Document ID: EPA-HQ-OPP-2008-0673-0003
Agency: epa
Document Type: Supporting & Related Material
Title: 
Posted Date: 2008-11-13T05:00Z

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"  4.2.2.1	Greenhouse and Nursery Crop
Clusters	  PAGEREF _Toc213732319 \h  40  

  HYPERLINK \l "_Toc213732320"  4.2.2.2	Field Crop Clusters	  PAGEREF
_Toc213732320 \h  45  

  HYPERLINK \l "_Toc213732321"  4.2.2.3	Mechanical Harvesting Cotton
Clusters	  PAGEREF _Toc213732321 \h  61  

  HYPERLINK \l "_Toc213732322"  4.2.2.4	Orchard Crop Clusters	  PAGEREF
_Toc213732322 \h  63  

  HYPERLINK \l "_Toc213732323"  4.2.2.5	Trellis Crop Clusters	  PAGEREF
_Toc213732323 \h  69  

  HYPERLINK \l "_Toc213732324"  4.2.2.6	Turf Clusters	  PAGEREF
_Toc213732324 \h  76  

  HYPERLINK \l "_Toc213732325"  4.2.2.7	Crop Irrigation Cluster	 
PAGEREF _Toc213732325 \h  78  

  HYPERLINK \l "_Toc213732326"  4.2.2.8	No/Low Contact Activities	 
PAGEREF _Toc213732326 \h  80  

  HYPERLINK \l "_Toc213732327"  4.2.2.9	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  4.2.2.1 :  Greenhouse and nursery
crops;

Section   REF _Ref213666316 \r \h  4.2.2.2 :  Field crops;

Section   REF _Ref213666331 \r \h  4.2.2.3 :  Mechanically-assisted
cotton harvest;

Section   REF _Ref213666343 \r \h  4.2.2.4 :  Orchard crops;

Section   REF _Ref213666359 \r \h  4.2.2.5 :  Trellis crops;

Section   REF _Ref213666375 \r \h  4.2.2.6 :  Turf production and
maintenance;

Section   REF _Ref213666390 \r \h  4.2.2.7 :  Hand-line irrigation; and

Section   REF _Ref213666403 \r \h  4.2.2.8 :  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 4.2.2.2).  

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

The activities in low height, waxy-leaf field crops proposed to be
represented by the two remaining two studies, AR1008 (scouting
cauliflower) and ARF050 (hand harvesting cabbage), are reasonable.  The
Agency did not consider any alternative clustering schemes.

Mechanical Harvesting Cotton Clusters

The ARTF study (AR1004) used to represent these clusters monitored two
types of mechanical harvesting and three distinct activities.  These
were:

Trailer harvesting

Tramping

Picker operator

Raker

Module harvesting

Picker operator

Raker

Module builder operator

ARTF Proposal:  The ARTF proposes that mechanically harvesting cotton be
defined by separate clusters because of its unique harvesting equipment
and distinct activities.  In addition, exposure is a result of contact
with cotton boll residues, rather than foliar residues, due to
defoliation prior to the harvest.  Therefore, unlike typical transfer
coefficients, those for the mechanical harvesting of cotton are derived
from cotton boll residues (measured as mass active ingredient per mass
cotton boll), and are expressed as grams/hr.  It follows that these
values are incompatible in exposure assessments for pesticides applied
prior to boll opening.  The ARTF-proposed clustering of these data is
presented in   REF _Ref179646208 \h  \* MERGEFORMAT  Table 19  and   REF
_Ref179646180 \h  \* MERGEFORMAT  Figure 20  below.

Table   SEQ Table \* ARABIC  19 :  ARTF Cotton – Mechanical Harvesting
Clusters

Representative Studies	Cluster Code	Post-application Exposure Assessment
Activities

Study Code	Activity/Crop

AR1004	Module builder operator	CHm	Module harvesting:  module builder
operator

	Picker operator	CHp	All harvesting methods:  picker operator and raker

	Raker

	Tramping	CHt	Trailer harvesting:  tramping

Geometric mean estimates appear to support the proposed three clusters,
with tramping and operation of the module builder having the highest and
lowest GM transfer coefficients, 3510 and 472 grams/hr, respectively,
while the picker operator and raker have similar intermediate values,
1960 and 1880 grams/hr, respectively.  However, statistical support for
separation into three distinct clusters is lacking (p = 0.09,
Kruskal-Wallis test; JMP 5.1.2).  Despite this, the Agency supports the
conclusions of the ARTF and agrees with the mechanical cotton harvesting
clusters shown in   REF _Ref179646208 \h  \* MERGEFORMAT  Table 19 .

Figure   SEQ Figure \* ARABIC  20 :  Cotton - Mechanical Harvesting
Activities

	MBO = module builder operator; PO = picker operator; R = raker; T =
tramper

Orchard Crop Clusters 

The clusters described below address all activities which occur in
agriculture related to the production of all types of tree fruits and
nuts.  Example crops classified as “orchard crops” for the purposes
of post-application exposure assessment include apples, peaches, figs,
olives, almonds, other nuts, cherries, and avocados.

In a majority of circumstances harvesting and other key activities are
completed using hand labor.  A range of cultural practices were captured
in these data in an attempt to account for potentially large differences
in exposure rates due to location or crop.  Mechanical harvest of
almonds is also included here even though this activity on almonds and
other tree nuts harvested in similar fashion are distinctly different
from the remaining crops.  Even though this activity involves intense
mechanical device use it also can lead to dermal exposures that can be
addressed using the TC approach.  The following ARTF studies were
conducted in orchard crops, presented below separated by activity:

Hand harvesting

Apples (ARF025)

Oranges (ARF028, ARF041)

Grapefruit (ARF042)

Peaches (AR1002, AR1014, 1021)

Mechanically Harvesting Almonds (AR1016)

Thinning Apples (AR1003)

Hand pruning

Apples (ARF047)

Olives (ARF033)

Propping Peaches (AR1017)

  REF _Ref179646286 \h  \* MERGEFORMAT  Figure 21  and   REF
_Ref213469403 \h  \* MERGEFORMAT  Figure 22  provide a graphic
illustration of some of the activities which were monitored in these
studies.

Figure   SEQ Figure \* ARABIC  21 :  Orange Harvest (Study ARF041)

 

Figure   SEQ Figure \* ARABIC  22 :  Apple Pruning (Study ARF047) 

 

	ARTF Proposal:  Unlike field crops, residue transfer during activities
in orchard crops does not appear to be significantly affected by leaf
texture.  For example,   REF _Ref211308166 \h  \* MERGEFORMAT  Figure 23
 below compares hand harvesting waxy-leaf (citrus) and smooth-leaf
(non-citrus) orchard crops.  There does not appear to be a visual
difference and the Wilcoxon test indicates no statistical significance
(p = 0.98; JMP 5.1.2).

Figure   SEQ Figure \* ARABIC  23 :  Hand Harvesting in Orchard Crops

The ARTF proposed clustering of the orchard crop monitoring studies is
shown in   REF _Ref179646341 \h  \* MERGEFORMAT  Table 20  below.

Table   SEQ Table \* ARABIC  20 :  ARTF Orchard Crop Clusters

Representative Studies	Cluster Code	Post-application Exposure Assessment
Activities

Study Code	Activity/Crop

ARF025	Hand harvesting apples	OH	Hand harvesting

ARF028	Hand harvesting oranges

ARF041	Hand harvesting oranges

Bagging

ARF042	Hand harvesting grapefruit

AR1002	Hand harvesting peaches

Dethorning

AR1003	Thinning apples

AR1014	Hand harvesting peaches

Cone harvesting (conifers)

AR1021	Hand harvesting peaches

Thinning

	Pollination

AR1016	Mechanical harvesting almonds	OHn	Mechanical harvesting (nuts)

ARF033	Hand pruning olives	OP	Hand pruning

	Scouting

ARF047	Hand pruning apples

Pinching

	Shaping (Christmas trees)

AR1017	Propping peaches	OW	Hand weeding

	Propping

	Animal control

	Baiting

	Grading/tagging (Christmas trees)

	Agency Review and Analysis:  The Agency generally agrees with the ARTF
proposal for orchard crop activities with the possible exception of
grouping thinning with harvesting which is discussed below.  The
remaining combinations are reasonable because similarities are evident
in both observation of the activities and the data collected.  For
example, harvesting orchard crops requires climbing in and out of the
canopy of the tree in order to retrieve much of the fruit not available
from the ground.  Pruning involves the use of shears or long handled
pruning saws which alters how trees are contacted for that activity. 
Mechanical harvesting in nuts is a unique activity involving mechanical
shakers and the majority (maybe all) nuts are harvested in this manner. 
Finally, propping requires low amounts of direct contact since it is
accomplished mostly from the ground using physical supports for tree
limbs.

From a quantitative perspective based on the monitoring data it does
appear that hand harvesting has a distinctly higher level of residue
transfer than other activities in the ARTF approach (see   REF
_Ref207691735 \h  \* MERGEFORMAT  Figure 24  below).  While the other
proposed clusters are fairly close in magnitude, the Agency supports
using the respective studies by type of activity to represent the
proposed activities.

Figure   SEQ Figure \* ARABIC  24 :  ARTF-proposed Orchard Crop Clusters

One potential alteration to the proposed crop grouping could be an
additional cluster for orchard crop thinning.  The Agency believes this
activity may be more contact-intensive and therefore could be considered
separately in exposure assessments.  This proposal is supported
statistically (p < 0.0001, Wilcoxon; JMP 5.1.2) and is shown in   REF
_Ref179646427 \h  \* MERGEFORMAT  Figure 25  below.  

Figure   SEQ Figure \* ARABIC  25 :  Comparison of Hand Harvesting and
Thinning in Orchard Crops

The Agency also believes there is consistency, both in terms of
quantitative transfer coefficients and in agricultural practice, between
some similar activities conducted in orchards and trellises, in
particular hand pruning, scouting, and weeding.  Therefore, combination
of cluster OP with similar activities in trellises may be possible, and
is discussed in the section outlining trellis crop clusters below.

Trellis Crop Clusters

In this section activities related to the production of trellis crops
are discussed.  Trellis crops can include various berries (e.g.,
raspberries and blackberries), grapes, kiwi, and hops which are grown
using a trellis system which makes it easier to control the growth of
the vines.  Grapes are grown in a variety of ways across the country and
practices vary widely depending upon why the crop is being produced. 
For example, grapes intended to be consumed directly as “table
grapes” would be handled more gingerly or the harvesters may work
slower to avoid damage which would make them less desirable in the
marketplace.  As a result, exposures may be reduced relative to the
harvest of raisins where less care is needed and more grapes could be
harvested at a faster pace.  Also, certain practices such as leaf
pulling or cane turning might be done to ensure proper sunlight
management across the plants over time as the grape bunches mature. 
Similarly, the timing of hand labor activities can vary depending upon
the desired outcome.  For example, a grower producing raisins or
reserving grapes for ice wine production may not be as concerned about
the timing of harvest activities as one producing table grapes where
actively managing harvest times based on crop sugar content is required.
 These nuances have been considered in the development of this cluster. 
The following ARTF studies were conducted in crops grown in trellises:

Hand harvesting

Blackberries (ARF020)

Juice/Wine Grapes (ARF048)

Table/Raisin Grapes (AR1020, AR1022)

Scouting

Table/Raisin Grapes (AR023)

Turning Table/Raisin Grape Canes (AR1015)

  REF _Ref179646562 \h  \* MERGEFORMAT  Figure 26  provides a graphic
illustration of one of the activities monitored in these studies.

Figure   SEQ Figure \* ARABIC  26 :  Grape Harvest (Study ARF048) 

 



ARTF Proposal:  The proposed cluster organization of these studies is
shown in   REF _Ref179646621 \h  \* MERGEFORMAT  Table 21  below.

Table   SEQ Table \* ARABIC  21 :  ARTF Trellis Crop Clusters

Representative Studies	Cluster Code	Post-application Exposure Assessment
Activities

Study Code	Activity/Crop

ARF020	Hand harvesting blackberries	THb	Caneberries	Hand harvesting

Tying

ARF048	Hand harvesting juice/wine grapes	THg	Grapes and Kiwi	Hand
harvesting

AR1020	Hand harvesting table/raisin grapes

	Tying

AR1022	Hand harvesting table/raisin grapes

	Leaf-pulling

ARF023	Scouting table/raisin grapes	TP	Hand pruning

	Bird Control

	Girdling

	Propagating

	Scouting

	Trellis repair

	Hand weeding

	Tying (low height, minimal foliage)

AR1015	Cane turning in table/raisin grapes	Tx	Harvesting hops

	Cane turning

Agency Review and Analysis:  First, it should be noted that all crops
grown in trellises are classified as smooth-leaf crops; therefore no
clustering based on leaf texture is considered.  However, it does appear
that certain activities have differing levels of residue transfer within
trellis crops.

Examining the studies comprising the proposed trellis crop clusters in  
REF _Ref211309110 \h  \* MERGEFORMAT  Figure 27  below, it appears that
cane turning (AR1015) and scouting table/raisin grapes (ARF023) have the
largest and smallest transfer coefficients, respectively.  This trend is
consistent with the observations from the studies.  Cane turning
involves heavy continuous contact with treated foliage and vines that
requires intense physical exertion to move the vines on interest into
appropriate positions.  Conversely, scouting involves examination and
documentation of all aspects of the vines and foliage to identify pest
pressure and diseases but the activity is not as physically demanding
and the contact is less intense.

Figure   SEQ Figure \* ARABIC  27 :  ARTF Studies in Trellis Crops

The separation of the remaining studies (ARF020, ARF048, AR1020, and
AR1022), to represent clusters THb and THg, is supported in   REF
_Ref179646674 \h  \* MERGEFORMAT  Figure 28  below.  The Wilcoxon test
also demonstrates statistical significance between the clusters (p <
0.0001; JMP 5.1.2).

Figure   SEQ Figure \* ARABIC  28 :  Comparison of ARTF Clusters THb &
THg

An alternative proposal may be to further separate the THg cluster by
having separate transfer levels for wine grapes and table/raisin grapes,
respectively.  In grape production, as indicated above, there is a wide
array cultural practices that can involve various activities, the
biological need for conducting the activities, and plant management
systems used.  For example, trellis design can vary widely as noted in  
REF _Ref179647118 \h  \* MERGEFORMAT  Figure 29  below.   Trellis design
can impact how workers contact plant foliage which could result in
differences in residue transfer and corresponding transfer coefficients.

Figure   SEQ Figure \* ARABIC  29 : Examples of Trellis Designs

 

Such a separation is supported statistically (p < 0.0001, Wilcoxon; JMP
5.1.2) by comparing a combination of the two hand harvesting studies
conducted on table/raisin grapes (AR1020, AR1022) with the hand
harvesting study conducted on wine grapes (AR048).    REF _Ref179647175
\h  \* MERGEFORMAT  Figure 30  below presents this comparison.

Figure   SEQ Figure \* ARABIC  30 :  Hand Harvesting Table/Raisin and
Wine Grapes

An additional alternative Agency is considering is to combine similar
activities conducted in trellises with those from orchards is being
considered.  The respective ARTF-proposed clusters OP and TP,
representing activities such as scouting and hand pruning, are very
similar in terms of residue transfer as shown in   REF _Ref179647199 \h 
\* MERGEFORMAT  Figure 31  below.  These activities are similar in some
ways because shears or other devices would be used which preclude some
level of contact with the treated plants.  There is no statistically
significant difference between the clusters as well (p = 0.90, Wilcoxon;
JMP 5.1.2).  

Figure   SEQ Figure \* ARABIC  31 :  Comparison of Similar Activities in
Orchard and Trellis Crops



Additionally, the Agency takes exception to including girdling in the
cluster represented by the scouting and hand pruning studies. 
Considering its potentially intense contact potential with foliage it
may be more appropriate to include in the cluster represented by the
cane turning study (cluster Tx).  Thus a potential alternative
clustering scheme for trellis crops is presented in   REF _Ref179647227
\h  \* MERGEFORMAT  Table 22  below.

Table   SEQ Table \* ARABIC  22 :  Alternative Trellis Crop Clusters

Representative Studies	Cluster Code	Post-application Exposure Assessment
Activities

Study Code	Activity/Crop

ARF020	Hand harvesting blackberries	THb	Caneberries	Hand harvesting

Tying

ARF048	Hand harvesting wine grapes	THwg	Wine grapes and kiwi	Hand
harvesting

Tying

Leaf-pulling

AR1020	Hand harvesting table/raisin grapes	THtg	Table/raisin grapes	Hand
harvesting

Tying

AR1022	Hand harvesting table/raisin grapes

	Leaf-pulling

ARF023	Scouting table/raisin grapes	OP/TP	Trellis Crops	Hand pruning

Bird Control

Propagating

ARF033	Hand pruning olives

	Scouting

Trellis repair

Hand weeding

Tying (low height, minimal foliage)

ARF047	Hand pruning apples

Orchard Crops	Hand pruning

Scouting

Pinching

Shaping (Christmas trees)

AR1015	Cane turning in table/raisin grapes	Tx	Harvesting hops

	Cane turning

	Girdling

Turf Clusters

Turf maintenance and sod production activities are included in the scope
of this document because occupational populations are required for the
production and maintenance of turf.  Golf course maintenance activities
include moving cups on greens, mowing, and weeding and are considered
typical of what a golf course employee would do over the course of a
day.  

ARF039 monitored workers while harvesting small slabs of sod (e.g., 1 ft
by 3 ft).  In this process slabs were cut with a typical harvester and
moved via conveyor to be stacked for market by the workers.  The
stacking/packaging part of the activity involves intense contact with
the sod and is the likely source of exposure.  The Agency believes it is
reasonable to use this study to represent all aspects of sod production
(i.e., transplanting, weeding, etc.) since it likely represents the
highest levels of residue contact.    REF _Ref211305568 \h  \*
MERGEFORMAT  Figure 32  and 33 provide a graphic illustration of some of
the activities which were monitored in these studies.

Figure   SEQ Figure \* ARABIC  32 :  Golf Course Maintenance (Study
ARF046)

 

Figure   SEQ Figure \* ARABIC  33 :  Sod Harvest (Study ARF039)

 

ARTF Proposal and Agency Review:  The ARTF-proposed grouping of turf
activities and data presentation are provided in   REF _Ref211305624 \h 
\* MERGEFORMAT  Table 23  and   REF _Ref211305609 \h  \* MERGEFORMAT 
Figure 34  below.  The Agency does not propose any alterations.

Table   SEQ Table \* ARABIC  23 :  ARTF Turf Clusters

Representative Studies	Cluster Code	Post-application Exposure Assessment
Activities

Study Code	Activity/Crop

AR035	Mechanical harvesting of sod	DH	Sod	Slab harvesting

Scouting

Hand weeding

Transplanting

AR057	Various golf course maintenance activities	DM	Golf Course Turf
Maintenance

Scouting

Transplanting

Hand weeding

Figure   SEQ Figure \* ARABIC  34 :  ARTF Turf Clusters

Crop Irrigation Cluster

Most types of irrigation activities which occur in agriculture do not
involve intense contact with treated foliage because in most cases the
irrigation systems are already permanently in place or move
automatically across treated fields.  As a result, in these cases
irrigators do not come into contact with treated foliage because the
task involves turning on a valve or switch to start the flow of
irrigation water or the operation of a device.  In some cases, furrow
irrigation is used where inversion tubes physically have to be primed
and placed in header ditches to commence water flow that does not
involve foliar contact so this activity is not included in the scope of
this discussion since the TC model does not apply.  

In other cases, irrigation requirements in certain crops and growing
conditions dictate that sprinkler systems or other types of systems be
moved in and out of treated fields in order to irrigate them.  The ARTF
proposes that the study monitoring this type of activity – hand-line
irrigation of potatoes (ARF036) – be used to represent hand-line
irrigation of all crops.    REF _Ref211308948 \h  \* MERGEFORMAT  Figure
35  provides a graphic illustration of the activity which was monitored.
The data are presented in   REF _Ref211308979 \h  \* MERGEFORMAT  Figure
36  below.  The Agency believes this study is adequate to represent hand
line irrigation in all crops since it is unlikely that this activity
will vary between crop groups and it is believed to represent a worst
case exposure situation.  For example, in this study it was a high row
crop and the workers did not wear rubber boots typical for irrigation
workers.  

Figure   SEQ Figure \* ARABIC  35 :  Potato Irrigation (Study ARF036)

 

Figure   SEQ Figure \* ARABIC  36 :  ARTF Irrigation (hand-line) Cluster

No/Low Contact Activities

A critical exposure and risk assessment element is the identification of
activities which may occur typically in agriculture but that are not
considered in a quantitative risk assessment process because they are
believed to represent negligible exposure levels.  These activities,
presented in Exhibit C, are indicated to be in cluster code “No TC”.
 Review of the classification of these activities as “no/low
contact” is part of the charge to the Panel.  This list was developed
based on expert judgment applying the criteria outlined in the
Agency’s Worker Protection Standard (WPS) which is presented in
40CFR170 for “no contact” and “low exposure potential”
activities (U.S. EPA, 2008).  Mechanized practices can be divided into
fully mechanized activities that are considered for the purposes of the
risk assessment process to meet the definition of “No contact” in
the WPS.  In the case of fully mechanized activities, the Agency does
not complete a quantitative exposure assessment but addresses these
types of potential exposures qualitatively by allowing early entry as
described in the Agency’s WPS (40CFR170).

  

“A worker may enter a treated area during a restricted-entry interval
if the agricultural employer assures that both of the following are met:
(1) The worker will have no contact with anything that has been treated
with the pesticide to which the restricted-entry interval applies
including, but not limited to, soil, water, air, or surfaces of plants;
and (2) no such entry is allowed until any inhalation exposure level
listed in the labeling has been reached or any ventilation criteria
established by § 170.110 (c)(3) or in the labeling have been met.”

In cases of partially mechanized activities where the potential for
significant exposure exists, the Agency quantitatively assesses the
resulting exposures.  However, there are a number of partially
mechanized activities that the Agency believes to have a “low exposure
potential” as defined in the WPS and these activities are assessed
qualitatively in the same manner as the “no contact” activities just
described.  The Agency also acknowledges that there is some potential
for exposure related to all of these activities because individuals
engaged in them often have excursion events for various reasons such as
unclogging machinery or equipment inspection for breakage.  In these
cases, the WPS § 170.112(c) Exception for short-term activities applies
which allows for such infrequent and unpredictable tasks to occur.

The ARTF in conjunction with regulatory agencies determined that a
number of activities for the workers generally result in no or very
little foliar contact.  These include mechanically-assisted harvesting,
frost control in orchards and trellises, and fertilizing soil. 
Therefore, as previously stated in discussion of the Agency risk
assessment methodology (Section 3.2), these types of tasks are addressed
on a case-by-case basis and would not typically be addressed
quantitatively in the Agency’s exposure assessment process.

  REF _Ref211306376 \h  \* MERGEFORMAT  Table 24  provides a summary of
these activities while Exhibit C should be referenced to review all of
the possible crop-activity combinations that would be considered to have
negligible exposures associated with them (i.e., transfer coefficients
would not be assigned to them).

Table   SEQ Table \* ARABIC  24 :  Crop-Activity Combinations without
Foliar Contact (No TC Assignment)

Crop-Type	Activity	Specific Crop

Orchards	Mechanical harvesting	All

	Irrigation (non hand-line)

Mechanical weeding

Frost control

Spreading bins

Fertilizing

Mechanical pruning

Hand pruning	Nuts

Smooth-leaf, Field	Irrigation (non hand-line)	All

	Fertilizing

Mechanical harvesting

Mechanical knifing

Mechanical swathing

Mechanical weeding

Straw bailing

Hand harvesting	Artichoke, Jerusalem

Potatoes

	Digging	Peanuts

	Flood harvesting	Cranberries

	Sanding

Transplanting

	Artichoke, Jerusalem

Sugarcane

Potatoes

Onion

Leek

Mint

Shallot

	Topping	Corn

Hairy-leaf, Field	Fertilizing	All

	Mechanical weeding

Irrigation (non hand-line)

Mechanical harvesting

Training

	Trellis	Burndown	All

	Frost control

Mechanical harvesting

Irrigation (non hand-line)

Mechanical weeding

Ditching

Discing	Hops

Waxy-leaf, Field	Mechanical harvesting	All

	Mechanical weeding

Injection fertilizing

	Turf	Aerating	Golf Courses

	Seeding

Roll harvesting	Sod

Greenhouse and Nursery	Fertilizing	All

	Mechanical harvesting

Mechanical weeding

Spreading bins	Nursery stock

Summary

It is clear that agriculture is a very dynamic industry and production
practices are often evolving 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.  The purpose of
this section was to illustrate the methods and considerations used to
outline the groupings which are applicable for exposure assessment
purposes based on what is known about hand labor activities in
agriculture and the exposure monitoring data generated by the ARTF. 
Additionally, the Agency has evaluated specific clusters proposed by
ARTF for assessment purposes and in some cases developed alternative
approaches for consideration.  It is likely that if additional
information becomes available, a similar process would be envisioned to
modify the proposals contained herein.

	Workday Duration in Farmworker Exposure Assessment

	As described above in Section 3.2, farmworkers post-application
exposure is calculated using an algorithm which requires inputs for the
duration of exposure, the source of exposure (e.g., DFRs), the transfer
coefficient, and body weight.  Currently, the Agency uses this algorithm
for deterministic exposure assessments, and will continue to do so in
future assessments, but could ultimately move toward probabilistic
methods as a way of better characterizing exposure estimates.  The
nature of the toxicological endpoints which are used in these
assessments is also a consideration in the routine characterization of
farmworker risks.  The intent of this section is to address workday
duration in the more holistic context of farmworker exposure and risk
assessment.  

	Inputs used in standard Agency assessment other than workday duration
are discussed in Section 4.3.1.  Section 4.3.2 discusses sources of data
that can be used to describe workday duration as well as its effect on
exposure estimates.  Section 4.3.3 characterizes the results of
post-application risk assessments in the context of the toxicological
endpoints typically selected for risk assessment.  Section 4.3.4
concludes the section.

Non-Duration Factors Used In Post-application Exposure Assessment

	This section discusses exposure factors other than workday duration
(i.e., DFRs, TCs, and body weight).  The purpose of this discussion is
to provide background information about the nature of these inputs with
respect to their use in the exposure assessment process.

Dislodgeable Foliar Residue (DFR):  As previously described in this
document, DFR provides a measure of the concentration of pesticide
residue per unit area of foliage (e.g., ug/cm2) that an individual can
contact over the course a workday.  Exposure can then be predicted as
described in Section 3.2 using a transfer coefficient based on the type
of crop and activity being performed and the workday duration.

When measured, DFR samples are collected on the day of application and
subsequent days (e.g., 1, 3, 5, 7 days) to ensure that the residue
dissipation can be quantified.  These studies are required to be
conducted at the maximum allowable (label) application rate in the major
crops for which use of the chemical of interest is sought.  It is also
required that multiple applications be completed if labels allow for
this and cultural practices dictate it.  Because not all applications
occur at the maximum allowable application rate and maximum allowable
application frequency, the Agency believes the resulting DFRs represent
high-end estimates of residue for risk assessment purposes.  In some
cases the Agency considers more typical application rates in its
determination of the DFR assessment inputs to reflect common
agricultural practices in is risk assessments.  However, these are
always considered in conjunction with what is the maximum condition
allowable in the specifications of the labels for a chemical being
evaluated.

Transfer Coefficient (TC):  The transfer coefficient has been the
subject of much of this document and will not be discussed in detail
here.  However, it should be noted that the data allows each transfer
coefficient for a given crop/activity cluster to be characterized as a
distribution.  Generally, transfer coefficients follow a lognormal
distribution as shown in   REF _Ref211306409 \h  \* MERGEFORMAT  Figure
37  below, from which the Agency routinely uses arithmetic mean transfer
coefficients in its exposure equation.

Figure   SEQ Figure \* ARABIC  37 :  Lognormal Probability Plot of
Transfer Coefficient Cluster HH

p = 0.15; Kolomogorov-Smirnov test (JMP 5.1.2)

Due to the intrinsic hierarchical structure of the data (e.g.,
measurements of the same worker on different reentry days, etc.), the
Agency recognizes that more refined statistical methods should be
considered for estimation of select statistics (e.g., variance component
or mixed-models).  At this point, the Agency has not completed this type
of analysis, though the ARTF discusses this issue in their database
report (Bruce et al, 2006).  Results of these types of analyses will add
to the overall characterization of the worker exposure estimates.

Body Weight (BW):  Body weight is a well known calculation input and
will not be addressed in detail here.  The Agency typically uses 70 kg,
a central tendency estimate for the general population including men and
women in its calculations.  Other values can be used for this input as
described in the Agency’s Exposure Factors Handbook but this value is
considered to best represent a typical individual in the population of
concern for the purposes of the Agency’s assessments.  It should be
noted, however, that most of the farmworker population are male (U.S.
DOL, 2005) and that the use of the 70 kg body weight input for assessing
risks in this population may result in a conservative estimate of
exposure for the majority of the actual exposed population (e.g., the
average body weight for males their 30’s is approximately 82 kg). 
Conversely, this may be less applicable to particular ethnic
subpopulations because of nutrition, genetic, and other health factors.

Workday Duration Considerations for Post-application Exposure and Risk
Assessment

The intent of this section is to provide context related to the use and
effect of workday duration in standard Agency post-application exposure
assessments.  As discussed above, the post-application exposure
assessment algorithm incorporates workday duration as an explicit
component (see Section 3.2).  It is an important component of the
exposure assessment because the algorithm assumes a proportionate
relationship between workday duration and exposure.  Both available data
to describe workday duration and the nature of the relationship between
duration and exposure are discussed below.  

Generally, a value of 8 hours per day for workday duration is used to
represent a broad average across the farmworker population.  However,
the Agency recognizes and illustrates below, that information is
available indicating that a workday can be shorter or longer (e.g., 6 or
12 hours).  To do this the Agency has summarized information from the
ARTF grower survey (Thompson, 1998) and the National Agricultural Worker
Survey (NAWS) conducted by the U.S. Department of Labor (U.S. DOL,
2005).

ARTF Grower Survey:  While the ARTF grower survey was mainly intended to
identify and categorize hand labor activities in agriculture based on
contact potential, it also included questions regarding workday
duration.  Workday duration for select crops and activities (mechanical
and hand harvesting, thinning, and mechanical and hand weeding in
tomatoes, cole crops, strawberries, and orchard crops) are presented
below.  

  REF _Ref211306876 \h  \* MERGEFORMAT  Figure 38  below presents a
summary for the selected crops across all activities in the form of
box-and-whisker plots indicating simple summary statistics.  The
percentiles are estimated from the survey results without distributional
assumptions and weighted by the response frequencies using SAS 9.1. 
Across all activities for the selected crops, the 50th percentile
workday duration is approximately 8 hours, while the 95th percentile
ranges between 10 and 12 hours.

Figure   SEQ Figure \* ARABIC  38 :  ARTF Grower Survey – Workday
Duration for Select Crop Categories

For orchard crops, to provide one specific example in   REF
_Ref211306947 \h  \* MERGEFORMAT  Figure 39  below, similar results are
found.  The 50th percentile workday duration for the majority of
activities is approximately 8 hours, while the majority of
activity-specific 95th percentiles range between 10 and 12 hours.  The
95th percentile durations of 16 and 24 hours for mechanically harvesting
cherries and mechanically weeding figs appear to be anomalous results,
but may indicate that mechanical operations can in fact have longer
durations.  The report of the grower survey including the questionnaire
and a summary of the results provided (Thompson, 1998) as well as a
summary of select data in Exhibit E.  These results demonstrate that
while an 8 hour day can in some cases be considered “typical” in the
farmworker community, longer days are certainly not atypical and
workdays can considerably exceed 8 hours.  

Figure   SEQ Figure \* ARABIC  39 :  ARTF Grower Survey – Workday
Duration for Orchard Crop Activities

Note:  box-and-whisker plot labeling is the same as in   REF
_Ref211306876 \h  \* MERGEFORMAT  Figure 38 .

National Agricultural Worker Survey (NAWS):  The results from available
NAWS information do not appear inconsistent with the results from the
ARTF grower survey.  As part of the survey, DOL asks interviewed
farmworkers how many hours they worked during their last work week at
their current farm job.  In a brief summary of this information from
2001-2002, DOL indicates that the average hours worked per week was 42,
up slightly from the average of 38 hours per week in 1993-1994.  The
breakdown of the surveyed population had 25% each working less than 35,
between 35 and 40, between 41 and 49, and 50 hours or more during their
last workweek.  

As well as select descriptions and summary analysis of NAWS, DOL
provides the raw survey data through 2006.  The Agency analyzed workday
duration for all crops and activities, and found that the years
following 2001-2002 were fairly similar to previous years.    REF
_Ref211306975 \h  \* MERGEFORMAT  Table 25  below summarizes this
analysis.  Using SAS 9.1, percentiles are estimated using the
within-year sampling weights provided in the data set.  The data set
used to complete this analysis can be downloaded from the DOL website.

Table   SEQ Table \* ARABIC  25 :  NAWS:  Hours Worked Per Week
2001-2006 (All Crops and Activities)

Year	N	50th	75th	90th	95th	99th	Maximum

2001	3070	42	50	60	63	80	97

2002	3305	41	50	59	62	75	93

2003	3529	40	49	56	61	80	91

2004	3021	45	50	65	84	88	97

2005	2206	44	51	60	70	76	97

2006	1502	44	50	58	63	80	90

Workday duration by farmworker task from NAWS is also presented in   REF
_Ref211306991 \h  \* MERGEFORMAT  Table 26  below.  The data was
combined across all years from 1993-2006 for summary purposes. 

Table   SEQ Table \* ARABIC  26 :  NAWS:  Hours Worked Per Week by
Farmworker Task (1993-2006)

Task	N	50th	75th	90th	95th	99th	Maximum

Harvest	10548	40	48	56	62	73	97

Post-Harvest	3452	40	48	56	63	82	97

Pre-Harvest	6716	40	47	55	60	80	97

Semi-Skilled	8377	44	50	60	65	80	97

Supervisor	65	45	50	60	60	78	78

Other	7558	42	50	60	65	87	97

Assuming an individual also works, on average, 5 or 6 days per week, a
daily workday duration based on the results in   REF _Ref211306975 \h 
\* MERGEFORMAT  Table 25  and   REF _Ref211306991 \h  \* MERGEFORMAT 
Table 26  above would be fairly similar to those from the ARTF grower
survey which asked respondents for the daily workday duration.  An
average of approximately 8 hours per day with the 95th percentile
between 10 and 12 hours does not appear to be inconsistent with the
data.  Though NAWS does include a question regarding the number of days
worked per week, the Agency was unsure of its relation to the
respondents’ current farm job; therefore these results were not used
to obtain a more refined estimate of daily workday duration.

However, the Agency is considering a joint effort with the Department of
Labor to establish a more robust dataset on workday duration for
farmworkers by adding additional questions/sub-questions to the NAWS
survey instrument for subsequent years of the survey.  This will include
a specific question on daily workday duration, corresponding to specific
crops and tasks.  There is discussion that this survey may be extended
to include longitudinal characteristics where interviewers seek to
obtain responses from the same farmworkers over the course of the
season.  This may inform longstanding post-application exposure and risk
assessment issues associated with exposure patterns that are described
in Section 4.3.3 below.

Effect of Workday Duration on Exposure:  In the post-application
exposure algorithm, it is assumed that there is a proportionate
relationship between workday duration and exposure.  There is some
evidence, however, that extrapolating to longer workday durations from
studies of short duration may overestimate exposure.  In other words, at
some point throughout the day, exposure may reach a level where
additional time does not yield a less than proportionate increase in
exposure such that exposure levels off or plateaus.  Though most of the
farmworkers monitored within the ARTF studies performed their activities
for less than a full workday due to logistical constraints,
extrapolation to a longer workday assuming that a proportionate
relationship between exposure and duration is considered reasonable. 
However, it is possible that this assumption may add conservatism to the
resulting assessments.  

The possible presence and magnitude of this effect was investigated
using a study that monitored peach harvesters (Spencer, 1995); the
results suggest that the rate of increase of exposure may indeed decline
with duration (and that exposure levels off or plateaus with increasing
time).  In this study, twenty-eight male workers were monitored for
azinphos-methyl (AZM) exposure while harvesting peaches on one workday. 
However – unlike most farmworker monitoring studies, including the
ARTF studies – these workers were monitored for different time
intervals (i.e., some for 2 hours, others for 5 or 7 hours).  Such a
design enables analysis of the effect of time on rate of exposure. 
Because some workers were monitored multiple times, the 28 farmworkers
yielded a total of 48 monitoring events.  The workers were paid by
piecework (i.e., bins of peaches), so dermal exposure was normalized for
production (i.e., dermal exposure per bin, or DE/bin) and analyzed
against their approximate workday intervals and grouped into 4
categories:  2, 3.5, ≥ 5, and 7 hours.  The authors concluded, as
illustrated in   REF _Ref211307085 \h  \* MERGEFORMAT  Figure 40  below,
that a significantly higher DE/bin was found for the lowest workday
interval – 2 hours.  Thus, extrapolation to an 8 hour exposure from
exposure monitoring data of less than ~3 hours could overestimate
exposure.

Figure   SEQ Figure \* ARABIC  40 :  AZM Dermal exposure/bin vs. time
for peach harvesters (Figure 3, Spencer et al., 1995)

  REF _Ref211307102 \h  \* MERGEFORMAT  Figure 41  below, from Ross et
al., (2000) shows a similar presentation of the same dataset.  They
present the AZM dermal exposure against bin production, a surrogate for
workday duration, and conclude that an extrapolation to a full workday
(12 bins) would be overestimated by 60% and 30% if exposure from that of
1/3 day (4 bins) and ½ day (6 bins), respectively, were used.

Figure   SEQ Figure \* ARABIC  41 :  Dermal monitoring of AZM residues
vs. daily peach harvest production (Figure 1, Ross et al., 1999; adapted
from Spencer et al., 1995)

 

Though based on limited information, these analyses do suggest that
extrapolation to a greater duration than that monitored will not
underestimate exposure and may actually overestimate exposure. 
Therefore, it is likely that using 8 hours per day, which is the value
typically used in Agency exposure assessments for farmworkers to
represent an average workday, is a reasonable representation of exposure
for longer workday durations because of this effect.

Toxicological Endpoint Considerations in Post-application Exposure and
Risk Assessments

Multi-Day Exposure and Risk Assessments

Multi-day farmworker risk assessments are based on toxicological
endpoints derived from repeated dosing animal studies in which the same
dose (or concentration) is administered dermally 5 days per week for 21
days or orally for 90 days.  The studies subject animals to controlled,
pre-defined and repeated concentrations in order to elicit a
dose-response relationship.  Thus, it follows that, when utilizing such
multi-day endpoints in its risk assessments, EPA should compare the
study dosing levels and durations with comparable estimates of exposure.
 In other words, when assessing worker risks, hazard endpoints derived
from multi-day repeated dosing toxicity studies in animals should be
compared to estimates of workers’ exposure over comparable multi-day
durations.  

However, though multi-day exposure is likely over the course of a
season, for numerous reasons including residue dissipation, field
re-treatments, reentry intervals, time spent in the field, intensity of
the post-application activity, and potential day-to-day movement between
fields, it is unlikely that a farmworker would experience a consistent,
steady level of repeated daily exposures to the same chemical over an
extended period of time that corresponds to the dosing pattern used to
derive the toxicology endpoint.  For example, it is unlikely that a
worker would be exposed to the same chemical concentration 7 days per
week for 90 days, as would be implied when comparing to a 90-day oral
toxicity study.  Instead, it is more likely that a farmworker would
experience an intermittent and variable exposure pattern as a result of
his day-to-day movement between fields or a pattern of decreasing daily
exposure if he were to repeatedly work in the same field.  However,
little is known about the probabilities associated with these various
possible exposure patterns.

With these types of exposure patterns and toxicological comparisons in
mind, the Agency’s approach to farmworker reentry assessments is to
utilize central tendency deterministic algorithm inputs, described in
Section 4.3.1, and combine these with additional conservative
assumptions that result in an estimate believed to represent a level of
exposure that is higher than actually experienced by large portion of
the farmworker population.  The following are some conservative elements
of Agency exposure and risk assessments that are not directly reflected
by the algorithm and likely result in overestimates of farmworker
reentry worker exposures:  

Workers repeatedly perform the same activity

Agency assessments assume that a farmworker repeats the same reentry
activity (e.g., scouting, hand harvesting, etc.) over an extended period
of time.  The ARTF grower survey (Thompson, 1998; Appendix 13) does
provide some information on the number of days per month an activity
would occur; however, the probability of these activities occurring
repeatedly on consecutive (or near-consecutive days) is not known. 
Therefore the Agency approach would not tend to underestimate risk for
those workers who do repeatedly perform the same activity on a
consecutive day-to-day basis, while perhaps significantly overestimating
risk for those who do not.

Workers are repeatedly exposed to the same chemical

Each pesticide active ingredient is assessed separately by the Agency;
therefore, farmworker exposure assessments assume exposure to the same
chemical for the duration of interest.  Like with the post-application
activity discussion above, the ARTF grower survey (Thompson, 1998;
Appendix 15) reports the number of pesticide applications per month by
crop, pesticide class (e.g., insecticide, fungicide, etc.) and pest
infestation levels.  In most cases, the frequency is once per month or
less per class of pesticide, which would make it unlikely that a worker
would be repeatedly exposed to the same chemical.  However, even if this
were the case, Agency approach would not underestimate exposure those
workers, while potentially overestimating exposure for those workers not
repeatedly experiencing exposure to the same chemical.

Workers experience residues following multiple applications at maximum
rates

Agency assessments assume that workers are exposed to residues following
the maximum number of applications at the maximum labeled rates.  Though
the probability that a site will have been treated in this fashion is
unknown, it is reasonable to assume that not all applications follow
this regimen.  This assumption does not underestimate exposure and would
tend to overestimate exposure.  

Workers repeatedly experience the same residue concentration

The ideal, and more accurate, DFR input in the exposure algorithm for a
multi-day exposure would be an average of the DFR measurements over a
sequence of actual work days.  A scenario for which exposures could
actually be estimated would be for a farmworker to continue to work in
one field for consecutive days and be exposed to a series of
sequentially declining residues, and then move to another field (treated
at a different time), and being exposed there to a different level of
declining residue over the next set of consecutive days.  However,
because little is known about the patterns of worker movement between
fields in relation to the timing of pesticide applications, the Agency
lacks information to estimate the average DFR to which a worker might be
subjected to over an extended period of time.  To do so would require a
number of assumptions regarding timing of treatments and of worker
movements between and activity patterns within fields.  

In order to maintain a conservative (i.e., health-protective) approach
in light of these uncertainties, the Agency chooses to use in its
exposure algorithm the single-day DFR measured on the day following
application, or at the end of a specified REI.  In practical terms, the
use of this value assumes that either 1) as a worker moves from
field-to-field (performing the same activity), he experiences the same
residue concentration (of the same chemical), or 2) the worker
repeatedly returns to the same field over the period of interest  in
which there is no dissipation or other degradation processes occurring. 
Both of these are believed to be unlikely situations and considered
conservative.

In sum, because many of the assumptions tend to overstate exposure, the
Agency believes its estimates of farmworkers’ multi-day exposure
likely fall at the high-end of the distribution of actual exposures for
the farmworker population.  However, the overall impact is difficult or
impossible to precisely quantify because of the infinite number of
potential exposure patterns that may occur.  On the other hand, the
impacts of the Agency’s use of other inputs in the exposure assessment
– like workday duration and the transfer coefficient – can be
quantified by using distributions in a probabilistic (e.g., Monte Carlo)
analysis.  Such an analysis is illustrated using strawberry harvesting
as an example.    REF _Ref213468830 \h  \* MERGEFORMAT  Table 27  below
outlines those inputs to the reentry exposure algorithm whose
distributions can be reasonably characterized.

Table   SEQ Table \* ARABIC  27 :  Hand Harvesting Strawberries –
Input Summary

Algorithm Input	Distribution	Parameters	Source

TC (cm2/hr)	Lognormal	Geometric mean = 944

Geometric Standard Deviation = 1.67	ARTF Cluster SH

WD (hrs/day)	Normal	Mean = 8.23

95%tile = 12	ARTF grower survey

BW (kg)	Lognormal	Mean = 77.9

Standard Deviation = 20.5	ARF025, ARF028, ARF041, ARF009, ARF010,
ARF012, ARF021, ARF020

Using a hypothetical DFR – 20 ug/cm2 measured 1 day after application
– and central tendency estimates for each input described in Section
4.3.1, the standard deterministic assessment for strawberry harvesting
results in an exposure estimate of 2.46 mg/kg-day,.  Using Crystal Ball
4.0, the distributions outlined in   REF _Ref213468830 \h  \*
MERGEFORMAT  Table 27 , and the hypothetical DFR estimate of 20 ug/cm2,
10,000 Monte Carlo simulations result in the exposure distribution
presented in   REF _Ref213468709 \h  \* MERGEFORMAT  Table 28  below.  

Table   SEQ Table \* ARABIC  28 :  Summary of Monte Carlo Simulation for
Hand Harvesting Strawberries

Percentile	Exposure (mg/kg-day)

50	1.99

55	2.16

60	2.34

65	2.55

70	2.81

75	3.10

80	3.47

85	3.92

90	4.58

95	5.79

100	23.90

The exposure estimate of 2.46 mg/kg-day derived from the deterministic
calculation represents a value between the 60th and 65th percentile of
this distribution derived from probabilistic inputs.

The comparison of the Agency’s standard deterministic exposure
estimate with the probabilistic estimate of the distribution of
exposures seems to suggest that the Agency’s standard estimates may
not represent the high-end of the exposure distribution.  The comparison
may be somewhat misleading, however, because the probabilistic
distribution still tends to overestimate exposure in three ways.  First,
it assumes that workers, who spend long hours (e.g., >11 hours/ day)
performing an activity, will spend the same (extended) amount of time
working at the same activity for multiple, consecutive days.  This
assumption would result in many values in the distribution reflecting
individuals who were working over 77 hours a week (i.e., 11+ hours per
day, 7 days per week) – a value that the NAWS dataset indicates would
fall at or near the 99th percentile of workers surveyed.  Second, the
distributions for TC and workday duration were not truncated to reflect
realistic limits.  Thus, the probabilistic distribution could include
values that reflect working more than 24 hours per day and getting many
times higher TC levels than actually measured.  To the extent that
values representing unrealistic or impossible limits were selected in
the Monte Carlo analysis, this could significantly overstate the
magnitude of exposure at the highest portion of the distribution. 
Third, this probabilistic distribution represents those workers
experiencing the same day 1 DFR for the duration of exposure (i.e., 100%
of the time) – a conservative assumption, since, over an extended
period of time workers will likely experience varying residues as a
result of declining residues within the same field or of moving from one
field to another throughout the workday or work week.  

With respect to the last factor, though not quantifiable, the Agency
believes the probability of an individual actually experiencing the same
concentration of the same chemical as they move from one field to
another performing the same activity for multiple, consecutive days is
quite low.  EPA developed two hypothetical scenarios to evaluate how
sensitive exposure estimates might be to different assumptions about
work patterns, in which workers were exposed to different DFR levels on
different days.  

Scenario A:  strawberry harvesters move between fields and have given
(hypothetical) probabilities of experiencing certain residue
concentrations;

Scenario B:  strawberry harvesters re-enter a field one day after
application and remain in the same field over 7 days.

Both examples address the low probability of workers experiencing the
same residue 100% of the time.  In the first example (Scenario A), it is
assumed that workers experience the hypothetical “day 1” residue (20
ug/cm2) 50% of the time, a “day 2” residue 30% of the time, and
“day 3” residue 20% of the time.  To calculate the “day 2” and
“day 3” residues, standard exponential decay with a hypothetical
dissipation rate of 10% per day is applied.  This situation perhaps
represents a staggered application pattern whereby workers enter new
fields at 1, 2, or 3 days following application of the same chemical. 
Using the same input distributions in   REF _Ref213468830 \h  \*
MERGEFORMAT  Table 27 , the exposure distribution resulting from workers
experiencing this pattern of residue is shown in   REF _Ref213468779 \h 
\* MERGEFORMAT  Table 29 .  Under Scenario A, the standard Agency
deterministic estimate of 2.46 mg/kg-day corresponds approximately to
the 75th percentile of the distribution, a slightly higher percentile in
this distribution than seen in the scenario where workers experience the
same “day 1” residue 100% of the time seen in   REF _Ref213468709 \h
 \* MERGEFORMAT  Table 28  and in which the transfer coefficient,
workday duration, and bodyweight are entered as distributions.

Table   SEQ Table \* ARABIC  29 :  Summary of Monte Carlo Simulation for
Scenario A 

Percentile	Exposure (mg/kg-day)

50	1.88

55	1.97

60	2.07

65	2.18

70	2.30

75	2.43

80	2.59

85	2.78

90	3.05

95	3.49

100	6.89

In Scenario B, workers are assumed to re-enter a field one day after
application and continue to harvest strawberries for 7 consecutive days
with no re-treatment of the field with the same chemical.  In this case,
the “day 1” residue is still 20 ug/cm2 and the same hypothetical
daily dissipation of 10% is assumed, so the workers would experience
7-day average DFR estimate of 15.1 ug/cm2.  Again, using the same input
distributions in   REF _Ref213468830 \h  \* MERGEFORMAT  Table 27 , the
exposure distribution resulting from workers experiencing this residue
pattern is shown in   REF _Ref213468889 \h  \* MERGEFORMAT  Table 30 .

Table   SEQ Table \* ARABIC  30 :  Summary of Monte Carlo Simulation for
Scenario B

Percentile	Exposure (mg/kg-day)

50	1.59

55	1.66

60	1.73

65	1.80

70	1.88

75	1.97

80	2.08

85	2.20

90	2.37

95	2.62

100	4.60

In the exposure distribution for Scenario B, the standard Agency
deterministic estimate of 2.46 mg/kg-day represents between the 90-95th
percentile of this exposure distribution.  Similar to Scenario A, this
would be expected since the DFR component in the equation is an average
over 7 days, a value approximately 25% lower than the DFR estimate used
in the standard Agency deterministic assessment.	 

	The Monte Carlo analyses provided above represent more realistic
patterns of farmworker exposure than that represented by the standard
Agency approach.  Not surprisingly, when these more realistic exposure
patterns are used, the Agency’s standard deterministic estimate of
2.46 mg/kg-day corresponds to higher percentiles of the exposure
distributions.  However, because the likelihood of the numerous exposure
pattern possibilities is unknown, the Agency believes – in the absence
of more accurate information – that assuming exposure to the same
chemical during the same activity at the same concentration for an
extended period of time is necessary.  But, the Agency further believes
that using high-end estimates for each quantifiable input in the
exposure algorithm – as opposed to the central tendency estimates
currently used – would result in an overall assessment that is
unreasonably conservative.

Single-Day Exposure and Risk Assessments

Some toxicological effects can occur, or it is reasonable to assume they
occur, following a single exposure.  These effects are typically either
cholinesterase inhibition, as seen in the N-methyl carbamates, or
developmental effects.  Unlike estimates of multi-day exposures which
reflect numerous conservative assumptions, it would not be unlikely that
farmworkers enter a field treated at the maximum application rate
immediately after the expiration of the REI and perform the same
activity for 1 day.  In fact, the Monte Carlo analysis in   REF
_Ref213468709 \h  \* MERGEFORMAT  Table 28  can be considered an
accurate representation of this scenario.  This is because it utilized a
single-day residue estimate (e.g., “day 1” residue) – an
appropriate input for a single-day assessment.  Thus, the Agency
exposure estimate of 2.46 mg/kg-day using standard Agency deterministic
inputs is an accurate reflection of approximately the 65th percentile of
a single-day exposure distribution for strawberry harvesters, an
estimate not considered to be at the high-end of an exposure
distribution.

It is the goal of the Agency to use high-end exposures to assess acute
risks in order to ensure that workers are appropriately protected.  With
that in mind, single-day assessments, like multi-day assessments, rely
on residues following maximum application rates as well as utilize a
proportional relationship between workday duration and exposure.  As
discussed above, both of these potentially add conservatism, though not
quantifiable, to the single-day assessments as well.  Despite this, the
Agency is concerned that current deterministic inputs may not adequately
yield an estimate at the high-end of single-day exposure distributions. 
Therefore the Agency is considering revising the approach to single-day
exposure and risk assessments with respect to the use of deterministic
inputs.  Potential changes include the use of a high-end estimate for
the transfer coefficient or workday duration or using an uncertainty
factor. 

Conclusions for Workday Duration and Post-application Exposure
Assessment

The Agency illustrated in Section 4.3.1 the varying amount of time
farmworkers spend per day conducting activities related to crop
production.  This generally ranged from 4 to 12 hours per day with 8
hours being a reasonable average estimate for most crop-activity
scenarios.  In Section 4.3.2, the Agency described the possibility that
exposure may increase at a slower rate as the workday progresses, such
that exposure following 10 or 12 hours of exposure may not, in reality,
be significantly higher than exposure following 7 or 8 hours of work. 
As a result, the standard Agency practice of linear extrapolation from
hours to exposure may actually be an overestimate.  Finally, in Section
4.3.3, the Agency presented additional aspects of the standard
post-application exposure process related to the nature of toxicological
endpoint selection.  The Agency demonstrated that the use of
conservative assumptions combined with central tendency inputs provides
an appropriately conservative estimate for the majority of Agency
exposure assessments.  After consideration of each of these aspects, the
Agency concludes that the use of 8 hours is an appropriate estimate for
workday duration for multi-day exposure assessments.  However, the
Agency recognizes the potential limitations of using similar inputs for
single-day assessments and is considering a revision to the approach to
these types of assessments.

Charge to the Panel

TOPIC A:  Crop-Activity Grouping/Clustering

In 1995, the Agency issued a data call-in (DCI) notice requiring the
development of information on the exposure potential associated with
labor activities in agriculture which occur in previously treated areas
(e.g., harvesting).  The central premise in the development and
collection of such exposure monitoring data is that activities which
exhibit similar magnitudes and patterns of exposure can be grouped
together for exposure assessment purposes.  It would also follow that
crop-activity combinations not actually monitored, but that were similar
from both ergonomic and agronomic perspectives, can be represented by
those that were monitored.  Based on this premise, the Agency has
identified several key factors for consideration by the Panel.  They
include the identification of labor activities in agriculture,
evaluation of the possible grouping approaches for similar crop-activity
combinations, and categorization of certain activities as no/low contact
in the Agency’s Worker Protection Standard (40CFR170).  Specifically,
the Agency identified the following issues for the Panel to consider:

Please comment on the strengths and limitations of the approaches and
data sources used to identify the universe of hand labor activities for
exposure assessment purposes.  Please identify any activities that EPA
has not listed for the crops included in the scope of the DCI.

The ARTF has recommended various crop-activities be grouped together or
clustered for the purposes of estimating exposure and has proposed and
conducted or purchased one or more exposure monitoring studies to be
used to represent each cluster.  The regulatory agencies also agree with
the concept of clustering like crop-activity combinations for this
purpose.  Please comment on the following:

The methods used by ARTF for the purposes of creating clusters for
exposure assessment purposes.

Statistical, agronomic, or other support for or against (1) the
ARTF-proposed clusters; (2) the Agency evaluation of the ARTF-proposed
clusters, and (3) the Agency-suggested alternative cluster schemes
outlined below.  Please include the rationale and reasoning for any
Panel-recommended changes or modifications.  The SAP Review Code in the
list refers to   REF _Ref213726121 \h  \* MERGEFORMAT  Table 31  below,
which provides a summary of the ARTF clusters, the Agency-suggested
alternatives, and relevant page numbers in the Agency’s background
document.  

Hairy Leaf Field Crops (clusters HH, HHt, and HS) [SAP Review Code A]

Smooth-leaf Field Crops (clusters SH, SSR, SSS, SW and Sx) [SAP Review
Code B]

Waxy-leaf Field Crops (clusters WIH, WIS, and Wm) [SAP Review Code C]

Orchard Crops

Cluster OH and the Agency suggestion for a separate cluster for thinning
[SAP Review Code D-1]

Clusters OHn and OW crop [SAP Review Codes D-2 and D-4]

Cluster OP [SAP Review Code E-3]

Trellis Crops

Cluster THb [SAP Review Code E-1]

Cluster THg and the Agency suggestions to further separate into clusters
for hand harvesting wine grapes (THwg) and table/raisin grapes (THtg) as
well as utilizing the hand harvesting table/raisin grape cluster to
represent girdling [SAP Review Code E-2]

Cluster TP and the Agency suggestion to group with cluster OP (see   REF
_Ref179647199 \h  \* MERGEFORMAT  Figure 31 ) [SAP Review Code E-3]

Cluster Tx [SAP Review Code E-4]

Greenhouse and Nursery Crops

Clusters GHf and GHv [SAP Review Code F-1]

Cluster GN and the Agency suggestion to have an additional cluster for 
hand-harvesting nursery crops (GHn) [SAP Review Code F-2]

Crop Irrigation (cluster I) [SAP Review Code G]

Mechanical Harvesting Cotton (clusters CHp, CHm, and CHt) [SAP Review
Code H]

Turf (clusters DH and DM) [SAP Review Code I]

Table   SEQ Table \* ARABIC  31 :  Reference Table for Charge Question
2 (b)

ARTF Study	ARTF Proposal	Agency Proposal	SAP Review Code	Page Number

Category/ Study Code	Crop	Activity	Cluster Code	Description	Summary of
Agency Review of ARTF Proposal	Cluster Code

Hairy-leaf, Field Crop Clusters

ARF045	Cucumbers	Hand Harvesting	HH	Hairy-leaf field crops:  hand
harvesting and similar contact activities	The Agency concurs with ARTF's
proposal	HH	A	54-59

ARF049	Summer Squash	Hand Harvesting

ARF024	Tobacco	Hand harvesting	HHt	Hairy-leaf (Tobacco):  hand
harvesting and canopy management	The Agency concurs with ARTF's proposal
HHt

ARF022	Sunflowers	Scouting	HS	Hairy-leaf field crops:  scouting and
similar contact activities	The Agency concurs with ARTF's proposal	HS

Smooth-leaf, Field Crop Clusters

ARF051	Tomato	Tying	SH	Smooth-leaf field crops:  hand harvesting and
tying	The Agency concurs with ARTF's proposal	SH	B	50-54

AR1001	Strawberry	Hand Harvesting

AR1023	Tomato	Hand Harvesting

AR1024	Strawberry	Hand Harvesting

AR1025	Cotton	Scouting	SSr	Smooth-leaf field crops:  scouting in row
conditions	The Agency concurs with ARTF's proposal	SSr

AR1027	Tomato	Scouting

ARF009	Corn	Scouting	SSs	Smooth-leaf field crops:  scouting in solid
stand conditions	The Agency concurs with ARTF's proposal	SSs

ARF021	Dry Pea	Scouting

AR1006	Cotton	Hand weeding	SW	Smooth-leaf field crops:  hand weeding,
thinning, and similar contact activities	The Agency concurs with ARTF's
proposal	SW

AR1018	Cotton	Hand weeding

AR1019	Dry Pea	Hand weeding

ARF010	Sweet Corn	Hand harvesting	Sx	Smooth-leaf field crops:  intense
contact activities	The Agency concurs with ARTF's proposal	Sx

Waxy-leaf, Field Crop Clusters

ARF050	Cabbage	Hand harvesting	WIH	Waxy-leaf field crops, low height: 
hand harvesting and similar contact activities	The Agency concurs with
ARTF's proposal	WIH	C	59-61

AR1008	Cauliflower	Scouting	WIS 	Waxy-leaf field crops, low height: 
scouting and similar contact activities	The Agency concurs with ARTF's
proposal	WIS 

ARF011	Cauliflower	Scouting	Wm	Waxy-leaf field crops, medium height: 
all activities, plus full foliage weeding	The Agency concurs with ARTF's
proposal	Wm

ARF012	Cauliflower	Hand harvesting

ARF037	Cabbage	Hand weeding

Orchard Crop Clusters

ARF025	Apples	Hand Harvesting	OH	Orchard crops:  hand harvesting and
similar contact activities	The Agency generally concurs with ARTF's
proposal.  However, one potential alteration to the proposed crop
grouping could be an additional cluster for orchard crop thinning.  The
Agency believes this activity may be more contact-intensive and
therefore could be considered separately in exposure assessments.
Possibly create a separate cluster for orchard crop thinning	D-1	63-69

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	The Agency concurs with ARTF's proposal	OHn	D-2

	ARF033	Olives	Hand Pruning	OP	Orchard crops:  hand pruning, scouting,
and similar contact activities	See Agency review comment for ARTF
Proposal for Cluster TP	See OP/TP	See E-3

	ARF047	Apples	Hand Pruning

AR1017	Peaches	Propping	OW	Orchard crops:  hand weeding and similar
contact activities	The Agency concurs with ARTF's proposal	OW	D-4

	Trellis Crop Clusters

ARF020	Blackberries	Hand harvesting	THb	Trellis crops:  hand harvesting
caneberries and similar contact activities	The Agency concurs with
ARTF's proposal	THb	E-1	69-76

ARF048	Juice/Wine Grapes	Hand harvesting	THg	Trellis crops:  hand
harvesting grapes and similar contact activities	The Agency is
considering to further separate the THg cluster by having separate
transfer coefficients for hand harvesting wine grapes and table/raisin
grapes, respectively.  The Agency also proposes to utilize the revised
THtg cluster to represent girdling.	THwg	E-2

	AR1020	Table / Raisin Grapes	Hand harvesting

THtg

AR1022	Table / Raisin Grapes	Hand harvesting

ARF023	Table / Raisin Grapes	Scouting	TP	Trellis crops:  hand pruning,
scouting, and similar contact activities	The Agency is considering
combining similar activities conducted in trellises and orchards.  The
respective ARTF-proposed clusters OP and TP, representing activities
such as scouting and hand pruning, are very similar because shears or
other devices would be used which preclude some level of contact with
the treated plants.  Also, corresponding to Review Code E-2, girdling
would be removed from this cluster.	OP/TP	E-3

	AR1015	Table / Raisin Grapes	Cane turning	Tx	Trellis crops:  intense
contact activities	The Agency concurs with ARTF's proposal	Tx	E-4

	Greenhouse and Nursery Crop Clusters

ARF055	Solidasters, Snapdragons, Lillies	Hand Harvesting	GHf	Greenhouse
and nursery floriculture hand harvesting:  all flowers and methods	The
Agency concurs with ARTF's proposal	GHf	F-1	40-45

ARF020	Blackberries	Hand Harvesting	GHv	Greenhouse vegetables: hand
harvesting and similar contact activities	The Agency concurs with ARTF's
proposal	GHv

ARF051	Tomatoes, fresh	Tying

ARF039	Chrysanthe-mums	Pinching	GN	Greenhouse and nursery crops:  all
activities	The Agency generally concurs with ARTF's proposal.  However,
the Agency believes that there could be support for additional
separation of hand harvesting nursery crops from other nursery crop
activities.	GN	F-2

	ARF043	Nursery Stock Citrus Trees	Hand Pruning

ARF044	Nursery Stock Citrus Trees	Hand Harvesting

All crops:  transplanting

GHn

Crop Irrigation Cluster

ARF036	Potatoes	Irrigation	I	Irrigation, any crop where hand line is
possible	The Agency concurs with ARTF's proposal	I	G	78-80

Mechanical Harvesting Cotton Clusters

AR1004	Cotton	Mechanical Harvesting	CHp	Cotton, mechanical harvesting: 
picker operator and raker (based on boll residues)	The Agency concurs
with ARTF's proposal	CHp	H	61-63

	CHm	Cotton, mechanical harvesting:  module builder operator (based on
boll residues)	The Agency concurs with ARTF's proposal	CHm

	CHt	Cotton, mechanical harvesting:  tramper (based on boll residues)
The Agency concurs with ARTF's proposal	CHt

Turf Clusters

ARF035	Sod	Mechanical Harvesting	DH	Sod:  mechanical harvesting,
scouting, transplanting, and hand weeding	The Agency concurs with ARTF's
proposal	DH	I	76-78

ARF057	Golf Course Turf	Maintenance	DM	Golf courses:  maintenance
activities	The Agency concurs with ARTF's proposal	DM



As indicated in the background document, the Agency recognizes the
limitations associated with using certain statistical tests (such as the
nonparametric Wilcoxon and Kruskal-Wallis tests) to provide a broad
rationale for the separation or combination of studies to form clusters.
 Specifically, these tests do not adequately account for or consider a
number of complex features of the data such as repeated measurements on
the same worker and nesting.   Again, as stated in the text, a
mixed-model approach that incorporates the hierarchical nature of the
data 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.  In Exhibit F, the Agency
provides a case study example of this alternate (mixed model) approach
for determining reasonable groupings of transfer coefficients (TCs) from
exposure studies involving various crop activities thought to be
ergonomically and/or agronomically similar.

The Agency believes the proposed approach illustrated in Exhibit F uses
more appropriate statistical and quantitative procedures for determining
which exposure monitoring studies can or should be combined.  Please
discuss thoughts and/or concerns with the analytical approach outlined
in Exhibit F and on the annotated SAS code provided as an attachment to
Exhibit F.  Please provide feedback on the results of the case study
which indicates that it would not be inappropriate to consider TC values
associated with hand harvesting activities in orchards to be distinct
from TC values associated with hand thinning activities in orchards (see
SAP Review Code D-1 in   REF _Ref213726121 \h  \* MERGEFORMAT  Table 31 
and   REF _Ref179646427 \h  \* MERGEFORMAT  Figure 25 ).

Please comment on the classification of crop-activity combinations in
Agency Exhibit C, identified with a cluster code of “No TC”, as
involving no or very low exposure.  Please identify any crop-activity
combinations classified as “No TC” in Exhibit C which should be
categorized differently because of their associated exposure potential. 
Likewise, please identify any combinations which should be categorized
as “No TC” which are currently included in other clusters.  Please
explain the basis for any such recommendations.

TOPIC B:  Workday Duration

The Agency discussed its methodology for assessing post-application
exposures with an emphasis on the workday duration input.  A central
tendency value of 8 hours per day is typically used by the Agency.  The
data also show, as seen in several sources, certain portions of the
population work longer over the course of a day (e.g., 10 or 12 hours). 
However, the Agency believes that, in most cases, employing a central
tendency estimate of 8 hours per day yields an appropriately protective
estimate of risk because of the combined impact of several other inputs
in the exposure and risk assessment process.  Specifically, the
following issues have been identified for the Panel to consider: 

Please comment on the strengths and limitations of the data sources used
to quantify the duration of a workday for farmworkers, as well as any
additional sources of information that could be used for the analysis of
farmworker workday duration.  If any are identified, please comment on
the possible impacts they might have on the results of the analysis
conducted by the Agency.

Please comment on the Agency’s conclusion that using 8 hours per day
for exposure assessment purposes and given the conservativeness of the
other inputs results in estimates of farmworker exposures at the high
end of the distribution of actual multi-day exposures.  To the extent
that the Panel believes that this is not the case, please suggest
alternative approaches.

Please comment on whether the Agency’s approach to single-day exposure
assessments results in farmworker exposure estimates that fall in the
high end of the distribution of actual single day exposures.  To the
extent the Panel thinks that is not the case, please suggest alternative
approaches that may generate such estimates.

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Labor, Office of the Assistant Secretary for Policy, Office of
Programmatic Policy, Research Report No. 9. March 2005. 

U.S. EPA (1980). Minutes of FIFRA SAP Subcommittee Meeting (February
21/22, 1980) Signed by Assistant Executive Anthony Inglis.

U.S. EPA (1981). Informal Review of Draft Proposed Pesticide
Registration Guidelines, Subpart K:  Exposure Data Requirements: Reentry
Protection (May 13/14, 1981), Signed by Executive Secretary Philip H.
Gray, Jr.

U.S. EPA (1984). Pesticide Assessment Guidelines, Subdivision K –
Exposure: Reentry Protection [NTIS Document Number PB85-120962]  

U.S.EPA 1986a). Pesticide Assessment Guidelines, Subdivision U –
Applicator Exposure Monitoring [NTIS Document Number PB87-133286
(October, 1986).]  

U.S. EPA (1986b). Transmittal of the Final FIFRA Science Advisory Panel
(SAP) Reports on the February 11-12, 1986 Meeting  

 

U.S. EPA (1989). Good Laboratory Practices (40CFR160)  

U.S. EPA (1992). Pesticide Handlers Exposure Database, (PHED) Version 1

U.S. EPA, (1993). Pesticide Rejection Rate Analysis: Occupational and
Residential Exposure:  [EPA Document 738-R-93-008]   

U.S. EPA (1994). Breakout Summary Report For Workshop On The Revisions
To Subdivision K Post-Application Exposure Guidelines (April 14-15,
1994)

U.S. EPA (1995.) Data Call-In Notice For Post-Application Exposure Data

U.S. EPA (1996). Residue Chemistry Test Guidelines OPPTS 860.1500 Crop
Field Trials, (EPA Document 712-C-96-183) available at:   HYPERLINK
"http://www.epa.gov/opptsfrs/publications/OPPTS_Harmonized/860_Residue_C
hemistry_Test_Guidelines/Series/860-1500.pdf" 
http://www.epa.gov/opptsfrs/publications/OPPTS_Harmonized/860_Residue_Ch
emistry_Test_Guidelines/Series/860-1500.pdf  

U.S. EPA (1998a). Draft Series 875 – Occupational and Residential
Exposure Test Guidelines, Group B – Post-application Exposure
Monitoring Test Guidelines - available at   HYPERLINK
"http://www.epa.gov/scipoly/sap/1998/march/contents.htm" 
http://www.epa.gov/scipoly/sap/1998/march/contents.htm  

U.S. EPA (1998b) Health Effects Test Guidelines – OPPTS 870.3200 21/28
Day Dermal Toxicity (EPA Document 712-C-98-201) available at:  
HYPERLINK
"http://www.epa.gov/opptsfrs/publications/OPPTS_Harmonized/870_Health_Ef
fects_Test_Guidelines/Series/870-3200.pdf" 
http://www.epa.gov/opptsfrs/publications/OPPTS_Harmonized/870_Health_Eff
ects_Test_Guidelines/Series/870-3200.pdf  

U.S. EPA (2000). Science Advisory Council For Exposure Policy 3.1,
Agricultural Transfer Coefficients (Original 5/7/98 revised 8/7/2000).

U.S. EPA (2008). 40CFR170 Worker Protection Standard, available at  
HYPERLINK
"http://www.access.gpo.gov/nara/cfr/waisidx_03/40cfr170_03.html" 
http://www.access.gpo.gov/nara/cfr/waisidx_03/40cfr170_03.html  

Zweig, G., Gao, R.-Y., Witt, J.M., Popendorf, W. and Bogen. K. (1984). 
Dermal exposure to carbaryl by strawberry harvesters. Journal of
Agricultural and Food Chemistry 32:1232-1236.

Zweig, G., Leffingwell, J.T. and Popendorf, W.  (1985). The relationship
between dermal pesticide exposure by fruit harvesters and dislodgeable
foliar residues.  Journal of Environmental Science and Health B20:27-59.

 The use of personal protective equipment or other types of equipment to
reduce exposures for post-application workers is not considered a viable
alternative for the regulatory process for a variety of reasons as
described in the Agency’s Worker Protection Standard (40CFR170).

 The conduct of both proposed and completed research which involves
intentional exposure to humans is now subject to ethical and scientific
review pursuant to 40 CFR 26, subpart K.  In 2006, the Agency
established the Human Studies Review Board (herein referred to as the
HSRB) that is charged with the evaluation of studies that involve
intentional exposure of human subjects, from both a scientific and
ethical perspective.  Review of the proposed research by the HSRB was
not required for these studies because they were conducted previous to
the date required for that review.  Review of the completed research by
the HSRB is not required for pre-rule studies unless the research was
for the purposes of identifying and quantifying a toxic endpoint which
the ARTF research does not.  Instead an internal Agency review of the
data for ethical concerns is required for studies conducted prior to
that date and all of the monitoring data developed by ARTF and/or
otherwise considered in the preparation for this meeting have been
reviewed and are in compliance with all applicable requirements
pertaining to these issues.

 Section 3 provides a detailed explanation of the use of TC in
post-application exposure assessment.

 Inhalation exposure measurements were required as well but only in
limited situations defined by the vapor pressure of the chemical (i.e.,
>10-4 mm Hg if applied in a field and >10-5 mm Hg if applied in a
greenhouse).   

   HYPERLINK "http://www.exposuretf.com/Home/ARTF/tabid/57/Default.aspx"
 http://www.exposuretf.com/Home/ARTF/tabid/57/Default.aspx 

   HYPERLINK "http://www.epa.gov/scipoly/sap/1998/march/contents.htm" 
http://www.epa.gov/scipoly/sap/1998/march/contents.htm 

   HYPERLINK
"http://www.epa.gov/scipoly/sap/meetings/2007/010907_mtg.htm" 
http://www.epa.gov/scipoly/sap/meetings/2007/010907_mtg.htm 

 As previously indicated, this information is only an overview of the
activities of ARTF.  The Agenda for the December 2008 meeting has a
significant allocation of time during which the ARTF will present more
details related to their scope, research program, milestones, and
results.  See Bruce and Korpalski (2008) for more information.

 The methodology for conducting exposure monitoring studies, which
involves the use of whole body dosimeters and face and hand wipes, was
the recent subject of the January 2007 SAP (see   REF _Ref212017237 \h 
\* MERGEFORMAT  Table 1 ).

 Details of this equation are further provided in Section 3.2.

 In several previous public meetings conducted in 2007 and 2008
including the January 2007 FIFRA SAP review of worker exposure
monitoring methods the Agency acknowledged that there are uncertainties
associated with the use of the handwash exposure monitoring method
(Bradman 2008; Fenske and Lu 1994; Fenske et al 1998).  In 2008 the
Agency proposed a strategy for accounting for these uncertainties that
adjusted hand exposure levels based on removal efficiency from the skin
and the distribution of exposure across the body.  The data provided in
this example have not yet been adjusted based on this approach.  All
other approaches and considerations will remain consistent with those
presented herein when the Agency completes its final quantitative
determinations of TCs based on the methods described in this paper.

 Worker monitoring was completed 1, 2, and 3 days after the second, and
final, application.

 No significant contamination was noted in any negative control samples
generated in this study.

 DFR sampling was conducted for many days after the second application,
but concurrent dermal exposure monitoring was only conducted on days 1,
2, and 3 after the second application.

 In some studies, exposure monitoring was conducted using inner and
outer dosimeters such that, if desired, exposure, and therefore TCs,
could be derived to represent an individual wearing a T-shirt and
shorts, or another alternative combination of clothing.

 If activities on turf are being evaluated (e.g., in golf course
maintenance) the Iwata DFR method does not apply and a different
technique is used to quantify residue dissipation.  DFRs have just been
used to describe this general process in this document since it applies
to most activities of concern.

 Section 4 provides more detail on the final approach which includes
complete use of the data.

 The level of concern is defined by an MOE which exceeds the applicable
uncertainty factors which have been established for the specific
chemical being evaluated.  A target MOE value of 100 is typically the
level where risks are no longer of concern.

   HYPERLINK "http://www.ipmcenters.org/index.cfm" 
http://www.ipmcenters.org/index.cfm 

   HYPERLINK "http://www.agcensus.usda.gov/" 
http://www.agcensus.usda.gov/ 

   HYPERLINK "http://www.ipmcenters.org/cropprofiles/cp_form.cfm" 
http://www.ipmcenters.org/cropprofiles/cp_form.cfm 

 This document also includes the results of a series of statistical
cluster analyses which represent the first attempt by the ARTF at
grouping similar crop-activity combinations.  This effort was abandoned
in favor of a more conventional, agronomically-based approach described
below in Section 4.2.  However, for the purposes of identifying all
possible hand labor activities in agriculture, this survey has been
extensively used.

   HYPERLINK "http://www.doleta.gov/agworker/naws.cfm" 
http://www.doleta.gov/agworker/naws.cfm 

 As previously noted, this approach was not the initial approach for
summarizing and using the data generated by ARTF.  Instead a statistical
clustering technique was attempted as described in the ARTF grower
survey (Thompson, 1998).  The peer review of this approach prompted the
development of the current agronomic method (Ross, 2001).  

 The annotation “TC, TDE” – Transfer Coefficient, Total Dermal
Exposure – in the analysis reflects the use of transfer coefficients
for workers wearing long-sleeve shirts and pants.

 Studies conducted by the ARTF have the prefix “ARF” in the study
code.  Some studies were completed before ARTF was formed.  These
studies were evaluated and purchased by the ARTF for inclusion into the
database after a collaborative review by ARTF and the regulatory
agencies.  These studies are noted by the prefix “AR”.

 For example, “HH” refers to hairy-leaf crops harvesting, while
“OH” refers to orchard-crop harvesting.  

 All tables within this section follow this same format. In the two
left-hand columns the ARTF study code and a description of the activity
completed in that study is provided similar to Table 11.  The middle
column is the cluster code assigned by ARTF (Bruce and Korpalski, 2008).
 Clusters represent an agronomic grouping that includes non-monitored
activities as well as the activity actually monitored.  Cluster codes
typically follow the format:  Agronomic group (e.g., G=greenhouse);
Subgrouping within agronomic group based on setting or activity (e.g.,
N=nursery or H=harvesting); and at times, further activity descriptors
(e.g., f=floriculture).  The two right hand columns describe the crops
and activities which the specific cluster is meant to represent with
actual monitored activities denoted using italics.

 Tobacco, a tall crop at harvest, may result in a more evenly
distributed total exposure across the body compared with cucumbers or
summer squash, low crops at harvest, where exposure may be concentrated
on the hands.

 An alternative mixed model analysis is provided in Exhibit F that uses
the data in the OH cluster as a case study to demonstrate a method 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.

 From   HYPERLINK
"http://cetulare.ucdavis.edu/pubgrape/tb994.htm#Figure1" 
http://cetulare.ucdavis.edu/pubgrape/tb994.htm#Figure1 

 DFR, as the acronym implies, refers specifically to foliage with
respect to conventional crops.  As previously described in Sections
4.2.2.3 and 4.2.2.6, though they are alternative residue measurements
based on different sampling techniques, DBR and TTR, for cotton and
turf, respectively, are used in the same fashion as DFR in exposure
assessments.  For simplicity, however, this section refers only to DFR.

 As described there, the crop/activity-specific transfer coefficient
(measured in cm2/hr) is the coefficient that relates the measured DFR
(ug/cm2) to the predicted hourly exposure rate (ug/hr).

 If a toxicological endpoint of concern is used that is developmental in
nature, a value of 60 kg is used to represent the central tendency body
weight value for women.  

 http://www.doleta.gov/agworker/naws.cfm

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3 Bradman (2008) indicated that there is a seasonal aspect in worker
activities which should be considered for exposure assessment purposes. 
Simcox (1997) investigated exposures to farmworkers during apple
thinning and found workers conducted the activity on several different
days after applications which in some cases were 1 day after application
up to 49 days after application

 Exposure (mg/kg-day) = [DFR (0.02 mg/cm2) * TC (1076 cm2/hr) * WD (8
hours)] / BW (70 kg) = 2.46

 Assuming inputs are representative of the population of strawberry
harvesters.

 Recall, however, that this distribution represents those workers
experiencing this “day 1” DFR for the duration of exposure (i.e.,
100% of the time) – a conservative assumption, since, over an extended
period of time workers will likely experience varying residues as a
result of declining residues within the same field or moving from one
field to another.  An alternative interpretation would be to think of
the sub-population of strawberry harvesters who are, in fact, exposed to
the same residue concentration for an extended period of time.  For this
sub-population, the deterministic estimate of 2.46 mg/kg-day would
represent between the 60th and 65th percentile.  However, as discussed,
the Agency believes that the number of farmworkers actually experiencing
this scenario is extremely low.

  These inputs are arbitrary but serve to illustrate the degree to which
exposure estimates may change based on a different pattern of reentry
worker movements between fields and the timing of field treatments.  

Page   PAGE  53  of   NUMPAGES  109