p H-pWBDWBDH-pH-pSamplers under long-sleeves/pants or more unless noted. Head/neck samplersalways unprotected unless noted. Green – combined samples, Red – not combined.For hand exposure; p = wore protective gloves & b = barehandedPage 26 of 114Page 27 of 114Samplers under long-sleeves/pants or more unless noted. Head/neck samplersalways unprotected unless noted.Green – combined samples,Red – not combined.For hand exposure; p = wore protective gloves & b = barehandedPHED Scenario 12 – Closed Cab AirblastFigure 2-3: Sampling Regimen Associated With Data From PHED Scenario 12WBDWBDWBDW W1000 (N=7) Gauze Patches, Forearm swipesDetergent HW, Urine collection, PSP= patch(transparent on back)WBD = whole body dosimeterS = socksH = HandwashW= wipe or swipeG = Gloves0523 (N=16) Cellulose Patches,10% IPA HW, PSP0460 (N=4) Gauze Patches,Ethanol HW, PSP1007 (N=5) Cottonseparates WBD, PSP, nohand samplesH-pH-pH-pH-pH-pH-pPHED contains a large amount of information about each monitoring event used to develop exposure estimates (these are referr ed to as a “replicate” in PHED). The PHED Reference Manual (USEPA, 1995a) describes all of the differe nt 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 deve loping the system (i.e., it is an image of an actual data entry sheet). Similar data would be collected under current Agency guideline s. [See the reference manual appendices for complete data entry forms (U.S. EPA, 1995a).]

Page 28 of 114The Agency has characterized estimates calcul ated using PHED by considering associated exposure factor information such as the amount s 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 re lating to various agricult ural 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 base d 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 expos ure 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 ex ample, 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) containi ng dry flowable formulations was not evaluated in the monitoring data available for this scenario. The meteorologi cal data, likewise, indicat e that conditions were typical for agricultural pesticide applications part icularly since temperatur es 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 deve lop unit exposure estimates based on skin loading rates. As illustrated by th e breadth and depth of the information available in PHED (see Table 2-2, Figure 2-5and USEP A, 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 th e Agency since the analyses of the data are standardized in the firs t tier of the process. Further analyses could be required depending upon the nature of indivi dual assessments (e.g., if only large acreage equipment for a specific chemical use pattern might be typical).

Page 29 of 114A 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 st udies which were well conducted and have adequate analyt ical quality control data. Th e 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 al ways use the best available data even if there could be quality issues associated with it (e.g., th ere 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 appropria te language would also accompany the assessment to characterize the resu lts). 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 nu mber 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 114Table 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 fiel ds 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 whic h characterize the conditions under which they were collected.

Table 2-4: Example Summary Of Raw Exposure Data Used To Calculat e Unit Exposure Values For PHED Scenario 1 – Open Mixing/Loading Of Dry Flowables

[Note: “0”s indicate no samples taken fo r that location. All residues are ug/cm2 and under clothing or protective gloves unle ss 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-Sh in 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 114As indicated above, PHED is a database which is used to store raw data records such as
those illustrated in Tables 2-2 a nd 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 re ferred 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 formulati on type (e.g., solids of varied types along with liquids) or the data can be segm ented based on specific solid type such as a dust or dry flowable (which is how the Agency uses the data). Tabl es 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 a nd long-sleeved shirts) along with some sort of protective gloves (i.e., they were not barehanded). In th e dataset upon which this calculation is based, there were also a sufficien t 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 mon itoring 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") a nd 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 te st (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 Statisti cs 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 Low er 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 base d 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 114Above, the Agency described its tiered appr oach for calculating occupational exposure estimates (OECD, 1997). In order to systemati cally 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 pr esented 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 re present individuals wear ing normal work clothing so the estimate for dermal exposure (excluding head, neck and hands) is reported below as 0.0382 mg/lb active ingredient handl ed which corresponds to the above PHED output for that
scenario. Likewise the head/neck and hand estima tes (for individuals we aring protective gloves) also correspond. It should also be noted that the number of observations ar e consistent in that 16 to 26 monitoring events were used to calcu late the dermal (nonha nd) exposures and 21 monitoring events were used to calculated hand exposures. The Agency has completed this sort
of calculation for all of the 37 s cenarios listed above the agricultu ral 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" capab ility 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 datase ts, 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, re alized that there are a number of possible refinements that could be implemen ted with additional data and development of

Page 33 of 114updated analytical methodologies. As a result, there has been a significant le vel of interest over the last few years in generating additional occ upational exposure monitoring data. The goal in these efforts has been to eliminate many of the lim itations inherent in much of the data which is currently used for regulatory purposes. Some of th e key limitations which have been identified as well as the Agency's proposed approach for ad dressing 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 us ed 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 wh en 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 curren t 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 re search proposal (AHETFa, 2006). Similarly, the AEATF has developed criteria for accepting existing data in its effort. These criteria are outlined in their acceptance criteria documen t (AEATFb, 2006).

Page 34 of 1143. Monitoring Methods

This section of the document provides the background information needed for the panel deliberations related to the sampling met hods 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 determ ine 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). Outsid e of these, there app ear to be limited other techniques for calculating risks fr om dermal exposure that can pr ovide similar types of results (e.g., first-principles models are very limited fo r this purpose). Thus, as the Agency moves toward the development of new, more refine d 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 possi ble 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 a nd 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 prep ared to proceed with th e 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., patche s, gloves, dosimeters or washes) that are included in current Agency guideline s 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 i nvestigators (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 predicti ng exposures. In addition to biological sampling media, the Agency also requires that a dditional 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 guida nce 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 cont ains large numbers of studies in which dermal exposure was estimated using patches but many ot her 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 monitori ng could be accomplished using cotton or Tyvek® gloves or varying wash media that have range d 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 114solubility 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 tran sformation and kinetics of dissipat ion 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 r uggedness and performance of the available exposure monitoring methods. These include:

• Examination of the potential for a systema tic bias between biological monitoring and passive dosimetry methods;

• Whether dermal absorption could cause a si gnificant 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 de termine if there is a systematic bias due
to whether the method is based on residue tr apping (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 ca n be addressed. Discussion pertaining to these topics
is included in Section 3.1: Relative Predictive Capabili ty Of Passive Dosimetry and Biological Monitoring . The remaining hand monitoring is sue 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 Pa ssive 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 metabolit es) at the target organ, tissue, cell, or cellular component need to be quantif ied. 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 114Even 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 a ssessment for pesticides often re lies on data that are used to quantify overall body burdens which are then compared to toxic e ndpoints that have been derived on a similar basis (e.g., mg/kg/body wei ght). 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 pur pose. It should be note d 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 targ et tissue estimates from body burdens. Agency guidelines allow for the collection of the follo wing 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 av ailable for inhalati on through the use of appropriate trapping devices.

• Biological monitoring measures body burden in selected tissues or fluids or the amount of pesticide or its meta bolites 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 toxi city from pesticide. Text in boxes represents techniques used to collect data used in es timating exposure to a pesticide. Human figure from Cull (1989).

Page 37 of 114Passive 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 availabl e 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 ac curate 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 m onitoring data. Comparisons among data sets generated using these diverse techniques pr ovide 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: Biol ogical Monitoring does the same for that method. [Note: These are summary in nature and more informa tion can be in the Agency guideline documents referenced above.] Section 3.1.3 Comparison of Passi ve 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 th e analyses contained in this section.

3.1.1. Passive Dosimetry

Dermal penetration and inhalation are the majo r routes of occupationa l pesticide exposure. Multiple studies with several different pesticides (outside of highly volatile chemicals such as fumigants) have shown that i nhalation 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 un certainties associated with dermal monitoring
methods that are not associated w ith 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 re sidue 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 (p atches) 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), Ches ter (1993), Fenske (1993), and Soutar et al. (2000). Exposure to hands is typically measured by e ither 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 diffe r 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 re sults. However, studies in which exposure monitoring intervals varied suggest that monitori ng 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 mon itoring intervals than after 3- to 7-hour intervals, s uggesting that extrapolat ion of short monitori ng intervals to longer workdays can result in overestimating exposure (Spencer et al. , 1995). It is assumed that the

Page 38 of 114amount of a dose penetrating skin can be extrapolated from labor atory studies, and that this
amount is consistent between individuals. Labora tory studies of dermal penetration may rely on human, non-human primate, or rode nt 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 partic ipants are typically under the supervision of the investigator during the entire period when exposure data are bei ng collected.” Passive dosimetry also allows creation of generic exposure databases, under th e assumption that in many cases deposition of residues on the skin is essentially independent of chemical propert ies of the pesticide (U.S. EPA, 1996a). Such databases contain data collect ed under known conditions, and allow a limited number of studies to support exposure esti mates 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 av ailable in an accessible biological medium;

• When adequate human pharmacokinetic data are lacking; or

• When there are obstacles to complete sampli ng 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 pest icide into the body must be estimated which would then be compared to an endpoint derived from an oral administ ration 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. [Not e: EPA tends not to use in vitro data.] Relationships between in

vivo and in vitro test results, or between laboratory an imal and human results, have not been reliably established for many classes of compou nds, 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 affect s estimates of absorbed dose is that for several pesticides percent dermal absorption has b een shown to vary invers ely 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 in creases, the total amount of pesticide absorbed
continues to increase with incr easing concentration on the skin. Applying a fixed percentage for dermal absorption for any amount of pesticide lik ely may overstate the ab sorbed dose for higher concentrations of pesticide. Conversely, fi xed dermal absorption estimates may be underestimated at lower dermal loading concentrations . However, as the significance of this effect

Page 39 of 114cannot be quantitatively ascertained under field c onditions, it adds to the uncertainty of estimating the magnitude of dermally absorbed doses.

In addition to the issues a ttendant to dermal absorption, a nother disadvantage of passive dosimetry is the uncertainty that results fr om the various methods of measuring dermal absorption. Patch dosimetry could over- or und erestimate 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 relativel y 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), ava ilable data show that exposure patterns differ by activities, suggesting that extr apolations from patch dosimetry might have varying effects on exposure estimates. Also, many investigators us e non-standard patch construction and placement, confounding comparisons between studies.

Materials used in patches or WBDs can resu lt in different exposure monitoring results, as pesticide residues on dosimeters can be unstable, and be lost th rough 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 es timates 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 gl oves overestimate exposure, da ta are lacking to support the accuracy of one method above the other. Several st udies 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 accu racy 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 pestic ide than an equivalent layer of skin, a glove does not have blood circulation that can carry aw ay absorbed residues. Hand washes supposedly measure the amount of pesticide lo aded onto the skin of a worker during a given period of time. This is an operational definition in that only th e amount of pesticide that can be washed off the skin is considered for exposure calculations. However, radiolabelle d 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 pe sticide loaded onto hands during occupational activities can be recovered by hand washes. Convers ely, in at least one study (Zweig et al ., 1984), the early

Page 40 of 114morning 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 monito ring (“biomonitoring”) to estimate worker exposure to pesticides in the fi eld typically uses compounds measur ed 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 ta rget compounds could be monitored in blood or other tissues or fluids, urine coll ection is less invasive than blood sample collection and urine is a major excretion route for metabolites of ma ny 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 sample s, 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. Fortunatel y, 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 asso ciated uncertainty depending upon the quality of the data used to develop the relationship. Howe ver, such studies tend to be conducted in humans only for compounds with anticipated low toxicities (Harris et al. , 2001).

Another assumption is that residues recove red from urine are entirely (or at least substantially) due to pesticides absorbed from expos ures 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 othe r sources such as diet. For alkylphosphates, this assumption is supported by mu ltiple studies (e.g., Nutley and Cocker, 1993).

Creatinine corrections assume a consta nt excretion of cr eatinine (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 informati on is available about the pesticide and if biomonitoring is conducted prop erly, a quantitative estimate of absorbed dose may be obtained. By definition, these resulti ng dose estimates integrate exposure across all routes. Such an estimate of absorbed dose, which avoids potential confounding from assumptions

Page 41 of 114of dermal penetration or inhala tion 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 si mple multiplication of the amount recovered from urine times the ratio of pesticide to metabol ite molecular weights and adjusting for other appropriate pharmacokinetic factor s (e.g., percentage of total parent equivalents excreted which
could be difficult to define ) gives an estimate of absorb ed 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 mi sleading 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 distribut ion 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 derm al); body composition (relative amounts of water and fat in the body); individual diffe rences in enzyme activities; and environmental factors such as concurrent exposure to other chem icals (Silvaggio and Mattison, 1994; Kuhn et al ., 1999;
Garfitt et al. , 2002). Data from well-conducted pharm acokinetic studies are essential to extrapolating absorbed doses from compounds anal yzed in urine samples, yet as acknowledged by Woollen (1993), even when these data are availabl e they can at best provide an “indication of the extent of this variability.” Pharmacokine tic 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 u nderestimation of exposure.

Biological monitoring genera lly 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. Participan ts may need to collect, store, and transport 24- hour urine samples before, duri ng, and after the exposure peri od (U.S. EPA, 1996a). While passive dosimetry can be supervised through th e 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 sa mples 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 inco mplete. Incomplete sampling can result in underestimation of exposure; attemp ts to correct results for sample incompleteness can result in
over- or underestimation.

For chemicals with a long half-life relati ve to the sampling period, biomonitoring results can be affected by absorbed mate rial not yet excreted (resulting in underestimated exposure) or by previous exposures (resulting in overestimated exposure). In part icular, the interval in which sequential 24-hour urine samples are collected must be long enough in order for the human body

Page 42 of 114to excrete nearly all of the chemical being mon itored. Even in cases where the target analyte urinary excretion half-life is le ss than 24 hours, the biomonitoring in terval may have to be longerthan 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 highe r results than samples collected during and following exposure (e.g., Honeycut and DeGeare, 1993). When the pre-expos ure 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 al so be noted that peak exposur es can be of concern depending upon the nature of the toxicological effect. For ex ample, with carbaryl cholinesterase inhibition is rapidly reversible which made peak exposures mo re 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 co mpounds 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 ma gnified when the dose is back-calculated from the amount of metabolite found. Exposures coul d 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. Furtherm ore, some pesticide metabolites are widely used chemicals (e.g., the parathion metabolite, 4-nitrophenol). Assuming
that the entire amount of these chemicals recove red 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 ca lculation that develops skin loading estimates from absorbed dose levels would require adjustin g these levels by an inverse of dermal absorption estimates which co uld create a level of uncertainty based on the potential differences in de rmal absorption rates.

3.1.3 Comparison of Passive Dosime try and Biomonitoring Techniques

Both passive dosimetry and biomonitoring have advantages and limita tions. Fenske and Day (2005) suggested that, as re gulatory agencies receive studie s conducted with both techniques concurrently, the concordance between results wi thin these studies can be examined in more detail. The Agency desires to evaluate the po ssible concordance between these two methods in
order to assess the level of conf idence associated with the use of either technique for regulatory purposes. Any conclusions would also be develope d in the context of the typical uncertainty and variability that is associated with such data.

Page 43 of 114The analysis below presents that examin ation. Several investigators have conducted comparisons of exposure estimates based on biomonitoring and estimates based on passive dosimetry. These results are summarized belo w along with similar analyses completed by the Agency and AHETF. These types of comparisons can take one of two a pproaches that include:

• Concurrent Analysis : A comparison of concurrent m onitoring results in which subjects wear dosimeters of some sort and also provi de 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 necessita ting use of dermal absorption factors together with results base d 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 lo ading concentration de rmal absorption can vary, for chlorpyrifos 1% is supported by Gri ffin 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 assessmen ts). 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 biologi cal monitoring does not quantify measurable residues, then it likely demonstrates that th ere was no or minimal breakthrough to the skin in a study. Conversely, if biological monitori ng 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 ha nds 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 retrospec tive analysis in which absorbed dose estimates generated using biological m onitoring 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 th e results are to be compared to generate estimates of exposure for the conditions unde r which from the biological monitoring was performed (e.g., for a similar scenario and activ ity, acres treated and application rate). Alternatively, researchers could conduct a sepa rate passive dosimetr y 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 result s 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 (AHETF d, 2006). The Agency essentially concurs
with their results but a more detailed anal ysis of this document is included below.

Page 44 of 114Prior to using the results from studies measuring absorbed dose from biological monitoring to review the concor dance with passive dosimetry m onitoring methods, the accuracy of the biological monitoring itself needs to be properly determined. However, because the review provided in this section is ba sed on existing studies not specif ically 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 a nd passive dosimetry monitoring methods is important. Uncertainties in both methods highlighted below may explain some of th e 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 chemi cal 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 presen ted in this section include:

• Incomplete urine collection from test subjects , previous exposure to the same chemical, and use of concurrent passive dosimet ry may intercept potential exposure.

• In the comparisons that are presented below from non concurrent anal ysis of biological and passive dosimetry estimates, minor differe nces in study design parameters (e.g., PPE, personal hygiene, equipment types and confi guration, nozzle pressures, product and dilute concentrations, etc.) will infl uence the potential exposure.

Estimating total absorbed dose from passive dosim etry monitoring methods is also dependent on many factors that affect the comp arison of methods in this secti on. For inhalation, nearly all of the monitoring techniques presented are based on personal air sampling us ing filter cassettes. Inhalation exposures are based on all of the part iculates collected on the filters, assuming all particulates are either inhala ble or respirable. Pump flow rates and breathing rates are standardized among most of the st udies. 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 ar e 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 measur ed clothing penetration factors that also
introduce uncertainties.

• Chlorpyrifos is used in many of the exampl es presented below and a 3 percent dermal absorption is used (Nolan et al 1984) while th e range of dermal abso rption for chlorpyrifos has been reported in the l iterature from 1 percent (Gri ffin 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 absorp tion over various loading concentrations is not expected to be constant (Zendzian 2000).

The following describes many of the analyses th at have been completed for the purposes of comparing passive dosimetry and biological monitori ng results. It should be noted that although

Page 45 of 114some of these results are inconc lusive, they were included to s how 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 a pplied 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 chlo rpyrifos, with urinary TCP considered to represent 70% of the absorbed chlorpyrifos and an elimina tion half-life of 26.9 hours. A more recent study suggested that the half-life of dermally appl ied chlorpyrifos yields an
elimination half-life of a bout 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 summar izes geometric mean internal doses reported by Fens ke and Day (2005), from exposure estimates calculated by Leighton (2000). Eight studies were used in the compar ison. In the eight studies summarized in Figure 3-2, the ratios of mean es timates 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 th e other. At higher pe rcentiles of exposure, these differences could vary which is difficult to predict because of the limited utility of PHED for developing distributio nal estimates of exposure.

Page 46 of 114Figure 3-2. Comparison of Concurrent Bi omonitoring and Passive Dosimetry Data from Selected Studies Monitoring Handler Exposure to Chlorpyrifos a

-20 -10

0 10 20 30 40 50 60

1234567
Handler DescriptionTotal 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 st udies 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 114Figure 3-3. Comparison of Concurrent Bi omonitoring 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 DescriptionTotal 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 summ arized in Figure 3-2, the absorbed doses estimated by passive dosimetry and biomonito ring for reentry workers differ at most by
about 3-fold. This is a small difference consid ering 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 (r 2 = 0.79 and r 2 = 0.86, respectively).

Geer et al . (2004) examined exposure monitoring datafrom 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 monito red five M/L/A doing termiticide applications with a handhe ld 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 paramete rs and that 29% of the variability in urinary TCP levels could be explained by the si mple 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 c ould impact any comparis on of results by as much as a factor of 10 since absorption estimates range from 1 to 10 percent.

Page 48 of 114Comparisons Involving Other Organ ophosphorus Pesticides (OPs): Several OPs in addition to chlorpyrifos have specific metabolites that have been quantified in urine. These include chlorfenvinphos, diazinon, fen itrothion, malathion, methyl-parathion and parathion (Maroni et al. , 2000; Wong and Anderson, 2000; Tuomainen et al. , 2002). Alternately, dialkylphosphate metabolites, wh ich 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 b een correlated with metabolites excreted in urine. Although these did not feature compar isons of absorbed dose estimates, as the correlations involving a range of exposures they provide suppor t for the idea that the two methods both give reasonable estimates of absorbed dose.

For example, Aprea et al . (2004) monitored respirator y and dermal exposures to dimethoate of eight applicators spraying olive tr ees. Absorbed doses were calculated from these exposures assuming 10% dermal abso rption and 100% inhalation absorption. Applicators also provided urine sample s 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 co rrelation between estimated ab sorbed dose and excreted alkylphosphates (r 2 = 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 concurre ntly in 48 harvesters at five study sites; an additional 22 harvesters participated in urin e 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 alkylphosphate s at the five sites resulted in a moderate correlation (r 2 = 0.64; p < 0.10).

In addition to the numerous studies where pa ssive 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 hour s; e.g., Krieger and Dinoff, 2000). Comparison of partial samples with full 24-hour urine collections s uggests 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 lan ce 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 114the gloves with ethanol. Re spiratory exposure was not monitored; however, full face respirators were worn by all three applicators, which would be antici pated to substantially decrease exposure. A regression of excret ed MMA against potential hand exposure to malathion (residues extracted from gloves) was highly correlated (R 2 = 0.82), while a regression of excreted MMA against potential total dermal exposure to malathion was (R 2
= 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 handler s applying a granular formulat ion of diazinon to planters and shrub areas. Dermal exposure was monito red 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 wor kday (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 (R 2 = 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 unc hanged pesticide; nonchemical-specific metabolites analogous to alkylphosphates are rare ly 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 cap tan exposure. However, de Cock et al. (1995) reported that while THPI recovered from th e urine of growers applying captan did not correlate well with total exposur e estimates, there were correlations with estimates of exposure to exposed skin, as well as use of protective equipment.

Some non-OP pesticides have major metabolite s that are excreted into urine. For example, Chester and Hart (1986) monitored a group of thirteen mi xer/loader/a pplicators (M/L/As) who applied the herbicide, fluazif op-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-expos ure and 7 days post-exposure); then, after urine collections were comp lete, 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 betw een the estimated dermal exposure of each M/L/A to fluazifop-butyl and the fluazif op residues recovered from the urine (R 2 = 0.009). Figure 3-4 shows the results of biomonitoring vs. estimated dermal exposure; Chester and Hart (1986) adjusted the dermal exposure estim ates to account for differences in amounts handled between the first and second applica tions (amounts handled were not reported).

Page 50 of 114However, Woollen (1993) reported on subse quent laboratory studies suggesting that dermal absorption was inversely related to dermal exposure at applied doses corresponding to the range estimated by Ch ester and Hart (1986). Depending on the magnitude of the effect, it could confound es timates of absorbed dose from passive dosimetry. Conversely, a correlation might be apparent if absorbed doses were normalized by amount of active ingredient handl ed. More information is needed about amounts of active ingredient ha ndled by each replicate, and about residues recovered from dosimeter sections, before the effectiven ess of the study design can be evaluated.

Figure 3-4. Comparison of Results of Sequential Biomonitoring and Passive Dosimetry of Mixer/Loader/Applic ators 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 replicat e doing two applications; the first application was monitored with 24-hour urine samples collected for 7 days and the second ap plication 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 c ities: Atlanta, Georgia, Cincinnati, Ohio, and Cleveland, Ohio. Concurrent passive dosim etry and biomonitoring were conducted with six handlers at each site, usi ng 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, assu ming 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 excr eted an average of
64.5% of the absorbed dose in the urine as the metabolite, dicarbot hioic acid (DCTA). During analysis of urine samples, DCTA wa s methylated to dith iopyr and quantified; however, none of the samples had dithiopyr re sidues above the limit of quantification of 5.4 ppb. About two-thirds of the samples ha d dithiopyr residues above the limit of detection (LOD); the LOD varied from day to day, but averaged 0.32 ppb. Absorbed

Page 51 of 114doses estimated from biomonitoring were ba sed on results above the LOD, with 1⁄2 LOD
substituted for non-detects. Estimates of ab sorbed dose calculated from passive dosimetry data averaged 0.0809 + 0.0575 μ g/kg body weight/lb active ingredient handled, nearly twice the average ab sorbed dose based on biomonitoring, 0.0460 + 0.0238 μ g/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 a nd its risk assessments and not just for the scenarios in the case study in order to identi fy 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 complete d over several years by the Agency. Much of the data which were identified have already been addressed in th e 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 ha s been calculated. If the
values exceed 1 then it illustrates that abso rbed dose estimates based on passive dosimetry exceed those calculated using concurrent biomonitoring.

Table 3-1: Summary of Concu rrent 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) 0.1 ug/kg 1.30 Chlorpyrifos 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 114Table 3-1: Summary of Concu rrent 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 114Table 3-1: Summary of Concu rrent 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 longunderwear 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), longpants (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 114Table 3-1: Summary of Concu rrent 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 resistantgloves, 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 end point 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% base d 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 estim ate the dermally absorbed
dose. (b) A total of 3 chlorotriazine degradates that comprise 12% was used for biomonitoring. Unable to verify complete urine volum e. No creatinine. Very variable field recoveries for fortified matrices (22-230%). In all cases but atrazine, dose is reported as ug/kg BW. For at razine, unit exposure estimates (mg/lb ai handled) were presented in the risk assessment which allowed for a similar comparison of the efficiency of passive do simetry 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 DescriptionRatio of Passive Dosimetry versusBiomonitoring

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 114Additional Retrospective Analysis: In addition to the analyses prepared above, the Agency evaluated all of the data in PHED a nd 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 dosimet ry 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 bi ological 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 biomonito ring 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 totale d (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 calcula ted using concurrent biomonitoring. It should be noted that the range of dermal abso rption estimates has been considered in this analysis in order to illustrate the sens itivity 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 ch emical 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 90 th percentile biomonitoring UE): 19.5

Page 56 of 114Table 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): 3.02 Siemer, S.R. (1995). The

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 90 th 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 resistan t 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 90 th percentile biomonitoring UE): 19.3

Page 57 of 114Table 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; chemi cal-resistantapron; and dust/mist filtering halfface respirator (OSHA/NIOSH approval number prefix TC-21C) (12) Ratio of unit exposures (dermal + inhalation PHED
and 90 th 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-sleevedshirt 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 114Figure 3-6. Ratios of Mean Passive Dosimetry versus Bi omonitoring 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 DescriptionRatio 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. No r 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 co mparing biological monitoring to passive dosimetry one method did not consistently resu lt 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 bi ological monitoring data.

• When comparing biological monitoring to passive dosimetry for other organophosphorus pesticides, numerous studies s howed that the passive dosimetry data correlated well with the biomonitoring results. However, several ot her studies showed that the absorbed dose
did not correlate with biomon itoring. 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 f actors make it difficult to identify any

Page 59 of 114definitive 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 biologi cal monitoring comparison presented in Table 3-1 indicates that the ratio of passive to bi ological monitoring me thods ranged from 0.01
to 5.73 with a ratio below 1 for 10 scenar ios (indicating passive dosimetry under predicting the biological abso rbed dose) and a ratio grea ter 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 replic ate clothing penetration factors, 10 scenarios
have ratios that are below 1 and seven scenario s have ratios that are greater than 1. For the chlorpyrifos examples, eight scenarios ha ve 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 dosimet ry to biological monitoring ra tios for the 40 scenarios used in EPA risk assessments (i.e., Tabl es 3-1 and 3-2) indicate that 70% of the scenarios were within an order of magnitude of the ratio of 1. The rang e of the ratios is from 0.01 to 56.9; based on the limitations of the data presented, to conclude anyt hing more than most of the data are within an order of magnitude of 1 or most of the data ra nge within 2 orders of magnitude, specific studies designed to “validate” the accuracy of passive dosi metry would be needed. Also of note is the fact that using biological monito ring data to develop a surrog ate 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) al ong with the need for accurate dermal absorption
data for the chemical to be a ssessed 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 revi ew of these documents. These include:

Page 60 of 114• The uncertainties associated with the AHETF an alysis 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 slight ly 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 informa tion to characterize dosimeter performance and quantify exposures that have not been captured using other methods (e.g., compare biomonitoring to wipe, i nhalation, and handwash results).

3.2 Systematic Evaluation of Hand Sampling Methods

Historically, hand sampling methods have in cluded two basic approaches that involve quantifying residues from the surf ace of the skin as describe d in the guideline documents referenced above. In some cases, monitors provide an absorptive la yer 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 material s as monitors such as cotton or Tyvek© gloves and many types of wash solutions that have incl uded 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 us e of gloves tended to ove r-estimate residue levels on the hands because cotton gloves trapped more re sidues 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 potenti al, conversely, to underestimate e xposure to the hands. Studies conducted by Fenske and colleagues are often ci ted as evidence of th e limited recovery of residues from hands by the hand rinse method (see be low for further information). There are a
number of factors that may influence the interpre tation 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 duratio n of the exposure monitoring event (one hour or several hours), the length of time the hand rinse was performed af ter the monitori ng event, the nature of the chemical being monitored (concentrat e, dilute spray, field re sidue/soil/plant material matrix), the concentration of the chemical on the skin (μg/cm 2 ), and the level of mechanical agitation that is used during samp ling (e.g., in some cases subjects shook their individual hands in soap solutions while in other cases subjects vi gorously washed their hands together).

If there is a systematic bias associated w ith hand sampling methods, the Agency wishes to address possible approaches for co mpensating in order to account for that bias. The Agency also
desires to better understa nd the ruggedness of the available met hods for application to settings

Page 61 of 114where concentrated liquids, dilute sprays, or soli d materials may reflect th e 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, K
ow , molecular weight, and othe r physical-chemical factors can influence hand exposure re sults. 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 Secti on 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 inte rpretation and conclusions based on the analyses contained in this section.

3.2.1: Mechanistic Hand Methods Analysis

The Agency is unaware of any comprehens ive study that compares the performance of hand rinses and absorbent gloves for estimating true hand exposure. However, articles have appeared in the published literat ure that address key factors th e Agency believes can impact the interpretation of exposure data relying on hand rinses. The semi nal study for this discussion is Davis et al., 1983 where the authors reported re sults of a study comp aring 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 ha nd rinse method described in Durham and Wolfe (1962) had limitations despite being a recognized sta ndard. The limitations include identifying
solvents that remove residues without in juring 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 worker s thinning apples in azinphos-methyl (azm) treated orchards. Two separate monitoring pe riods were evaluated for a duration of two hours. One monitoring period was two days af ter an application of azm and another 9 days after the applicat ion. Unfortunately, a description or discussion of the absorptive

Page 62 of 114capacity of the type of cotton gloves used in the study was not provided. Nor did the authors attempt to discern what percent of th e 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 expos ure duration (hours) on
the differences between the use of hand rins es 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. Resu lts from the shorter exposure durations demonstrated that gloves produced significantl y 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 (defin ed as total mass of captan deposited on the glove) as a possible explanation for the l ack of significant differences in the hand rinse/glove comparison for the longer exposure durations. They also hypothesized that decreases in the absorptive cap acity of the gloves may have b een due to moisture, soil and or sweat combined with captan residues. The authors also noted captan residues were recovered beneath gloved hands indicating br eakthrough. Summary statistics from this study are shown in Table 3-3.

Table 3-3: Glove and handwas h exposure rates by sampling time Glove exposure rate (mg/hr) Hand exposure rate (mg/hr) Time (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/cm 2 ) 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 114Comparison of Three Methods for Asse ssment of Hand Exposure to AzinphosMethyl (Guthion) During Apple Thinning : Richard A. Fenske, Nancy J. Simcox, Janice E. Camp, and Cynthia J. Hines. A pplied Occupational and Environmental Hygiene Volume 14(9): 618-623, 1999.

Hand measurement techniques used in this st udy consisted of cotton gloves, hand rinses (washing hands in a polyethylene bag containing a surfactant/distille d 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) da ta collected by Fenske Sample day(s) Days post application N Mean foliar
residue (μg/cm 2 ) 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 1.22 A 27 Glove

2 and 3
-- 8 1.23 A 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 th e 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 68 B
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 114removal efficiency of azinphos-methyl is unknown, the authors assumed that azm may have a rinse efficiency similar to captan becau se both chemical’s have similar log octanol: water partition coefficients (K o/w ). The octanol/water partition coefficient is defined as the
ratio of a chemical's concentration in the octa nol 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 wa sh removal is presented in Table 3-6.

Table 3-6: Comparison Of Factors Related To Handwash Removal Of Residues

Chemical log K o/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 Capt an from Skin by Standard Handwash Exposure Assessment Procedures . R.A. Fenske, C. Schulter, C. Lu and E.H. Allen.
Bull. Environ. Contam. T oxicol. (1998) 61:194-201.

The hand rinse removal efficiency of captan us ing rinses was investigated by Fenske and colleagues in a laboratory study designed to simulate occupational hand exposure.
Volunteers contacted test tube s 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 perfor med a similar routine.

The hand rinses (washes) consisted of two washes using separate polyethylene bags containing isopropanol in dist illed water. In this mass balance study, captan was
measured on the hands and the amount that was transferred to th e 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 ab ility to explore the im pact of the timing of
hand rinse collection after e xposure/contact (immediately and 1 hour after exposure).

Table 3-7: Summary Data For Capt an 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 capt an (as reported in Fenske et al., 1989 and discussed above) has been reported at rate s of 10 to 20 mg/hour with DFR’s of 6.4 μg/cm 2 . This study highlights the impact of tim e with respect to hand wash collection.

Page 65 of 114Determination of Handwash Removal E fficiency: 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 desc ribed above but has a similar mass balance study design and investigated the rinse efficiency of ch lorpyrifos 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 effici encies were observed when hand rinses were performed immediately after contact with the test tubes. The hands we re 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 Remova l Using Handwash Methodology Removal efficiency (%) Time (hr) N Test tube spike
level (μg) Transfer efficiency Skin loading (μg/cm 2 ) # 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. Sanders on, Marcia Nishioka and Wayne Buhler. Journal of Exposure Analysis and Envi ronmental Epidemiology (2003) 13, 203-210.

As noted, the efficiency of ha nd 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 collea gues studied the effectiveness of soap and water washes for removing pesticide residues from field workers’ hands after harvesting
tobacco treated with the wate r soluble pesticide acephate. Hand wipes as described in Geno et al., 1996) were used to sample the ha nds. 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, da y 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 rem oved any doubt among the investigators that

Page 66 of 114potential handedness (left-handedne ss versus right-handedness) wa s a factor. Therefore, the investigators modified the sampling scheme to account for “inter-worker variability by randomizing the workers to have either (i) th eir 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 in structed 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 cm 2 . Upper and lower leaf surfaces were wiped for a combined surface area of 400 cm 2 . Hand rinses were converted from μg/sample to μg/cm 2 by assuming a single hand surface area of 420

cm 2 . The authors reported that hand washi ng reduced the acephate residues on the hands by 96%. The high efficiency of the hand wa sh 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/cm 2 ) GSD Range (ng/cm 2 )

Prewash 18 6.6 4.0 0.6-103 1

Postwash 18 0.2 3.2 0.04-1.2
Prewash 12 12.6 4.2 0.5-64.7 2
Postwash 12 0.7 2.8 0.07-2.4

Prewash 6 8.1 5.3 0.4-36.6 3
Postwash 6 0.4 1.6 0.2-0.68
Prewash 6 59.0 2.3 32.1-257 4

Postwash 6 2.3 1.3 1.7-3.7
Prewash 6 6.5 10.3 0.07-46.5 5
Postwash 6 0.1 1.7 0.06-02

In a similar study evaluating nicotine expos ure (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 e fficiency results based on different hand rinsing procedures for various pesticides (one non-pest icide) is presented. In Table 3-10, results of hand rinse efficiency studies conducted by Fenske and inve stigators 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 Marqua rt) the investigators relied on direct spiking of residues onto th e 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 114Table 3-10: Comparison Of Ha nd Wash Efficiency Studies

Pesticide Method Loading (μg) Wash efficiency (%)

Mean Std (%) N Reference

B 1500 b

15000 b 94

63 11

13 4

4 Brouwer et al

., 2000
a Captan

B 5620 c (1 h)

5250 c (0 h) 68

78 5

14 6

3 Fenske et al

., 1998
Carbendazim B 500 b

5000 b 94

59 8

13 3

3 Brouwer et al

., 2000
a Chlorothalonil A 4400 b 74 11 4 Brouwer et al

., 2000
a Chlorpyrifos B

B
C 1700 c (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 2275 b

2275 b
5000; 15,000; 30,000 b 81

66

86 10

5

5 4

5

12 Brouwer et al

. 2000
a , 1992 Marquart et al

., 2002
Methiocarb D 500 b

1800 b

7000 b 77
84

84 3

3

6 4

4

4 Brouwer et al

., 2000
a

Methomyl D 300 b

1490 b 71

70 3

4 4

4 Brouwer et al

., 2000
a Prochloraz B 500 b

5000 b 95

96 14

6 4

4 Brouwer et al

., 2000
a 175 b

575 b

1400 b Brouwer et al

., 2000 a,b Propoxur D

500, 5000,

7500 b 66

71

72

46 8

13

10

3 4

4

4

12 Marquart et al

., 2002

Vinclozolin D 59.2

227

384 b 81 5 3 Brouwer et al

., 2000 a,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 s ubjects per level of loading.

b Direct repeated spiki ng of 0.5 ml on the hands.

c Mass balance approach from tran sfer 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 effici ent than the described hand rinse methods.

Page 68 of 114Comparison 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 solutio ns were compared in a trial using dorsal porcine skin fortified with four different pe sticides at varying concentrations (0.5, 2.0 and 8.0 μg/cm 2 ). The pesticides were selected on th e 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) a nd a mixture of surfactants in propylene glycol (D-TAM). Wipe samples were collec ted after the pesticide had dried on the skin.
The residence time was 90 mi nutes (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 th e amount of pesticide recovered from the skin (wash efficiency). In general higher effi ciencies were seen at the higher pesticide concentrations (2.0 and 8.0 μg/cm 2 ). According to the author s, “The overall recovery of the four pesticides with 1-propanol was signi ficantly higher (p < 0.05) than recoveries with all other solvents. Soap and water recovered significantl y more (p < 0.05) of the four pesticides than either PEG or D-TAM. Fo r glyphosate, alachlor, and methyl parathion, there were no significant di fferences (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 Va rious 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 114Table 3-11: Removal Efficiency For Va rious 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 fo r 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 a ppeared to be a reasonable amount of time before exposure study investig ators could collect hand rins e samples from volunteers. The authors also recognize the higher recoveri es using hand rinses described in Geno et al., 1996, (~90%) were likely due to wiping the hands immediat ely after loading, that is
before the substances could be bound to the skin. The author s 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 th is reason, some investigators have avoided using alcohol based rinses.

Methods of Assessing Dermal Absorpti on 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 contamin ant, as well as the interval between the exposure event and washing, are important pa rameters 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 volatili zation, washing, desquamation (shedding of skin) or contact with other surfaces.

A number of the earlier agricultural reentr y studies reported in the literature were evaluated in this paper. Th ese studies covered a wide vari ety of reentry activities and pesticides and are shown in Table 3-12.

Table 3-12: Activities Addr essed 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 114Table 3-12: Activities Addr essed 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

., 1992Azinphosmethyl 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 wash es or gloves. The hand measurements were converted from μg/hr to μg/(cm 2 × hr) by using a surface area of the 840 cm 2 (both hands). These hand conversions were then pl otted on a log scale w ith their corresponding dislodgeable foliar residue (DFR) measurements (μg/cm 2 ). The transfer rate (TR) relationship to DFR was expressed as:

Log TR = 1.09 log DFR + 0.05.

The reported correlation co efficient was 0.78 with significance at p < 0.00001. The authors assert that the “equa tion indicates that the transf er rate, expressed as μg/(cm 2 × hr), is approximately equal to the DFR expressed as μg/cm 2 ...and that the transfer rate from the vegetation to the skin surface appear s to be solely depende nt 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 cont acts. This phenomenon has been considered
and evaluated in the context of industrial settings (Brouwer, Kroses, and van Hemmen,

Page 71 of 1141999) as Brouwer and colleagues explored the impact of repeated hand contacts for modeling dermal exposure to industrial wo rkplace 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/cm 2 ) to come into equilibrium with
treated surface concentr ation (DFR’s - μg/cm 2 ) can impact the interpretation of worker dermal exposure studies. In particular, wh en using dermal exposure studies that have
been conducted for short durations and usi ng 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 cm 2 /hr) was explored by California Department of Pesticide Regulation (then, the California Department of Food and Agriculture).

Long and Short Intervals of Dermal Ex posure 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 reen try studies (e.g., Fenske et al., 1989; Davis et al., 1983), Spencer and colleagues found that these data su ggested 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 hypothe sized that: “if monitoring media exhibit uneven acquisition or loading effects, then ex trapolating 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 linear ly 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 Letter s 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 reen try. The Stanislaus C ounty 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, dosime ters and hand wipes were evaluated for 7 workers after 1.5 hours, 3 hours, and 5 hours, an d 8 workers at 7 hours (entire work day). On the second day, workers hand wipes and dos imeters 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 114Table 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 th e last three intervals. Th is 1.5 fold higher exposure for a two hour interval compared to longer interv als indicates that samp ling 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 ex trapolated 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 T oxicology 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 sugge st 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 ha nd exposures were similar regardless of numbers of bins filled. Perhaps the figure is also illustra tive 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 114Figure 3-7. Dermal Monito ring of Azinphos-Methyl Residents vs. Daily Peach Harvest Production (adapted from Spencer et al . 1995)

The study synopses presented in this section high lighted factors that can impact the results of worker exposure studies relying on hand rinses. They are as follows:

Physical chemical propertie s (solubility, octanol/wate r partition coefficient (K o/w )); Residence time on the skin before rinsing; Type of solvent rinse (e.g., alcohol or soap and water); Skin concentration (μg/cm 2 ); 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 mixtur e of pesticide residues, soil, plant materials and sweat.]

The mass balance studies performed by Fens ke and colleagues involving test tubes allowed for potential comparisons of hand rinse efficiency based on a chemical’s K o/w

. Curwin noted that a pesticide’s water sol ubility may have an impact on rinse removal efficiency. Campbell and collea gues looked at the effi ciency of the various types of rinse mixtures available to investig ators 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 larg e a role (e.g., propoxur). In general rinse efficiencies were high for all chemicals re gardless of concentration and/or physical

Page 74 of 114chemical 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 chem ical binding to the sk in 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 relati onship 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 pe rformed 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/p lant 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 requ ired based on their comparison of exposures made using passive dosimetry and biological mo nitoring. However, the Agency believes it is a prudent to consider such an adjustme nt 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 individu als wearing chemical resistan t gloves. This adjustment may have a larger impact on studies a ddressing reentry expos ure or residential applications where the use of pr otective gloves is not considered.

The Agency invites the panel to consider op tions the Agency may consider to adjust exposure estimates when relying on studies us ing hand rinses. Vari ous options include:

• Consider the slope of a regression equation ba sed in plots of hand rinse efficiency and log Ko/w. These can be based on such fact ors such as skin concentrations, rinse solvent, residence time. The adjustment would be identical to the adjustments commonly made for laborator y/field fortifications.

• As part of a field study design, select a su rrogate chemical with a known and reliable biomarker and add any remaining residues based on what was found in the urine. (adjustments must consider th e inhalation pathway as well).

• Consider additional laboratory-based hand-rins e 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 1143.2.2: Hand Method Field Performance Data

In addition to the mechanistic analysis a bove, an additional evaluation of hand sampling methods was conducted using the data from the PH ED-based case study. In this analysis the unit exposures (μg/lb active ingredie nt handled) were calculated for the combined exposure of both hands. Data were then segmented based on the ty pe 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 whet her or not protective (e.g., chemical resistant) gloves were worn. [Note: AHETF proposed studies al l require the use of protective gl oves 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 an alysis from the PHED case study were from 3 mixer/loader and 3 applicator e xposure scenarios. The data repr esent 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). A ll 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 prot ective gloves);

• 8 Sampling media used;

o Removal methods (i.e., washes): etha nol, methanol, acetone, isopropanol,
soapy water

o Trapping methods: cotton gloves, Tyvek© gloves, patches

• Some wore protective (e.g., chem ical resistant) gloves, othe rs 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 es timates that have not been normalized by the pounds of active ingredient handl ed while Figure 3-10 presents hand unit exposure estimates which have been normalized. [Note: More in formation is also available in Exhibit B.]

Page 76 of 114Table 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

AcetoneProtGL AqDeterBare AqDeterProtGl Ethanol-Bare EthanolProtGl CottonGlBare CottonGlProtGl IsopropanolProtGl MethanolBare MethanolProtGl Patches-Bare Tyvek-Bare

Sampling Media & Hand Protection LevelNumber of Data Points (N)

Figure 3-8

Page 77 of 114PHED 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) HandledTotal 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) HandledUnit 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 ha ve been constrained to represent only those results for applicators who wore protectiv e gloves. It should be noted that these data also include only 4 of the 8 sampling media.

Page 78 of 114PHED 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) HandledUnit Exposure (ug/lb ai) AqDeter-ProtGl

Ethanol-ProtGl

CottonGl-ProtGl

Isopropanol-ProtGl

Figure 3-11

In order to supplement the litera ture data available for inves tigating the efficiency of hand wash recoveries, the Agency conducted an explorat ory analysis to determin e 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 “cott on” (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 ex posure 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 signif icance test are based on the standard ANOVA assumptions that 1) the data being analyzed ar e normally distributed, 2) the group variances are equal, and 3) samples within a level are independent.

In order to address the firs t assumption that the data are normally distributed, the logarithms of unit exposure values were analy zed. Probability plots are a useful tool for qualitatively assessing whether or not the data ar e 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 pl ot along an approximately straight line then they are normally distributed (since the logs of the un it exposure values are pl ot, the log-normality of

Page 79 of 114the unit exposure values is being assessed). For this particular s cenario, log transforming the data effectively converts the unit exposure data into normal distributions.

The second assumption that needs to be verifi ed 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” vari ance is decidedly different.

The third assumption is that the samples with in a hand method are independent. In Figure 3-12, the scatter plots (the left graph with th e green diamonds) use colored symbols to uniquely identify samples from the same study. A visual in spection of the scatter pl ots indicate that the samples obtained from the same study tend to “clust ered” together. For example, the black, gray,
and blue circles are clustered t ogether in the cotton glove sca tter plot. This clustering of
observations is indicative of in tra-study correlation. The intr a-study correlation evidenced by the scatter plots implies that the samples are not i ndependent 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 Gloveslog Unit_Exposure

-3 -2 -1 0 1 2

Cotton Soap Wash

Sampler Cotton Soap Wash .01 .05 .10 .25 .50 .75 .90 .95 .99

-3 -2 -1 0 1 2 3

Normal Quantile

When evaluating the three underlying assump tions of a simple ANOVA analysis and the related significance testing for this exploratory an alysis, it was generally found for all scenarios that (1) log transforming the unit exposure va lues effectively convert ed the data to normal distributions, (2) the variance of the unit exposur e values from at least one hand wash methods was not equal to the other, and (3) the sample s from within a study appear to be correlated indicating a lack of indepe ndence between the samples within a hand wash method.

Page 80 of 114Violation of the variance equality assumpti on 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 ty pe of non-independence observed in the hand
wash data informs the violation of the varian ce 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 expl oratory purposes. The more complex error component can be better modeled by more sophis ticated techniques, such as nested ANOVA or mixed linear models. This improper modeling of th e error structures in turn affects the modeled group variances of the hand wash methods.

Even given the violation of some basic assump tions, 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 met hod 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 obs ervation of the data wa s that the study-to-study variation within hand wash methods was generall y greater than the method-to-method variation. This observation implies that even with a more s ophisticated analysis that appropriately models the nested error structure, much larger differen ces in unit exposure valu es between hand wash
methods would have to be obser ved to conclude that significa nt 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 techniqu es have been used extensively to estimate exposure to pesticides. Each technique has ad vantages and disadvantag es, and the choice of which to use is determined by how the resulting da ta will be used. Seve ral studies incorporating both methods concurrently have been reviewed in this document. Concurrent passive dosimetry and biomonitoring studies often give similar or at least correlated re sults. Likewise , retrospective analyses give similar results. In some cases, in sufficient information is available to adequately assess the relationship between results obtaine d 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, wh ere conditions are less co ntrolled and the natural differences between workers are captured in the re sults. Because of this variability, ratios in study means that differ by less than an order of ma gnitude, for example, may be considered to be fairly small. Ratios in mean absorbed doses es timated from passive dosimetry and biomonitoring for the most-studies active ingred ient, chlorpyrifos, are within 3-fo ld of one another for nearly all handler and reentry studies revi ewed. As passive dosimetry and biomonitoring represent very diverse approaches, these result s tend to support both as prov iding reasonably consistent estimates of exposure. Given this premise, it is unlikely that dermal absorption during sample collection or breakthrough through de rmal dosimeters likely contribute s to a negative bias in any

Page 81 of 114pragmatic application of the results in Agency assessments. The Agency acknowledges that certainly from a theoretical and physical perspect ive that these events likely take place. One adjustment that could be made to address th e possibility of breakth rough 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 activ e ingredients that have well characterized pharmacokinetics. This would enab le biological samples to be co llected to characterize sampling method performance.

Hand exposure monitoring techniques have also been extensively evaluated as described above. At this point, both the mechanistic and fi eld performance analyses that have been most studied appear to be equivocal w ith regard to a bias pertaining to the selection of a specific
method. This should also be considered with the notion that there are a nu mber of uncertainties associated with any selection related to hand monitoring methodology.

Page 82 of 1144. Unit Exposure & Applicability Across Anticipated Working Conditions

The Agency’s use of unit exposures, defi ned previously in this document as exposure normalized by the amount of active in gredient handled is based historically upon the broad assertion that levels of expo sure are most dependent upon the physical parameters of handling pesticides, not its ch emical properties (Severn, 1982; Hackathorn and Eberhart, 1985; Reinart and Severn, 1985; Honeycutt, 1985; USEPA, 1986a). More specifically, Reinart and Severn (1985), sai d, “...it is our experien ce that the proper (and logical) conversion factors ar e amounts of pesticide app lied 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 mi xing-loading) must be used.”

It is the Agency’s intention in this sec tion 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 th e Agency’s pesticide handler exposure assessments. This metric is not only pract ical 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 pestic ide an applicator can handle).

This section of the document discusses th e Agency’s exploration of the current PHED data with respect to th e 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 a nd appropriate statistical con cepts and designs in their

Page 83 of 114analyses to distinguish between complete pr oportionality and comp lete 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 examin e, using our current database, the extent to which this relationship is demonstrated and applicable across the anticipated pesticide handler working conditions. The Agency ha s presented both a discussion of the methodology used to do so using dermal e xposure and findings regarding proportionality
for the six PHED scenarios in our case st udy. Though not considered for the purposes of this exercise, the Agency believes that th e 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 differe nt 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 us ed 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 sponsor ed by the Internati onal Life Sciences Institute (ILSI) Risk Science In stitute (RSI) and described in Investigating Probabilistic
Methods to Assess Mixer/Loade r Exposure to Pesticides (ILSI, 2003)). The authors of this document recognized that the studies w ithin PHED did not follow the same design in terms of location and number of patch dosim eters used and many records were therefore considered incomplete in terms of representi ng 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 orde r to “ensure that the body parts measured were consistent across records and that they we re the parts likely to be most exposed in the scenario queried.” In other words, th e ILSI authors recogni zed the limitations of PHED and determined that it was most appr opriate to use only t hose records which, in their opinion, were sufficiently complete in terms of critical body pa rts such that total dermal exposures could be reasonably estim ated. Following this methodology, the current analysis of the relationship between expos ure 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 th e specific scenario of interest 2

.

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 1144.1.2 Constructing Proportionality Plots

The model the Agency assumes for its unit exposures in pesticide handler exposure assessments is a power relationship:

E = AW x

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 regressi on line for the log convers ion equation of this
model:

log (E) = log (A ) + x(log W) with intercept log (A) and slope x

.

If the relationship between exposure and Aa iH is directly pro portional, then the resultant slope ( x ) should be 1. A non-zero slope ot her than 1 would indicate an alternative power relationship (e.g., E = AW 0.6 ) and a slope of 0 would suggest independence between the variables.

We recognize that there are a number of st atistical limitations and caveats to the use of OLS regression with th e 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 perf ormed here. Nevertheless, this exercise and the resultant plots are believed to be valuable exploratory tool s which graphically illustrate the relationship betw een exposure and AaiH 3

.

methodology the Agency would have had to plot separately studies utilizing patch dosimetry and studies using WBD, thus minimizing the amount of data for th is 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 measuremen ts (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 examini ng 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 (cluste r) may not be treated as independent, but the studies (clusters) themselves ar e independent. Note that the AHETF recognizes these statistical issues and is proposing more appropriate st atistical techniques to be used with its data.

Page 85 of 114Plots illustrating the Agency’s examina tion of proportionality between exposure and AaiH were generated using the following su bsetting 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 clothi ng 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 si ngle layer of clothing or PPE

The purpose of this data formatting and presentation was to maximize the Agency’s ability to investig ate proportionality between e xposure 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 ex amining proportionality using our current database. However, because pesticide ha ndlers 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 exposur e can then be estimated depending on the “clothing” worn (e.g., exposure to the body while wearing a single laye r of clothing plus hand exposure with or without gloves plus expos ure 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 exposur e as a function of the log of AaiH, should result in a regression line with a slope not significantly differe nt than 1. Alternatively, one can also see whether the slope of the regressi on 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 pr ovided 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 mixi ng/loading granules and open-cab airblast applications – are provided as examples in this section. Resu lts for all six scenarios within the case study can be seen in the Proportio nality Analysis Spreadsheets provided in
Exhibit C.

Page 86 of 114The log-log regression plots for each cas e study scenario and corresponding subsets (e.g., various body part and clothi ng combinations) contain:

• Individual data points of each study: represented by different colors and/or characters;

• Regression line: represen ted by a solid black line;

• “Slope = 1” line for comparison: repr esented 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 limitati ons of PHED studies (described in Table 2.7) and the statistical limitations of our me thodology (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 Mixi ng/Loading Dry Flowables

Scenario #1 includes individuals expo sed during open mixing and loading of chemicals formulated as dry flowables (e.g., wa ter-dispersible granules ). Shown in Table
4.1 below are the various subsets used to eval uate proportionality for this scenario. These include the relationship between protected ha nd exposure (e.g., with chemical resistant gloves) and AaiH for which there are 37 meas urements with data quality grades of A through E and the relationship between total de rmal 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 gr aded A through C. The minimum-critical body parts required to represent exposure to the rest of th e body (RoB) are listed, as are the number of records available having conc urrent hand plus RoB exposure representing total dermal exposure.

Table 4.1 Scenario #1: Open Mixing/Loading Dry Flowables # Records by PHED Grade, Dosimet ry 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 expo sed during open loading of chemicals formulated as granules. Shown in Table 4.2 below are the various subsets used to evaluate proportionality for this scenari o. These include the relationship between unprotected (bare) hand exposur e and AaiH for which there are 10 measurements with data quality grades of D and E and the rela tionship between protected hand exposure (e.g.,

Page 87 of 114with chemical resistant gloves) and AaiH fo r which there are 67 measurements graded A through C. The minimum-critical body parts requi red 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, Dosimet ry 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

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 eith er D or E grade data . Figure 4.1, below, shows that the slope of the regression line is significantly differe nt 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 114Figure 4.1 Scenario #2: Open Loa ding Granules (PHED Grades D-E) Outer Dosimetry - Hand Exposure vs. lb ai handled

100 1000 10000

10 100 1000 10000

lbs ai handledExposure (ug)

Study 425 Study 448 Regression Line Slope = 1 95% Confidence Limit Regression Slope = -0.26

95% CI slope = -0.61, 0.09

R 2 = 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 th e minimum-critical body parts required to represent dermal body exposure fo r 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, e xposure 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 th e body parts measured) and is included in the “LOG-LOG
REGRESSION SUMMARY” tabs in the f ile ExhibitC_II_b_L_Gran_outer.xls.

Page 89 of 114Figure 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 handledExposure (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% CI slope = 0.20, 0.78

R 2 = 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 si gnificantly different than 1, however also significantly different than 0. This suggest s that while the relationship may not be proportional, exposure is not independent of AaiH.

Page 90 of 114Figure 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 handledExposure (ug)

Study 422 Study 427 Study 1003 Study 1004 Study 1011 Regression Line Slope = 1 95% Confidence Limit Regression Slope = 0.74

95% CI slope = 0.57, 0.91

R 2 = 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, th at had at least the minimum-critical body parts required to represent dermal body exposure fo r 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 diffe rent than 0. This suggests that while the relationship may not be proportional, e xposure is not independent of AaiH.

Page 91 of 114Figure 4.4 Scenario #2: Open Loading Granules (PHED Grade A) Inner Dosimetry - RoB Exposure vs. lb ai handled

1 10 100 1000 10000

1 1 0

lbs ai handledExposure (ug)

1 0 0

Study 1004 Study 1011 Regression Line Slope = 1 95% Confidence Limit Regression Slope = 2.17

95% CI slope = 1.75, 2.60

R 2 = 0.7155

n = 45

Open Loading Granules – Total Dermal Exposure underneath a Single Layer Clothing/PPE (gloved hands pl us 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 leas t 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, be low, the slope of the regression line was significantly different than 1, how ever also significantly differ ent than 0. This suggests that while the relationship may not be proportion al, 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 singl e 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 fo r each of the two studi es 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 usi ng gloves, hand exposure becomes a minimal driver in relation to the rest of the body.

Page 92 of 114Figure 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 1 0

lbs ai handledExposure (ug)

1 0 0

Study 1004 Study 1011 Regression Line Slope = 1 95% Confidence Limit Regression Slope = 2.21

95% CI slope = 1.78, 2.64

R 2 = 0.7294

n = 42

Scenario #3: Open Mixing/Loading Liquids

Scenario #3 includes individuals expo sed during open mixing and loading of chemicals formulated as liquids (e.g., solubl e concentrates). S hown in Table 4.3 below are the various subsets used to evaluate propo rtionality for this scenario. These include the relationship between exposur e 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) w ith protective clothing (e.g., single layer PPE with chemical resistant gloves) and AaiH fo r which there are 50 measurements graded A through C. The minimum-critical body parts requi red 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 representi ng total dermal exposure.

Table 4.3 Scenario #3: Op en Mixing/Loading Liquids # Records by PHED Grade, Dosimet ry 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 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 114Scenario #11: Open-cab Airblast Applications

Scenario #11 includes indivi duals exposed during the a pplication of pesticides using airblast equipment that are not equippe d 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 s cenario. 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 exposureand AaiH for which there are 39 measurements graded A through C. The minimumcritical body parts required to represent exposur e 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, Dosimet ry Location, and Body Section [Minimum-critical Body Parts representing RoB Exposure: forearms, chest, back, thighs] Inner Dosimetry Outer Dosimetry HED
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 – Exposu re 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 handle d, that had measurements for at least the minimum-critical body parts required to repr esent dermal body exposure. These were
classified as either A or C grade data. In Figure 4.6, be low, the slope of the regression line was not significantly than 1 and also significantly different than 0, suggesting proportionality is reasonable.

Page 94 of 114Figure 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 handledExposure (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% CI slope = 0.54, 1.03

R 2 = 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 expos ure measurements. Therefore, the Agency opted to only present the RoB exposure (Fig ure 4.6, above) as an ad equate representation of total dermal exposure.

Open-cab Airblast Applications – Expo sure 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 signi ficant potential exposur e driver for open-cab airblast applications.

There were 62 monitoring events, where betw een approximately 0.8 and 110 lbs ai were handled, that had bare head exposure measur ements. 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 signifi cantly different than 1 and also significantly different than 0, suggesting pr oportionality is reasonable.

Page 95 of 114Figure 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 handledExposure (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% CI slope = 0.54, 1.06

R 2 = 0.3902

n = 62

Open-cab Airblast Applications – Ex posure 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 re presenting a single layer of clothing without gloves ), the Agency examined proportionality for derm al exposure underneath a single layer of clothing excluding the hands. Additionally, the data w ould have been minimized considerably having to use only those individua ls that had concurrent measurements for gloved hands and body exposure underne ath a single layer of clothing.

There were 39 monitoring events, where betw een approximately 0.8 and 13 lbs ai were handled, that had at the leas t the minimum-critical body part s 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 Fi gure 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 114Figure 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 handledExposure (ug)

Study 460 Study 517 Study 518 Study 1000 Study 1006 Regression Line Slope = 1 95% Confidence Limit Regression Slope = 1.58

95% CI slope = 1.07, 2.10

R 2 = 0.5093

n = 39

Scenario #12: Closed-c ab Airblast Applications

Scenario #12 includes indivi duals exposed during the a pplication of pesticides using airblast equipment equipped with an encl osed structure in whic h the applicator sits (i.e., closed-cab). Shown in Table 4.5 belo w are the various subsets used to evaluate proportionality for this scenario. These in clude the relationship between unprotected body exposure (represented by outer dosimetry) and AaiH for whic h 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 wh ich there are 20 measurements graded A and B. The minimum-critical body part s required to represent exposu re to the rest of the body (RoB) are listed, as are the number of record s available having concurrent hand plus RoB exposure representing total dermal exposure.

Table 4.5. Scenario #12: Closed-cab Airblast Applications # Records by PHED Grade, Dosimet ry Location, and Body Section [Minimum-critical Body Parts representing RoB Exposure: forearms, chest, back, thighs] Inner Dosimetry Outer Dosimetry PHED
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 114Scenario #15 includes individuals exposed during th e application of solid formulation pesticides (e.g., granules) usi ng a tractor-drawn broa dcast spreader not equipped with an enclosed structure in which th e applicator sits (i.e ., open-cab). Shown in Table 4.6 below is the only subset used to ev aluate proportionality for this scenario: exposure to the body underneath a single laye r 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, Dosimet ry Location, and Body Section [Minimum-critical Body Parts representing RoB Exposure: forearms, chest, back, thighs] Inner Dosimetry Outer Dosimetry PHED
Grade RoB RoB

A – B 4
See ExhibitC_VI_c_APP_OpencabSolidSpreader _inner.xls No ExhibitC file

Overall, results of the Agency’s proportiona lity 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 woul d suggest proportionali ty is a reasonable assumption, while in others the slope of the re gression line was signifi cantly different than 1 but not significantly different than 0, whic h 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 it self (e.g., disparate st udy designs and others previously discussed in Section 2 and Ta ble 2.7) or that the assumed proportional relationship is, in actuality, not th e proper one for certain scenarios.

4.1.4 Conclusion and Discussion

As shown in the open mixing/loading granul es and open-cab airblast applications examples above and in the additional case st udies 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 func tion of limitations of our database potentially solved by additional/replacement data or whet her proportionality in some cases is not a reasonable assumption.

In cases where proportionality betwee n exposure and AaiH may not be a reasonable assumption, the Agency believes that information on additional ancillary
variables such as mix concentr ation, duration of exposure, numb er of tank mixes, etc., also collected during field trials, ma y 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 evalua te the resulting models using likelihood ratio tests, stepwise regression, a nd 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 114Proposed study designs by the AHETF for a dditional data for various pesticide handling scenarios will allow for a more robus t examination of propor tionality in certain cases. The methodology for examining proporti onality proposed by the AHETF includes accounting for the “clustering” effects of studi es by using appropriate statistical modeling including nested models.

The Agency believes that, though neither th e studies in our current database nor the proposed studies by the AHETF were desi gned 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 pos sibility for more sophisticated statistical analysis.

Page 99 of 1145. Scope of Research Plan

In previous sections we have seen examples of handler exposure s cenario analyses having both large and small numbers of samples (N). In a ll 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 us e as large a range of amount of active ingredient handled as possible. These mo nitored activities would be performed by as many
different individuals and at as ma ny 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., patc hes 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 increa sed 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 pr evious SAP, (US EPA, 1986b), the Agency proposed requiring 9 samples for studies of exposure of ae rial 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 agr eed with the Agency’s approach of determining sample size requirements based on balancing the st atistical advantages of large samples and the cost and practicality of conducti ng these kinds of studies. The Panel also found the sample size requirements to be too rigid. A lternatively, the SAP suggested th at 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 gr ouped by scenario as described above in Section 2. However over time, the limitations of PHED have been acknowledged, examples of which have been il lustrated in this document.

The focus of this section are the documents entitled Agricultural Handlers Exposure Task Force (AHETF) Technical Summary Document Fo r 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-Antimicrobi als Exposure Assessment Task Force II (AEATF II) Background and Scoping Summary (ACCa, 2006) . However, it is important to characterize the number of agricult ural pesticide applicators presently in the United States in order to provide some context for the discussion s related to the overall sa mpling plan proposed by AHETF and AEATF, the proposed sampling intensit y, and its representativeness. The Agency believes the nature and the size of the exposed p opulation has a direct be aring on the development of the research plan including the numbers of samples that woul d be required to adequately predict exposures for that population.

The Agency is not aware of any direct cen sus 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 develo ped an estimate of the approximate number of domestic aerial applicators and can identify th e number of farms that receive agricultural

Page 100 of 114pesticide applications categori zed by pesticide formulation, a pplication method, and type of applicator. The Agency estimates that there ar e approximately 3,500 to 4,000 aerial applicators in the United States. Table 5-1 reports the a pproximate number of farms reporting pesticide applications categorized by formul ation, 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 pes ticides, categorized by formulation, application method, and type of appl icator. 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 fa rm. 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 th e row and column totals do not sum. Further, the row totals include records where the type of applicator is unknow n. 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 include s 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 scen arios as part of developing an exposure database, referred to as AHED (Agricultural Handlers Exposure Databa se). Table 5.2 provides a comparison of the AHETF-proposed sample sizes for th e AHED scenarios and the samples sizes currently available to the Ag ency from the PHED. The proposed number of samples for
each scenario will be re-evaluated as the AH ETF 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 s cenario 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 samp le 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 114same monitoring and sample collection pr otocol, 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 aer ial 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 114Table 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 ju stification 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 agro chemical market. Overall, the procedures for performing the studies reflect se veral 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 th an the PHED studies currently used.

The proposed AEATF exposure scenarios and corre sponding numbers of m onitoring events are presented in Table 5-3. The pr oposed 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 wi ll be performed in simulated circumstances (e.g., mopping floors, wiping walls at a re nted reception hall). While other studies will be performed in a simulated room/chamber at a laboratory to contro l air exchange rates, et c. (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 1145.1 Within-worker and Between-worker Variability

To date, all regulatory agencies have assume d that all observed vari ability is betweenhandler variability. That is, repeated measurem ents on the same individual performing the same task would be considered as separa te (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 114A 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 individual s 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 Aa iH (i.e, . unit exposures). On e 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 wh ether they are repeat
measurements of the same individual or of differe nt 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 effec tive from a statis tical point of view to increase the number of clusters, or st udies rather than the num ber of monitoring units within each study. The AHETF fu rther asserts that resources would be better spent monitoring single handler-days and increasing the number of st udies 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 develo p 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 reque sting the Panel’s assist ance in addressing.

Page 105 of 1146. Bibliography

AHETFa, 2006. Agricultural Handlers E xposure Task Force (AHETF) Technical Summary Document For a Multi-Year Pestic ide 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 Be nchmark 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 Monitori ng 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-Antimicrobial s 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 Acceptanc e 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).

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