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UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

WASHINGTON, D.C. 20460 

OFFICE OF CHEMICAL SAFETY

AND POLLUTION PREVENTION

 

MEMORANDUM (DRAFT)

Date: March 24, 2015

SUBJECT: Review of Agricultural Handler Exposure Task Force (AHETF) Monograph: 

“Backpack Application of Liquid Sprays in Utilities Rights-of-Way” (AHE1012)

PC Code: -- DP Barcode: D424145

Decision No.: -- Registration No.: --

Petition No.: -- Regulatory Action: --

Risk Assessment Type: -- Case No.: --

TXR No.: -- CAS No.: --

MRID No.: 49478601 40 CFR: --

 Ver.Apr.08

 

FROM: Matthew Crowley, Biologist

Chemistry and Exposure Branch

Health Effects Division

THROUGH: David J. Miller, Chief

Chemistry and Exposure Branch

Health Effects Division

TO: Richard Dumas

Pesticide Registration Division

This memorandum presents EPA’s review of the occupational handler exposure scenario 

monograph “Backpack Application of Liquid Sprays in Utilities Rights-of-Way” (AHE1012) 

submitted by the Agricultural Handler Exposure Task Force. Scientific review of the field and 

analytical report (AHE400 – Bruce, E., 2014) that outlines the monitoring data collected to 

support this scenario can be found in a separate data evaluation review (DER) memorandum 

(Crowley, 2015). 

The AHETF satisfactorily followed the study protocols, sampling design, and data analysis plan. 

Though data analysis objectives/benchmarks were not fully met, EPA considers the backpack 

rights-of-way scenario complete and its results are recommended for use in routine assessment of 

exposure and risk.
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1.0 Executive Summary

This document represents the Health Effects Division (HED) review of the Agricultural Handler 

Exposure Task Force (AHETF) Study AHE1012: Backpack Application of Liquid Sprays in 

Utilities Rights-of-Way (Bruce, et al, 2014). AHETF Report AHE400 (Bruce, E., 2014), 

“Determination of Dermal and Inhalation Exposure to Workers during Backpack and Handgun 

Application of Liquid Sprays in Utilities Rights-of-Way”, provides the exposure monitoring field 

and analytical results, including laboratory analyses; details can be found in both the submitted 

study report and its corresponding EPA review (Crowley, 2015). The scenario monograph

(AHE12012) subject to this review compiles the exposure monitoring results as outlined in the 

submitted AHE400 into a formal generic exposure scenario which can be utilized by pesticide 

regulatory agencies for exposure assessment purposes. 

Overall, the AHETF adequately followed the general study design outlined in the AHETF 

Governing Document (AHETF, 2008 and 2010a) and specific scenario sampling and data 

analysis plan (AHETF, 2010b). AHETF efforts represented a well-designed, concerted process

to collect reliable, internally-consistent, and contemporary exposure data in a way that takes 

advantage of and incorporates a more robust statistical design, better analytical methods, and 

improved data handling techniques. The AHETF data and associated unit exposures are 

considered superior to the existing used to assess exposure and risk for this scenario.1 The data

are considered the most reliable data for assessing exposure and risk to individuals applying 

liquid spray pesticides2via backpack sprayer, including frill applications, in utility rights-ofways (ROW) or areas of similar terrain and foliage/vegetation characteristics while wearing the 

following personal protective equipment (PPE): long-sleeved shirts, long pants, shoes, socks, 

chemical-resistant gloves, and no respirator3. Importantly, the data represents exposure during 

loading and application only – it does not represent exposures during the mixing of pesticide

spray solutions.

The primary quantitative objective was for dermal exposure results (normalized to the amount of 

active ingredient handled) to be accurate within 3-fold at the geometric mean, arithmetic mean 

and 95th percentile. This objective was not met: AHETF results showed an accuracy of 

approximately 4-fold. The secondary objective to evaluate proportionality versus independence 

between dermal exposure and the amount of active ingredient handled with 80% statistical power 

– a key assumption in the use of exposure data as “unit exposures” – was met. Additionally, the 

AHETF estimate of the slope of log dermal exposure-log amount of active ingredient handled 

(AaiH) was 1.12 (95% CI: 0.56 – 1.67) – a result consistent with the assumption of 

proportionality. Thus, for this scenario, HED will continue to use the exposure data normalized 

by the amount of active ingredient as a default condition for exposure assessment purposes.

 

1 Beard, K.K. (1997). Evaluation of Applicator Exposures to SURFLAN® A.S. During Mixing, Loading, and 

Application with Backpack Sprayers. EPA MRID 44339801. See: http://www.epa.gov/opp00001/science/handlerexposure-data.html

2 The data are not applicable to volatile chemicals (e.g., fumigants).

3 Adjustments to this dataset would be required to represent alternative personal protective equipment (e.g., applying 

a protection factor to represent exposure when using a respirator or additional protective clothing). These types of 

adjustments would be used in risk assessments as appropriate, given the availability of reliable factors, and are not 

addressed in this review.
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After adjustments by EPA for potential inefficiencies of the hand wash and face/neck wipe 

residue collection methodologies, results of the benchmark objective analyses were nearly

identical to those described above. That is, the adjustments did not alter the outcomes of the 

benchmark analyses and conclusions were unaffected. However, as would be expected, the 

adjustments to the data result in slightly different estimates of exposure statistics (i.e., means and 

percentiles) than those calculated without the adjustments. Section 3.2.1 discusses this in more 

detail.

Select summary statistics for this backpack applicator scenario are presented in Table 1 below, as 

well as the value previously used (Beard, 1997 – EPA MRID 44339801) to assess pesticide 

exposure/risk for this scenario for comparison.

Table 1. Unit Exposures (μg/lb ai handled): Backpack Applicators

Exposure Route

Beard, K.K., 1997 

(EPA MRID 

44339801)

AHETFa

Arithmetic Mean Geometric Mean Arithmetic Meanc95th Percentiled

Dermalb8,260 6843 30,394 117,564

Inhalation 2.58 16.8 39.8 146

a

Statistics are estimated using a variance component model accounting for correlation between measurements 

conducted within the same field study (i.e., measurements collected during the same time and at the same location). 

Additional model estimates (e.g., empirical and simple random sample assumptions) are described in Section 3.0.

b

Per current EPA policy, dermal unit exposures reflect 2X adjustment of hand and face/neck measurements to 

address potential inefficiencies in those exposure monitoring methods since the average percent contribution to total 

dermal exposure by the hands, face, and neck is greater than 20% (see Section 3.1).

c Arithmetic Mean (AM) = GM * exp{0.5*((lnGSD)^2)}

d

95th percentile = GM * GSD^1.645

2.0 Background

The following provides background on the AHETF objectives and also discusses review of the 

backpack rights-of-way applicator scenario by the Human Studies Review Board (HSRB).

2.1 AHETF Objectives

The AHETF is developing a database (Agricultural Handlers Exposure Database or AHED) 

which can be used to estimate worker exposures associated with major agricultural and nonagricultural handler scenarios. A scenario is defined as a pesticide handling task based on 

activity such as mixing/loading or application. Other factors such as formulation (e.g., liquids, 

granules) application equipment type (e.g., tractor-mounted boom sprayers, backpack sprayers) 

are also key criteria for defining scenarios. AHETF-sponsored studies are typically designed to 

represent individuals wearing long-sleeved shirts, long pants, shoes, socks, chemical-resistant 

gloves as appropriate, and no respirators. In some cases, an engineering control (e.g., enclosed 

cabs on tractors) or additional personal protective equipment/clothing may also be a key element 

of the scenario.

AHETF studies use dosimetry methods intended to define pesticide handler dermal and 

inhalation exposures, attempting to represent the chemical exposure "deposited on or to-the-skin"
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or “in the breathing zone.” For the purposes of pesticide handler exposure assessment, dermal 

and inhalation exposures are expressed as “unit exposures” – exposure per the weight-based 

amount of pesticide handled. Mathematically, unit exposures are expressed as exposure 

normalized by the amount active ingredient handled (AaiH) by participants in scenario-specific 

exposure studies (e.g., mg exposure/lb ai handled). Scenario-specific unit exposures are then 

used generically to predict exposure for other chemical and/or application conditions such as 

different application rates.

Two major assumptions underlie the use of exposure data in this fashion. First, the expected 

external exposure is unrelated to the identity of the specific active ingredient in the pesticide 

formulation. That is, the physical characteristics of a scenario such as the pesticide formulation 

(e.g., formulation type – wettable powder, liquid concentrate, dry flowable, etc.), packaging 

(e.g., bottle or water-soluble packet), or the equipment type used to apply the pesticide, influence 

exposure more than the specific pesticide active ingredient (Hackathorn and Eberhart, 1985). 

Thus, for example, exposure data for spraying one chemical using a backpack sprayer in a utility

right-of-way can be used to estimate exposure for another chemical used in the same manner. 

Second, dermal and inhalation exposure are assumed proportional to the amount of active 

ingredient handled. In other words, if one doubles the amount of pesticide handled, exposure is 

expected to double.

The AHETF approach for monitoring occupational handler exposure was based on criteria

reviewed by EPA and presented to the Human Studies Review Board (HSRB) for determining 

when a scenario is considered complete and operative. Outlined in the AHETF Governing 

Document (AHETF, 2008 and 2010a), the criteria can be briefly summarized as follows:

 The primary objective of the study design is to be 95% confident that key statistics of 

dermal exposure (normalized to the amount of active ingredient handled, i.e., dermal 

“unit exposures”) are accurate within 3-fold. Specifically, the upper and lower 95% 

confidence limits should be no more than 3-fold higher or lower than the estimates for 

each the geometric mean, arithmetic mean, and 95th percentile dermal unit exposures. To 

meet this primary objective AHETF proposed an experimental design that provides a 

sufficient number of field trials and a sufficient number of monitored individuals. Note 

that this “fold relative accuracy” (fRA) objective does not apply to normalized inhalation 

exposure, though estimates are provided for reference.

 The secondary objective is to evaluate the assumption of proportionality between dermal 

exposure and amount of active ingredient handled (AaiH) in order to be able to use the 

AHETF data generically across application conditions. To meet this objective, the 

AHETF proposed a log-log regression test to distinguish complete proportionality (slope 

= 1) from complete independence (slope = 0), with 80% statistical power, achieved when 

the width of the 95th confidence interval of the regression slope is 1.4 or less. Note, 

again, that this objective does not apply to normalized inhalation exposure; however the 

tests are performed for informational purposes.

To simultaneously achieve both the primary and secondary objectives described above and

maximize logistical/cost efficiently while minimizing the number of participating workers, the 
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AHETF developed a study design employing a ‘cluster’ strategy. A cluster, from a sample size 

perspective, is defined as a set of workers monitored in spatial and temporal proximity. While

cluster sampling is logistically more efficient and cost effective, importantly, in terms of a 

sampling strategy, there is assumed to be some level of correlation within clusters. For AHETF 

purposes, clusters are generally defined by a few contiguous counties in a given state(s) within a 

US EPA agricultural growing region. 

Though other configurations may also satisfy study objectives, for most handler scenarios the 

optimal configuration for the AHETF is 5 regional clusters each consisting of 5 participants. The 

25 total participants together with the conditions under which the worker handles the active 

ingredient are referred to as monitoring units (MUs). Within each cluster, the AHETF partitions 

the practical AaiH range handled by the participants in each cluster appropriate to a given 

scenario. In general, the strata of AaiH for any given scenario is commensurate with typical 

commercial production agriculture and EPA handler risk assessment with respect to amount of 

area that could be treated or amount of dilute solution that could be sprayed in a work day.

2.2 Previous HSRB Review and Comments

The ability of the EPA to use the backpack ROW applicator exposure monitoring studies to 

develop regulatory decisions is contingent upon compliance with the final regulation establishing 

requirements for the protection of subjects in human research (40 CFR Part 26), including review 

by the Human Studies Review Board4. 

The protocol and sampling plan for this exposure data and scenario (AHETF, 2010b) was 

presented to the HSRB in October 2010. The meeting report (HSRB, 2010) stated that the 

proposed approach would generate reliable data for assessing exposure for backpack sprayers 

applying pesticides in utility rights-of-way. The Board agreed that the proposal had clear 

scientific objectives and a reasonable experimental design to meet the objectives, including 

appropriate justification of and analytical plan for measuring test substances as well as 

justification for sample size and study site selection.

However, the HSRB also commented about some potential weaknesses of the study. The HSRB 

was concerned that exposure variability would be extremely high so as to be unrepeatable as well 

as potentially preventing evaluation of proportionality with amount of active ingredient handled. 

The Board also recommended that the field notes be adequate such that “time-on-task” could be 

estimated as a fraction of total monitored time, particularly if a minimum of 4 hours was 

required.

While the data did prove highly variable, it did not appear to hamper evaluation of 

proportionality (see Section 3.2.2), and since the study is unlikely to be repeated it is unknown 

whether such results could be reproduced. However, given the very high variability observed, it 

is unlikely that a repeat study would demonstrate even higher variability. While a 4-hour 

minimum was included in the protocol (to ensure detectable residues – with the additional effect 

of skewing the data to higher exposures), a few workers did work less than 4 hours and were 

 

4

http://www2.epa.gov/programs-office-science-advisor-osa/human-studies-review-board
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noted as protocol deviations. However, even with less-than-4 hour monitoring, there were very

few non-detect results on the exposure matrices. Should additional analysis be desired to address 

concerns with the time requirement, the field notes/observations are fairly detailed and could be 

used to estimate time-on-task.

3.0 Exposure Study Conduct and Monitoring Results

Field monitoring and analytical results, as well as protocol amendments and deviations, were 

reported in AHE400 and reviewed by EPA (Crowley, 2015). The following sections summarize 

the conduct of the study, the exposure monitoring results and the scenario benchmark statistical 

analyses presented in the AHETF scenario monograph (Bruce, et al, 2014).

3.1 Exposure Study Design and Characteristics

This scenario is application of liquid spray pesticides via backpack sprayer, including frill 

applications5, in rights-of-ways or areas of similar terrain and foliage/vegetation characteristics 

such as a public park or drainage ditches while wearing the following work clothing/PPE: longsleeved shirts, long pants, shoes, socks, chemical-resistant gloves, and no respirator. Dermal and 

inhalation exposure monitoring was conducted for 19 different workers6and reported in the 

AHETF submission “Determination of Dermal and Inhalation Exposure to Workers during 

Backpack and Handgun Application of Liquid Sprays in Utilities Rights-of-Way” (AHE400; 

Bruce, E., 2014).

The figures below (from AHE1012 Appendix C; Bruce, et al, 2014) depict examples of activities

for which the exposure data are applicable.

 

5 A frill application, also known as “hack and squirt”, consists of workers using machetes or hatchets to make slices 

at the base of trunks of trees or bushes (forming a “frill”) in which to spray pesticide.

6 The original sampling plan called for monitoring 21 workers. The AHETF terminated sampling at 19 workers due 

to logistical difficulties and the duration of the study at that point. Additionally, only 17 of the 19 workers had valid 

inhalation monitoring due to lack of analyte or pump malfunction.
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Figure 1: Application in Distribution Right-of-Way

Figure 2: Frill Application (“hack-and-squirt”) in addition to Backpack Spraying
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Figure 3: Application to invasive grass in a public park

In order the capture the expected range of exposures within this scenario (with a small sample), 

the monitoring plan outlined a strategy to target a diverse set of conditions in terms of 

application sites, equipment, workers, and other potential exposure factors. For each targeted 

area, the AHETF developed lists of potential utility companies and application companies that 

might be able to provide eligible workers to monitor. From a universal list of all companies who 

may conduct rights-of-way applications, AHETF constructed additional lists of “qualified 

employers” and then “potentially eligible” employers via randomized telephone calls and 

questionnaires, from which they would schedule monitoring. When multiple workers in a given 

area/time were available, the monitored workers were selected at random.

The sampling plan for this scenario (AHETF, 2010b) intended for a ‘7x3’ design – monitoring of 

a total of 21 different workers, 3 workers in each of 7 separate ‘clusters’ or monitoring areas 

represented by the U.S. states: WV, NC, FL, AR, IN, MN, and eastern TX. Monitoring locations 

were all places where undesirable vegetation such as shrubs, vines, or bushes were meant to be 

controlled. Most of the monitoring was in utility rights-of-ways, such as areas where electric 

transmission and distribution lines or pipelines run through. These are areas where controlling 

vegetation is important so as to provide easy access to utility personnel and vehicles, but also 

because the vegetation can damage utility distribution equipment. 

Following recruitment difficulties, some monitoring areas were expanded to include additional 

states, as well as to expand beyond strictly rights-of-way areas to additional use sites of similar 

terrain and foliage/vegetation such as wildlife refuges, parks, and drainage ditches. While these 

are not rights-of-way, both the purpose of the application (i.e., controlling undesirable 

vegetation) and location characteristics such as terrain were similar to the right-of-way locations 

that constituted the bulk of the monitoring. Additionally, monitoring was conducted over an 

extended period of time from May 2011 to September 2013. 
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Thus, the actual conduct of the study utilized the (expanded) 7 monitoring areas, but because of 

temporal differences, effectively constituted of 15 clusters, with 1 or 2 workers per cluster, 

totaling 19 monitored workers. Because of the length of time and cost expended to conduct 

monitoring for 19 workers – and the belief that study objectives would be met with 19 – AHETF 

elected to terminate the monitoring at 19 total workers instead of 21. Section 3.2.1 provides 

more discussion on this issue.

Monitoring was conducted across 3 years and 9 different U.S. states, providing both spatial and 

temporal diversity in the sample. Additionally, there were no repeat measurements on the same 

worker, and only two of those workers worked for the same employer. In the same-employer 

case, the workers were monitored in different calendar years, at different job sites, and were part 

of different application crews. By diversifying locations, employers and workers, as well as the 

amount of active ingredient handled (to accommodate the secondary objective), brands/types of 

backpacks and configurations and spray techniques were also indirectly varied. For more details 

on these conditions see the monograph submission (AHE1012), the data submission (AHE400) 

and EPA review of AHE400 (Crowley, 2015).

3.2 Exposure Monitoring and Calculations

Monitored on actual days of work, participants handled between 0.03 to 9.65 lbs of active 

ingredient (fosamine, glyphosate, or imazapyr), spraying 4.5 to 64.5 gallons of solution in 2 to 11

hours. Dermal exposure was measured using 100% cotton “whole body dosimeters” (WBD) 

underneath normal work clothing (e.g., long-sleeved shirt, long pants, socks and shoes), hand 

rinses (collected at the end of the day and during restroom and lunch breaks), and face/neck 

wipes (adjusted to extrapolate to portions of the head covered by protective eyewear, respirators, 

and/or hair). Per AHETF goals, monitoring of these backpack applications was conducted to 

represent exposure for workers wearing long-sleeve shirts, pants, shoes/socks, chemical-resistant 

gloves and no respiratory protection. While this was largely the case, because of the nature of 

the terrain and environment nearly all workers wore (company-required) hard hats and 7 of 19

wore additional leg coverings to protect from thorns or snakes. Additional evaluation of some of 

this additional clothing is reviewed in Section 3.3.

Additionally, as presented at a June 2007 HSRB meeting, in order to account for potential 

residue collection method inefficiencies7, EPA made adjustments in the AHETF-supplied data to 

hand and face/neck field study measurements as follows:

 if measured exposures from hands, face and neck contribute less than 20% as an 

average across all workers, no action is required;

 if measured exposure contribution from hands and face/neck represents between 20% 

and 60% of total, the measurements shall be adjusted upward by a factor of 2, or 

submission of a validation study to support the residue collection method;

 

7 The terminology used to describe this are “method efficiency adjusted” (MEA) or “method efficiency corrected” 

(MEC).
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 if measured exposure contribution from hands and face/neck represents is greater than 

60%, a validation study demonstrating the efficiency of the residue collection 

methods is required.

For these studies, the measurements fell in the second category – on average a contribution of 

27% to total dermal exposure – and hand rinse and face/neck wipe measurements have been 

adjusted upward by a factor of 2 (i.e., multiplied by 2). 

Inhalation exposure is measured using a personal air sampling pump and an OSHA Versatile 

Sampler (OVS) tube. The tube is attached to the worker’s shirt collar to continuously sample air 

from the breathing zone. All samples are adjusted as appropriate according to recovery results 

from field fortification samples.

Total dermal exposure was calculated by summing exposure across all body parts for each 

individual monitored. Total inhalation exposures were calculated by adjusting the measured air 

concentration (i.e., ug/L) using a breathing rate of 16.7 liters per minutes, representing light 

activities (NAFTA, 1998), and total work/monitoring time.

8

 Dermal and inhalation unit 

exposures (i.e., ug/lb ai handled) are then calculated by dividing the summed total exposure by

the amount of active ingredient handled.

A summary of the 19 backpack applicator MUs is provided in Table 2 below, with data plots 

shown in Figures 4 and 5. All field measurements were adjusted by their corresponding field 

fortification recovery values. In additional, though alternate methods can be applied by data 

users (e.g., maximum likelihood estimation), residues with results less than analytical limits use 

the “1⁄2 analytical limit” (either 1⁄2 LOD or LOQ) convention.

For dermal exposure, both hand rinse and face/neck wipe method efficiency adjusted (MEA) 

data and unadjusted results are presented. Note for inhalation exposure, there were a total of 17

(rather than 19) measurements due to sampling pump malfunction or lack of remaining sample 

for analysis (MU A1 – sample extract was used up from previous analysis with an improper 

analytical method).

Table 2. Backpack Application MU Summary

MU 

ID State Application Site

Work/

Monitoring 

Time

(hours)

Solution 

Sprayed

(gallons)

AaiH

(lbs)

Unit Exposure (ug/lb ai)

Dermal

Inhalation Non-MEA MEA

A27 PA Distribution 3.5 4.5 1.44 712 978 --

A33 PA Transmission 9 28.5 4.49 884 1436 7.95

A34 PA Park 2 13.5 0.81 13485 13977 3.07

A8 NC Distribution 4 12.25 2.11 3541 5502 36.5

A10 GA Transmission 6.4 64.5 9.65 16096 16195 14.1

A20 SC Distribution 5.1 48 0.48 12896 15628 12.5

A11 FL Wildlife Refuge 8.2 22.5 0.051 400 675 14.1

 

8

Inhalation Exposure (ug) = collected air residue (ug) x [breathing rate (L/min) ÷ average pump flow rate (L/min)]
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A12 FL Transmission 5 16.5 1.9 2710 3990 98.4

A13 FL Transmission 5 20 2.27 1329 2095 85.9

A1 AR Distribution 10.7 33.25 6.65 3586 5209 --

A23 AR Distribution 6 14.5 3.11 238799 241923 49.5

A24 AR Transmission 4.3 33 0.37 167305 168771 46.8

A16 IN Pipeline 10.2 15.875 5.08 4507 6255 112

A38 IN Distribution 8.8 14.75 1.83 33279 34234 7.98

A31 MI Park 6.3 16.5 1.13 949 1024 2.33

A40 MI Drainage Ditch 7.8 7.5 0.62 3537 3654 1.74

A2 TX Transmission 7.5 16.5 3.62 3763 4075 51.1

A4 TX Transmission 6.1 16 0.03 2823 4250 18.1

A25 TX Transmission 3.6 19.5 3.51 61489 64311 33

Figure 4: Dermal Unit Exposures (MEA) (ug/lb ai)

Figure 5: Inhalation Unit Exposures (ug/lb ai)
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3.3 Evaluation of Scenario Benchmark Objectives

The AHETF monograph details the extent to which the backpack ROW applicator scenario 

meets objectives described in Section 2.1. The monograph states that while the primary

objective (3-fold accuracy) was not met, the secondary objective (adequate analytical power)

was met. EPA agrees with the methodologies used to assess these objectives (Appendix E of 

Bruce, et al, 2014) and has independently confirmed the results by re-analyzing the data with the 

AHETF-supplied statistical programming code.

3.3.1 Primary Objective: fold Relative Accuracy (fRA)

The primary benchmark objective for AHETF scenarios is for select statistics – the geometric 

mean (GM), the arithmetic mean (AM), and the 95th percentile (P95) – to be accurate within 3-

fold with 95% confidence (i.e., “fold relative accuracy”). The AHETF analyzed the data using 

various statistical techniques to evaluate this benchmark. Importantly, the AHETF presented 

results in the monograph without the MEA factors previously described. As described in 

previous sections of this review, EPA applied the MEA factor to the AHETF results. This 

section discusses results with and without the EPA-incorporated MEA factors.

First, both dermal and inhalation unit exposures were shown to fit lognormal distributions 

reasonably well. Normal and lognormal probability plots are provided as Appendix A. Next, the 

AHETF calculated estimates of the GM, AM and P95 based on three variations of the data:

 Non-parametric empirical (i.e., ranked) estimates;

 Assuming a lognormal distribution and a simple random sample (SRS); and,
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 Hierarchical variance component modeling to account for potential MU correlations.

As presented in Appendix C of the AHETF Governing Document (AHETF, 2008 and AHETF, 

2010a) and Appendix E of the scenario monograph (Bruce et al, 2014), the 95% confidence 

limits for each of these estimates were obtained by generating 10,000 parametric bootstrap 

samples. Then, the fRA for each was determined as the maximum of the two ratios of the 

statistical point estimates with their respective upper and lower 95% confidence limits. 

EPA performed the method efficiency adjustments on the dermal exposure, utilizing the same 

(SAS) statistical programming code submitted by the AHETF, except substituting the input data 

with the MEA data. The primary benchmark of 3-fold accuracy for select statistics was not met 

for dermal exposure data both adjusted and unadjusted for potential hand rinse and face/neck 

wipe method inefficiencies. Results for the unadjusted and adjusted dermal exposure data are 

presented below in Table 3 and inhalation exposure in Table 4. 

Both the means and percentiles and fRA95 values for the MEA dermal exposure data are slightly

lower than the unadjusted data. The adjustments have the (obvious) effect of increasing the 

exposure estimates on an individual level – and non-parametric statistics reflect that result (e.g., 

the simple MEA arithmetic mean is 31,273 ug/lb ai while the non-MEA arithmetic mean is 

30,110 ug/lb ai). But on the sample level, due to the relative effect of the adjustments on 

individual workers (workers with larger total exposures were relatively unaffected by the 

adjustment) and the resulting decrease in variability, parametric statistics are lower than the 

unadjusted data. Despite this result, in order to apply consistent policy, EPA will rely on the data 

adjusted for potential inefficiencies for the hand rinse and face/neck wipes.

Table 3. Backpack ROW Application – Results of Primary Benchmark Analysis for Dermal Exposure

Statistic

Dermal (MEA) a Dermal (non-MEA)

Unit Exposure (ug/lb ai) fRA95Unit Exposure (ug/lb ai) fRA95 Estimate 95% CI Estimate 95% CI

GMS 7,356 2,924-15,976 2.3 5,886 2,222-13,785 2.5

GSDS 5.33 2.87-9.11 -- 6.09 3.14-10.89 --

GMM 6,843 3,006-15,664 2.3 5,508 2,270-13,386 2.4

GSDM 5.13 2.88-9.15 -- 5.86 3.17-10.91 --

ICC 0.75 0.00-0.97 -- 0.70 0.00-0.96 --

GMS = geometric mean assuming SRS = “exp(average of 19 ln(UE)) values”.

GSDS = geometric standard deviation assuming SRS = “exp(standard deviation of 19 ln(UE)) values”

GMM = variance component model-based geometric mean

GSDM = variance component model-based geometric standard deviation

ICC = intra-cluster correlation

AMS 31,273 6,949-78,821 3.5 30,110 5,980-88,678 4.1

AMU 29,784 7,891-101,565 3.5 30,096 6,973-121,370 4.1

AMM 26,052 8,144-101,555 3.5 26,317 7,166-122,551 4.1

AMS = simple average of 19 unit exposures

AMU = arithmetic mean based on GMS = GMS*exp{0.5*((lnGSDS)^2}

AMM = variance component model-based arithmetic mean = GMM* exp{0.5*((lnGSDM)^2}

P95S 241,923 27,500-917,904 6.4 238,799 25,195-1,097,817 7.5

P95U 115,166 27,639-351,621 3.6 114,902 25,093-383,796 3.9

P95M 100,769 28,549-347,158 3.5 101,020 26,148-382,338 3.8

P95S = 95th percentile (i.e., the 18th unit exposure out of 19 ranked in ascending order)

P95U = 95th percentile based on GMS = GMS * GSDS^1.645
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As shown in the tables above, for AHETF-calculated dermal exposures, while estimates of the 

geometric mean were accurate to approximately 2-fold, both the arithmetic mean and 95th

percentiles are accurate to only approximately 4-fold. For MEA dermal exposures calculated by 

EPA the mean and 95th percentiles were accurate to within 3.5-fold. Though not of primary

interest for the accuracy benchmark, mixed-model estimates of inhalation data were accurate to 

within 3-fold.

The AHETF recognized the failure to meet the accuracy benchmark (3-fold) for dermal exposure 

and addressed it in the monograph report. This outcome could be the result of several reasons: 

small sample size, extreme variability, or a high ICC. Prior to study conduct, study design 

analysis utilized estimates of 4 and 0.3 for the geometric standard deviation (a measure of 

variability) and the ICC, respectively, to determine the sample size. While the ICC was higherthan-expected at approximately 0.7, there were fewer workers within clusters than originally 

planned. Due to recruiting/logistical difficulties, 19 workers in 15 clusters, compared with the 

original 7x3 design; thus, the effect of the high ICC is muted. However, the observed variability

(GSD ≈ 6) was much higher than that assumed in the sampling plan, and is the prime reason for 

not meeting the within 3-fold accuracy benchmark.

Due to the high variability, the AHETF demonstrated through simulations that it would take 8 

more clusters of 2 workers each (totaling 16) or 10 more clusters of 1 worker to meet the goal of 

P95M = variance component model-based 95th percentile = GMM* GSDM^1.645

a Dermal exposure values reflect 2X default adjustment for hands and face/neck measurements.

Table 4. Backpack ROW Application – Results of Primary Benchmark Analysis for Inhalation Exposure

Statistic

Inhalation

Unit Exposure (ug/lb ai) fRA95 Estimate 95% CI

GMS 18.9 8.0-35.6 2.1

GSDS 3.65 2.22-6.19 --

GMM 16.8 8.3-34.3 2.0

GSDM 3.72 2.25-6.21 --

ICC 0.85 0.38-0.98 --

GMS = geometric mean assuming SRS = “exp(average of 17 ln(UE)) values”.

GSDS = geometric standard deviation assuming SRS = “exp(standard deviation of 17 ln(UE)) values”

GMM = variance component model-based geometric mean

GSDM = variance component model-based geometric standard deviation

ICC = intra-cluster correlation

AMS 35 14.5-100.3 2.7

AMU 43.7 15.3-113.7 2.7

AMM 39.8 15.8-113.1 2.7

AMS = simple average of 17 unit exposures

AMU = arithmetic mean based on GMS = GMS*exp{0.5*((lnGSDS)^2}

AMM = variance component model-based arithmetic mean = GMM* exp{0.5*((lnGSDM)^2}

P95S 112 45.1-836.1 4.4

P95U 158.9 46.3-431.1 3.1

P95M 145.8 47.9-427.6 3.0

P95S = 95th percentile (i.e., the 16th unit exposure out of 17 ranked in ascending order)

P95U = 95th percentile based on GMS = GMS * GSDS^1.645

P95M = variance component model-based 95th percentile = GMM* GSDM^1.645
Page 15 of 25

3-fold accuracy9. They also conclude that while accuracy may be improved it is unlikely

exposure estimates would be greatly affected. Finally the AHETF believes that it would not be 

worth the multi-year delay – nor ethically justifiable – to monitor these additional workers to 

meet the accuracy benchmark. 

3.3.2 Secondary Objective: Evaluating Proportionality

The secondary objective of AHETF studies is to be able to distinguish, with 80% statistical 

power, complete proportionality from complete independence between dermal exposure and 

amount of active ingredient handled. Based on the AHETF analysis this benchmark was met.

To evaluate the relationship for this scenario the AHETF performed regression analysis of 

ln(exposure) and ln(AaiH) to determine if the slope is not significantly different than 1 –

providing support for a proportional relationship – or if the slope is not significantly different 

than 0 – providing support for an independent relationship. Both simple linear regression and 

mixed-effect regression were performed to evaluate the relationship between dermal exposure 

(both standard and adjusted for exposure method collection inefficiencies) and AaiH. A 

confidence interval of 1.4 (or less) indicates at least 80% statistical power. The resulting

regression slopes and confidence intervals are summarized in Table 5.

Table 5. Summary Results of log-log Regression Slopes

Model

Dermal Exposure Inhalation Exposure Standard (non-MEA) MEA

Est. 95% CI CI 

Width Est. 95% CI CI Width Est. 95% CI CI Width

Simple 

Linear 1.22 0.63 – 1.82 1.19 1.20 0.64 – 1.75 1.11 1.20 0.76 – 1.65 0.89

MixedEffects 1.12 0.56 – 1.67 1.11 1.06 0.56 – 1.58 1.01 1.22 1.00 – 1.44 0.44

Note: results shown using the Kenward-Rogers denominator degrees of freedom method. AHETF statistical 

analysis (AHE1012 Appendices E and F) provides results using the Containment method as well. Results were not 

substantially different.

For dermal exposure, the slope of the mixed-effects regression – preferred since it accounts for 

within-cluster correlation – is 1.12 with a 95% confidence interval that excludes 0 and includes

1, suggesting a proportional relationship is more consistent with the data than an independent 

relationship. For inhalation exposure, the mixed-effects regression slope is 1.22, with a 95% 

confidence intervals that excludes 0 and includes 1, suggesting a proportional relationship is 

more consistent with the data than an independent relationship. In terms of the secondary

objective, the width of the confidence interval for both dermal and inhalation exposure was less

than 1.4, indicating the power to detect complete independence from complete proportionality

was greater than 80%.

 

9 Note that the AHETF terminated sampling at 19 workers, two short of the original plan for 21 monitored workers. 

It can be concluded from the results and additional AHETF analysis that had 21 workers been monitored this would 

also have been inadequate to meet the accuracy target.
Page 16 of 25

Adjustments for hand rinse and face/neck wipe inefficiencies do not alter these conclusions. For 

MEA dermal exposures, the 95% confidence intervals for the mixed-effects log-log regression 

slope also excludes 0 and includes 1 and the width of the interval is less than 1.4.

3.4 Additional EPA Analysis of Scenario Characteristics

While exposure normalized to active ingredient handled is the preferred format of the data for 

use in exposure assessment, it is worth evaluating other aspects of the backpack ROW applicator 

dataset. Due to routine application and worker characteristics observed during recruitment and 

monitoring, some aspects of the data were further evaluated for potential effects on the data. For 

example, the use of additional application sites considered similar to rights-of-way in terms of 

terrain and vegetation type/density; the inclusion of workers who wear leggings/chaps for 

physical protection; prevalence of overhead spraying; and employment of the frill (“hack-andsquirt”) methodology. In this scenario the extreme variability in itself warrants examination of 

the data.

It is important (and incumbent upon anyone analyzing this data) to note that the data were not 

collected in a way to be able to, with any meaningful analytical power (with the exception of 

amount of active ingredient handled), detect or determine any differences due to any particular 

factors. In fact, with a primary goal of capturing variability and diversity within the scenario, the 

sampling methodology use by the AHETF runs counter to the methodology (i.e., holding 

parameters equal) that would be employed to evaluate the significance of certain exposure 

factors. Nevertheless, it is still worthwhile to at least visually examine the data for any trends for 

further evaluation, particularly in a regulatory context.

Site of Application

As previously described, the AHETF amended the protocol to include additional sites that were 

not strictly rights-of-way (such as pipeline or electricity rights-of-way), but similar in terms of 

terrain and the undesirable target vegetation. For this backpack scenario, this included areas 

such as a drainage ditch, parks, and a wildlife refuge. Figure 4 below shows the normalized 

(unit) exposures for each site-category monitored.
Page 17 of 25

Figure 4

Prevalence of Overhead Spraying

Overhead spraying is often thought to yield higher exposures than downward spraying simply

due to the potential for the spray plume/droplets to settle down upon the worker. For this 

backpack monitoring data the worker observations were reviewed for occurrences of overhead 

spraying and (crudely) quantified into the categories “Yes/Often”, “Some”, and “No/Rarely”. 

Figure 5 below presents these results. It should not be surprising that frequent overhead spraying 

with backpacks in rights-of-way areas is infrequent (only 2 of 19 workers), since they are 

generally low-pressure sprayers. Should significant overhead spraying be required (to reach 

higher trees or foliage) another application method or equipment would likely be used.
Page 18 of 25

Figure 5

Frill (“Hack-and-Squirt”) Applications

Because it is sometimes a routine practice of backpack applications in rights-of-way areas, 

workers who, in addition to their backpack spraying, employed frill applications were not 

excluded from the dataset. However, users of the data aware of this practice may wish to know 

whether that method has the potential to result in higher exposures. Figure 6 below displays 

exposure results for backpack applicators who did (3 of 19) and did not (16 of 19) make frill 

applications.
Page 19 of 25

Figure 6

Use of Protective Leggings/Chaps

During recruitment it became obvious that excluding workers who wear protective 

leggings/chaps (to protect against snakes and thorns) from the monitoring (in order to avoid 

confounding) would significantly reduce the pool of available workers. Thus the AHETF 

amended the protocol to relax this restriction. Ultimately, slightly less than half (7 of 19) of the 

backpack applicators used protective leg gear. Users of the data may wish to evaluate whether 

there is any potential for the protective leg gear to reduce (or increase) exposure. Figure 7 below 

presents the data.
Page 20 of 25

Figure 7

Overall, it does not appear from simple visual observations of the particular variables above, that 

any would be particularly useful in a regulatory context. But, again, because of the exposure 

sampling methodology and lack of analytical power, there could be a (true) effect, but it is not 

observable in the data. In addition, while difficult to quantify, a read through the worker 

observations also does not provide much more additional information than that which has been 

quantified above.

4.0 Data Generalizations and Limitations

The need for an upgraded generic pesticide handler exposure database has been publicly 

discussed and established (Christian, 2007). No existing exposure data for backpack applicators 

in rights-of-way areas met AHETF criteria for inclusion in an updated database; thus the data 

outlined in AHE400 (field study) and AHE1012 (scenario monograph) will serve to complete the 

scenario. 

The data will be used generically to assess exposure for applicators applying any conventional 

pesticide applied as a spray using backpack sprayers to areas containing undesirable vegetation 

such as shrubs/bushes/vines in rights-of-way or similar areas. However, certain limitations need 

to be recognized with respect to collection, use, and interpretation of the exposure data.
Page 21 of 25

4.1 Generic Use in Exposure Assessment

The data comprising this scenario are acceptable for use in assessing exposure for applicators 

applying any conventional pesticide applied as a spray using backpack sprayers to areas 

containing undesirable vegetation such as shrubs/bushes/vines in rights-of-way or similar areas

while wearing a long-sleeve shirt, pants, shoes/socks, and chemical resistant gloves. This 

includes additional practices in backpack applications such as the use of frill applications and 

protective leg gear, as described in Section 3.3.

Importantly, use of the data generically in a regulatory context implies that the pesticide active 

ingredient being reviewed has a use pattern consistent with the activities and conditions 

represented by the data for this scenario. In other words, other pesticides may be used in rightsof-way areas for a particular purpose, but that does not automatically mean this dataset would 

apply for that pesticide. If, for example, a pesticide has more limited use as a spot treatment or 

does not require workers to fully immerse in the target vegetation, another dataset might be a 

better surrogate. 

Additionally, even for this specific scenario, the availability of this data does not preclude 

additional consideration or use of acceptable available chemical-specific studies, biomonitoring 

studies, or other circumstances in which exposure data can be acceptably used in lieu of these

data.

4.2 Applicability of AHETF Data for Volatile Chemicals

The data generated in this study are acceptable to use as surrogate data for assessing applicator 

exposure to other conventional pesticides used in backpack sprayers, which are generally

chemicals of low volatility. Since they are not typically used in backpack sprayers, it is not 

expected that this dataset would be used to support regulatory decisions for high volatility

pesticides (e.g., fumigants).

4.3 Use of “Unit Exposures”

As previously shown, statistical analyses provide general support for use of the exposure data 

normalized by the amount of active ingredient handled. Thus, EPA will continue to recommend 

use of the exposure data normalized by the amount of active ingredient handled as a default 

condition.

4.4 Representativeness and Extrapolation to Exposed Population

Targeting and selecting specific monitoring characteristics (i.e., “purposive sampling”) as well as 

certain restrictions necessary for logistical purposes (e.g., selection of certain U.S. states to 

ensure a large pool of potential applicators; requiring potential applicators to use certain 

pesticides to ensure laboratory analysis of exposure monitoring matrices; and requiring selection 

of workers who normally wear the scenario-defined minimal PPE), made the studies comprising 

this scenario neither purely observational nor random to allow for characterization of the dataset 

as representative of the population of backpack applicators in rights-of-way areas. 
Page 22 of 25

It is important to recognize this as a limitation when making use of the data. 

It appears however, that the dataset has captured routine behavior as well as limiting the 

likelihood of “low-end” exposures via certain scripting aspects (e.g., monitoring time 

requirements to avoid non-detects), both of which are valuable for regulatory assessment 

purposes. Also, the random elements incorporated into the recruitment process likely mitigated

selection bias on the part of participants or recruiters. Thus, with respect to costs, feasibility, and 

utility, the resulting dataset is considered a reasonable approximation of expected exposure for 

this population.

5.0 Conclusions

EPA has reviewed the AHETF Backpack ROW Application scenario monograph and concurs 

with the technical analysis of the data as well as the evaluation of the statistical benchmarks 

objectives. Conclusions are as follows:

 Deficiencies in the existing scenario dataset have been recognized and the need for new 

data established.

 The AHETF data developed and outlined in the monograph and this review represent the 

most reliable data for assessing backpack application exposure in rights-of-way or similar 

areas.

 The primary (quantitative) objective was not met: estimates of the GM, AM, and P95 

dermal exposures were not shown to be accurate within 3-fold with 95% confidence.

 The secondary (quantitative) objective was met: the dataset provided adequate statistical 

power to distinguish proportionality from independence between dermal exposure and 

AaiH.

 The assumption of proportionality between both dermal and inhalation exposure and the 

amount of active ingredient handled was not rejected. As a result, EPA will continue 

using exposures normalized by AaiH as a default condition for exposure assessment 

purposes for the foreseeable future.

6.0 References

AHETF, 2008. Volume IV AHETF Revised Governing Document for a Multi-Year Pesticide 

Handler Worker Exposure Monitoring Program. Version Number: 1. April 7, 2008. 

Agricultural Handlers Exposure Task Force (AHETF). [MRID 47172401]

AHETF, (2010a). Governing Document for a Multi-Year Pesticide Handler Exposure 

Monitoring Program, Version 2, August 12, 2010.

AHETF, (2010b). Monitoring Unit Selection and Construction Plan for Scenarios: 1. 

BACKPACK APPLICATION OF LIQUID SPRAYS IN UTILITIES RIGHTS-OF-WAY 2. 

HANDGUN APPLICATION OF LIQUID SPRAYS IN UTILITIES RIGHTS-OF-WAY. June 

4, 2010.
Page 23 of 25

Bruce, E. (2014). Determination of Dermal and Inhalation Exposure to Workers during 

Backpack and Handgun Application of Liquid Sprays in Utilities Rights-of-Way. Study Number 

AHE400. Unpublished study sponsored by the Agricultural Handler Exposure Task Force. 1430 

p. September 18, 2014. EPA MRID 49472001.

Bruce, E., Holden, L., and Klonne, D. (2014). Agricultural Handler Exposure Scenario 

Monograph: Backpack Application of Liquid Sprays in Utilities Rights-of-Way. Report 

Number AHE1012. Unpublished study sponsored by the Agricultural Handlers Exposure Task 

Force. 224 p. September 29, 2014. EPA MRID 49478601. 

Christian, Myrta, 2007. Memorandum: Transmittal of Meeting Minutes of the FIFRA Scientific 

Advisory Panel Meeting Held January 9 – 12, 2007 on the Review of Worker Exposure 

Assessment Methods. U.S. Environmental Protection Agency.

Crowley, M., 2015. Review of “Determination of Dermal and Inhalation Exposure to Workers 

during Backpack and Handgun Application of Liquid Sprays in Utilities Rights-of-Way”. 

AHETF Report AHE400. Memorandum to Richard Dumas. March 24, 2015. EPA Review 

D424153.

Hackathorn, D.R. and D.C. Eberhart. 1985. Data Base Proposal for Use in Predicting Mixerloader-applicator Exposure. American Chemical Society Symposium Series 273, pp. 341-355.

Human Studies Review Board Meeting Report – October 27-28, 2010. EPA-HSRB-10-02. 

December 13, 2010.

NAFTA - Dept. of Pesticide Regulation (DPR), California EPA, HSM-98014, April 24, 1998.
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Appendix A

Normal and Lognormal Probability Plots of Dermal and Inhalation Unit Exposures
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