Scientific Issues Associated

with Worker Reentry

Exposure Assessment

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:

December 2-5, 2008

Table of Contents

  TOC \o "1-4" \h \z \u    HYPERLINK \l "_Toc213732304"  Exhibits	  PAGEREF _Toc213732304 \h  5

  HYPERLINK \l "_Toc213732305"  1	Introduction	  PAGEREF _Toc213732305 \h  6

  HYPERLINK \l "_Toc213732306"  1.1	Background	  PAGEREF _Toc213732306 \h  6

  HYPERLINK \l "_Toc213732307"  1.2	Report Organization	  PAGEREF _Toc213732307 \h  8

  HYPERLINK \l "_Toc213732308"  2	Historical Perspective	  PAGEREF _Toc213732308 \h  9

  HYPERLINK \l "_Toc213732309"  2.1	Early Exposure Assessment Developments	  PAGEREF _Toc213732309 \h  9

  HYPERLINK \l "_Toc213732310"  2.2	ARTF Activities and Achievements	  PAGEREF _Toc213732310 \h  12

  HYPERLINK \l "_Toc213732311"  3	Exposure and Risk Assessment Overview	  PAGEREF _Toc213732311 \h  15

  HYPERLINK \l "_Toc213732312"  3.1	Calculation of Transfer Coefficients   PAGEREF _Toc213732312 \h  15

  HYPERLINK \l "_Toc213732313"  3.2	Risk Assessment Approach	  PAGEREF _Toc213732313 \h  21

  HYPERLINK \l "_Toc213732314"  4	Topics for SAP Consideration	  PAGEREF _Toc213732314 \h  27

  HYPERLINK \l "_Toc213732315"  4.1	Identification of Farmworker Activities	  PAGEREF _Toc213732315 \h  27

  HYPERLINK \l "_Toc213732316"  4.2	Crop-Activity Clusters:  Summary and Analysis	  PAGEREF _Toc213732316 \h  34

  HYPERLINK \l "_Toc213732317"  4.2.1	Farmworker Exposure Monitoring Data	  PAGEREF _Toc213732317 \h  35

  HYPERLINK \l "_Toc213732318"  4.2.2	Identification and Analysis of Clusters	  PAGEREF _Toc213732318 \h  39

  HYPERLINK \l "_Toc213732319"  <IP_ADDRESS>	Greenhouse and Nursery Crop Clusters	  PAGEREF _Toc213732319 \h  40

  HYPERLINK \l "_Toc213732320"  <IP_ADDRESS>	Field Crop Clusters	  PAGEREF _Toc213732320 \h  45

  HYPERLINK \l "_Toc213732321"  <IP_ADDRESS>	Mechanical Harvesting Cotton Clusters	  PAGEREF _Toc213732321 \h  61

  HYPERLINK \l "_Toc213732322"  <IP_ADDRESS>	Orchard Crop Clusters	  PAGEREF _Toc213732322 \h  63

  HYPERLINK \l "_Toc213732323"  <IP_ADDRESS>	Trellis Crop Clusters	  PAGEREF _Toc213732323 \h  69

  HYPERLINK \l "_Toc213732324"  <IP_ADDRESS>	Turf Clusters	  PAGEREF _Toc213732324 \h  76

  HYPERLINK \l "_Toc213732325"  <IP_ADDRESS>	Crop Irrigation Cluster	  PAGEREF _Toc213732325 \h  78

  HYPERLINK \l "_Toc213732326"  <IP_ADDRESS>	No/Low Contact Activities	  PAGEREF _Toc213732326 \h  80

  HYPERLINK \l "_Toc213732327"  <IP_ADDRESS>	Summary	  PAGEREF _Toc213732327 \h  82

  HYPERLINK \l "_Toc213732328"  4.3	Workday Duration in Farmworker Exposure Assessment	  PAGEREF _Toc213732328 \h  82

  HYPERLINK \l "_Toc213732329"  4.3.1	Non-Duration Factors Used In Post-application Exposure Assessment	  PAGEREF _Toc213732329 \h  83

  HYPERLINK \l "_Toc213732330"  4.3.2	Workday Duration Considerations for Post-application Exposure and Risk Assessment	  PAGEREF _Toc213732330 \h  85

  HYPERLINK \l "_Toc213732331"  4.3.3	Toxicological Endpoint Considerations in Post-application Exposure and Risk Assessments	  PAGEREF _Toc213732331 \h  91

  HYPERLINK \l "_Toc213732332"  4.3.4	Conclusions for Workday Duration and Post-application Exposure Assessment	  PAGEREF _Toc213732332 \h  97

  HYPERLINK \l "_Toc213732333"  5	Charge to the Panel	  PAGEREF _Toc213732333 \h  98

  HYPERLINK \l "_Toc213732334"  6	Bibliography	  PAGEREF _Toc213732334 \h  106

Table of Tables

  TOC \h \z \c "Table"    HYPERLINK \l "_Toc213732335"  Table 1:  Summary of Activities and Document Milestones Associated with EPA Occupational Pesticide Exposure Assessments	  PAGEREF _Toc213732335 \h  10

  HYPERLINK \l "_Toc213732336"  Table 2:  Field Recovery (%) by Sampling Media and Fortification Level	  PAGEREF _Toc213732336 \h  18

  HYPERLINK \l "_Toc213732337"  Table 3:  Adjustment Factors for Sampling Media Based on Level Found	  PAGEREF _Toc213732337 \h  18

  HYPERLINK \l "_Toc213732338"  Table 4:  Summary of Dermal Exposure Monitoring	  PAGEREF _Toc213732338 \h  19

  HYPERLINK \l "_Toc213732339"  Table 5:  Summary of DFR Sampling	  PAGEREF _Toc213732339 \h  19

  HYPERLINK \l "_Toc213732340"  Table 6:  Tobacco Harvesting – Transfer Coefficient Summarya	  PAGEREF _Toc213732340 \h  21

  HYPERLINK \l "_Toc213732341"  Table 7:  Example Summary of Farmworker Risks	  PAGEREF _Toc213732341 \h  25

  HYPERLINK \l "_Toc213732342"  Table 8:  Primary Crop at Current Farm Job (Table 5.1 in U.S. DOL, 2005)	  PAGEREF _Toc213732342 \h  32

  HYPERLINK \l "_Toc213732343"  Table 9:  Primary Task at Current Farm Job (Table 5.3 in U.S. DOL, 2005)	  PAGEREF _Toc213732343 \h  33

  HYPERLINK \l "_Toc213732344"  Table 10:  Summary of Reentry Exposure Monitoring Studies in ARTF Database	  PAGEREF _Toc213732344 \h  35

  HYPERLINK \l "_Toc213732345"  Table 11:  Organization of ARTF Studies by Cluster	  PAGEREF _Toc213732345 \h  37

  HYPERLINK \l "_Toc213732346"  Table 12:  ARTF Greenhouse and Nursery Clusters	  PAGEREF _Toc213732346 \h  41

  HYPERLINK \l "_Toc213732347"  Table 13:  ARTF Greenhouse Vegetable Cluster	  PAGEREF _Toc213732347 \h  42

  HYPERLINK \l "_Toc213732348"  Table 14:  Alternative Greenhouse and Nursery Clusters	  PAGEREF _Toc213732348 \h  44

  HYPERLINK \l "_Toc213732349"  Table 15:  ARTF Field Crop Studies by Leaf Texture	  PAGEREF _Toc213732349 \h  49

  HYPERLINK \l "_Toc213732350"  Table 16:  ARTF Smooth-leaf Field Crop Clusters	  PAGEREF _Toc213732350 \h  51

  HYPERLINK \l "_Toc213732351"  Table 17:  ARTF Hairy-leaf Field Crop Clusters	  PAGEREF _Toc213732351 \h  55

  HYPERLINK \l "_Toc213732352"  Table 18:  ARTF Waxy-leaf Field Crop Clusters	  PAGEREF _Toc213732352 \h  60

  HYPERLINK \l "_Toc213732353"  Table 19:  ARTF Cotton – Mechanical Harvesting Clusters	  PAGEREF _Toc213732353 \h  62

  HYPERLINK \l "_Toc213732354"  Table 20:  ARTF Orchard Crop Clusters	  PAGEREF _Toc213732354 \h  66

  HYPERLINK \l "_Toc213732355"  Table 21:  ARTF Trellis Crop Clusters	  PAGEREF _Toc213732355 \h  71

  HYPERLINK \l "_Toc213732356"  Table 22:  Alternative Trellis Crop Clusters	  PAGEREF _Toc213732356 \h  76

  HYPERLINK \l "_Toc213732357"  Table 23:  ARTF Turf Clusters	  PAGEREF _Toc213732357 \h  77

  HYPERLINK \l "_Toc213732358"  Table 24:  Crop-Activity Combinations without Foliar Contact (No TC Assignment)	  PAGEREF _Toc213732358 \h  81

  HYPERLINK \l "_Toc213732359"  Table 25:  NAWS:  Hours Worked Per Week 2001-2006 (All Crops and Activities)	  PAGEREF _Toc213732359 \h  88

  HYPERLINK \l "_Toc213732360"  Table 26:  NAWS:  Hours Worked Per Week by Farmworker Task (1993-2006)	  PAGEREF _Toc213732360 \h  88

  HYPERLINK \l "_Toc213732361"  Table 27:  Hand Harvesting Strawberries – Input Summary	  PAGEREF _Toc213732361 \h  93

  HYPERLINK \l "_Toc213732362"  Table 28:  Summary of Monte Carlo Simulation for Hand Harvesting Strawberries	  PAGEREF _Toc213732362 \h  93

  HYPERLINK \l "_Toc213732363"  Table 29:  Summary of Monte Carlo Simulation for Scenario A	  PAGEREF _Toc213732363 \h  95

  HYPERLINK \l "_Toc213732364"  Table 30:  Summary of Monte Carlo Simulation for Scenario B	  PAGEREF _Toc213732364 \h  95

  HYPERLINK \l "_Toc213732365"  Table 31:  Reference Table for Charge Question 2 (b)	  PAGEREF _Toc213732365 \h  100

 Table of Figures

  TOC \h \z \c "Figure"    HYPERLINK \l "_Toc213732366"  Figure 1:  Example of Farmworker Activities Reported in Grower Survey (Excerpt from Table 8 of Thompson, 1998)	  PAGEREF _Toc213732366 \h  29

  HYPERLINK \l "_Toc213732367"  Figure 2:  Example of Crops, Regions, and Sample Size Table Reported in Grower Survey (Excerpt from Table 5 of Thompson, 1998)	  PAGEREF _Toc213732367 \h  30

  HYPERLINK \l "_Toc213732368"  Figure 3:  Regions Specified In Residue Chemistry Requirements	  PAGEREF _Toc213732368 \h  31

  HYPERLINK \l "_Toc213732369"  Figure 4:  Solid Aster Harvest (Study ARF055)	  PAGEREF _Toc213732369 \h  41

  HYPERLINK \l "_Toc213732370"  Figure 5:  ARTF Studies - Greenhouse and Nursery	  PAGEREF _Toc213732370 \h  43

  HYPERLINK \l "_Toc213732371"  Figure 6:  ARTF Studies - Greenhouse and Nursery	  PAGEREF _Toc213732371 \h  44

  HYPERLINK \l "_Toc213732372"  Figure 7:  Cucumber Harvest (Study ARF045)	  PAGEREF _Toc213732372 \h  47

  HYPERLINK \l "_Toc213732373"  Figure 8:  Cabbage Weeding (Study ARF037)	  PAGEREF _Toc213732373 \h  47

  HYPERLINK \l "_Toc213732374"  Figure 9:  Dry Pea Scouting (Study ARF021)	  PAGEREF _Toc213732374 \h  48

  HYPERLINK \l "_Toc213732375"  Figure 10:  Tomato Tying (Study ARF038)	  PAGEREF _Toc213732375 \h  48

  HYPERLINK \l "_Toc213732376"  Figure 11:  Field Crop Scouting - Leaf Texture Comparison	  PAGEREF _Toc213732376 \h  50

  HYPERLINK \l "_Toc213732377"  Figure 12:  ARTF Smooth-leaf Field Crop Studies	  PAGEREF _Toc213732377 \h  52

  HYPERLINK \l "_Toc213732378"  Figure 13:  Comparing Clusters SSs and SSr	  PAGEREF _Toc213732378 \h  53

  HYPERLINK \l "_Toc213732379"  Figure 14:  Smooth-leaf Field Crop Clusters	  PAGEREF _Toc213732379 \h  54

  HYPERLINK \l "_Toc213732380"  Figure 15:  ARTF Hairy-leaf Field Crop Studies	  PAGEREF _Toc213732380 \h  56

  HYPERLINK \l "_Toc213732381"  Figure 16:  Hand Harvesting Hairy-leaf Field Crops	  PAGEREF _Toc213732381 \h  57

  HYPERLINK \l "_Toc213732382"  Figure 17:  Hand Harvesting Tobacco (ARF024)	  PAGEREF _Toc213732382 \h  58

  HYPERLINK \l "_Toc213732383"  Figure 18:  Clusters HH & HHt - % Hand and RoB TC, TDE Contribution	  PAGEREF _Toc213732383 \h  59

  HYPERLINK \l "_Toc213732384"  Figure 19:  Waxy-leaf Field Crop ARTF Studies	  PAGEREF _Toc213732384 \h  61

  HYPERLINK \l "_Toc213732385"  Figure 20:  Cotton - Mechanical Harvesting Activities	  PAGEREF _Toc213732385 \h  63

  HYPERLINK \l "_Toc213732386"  Figure 21:  Orange Harvest (Study ARF041)	  PAGEREF _Toc213732386 \h  64

  HYPERLINK \l "_Toc213732387"  Figure 22:  Apple Pruning (Study ARF047)   PAGEREF _Toc213732387 \h  65

  HYPERLINK \l "_Toc213732388"  Figure 23:  Hand Harvesting in Orchard Crops	  PAGEREF _Toc213732388 \h  66

  HYPERLINK \l "_Toc213732389"  Figure 24:  ARTF-proposed Orchard Crop Clusters	  PAGEREF _Toc213732389 \h  68

  HYPERLINK \l "_Toc213732390"  Figure 25:  Comparison of Hand Harvesting and Thinning in Orchard Crops	  PAGEREF _Toc213732390 \h  69

  HYPERLINK \l "_Toc213732391"  Figure 26:  Grape Harvest (Study ARF048)   PAGEREF _Toc213732391 \h  70

  HYPERLINK \l "_Toc213732392"  Figure 27:  ARTF Studies in Trellis Crops	  PAGEREF _Toc213732392 \h  72

  HYPERLINK \l "_Toc213732393"  Figure 28:  Comparison of ARTF Clusters THb & THg	  PAGEREF _Toc213732393 \h  73

  HYPERLINK \l "_Toc213732394"  Figure 29: Examples of Trellis Designs	  PAGEREF _Toc213732394 \h  74

  HYPERLINK \l "_Toc213732395"  Figure 30:  Hand Harvesting Table/Raisin and Wine Grapes	  PAGEREF _Toc213732395 \h  74

  HYPERLINK \l "_Toc213732396"  Figure 31:  Comparison of Similar Activities in Orchard and Trellis Crops	  PAGEREF _Toc213732396 \h  75

  HYPERLINK \l "_Toc213732397"  Figure 32:  Golf Course Maintenance (Study ARF046)	  PAGEREF _Toc213732397 \h  77

  HYPERLINK \l "_Toc213732398"  Figure 33:  Sod Harvest (Study ARF039)	  PAGEREF _Toc213732398 \h  77

  HYPERLINK \l "_Toc213732399"  Figure 34:  ARTF Turf Clusters	  PAGEREF _Toc213732399 \h  78

  HYPERLINK \l "_Toc213732400"  Figure 35:  Potato Irrigation (Study ARF036)	  PAGEREF _Toc213732400 \h  79

  HYPERLINK \l "_Toc213732401"  Figure 36:  ARTF Irrigation (hand-line) Cluster	  PAGEREF _Toc213732401 \h  80

  HYPERLINK \l "_Toc213732402"  Figure 37:  Lognormal Probability Plot of Transfer Coefficient Cluster HH	  PAGEREF _Toc213732402 \h  84

  HYPERLINK \l "_Toc213732403"  Figure 38:  ARTF Grower Survey – Workday Duration for Select Crop Categories	  PAGEREF _Toc213732403 \h  86

  HYPERLINK \l "_Toc213732404"  Figure 39:  ARTF Grower Survey – Workday Duration for Orchard Crop Activities	  PAGEREF _Toc213732404 \h  87

  HYPERLINK \l "_Toc213732405"  Figure 40:  AZM Dermal exposure/bin vs. time for peach harvesters (Figure 3, Spencer et al., 1995)	  PAGEREF _Toc213732405 \h  89

  HYPERLINK \l "_Toc213732406"  Figure 41:  Dermal monitoring of AZM residues vs. daily peach harvest production (Figure 1, Ross et al., 1999; adapted from Spencer et al., 1995)	  PAGEREF _Toc213732406 \h  90

Exhibits

Exhibit A:  TC Calculation Example Using ARF024

Exhibit B:  TC Calculation Example – ARF024 Study Report (EPA MRID 45005911)

Exhibit C:  List of 4519 Identified Crop-Activity Combinations

Exhibit D:  ARTF Transfer Coefficient Data Summary

Exhibit E:  ARTF Grower Survey – Workday Duration Summary

Exhibit F:  Supplemental Statistical Analysis for ARTF Cluster OH

Introduction

Over the past 25 years, the Agency has been actively engaged in the refinement of its methodologies and development of data for assessing exposures that may occur while performing agricultural activities in areas that have previously been treated with pesticides.  These are generally referred to as occupational “post-application” exposures.  This effort has been collaborative with various other regulatory agencies, experts in crop production, growers, and the pesticide industry in the form of the Agricultural Reentry Task Force (ARTF).  This document summarizes these efforts and offers alternative approaches for interpreting the data in some circumstances.  Additional background and detail are provided in several accompanying documents and introduced in the ARTF SAP submission (Bruce and Korpalski, 2008).  Please refer to that document and its references for additional information.

Typical activities that may lead to such exposures include hand harvesting crops such as grapes and vegetables, thinning orchard crops, scouting fields for pests, working in greenhouses, and golf course maintenance.  Post-application exposure assessments are typically used by the Agency as a component of regulatory risk management decisions to establish a Restricted Entry Interval (REI).  REIs establish a minimum period of time before which workers are prohibited from re-entering treated areas.  The intent of REIs is to ensure that farmworkers cannot enter previously treated areas to perform activities until pesticide residues have dissipated to levels considered acceptable by the Agency.  As a result of using much of the data and methodologies described in this document the Agency has lengthened over 750 REIs associated with 50 different pesticide active ingredients including fungicides, insecticides, and herbicides over approximately the last decade during the recently completed re-registration process.

Background

Historically, there has been interest in a variety of topics and issues associated with exposures to pesticides of farmers, farmworkers, and their families.  The Agency has been involved in efforts associated with these issues including investigating farmworker pesticide exposure incidents (e.g. NIOSH’s SENSOR program), investigating longer-term effects on health from pesticide exposures (e.g., NCI’s Agricultural Health Study, the industry-funded Farm Family Health Study, and Agency STAR grants), and training efforts to ensure farmworker safety and reducing exposures to pesticides through improvements to its Worker Protection Standard - 40CFR170 (CEQ, 1974; U.S. EPA, 1984).  However, though those issues remain very important, the purpose of this meeting is to narrowly focus on technical issues related to specific aspects of the Agency’s conduct of farmworker exposure assessments.  These types of assessments and the REIs which are established based on their results are but one element of the overall Agency effort to ensure that the levels of pesticides that farmworkers and their families are exposed to do not result in adverse health consequences.

The first quantitative farmworker assessments were completed by the Agency in the late 1970s followed by the issuance of the first guideline document addressing farmworker exposure by the Agency in 1984 (CEQ, 1974;  U.S EPA, 1984).  These early assessments used a screening level approach to determine risks based on monitoring data from a limited number of high exposure hand labor tasks collected in research studies (Popendorf, et al, 1980 & 1982; Zweig, et al, 1984 & 1985).

Recognizing the initial screening level approach was insufficient to represent the diversity of crops and farmworker tasks in agriculture, the Agency, under Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) authority, required additional data which would allow for a more refined farmworker exposure assessment.  As a result, a Data-Call-In (DCI) was issued to pesticide registrants in 1995.  This DCI impacted all pesticide registrants who produced products that could lead to post-application farmworker exposure.  In anticipation of the need to provide these data to the Agency, the industry-based ARTF was formed prior to the time that the DCI was issued.  Ultimately – based on the information provided by ARTF and working in conjunction with the California Department of Pesticide Regulation (DPR), Canada’s Pest Management Regulatory Agency (PMRA), and the U.S. Department of Agriculture (USDA) – an agronomically-based, task-specific approach for assessing farmworker exposure was developed.  This approach is the primary subject of this FIFRA Scientific Advisory Panel (SAP).  It is based on 47 farmworker exposure monitoring studies and other data which would currently cost approximately $40 million to complete.  It differs from previous efforts in defining and quantifying farmworker exposures in that it is substantially broader and is intended to provide a tool for assessing risks associated for all hand labor tasks performed in contemporary agriculture (i.e., ~ 4500 crop/activity combinations have been identified).

At this meeting, the Agency is seeking review and comment on three basic areas of post-application (farmworker) exposure assessment which the Agency is seeking guidance from the panel.  These include:  (1) the identification of activities for consideration in the assessment process; (2) grouping of similar activities for assessment purposes including no/low contact activities; and (3) consideration of workday duration in exposure calculations.  The ARTF has conducted an analysis of the data it has generated in response to the DCI and, in most cases, the Agency concurs with this analysis and the conclusions drawn from these data, though in some cases alternative analyses are presented for consideration by the Panel.

Report Organization

The purpose of this report is to provide the SAP with background information on the Agency’s farmworker exposure assessment approach and to present the topics that the Agency is specifically requesting the SAP address.  The sections of the report include:

Section 2:  Historical Perspective – This section provides a history of critical events that pertain to Agency farmworker exposure/risk assessments.  These include addressing how assessments have been completed in the past including the use of interim REIs, the use of labor statistics and other sources of information to characterize the farmworker population, and the 1995 DCI.

Section 3:  Exposure and Risk Assessment Overview – This section provides an overview of the data and methodologies typically used by the Agency for completing farmworker exposure/risk assessments.

Section 4: Topics for SAP Consideration – This section addresses the specific topics to be considered in this meeting:

The identification and grouping/clustering of agricultural occupational post-application exposure activities proposed by the ARTF and reviewed by the Agency.  The discussion includes a description of the specific exposure monitoring data for farmworkers, how those results were integrated into a framework which will allow for assessments to be completed for all hand labor tasks in agriculture, and identification of those tasks considered as no/low contact for the purposes of Agency exposure/risk assessment.

The consideration of the importance of workday duration in the context of the overall Agency exposure and risk assessment methodology.  This includes discussion of the importance of other key factors in farmworker exposure and risk assessment.

Section 5:  Charge – This section provides the specific charge to the Panel related to the topics discussed in Section 4.

Section 6: Bibliography – This section provides the citations used in the development of this document.

Historical Perspective

	 This section provides a historical perspective on the scientific and regulatory developments that have lead to establishment of the Agency’s farmworker exposure assessment methods and creation of the ARTF.  It specifically highlights regulatory milestones in protecting farmworkers from post-application pesticide exposures, describes developments that led to the establishment of the ARTF, and summarizes the ARTF’s activities and achievements to date.

Early Exposure Assessment Developments

As early as the 1950s, researchers such as Durham and Wolfe recognized a concern over those being occupationally exposed to pesticides and began to develop methods to quantify these exposures.  It was apparent, particularly after the introduction of organophosphate pesticides, that toxic effects were seen in farmworkers; however, it was difficult to investigate the problem due to the erratic nature of incidents (Durham and Wolfe, 1962; U.S. EPA, 1984).  In 1974, the President’s Council On Environmental Quality (CEQ) established a Task Group which focused on occupational exposure to pesticides and identified a need to evaluate exposures from plant surfaces with a focus on contact-intensive field operations (e.g., harvesting, thinning) in order to establish REIs.  Key recommendations included requiring monitoring data; evaluating geographical and other factors which influence exposures; establishing surveillance systems; and developing lower risk alternatives.  This guidance stimulated Agency activity in the area of occupational exposure assessment and lead to the development of the Agency’s first guidelines related to the assessment of occupational exposures (CEQ, 1974).  An “epidemiological” model was initially the method used to establish REIs, whereby a REI would be postulated and subsequently tested in the field using monitoring data.  This model was later deemed insufficient and a new method, utilizing the relationship or correlation between field residues and exposure, was proposed (USEPA, 1984 and 1998a).  This relationship would become known as the transfer coefficient (TC) and is a major topic of discussion throughout this document.

In the late 1970s and early 1980s, much of the seminal work pertaining to farmworker post-application exposure assessments was completed starting with the development of the dislodgeable foliar residue (DFR) sampling method (Iwata,1977).  The DFR can be thought of as the amount of residue available for transfer to a worker’s skin on the surface of a treated plant.  DFR values are chemical-specific and can be impacted by a number of factors such as crop, climate, or application method.  Therefore, it is common that more than one source of DFR data may be used in an assessment to represent the varied conditions under which a chemical is used.  It was also during this time that the concept of the transfer coefficient was more fully investigated (Popendorf et al, 1980 & 1982; Zweig et al 1984 & 1985).  The TC is an exposure metric that relates total exposure for a particular hand labor task to the amount of residues on the surface of the crop per unit time.  Using the TC generically for other chemicals where the same hand labor task may occur, exposure can be estimated using these values in conjunction with chemical-specific DFR values and an estimate of exposure duration (Popendorf et al, 1980 & 1982; U.S. EPA 1984 & 1988a).  The Agency began using the initial TCs established by Popendorf and other researchers as a screening level tool for evaluating all farmworker exposures.  In other words, the early TCs established in published literature (e.g., fruit tree harvesting and strawberry harvesting) were used to assess exposure and risk for all crops and activities.  The Agency recognized that this approach was insufficient to represent the large number of possible farmworker activities.  As a result, the Agency requested that industry provide a much more extensive database for future exposure and risk assessment processes in its 1995 DCI.  This action by the Agency required pesticide registrants whose products have the potential for post-application worker exposure to generate data which could be used to quantify these exposures, and initiated the current model for developing large datasets of non-dietary exposure data in a systematic manner (USEPA, 1995).

The DCI was issued under the authority in section 3(c)(2)(B) of the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA), 7 U.S.C. section 136a(c)(2)(B).  Data were required for pesticide products if they were registered for many use sites including commercial agricultural, ornamental, or tree crops grown in a field, nursery, greenhouse, or any other crop growing site.  Specifically, foliar residue dissipation studies, quantifying surface residue concentrations over time, were required, as well as dermal exposure measurements “upon reentry to a treated area.”  In addition, the DCI included 319 pesticide active ingredients that represented all known uses of pesticides in which exposure to farmworkers were expected to occur.

A more complete summary of significant Agency activities and milestones relating to pesticide occupational exposure assessments are presented in   REF _Ref212017237 \h  \* MERGEFORMAT  Table 1 .

Table   SEQ Table \* ARABIC  1 :  Summary of Activities and Document Milestones Associated with EPA Occupational Pesticide Exposure Assessments

Year	Document	Description

1980

	FIFRA Scientific Advisory Panel (SAP) Informal Review Of Subdivision K Guidelines	The first iteration of the post-application worker risk assessment guidelines was reviewed.  Topics addressed included when to require data, specific data collection methods, human exposure monitoring, and the calculation of reentry intervals.  Agency proposals were reviewed in subcommittee and then by the SAP (U.S. EPA, 1980 & 1981).

1981

1984	Pesticide Assessment Guidelines, Subdivision K – Exposure: Reentry Protection [NTIS Document Number PB85-120962 (October, 1984)] Much of the seminal research related to exposure monitoring was conducted between 1974 and 1984.  The Agency synthesized this information (a summary of key studies is included in the preamble) and established the testing guidelines for monitoring post-application worker exposures (USEPA, 1984).

1986	Pesticide Assessment Guidelines, Subdivision U – Applicator Exposure Monitoring [NTIS Document Number PB87-133286 (October, 1986)] Similar guidelines for occupational pesticide handlers (i.e., those individuals involved with mixing, loading or applying) were established soon after those for post-application exposures. The concept of the use of surrogate data and a more thoughtful consideration of sampling and analysis methods are presented (USEPA, 1986a).

1986	FIFRA Scientific Advisory Panel (SAP) Review Of Subdivision U Guidelines

	This effort represents the first SAP review of the Agency’s exposure monitoring guidelines.  Key recommendations from this review that are germane to the current SAP evaluation include:  the use of concurrent dosimetry and biological monitoring and the use of either handwashes or lightweight gloves for monitoring hand exposures with some chemicals needing special consideration because of rapid absorption, persistence in the skin, etc.  The Panel agreed with the utility of exposure monitoring data in a generic sense although advised that factors that affect bioavailability should be considered when the approach is used (USEPA, 1986b).

1989	Good Laboratory Practices (40CFR160)	This document provides generalized guidance related to ensuring the quality control and quality assurance elements of data generated for pesticide registration purposes.  Key elements related to exposure monitoring include recordkeeping, field calibration, laboratory quality control, and sample integrity (USEPA, 1989).

1992	Pesticide Handlers Exposure Database (PHED)

	This database assembled the available data for pesticide handlers into a system that is still used today to generically address occupational pesticide handler exposures (Hackathorn and Eberhart, 1985 & U.S. EPA, 1992).  This system established many precedents related to the use of data in a generic manner (e.g., use of passive dosimetry exposure monitoring techniques, correction for quality control, exposure data grouping, and monitoring refinements).

1993	Pesticide Rejection Rate Analysis: Occupational and Residential Exposure [EPA Document 738-R-93-008, September, 1993]	 In many scientific disciplines, there was a high rejection rate among studies submitted to the Agency for pesticide registration purposes.  Therefore, in 1991 the Agency decided to evaluate the factors that most frequently caused studies to be rejected in order to assess the adequacy of EPA’s guidance documents and to determine if rejection issues were avoidable.  The key factors related to worker exposure monitoring were identified as inadequate quality assurance/quality control and inadequate sampling for residue decay studies (USEPA, 1993).  As a result, the Agency encouraged industry to form task forces to address data requirements applicable to all companies.

1994 	Workshops On Revisions To Agency Guidelines For Exposure Assessment

	After the release of the rejection rate analysis for exposure data and the experience gained during the development and review of many studies, it was clear that the current Agency guidelines for worker exposure required updating and that additional data were required to address various exposure issues.  As such, the Agency along with other regulatory bodies (i.e., Health Canada, California Department of Pesticide Regulation, and the Organization for Economic Cooperation and Development) sponsored workshops to determine how the current guidelines could be refined (PMRA & U.S. EPA, 1997 & USEPA, 1994).

1997

1994	Formation of the Agricultural Reentry Task Force (ARTF)

	The ARTF was formed to generate the data that the Agency would ultimately require of pesticide registrants related to farmworker post-application exposure. The efforts by the ARTF will be an integral part of the discussions at this SAP meeting.  The ARTF has submitted background materials on their efforts and are reviewed and referenced throughout this document.

1995	Data Call-In For Post-Application Worker Exposure Data

	This action by the Agency required pesticide registrants whose products have the potential for post-application worker exposure to generate data which would be used to quantify these exposures.  This effort initiated the current model for developing large datasets of non-dietary exposure data in a systematic manner (USEPA, 1995).

1998	Draft Series 875 – Occupational and Residential Exposure Test Guidelines, Group B – Post-application 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).

2007	FIFRA Scientific Advisory Panel (SAP) Review Of Worker Exposure Assessment Methods

	The exposure assessment methods used by the Agency were reviewed by the FIFRA Scientific Advisory Panel in January, 2007. The key factors that were discussed included identifying a need for additional pesticide handler exposure data, considerations with regard to dermal sampling methods in comparison to biological monitoring, and development of a research plan based on stratification of handler tasks and statistical considerations.

ARTF Activities and Achievements

The ARTF was organized to address the requirements of PR Notice 94-9 issued on December 9, 1994 prior to the issuance of the 1995 DCI.  Since its formation, the ARTF has served as the major conduit through which farmworker post-application exposure monitoring data have been generated.  The approach which has been taken by ARTF, developed in consultation with the regulatory agencies, is based on the monitoring of selected activities that are used to represent similar types of activities as defined by the crop, the ergonomics of the post-application activity, and the potential for contact with pesticide residues.  For example, it is believed that harvesting oranges and apples have similar exposure potential because both crops are grown similarly in orchards making the physical act of harvesting and potential for contact with foliar residues essentially the same.  Due to these similarities it would then be expected that each activity’s TC would be similar since it is a measure of potential contact with pesticide residue.

Estimated at approximately 4500 combinations, there are clearly a wide range of crops and hand labor tasks.  Generating data and TCs for each crop-activity combination would be prohibitively expensive and unnecessary given the reasonable similarities between crops and activities.  Consequently, a number of groupings, or clusters, were created to represent naturally similar crop-activity types.  These groupings and their associated transfer coefficients represent all hand labor tasks in agriculture that would be routinely used to calculate risks and is the major work product of the ARTF.  There were many important activities and achievements since the formation of the ARTF and the regulatory consultation process which lead to the ultimate decision to represent hand labor exposure potentials in agriculture through a series of groupings.  Ultimately, it was agreed that through the conduct of 47 studies that contain 824 exposure monitoring days, sufficient information could be produced which would satisfy the Agency’s data needs.  These ARTF activities and achievements include:

Development and use of standard monitoring protocols, dosimetry methods, DFR measurement methods, and reporting guidance which allowed for a systematic approach for collecting monitoring data.

Implementation of a survey of experts (e.g., extension agents, agronomists, etc.) elucidating the types of hand labor activities conducted in agriculture and the nature of those activities on a national basis.  A total of 578 questionnaires were collected which focused on 90 important crops as defined by the census of agriculture in the United States and Canada (ARTF, 1996).

Implementation of a survey of growers, based on information gained from the expert survey, on a national level to further elucidate the types of hand labor activities conducted in agriculture and the nature of those activities on a national basis.  A total of 3,850 questionnaires were completed resulting in 11,101 responses involving data on contact with foliage or fruit from at least one of 82 different post-application activities in 96 different crops.  Data were collected in each of 16 agricultural regions in the United States and Canada representing different crop heights and amounts of foliage.  The total number of growers who were contacted in this survey was 39,424 (Thompson, 1998).

Establishment of a clustering scheme to group activities with similar exposure potential based on a statistical analysis of the grower survey responses related to crop maturity and the ergonomics of hand labor activities of interest.

Completion of a peer review of the project (Ross, 2001).

Revision of the clustering scheme to an agronomically-based framework based on the comments of the peer review process (Bruce, et al 2006; Bruce and Korpalski, 2008).

Development of a proposal to use collected exposure information and information pertaining to specific hand labor activities conducted in agriculture to assign transfer coefficients for exposure assessment purposes to all hand labor activities conducted in agriculture.  This includes a proposed list of no/low contact activities which are typically excluded from routine exposure assessment.

In summary, researchers recognized a potential concern with respect to occupational exposure as early as the 1950s and began to develop methods to quantify these exposures.  The Agency began intense activity in this regard in the early 1980s with the development of the first guidelines focused on collecting exposure monitoring data to quantify farmworker exposures and the development of the first regulatory risk assessments focused on this population.  The studies which were collected at first focused on major exposure activities which impacted large numbers of workers such as fruit tree harvesting, but were insufficient to address the wide variety of hand labor activities in agriculture.  As a result, the Agency issued a DCI that required industry to develop data that could be used to evaluate the exposure potential for all hand labor activities in agriculture.  This resulted in the formation of ARTF.  Since its inception, the ARTF has been the primary data generator of monitoring data used by the Agency to quantify farmworker post-application exposures in United States and Canadian agriculture.  The results of this effort are the primary focus of this meeting and the efforts by the ARTF will be an integral part of the discussions at the SAP meeting.

Exposure and Risk Assessment Overview

  	The overall goal of the Agency is to have a scientifically sound method for conducting assessments of post-application farmworker exposure.  A large amount of information has been collected, a number of studies have been conducted, and many discussions have occurred to develop an approach aimed at providing for a consistent manner in which the Agency conducts post-application risk assessments.

This section provides a description of the process used to calculate transfer coefficients used to estimate post-application exposures for various tasks and then illustrates how they are used in risk assessments completed by the Agency.  Section 3.1 below illustrates how a transfer coefficient is calculated from a single monitoring study, using tobacco harvesting as an example.  Section 3.2 describes how transfer coefficients would be used generically in typical Agency exposure and risk assessments.  The example in Section 3.2 illustrates the use of several transfer coefficients, all calculated in a manner similar to that described in Section 3.1.

Calculation of Transfer Coefficients

This section illustrates how a transfer coefficient is calculated for a single hand labor activity using monitoring data.  Transfer coefficients are applied generically in the Agency’s risk assessment process based on the premise that they are reasonably similar for a specific activity and crop combination regardless of chemical-specific information.  This “genericness” is historically rooted in occupational pesticide exposure assessment, but is discussed in more detail by the ARTF (Bruce and Korpalski, 2008; Bruce et al 2003).  The major benefit of using TCs both generically and to represent similar hand labor tasks in the form of a cluster is to save resources by avoiding repetitive requirements for similar chemical-, crop-, and task-specific farmworker exposure monitoring studies.  Given the amount of possible pesticide-crop-activity combinations, the Agency believes such an approach is reasonable.

Transfer coefficients are calculated based on actual exposure monitoring of individuals involved in a specific hand labor task.  The results obtained from exposure monitoring are used to determine total dermal exposure and normalized per hour worked.  A ratio is then derived between this value and a representative concentration of the available surface residue in the area where the farmworker conducted the activity (i.e., the residues that are not absorbed but rather are on the surface and available for transfer to an individual that may come in contact with the crop) – also known as dislodgeable foliar residue (DFR).  This ratio is the TC and is expressed as cm2/hr.  Expressed mathematically, the calculation of transfer coefficients is as follows:

TC (cm2/hr) = [Exposure (µg) ( time worked (hours)] ( DFR ((g/cm2)

Once a transfer coefficient (TC) has been calculated, it is then used to estimate the dermal exposure (DE) from any similar task performed on a similar crop for which dislodgeable foliar residue (DFR) data are available using the following equation:

DE (mg/day) = [(DFR (mg/cm2) x TC (cm2/hr) x Hr/Day]

Note that the residue measurement is not the “true” measure of what is available for transfer to the skin, it is a relative indicator of exposure based on the quantification of surface residues using a reproducible methodology for typical foliage (i.e., method of Iwata et al, 1977) or turf (i.e., modified California roller method of ORETF, 1998).  These specific techniques are used to measure the surface residue rather than quantifying the total residues contained both on the surface and absorbed into treated leaves because the methods are reproducible and reflect the surface residue which is of most interest.  Since it has been determined that DFR values can vary both for different pesticides as well as different pesticide formulations, DFR data are required for each individual pesticide product in order to determine a worker’s exposure to a specific pesticide product.

The use of transfer coefficients can be illustrated by the following example.  Consider two tomato fields for which the amount of chemical on treated leaf surfaces that can be rubbed off (i.e., can be dislodged) on the skin is the same.  One field has been treated with chemical A while the other field has been treated in a similar manner with chemical B.  If an individual harvests the same amount of tomatoes for the same amount of time in a day in each field, the individual would be expected to be subjected to similar exposures on the basis of this “genericness” premise.  The transfer coefficient would also be expected to be similar for each field and chemical because the ratio of exposure per hour to dislodgeable foliar residue would be the same.  If, on the other hand, this same individual were to perform another activity in those fields such as scouting for pests or vegetable tying, the resulting transfer coefficients would be different from that estimated for tomato harvesting because the different activities yield different exposures over the course of the day since they have a different amount of contact with the treated foliage.  As indicated above, the goal of this effort is to evaluate all possible hand labor tasks across agriculture and develop a library, or database, of transfer coefficients that can be used to assess exposure and risks for them.

In order to illustrate the details of the calculation of a transfer coefficient, an example has been prepared using the data which was collected in the ARTF field study which monitored workers while harvesting tobacco (ARF024) after the application of carbaryl (Klonne et al, 1999).  All details of the calculations and the field report for this example study are provided for illustrative purposes in Exhibits A and B, respectively.  In this study, eight (8) farmworkers were monitored from July 10-12, 1998 while harvesting tobacco in North Carolina.  The exposure of each worker was monitored on three different days after application while harvesting (i.e., 1, 2, and 3 days after application).  In this study the insecticide carbaryl was applied using a groundboom tractor at approximately 2 lb/acre on July 1 and again on July 9, the day before worker exposure monitoring began.  Each worker was monitored for approximately 6 hours on each day.

In this study, exposure monitoring was completed using passive dosimetry techniques which were reviewed by the FIFRA SAP most recently in January, 2007.  Dermal exposure to the body (i.e., minus hands, face, and neck) was measured using inner and outer whole body dosimeters (WBDs).  The outer dosimeters consisted of 100 percent cotton long-sleeved shirts and pants. The inner dosimeters were also 100 percent cotton but the garment was one piece.  The results obtained from the inner dosimeter represent residues which would deposit on an individual’s skin underneath their normal work clothing.  At the end of each monitoring day, each inner and outer dosimeter was sectioned for separate analysis into each of six (6) sections:  upper arms, lower arms, upper legs, lower legs, front torso, and rear torso.  Hand exposures were measured using detergent hand washes collected at restroom and eating breaks and at the conclusion of each monitoring day.  At each sample collection interval, workers scrubbed their hands together for 30 seconds while the field investigator poured 250 mL of 0.01 percent Aerosol( OT-75 (AOT) solution (sodium dioctyl sulfosuccinate in distilled water) over them into a bowl and the resulting solutions were collected.  Face and neck wipes were collected at lunch and again at the conclusion of each monitoring day using an 8-layer, 4x4 inch, 100 percent cotton gauze moistened with the AOT solution.  The results for each hand sample was added together to obtain a value for the entire exposure interval.  Similarly, results from the wipe samples were also composited.  Inhalation monitoring was also completed but these data are not included in the calculation of dermal transfer coefficients.

Dislodgeable foliar residues (DFRs) were collected using the Iwata (1977) method that involves obtaining leaf punch samples of a known surface area (i.e., typically 400 cm2 of area) then washing these punches in the same 0.01 percent Aerosol( OT-75 (AOT) solution (sodium dioctyl sulfosuccinate in distilled water) as is used for collecting the handwash samples described above.  The resulting wash solutions were then analyzed for carbaryl residues.  In this study, triplicate samples were collected at each interval.  Samples were collected 2 days prior to and on the day of the first application.  Samples were also collected on the day of the second application, as well as 1, 2, 3, 4, 5, 6, 7, 14, 22, 28, and 35 days after the second application (DAA).

	This study incorporated a series of analytical procedures to determine loss of residue from the sampling media under field conditions, how stable the residues were until analysis, and also how much of the collected residues could actually be extracted from the media and quantified.  Once these loss and stability rates were known they were used by investigators to make appropriate adjustments to residue levels quantified in the field monitoring samples.  In this study, both negative (to check for procedural contamination) and positive field control samples were generated.  Positive field controls of each sampling media were spiked with known quantities of analyte (in this case, carbaryl) then exposed to field conditions to ensure these samples are subjected to similar conditions as the media used to conduct the actual monitoring .  After monitoring periods were complete the controls were collected, stored, shipped, and analyzed concurrently with field samples.  The results for the field recovery samples are presented in   REF _Ref179643430 \h  \* MERGEFORMAT  Table 2  below.

Table   SEQ Table \* ARABIC  2 :  Field Recovery (%) by Sampling Media and Fortification Level

Sampling Day	Sampling Media Fortification Levels (ug)

	Outer Dosimeter	Inner Dosimeter	Face/Neck Wipe	Hand Wash	DFR

	Pants	Shirt

	500	10000	500	10000	5	1000	50	1000	500	10000	50	1000

1 DAA	69.9	94.7	70.3	105	53.2	93.6	91.3	99.4	111	103	111	114

	71.2	95.5	59.5	102	51.7	93.0	88.5	96.6	108	118	113	115

	72.9	93.6	58.4	106	49.2	94.0	88.5	94.5	109	125	114	110

2 DAA	73.7	97.9	63.3	98.6	64.4	94.5	89.2	98.4	108	120	91.4	115

	73.2	96.8	69.3	102	61.8	99.6	88.7	102	108	103	119	117

	73.2	96.3	68.2	100	59.6	98.0	87.5	105	106	118	118	123

3 DAA	66.8	102	55.7	97.1	47.2	96.0	87.0	106	108	112	111	115

	71.2	103	64.4	94.9	49.7	98.4	86.2	110	115	119	116	125

	71.5	105	56.2	91.7	44.1	106	87.1	111	115	115	112	119

Average	71.5	98.3	62.8	99.7	53.4	97.0	88.2	103	110	115	112	117

SD	2.2	4.0	5.6	4.7	7.0	4.1	1.5	5.8	3.2	7.6	8.2	4.7

Based on the results in   REF _Ref179643430 \h  \* MERGEFORMAT  Table 2  the investigators developed a series of correction factors to account for field fortification by correcting (i.e., dividing) each field measurement using the factors shown in   REF _Ref179643483 \h  \* MERGEFORMAT  Table 3  below.  Results were adjusted based on the average percentage recovery based on the corresponding residue levels in the field sampling media.  The correction/adjustment factor is applied depending on the magnitude of the resulting residue, with the threshold being the midpoint between each fortification level.  If the chemical residue magnitude is closer to the higher fortification amount, it is adjusted based on that level’s recovery.

Table   SEQ Table \* ARABIC  3 :  Adjustment Factors for Sampling Media Based on Level Found

Outer Dosimeter (ug)	Inner Dosimeter (ug)	Hand Wash (ug)	Face/Neck Wipe (ug)	DFR (ug)

Pants	Shirt

(5250

>5250

(5250

>5250

(503

>503

(5250

>5250

(525

>525

(525

>525

0.715	0.983	0.628	0.997	0.534	0.970	1.10	1.15	0.882	1.03	1.12	1.17

A summary of the dermal exposure monitoring is provided below in   REF _Ref179643502 \h  \* MERGEFORMAT  Table 4 .  All data have already been adjusted for field recovery.  Inner and outer dosimeters are summed across all body regions, and hand wash and face/neck wipes samples are totals for each day in the event multiple samples were taken.

Table   SEQ Table \* ARABIC  4 :  Summary of Dermal Exposure Monitoring

DAA	MU#	Worker ID	Results Adjusted for Field Recovery (ug)

	Total Outer Dosimeter	Total Inner Dosimeter	Hands	Face/Neck

1	1	A	129690	30150	3906	24.4

1	2	B	145770	28784	3334	27.1

1	3	C	155200	35670	4395	69.3

1	4	D	152690	26080	2943	18.5

1	5	E	186300	32820	4013	19.3

1	6	F	142720	38140	2672	30.4

1	7	G	198300	37980	2887	72.3

1	8	H	182500	27639	3086	21.4

2	9	A	60990	5528	6360	7.3

2	10	B	83970	6558	6690	9.5

2	11	C	59000	7789	6900	18.7

2	12	D	71810	7338	6960	2.2

2	13	E	63760	7919	9330	2.6

2	14	F	84470	7746	6160	8.6

2	15	G	53400	8974	6700	10.8

2	16	H	69290	6282	6160	7.5

3	17	A	90650	9679	5650	5.9

3	18	B	145490	12779	3400	13.3

3	19	C	96220	16939	5030	12.9

3	20	D	111790	9886	4910	3.6

3	21	E	99600	14838	6840	4.8

3	22	F	113870	19080	5370	16.6

3	23	G	103520	19894	5160	25.9

3	24	H	111090	15872	4190	7.4

The results for each day of DFR sampling are presented in   REF _Ref179643545 \h  \* MERGEFORMAT  Table 5  below.  Like the dermal exposure monitoring presented above, they are corrected for field recoveries.  Detectable residues were found in all samples collected in this study as long as 35 days after application (i.e., LOQ = 1 µg/sample).

Table   SEQ Table \* ARABIC  5 :  Summary of DFR Sampling

Applicationa	DAA	Total Residue for Each Daily Sample (ug)	Concentration for Each Daily Sample (ug/cm2)b	Overall Concentration (ug/cm2)

1	2	3	1	2	3	Avg.	GMc

1	0	2222.2	2170.9	2260	5.550	5.425	5.650	5.55	5.55

2	0	1649.6	1598.3	1860	4.125	4.000	4.650	4.25	4.25

2	1	1461.5	1940.2	2620	3.650	4.850	6.550	5.03	4.88

2	2	1675.2	2085.5	1910	4.200	5.225	4.775	4.73	4.73

2	3	1735.0	1957.3	1740	4.350	4.900	4.350	4.53	4.53

2	4	923.1	1222.2	1470	2.308	3.050	3.675	3.00	2.95

2	5	474.4	701.7	561	1.185	1.755	1.403	1.45	1.43

2	6	777.8	957.3	821	1.945	2.393	2.053	2.13	2.12

2	7	408.0	478.6	405	1.020	1.198	1.013	1.08	1.07

2	14	20.0	12.9	14.6	0.050	0.032	0.037	0.040	0.039

2	22	5.8	35.8	11.2	0.015	0.090	0.028	0.044	0.033

2	28	3.7	4.0	9.9	0.009	0.010	0.025	0.015	0.013

2	35	4.8	2.8	3.9	0.012	0.007	0.010	0.010	0.009

a Application 2 occurred eight days after Application 1

b Normalized for total surface area of leaf punches (400 cm2)

b GM = geometric mean

 The DFR samples, whose protocols attempt to estimate representative exposure sources by sampling different parts of the plant in different areas of a treated field, serve as the residue component of the ratio.  Whether the resulting transfer coefficient should be calculated using the arithmetic mean of the 3 daily samples or the geometric mean has been a frequent point of discussion.  It has been argued that the geometric mean DFR is the correct measure to use because DFR data are lognormally distributed (Korpalski, et al., 2005).  A predicted residue value for each day of exposure monitoring based on an exponential decay model can also be used, however, in most cases, regardless of the residue estimation (e.g., arithmetic mean, geometric mean, or predicted residue) the resulting transfer coefficients in most cases are not significantly different and do not have an impact upon exposure and risk estimates calculated using this methodology.

As described at the beginning of this section, a TC is simply the ratio of concurrently measured hourly exposure rate to the amount of residues on foliage measured from the area in which the exposed individual worked (i.e., represented by DFR data) and expressed in units cm2/hr.  Typically, TCs are calculated based on total body exposure under normal work clothing as is done in this example (i.e., hands, face/neck and all body regions under long sleeved shirts and long pants are included together).   Also in this example, as well as most other studies, transfer coefficients for each worker can be segmented for each body region since the dosimeters worn by the subjects in the tobacco harvesting study were sectioned and analyzed separately to represent different body regions (e.g., lower and upper arms provided separate samples).  The Agency does not tend to use the data in this manner since the Agency does not use TCs which reflect the use of gloves, aprons or other devices for farmworkers since they are not considered widely practical or their use enforceable to control the exposures of workers in treated fields.  Instead, REIs are established which preclude worker activities until residues dissipate to an acceptable level.  These levels are determined based on risk assessment results and also, under certain circumstances, by considering risks of exposure to the chemical relative to the benefits associated with its use as required under FIFRA.  Transfer coefficients for the tobacco harvesting study based on both the arithmetic mean and geometric mean DFRs are presented in   REF _Ref179643576 \h  \* MERGEFORMAT  Table 6 .

Table   SEQ Table \* ARABIC  6 :  Tobacco Harvesting – Transfer Coefficient Summarya

Days After 2nd

App.	Worker ID	Time Worked

(hours)	Exposure (ug)b	DFR (ug/cm2)	Transfer Coefficient (cm2/hr)c

	Total Inner Dosimeter	Hands	Face/Neck	Arith. Mean	Geo.Mean	Based On Arith. Mean DFR	Based On Geo.

Mean DFR

1	A	6.08	30150	3906	24.4	5.03	4.88	1110	1150

1	B	6.08	28784	3334	27.1	5.03	4.88	1050	1080

1	C	6.08	35670	4395	69.3	5.03	4.88	1310	1350

1	D	6.08	26080	2943	18.5	5.03	4.88	950	979

1	E	6.08	32820	4013	19.3	5.03	4.88	1200	1240

1	F	5.05	38140	2672	30.4	5.03	4.88	1610	1660

1	G	6.08	37980	2887	72.3	5.03	4.88	1340	1380

1	H	6.08	27639	3086	21.4	5.03	4.88	1010	1040

2	A	6.22	5528	6360	7.3	4.73	4.73	404	404

2	B	6.22	6558	6690	9.5	4.73	4.73	451	451

2	C	6.22	7789	6900	18.7	4.73	4.73	500	500

2	D	6.22	7338	6960	2.2	4.73	4.73	486	486

2	E	6.22	7919	9330	2.6	4.73	4.73	586	586

2	F	6.22	7746	6160	8.6	4.73	4.73	473	473

2	G	6.22	8974	6700	10.8	4.73	4.73	533	533

2	H	6.22	6282	6160	7.5	4.73	4.73	423	423

3	A	6.43	9679	5650	5.9	4.53	4.53	526	526

3	B	6.43	12779	3400	13.3	4.53	4.53	556	556

3	C	6.43	16939	5030	12.9	4.53	4.53	755	755

3	D	6.43	9886	4910	3.6	4.53	4.53	508	508

3	E	6.43	14838	6840	4.8	4.53	4.53	744	744

3	F	6.43	19080	5370	16.6	4.53	4.53	840	840

3	G	6.43	19894	5160	25.9	4.53	4.53	861	861

3	H	6.43	15872	4190	7.4	4.53	4.53	689	689

a Detailed calculations and the field report for this study are available as mentioned above in Exhibits A and B, respectively.

b Based on the sum of exposure across all body regions (Total Inner Dosimeter + Hands + Face/Neck).

c Transfer Coefficient (cm2/hr) = [Exposure (ug) ( time worked (hours)] ( DFR ((g/cm2).

Risk Assessment Approach

This section illustrates how the Agency uses transfer coefficients in its risk assessment process to predict exposures associated with the hand labor activities required to produce the crops where a chemical is used.  The goal in Agency risk assessments for farmworkers is to ensure that a sufficient period of time has elapsed between application and worker reentry such that exposures through contact with foliage are below the Agency’s threshold of concern.  This requires the Agency to define the timeframe it takes for residues to decline to acceptable levels for those who enter previously treated fields or areas (e.g., greenhouses) to work.  This is done by coupling information about the dissipation rate of a chemical (obtained by measuring DFRs at varying amounts of time following treatment with a pesticide) with the TCs for the suite of activities and crops that are associated with the use of a particular chemical.  The scope of such assessments is typically defined by the labeled uses of the particular pesticide being evaluated.  In some cases, all labeled uses are considered while in other cases only a few or even a single use pattern is considered depending upon the nature of the action being developed by the Agency.  For the purposes of this discussion an example is provided below for an assessment that has many possible hand labor tasks which illustrates how hand labor tasks are grouped together and evaluated using their respective transfer coefficients.

The Agency uses exposure scenarios to categorize the kinds of exposures that occur related to the use of a chemical.  The use of exposure scenarios as a basis for exposure assessment is very common as described in the U.S. EPA Guidelines For Exposure Assessment (U.S. EPA; Federal Register Volume 57, Number 104; May 29, 1992).  The steps involved in completing a post-application exposure assessment for farmworkers based on this approach include:

Defining the use pattern;

Assembling and analyzing the DFR data;

Specifying the  Crop Groups and Hand Labor Activities;

Calculating Dermal Exposures;

Calculating Dermal Risks;

Presenting the Risks; and

Characterizing the Risks

Each of these steps is described in more detail below.

Step 1 – Defining the Use Pattern:  For farmworkers, the process first entails defining how a particular pesticide is used.  This includes determining what crops it is used to treat, the timing of the applications in each crop relative to crop maturity and pest pressures, the application rates, and what hand labor activities are needed and when they are performed to produce that crop in a commercial context.

Step 2 – Assembling and Analyzing the DFR Data:  The next step in the assessment process is to assemble the available data for quantifying residue dissipation based on the dislodgeable foliar residue (DFR) method of Iwata (1977).  In this technique, surface residues are collected from leaf samples of known surface area using a soap solution which is then analyzed and the results are typically presented in units of (µg/cm2).  In most cases, empirical DFR data are available for assessments but in some cases a screening level approach for defining DFR values from application rates is used to provide those values for assessment purposes.  The dissipation kinetics for DFR or other residues are typically quantified based on a semi-log regression of the empirical dissipation data using a commercial spreadsheet linear regression function.  Half-lives are calculated using the algorithm (T1/2 = - ln 2/slope).  The results of these analyses are used to calculate best fit concentrations over time using the following pseudo-first order equation:

 EQ C\S\do4(envir(t)) = C\S\do4(envir(0))e\S\up4(PAI\S\do4((t))*M)

Where:

Cenvir(t) = dislodgeable foliar or turf transferable residue concentration (μg/cm2) that represents the amount of residue on the surface of a contacted leaf surface that is available for dermal exposure at time (t);

Cenvir(o) = dislodgeable foliar or turf transferable residue concentration (μg/cm2) that represents the amount of residue on the surface of a contacted leaf surface that is available for dermal exposure at time (0);

e =natural logarithms base function;

PAIt = post-application interval or dissipation time (e.g., days after treatment or DAT); and

M = slope of semi-log regression [ln(Cenvir) vs post-application interval (PAI)].

Step 3 – Specifying the Crop Groups and Hand Labor Activities:  This activity is a key focus of this FIFRA SAP meeting because the Agency desires to move beyond its interim approach (U.S. EPA, 2000) for identifying hand labor activities and crop groups to a final approach which uses all of the available data.  This step involves identification of specific hand-labor tasks which are likely to result in exposure to the pesticide being assessed (i.e., ~ 4500 crop/activity combinations have been identified).  To facilitate consistency, the Agency has developed a series of general scenario-based descriptions for tasks in order to categorize them for risk assessment purposes.  Common examples include: harvesters, scouting, crop maintenance tasks (e.g., irrigating, hoeing and weeding), and turf maintenance (golf course mowing and sod harvesting).

A number of factors dictated that an agronomic approach be used as the basis for this categorization followed by further delineation based on the amount of exposure associated with the activities associated with the crops contained in each agronomic group (see Bruce and Korpalski, 2008 for more information).  For illustrative purposes, risk values are presented for a hypothetical chemical used in the following agronomic groups:

Field/row crops (e.g., beans, corn, peanuts, peas, sorghum, sunflowers);

Tobacco;

Cut flowers (e.g., floriculture crops);

Vine/trellis (e.g., blackberries, blueberries, grapes, kiwi, raspberries); and

Nursery crops (e.g., container, ball, and burlap ornamentals).

Within each agronomic group, a variety of cultural practices are required to maintain the included crops.  These practices are varied and typically involve light to heavy contact with immature plants as well as with more mature plants.  Due to the variety in the hand labor tasks required to produce these crops, different transfer coefficients are used to predict the range of exposures within each agronomic group.  This hypothetical matrix of agronomic groupings and associated transfer coefficients within each group represent the concept of a library of transfer coefficients desired by the Agency for future purposes.

It should also be noted that there are some tasks that have been identified in agriculture which are either highly mechanized or are otherwise conducted in a manner that is believed to result in little or no exposure as a result of contact with treated foliage. For these activities, the need for assessments is determined on a case-by-case basis.  In those cases where the activities are believed to result in exposure, the transfer coefficient/foliar contact approach described throughout does not apply and other methods are used to address them (e.g., soil residue levels).  However, most are treated as having a negligible potential for exposure that do not warrant a quantitative evaluation of risk.

Step 4 – Calculating Dermal Exposures:  The next step in the risk assessment process is to calculate dermal exposure on each individual post-application day and crop/activity combination of concern after application using the following equation:

DE(t, task) (mg/kg-day) = [(DFR(t) (mg/cm2) x TC(task) (cm2/hr) x Hr/Day]/BW

Where:

DE(t, task) =	Daily exposure or amount deposited on the surface of the skin at time (t) attributable for activity in a previously treated area, also referred to as potential dose (mg ai/day);

DFR(t) =	Dislodgeable foliar residues at time (t) where the longest duration is dictated by the decay time observed in the studies (µg/cm2), analysis is to be completed for each crop of concern;

TC(task) 	=	Transfer Coefficient (cm2/hour) for each crop-activity combination;

BW	=	Body weight (kg); and

Hr/day	=	Exposure duration meant to represent a typical workday.

Step 5 – Calculating Dermal Risks:  Once normalized daily exposure values are calculated, the calculation of daily absorbed dose (if required based on the endpoint) and the resulting risk estimates (i.e., typically expressed as margins of exposure or MOEs) are completed by comparing exposure levels to the point of departure (e.g., a no observed adverse effect level or NOAEL determined in an appropriate animal toxicity study) using the following equation:

MOE(dermal, t,task) = POD (mg/kg/day)/DE(t, task) (mg/kg/day)

Where:

MOE (dermal, t, task) = 	Margin of exposure; used by the Agency to represent risk or how close a chemical exposure is to being a concern (unitless).  These values are calculated based on an exposure estimate for each day after application and for each task of concern;

DE (t, task) = 	Daily exposure or amount deposited on the surface of the skin at time (t) attributable for activity in a previously treated area, also referred to as potential dose (mg ai/day); and

POD = 	Point of departure determined by the toxicity of a chemical, typically the dose level in a toxicity study, where no observed adverse effects occurred (NOAEL) in that study or the lowest dose level where an adverse effect occurred (LOAEL) in the study (mg pesticide active ingredient/kg body weight/day).

Step 6 – Presenting the Risks:   An example showing risk results for a chemical used on many crops with many associated hand labor activities is presented in   REF _Ref179643716 \h  \* MERGEFORMAT  Table 7 . The results are presented in two different manners that could be useful for risk managers.  They include “MOE Day 0” which is a measure of the risks associated with a specific crop/hand labor activity if it was conducted on the actual day of application.  In this example, the MOE on the day of application for a high exposure activity such as harvesting cut flowers is 11 and the desired target MOE is 100 which indicates that hand harvest should not occur in cut flowers until time passes to allow residues to dissipate to appropriate levels.

The other type of result – “Days for MOE≥UF” – specifies the number of days it would take for residues to dissipate to levels that would not be of concern to the Agency for a specific crop group/hand labor activity combination (i.e., where the MOEs for each crop group/TC combination are ≥100).  Within each crop group there are a number of activities required to produce the crop and these activities which result in a range of exposures (e.g., hand harvesting may be considered to result in very high exposures while scouting may result in low exposures).  For ease of presentation, general terms from “very low” to “very high” have been used to represent these trends which in effect reflect the varied TCs within each crop group.  This is easier than attempting to present each range of TC values numerically because there is such a wide range of values across all crop groups.  Based on this example, it is clear that there are risk concerns on the day of application and that time is required, based on the risk estimates, for residues to dissipate prior to allowing workers to perform tasks.

Table   SEQ Table \* ARABIC  7 :  Example Summary of Farmworker Risks

Crop Group

Result Type

Results For Range of TCs Applicable To Crop Group

Low

Medium

High

Very High

Field/Row Crops

MOE Day 0

982

65

39

NA

≥ UF

0

3

5

NA

Tobacco

MOE Day 0

411

32

21

NA

Days For MOE > UF

0

6

8

NA

Cut Flowers

MOE Day 0

30

18

11

NA

Days For MOE ≥ UF

7

9

12

NA

Vine/trellis

MOE Day 0

147

74

15

7

Days For MOE ≥ UF

0

2

11

14

Nursery/

Ornamentals

MOE Day 0

669

421

184

NA

Days For MOE≥ UF

0

0

0

NA

NA = No activity in the category for the particular crop group.

	Step 7 – Characterizing the Risks:  In summary, when the Agency conducts a pesticide risk assessment, it considers all applicable tasks related to the production of all crops listed on product labels for the pesticide being evaluated.  In most cases, the exposures related to these hand labor tasks are quantitatively assessed using the approach outlined above where the applicable exposure rates (i.e., averages are used), defined by the selected transfer coefficients, are used to represent agronomically similar activities.  In other cases activities have been identified which are thought to have a negligible exposure potential associated with them.  These are assessed qualitatively as described above under the criteria outlined in the Agency’s Worker Protection Standard (40CFR170) and are discussed in more detail in Section 4 below.  The purpose of this section has been to illustrate these processes so that the discussions of transfer coefficients and related topics in this meeting can be more informed.

Finally, a brief overview of how these risk estimates are considered by the Agency is warranted.  In the regulatory process used by the Agency as specified under FIFRA, risk assessments for occupational populations are provided to risk managers to inform them of the risks associated with the use of a particular chemical.  In this case, the risk assessments are used in the determination of REIs.  These risk assessments along with other factors, including the benefits associated with the use of a chemical, are considered in the determination of REIs as dictated by the requirements of FIFRA.  How this process works varies depending upon the predicted risks and the benefits associated with the use of the particular chemical on the crop.  In many cases, the values predicted in the risk assessments are used directly for determining REIs.  But as described above, a risk-benefit based decision can also be made depending on the circumstance.

For example, if a risk assessment predicted that worker risks do not achieve the target MOE until 7 days after application (e.g., MOEDay 7 = 110 where risks of no concern are for MOEs > 100) but a critical hand labor operation is necessary for crop production 3 days after application (e.g., bud thinning) where the MOEDay 3 = 65 a risk manager could opt to define the REI as 3 days, depending on the benefits resulting from this shorter REI.  Many factors would be considered in such a decision including the severity of the toxic effect which could occur as a result of the exposure, the toxicological dose response curve associated with the exposure, the frequency of the hand labor activity, how critical the activity is relative to the production of the crop, and the economics of cultivating the crop under current practices or possible alternative practices.  This could also mean that benefits could be interpreted differently for a chemical used on the same crop in different geographical regions where pest pressures differ.

Topics for SAP Consideration

	This section presents background information on the specific topics that are included in the charge to the panel.  The Agency has worked very closely with its regulatory partners (PMRA and DPR) providing review of and consultation to the ARTF during the development of field monitoring protocols, conduct of field studies, data analysis, as well as other functions.  The specific products of the ARTF are referenced wherever possible to avoid duplication with any additional documentation or summary that could be developed.  ARTF has produced and submitted to the record a document which can be used in conjunction with the information included herein to define what information is available and where it can be located (Bruce and Korpalski, 2008).

	There are three basic areas of post-application farmworker exposure assessment for which the Agency is seeking guidance from the panel.  These include:  (1) the identification of activities for consideration in the assessment process; (2) grouping of similar activities for assessment purposes including no/low contact activities; and (3) consideration of workday duration in exposure calculations (Sections 4.1, 4.2, and 4.3, respectively).

Identification of Farmworker Activities

One of the major efforts which the Agency, its regulatory partners, and the ARTF have engaged in is the identification of all of the activities which occur in agriculture that are necessary for the production of a crop.  The other major aspect of this process involved distinguishing between those hand labor activities that have routine, substantive exposures associated with them and those activities which have a negligible exposure potential associated with them.  Each aspect is addressed in this section.

The identification of all activities in agriculture which are needed to produce a crop was an extensive process that involved a significant amount of internal consultation and outside consultation with those involved in agriculture and other reliable sources such as crop profiles, agronomic texts, and Agency guidance for other types of data requirements such as residue chemistry.  The major focus of this effort was based on the following sources of information:

United States Department of Agriculture

Staff;

Regional IPM (Integrated Pest Management) centers;

Census of agriculture; and,

Crop profiles.  An example excerpt from a crop profile for strawberry production in Florida as it pertains to worker activities is provided below.

ARTF Expert Survey (ARTF, 1996):  This document presents a survey of agronomic experts (e.g., extension agents and university researchers) about what activities are required for the production of crops, how frequently they occur, the nature of the exposures, and the degree of mechanization.

ARTF Grower Survey (Thompson, 1998): This document presents a survey of growers about what activities are required for the production of crops, how frequently they occur, the nature of the exposures, and the degree of mechanization.

The grower and expert surveys completed by ARTF (Thompson, 1998 & ARTF, 1996) provided much of the information that was used to identify the specific activities that occur in agriculture.  These surveys also include many types of critical ancillary information useful to the Agency when developing exposure assessments and characterizing the results (e.g., frequency of the activity by crop and location).  Examples of the kinds of information that are included in this survey are presented in   REF _Ref179644409 \h  \* MERGEFORMAT  Figure 1  and   REF _Ref179644417 \h  \* MERGEFORMAT  Figure 2 .    REF _Ref179644409 \h  \* MERGEFORMAT  Figure 1  presents a partial list of the activities which were identified in the survey.  Please refer to the specific documents which are included in the background information for this meeting for more information pertaining to the survey design, how individuals were identified for participation, information on response rates, geographical regions and crops covered in the surveys, and more detailed information on activities.

Figure   SEQ Figure \* ARABIC  1 :  Example of Farmworker Activities Reported in Grower Survey (Excerpt from Table 8 of Thompson, 1998)

Similarly,   REF _Ref179644417 \h  \* MERGEFORMAT  Figure 2  presents an example of crops which were identified, the regions where questionnaires were collected to address issues pertaining to those crops, and the numbers of questionnaires received related to the particular crop by region.  It should be noted, which is explained below, that growers were also asked about exposures and the responses are reflected in the columns with the term “contact” in them.  The purpose of using this table to illustrate the survey is because it shows the number of responses associated with the information pertaining to each crop.

Figure   SEQ Figure \* ARABIC  2 :  Example of Crops, Regions, and Sample Size Table Reported in Grower Survey (Excerpt from Table 5 of Thompson, 1998)

The ARTF conducted a survey of experts (e.g., extension agents, agronomists, etc.) to elucidate the types of hand labor activities conducted in agriculture and the nature of those activities on a national basis.  In this survey, a total of 578 surveys were collected which focused on 90 important crops as defined by the census of agriculture in the United States and Canada (ARTF, 1996).  In the grower survey (Thompson, 1998) a total of 3,850 questionnaires were completed resulting in 11,101 responses involving data on contact with foliage or fruit from at least one of 82 different post-application activities in 96 different crops.  Data were collected in each of 16 agricultural regions in the United States and Canada representing different crop heights and amounts of foliage.  The total pool of growers who were contacted in this survey was 39,424.  The segmentation of the survey into agronomically distinct regions is similar to that used for defining residue trials that are routinely required by the Agency.    REF _Ref179644363 \h  \* MERGEFORMAT  Figure 3  provides an illustration of the regional segmentation required in defining residue chemistry studies.  More information is available pertaining to the precedent of using agronomy to establish sample design criteria (U.S. EPA, 1996).

Figure   SEQ Figure \* ARABIC  3 :  Regions Specified In Residue Chemistry Requirements

	The sources described above represent the major resources used in defining a list of hand labor activities considered by the Agency in its assessments.  The Agency has also attempted to independently correlate the conclusions drawn based on this information with other independent sources of information such as the National Agricultural Workers Survey (NAWS) by the United States Department of Labor (U.S. DOL, 2005).  For example, the grower survey (Thompson, 1998) indicates that strawberry harvest in one of the regions is sustained over a 6 month period and the frequency of harvest during that 6 month period ranges from about 12 to 20 days per month.  Information from the expert survey and also the crop profile summary excerpt provided above also indicate strawberries are harvested approximately every 3 days during the season. NAWS also indicates that activities completed in fruit and nut production account for the largest percentage of farmworker jobs and harvesting is a principal task for farmworkers.

	NAWS is conducted on a routine basis every year to define many characteristics of the farmworker population.  The results of this survey and other sources have been used to assist the Agency in characterizing the farmworker population in the United States and it is also important for framing issues at this meeting because the population is so large and the activities it is engaged in are quite diverse.  The total number of farmworkers is estimated to range from between 3.5 and 4 million people.  The information included in NAWS is obtained directly from farm workers through face-to-face interviews.  Since 1988, when the survey began, nearly 50,000 workers have been interviewed.  Depending on the information needs, between 1,500 and 4,000 workers are interviewed each year.  The most recent survey results available at the referenced website (for 2001 through 2002 collected in 6472 interviews) include:  78 percent were born outside the United States; the average age is 33 years; 79 percent are men; on average most worked for a single farm employer and were directly hired by that employer.  Results also indicated in 2001 to 2002, the average work week was 42 hours, compared to 38 in 1993-1994.  In 2001-2002, approximately one quarter each worked less than 35 hours, between 35 and 40, 41 and 49, and 50 hours or more.  The primary crop type where individuals were employed were fruit and nuts (34 percent), vegetables (31 percent) followed by tasks in horticulture (18 percent).  Taking a crop from field to market encompasses a wide variety of tasks that hired crop workers perform.  In 2001-2002, at the time of their interview, 16 percent of the workers were performing pre-harvest tasks, such as hoeing, thinning, and transplanting, 30 percent were doing harvest tasks, and 9 percent were involved in post-harvest activities, such as field packing, sorting, and grading.  Seventeen percent of the crop workers were performing technical production tasks, such as pruning, irrigating, and operating machinery.  Examples of the types of information which the Agency considered in identifying hand labor tasks of concern are included in   REF _Ref179644200 \h  \* MERGEFORMAT  Table 8  and   REF _Ref179644209 \h  \* MERGEFORMAT  Table 9  (U.S. DOL, 2005).

Table   SEQ Table \* ARABIC  8 :  Primary Crop at Current Farm Job (Table 5.1 in U.S. DOL, 2005)

Primary Crop Type	Percentage of Hired Crop Workers

Total	100%

Fruit & nut	34%

Vegetable	31%

Horticultural	18%

Field	14%

Miscellaneous	4%

Note:  Sum of portions is not equal to 100 percent because of rounding.

Table   SEQ Table \* ARABIC  9 :  Primary Task at Current Farm Job (Table 5.3 in U.S. DOL, 2005)

Primary Task Type	Percentage of Hired Crop Workers

Total	100%

Pre-harvest	16%

Harvest	30%

Post-harvest	9%

Technical Production	17%

Other	27%

Note: Sum of portions is not equal to 100 percent because of rounding.

Based on the above information a list of all possible agricultural activities totaling approximately 4500 crop-activity combinations was produced and is included as Exhibit C.  A description of some of these agricultural activities to provide context for the next discussion in Section 4.2 on the ARTF data and database grouping of crops and activities is provided below.

Harvesting:  gathering of a commodity or fiber material from production fields, vineyards, or groves/orchards at desired time (e.g., fruit ripening).  May be completed by hand; by hand with tools such as knives or bins; or it may be partially (e.g., mules) or fully mechanized (e.g., combines for grain harvest).

Thinning:  hand labor activity intended to remove excess blossoms or immature fruit intended to produce larger, more robust fruits, vegetables, or flowers by reducing competition within a plant for food resources.

Pruning: activity intended to remove errant or diseased plant components, can be done to eliminate competition for food resources within a plant, to manage a plant canopy, or aesthetic reasons (e.g., Christmas trees), this activity is mostly done by hand with an appropriate tool but it can be mechanically assisted as well.

Topping: Form of pruning intended to impede vertical growth in a plant, often completed in orchards to limit tree growth which forces better fruit yields and allows easier access for hand labor activities such as harvest.

Propping:  Physical act of adding a support to tree limbs which are heavily laden with fruit to aid them in supporting the weight of the fruit and keep them from breaking.

Turning:  Physical act of turning certain elements of a plant of interest in the field to aid in better growth potential, aid in fruit ripening, or to prevent rot, this activity is more prevalent in crops such as melons and grapes, for example watermelons would be turned to ensure more even ripening and to prevent rot.

Tying:  Physical act of tying heavy fruit laden plants to keep them from breaking, most often completed on a commercial scale in row vegetable crops such as tomatoes where production is in tarped raised beds and the plants are tied to a central stake support system running up and down the rows lengthwise.

Scouting:  Physical act of entering a field to examine the crop in order to ascertain the degree of pest pressure, weed infestation levels, disease levels or water/food resource needs, this activity often involves direct immersion and contact with plant canopies over extended periods.

There are many other activities which were identified in this process including grafting, propagating, staking, pinching, detasseling (corn), suckering, and grape girdling.  Their intent is similar to the others described above and they essentially fall into categories which can be described as propagation, canopy management, nutrient management, or crop optimization.  It is clear that agriculture is a very dynamic industry and production practices may evolve to improve production yields, minimize costs, adjust to market conditions or respond to water/resource issues or changing pest and disease pressures.  Given this premise, the Agency intends to constantly be engaged with its partners and impacted stakeholders to keep apprised of changing conditions in agriculture so that its assessments reflect current cultural practices.  This could, in the future, result in activities being dropped from the routine assessment process or conversely being added as new ones are identified.

Crop-Activity Clusters:  Summary and Analysis

Given the large number and variety of farmworker crop-activity combinations identified and the impracticality of developing individual TCs for each and every combination, the regulatory agencies consider the method of grouping TCs for various hand labor activities based on an agronomic framework appropriate for the purposes of occupational post-application exposure assessment.  The agencies also believe it is reasonable to represent the exposures of those groups with selectively developed exposure monitoring data that can serve as an indicator for that group.  These groupings (or clusters for the purposes of this document) are based on the premise that each has a similar pattern of exposure and magnitude of exposure because of certain similarities (e.g., crop-type, level of maturity, ergonomic factors, etc).

The ARTF proposed an agronomically-based clustering scheme for hand labor activities which occur in agriculture (Bruce et al, 2006).  This section presents a review of this approach, developed with input from the various regulatory agencies.  The goal is not to provide a complete reproduction of the approach (which can be found in Bruce et al, 2006) but to provide a summary overview of the process and to identify possible alternatives to the ARTF proposals where appropriate.

Farmworker Exposure Monitoring Data

The exposure monitoring data which were generated as a result of the activities of ARTF are summarized below.  The conduct of each study has been reviewed by the Agency and all have been found to be of sufficient quality for use in risk assessment.  As previously described, initial identification of potential crop and activity groupings were derived mainly from expert agronomic opinion, but were also quantitatively analyzed by the ARTF using the completed data from various studies (Bruce et al, 2006).  This section examines the proposed clustering of the various ARTF studies both from an agronomic (expert-opinion) based perspective and quantitative (i.e., statistical) perspective.  For this statistically-based comparison, transfer coefficients for total dermal exposure (TDE) were used as quantitative basis of comparison.  The data used in these analyses are supplied as Exhibit D.

The ARTF database consists of a total of 47 studies each representing a single crop and a single activity.  Each study has been either combined with the results of others or it has been used alone to represent a cluster of hand labor activities in the exposure assessment process (Bruce et al, 2006).  This practice allows for estimating farmworker exposure during activities in crops not explicitly monitored.    REF _Ref179644639 \h  \* MERGEFORMAT  Table 10  provides a summary of the ARTF studies that make up the database.

Table   SEQ Table \* ARABIC  10 :  Summary of Reentry Exposure Monitoring Studies in ARTF Database

Study Code	Crop	Reentry Activity	N

ARF009	Sweet corn, fresh	Scouting	24

ARF010	Sweet corn, fresh	Harvesting, hand	17

ARF011	Cauliflower	Scouting	24

ARF012	Cauliflower	Harvesting, hand	16

ARF020	Blackberry	Harvesting, hand	15

ARF021	Bean/Peas, dry	Scouting	15

ARF022	Sunflower	Scouting	14

ARF023	Grape, raisin/table	Scouting	15

ARF024	Tobacco	Harvesting, hand	24

ARF025	Apple	Harvesting, hand	15

ARF028	Orange	Harvesting, hand	15

ARF033	Olive	Pruning, hand	15

ARF035	Sod	Harvesting, slab (mechanical)	17

ARF036	Potato	Irrigation	15

ARF037	Cabbage	Weeding, hand	13

ARF039	Floriculture crops	Pinching	15

ARF041	Orange	Harvesting, hand	15

ARF042	Grapefruit	Harvesting, hand	15

ARF043	Nursery crops	Pruning, hand	15

ARF044	Nursery crops	Harvesting, hand	15

ARF045	Cucumber	Harvesting, hand	15

ARF047	Apple	Pruning, hand	15

ARF048	Grape, wine	Harvesting, hand	15

ARF049	Squash, summer	Harvesting, hand	24

ARF050	Cabbage	Harvesting, hand	15

ARF051	Tomato, fresh	Tying	15

ARF055	Floriculture Crops	Harvesting, hand	31

ARF057	Golf Course	Maintenance	44

AR1001	Strawberry	Harvesting, hand	20

AR1002	Peach	Harvesting, hand	18

AR1003	Apple	Thinning	20

AR1004	Cotton	Harvesting, Mechanical	24

AR1006	Cotton	Weeding, hand	12

AR1008	Cauliflower	Scouting	5

AR1014	Peach	Harvesting, hand	23

AR1015	Grape, raisin/table	Turning	30

AR1016	Almond	Harvesting, mechanical	10

AR1017	Peach	Propping	10

AR1018	Cotton	Weeding, hand	10

AR1019	Beans/Peas, dry	Weeding, hand	10

AR1020	Grape, raisin/table	Harvesting, hand	10

AR1021	Peach	Harvesting, hand	10

AR1022	Grape, raisin/table	Harvesting, hand	10

AR1023	Tomato, fresh	Harvesting, hand	10

AR1024	Strawberry	Harvesting, hand	10

AR1025	Cotton	Scouting	17

AR1027	Tomato, fresh	Scouting	3

Total = 47

	Total = 765

Each row in this table represents a single monitoring study and (N) represents the number of exposure monitoring units completed within that study.  Individuals in many cases have been monitored more than once in a study but on different days after application.

The 47 studies were then grouped by the ARTF into clusters based on both qualitative professional judgment and quantitative analysis (Bruce, et al, 2003 and 2006).    REF _Ref179644678 \h  \* MERGEFORMAT  Table 11  below summarizes the ARTF organization of the studies into proposed clusters.  Each cluster code is based on the corresponding exposure monitoring studies and generally refers to the crop-type and activity monitored.  As shown, multiple monitoring studies (or, in many cases, one study) are being used to represent a group of crops and activities.  A more detailed description of the activities and crops are detailed in Exhibit C.  The process used to develop the cluster arrangement is presented and discussed in Section 4.2.2 below.

Table   SEQ Table \* ARABIC  11 :  Organization of ARTF Studies by Cluster

ARTF Study	Proposed Cluster

Code	Crop	Activity	Code	Description

ARF045	Cucumbers	Hand Harvesting	HH	Hairy-leaf field crops:  hand harvesting and similar contact activities

ARF049	Summer Squash	Hand Harvesting

ARF024	Tobacco	Hand harvesting	HHt	Hairy-leaf (Tobacco):  hand harvesting and canopy management

ARF022	Sunflowers	Scouting	HS	Hairy-leaf field crops:  scouting and similar contact activities

ARF051	Tomato	Tying	SH	Smooth-leaf field crops:  hand harvesting and tying

AR1001	Strawberry	Hand Harvesting

AR1023	Tomato	Hand Harvesting

AR1024	Strawberry	Hand Harvesting

AR1025	Cotton	Scouting	SSr	Smooth-leaf field crops:  scouting in row conditions

AR1027	Tomato	Scouting

ARF009	Corn	Scouting	SSs	Smooth-leaf field crops:  scouting in solid stand conditions

ARF021	Dry Pea	Scouting

AR1006	Cotton	Hand weeding	SW	Smooth-leaf field crops:  hand weeding, thinning, and similar contact activities

AR1018	Cotton	Hand weeding

AR1019	Dry Pea	Hand weeding

ARF010	Sweet Corn	Hand harvesting	Sx	Smooth-leaf field crops:  intense contact activities

ARF050	Cabbage	Hand harvesting	WIH	Waxy-leaf field crops, low height:  hand harvesting and similar contact activities

AR1008	Cauliflower	Scouting	WIS	Waxy-leaf field crops, low height:  scouting and similar contact activities

ARF011	Cauliflower	Scouting	Wm	Waxy-leaf field crops, medium height:  all activities, plus full foliage weeding

ARF012	Cauliflower	Hand harvesting

ARF037	Cabbage	Hand weeding

ARF025	Apples	Hand Harvesting	OH	Orchard crops:  hand harvesting and similar contact activities

ARF028	Oranges	Hand Harvesting

ARF041	Oranges	Hand Harvesting

ARF042	Grapefruit	Hand Harvesting

AR1002	Peaches	Hand Harvesting

AR1003	Apples	Thinning

AR1014	Peaches	Hand Harvesting

AR1021	Peaches	Hand Harvesting

AR1016	Almonds	Mechanical Harvesting	OHn	Orchard crops:  mechanically harvesting nuts

ARF033	Olives	Hand Pruning	OP	Orchard crops:  hand pruning, scouting, and similar contact activities

ARF047	Apples	Hand Pruning

AR1017	Peaches	Propping	OW	Orchard crops:  hand weeding and similar contact activities

ARF020	Blackberries	Hand harvesting	THb	Trellis crops:  hand harvesting caneberries and similar contact activities

ARF048	Juice/Wine Grapes	Hand harvesting	THg	Trellis crops:  hand harvesting grapes and similar contact activities

AR1020	Table / Raisin Grapes	Hand harvesting

AR1022	Table / Raisin Grapes	Hand harvesting

ARF023	Table / Raisin Grapes	Scouting	TP	Trellis crops:  hand pruning, scouting, and similar contact activities

AR1015	Table / Raisin Grapes	Cane turning	Tx	Trellis crops:  intense contact activities

ARF055	Solidasters, Snapdragons, Lillies	Hand Harvesting	GHf	Greenhouse and nursery floriculture hand harvesting:  all flowers and methods

ARF020	Blackberries	Hand Harvesting	GHv	Greenhouse vegetables: hand harvesting and similar contact activities

ARF051	Tomatoes, fresh	Tying

ARF039	Chrysanthemums	Pinching	GN	Greenhouse and nursery crops:  all activities

ARF043	Nursery Stock Citrus Trees	Hand Pruning

ARF044	Nursery Stock Citrus Trees	Hand Harvesting

All crops:  transplanting

ARF036	Potatoes	Irrigation	I	Irrigation, any crop where hand line is possible

AR1004	Cotton	Mechanical Harvesting	CHp	Cotton, mechanical harvesting:  picker operator and raker (based on boll residues)

	CHm	Cotton, mechanical harvesting:  module builder operator (based on boll residues)

	CHt	Cotton, mechanical harvesting:  tramper (based on boll residues)

ARF035	Sod	Mechanical Harvesting	DH	Sod:  mechanical harvesting, scouting, transplanting, and hand weeding

ARF057	Golf Course Turf	Maintenance	DM	Golf courses:  maintenance activities

Identification and Analysis of Clusters

The major focus of the analysis below is the discussion of the studies and organization for the following obvious major agronomic groupings including:  field, orchard, trellis, and greenhouse/nursery crops.  However, additional, “specialty” clusters, such as those for turf, crop irrigation, and mechanically harvesting cotton, will be briefly described as well those activities that are considered to have no/low contact.  The assignment to a cluster for those activities which have not been explicitly monitored is also addressed.

The Agency recognizes that the monitored studies were not conducted using a rigorous statistical sampling design.  Therefore this section does not attempt to conduct advanced statistical analyses on the datasets.  The nonparametric Wilcoxon and Kruskal-Wallis tests are employed to provide a broad rationale for the separation or combination of studies to form clusters.  Though the Agency recognizes the limitations in using these tests due to the non-independence and structure of the data sets (e.g., same workers monitored on different days), we believe that reasonable distinctions, based on both knowledge of agricultural practices and the resulting exposure monitoring data as well as consultation with agricultural experts prior to and after collection of the data, can be made between data sets for the purposes of establishing clusters of data that can be used in exposure assessments.  The data provided as Exhibit D includes information about each individual worker monitored which would allow for more advanced analyses to be conducted if desired.  In addition, Exhibit F provides a case study analysis using a mixed-model approach that incorporates the hierarchical nature of the data and is likely to be more appropriate and to more definitively address the issues of interest regarding the degree to which specified crop-activity combinations might be combined.

Part of the process of developing a generic database for use in occupational post-application exposure assessment was recognizing that, for logistical and economic reasons, exposure monitoring studies could not be conducted for the approximately 4500 possible crop and activity combinations that were identified.  Therefore, based on both professional judgment with respect to similarities with those crops and activities that were monitored, non-monitored crops and activities were assigned to certain clusters.  Such crop and activities are presented throughout the following sections in tables showing the ARTF-proposed clusters.  For example, tying and leaf-pulling in trellis crops are included in clusters represented only by studies monitoring hand harvesting (i.e., THb and THg).  Another example is transplanting of all crops represented by the GN cluster (i.e., greenhouse and nursery crops) because it involves handling crops when they are very small.  The list of all possible crops and activities and their cluster assignments in Exhibit C should be consulted for further detail.  These assignments are an attempt to capture, and enable post-application exposure assessment of, all possible agricultural crops and activities.

As indicated above, the regulatory agencies have reviewed and provided comment to the ARTF throughout the development of this project including the development of the proposed clustering scheme.  The regulatory agencies concur with a clustering approach.  This analysis provides a review of the ARTF scheme and some possible alternatives for certain situations for consideration by the panel.  The analysis which has been completed below is based on the following groupings which encompass the clusters proposed in   REF _Ref179644678 \h  \* MERGEFORMAT  Table 11 :

Section   REF _Ref213666286 \r \h  <IP_ADDRESS> :  Greenhouse and nursery crops;

Section   REF _Ref213666316 \r \h  <IP_ADDRESS> :  Field crops;

Section   REF _Ref213666331 \r \h  <IP_ADDRESS> :  Mechanically-assisted cotton harvest;

Section   REF _Ref213666343 \r \h  <IP_ADDRESS> :  Orchard crops;

Section   REF _Ref213666359 \r \h  <IP_ADDRESS> :  Trellis crops;

Section   REF _Ref213666375 \r \h  <IP_ADDRESS> :  Turf production and maintenance;

Section   REF _Ref213666390 \r \h  <IP_ADDRESS> :  Hand-line irrigation; and

Section   REF _Ref213666403 \r \h  <IP_ADDRESS> :  No/low contact activities.

Greenhouse and Nursery Crop Clusters

The objective in this cluster was to identify activities which occur in the routine production of various plants and commodities in the greenhouse and nursery industries.  This industry varies extremely with respect to the types of facilities used (e.g., shade houses or fully enclosed structures, field grown plants, etc.), the crops or plant materials produced (e.g., vegetables, container ornamentals, cuttings, etc.), and the types of activities that are required for the production of the crops.  Based on the available surveys as well as other information it was jointly decided by the Agency and ARTF that the following post-application crops and activities in greenhouses or nurseries, monitored in ARTF studies, be used to represent these clusters (Bruce and Korpalski, 2008):

Hand harvesting nursery stock citrus trees (ARF044);

Pinching chrysanthemums (ARF039);

Hand pruning nursery stock citrus trees (ARF043);

Hand harvesting greenhouse-grown snapdragons, lilies, and solid asters (ARF055)

For graphic illustration   REF _Ref179645005 \h  \* MERGEFORMAT  Figure 4  is a photograph from ARF055.

Figure   SEQ Figure \* ARABIC  4 :  Solid Aster Harvest (Study ARF055)

ARTF Proposal:  The ARTF-proposed clustering of these datasets as shown in   REF _Ref179645110 \h  \* MERGEFORMAT  Table 12  below.  In   REF _Ref179645110 \h  \* MERGEFORMAT  Table 12 , the TCs assigned for cluster code GN would apply to all 4 activities for floriculture crops, all 9 activities listed for nursery stock crops and transplanting for all crops, and are based on the 3 studies listed in the left hand column.  For clarity, the activity actually monitored in the study used to represent the cluster is italicized while those for which no actual monitoring exists but were deemed by expert opinion to be sufficiently similar – and to which the measured values are extrapolated – are shown in regular font.

Table   SEQ Table \* ARABIC  12 :  ARTF Greenhouse and Nursery Clusters

Representative Studies	Cluster Code	Post-application Exposure Assessment Activities

Study Code	Activity/Crop

ARF055	Hand harvesting greenhouse-grown snapdragons, lilies, and solidasters	GHf	Hand harvesting (all methods) greenhouse and nursery floriculture crops

ARF044	Hand harvesting nursery stock citrus trees	GN	Floriculture Crops Pinching

Hand pruning

Scouting

Hand weeding

	Nursery Stock Crops	Grafting

ARF039	Pinching chrysanthemums

	Hand harvesting

Pinching

Propagating

Hand pruning

Scouting

Staking

ARF043	Hand pruning ornamental citrus trees in a nursery

	Tying

Hand weeding

	All crops:  transplanting

The ARTF also believes that the greenhouse and nursery studies outlined above are inadequate to represent activities associated with greenhouse-grown vegetables because of specialized techniques used to grow vegetables in greenhouses.  As a result, the ARTF proposes a separate cluster outlined in   REF _Ref213467388 \h  \* MERGEFORMAT  Table 13  below:

Table   SEQ Table \* ARABIC  13 :  ARTF Greenhouse Vegetable Cluster

Representative Studies	Cluster Code	Post-application Exposure Assessment Activities

Study Code	Activity/Crop

ARF020	Hand harvesting blackberries	GHv	Hand harvesting

	Pinching/Suckering

	Pollination

	Propagating

ARF051	Tying tomatoes

Hand pruning

	Scouting

	Turning

	Tying/Clipping/Winding

	Hand weeding

Agency Review and Conclusion:   The separation of activities into distinct clusters based on both agronomic and ergonomic factors thought to result in different levels and types of exposure for workers is supported by the associated exposure monitoring data.  In this case, the Agency agrees that hand harvesting floriculture crops (e.g., cut flowers) can be considered separately from other greenhouse or nursery crops in post-application exposure assessments due to its observable high residue transfer level in   REF _Ref179645176 \h  \* MERGEFORMAT  Figure 5  below (p < 0.0001, Kruskal-Wallis test; JMP 5.1.2).  This is also supported intuitively based on the nature of the activity compared to the others in this group.  In this study, workers clipped treated flowers (i.e., 3 to 5 ft. tall lilies, snapdragons, and asters) then cradled them or placed them in trays, then subsequently placed them in handling/shipping containers.  This activity involved extensive contact of the hands and upper body, with plant material, which is reflected in the exposure monitoring data.

Figure   SEQ Figure \* ARABIC  5 :  ARTF Studies - Greenhouse and Nursery

However, the Agency believes that there could be support for additional separation of hand harvesting nursery crops from other nursery crop activities (  REF _Ref179645369 \h  \* MERGEFORMAT  Figure 6 ; p < 0.0001, Kruskal-Wallis test; JMP 5.1.2.  In these studies, harvesting of nursery stock appears to be the most physically demanding because it involved preparing and moving large containerized ornamental trees in pot sizes from 5 to 15 gallons and even trees in 2 foot square wooden boxes which would be extremely heavy and cumbersome.  Given this context, the Agency believes that the physical awkwardness of handling such containers could be the cause of the more intense exposure rates noted in the monitoring data.  Hand harvesting is consistently an activity with relatively high residue transfer levels within other crop groups as well also likely due to more intense and physically awkward contacts with foliage.

Figure   SEQ Figure \* ARABIC  6 :  ARTF Studies - Greenhouse and Nursery

Indeed, there would be statistical support for having separate clusters represented each by the hand pruning and pinching studies.   It is also intuitive that these activities could be separated given the nature of the activities (i.e., using shears to prune a sparsely foliated ornamental tree versus pinching buds off of mums in small pots on benches) and given the different patterns of exposure across the body (i.e., most on the upper legs and hands for pruners and lower arms and hands for pinching).  The Agency; however, considers their combination to be reasonable for the purposes of assessing exposure to activities other than hand harvesting in greenhouses and nurseries because the pruning and pinching are lower exposure activities compared to harvesting and the TC distributions based on total body exposure overlap in ARF039 and ARF043 as illustrated in   REF _Ref213481074 \h  \* MERGEFORMAT  Figure 6  above.  Thus, EPA proposed a potential alternative organization for greenhouse and nursery crops and activities as shown in   REF _Ref179645259 \h  \* MERGEFORMAT  Table 14  below.

Table   SEQ Table \* ARABIC  14 :  Alternative Greenhouse and Nursery Clusters

Representative Studies	Cluster Code	Post-application Exposure Assessment Activities

Study Code	Activity/Crop

ARF055	Hand harvesting greenhouse-grown snapdragons, lilies, and solidasters	GHf	Hand harvesting (all methods) greenhouse and nursery floriculture crops

ARF044	Hand harvesting nursery stock citrus trees	GHn	Hand harvesting nursery stock crops

ARF039	Pinching chrysanthemums	GN	Floriculture Crops	Pinching

Hand pruning

Scouting

Hand weeding

	Nursery Stock Crops	Grafting

Pinching

ARF043	Hand pruning nursery stock citrus trees

	Propagating

Hand pruning

Scouting

Staking

Tying

Hand weeding

	All crops:  transplanting

	With regard to greenhouse-grown vegetables, the Agency agrees with the ARTF that the studies monitoring workers performing activities in greenhouse and nursery grown ornamental and floriculture crops are not suitable surrogates.  No specific monitoring data were generated for these activities but ARTF proposed using monitoring data from other settings to assess risks associated with the production of greenhouse-grown vegetables.  These include blackberry harvesting (ARF020) and tomato tying (ARF050), both developed for use in other clusters representing standard field conditions (see Section <IP_ADDRESS>).

The Agency believes that addressing greenhouse-grown vegetables in this manner is appropriate because of the manner in which greenhouse vegetables are produced is much more like the conditions observed in these two studies than other aspects of agriculture.  For example, a large percentage of the table ready fresh tomato market is supplied by greenhouse operations which typically provide clusters of 3 to 4 tomatoes still attached to a vine segment for consumers.  They are not produced using typical varieties but are instead produced from varieties of tomatoes specifically developed for greenhouse production that are essentially long vines which are hung from greenhouse ceilings and interacted with by workers in a manner similar to the two referenced exposure studies for harvesting blackberries and field tying tomatoes.

Field Crop Clusters

Crops classified as “field crops” for the purposes of post-application exposure assessment include corn, tomatoes, strawberries, cucumbers, melons, tobacco, eggplant, peppers, beans/peas, lettuce, cabbage, parsley, potatoes, beets, onions, and cauliflower.  The following field crops and activities were monitored in ARTF studies:

Hand harvesting

Tobacco (ARF024)

Cucumbers (ARF045)

Summer squash (ARF049)

Sweet corn (ARF010)

Strawberries (AR1024, AR1001)

Tomatoes (AR1023)

Cauliflower (AR012)

Cabbage (ARF050)

Scouting

Sunflower (ARF022)

Dry Beans/Peas (ARF021)

Sweet Corn (ARF009)

Tomatoes (AR1027)

Cotton (AR1025)

Cauliflower (AR011, AR1008)

Tying Tomatoes (ARF051)

Hand weeding

Cotton (AR1006, AR1018)

Dry Beans/Peas (AR1019)

Cabbage (ARF037)

  REF _Ref211307653 \h  \* MERGEFORMAT  Figure 7  through   REF _Ref211307661 \h  \* MERGEFORMAT  Figure 10  provide photographs illustrating of some of the activities which were monitored in these studies.

Figure   SEQ Figure \* ARABIC  7 :  Cucumber Harvest (Study ARF045)

Figure   SEQ Figure \* ARABIC  8 :  Cabbage Weeding (Study ARF037)

Figure   SEQ Figure \* ARABIC  9 :  Dry Pea Scouting (Study ARF021)

Figure   SEQ Figure \* ARABIC  10 :  Tomato Tying (Study ARF038)

ARTF Proposal

Effect of Leaf Texture on Field Crop Grouping:  Upon analysis of exposure monitoring data for field crops it became apparent that, in general, leaf texture may have a significant impact on available residue and subsequent transfer to field workers, and thus could be used to help form crop-activity groupings.  The ARTF analyzed DFR data following pesticide applications and found that waxy and hairy leaves have the lowest and highest DFR levels, respectively, suggesting that waxy leaves are less efficient than hairy leaves at retaining pesticide residues following applications (Bruce et al, 2003; Bruce and Korpalski, 2008).  This phenomenon corresponds to what is seen when analyzing transfer coefficients for these respective groups and is described below:  the higher level of residue transfer from waxy-leaf field crops likely corresponds to their inefficiency at retaining pesticide residues.

The ARTF field crop studies, characterized and separated into three leaf-type groups:  waxy-, hairy-, and smooth-leaf crops, is shown in   REF _Ref211307687 \h  \* MERGEFORMAT  Table 15  below.  A list characterizing crops by leaf texture is provided in the ARTF “database report” (Bruce et al, 2006).

Table   SEQ Table \* ARABIC  15 :  ARTF Field Crop Studies by Leaf Texture

Leaf-Type	Crops 	ARTF Study Code(s)

Waxy	Cauliflower	ARF1012, ARF011, ARF1008

	Cabbage	AR037, ARF050

Smooth	Sweet Corn	ARF010, ARF009

	Dry Beans/Peas	ARF021, AR1019

	Strawberries	AR1024, AR1001

	Tomatoes	AR1023, ARF051

	Cotton	AR1025, AR1006, AR1018

Hairy	Tobacco	ARF024

	Cucumber	ARF045

	Summer Squash	ARF049

	Sunflower	ARF022

	Agency Review and Analysis:  The Agency generally agrees with the approach to use leaf texture as an initial grouping scheme for field crops, though it is recognized that many other factors are confounding such a definitive conclusion (e.g., crop height).  However, some limited analysis does provide support for separation by leaf texture.  For example,   REF _Ref211309061 \h  \* MERGEFORMAT  Figure 11  below demonstrates that for a given field crop activity, in this case scouting, residue transfer appears to differ among waxy, smooth, and hairy-leaf crops (p < 0.0001, Kruskal-Wallis test; JMP 5.1.2).  Furthermore, the order appears to follow the “waxy > smooth > hairy” leaf texture hypothesis.

Figure   SEQ Figure \* ARABIC  11 :  Field Crop Scouting - Leaf Texture Comparison

The field crops grouped by leaf texture are then further clustered by activity.  These smooth-, hairy-, and waxy-leaf clusters are described below.

Analysis of Smooth-leaf, Field Crop Clusters  (Sx, SSs, SSr, SH, SW):  Crops classified as “smooth-leaf, field crops” for the purposes of post-application exposure assessment include corn, tomatoes, strawberries, beans/peas, peppers, celery, beets, sugarcane, and wheat.  The following smooth-leaf field crops and activities were monitored in ARTF studies:

Hand harvesting

Sweet corn (ARF010)

Strawberries (AR1024, AR1001)

Tomatoes (AR1023)

Scouting

Dry Beans/Peas (ARF021)

Sweet Corn (ARF009)

Tomatoes (AR1027)

Cotton (AR1025)

Tying Tomatoes (ARF051)

Hand weeding

Cotton (AR1006, AR1018)

Dry Beans/Peas (AR1019)

ARTF Proposal:  The ARTF-proposed clustering of these datasets is shown in   REF _Ref179645658 \h  \* MERGEFORMAT  Table 16  below.  ARTF formed these clusters intuitively by grouping similar activities.  There are two notable exceptions for clusters for hand harvesting corn or sugarcane where extreme immersion is involved (Sx) and hand weeding various smooth leaf crops (SW).  In cluster (Sx) detasseling seed corn is included because the practice also involves immersion into the crop resulting in extensive physical contact to complete the activity in a manner analogous to that required for hand harvesting.  Also note that in rare cases where sugarcane is hand harvested that the corn study would also be used.  In cluster (SW), exposures were consistently less for hand weeding activities compared to other smooth-leaf clusters.  This would be expected for hand weeding activities in these types of crops because crops were not fully mature compared to other types of activities.  Further contributing to the comparatively lower exposures was that low weed pressure was noted in the bean study, and hoes were used in all three studies a majority of the time which minimizes direct plant contact in those cases.  Most weeding in commercial agriculture is done with hoes and these studies are considered representative of typical conditions for weeding.

Table   SEQ Table \* ARABIC  16 :  ARTF Smooth-leaf Field Crop Clusters

Representative Studies	Cluster Code	Post-application Exposure Assessment Activities

Study Code	Activity/Crop

ARF010	Hand harvesting corn	Sx	Hand harvesting all corn types and sugar cane

	Detasseling seed corn

AR1006	Hand weeding cotton	SW	Hand weeding

	Thinning

AR1018	Hand weeding cotton

Staking

	Canopy management (strawberries)

AR1019	Hand weeding dry beans/peas

Hand pruning

	Mulching

ARF009	Scouting corn	SSs	Scouting, solid-stand conditions

ARF021	Scouting dry beans/peas

AR1025	Scouting cotton	SSr	Scouting, row conditions

AR1027	Scouting tomatoes

ARF051	Tying tomatoes	SH	Hand harvesting (except corn and sugar cane)

ARF1001	Hand harvesting strawberries

AR1023	Hand harvesting tomatoes

Tying

AR1024	Hand harvesting strawberries

Agency Review and Analysis:  The Agency concurs with the clusters proposed by ARTF.  Examining the various data sets for smooth-leaf field crops it is obvious that harvesting sweet corn (ARF010) stands out as an activity with very high residue transferability in   REF _Ref179645697 \h  \* MERGEFORMAT  Figure 12  below.  The ARTF proposes that ARF010 be used to represent other potentially high contact activities in smooth-leaf field crops such as hand harvesting all corn types and sugar cane and detasseling seed corn.

It also appears that hand weeding smooth-leaf field crops (see AR1006, AR1018, and AR1019 in   REF _Ref179645697 \h  \* MERGEFORMAT  Figure 12  below) is a relatively low contact activity compared with the rest of the activities in the smooth leaf agronomic group.  The ARTF proposes that these studies represent activities that, like weeding, will have relatively low foliar contact in smooth-leaf field crops, including thinning, staking, and hand pruning (see   REF _Ref179645658 \h  \* MERGEFORMAT  Table 16  above).  Low foliar contact would be expected since the studies were conducted in immature crops with less dense foliage.

Figure   SEQ Figure \* ARABIC  12 :  ARTF Smooth-leaf Field Crop Studies

The ARTF proposes three additional clusters for smooth-leaf field crops:  SSs, SSr, and SH.   Though both the SSs and SSr clusters include scouting smooth-leaf field crops, the ARTF proposes two separate clusters because it appears that crops grown in row conditions (e.g., tomatoes and cotton) have lower residue transfer levels than crops grown more densely in solid standing conditions (e.g., dry beans and corn).

The comparison can be seen in   REF _Ref179645758 \h  \* MERGEFORMAT  Figure 13  below and is supported statistically using the Wilcoxon test (p < 0.0001; JMP 5.1.2).  The studies used to comprise cluster SSs (ARF009 and ARF021) collectively have a higher level of transfer than those that collectively make up cluster SSr (AR1025 and AR1027).

Figure   SEQ Figure \* ARABIC  13 :  Comparing Clusters SSs and SSr

	The final smooth-leaf field crop cluster (SH) is proposed to represent harvesting (except corn and sugar cane) and other similar contact activities such as tying and thinning (see   REF _Ref179645658 \h  \* MERGEFORMAT  Table 16  above).  As seen in   REF _Ref179645817 \h  \* MERGEFORMAT  Figure 14  below, the SH cluster appears to have higher and lower transfer levels than the SW and Sx clusters, respectively, however it appears relatively close to the remaining two clusters (SSr and SSs).

Figure   SEQ Figure \* ARABIC  14 :  Smooth-leaf Field Crop Clusters

Though the Agency could consider combining the datasets comprising clusters SH and SSs because their transfer coefficients are not statistically different (p = 0.88, Wilcoxon test; JMP 5.1.2) the Agency will continue to qualitatively separate them for the purposes of exposure assessment.  Because the activities are intuitively separate (i.e., harvesting vs. scouting-like activities) it is easier for exposure assessment purposes for the datasets to remain separate.  In the dry pea study, it was noted that it was difficult to walk through the fields due to crop growth which would likely not be the case while harvesting tomatoes and strawberries.

Analysis of Hairy-leaf, Field Crop Clusters (HS, HH, HHt):  Crops classified as “hairy-leaf, field crops” for the purposes of post-application exposure assessment include cucumbers, squash, melons, pumpkins, eggplant, cantaloupe, sunflower, and tobacco.  The following hairy-leaf field crops and activities were monitored in ARTF studies:

Hand harvesting

Cucumbers (ARF045)

Summer Squash (ARF049)

Tobacco (ARF024)

Scouting

Sunflowers (ARF022)

ARTF Proposal:  The ARTF-proposed clustering of these datasets is shown in   REF _Ref179645872 \h  \* MERGEFORMAT  Table 17  below.  Qualitatively, they are intuitive because activities likely to result in similar foliar contact and residue transfer are grouped together (e.g., grouping harvesting with turning and scouting with pruning).  There is one notable exception, where the tobacco harvesting study is used to represent only tobacco-related production activities, only (cluster HHt), and is further discussed below.

Table   SEQ Table \* ARABIC  17 :  ARTF Hairy-leaf Field Crop Clusters

Representative Studies	Cluster Code	Post-application Exposure Assessment Activities

Study Code	Activity/Crop

ARF045	Hand harvesting cucumber	HH	Hand harvesting (except tobacco)

ARF049	Hand harvesting summer squash

Tying

	Turning (melons)

ARF024	Hand harvesting tobacco	HHt	Hand harvesting

	Hand pruning

	Suckering

	Thinning

	Topping

ARF022	Scouting sunflowers	HS	Scouting

	Thinning (except tobacco)

	Hand pruning (except tobacco)

	Hand weeding

	Staking

	Bird control

	Agency Review and Analysis:  The Agency concurs with the ARTF proposals.  Among the studies monitoring post-application activities in hairy-leaf field crops, it is apparent, and can be seen in   REF _Ref211307802 \h  \* MERGEFORMAT  Figure 15  below, that scouting in sunflowers (ARF022) results in the lowest level of residue transfer.  This supports the proposed HS cluster including scouting and similar contact activities (e.g., hand weeding, staking, etc.) for hairy-leaf field crops.  Tobacco harvesting, even though it looks similar based on the information presented below, has been kept separate essentially based on observations made during the conduct of the study.  More information is provided below concerning this issue.

Figure   SEQ Figure \* ARABIC  15 :  ARTF Hairy-leaf Field Crop Studies

As indicated above, tobacco production activities appear somewhat unique when compared to harvesting and scouting other hairy-leaf field crops because of the high amount of tobacco resin observed in the harvested leaves.  This resin clearly impinged upon the worker’s clothing which was observed in the study on many workers.  It is because of this phenomena and the uncertainty it introduced that it was decided not to group the results of this study with others in a cluster.  The proposed separation of activities in tobacco (HHt) from other hairy-leaf field crops (HH) is also supported in   REF _Ref211308771 \h  \* MERGEFORMAT  Figure 16  below (p = 0.006, Wilcoxon; JMP 5.1.2).

Figure   SEQ Figure \* ARABIC  16 :  Hand Harvesting Hairy-leaf Field Crops

It should be noted however, that in the tobacco hand harvesting monitoring study (ARF024), reentry day 1 (i.e., the day after application) resulted in significantly higher TCs than subsequent reentry days (i.e., days 2 and 3), and is likely the source for the appearance of higher transfer coefficients overall for hand harvesting tobacco (see   REF _Ref179646081 \h  \* MERGEFORMAT  Figure 17 ).  The field report indicates “heavy dew” on reentry day 1 which could potentially be the source for the observable difference between reentry day 1 and the other reentry days within this study.  This illustrates a significant issue that was considered in the process for deciding that transfer coefficients could be used generically across many types of field conditions.  For example, the potential effect of both different reentry days and moisture on residue transfer within similar activities is discussed by the ARTF in their “genericness report” (Bruce, et al 2003).

Figure   SEQ Figure \* ARABIC  17 :  Hand Harvesting Tobacco (ARF024)

DAA = day after application

Even if tobacco harvesting did not exhibit a distinctly different residue transfer level (i.e., higher TCs) from other hairy-leaf field crops, the ARTF indicates that the distribution of exposure across the different sections of the body (i.e., hands, legs, forearms, etc.) is different for hand harvesting tobacco compared with hand harvesting the other hairy-leaf field crops in ARTF studies (cucumbers and summer squash), perhaps because of crop height differences.    REF _Ref211308057 \h  \* MERGEFORMAT  Figure 18  below presents support for this observation by showing the percent of the total transfer coefficient contributed by the hands and the rest of the body (RoB) for each monitored individual in clusters HH and HHt.

Figure   SEQ Figure \* ARABIC  18 :  Clusters HH & HHt - % Hand and RoB TC, TDE Contribution

Hands

Because the Agency generally does not conduct post-application risk mitigation in the form of personal protective equipment, such as chemical-resistant gloves or coveralls, the practical implications of this observation will be limited.  In other words, regardless of the distribution of exposure across the body, the transfer coefficient representing total body exposure is typically used in the exposure assessment.  However, if this information is deemed useful in the future, exposure distribution information, such as that described here, is valuable.

The Agency supports the ARTF proposed clusters for hairy-leaf field crops without revision.

Analysis of Waxy-leaf Field Crop Clusters (WIH, WIS, Wm):  Crops classified as “waxy-leaf, field crops” for the purposes of post-application exposure assessment include broccoli, cabbage, cauliflower, onions, and brussel sprouts.  The following waxy-leaf field crops and activities were monitored in ARTF studies:

Hand harvesting

Cabbage (ARF050)

Cauliflower (ARF012)

Scouting

Cauliflower (AR1008, ARF011)

Hand weeding

Cabbage (ARF037)

ARTF Proposal:  The ARTF proposed clustering the data as shown in   REF _Ref179646030 \h  \* MERGEFORMAT  Table 18 . Unlike previous groupings, crop height appears to be a larger contributor to residue transfer within waxy-leaf field crops.  However, though cabbage would be classified as a “low crop” the relatively higher transfer coefficients from the cabbage weeding study (ARF037) are more comparable to, and are therefore grouped with, activities associated with medium-height, waxy-leaf field crops.  One possible theory is that harvesters experience less residue transfer because they contact the inner portions of the plant more than the outer for this task which, as a result of being more physically protected from sprays during application, have lower residues for workers to contact on that part of the plant.  For weeding on the other hand, it could be theorized that this activity results in contact with foliage less protected from sprays during application than the inner leaves contacted by harvesters.  The age of the plants could also impact the relative differences in exposure with cabbage at harvest time possibly having less vigorously growing foliage compared to the more vigorously growing plants that would be expected in the weeding study.

Table   SEQ Table \* ARABIC  18 :  ARTF Waxy-leaf Field Crop Clusters

Representative Studies	Cluster Code	Post-application Exposure Assessment Activities

Study Code	Activity/Crop

ARF050	Hand harvesting cabbage	WIH	Low height	Hand harvesting

Scouting (full foliage)

Hand weeding (minimal foliage)

AR1008	Scouting cauliflower	WIS	Low height	Scouting (minimal foliage)

Thinning

Topping

ARF011	Scouting cauliflower	Wm	Medium height	Hand Harvesting

ARF012	Hand harvesting cauliflower

	Scouting

ARF037	Hand weeding cabbage

	Tying

	Low height	Hand weeding (full foliage)

	Agency Review and Analysis:  Based on their categorization as shown in   REF _Ref179646030 \h  \* MERGEFORMAT  Table 18 , the ARTF generally proposes clustering of waxy-leaf field crops by crop height, with the exception of hand weeding for full foliage crops.  This proposal does have some quantitative support in   REF _Ref179646146 \h  \* MERGEFORMAT  Figure 19  below.  The two studies monitoring “medium height” waxy-leaf field crops (ARF011 and ARF012) and the hand weeding cabbage study (ARF037) do have a significantly higher level of residue transfer compared with the other waxy-leaf field crops and activities.

Figure   SEQ Figure \* ARABIC  19 :  Waxy-leaf Field Crop ARTF Studies