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UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

WASHINGTON, D.C. 20460 

OFFICE OF CHEMICAL SAFETY

AND POLLUTION PREVENTION

 

MEMORANDUM

Date: March 11, 2011

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

Open Cab Airblast Application of Liquid Sprays 

PC Code: -- DP Barcode: D387287

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

Petition No.: -- Regulatory Action: --

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

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

MRID No.: 48326701 40 CFR: --

 Ver.Apr.08

 

FROM: Matthew Crowley, Biologist

Chemistry and Exposure Branch

Health Effects Division

Bayazid Sarkar, Mathematical Statistician

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 the Health Effects Division review of the occupational handler 

exposure scenario monograph “Open Cab Airblast Application of Liquid Sprays” submitted by 

the Agricultural Handler Exposure Task Force (AHETF, 2010). Scientific review of the five 

exposure studies comprising this scenario can be found in a separate data evaluation review 

(DER) memorandum (Crowley, 2011; D387287). The AHETF satisfactorily followed the study 

protocols and satisfied data analysis objectives. EPA considers the open cab airblast scenario 

complete and its results are recommended for use in routine assessment of exposure and risk for 

open cab airblast applicators.
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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) Monograph: Open Cab Airblast Application of Liquid Sprays 

(AHETF, 2010). HED confirms that the data meets the study design objectives outlined in the 

AHETF Governing Document (AHETF, 2008) and is considered the most reliable data for 

assessing exposure and risk to individuals applying liquid spray pesticides1 with open cab 

airblast equipment while wearing the following personal protective equipment (PPE): longsleeved shirts, long pants, shoes, socks, chemical-resistant gloves, with or without chemicalresistant hats, and no respirator2

. The AHETF data and associated “unit exposures” are 

considered superior to the existing open cab airblast applicator dataset.3

Select summary statistics for the open cab airblast applicator scenario “unit exposures” are 

presented in 

 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 primary objective 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 

was met. The secondary objective to evaluate proportionality between dermal and inhalation 

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 not met. Despite having lessthan-expected statistical power, regression analysis does not reject proportionality between 

exposure and the amount of active ingredient handled for either the dermal and inhalation routes 

of exposure. 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.

Table 1 below, as well as the previous value used (from the Pesticide Handler 

Exposure Database, PHED) for comparison.

 1 The data is not applicable to volatile chemicals (e.g., fumigants).

2 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.

3 Pesticide Handlers Exposure Database (PHED) Scenario 11: Airblast Application, Open Cab (APPL)
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Table 1. Unit Exposures (ug/lb ai handled): Open Cab Airblast Applicators

Exposure 

Route PPE PHED AHETFb

“Best Fit” Geometric Mean Arithmetic Meand 95th Percentilee

Dermala

single-layer, CR gloves, 

CR hatc unavailable 66.4 215 826

single-layer, CR gloves 240 387 1590 6148

Inhalation no respirator 4.5 0.781 4.71 17.6 a CR = chemical-resistant; single-layer = long-sleeve shirt, pants, shoes/socks

b 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.

c Per current EPA policy, dermal unit exposures reflect 50% adjustment of hand and face/neck measurements, since 

the average percent contribution to total dermal exposure by the hands, face, and neck is approximately 50%.

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

e 95th percentile = GM * GSD^1.645

2.0 Background

The following provides background on the AHETF objectives for this research and also 

discusses previous reviews of the open cab airblast 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 define worker exposures associated with major agricultural and nonagricultural handler scenarios. A scenario can be defined as a pesticide handling task based on 

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

granules), tractor type (e.g., open or closed cab), and/or application equipment type (e.g., 

airblast, aircraft or boom sprayers) are also key criteria for defining scenarios. AHETFsponsored 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, such as the scenario addressed by this monograph, additional personal protective 

equipment/clothing may also be a key element of the scenario (e.g., certain types of headgear to 

reduce overhead exposures).

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

inhalation exposures, which represent the chemical exposure “to-the-skin" (i.e., "deposited on”) 

and “in the breathing zone.” For the purposes of pesticide handler exposure assessment, dermal 

and inhalation exposures are expressed as “unit exposures” – expressed as exposure per weightunit chemical 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). Unit exposures are then used generically to predict 

exposure for other chemicals having the same or 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 
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(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 open cab airblast equipment 

can be used to estimate exposure while spraying another chemical proposed for use with open

cab airblast equipment. 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, one doubles the exposure.

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

reviewed by HED 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), 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 (see Table 4).

• The secondary objective is to evaluate the assumption of proportionality between 

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

AHETF data generically across application rates. 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 

contain costs, the AHETF developed a study design employing a ‘cluster’ strategy. Each cluster 

is defined by a region. Typically, these regions are defined by a few contiguous counties in a 

given state(s) within a US EPA growing region. For most handler scenarios a configuration of 5 

regional clusters each consisting of 5 participants is used to meet the objectives from a statistical 

sample size perspective. In some cases, the presence of existing data incorporated into the 

scenario requires alterations to the study design. For the open cab airblast applicator scenario, 15 

monitored workers from a study conducted in 2004 (Smith, L., 2004; EPA Review: Dawson, J., 

2006, D316628) were available to include in the open cab airblast exposure scenario. Thus, new 

data collected was proposed for 3 new clusters with 5 workers per cluster. The participants 

together with the conditions under which the worker handles the active ingredient are sometimes 

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 
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production agriculture and HED handler risk assessments considerations with respect to amount 

of area that could be treated in a single work day.

In this case, the scenario is application of liquid spray pesticides using airblast sprayers hauled 

by trucks or tractors with open cabs while wearing the following personal protective equipment 

(PPE): long-sleeved shirts, long pants, shoes, socks, chemical-resistant gloves, with and without 

chemical-resistant hats, and no respirator. Dermal and inhalation exposure monitoring was 

conducted for 28 workers4 (referred to as “monitoring units”, or MUs) applying liquid spray 

pesticides using open cab airblast equipment5

Table 2

. Three new monitoring studies were conducted,

each monitoring different workers while spraying tree or trellis crops in 3 different states in the 

U.S. where airblast equipment would typically be used – pecans in Oklahoma, and grapes in each 

California and New York. The monitoring conducted in these studies would be combined with 

previously conducted monitoring on peaches in Georgia, apples and pears in Idaho, and oranges 

in Florida. References for the studies are in below.

Table 2. AHETF Open Cab Airblast Applicator Studies

Study ID

Study Title AHE# EPA 

MRID

AHE07 46448201 Determination of Dermal and Inhalation Exposure to Workers During Airblast Applications 

of a Liquid Pesticide Product by Open Cab Airblast Application to Orchard Crops

AHE62 48289611 Determination of Dermal and Inhalation Exposure to Workers During Airblast Applications 

of Liquid Sprays Using Open Cab Equipment in California Trellis Crops

AHE63 48289612 Determination of Dermal and Inhalation Exposure to Workers During Airblast Applications 

of Liquid Sprays Using Open Cab Equipment in New York Trellis Crops

AHE64 48289613 Determination of Dermal and Inhalation Exposure to Workers During Airblast Applications 

of Liquid Sprays Using Open Cab Equipment in Oklahoma Tree Nuts

The figures below (from AHETF, 2010) depict examples of this activity for which the exposure 

data are applicable.

 4 Only 3 workers out of a planned 5 were monitored in AHE62. Thus, the total of 28 instead of 30 monitored 

workers (when combined with the 15 from AHE07). See Section 3.1 and 3.2.

5 Some open cab vehicles had a canopy or roof. Potential effects on dermal exposure are discussion in Section 3.1.
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Figure 1: Open Cab airblast application in GA peaches

Figure 2: Open Cab airblast application in ID apples
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Figure 3: Open cab airblast application (with canopy) in FL oranges

2.2 HSRB Review and Comments

The ability of the EPA to use the open cab airblast 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 Board6

Table 3. Open Cab Airblast Application Scenario – HSRB Review Timeline

. The following is a timeline of HSRB reviews related to 

this scenario:

Date HSRB Review

June 2006 AHE62 (CA-grapes) Protocol (at that time titled “AHE36”)

AHE63 (NY-grape) Protocol (at that time titled “AHE37”)

October 2008

Open Cab Airblast Application Scenario Design

AHE62 (CA-grapes) Protocol (revised)

AHE63 (NY-grape) Protocol (revised)

AHE64 (OK-pecans) Protocol

Execution of the field studies followed favorable reviews by the HSRB; however, throughout the 

review process, numerous comments and suggestions were noted and incorporated when 

possible. Appendix D of the AHETF scenario monograph (AHETF, 2010) outlines both 

scientific and ethical issues related to the open cab airblast scenario that were addressed. The 

following summarizes the more substantive scientific HSRB review comments related to this 

process and how the AHETF responded.

2.2.1 Characterization of non-respondents and responders who declined to 

participate (11/14/08 and 12/30/08 HSRB Meeting Reports)

 6 http://www.epa.gov/osa/hsrb/; HSRB review required only for protocols for AHE62, 63, and 64 – AHE07 was 

conducted in 2003 and not subject to HSRB review.
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The HSRB was concerned with the inability to evaluate study participants against the universe of 

open cab airblast applicators, considering the AHETF indicated they would experience a very 

low response rate. Continued AHETF attempts to contact non-responders was unsuccessful; 

thus, comparison with those eligible for participation was not possible. However, AHETF did 

attempt to address the HSRB comment by comparing study participants with those eligible nonparticipants via an informal survey of local agricultural experts.

The surveyed experts were asked to evaluate how the selected employers and equipment 

compares to the local population of airblast applicators, using the following characteristics to 

determine whether they were typical of other growers/applicators in the area where the 

monitoring was conducted:

• Whether the participant was the grower, employed by a grower or was a commercial 

applicator;

• The total acres of target crop (for grower MUs only)

• # of employed experienced airblast applicators

• Equipment type

It appears based on this informal survey/poll of local experts that the participants in these studies 

were not atypical of the population of open cab airblast applicators. EPA believes that this 

methodology, however, could be improved for future AHETF studies, perhaps via a more 

systematic database compilation of the information obtained during the recruitment phase. A 

summary of the findings is provided in Table 44 below.

Table 4. Synopsis of Experts Used to Evaluate the Representativeness of Monitored Workers

Study ID Recruited Responded Response

AHE62

(CA-grape) 5 USDA agricultural extension agents 5

• 4 of 5 agreed that the study participants 

were typical in the counties monitored.

• 1 thought acreage was unrepresentative 

of some counties monitored

AHE63

(NY-grape)

3 USDA agricultural extension agents

(Cornell University)

4

• 3 of 4 agreed that the study participants 

were typical in the counties monitored.

• 1 of 4 did not agree due to the absence of 

the “Kinkelder” airblast sprayer – an 

older model prone to spray drift.

1 pesticide application technology 

specialist

1 USDA agricultural extension agent 

(Pennsylvania State University)

AHE64

(OK-pecans)

1 professor of Entomology and Plant 

Physiology (OK State University)

7

• 4 of 7 agreed that the study participants 

were typical in the counties monitored.

• 3 of 7 did not agree

o 1 said acreage should be larger than 

that monitored

o 2 said enclosed cabs were more 

representative

1 professor of Horticulture and 

Landscape Architecture (OK State 

University)

10 USDA agricultural extension 

agents

2.2.2 Documented Survey Implementation Expertise (11/14/08 and 12/30/08 

HSRB Meeting Reports)
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Given the admittedly difficult attempts at recruitment for occupational pesticide exposure 

monitoring studies, the HSRB advised the AHETF to employ individuals with expertise in 

survey implementation. As a result, the AHETF abandoned use of so-called Local Site 

Coordinators for recruitment purposes and employed individuals familiar with survey 

methodology.

2.2.3 No more than 1 worker from the same employer (11/14/08 and 12/30/08

HSRB Meeting Reports)

The HSRB noted that to accurately represent the assumptions of the statistical model and 

“nested” sample design, the AHETF could utilize no more than 1 worker per employer due to 

potential exposure correlations for workers of the same employer (e.g., training similarities, etc.). 

The AHETF responded by indicating that, for the airblast studies, no more than 1 employee of a 

grower or commercial applicator would be monitored. This was accurately reflected in the 

executed exposure monitoring.

2.2.4 Better Characterization of the Recruitment Process (11/14/08 HSRB 

Meeting Report)

The HSRB recommended that the AHETF better define the recruitment process so as to identify 

the individuals or organizations contacted. Specifically, this arose from concerns that the 

recruitment will focus on growers with multiple workers at the expense of those who employ 

only 1 pesticide operator or growers who treat their own farm. The AHETF responded by 

producing a full set of recruitment-related standard operating procedures (SOPs). Among other 

issues, for the purposes of contacting growers on the “call” list, no distinction is made between a 

single grower working his own farm, a grower with a single employee, and a grower with 

multiple employees.

2.2.5 Capturing Applicator Behavior (11/14/08 and 12/30/08 HSRB Meeting 

Reports)

The HSRB was concerned that the scripted nature of the exposure monitoring would not capture 

the extent of exposures typical of open cab airblast applicator behavior under normal 

circumstances. Per protocol, the AHETF employed observers who recorded applicator behavior 

throughout the workday. All manner of behaviors were captured, from specifics of the 

application procedures (i.e., sequence of row treatments) to observations of spray drift and 

contact with treated foliage.

2.2.6 Exclusion of Monitoring Exposure During Applications to Dormant 

Crops and Hops (11/14/08 HSRB Meeting Report)

The HSRB expressed concern with the exclusion of monitoring exposure for both dormant crops 

as well as hops, despite pesticide applications made to both using open cab airblast applications. 

The AHETF noted that with consultation with EPA, that the monitoring conducted for the array 

of non-dormant crops would be considered sufficiently adequate for assessment of open cab 

airblast exposures to dormant crops and hops – as well as other crops not specifically monitored. 
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Given the logistical considerations of the sampling design (i.e., the increased chances of finding 

willing participants for more common pesticide applications), HED agrees that for the purposes 

of the generic database, the proposed studies are adequate for assessment of open cab airblast 

exposure.

2.2.7 Effect of Product and Packaging (11/4/08 HSRB Meeting Report)

The HSRB noted that a rationale was not provided for the statement that neither the product nor 

packaging would have any influence on exposure, citing the potential for increased exposure due 

to cleaning spray nozzles clogged from use of solid formulations diluted in water. The AHETF 

recognized that formulation could potentially affect exposure during applications of liquid sprays

if a solid formulation were to clog nozzles and require cleaning by the applicator. In these 

studies, it is apparent that workers did interact with the spray nozzles; however, since all 

formulations used in these studies were liquid concentrates, attribution of these interactions to 

use of solid formulations cannot be made.

2.2.8 Consideration of Alternative Study Design (11/4/08 HSRB Meeting 

Report)

Due to statistical concerns with the AHETF sampling design expressed by the HSRB during 

earlier review meetings, including selection of workers and sample representativeness, the HSRB 

outlined an alternative sampling approach, which included a more robust approach at identifying 

and recruiting potential participants. With respect to recruitment procedures such as developing 

the universe of potential participants, writing recruitment letters, employing experienced 

interviewers, and comparing participant characteristics with those of the applicator population, 

the AHETF believes, and EPA agrees that, they have followed the fundamental principles of the 

HSRB recommendations. The changes in recruitment procedures also satisfied the EPA 

requirement to incorporate “random elements” whenever feasible in the sampling process.

2.2.9 Evaluation of Combining AHE07 with new monitoring in AHE62-64

[12/30/08 HSRB Meeting Report]

The HSRB recommended an evaluation of the AHE07 dataset against new exposure monitoring 

in AHE62-64, in the event obvious or significant differences are apparent which could 

complicate utilizing a combined dataset. The Agency agrees that the datasets should not be 

combined without adequate review. Results are presented in Section 3.1 for the entire combined 

dataset as well as a comparison showing relatively insignificant differences between the datasets. 

The Agency has no concerns with combining the datasets and feels the mixed-model approach 

for statistical analysis properly accounts for potential data clustering for both the existing data 

and new monitoring.

3.0 Results

Exposure results were reported in reports for each study and reviewed in Crowley, 2011

(D387287). The following sections summarize the exposure monitoring results and the scenario 

benchmark statistical analyses presented in the AHETF scenario monograph (AHETF, 2010).
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3.1 Exposure Monitoring and Calculations

Monitored on actual days of work, participants handled between 5 to 90 lbs of active ingredient 

(carbaryl or malathion), spraying 3 to 30 acres in 1.4 to 10.6 hours. Dermal exposure was 

measured using hand washes, face/neck wipes, whole body dosimeters (100% cotton union suits) 

for the remainder of the body (torso, arms, and legs), and gauze patches on the inside and outside 

of chemical-resistant (CR) hats for exposure to the head. Inhalation exposure was measured 

using personal air sampling pumps and OSHA Versatile Samplers (OVS) mounted on the shirt 

collar. Results represent dermal exposure with and without chemical-resistant hats while 

wearing a long-sleeved shirt, pants, shoes/socks, and chemical-resistant gloves, and inhalation 

exposure without respiratory protection.

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

residue collection method inefficiencies7

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

average across all workers, no action is required;

, the AHETF has made adjustments to hand and 

face/neck field study measurements according to EPA directions as follows:

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

and 60% of total, the measurements shall be adjusted upward by 50%, or submission 

of a validation study to support the residue collection method;

• 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, measurements for dermal exposure with chemical-resistant hats fell in the 

second category and hand rinse and face/neck wipe measurements have been adjusted upward by 

50% (i.e., multiplied by 2). Measurements for dermal exposure without chemical-resistant hats 

fell in the first category and were not adjusted.

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

Sampler (OVS) tube with a glass fiber filter and Chromosorb 102 solvent. 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 8.3 liters per minutes (LPM; converted from 

1.0 m3

/hr), representing light activities such as mixing/loading light packages (NAFTA, 1998),

and total work/monitoring time.

8

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

(MEC).

8 Inhalation Exposure (ug) = collected air residue (ug) x [breathing rate (L/min) ÷ average pump flow rate (L/min)]

 Dermal unit exposures (i.e., ug/lb ai handled) are then 

calculated by dividing the summed total exposure by the amount of active ingredient handled.
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Results represent dermal exposure while wearing a long-sleeved shirt, pants, shoes/socks,

chemical-resistant gloves, with and without chemical-resistant hats and inhalation exposure 

without respiratory protection. Though some open cab vehicles were equipped with canopies, 

this did not offer any additional protection as no difference was observed in the dermal exposure 

results. This was initially addressed in the data review for AHE07 (D316628), but also included 

in the reviews for AHE62-64 (Crowley, 2011; D387287).

A summary of the 28 open cab airblast applicator MUs is provided in Table 3 below. Additional 

evaluation of exposure data are presented in Sections 3.2-3.4 and more detailed exposure data are

provided in Appendix A, Table 1.

Table 3. Open Cab Airblast Application MU Summary

Study 

ID

MU 

ID State Crop

Work/

Monitoring 

Time

(hours)

Area 

Treated

(acres)

AaiH

(lbs)

Unit Exposure (ug/lb ai)

Dermal

without Inhalation

CR hatsa

with CR 

hats

AHE07

1 GA peach 5.5 25 75 128 17.4 0.205

3 GA peach 6.6 15 45 1075 138 1.20

4 GA peach 5.8 25 75 51.9 11.7 0.249

6 GA peach 8.1 20 60 327 24.0 0.803

8 GA peach 8.3 17 52 1673 246 5.68

10 ID apple 7.7 16 32 566 149 1.07

12 ID apple 7.9 16 33 228 23.5 1.28

13 ID apple 6.5 18 36 1464 45.0 1.09

15 ID apple 7.2 12 24 1363 158 1.47

16 ID apple 6.7 20 40 499 51.8 0.800

17 ID apple & pear 6.2 17 34 116 6.4 0.548

22 FL orange 4.8 20 60 406 103 0.851

23 FL orange 7.0 30 90 335 78.9 0.392

26 FL orange 4.8 30 90 148 59.2 0.337

27 FL orange 7.4 30 90 1211 286 5.88

AHE62

A1 CA grape 5.1 20 34.3 134 26.0 0.889

A2 CA grape 2.9 12 5.0 287 89.2 7.13

A3 CA grape 4.6 9.5 10.4 290 43.5 4.26

AHE63

A1 NY grape 10.6 24 48.4 1531 146 2.42

A2 NY grape 6.9 17.5 35.6 1098 206 0.612

A3 NY grape 6.2 12 24.4 2073 46.9 2.77

A4 NY grape 4.2 7.5 15.2 17.8 4.0 0.214

A5 NY grape 1.4 3 6.1 302 54.9 4.97

AHE64

A1 OK pecan 7.8 15 63.1 317 67.7 0.677

A2 OK pecan 2.7 5 10.1 9355 2524 1.44

A3 OK pecan 3.0 7 35.3 95.0 49.1 0.380

A4 OK pecan 3.3 5 25.2 9250 3202 0.264

A5 OK pecan 2.5 9 18.2 3.8 6.5 0.00026 a Dermal exposure with CR hats reflects hand rinse and face/neck wipe method efficiency adjusted (MEA) data.

Figures 4 through 6 below present the data in graphical form. The full range of both dermal and 

inhalation unit exposures is visible, as well as a side-by-side study comparison. Section 3.5 

combines and treats the data statistically as one distribution, so there is value in comparing them 

in this way. Additionally, inhalation monitoring for MU A5 in AHE64 (OK-pecan) is shown to 
Page 13 of 40

be significantly lower in magnitude than the rest of the dataset in Figure 6. Additional treatment 

of this monitoring is considered in Section 3.2.3. 

Figure 4: Dermal Unit Exposures with CR hats (ug/lb ai)

Figure 5: Dermal Unit Exposures w/o CR hats (ug/lb ai)
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Figure 6: Inhalation Unit Exposures (ug/lb ai)

3.2 Evaluation of Scenario Benchmark Objectives

The AHETF monograph details the extent to which the open cab airblast applicator scenario 

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

objective was met, the secondary objective was not. As for reviews of previous AHETF scenario 

monographs (Sarkar, B., 2010), EPA (OPP/HED/CEB) has independently confirmed these 

results.

3.2.1 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. First, both dermal and inhalation unit 

exposures were shown to fit lognormal distributions reasonably well. Lognormal probability 

plots are provided as Appendix B.

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,

• Hierarchical variance component modeling to account for potential MU correlations.

As presented in Appendix C of the AHETF Governing Document (AHETF, 2008), the 95% 

confidence limits for each of these estimates were obtained by generating 10,000 parametric 
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bootstrap samples. Then, the fRA for each statistic was determined as the maximum of the two 

ratios of the statistical point estimates with their respective upper and lower 95% confidence 

limits. The primary benchmark of 3-fold accuracy for select statistics was met for dermal 

exposure data with and without chemical-resistant hats, including when adjusting dermal 

exposure with chemical-resistant hats using MEA hand rinse and face/neck wipe results. Note, 

though not applicable to the benchmark, the fRA values for inhalation are also presented and are 

below 3-fold at geometric mean but above 3-fold at the arithmetic mean and 95th percentile.

3.2.2 Secondary Objective: Testing 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, in which the relationship 

between exposure and amount of active ingredient handled is tested using a mixed-effect log-log

regression, this benchmark was not met.

3.2.2.1 AHETF Analysis

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 

Table 4. Open Cab Airblast Application Scenario – Results of Primary Benchmark Analysis

Statistic

Dermal Unit Exposure (ug/lb ai) Inhalation Unit Exposure 

With CR hats (ug/lb ai) a Without CR hats

Estimate 95% CI fRA Estimate 95% CI fRA Estimate 95% CI fRA

GMS 66.4 37.3 – 117 1.8 387 206 – 720 1.9 0.764 0.335 – 1.81 2.4

GSDS 4.63 3.08 – 6.99 1.5 5.37 3.43 – 8.42 1.6 6.56 3.95 – 10.9 1.7

GMM 66.4 37.3 – 117 1.8 387 206 – 719 1.9 0.781 0.335 – 1.82 2.3

GSDM 4.63 3.08 – 7.06 1.5 5.37 3.44 – 8.52 1.6 6.65 3.98 – 11.3 1.7

ICC 0.00 0.00 – 0.32 -- 0.00 0.00 – 0.32 -- 0.10 0.00 – 0.46 --

GMS = geometric mean assuming SRS = “exp(average of 28 ln(UE)) values”.

GSDS = geometric standard deviation assuming SRS = “exp(standard deviation of 28 ln(UE)) values”

GMM = variance component model-based geometric mean

GSDM = variance component model-based geometric standard deviation

ICC = intra-class correlation

AMS 281 85.9 – 524 3.3 1227 546 – 4379 3.6 1.71 1.13 – 15.1 8.8

AMU 215 94.7 – 529 2.5 1590 619 – 4523 2.8 4.48 1.36 – 17.6 3.9

AMM 215 94.9 – 536 2.5 1590 625 – 4623 2.9 4.71 1.39 – 19.1 4.1

AMS = average of 28 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 2524 256 – 2527 9.9 9250 1701 – 20929 5.4 5.88 3.82 – 73.0 12.4

P95U 826 336 – 1995 2.5 6148 2290 – 16154 2.7 16.8 5.17 – 55.1 3.3

P95M 826 338 – 2016 2.4 6148 2304 – 16340 2.7 17.6 5.26 – 57.7 3.4

P95S = 95th percentile (i.e., the 27th unit exposure out of 28 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 a Dermal exposure values reflect 2X default adjustment for hands and face/neck measurements.
Page 16 of 40

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

and AaiH. A confidence interval width 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 With CR hatsa Without CR hatsb

Est. 95% CI CI 

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

Simple 

Linear no AHETF analysis 0.99 0.12 – 1.85 1.74 0.57 -0.09 – 1.22 1.30

MixedEffects 0.87 0.08 – 1.67 1.59 0.99 0.12 – 1.85 1.74 0.53 -0.25 – 1.31 1.57

a Reflects use of MEA hand and face/neck wipe data.

b Note because the correlation estimate (i.e., the “intra-class correlation”, or ICC) is 0, the slope estimates for the 

simple linear regression and the mixed model are identical.

For dermal exposure with and without chemical-resistant hats, the slopes of the mixed-effects 

regressions are 0.87 and 0.99, respectively with both sets of 95% confidence intervals excluding 

0 while including 1, suggesting that a proportional relationship is consistent with the data. For 

inhalation exposure the mixed-effects regression slope is 0.53, with 95% confidence intervals 

including both 0 and 1, suggesting that either an independent or a proportional relationship is 

consistent with the data. In terms of the secondary objective, in no case was the width of the 

mixed effect regression 95% confidence interval less than 1.4, indicating the power to detect 

complete independence from complete proportionality was less than 80%.9

3.2.2.2 Additional ICC Considerations (EPA Analysis)

 The AHETF 

suggests, and EPA concurs, that this may be the result of the range of AaiH being small relative 

to the range in exposure observed.

Considering discussions from a meeting of the HSRB in October 2010 and a similar analysis 

presented in a review of the AHETF Closed Cab Airblast Application Scenario Monograph 

discussed at the January 2011 HSRB meeting, EPA conducted additional analysis with respect to 

statistical procedures and the intra-class correlation coefficient (ICC) for the open cab airblast 

applicator dataset. The AHETF statistical analysis for proportionality used the Kenward-Rogers 

denominator degrees of freedom (DDF) method to calculate confidence intervals for the log-log 

regression slope10

 9 Despite this being valuable with respect to post-hoc evaluation of the study design criteria, this may not be relevant 

for dermal exposure since the test did not rule out proportionality.

10 Note that the choice of denominator degrees of freedom method does not affect the estimated slope and its 

standard error, but it can affect the confidence interval. Since a bootstrap method was used to compute confidence 

intervals and fold relative accuracy for the normalized exposure summary statistics (arithmetic mean, 95th percentile, 

etc.), this issue does not impact those calculations.

. However the Kenward-Rogers method in PROC MIXED in SAS 9.2 ignores 

covariance parameters with zero variances, suggesting that other methods should be used when 

the ICC is zero – such as the case for the dermal exposure (ICC estimate for inhalation exposure 

is non-zero). Under contract with EPA/OPP, ICF, Inc. investigated the alternate approaches to 

calculate the DDF for a similar set of exposure monitoring studies conducted by the 
Page 17 of 40

Antimicrobial Exposure Assessment Task Force II (AEATF-II)11. The ICF memo reviewed the 

different methods for calculating the DDF for fixed effects in a mixed model using the SAS 

MIXED procedure based on an article by Schaalje, et al12

Table 6

and concluded that the “containment” 

method is most appropriate for calculating mixed-effect model confidence intervals when the 

ICC=0. below summarizes the five available methods outlined in the ICF memo.

Table 6. Summary of SAS Methods for Computing Fixed-Effects DDF in PROC MIXEDa

DDF Method SAS 

Abbreviation Comments

Residual residual Uses residual degrees of freedom. Ignores covariance structure as defined by 

the RANDOM and REPEATED statements. This method is not recommended.

Containment contain

Default method when RANDOM statements are present. Accounts for the 

minimum contribution of the random effects that syntactically contain the fixed 

effects of interest.

Between-Within bw

Default method when REPEATED statements are present and RANDOM 

statements are not present. Only exact when the data are balanced and the 

design is a repeated measures design with compound symmetry, and where the 

levels of the within-subjects effects are not replicated within any of the 

subjects. Otherwise the method is at best approximate and can be 

unpredictable.

Satterthwaite / 

Fai-Cornelius satterth Designed to approximate the denominator degrees of freedom for split-plot designs with complicated covariance structures and/or unbalanced data sets.

KenwardRogers kr

Designed to approximate the denominator degrees of freedom for designs with 

complicated covariance structures and/or unbalanced data sets. Results from 

simulations suggest better performance than the Satterthwaite method. If a 

covariance parameter has zero variance then this method ignores that 

covariance. a RANDOM statement used to define the cluster effect.

For dermal exposure with and without chemical-resistant hats the ICC estimate is zero for which 

the “containment” method is recommended. Using this method, however, does not alter the 

overall conclusions with respect to the secondary study objective. That is, the 95% confidence 

intervals for dermal exposure with and without chemical-resistant hats still include 1 and 

excludes 0 (0.072 – 1.68 and 0.11 – 1.86, respectively). The confidence interval widths are still 

greater than 1.4 as well. Additional details for this analysis are provided in Appendix C. The 

SAS code is provided in Appendix D.

3.2.2.3 Evaluation of Regression Diagnostics for Normality and Constant 

Variance of Residuals

Following discussions at the January 2011 HSRB meeting for the AHETF Closed Cab Airblast 

Application Scenario Monograph, EPA conducted standard linear regression diagnostic analyses 

for the open cab airblast applicator dataset. For both normalized dermal and inhalation 

exposures, residuals were demonstrated to be normally distributed and no pattern was observed 

 11 “Additional statistical issues for the AEATF Mop Study Statistical Review for HSRB”. Contract No.: EP-W-06-

091.

12 Schaalje, G. B., J. B. McBride, G. W. Fellingham. “Approximations to Distributions of Test Statistics in Complex 

Mixed Linear Models Using SAS® Proc MIXED” Proceedings of the Twenty Sixth Annual SAS Users Group 

International Conference. April 2001. Long Beach, CA. ISBN 1-58025-864-6. SAS Institute, Cary, NC 27513. 
Page 18 of 40

that would violate the constant variance assumption. Additional details for this analysis are 

provided in Appendix C. The SAS code is provided in Appendix D.

3.2.3 Consideration of AHE64 (OK-pecan) MU A5 for Inhalation Exposure

The AHETF described the influence of the inhalation exposure MU A5 in AHE64 (OK-pecan) in 

the monograph report saying, “Much of the difficulty with the inhalation data was the result of a 

single MU with an exceptionally small exposure value (about 3 orders of magnitude lower than 

the next highest value) – the separation of this observation from the dataset is clear in Figure 6. 

The AHETF demonstrated that the widths of the 95% confidence interval of the statistics of the 

inhalation unit exposure distributions are decreased when AHE64-A5 is excluded (shown in 

Table-9 below). Also shown in Table 9 is additional AHETF analysis demonstrating that both 

the mixed effect regression slope and its confidence interval width decrease when MU A5 in 

AHE64 is excluded.

Table 9. Statistical Results when Excluding Inhalation Observation AHE64 MUA5

Exposure Route

Inhalation Unit Exposure (ug/lb ai) Statistics 

(95% CI)a

Mixed-Effect Regression 

Analysis

Geometric 

Mean

Arithmetic 

Meanb

95th

Percentilec

Est. 

slope 95% CI CI Width

Inhalation

(no respirator)

All 28 

observations

0.781

(0.335 – 1.82)

4.71

(1.39 – 19.1)

17.6

(5.26 – 57.7) 0.53 -0.25 – 1.31 1.57

Excluding 

AHE64-A5

1.03

(0.683 – 1.53)

1.81

(1.11 – 3.06)

5.90

(3.16 – 10.9) 0.40 -0.10-0.90 1.00

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 Arithmetic Mean (AM) = GM * exp{0.5*((lnGSD)^2)} c 95th percentile = GM * GSD^1.645

EPA conducted additional analysis (more fully presented in Appendix C with corresponding 

SAS code in Appendix D) confirming the significant influence of this observation on both the 

magnitude and precision of parameter estimates of the “ln(inhalation exposure) vs. ln(AaiH)”

regression model. It can be seen from the additional analysis that MU A5 (AHE64) exerts

influence primarily on the estimates of the fixed effects and their precision.

Interestingly, while the geometric mean unit exposure is slightly increased by excluding AHE64-

A5, modeled estimates of both the arithmetic mean and 95th percentile values are decreased13

 13 Though perhaps counterintuitive – excluding a low value might lead one to believe that the dataset is shifted 

upward – the exclusion of this value decreases the geometric standard deviation (GSD) of the lognormal 

distribution. As a result, low-end percentiles will increase, but high-end percentiles (i.e., arithmetic mean, 95th

percentile) will decrease. Additionally, because the magnitude of the excluded value is so much lower than the rest 

of the observations, its exclusion has hardly any effect on non-parametric estimates.

. 

Though perhaps statistically anomalous, in additional correspondence with the Agency (provided 

as Appendix E), the AHETF did not think that this observation resulted from faulty equipment or 

other measurement error. Thus, EPA will continue to include it in the full dataset, which also 

results in higher estimates at the upper-end of the distribution, a reasonable outcome for 

regulatory assessment. 
Page 19 of 40

3.3 Data Generalizations and Limitations

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

discussed and established (Christian, 2007). The data will be used generically to assess exposure 

for applicators applying any conventional chemical applied as a spray using airblast equipment 

hauled by trucks or tractors with an open cab. However, certain limitations need to be 

recognized with respect to collection, use, and interpretation of the exposure data.

3.3.1 Generic Use in Exposure Assessment

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

applying pesticides to any crop using any type of open cab airblast equipment, while wearing a 

long-sleeve shirt, pants, shoes/socks, chemical resistant gloves, with or without chemicalresistant hats, and no respirator. This 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.

3.3.2 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 open cab airblast equipment, which are 

generally chemicals of low volatility. Since they are not typically used in airblast sprayers, the 

Agency does not expect for it to be used to support regulatory decisions for high volatility 

pesticides (e.g., fumigants).

3.3.3 Use of “Unit Exposures”

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

by the amount of active ingredient handled, though with less-than-expected statistical power. 

HED will continue to recommend use of the exposure data normalized by the amount of active 

ingredient handled as a default condition for the foreseeable future.

3.3.4 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 major crops that use open

cab airblast application methods to ensure a large pool of potential applicators; requiring 

potential applicators to use certain pesticides due 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 open

cab airblast applicators. Thus, it is important to recognize these limitations in considering this 

dataset as representative of all open cab airblast applicators. 
Page 20 of 40

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-detect exposures), 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.

3.4 Conclusions

HED has reviewed the AHETF Open Cab Airblast 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 open cab airblast application scenario dataset (i.e., PHED) 

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 open cab airblast application exposure.

• Per stated objectives, estimates of the GM, AM, and P95 were shown to be accurate 

within 3-fold with 95% confidence;

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

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

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

purposes for the foreseeable future.

4.0 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, 2010. Agricultural Handler Exposure Scenario Monograph: Open Cab Airblast 

Application of Liquid Sprays. Report Number AHE1006. December 14, 2010. [MRID 

48326701]

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., 2011. Memorandum: Review of Agricultural Handler Exposure Task Force 

(AHETF) Open Cab Airblast Applicator Exposure Monitoring Studies: AHE62, AHE63, 

AHE64. D387287. March 11, 2011

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.
Page 21 of 40

Human Studies Review Board Meeting Report – June 24-25, 2008. EPA-HSRB-08-03. 

November 14, 2008. http://www.epa.gov/osa/hsrb/files/june2008hsrbfinalreport111408.pdf

Human Studies Review Board Meeting Report – October 21-22, 2008. EPA-HSRB-08-04. 

December 30, 2008. http://www.epa.gov/osa/hsrb/files/October2008HSRBfinalreport123008.pdf

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

Sarkar, Bayazid, 2010. Memorandum: Review of Statistical Analyses in Agricultural Handler 

Exposure Task Force (AHETF) Monographs. U.S. Environmental Protection Agency. June 25, 

2010. D000000. 
Page 22 of 40

Appendix A

Supplemental Table 1: AHETF Open Cab Airblast Applicator Dermal and Inhalation 

Exposure Data
Page 23 of 40

Table 1. Open Cab Airblast Application – Dermal and Inhalation Exposures

Study 

ID

MU 

ID

AaiH 

(lbs)

Dermal Inhalation

Inner 

WBDa

(μg)

Hand

(μg)

Head

(μg) Feetb

(μg)

Total Exposure 

(μg)

Unit Exposure

(μg/lb ai) Total

(ug)

Unit 

Exposure

Non- (μg/lb ai)

MEA MEA with CR hat

w/o CR 

hat

with CR 

hatc

w/o CR 

hat

with CR 

hatc

w/o CR 

hat

AHE07

1 1 285 424 848 170 8859 2.2 1305 9570 17.4 128 15.4 0.205

3 3 2166 1851 3702 340 44348 1.7 6210 48367 138 1075 54.0 1.20

4 4 454 179 358 67.1 3257 0.39 879 3890 11.7 51.9 18.7 0.249

6 6 814 217 434 189 18569 0.85 1438 19601 24.0 327 48.2 0.803

8 8 2507 4146 8292 1971 80298 23.3 12793 86974 246 1673 296 5.68

10 10 4084 275 550 123 13755 0.92 4758 18115 149 566 34.4 1.07

12 12 558 51.4 103 115 6923 0.79 777 7533 23.5 228 42.3 1.28

13 13 1101 203 406 111 51416 0.59 1619 52721 45.0 1464 39.3 1.09

15 15 2878 160 320 595 29664 6.6 3800 32709 158 1363 35.2 1.47

16 16 1172 389 778 112 18390 8.4 2070 19959 51.8 499 32.0 0.800

17 17 161 11.4 22.8 31.5 3754 0.97 216 3927 6.4 116 18.6 0.548

22 22 2266 1614 3228 690 20476 1.2 6185 24357 103 406 51.0 0.851

23 23 3960 877 1754 1380 25267 6.9 7101 30111 78.9 335 35.3 0.392

26 26 2595 840 1680 1045 9856 7.2 5327 13298 59.2 148 30.4 0.337

27 27 14852 2869 5738 5072 91118 108 25770 108947 286 1211 529 5.88

AHE62

A1 A1 288 271 542 61.3 4050 -- 891 4609 26.0 134 30.5 0.889

A2 A2 279 15.0 30.0 137 1139 -- 446 1433 89.2 287 35.7 7.13

A3 A3 239 13.8 27.6 185 2767 -- 452 3020 43.5 290 44.3 4.26

AHE63

A1 A1 5362 242 484 1232 68487 -- 7078 74091 146 1531 117 2.42

A2 A2 6786 225 450 110 32086 -- 7346 39097 206 1098 21.8 0.612

A3 A3 750 58.9 118 277 49771 -- 1145 50580 46.9 2073 67.6 2.77

A4 A4 20.7 16.1 32.2 7.4 233 -- 60.3 270 4.0 17.8 3.25 0.214

A5 A5 191 29.9 59.8 84.0 1623 -- 335 1844 54.9 302 30.3 4.97

AHE64

A1 A1 2773 486 972 529 16752 -- 4274 20011 67.7 317 42.7 0.677

A2 A2 16593 1029 2058 6842 76863 -- 25493 94485 2524 9355 14.5 1.44

A3 A3 781 416 832 119 2158 -- 1732 3355 49.1 95.0 13.4 0.380

A4 A4 72075 1380 2760 5867 159634 -- 80702 233089 3202 9250 6.66 0.264

A5 A5 4.5 0.5 1.0 112 64.8 -- 118 69.8 6.5 3.8 0.294 0.00026 a Represents the sum of six body sections (upper and lower arms, front and back torso, upper and lower legs)

b AHE07 measured foot exposure. Its low contribution (< 1%) to total dermal exposure led to exclusion from monitoring workers’ feet in AHE62-64. c

Dermal exposure with CR hats reflects use of MEA data. 
Page 24 of 40

Appendix B

Lognormal Probability Plots of Dermal and Inhalation Unit Exposures
Page 25 of 40
Page 26 of 40

Appendix C

Additional Analysis for Section 3.2.2.2, 3.2.2.3, and 3.2.3

I. Analysis for Section 3.2.2.2 (Additional ICC Considerations)

a. Dermal- no CR hat:

Solution for Fixed Effects

Effect Estimate

Standard 

Error DF t Value Pr > |t| Alpha Lower Upper

lnAaiH 0.9850 0.4230 21 2.33 0.0300 0.05 0.1052 1.8647

 Width of the 95% confidence interval of the slope =1.76

b. MEA corrected Dermal- with CR hat:

Solution for Fixed Effects

Effect Estimate

Standard 

Error DF t Value Pr > |t| Alpha Lower Upper

lnAaiH 0.8743 0.3856 21 2.27 0.0340 0.05 0.07238 1.6763

 Width of the 95% confidence interval of the slope =1.60

II. Analysis for Section 3.2.2.3 (Regression Diagnostics)

a. Dermal with no CR hat

Because ICC is zero for the model “ln(dermal exposure, no CR hat) vs. ln(amt of ai handled)” 

EPA decided to use studentized residual instead of conditional studentized residual for 

diagnostics purposes. The difference between conditional residual and residual is that 

conditional residual takes account of the random effects. Figure 1 below shows a Q-Q plot of the

studentized residuals from the mixed effect model fitted to the dermal data with no CR hat. The 

Q-Q plot comes close to a straight line; thus, we can accept that the studentized residuals are

normally distributed. Also no specific pattern is observed in the “residual vs. predicted” plot; 

thus, there is no strong indication against assumption of constant variance.
Page 27 of 40

Appendix C, Figure 1: Residual diagnostics of dermal exposure, no CR hat

In addition to the above probability plot of the studentized residual, EPA also used formal 

statistical test to assess the normality of the residuals. All four tests shown below provide 

evidence that the studentized residuals are normally distributed (p>0.05).

 Tests for Normality

 Test --Statistic--- -----p Value------

 Shapiro-Wilk W 0.959419 Pr < W 0.3378

 Kolmogorov-Smirnov D 0.098612 Pr > D >0.1500

Cramer-von Mises W-Sq 0.062413 Pr > W-Sq >0.2500

 Anderson-Darling A-Sq 0.426028 Pr > A-Sq >0.2500

b. Dermal with CR hat (MEA corrected):

Because ICC is zero for the model “ln(MEA corrected dermal exposure, with CR hat) vs. ln(amt 

of ai handled)” EPA decided to use studentized residual instead of conditional studentized 

residual for diagnostics purposes. Figure 3 shows a Q-Q plot of the studentized residuals from 

the mixed effect model fitted to the dermal data (MEA corrected) with CR hat. The Q-Q plot 

comes close to a straight line; thus, we can accept that the studentized residuals are normally 

distributed. Also no specific pattern is observed in the “residual vs. predicted” plot; thus, there is 

no strong indication against assumption of constant variance.
Page 28 of 40

Appendix C, Figure 3: Residual diagnostics of dermal exposure, with CR hat with MEA 

correction

In addition to the above probability plot of the studentized residual, EPA also used formal 

statistical test to assess the normality of the residuals. All four tests shown below provide 

evidence that the studentized residuals are normally distributed (p>0.05).

 Tests for Normality

 Test --Statistic--- -----p Value------

 Shapiro-Wilk W 0.953824 Pr < W 0.2468

 Kolmogorov-Smirnov D 0.115966 Pr > D >0.1500

 Cramer-von Mises W-Sq 0.056386 Pr > W-Sq >0.2500

 Anderson-Darling A-Sq 0.43476 Pr > A-Sq >0.2500

c. Inhalation

Figure 4 below shows a Q-Q plot of the conditional studentized residuals from the mixed effect 

model fitted to the inhalation data. In the inhalation exposure model, ICC is not zero, so EPA 

used conditional residual instead of regular residual because it takes account of random effects 

due to clustering. The Q-Q plot comes close to a straight line; thus, we can accept that the

conditional studentized residuals are normally distributed. Also no specific pattern is observed 

in the “residual vs. predicted” plot; thus, there is no strong indication against assumption of 

constant variance.
Page 29 of 40

Figure 4: Residual diagnostics of inhalation exposure

III.Analysis for Section 3.2.3 (Inhalation Exposure)

AHETF commented in their monograph that relationship between inhalation exposure and 

amount of active ingredient handled has the potential to be strongly influenced by the exposure 

for MU A5 in AHE64 (OK-pecan). From the residual plot shown in Figure 4 above, it was 

found that MU AHE64-A5 has conditional studentized residual > 3.0. EPA decided to conduct

additional analysis to determine the influence of MU AHE64-A5.

In linear mixed models fit by restricted maximum likelihood (REML), an overall influence 

measure is the restricted likelihood distance (Cook and Weisberg 1982). The likelihood distance 

gives the amount by which the restricted log-likelihood of the full data changes if one were to 

evaluate it at the reduced-data estimates (Mixed Model Influence Diagnostics, Oliver 

Schabenberger, SAS Institute). Figure 5 below shows restricted maximum likelihood distance 

for the inhalation exposure model. 

Appendix C, Figure 5: Restricted likelihood distance of inhalation exposure model (log-log 

regression) using mixed effects
Page 30 of 40

Clearly, observation 28 (MU AHE64-A5) stands out in terms of the summary statistic restricted 

likelihood distance (RLD). Because the RLD suggests that the MU AHE64-A5 is influential, the 

nature of that influence should be determined. Using Cook’s D and CovRatio of inhalation 

exposure model (log-log regression), shown in Figure 6 below, the observation with the largest 

effect on the fixed effects estimates is 28 (Cook’s D). The same observation has CovRATIO 

values of less than one. Deleting it from the analysis increases the estimated precision of the 

fixed effects estimates. MU AHE64-A5 exerts influence primarily on the estimates of the fixed 

effects and their precision, as can be seen from the Cook’s D, and CovRATIO statistic for the 

fixed effects parameter. 

Figure 5: Cook’s D and CovRatio of inhalation exposure model (log-log regression) using 

mixed effects
Page 31 of 40

References (specific to Appendix C)

Cook, R.D. and Weisberg, S. (1982), Residuals and Influence in Regression, New York: 

Chapman and Hall

Schabenberger, O. (2004), Mixed Model Influence Diagnostics, NC: SAS Institute Inc.
Page 32 of 40

Appendix D

SAS Code for Analysis in Sections 3.2.2.2, 3.2.2.3, and 3.2.3
Page 33 of 40

SAS Code for “Dermal Exposure without CR hats (No MEA)"

data Derm_NOMEA_Nohats;

input cluster $ MU $ ai exp;

datalines;

AHE07_GA 1 75 9570

AHE07_GA 3 45 48367

AHE07_GA 4 75 3890

AHE07_GA 6 60 19601

AHE07_GA 8 52 86974

AHE07_ID 10 32 18115

AHE07_ID 12 33 7533

AHE07_ID 13 36 52721

AHE07_ID 15 24 32709

AHE07_ID 16 40 19959

AHE07_ID 17 34 3927

AHE07_FL 22 60 24357

AHE07_FL 23 90 30111

AHE07_FL 26 90 13298

AHE07_FL 27 90 108947

AHE62_CA A1 34.3 4609

AHE62_CA A2 5 1433

AHE62_CA A3 10.4 3020

AHE63_NY A1 48.4 74091

AHE63_NY A2 35.6 39097

AHE63_NY A3 24.4 50580

AHE63_NY A4 15.2 270

AHE63_NY A5 6.1 1844

AHE64_OK A1 63.1 20011

AHE64_OK A2 10.1 94485

AHE64_OK A3 35.3 3355

AHE64_OK A4 25.2 233089

AHE64_OK A5 18.2 69.8

;

run;

data Derm_NOMEA_Nohats;

set Derm_NOMEA_Nohats;

lnAaiH=log(ai);

lnExp=log(Exp);

run;

ods graphics on;

ods rtf;

Proc Mixed Data=Derm_NOMEA_Nohats Method=REML cl covtest;

 Title3 "Mixed-Effects Regression of Ln Exposure on Ln Amt AI Handled";

 Class Cluster;

 Model LnExp = LnAaiH / solution alpha=0.05 DDFM = contain residual

outp=hsrb ;

 random cluster;

 

run;

ods rtf close;

proc print data=hsrb;
Page 34 of 40

run;

proc univariate normal plot data=hsrb;

var studentresid;

run;

SAS Code for “Dermal Exposure with CR hats (MEA)"

data DermMEA;

input cluster $ MUID $ ai exposure ;

datalines;

AHE07_GA 1 75 1305

AHE07_GA 3 45 6210

AHE07_GA 4 75 879

AHE07_GA 6 60 1438

AHE07_GA 8 52 12793

AHE07_ID 10 32 4758

AHE07_ID 12 33 777

AHE07_ID 13 36 1619

AHE07_ID 15 24 3800

AHE07_ID 16 40 2070

AHE07_ID 17 34 216

AHE07_FL 22 60 6185

AHE07_FL 23 90 7101

AHE07_FL 26 90 5327

AHE07_FL 27 90 25770

AHE62_CA A1 34.3 891

AHE62_CA A2 5 446

AHE62_CA A3 10.4 452

AHE63_NY A1 48.4 7078

AHE63_NY A2 35.6 7346

AHE63_NY A3 24.4 1145

AHE63_NY A4 15.2 60.3

AHE63_NY A5 6.1 335

AHE64_OK A1 63.1 4274

AHE64_OK A2 10.1 25493

AHE64_OK A3 35.3 1732

AHE64_OK A4 25.2 80702

AHE64_OK A5 18.2 118

;

data DermMEA;

set DermMEA;

lnAaiH=log(ai);

lnExp=log(Exposure);

run;

ods graphics on;

ods rtf;

Proc Mixed Data=DermMEA Method=REML cl covtest;

 Title3 "Mixed-Effects Regression of Ln Exposure on Ln Amt AI Handled";

 Class Cluster;

 Model LnExp = LnAaiH /solution alpha=0.05 DDFM = contain residual

outp=hsrb1;

 random cluster;
Page 35 of 40

 Run;

 ods rtf close;

proc univariate normal plot data=hsrb1;

var studentresid;

run;

SAS Code for “Inhalation Exposure"

data inhalation;

input cluster $ MU_ID $ ai inhalation_exposure ;

datalines;

AHE07_GA 1 75 15.4

AHE07_GA 3 45 54

AHE07_GA 4 75 18.7

AHE07_GA 6 60 48.2

AHE07_GA 8 52 296

AHE07_ID 10 32 34.4

AHE07_ID 12 33 42.3

AHE07_ID 13 36 39.3

AHE07_ID 15 24 35.2

AHE07_ID 16 40 32

AHE07_ID 17 34 18.6

AHE07_FL 22 60 51

AHE07_FL 23 90 35.3

AHE07_FL 26 90 30.4

AHE07_FL 27 90 529

AHE62_CA A1 34.3 30.5

AHE62_CA A2 5 35.7

AHE62_CA A3 10.4 44.3

AHE63_NY A1 48.4 117

AHE63_NY A2 35.6 21.8

AHE63_NY A3 24.4 67.6

AHE63_NY A4 15.2 3.25

AHE63_NY A5 6.1 30.3

AHE64_OK A1 63.1 42.7

AHE64_OK A2 10.1 14.5

AHE64_OK A3 35.3 13.4

AHE64_OK A4 25.2 6.66

AHE64_OK A5 18.2 0.294

;

run;

data inhalation;

set inhalation;

lnAaiH=log(ai);

lnExp=log(inhalation_exposure);

run;

ods graphics on;

ods rtf;

Proc Mixed Data=inhalation Method=REML cl covtest;

 Title3 "Mixed-Effects Regression of Ln Exposure on Ln Amt AI Handled";
Page 36 of 40

 Class Cluster;

 Model LnExp = LnAaiH /influence (iter=5) solution alpha=0.05 DDFM = 

KenRog residual ;

 random cluster; 

Run; 

ods rtf close;

proc print data=hsrb;

run;
Page 37 of 40

Appendix E

AHETF Correspondence to EPA Regarding AHE64 MUA5
Page 38 of 40

Toxicology & ExposureAssessment Services, Inc.

Dennis R. Klonne, M.S., Ph.D., DABT Phone: (919) 870-

5176

2901 Patrie Place Fax: (919) 518-1573

Raleigh, NC 27613 E-mail: dklonne@bellsouth.net

______________________________________________________________________________

To: Matt Crowley 

Date: March 7, 2011

Re: MU A5 in AHE64 of the Open Cab Airblast Scenario

CC: Dave Johnson, Victor Canez, and Dave Barnekow

From: Larry Smith and Dennis Klonne

There appears to be a question regarding the exposure data (primarily the inhalation exposure 

data) for worker MU A5 in the AHE64 study of the Open Cab Airblast Scenario. In summary, 

there were no measurement or analytical problems or errors for the exposure matrices for this 

worker. There was no discussion in the Monograph regarding the low exposure values for 

inhalation and dermal exposures (either absolute values or normalized values) because no 

obvious reason was found for the results (see below). Only the effects of these data on the 

statistical objectives were discussed in the monograph.

The AHETF did observe the exceptionally low inhalation and dermal exposure (both absolute 

and normalized) values for this worker. An evaluation was conducted and several observations 

described below were made.

The following observations can be made regarding MU A5 in study AHE64:

o He had the lowest unit exposure values for both the dermal and inhalation routes. It makes 

sense that both values would tend to move in tandem with this open cab airblast scenario.

o He had the most experience of the workers in this scenario – 40 years. However, another 

worker in the scenario, also tied with 40 years of experience, had a much higher unit 

exposure for both dermal and inhalation routes.

o He used the same tank size as many other workers – 500 gal.

o He applied the smallest spray volume used in the scenario – 300 gal. However, another 

worker (A5, AHE63), also tied with a 300 gal spray volume, had a much higher unit 

exposure for both dermal and inhalation routes.

o He did have the lowest gallon-per-acre spray rate (33 gal/A) but a MU with a similar, albeit it 

slightly higher spray rate of 38 gal/A, had a much higher unit exposure for both dermal and 

inhalation routes.

o Although he applied only 2 loads, several other MUs that also applied 2 loads had much 

higher unit exposures for both dermal and inhalation routes.
Page 39 of 40

o MUs who treated fewer acres had higher unit exposures for both dermal and inhalation 

routes.

o He had the 2nd shortest monitoring time in the scenario – 2.5 hrs. However, the MU with the 

shortest monitoring time (1.4 hr) and another with a very similar monitoring time (2.7 hr) had 

much higher unit exposures for both dermal and inhalation routes.

The one value that seems to really stand out about this particular worker is the percentage of the 

total dermal exposure that was on the hands and face/neck (78%); whereas the next highest value 

in the scenario is 14%. However, remember that this is despite the lowest absolute dermal 

exposure (69.8 μg) in the scenario. It is apparent that this is due to a lack of exposure on all the 

other body parts under the work clothing. The explanation for that could be due to one factor, 

such as the exceptional work habits of this individual, or to a multitude of factors, such as work 

habits, direction and configuration of nozzles, operating pressure of nozzles, tractor speed, wind 

speed and direction, etc., etc.

The detailed observations of the worker indicated no obvious explanation for the findings. The 

raw field data for AHE64 MU A5 was reviewed for indications that the OVS sample may have 

been compromised. The monitoring was conducted on 8/29/2009, starting at 0811 and ending at 

1040 when the pump was turned off. Pump flow was measured at the start and end of 

monitoring (start = 2.041 L/min, end = 1.996 L/min). Additionally, the pump operation was 

visually confirmed during the monitoring period at the following times; 0811, 0841, 0919, 0932, 

and 1036. These data indicate the pump was operating normally throughout the monitoring 

period. There were no pump failures noted.

The worker observer's notes indicate the worker was prudent in his application techniques. For 

example, he turned off the sprayer when he made his turns at the end of each row. The worker 

wore a half-face respirator, but this would not have covered the OVS tube cartridge.

The sample handling and storage records show no anomalies. There are no indications of 

analytical anomalies for the A5 OVS tube analysis or laboratory control recoveries during the 

analytical runs.

In summary, although it was apparent that this worker’s exposure was exceptionally low, there 

were no obvious explanations for the result. Thus, further discussion in the monograph along 

these lines was not deemed to be necessary.

The discussion of this worker in the scenario was done with the intent of demonstrating the 

effects of the unit exposures on the primary and secondary statistical objectives. As with any 

apparent outlying value, be it an exceptionally high or exceptionally low value, it can have some 

dramatic effects on the statistics and attainment of the statistical objectives. This was the case 

for this scenario, in which the inclusion of the inhalation unit exposure value for MU A5 in 

AHE64 prevented attainment of the primary statistical objective (i.e., 3-fold accuracy), even 

though the inhalation data are not held to this standard. The conclusion from this exercise was 

that “This MU produced an atypically small normalized inhalation exposure value that some 

users may choose to eliminate from the dataset when describing ‘typical’ inhalation exposure.” 

(page 76 of the monograph)
Page 40 of 40

Just as we also do not eliminate exceptionally high values from consideration, we do evaluate the 

impact of some of these values on the statistical description of the scenario. In this case, we 

performed the same exercise with some values that were exceptionally low. As noted in the 

monograph, it is up to future users of the database to determine the most appropriate use of the 

data, as objectives and needs can differ between users.