[Federal Register Volume 76, Number 220 (Tuesday, November 15, 2011)]
[Notices]
[Page 70721]
[FR Doc No: 2011-29404]

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DEPARTMENT OF ENERGY

Federal Energy Regulatory Commission

[Docket No. AD12-5-000]

 Voltage Coordination on High Voltage Grids; Notice of Staff
Workshop

    Take notice that the Federal Energy Regulatory Commission will hold
a Workshop on Voltage Coordination on High Voltage Grids on Thursday,
December 1, 2011 from 9 a.m. to 4:30 p.m. This staff-led workshop will
be held at the Federal Energy Regulatory Commission, 888 First Street
NE., Washington, DC 20426. The workshop will be open for the public to
attend and advance registration is not required.
    The Commission is interested in better understanding the
interaction between voltage control, reliability, and economic
dispatch. In addition, the Commission will consider how improvements to
dispatch and voltage control software could improve reliability and
market efficiency. The workshop will address how entities currently
coordinate economic dispatch and voltage control and the capability of
existing and emerging software to improve coordination and optimization
of transfer capability across the Bulk-Power System from a reliability
and economic perspective.
    The agenda for this workshop will be issued at a later date.
Information will be posted on the calendar page for this event on the
Commission's web site, http://www.ferc.gov, prior to the event. This
event will not be Web cast nor transcribed.
    Commission workshops are accessible under section 508 of the
Rehabilitation Act of 1973. For accessibility accommodations, please
send an email to<EMAIL_ADDRESS>or call toll free 1-(866) 208-
3372 (voice) or<PHONE_NUMBER> (TTY), or send a FAX to<PHONE_NUMBER>
with the required accommodations.
    For more information about this conference, please contact: Sarah
McKinley, Office of External Affairs, Federal Energy Regulatory
Commission, 888 First Street NE., Washington, DC 20426,<PHONE_NUMBER>,
<EMAIL_ADDRESS>
    Dated: November 8, 2011.
Kimberly D. Bose,
Secretary.
[FR Doc. 2011-29404 Filed 11-14-11; 8:45 am]
BILLING CODE 6717-01-P


[Federal Register: October 3, 2005 (Volume 70, Number 190)]
[Notices]
[Page 57581]
[DOCID:fr03oc05-71]

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DEPARTMENT OF ENERGY

Federal Energy Regulatory Commission

[Docket No. RP05-393-001]

Midwestern Gas Transmission Company; Notice of Compliance Filing

September 27, 2005.
    Take notice that on September 21, 2005, Midwestern Gas Transmission
Company (Midwestern) tendered for filing to become part of its FERC Gas
Tariff, Third Revised Volume No. 1, the following tariff sheets to
become effective September 1, 2005:

Substitute Sixth Revised Sheet No. 202
Substitute Eighth Revised Sheet No. 204
Substitute Second Revised Sheet No. 222A
Fifth Revised Sheet No. 224
Substitute Second Revised Sheet No. 230A
Substitute Fourth Revised Sheet No. 235
Substitute Third Revised Sheet No. 242
Substitute Fifth Revised Sheet No. 272

    Midwestern states that this filing is being made to comply with the
Commission's Letter Order dated September 6, 2005 regarding compliance
with the Federal Energy Regulatory Commission's Order No. 587-S .
    Any person desiring to protest this filing must file in accordance
with Rule 211 of the Commission's Rules of Practice and Procedure (18
CFR 385.211). Protests to this filing will be considered by the
Commission in determining the appropriate action to be taken, but will
not serve to make protestants parties to the proceeding. Such protests
must be filed in accordance with the provisions of Section 154.210 of
the Commission's regulations (18 CFR 154.210). Anyone filing a protest
must serve a copy of that document on all the parties to the
proceeding.
    The Commission encourages electronic submission of protests in lieu
of paper using the ``eFiling'' link at http://www.ferc.gov. Persons

unable to file electronically should submit an original and 14 copies
of the protest to the Federal Energy Regulatory Commission, 888 First
Street, NE., Washington, DC 20426.
    This filing is accessible online at http://www.ferc.gov, using the

``eLibrary'' link and is available for review in the Commission's
Public Reference Room in Washington, DC. There is an ``eSubscription''
link on the Web site that enables subscribers to receive email
notification when a document is added to a subscribed docket(s). For
assistance with any FERC Online service, please e-mail
<EMAIL_ADDRESS>or call<PHONE_NUMBER> (toll free). For

TTY, call<PHONE_NUMBER>.

Magalie R. Salas,
Secretary.
 [FR Doc. E5-5405 Filed 9-30-05; 8:45 am]

BILLING CODE 6717-01-P


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.

Bibliography

ARTF (1996) Agricultural Reentry Activities and Pesticide Exposures in The United States and Canada:  Results of Crop Production Experts. Doane Marketing Research, Inc.

Bradman, A.; Salvatore, A.L. Boeniger, M.; Castorina, R.; Snyder, J.; Barr, D.; Jewell, N.P.; Kavanaugh-Baird, G.; Striley, C.; Eskenazi, B. (2008) Community-based intervention to reduce pesticide exposure to farmworkers and potential take-home exposure to their families.  Journal of Exposure Science and Environmental Epidemiology (2008) 1-11

Bruce, E., Holden, L. and Korpalski, S. (2003), Transfer Coefficient:  A Generic Tool for Estimating Pesticide Exposure to Agricultural Workers Who Re-Enter Treated Crops, ARTF LLC; June 27, 2003.  (MRID 46040301)

Bruce, E., and Korpalski, S. (2008), Development of the ARTF Transfer Coefficient Database, ARTF LLC.

Bruce, E., Holden, L. and Korpalski, S., (2006), Reissued Report: Agricultural Reentry Task Force Transfer Coefficient Database: A Summary of its Development and Use, ARTF LLC, January 13, 2006.  (MRID 46734002)

CEQ - Council on Environmental Quality Task Group on Occupational Exposure to Pesticides (1974)  Occupational Exposure to Pesticides, Report to the Federal Working Group on Pest Management From the Task Group on Occupational Exposure to Pesticides, January 1974.

Durham W.F. and Wolfe, H.R. (1962).  Measurement of the exposure of workers to pesticides.  Bulletin of the WHO 26:75-91.

Fenske RA, Lu C. (1994).  Determination of handwash removal efficiency: incomplete removal of the pesticide chlorpyrifos from skin by standard handwash techniques.  Am Ind Hyg Assoc J. 55(5):425-432.

Fenske, R.A., Schulter, C., Lu, C. and Allen, E.H. (1998).  Incomplete removal of the pesticide captan from skin by standard handwash exposure assessment procedures.  Bulletin of Environmental Contamination and Toxicology 61:194-201.

Hackathorn, D.R. and D.C. Eberhart. (1985). Data base proposal for use in predicting mixer-loader/applicator exposure.  American Chemical Society Symposium Series 273, pp. 341–355

Iwata, Y.; Knaak, J.B.; Spear, R.C.; Foster, R.J. (1977). Worker Reentry into Pesticide Treated Crops.  Procedure For The Determination of Dislodgeable Residues on Foliage, Bull. Environ. Contam. Toxicol. 18:649-655.

Klonne, D.R., Artz, S.C., Prochaska, C., Rotondaro, A. (1999) Determination of Dermal and Inhalation Exposure To Reentry Workers During Harvesting In Tobacco, Study Number ARF024, EPA MRID 450059-11.

Korpalski, S., Bruce E., Holden, L., and Klonne, D. (2005) Dislodgeable Foliar Residues are Lognormally Distributed for Agricultural Reentry Studies, J. Exposure Analysis and Envir. Epid.,  Vol. 15 (2), pp. 160-163.

ORETF (1998) Transferable Turf Residue Sampling Technique, Field Study Procedures, Outdoor Residential Exposure Task Force Standard Operating Procedure (10.E.1).

Popendorf, W (1980) Exploring Citrus Harvesters’ Exposure To Pesticide Contaminated

Foliar Dust, Am. Industr. Hygiene Assoc. J. 41:652-659.

Popendorf, W.J. and J.T. Leffingwell (1982) Regulating OP Pesticide Residues For Farmworker Protection, Residue Reviews 82: 125-201.

PMRA & U.S. EPA. (1997). Workshop On Post-application Exposure Assessment – Final Report.

Ross, J.H., Dong, M.H., and Krieger R.I. (2000) Conservatism in Pesticide Exposure Assessment, Regulatory Toxicology and Pharmacology 31: 53-58

Ross, J. (2001).  Peer Review Summary Report and ARTF Response to Peer Review Comments, ARTF Peer Review Panel, August, 2001.  (MRID 45491903)

Simcox, N.J.; Camp, J.; Fenske, R.; Stebbins, A.; McDonald, B.; Lee, I-Chwen; Bellamy, G.; Kalman, D. (1997) Farmworker Exposure To Pesticide Residue During Apple Thinning; University of Washington, Department of Environmental Health, Seattle WA 98195 (June 15, 1997)

Spencer, J.R., Sanborn J.R., Hernandez, B.Z., Krieger, R.I., Margetich, S.S., and Schneider, F.A. (1995). Long vs. Short Monitoring Intervals For Peach Harvesters Exposed To Foliar Azinphos Methyl Residues, Toxicology Letters 78: 17-24.

Thompson, R. (1998). Agricultural Worker Crop Contact from Reentry Activities Performed in the USA and Canada: Grower Results, Doane Marketing Research, Inc., MRID 44802601.

U.S. DOL (2005).   HYPERLINK "http://www.doleta.gov/agworker/report9/toc.cfm"  Findings from the National Agricultural Workers Survey (NAWS) 2001 - 2002. A Demographic and Employment Profile of United States Farm Workers. U.S. Department of 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 <IP_ADDRESS> and <IP_ADDRESS>, 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