Page 1 of 40 

### UNITED STATES ENVIRONMENTAL PROTECTION AGENCY WASHINGTON, D.C. 20460

OFFICE OF CHEMICAL SAFETY

AND POLLUTION PREVENTION



### MEMORAN DUM

Date: March 1 1 , 201 1

SUBJECT: Review of Agricultural Handler Exposure Task Force (AHETF) Monograph: Open Cab Airblast Application of Liquid Sprays

PC Code:

-- DP Barcode: D 387287

Decision No.:

-- Registration No. :

-- Petition No.:

-- Regul atory Action:

-- Risk Assessment Type:

-- Case No.:

-- T XR No.:

-- CAS No.:

-- MRID No.: 48 3 26701 40 CFR:

-- Ver.Apr.08



### FROM: Matthew Crowley , Biologist Chemistry and Exposure Branch

Health Effects Division

Ba yazid 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, 201 1 ; D 387287 )

. 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.Page 2 of 40 

### 1.0 Executive Summary

This document represen ts 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, 200 8 ) and is considered the most reliable data for assessing exposure and risk to individuals applying liquid spray pesticides 1 

### with open cab airblast equipment while wearing the following personal protective equipment (PPE): l ong

sleeved shirts, long pants, shoes, socks, chemical

- resistant gloves, with or without chemical

resistant hats, and no respirator 2



### . The AHETF data and associated “ unit exposures ” are considered superior to the existing open cab airblast applicator datas et. 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

c ontemporary 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 95 th 

### percentile was met. The secondary o bjective 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 less

than

- e xpected 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 expo sure 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 altern ative 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 o f reliable factors, and are not

addressed in this review.

3 Pesticide Handlers Exposure Database (PHED) Scenario 11: Airblast Application, Open Cab (APPL)Page 3 of 40 Table 1

. Unit Exposures ( u g/lb ai handled): Open Cab Airblast Applicators
Exposure

Route PPE PHED AHETF b

“Best Fit” Geometric Mean Arithmetic Mean d 95 th Percentile e

Dermal a single

- layer, CR gloves,

CR hat c unavailable 66.4 215 826

single

- layer, CR gloves 240 387 1590 6148

Inhalation n o respirator 4.5 0.781 4.71 17.6

a CR = chemical

- resist ant; 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 a nd 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 95 th percentile = GM * GSD^1.645



### 2.0 Background

The following provides background on the AHETF obje ctives for this research a nd also

discusses previous reviews of the open cab airblast scenario by the Human Studies Review Board (HSRB).

2.1 A HETF Objectives

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

which can be us ed to define worker exposures associated with major agricultural and non

agricultural 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. AHETF

sponsored studies are typically designed to represent indivi duals 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 weight

unit chemical handled. Mathematically, unit exposu res 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 chemica ls 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.) , packagingPage 4 of 40 

### (e.g., bottle or water

- soluble packet), or the equipment type used to appl y 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 spra ying 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 doubl es 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 i n the AHETF Governing Document (AHETF, 200 8 ), 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 95 th 

### 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” ( f

RA) objective does not appl y 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 ingred ient 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 95 th 

### 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 inform ational 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 per spective. 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 Revie w: 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 A aiH for any given scenario is commensurate with typical commercialPage 5 of 40 

### 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

chem ical

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

conducted for 2 8 workers 4 

### (referred to as “monitoring units”, or MUs) applying liquid spray pesticides using open cab airblast equipment 5



### 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 con ducted 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

AHE 07 4 6448201 Determination of Dermal and Inhalation Exposure to Workers During Airblast Applications

of a Liquid Pesticide Product by Open Cab Airbla st 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 Inhala tion 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 Ca b 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 mon itored 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

.Page 6 of 40 Figure 1 : Open Cab airblast application in GA pe aches

Figure 2 : Open Cab airblast application in ID applesPage 7 of 40 Figure 3 : Open cab airblast application (with canopy) in FL orange s



### 2.2 HSRB Review and Comments

The ability of the EPA to use the open cab airblast applicator exposure m onitoring 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 Board 6

Table 3

. Open Cab Airblast Application Scenario

– HSRB Review Timeline 

### . The following is a timeline of HSRB review s related to

this scenario:

Date HSRB Review

June 2006 AHE6 2 ( CA

- grapes ) Protocol (at that time titled “AHE3 6 ”)

AHE 63 ( NY

- grape ) Protocol (a t that time titled “AHE3 7 ”)

October 2008 Open Cab Airblast Application Scenario Design

AHE 62 ( CA

- grapes ) Protocol (revised)

AHE 63 ( NY

- grape) Protocol (revised)

AHE 64 ( OK

- pecans ) Protocol



### Execution of the field studies follow ed favorable reviews by the HSRB; however, t hroughout 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 ai rblast 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 n on

- respondents and r esponders who declined to participate ( 11/14/ 08 and 12/30/08 HSRB Meeting Report s )

6 http://www.ep a.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.Page 8 of 40 

### The HSRB was concerned with the inability to evaluate study participant s against the universe of

open cab airblast applicators , considering the AHETF indicated they would experience a very low response rate. Continue d 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 compar ing study participants with those eligible non

participan ts 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 whethe r 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 employe d 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 t his

methodol o gy, 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 4 

### 4 below

.

Table 4

. Synopsis of Experts Used to Evaluat e the Representativeness of Monitored Workers Study ID Recruited Responded Response

AHE 62 ( CA

- grape ) 5 USDA agricultural extension agents 5 **•** 4 of 5 agreed that the study participants were typical in t he counties monitored.

**•** 1 thought acreage was unrepresentative

of some counties monitored

AHE6 3 ( 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)

AHE 64 ( OK

- pecans ) 1 profe ssor 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

repres entative 1 professor of Horticulture and Landscape Architecture (OK State University) 10 USDA agricultural extension

agents



### 2.2.2 Documented S urvey Implementation Expertise ( 11/14/08 and 12/30/08 HSRB Meeting Report s )Page 9 of 40 

### Given the admittedly difficult a ttempts at recruitment for occupational pesticide exposure monitoring studies, the HSRB advised the AHETF to employ individuals with expertise in survey implementation. As a result, t he AHETF abandoned use of so

- called Local Site

Coordinators for recruitm ent 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 Report s )

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

“ne sted” 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 th at 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

onl y 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). A mong other issues, for the purposes of contacting growers on the “call” list, no distinc tion 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 Report s )

The HSRB was concerned that the scripted nature o f the exposure monitoring would not capture the extent of exposures typical of open cab airblast applicator behavior under normal

circumstances. Per protocol, t he AHETF employed observers who recorded applicator behavior throughout the workday

. All manne r 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 consult ation 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.Page 10 of 40 

### G iven t he 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 assess ment 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 p otential 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 t o 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 wo rkers 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 principl es 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 H SRB 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 t hat 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 wi th 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 stud y and revi ewed in Crowley, 201 1 (D 387287 ). The following sections summarize the exposure monitoring results and the scenario benchmark statistical analyses presented in the AHETF scenario monograph ( AHETF, 2010 ).Page 1 1 of 40 

### 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 un ion 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 S amplers (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 protec tion.

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

residue collection method inefficiencies 7



### • 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 :

**•** i f 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 validat ion study to support the residue collection method ;

**•** i f measured exposure contribution from hands and face/neck represents is greater than 60%, a validation study demonstrating the efficiency of the resid ue 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). Measu rements 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 Chromos orb 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 fro m field fortification samples.

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

individual monitored. Total i nhalation exposures we re 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 m 3 

### /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” (ME C).

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

### Dermal unit exposures (i.e., u g/lb ai handled) are then calculated by dividing the summed total exposure by the amount of active ing redient handled.Page 12 of 40 

### 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 ve hicles 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; D 387287 ).

A summary of the 2 8 open cab airblast applicator MUs is provided in Table 3 b elow

. Additional evaluation of exposure data are presented in Sections 3.2

- 3.4 and m ore 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

Inhalation without

CR hats a with CR

hats

AHE 07 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 3 2 566 149 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

2 3 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

AHE6 2 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

AHE 63 A1 NY g rape 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

AHE 64 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 reflect s h and rinse and face/neck wipe method efficiency adjusted (MEA) dat a

.



### 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 distribut ion, so there is value in comparing them in this way. Additionally, inhalation monitoring for MU A5 in AHE64 (OK

- pecan) is shown toPage 13 of 40 

### be significantly lower in magnitude than the rest of the dataset in Figure 6 . Additional treatment of this monitoring is c onsidered 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)Page 14 of 40 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 t hese

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 95 th 

### percentile (P95)

– to be accurate within 3

- fold with 95% confidence (i.e., “fold relative accuracy”). The AHETF analyzed the data using various statistical techniq ues to evaluate this benchmark. First, both dermal and inhalation unit exposures were shown to fit lognormal distributions reasonably well. L ognormal probability plots are provided as A ppendix B

.

Next, the AHETF calculated estimates of the GM, AM and P 95 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 pr esented in Appendix C of the AHETF Governing Document (AHETF, 2008), t he 95% confidence limits for each of these estimates were obtained by generating 10,000 parametricPage 15 of 40 

### bootstrap samples. Then, the fRA for each statistic was determined as the maximum of t he 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, includi ng 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 95 th 

### 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 pro portionality 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 suppor t 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 (ug/lb ai) With CR h ats a Without CR hats

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

GM S 66.4 37.3

– 117 1.8 387 206

– 720 1.9 0.764 0.335

– 1.81 2.4

GSD S 4.63 3.08

– 6.99 1.5 5.37 3.43

– 8.42 1.6 6.56 3.95

– 10.9 1.7

GM M 66.4 37.3

– 117 1.8 387 206

– 71 9 1.9 0.781 0.335

– 1.82 2.3

GSD M 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

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

GSD S = geometric sta ndard deviation assuming SRS = “exp(standard deviation of 28 ln(UE)) values”

GM M = variance component model

- based geometric mean

GSD M = variance component model

- based geometric standard deviation

ICC = intra

- class correlation

AM S 281 85.9

– 524 3.3 1227 5 46

– 4379 3.6 1.71 1.13

– 15.1 8.8

AM U 215 94.7

– 529 2.5 1590 619

– 4523 2.8 4.48 1.36

– 17.6 3.9

AM M 215 94.9

– 536 2.5 1590 625

– 4623 2.9 4.71 1.39

– 19.1 4.1

AM S = average of 28 unit exposures

AM U = arithmetic mean based on GM S = GM S *exp{0.5*( ( lnGS D S )^2 ) }

AM M = variance component model

- based arithmetic mean = GM M * exp{0.5*( ( lnGSD M )^2 ) }

P95 S 2524 256

– 2527 9.9 9250 1701

– 20929 5.4 5.88 3.82

– 73.0 12.4

P95 U 826 336

– 1995 2.5 6148 2290

– 16154 2.7 16.8 5.17

– 55.1 3.3

P95 M 826 338

– 2016 2.4 614 8 2304

– 16340 2.7 17.6 5.26

– 57.7 3.4

P95 S = 95 th percentile (i.e., the 27 th unit exposure out of 28 ranked in ascending order)

P95 U = 95 th percentile based on GM S = GM S * GSD S ^1.645

P95 M = variance component model

- based 95 th percentile = GM M * GSD M ^1.64 5

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 relat ionship between de rmal 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 hats a Without CR hats b

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

Mixed

Effects 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, th e slope estimates for the simple linear regression and the mixed model are identical.



### For dermal exposure with and without chemical

- resistant hats , the slope s of the mixed

- effects regression s are 0. 87 and 0.99, respectively with both sets of 95% confiden ce 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 i ndependence 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.

Cons idering 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 r espect 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 calc ulate confidence intervals for the log

- log regression slope 10

9 Despite this being valuable with respect to post

- hoc evaluation of the study design criteria, this may not be relevant for dermal exp osure 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, 95 th 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 cas e 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 thePage 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 al 12



### Table 6 and concluded that the “containmen t” method is most appropriate for calculating mixed

- effect model confidence intervals when the

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

Table 6 . Summary of SAS Methods for Computing Fixed

- Effects DDF in PROC MIXED a

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 REPEA TED 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.

Kenward

Rogers 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 “con tainment” 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 in clude 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

Varianc e 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 analyse s 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 Sta tistical 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 detail s 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 monogra ph 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 datase t 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 addit ional 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 Inha lation Unit Exposure (ug/lb ai) Statistics (95% CI) a Mixed

- Effect Regression Analysis

Geometric

Mean Arithmetic

Mean b 95 th

Percentile c 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 95 th 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 precisio n of parameter estimates of the “ ln(inhalation exposure) vs. ln(AaiH) ” regression m odel. It can be seen from the additional analysis that MU A5 ( AHE64 ) exert s influence primarily on th e estimates of the fixed effects and their precision

.

I nterestingly, w hile the geometric mean unit exposure is slightly increased by excluding AHE64

- A5, modeled estimates of both the arithmetic mean and 95 th 

### percentile values are decreased 13

13 Though perhaps counterintuitive

– excluding a low value migh t 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., arithmet ic mean, 95 th
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. 

### . T hough perhaps statistically anomalous, in additional correspondence with the Agenc y (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 Limi t ations

The need for an upgraded generic pesticide handler exposure database has been publicly discussed and established (Christian, 2007). The data will be use d 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, u se, and interpretation of the exposure data

.

3.3.1 Gener ic Use in Exposure Assessment

The data comprising this scenario are acceptable for use in assessing exposure for applicators applying pesticides to any crop using a ny type of open cab airblast equipment, while wearing a long

- sleeve shirt, pants, shoes/socks, chemical resistant gloves , with or without chemical

resistant hats, and no respirator . This does not preclude additional consideration or use of acceptable available chemical

- specific studies, biomoni toring studies, or other circumstances in

which exposure data can be acceptably used in lieu of th ese data

.

3.3.2 A pplicability of AHETF Data for V olatil e Chemicals

The data generated in this study are acceptable to use as surrogate data for assessing applicat or 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 d ecisions 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

- expecte d statistical power. HED will continue to recommend use of the exposure data normalized by the amount of active ingredient h andled as a default condition for the foreseeable future

.

3.3.4 Representativeness and Extrapolation to Exposed Population

Targeting an d selecting specific monitoring characteristics (i.e., “p urposive 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 potentia l 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 the se limitations in considering this

dataset as re presentative 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 no n

- detect exposures) , both of which are valuable for regulatory assessment purposes. Also, the random elements incorporated into the recruitment process likely

m itigated 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 an alysis 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 est ablished.

**•** 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 conditi on for exposure assessment

purposes for the foreseeable future

.

4.0 References

AHETF , 200 8

. 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 AHE100 6 . December 14 , 2010. [MRID

483 26701 ]

Christian, Myrta, 2007. M emorandum: 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., 201 1 . Memorandum: Review of Agri cultural Handler Exposure Task Force (AHETF) Open Cab Airblast Applicator Exposure Monitoring Studies: AHE 62 , AHE 63

, AHE 64

. D 387287

. March 1 1

, 201 1

Hackathorn, D.R. and D.C. Eberhart. 1985. Data Base Proposal for Use in Predicting Mixer

loader

- applica tor 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 Studie s 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.

Sarka r, 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 DataPage 23 of 40 Table 1. Open Cab Airblast Application

– Dermal and Inhalation Exposures

Study

ID MU

ID AaiH (lbs) Dermal Inhalation

Inner

WBD a

(μg) Hand (μg) Head (μg) Feet b
(μg) Total Exposure (μg) Unit Exposure (μg/ lb ai) Total (ug) Unit Exposure (μg/lb ai) Non

- MEA MEA w ith

CR h at w / o CR

h at w ith CR

h at c w / o CR

h at w ith CR

h at c w / o CR

h at

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 5 4.0 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 56 6 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 7.2 532 7 13298 59.2 148 30.4 0.337

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

AHE6 2 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 27.6 185 276 7

-- 452 3020 43.5 290 44.3 4.26

AHE 63 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

AHE 64 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 41 6 832 119 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 hat s reflects use of MEA data.Page 24 of 40 

### A ppendix B

Lognormal Probability Plots of Dermal and Inhalation Unit ExposuresPage 25 of 40Page 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 Eff ects

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 Fix ed 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 Diagnos tics)

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 show s a Q

- Q plot of the

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

- Q plot comes clo se 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, Fig ure 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

- W ilk 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

res idual for diagnostics purposes. Figure 3 show s a Q

- Q plot of the studentized residuals from

the mixed effec t 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 studentize d 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, Fig ure 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 res iduals 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 b elow 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

use d conditional residual instead of regular residual because it takes acco unt 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. A nalysis for Section 3.2.3 (Inhalation Exposure)

AHETF commented in their monograph that relationship between inhalatio n 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 residua l > 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 s hows 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 effectsPage 30 of 40 

### Clearly, observation 28 (MU AHE64

- A5) stands o ut 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, t he observation with the largest effect on the fixed effects estimates is 28 (Cook’s D). T he same observation has C ov RATIO

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

fixed effects estimates. MU AHE64

- A5 exert s influence primarily on the estimates of the fixed effects and their precision, as can be seen from the Cook’s D, and C ov RATIO statistic for the fixed effects parameter.

Figure 5: Cook’s D and CovRatio of i nhalation exposure model (log

- log regression) using

mixed effectsPage 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 3.2.3Page 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

AHE 07_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_O K 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 p lot 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(a i);
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 = cont ain 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 ;

A HE07_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 Reg ression 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 Co rrespondence to EPA Regarding AHE64 MUA5Page 38 of 40 

# T oxicology & 

# E xposure 

# A ssessment 

# S ervices , 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 measuremen t 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 r eason 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 f or 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 mu ch 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 s pray 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 2 nd 

### 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 i nhalation 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 **μ** g) 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 t he 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 w orker 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 th e 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 t he 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 a nomalies 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 di scussion 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 produce d 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 user s of the database to determine the most appropriate use of the data, as objectives and needs can differ between users.