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

OFFICE OF CHEMICAL SAFETY
AND POLLUTION PREVENTION



### MEMORANDUM

Date: January 7, 2011

SUBJECT: Review of Agricultural Handler Exposu re Task Force (AHETF) Monograph: Closed Cab Airblast Applic ation of Liquid Sprays

PC Code:

-- DP Barcode: D381148
Decision No.: -- Registration No.:

-- Petition No.:

-- Regulatory Action:

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

-- TXR No.:

-- CAS No.: --
MRID No.: 48314201 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 “Closed Cab Airbla st Application of Liquid Sprays” submitted by the Agricultural Handler Exposure Task Force. Scientific review of the five exposure studies comprising this scenario can be found in a sepa rate data evaluation review (DER) memorandum (Crowley, 2011 – D381148). The AHETF satisfact orily followed the study protocols and satisfied data analysis objectives. EPA consider s the closed cab airblast scenario complete and
its results are recommended for use in routine assessment of exposure and risk for closed cab airblast applicators.

Page 1 of 62

### 1.0 Executive Summary

This document represents the Health Effects Divi sion (HED) review of the Agricultural Handler Exposure Task Force (AHETF) Monograph: Closed Cab Airblast Application of Liquid Sprays (AHETF, 2010). HED confirms that the data me ets the study design objectiv es outlined in the AHETF Governing Document (AH ETF, 2008) and is considered the most reliable data for assessing exposure and risk to indivi duals applying liquid spray pesticides 1 

### with closed cab airblast equipment wh ile wearing the following personal pr otective equipment (PPE): longsleeved shirts, long pants, shoes, socks, chemical-resistant gloves, and no respirator 2 

### . The AHETF data and associated unit exposures are c onsidered superior to the existing closed cab airblast applicator dataset. 3 

### AHETF efforts represented a we ll-designed, concerted process to collect reliable, internally-consistent, and cont emporary exposure data in a way that takes advantage of and incorporates a more robust st atistical 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 objective to evaluate proportionality between dermal and inhalation exposure and the amount of activ e ingredient handled with 80 % statistical power – a key assumption in the use of exposure data as “uni t exposures” – was not met. Though with lessthan-expected statistical power , regression analysis does not reject proportionality between exposure and the amount of active in gredient handled for either the dermal and inhalation routes of exposure. Additional analyses point to incide ntal exposure sources such as contacts with exterior surfaces having a more substantial impact on exposure. This is not unexpected given the use of the enclosed cab which, as a physical barr ier, is intended to prevent exposure from the airblast overspray. Thus, for this scenario, HED will investigate alternative options for use of this data, but continue for the foreseeable fu ture to use the exposure data normalized by the amount of active ingredient as a default condition for exposure assessment purposes.

Select summary statistics for the closed cab airb last applicator scenario “unit exposures” are presented in Table 1 below, as well as the PHED valu e previously used for comparison.

Table 1. Unit Exposures (ug/lb ai ha ndled): Closed Cab Airblast Applicators

Exposure Route PHED AHETF a

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

Dermal b 19 4.1 14.6 56.4
Inhalation 0.45 0.033 0.068 0.240

a Statistics are estimated using a variance component model accounting for correlation between measurements
conducted within the same fiel d study (i.e., measurements co llected during the same time and at the same location). Additional model estimates (e.g., empirical and simple ra ndom sample assumptions) are described in Section 3.0.

b Per current EPA policy, dermal unit exposures reflect 50% adjustment of hand and face/neck measurements, since the average percent contribution of dermal exposure by the hands, face, and neck is approximately 50%.

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

2 Adjustments to this dataset would be required to represen t 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 appropri ate, given the availability of reliable factors, and are not
addressed in this review.

3 Pesticide Handlers Exposure Database (PHED) Scenar io 12: Airblast Application, Enclosed Cab (APPL)

Page 2 of 62c Arithmetic Mean (AM) = GM * exp{0.5*((lnGSD)^2)}

d 95 th 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 closed cab airblast scenario by the Human Studies Review Board (HSRB).

2.1 AHETF Objectives

The AHETF is developing a database (Agric ultural 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 pesticid e handling task based on activity such as mixing/loading or application. Other factors such as formulation (e.g., liquids, granules), tractor type (e.g., ope n or closed cab), and/or ap plication equipment type (e.g., airblast, aircraft or boom spra yers) are also key criteria fo r defining scenarios. AHETFsponsored studies are typically designed to re present individuals wearing long-sleeved shirts, long pants, shoes, socks, chemical-resistant glove s as appropriate, and no re spirators. In some cases, such as the scenario addressed by this m onograph, an engineering c ontrol (i.e., the closed cabs for this scenario) or additional personal pr otective 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 "deposited on or to-the-skin" or “in the breathing zone.” For the purposes of pes ticide handler exposure assessment, dermal and inhalation exposures are expresse d as “unit exposures” – expre ssed as exposure per weight-unit chemical handled. Mathematically, unit exposures are expressed as exposure normalized by the amount active ingredient handled (AaiH) by participants in scen ario-specific exposure studies (e.g., mg exposure/lb ai handled). Unit exposures are then used generical ly to predict exposure for other chemicals having the same or different a pplication rates.

Two major assumptions underlie the use of exposure data in this fashion. First, the expected external exposure is unrelated to the identity of the specific active ingredient in the pesticide formulation. That is, the physical characteristics of a scenario such as the pesticide formulation (e.g., formulation type – wettable powder, liquid concentrate, dry flowable, etc.), packaging (e.g., bottle or water-soluble packet), or the equipm ent type used to apply the pesticide influence exposure more than the specific pesticide active ingredient (H ackathorn and Eberhart, 1985). Thus, for example, exposure data for spraying one chemical using closed cab airblast equipment can be used to estimate exposure while spraying another chemical proposed for use with closed cab airblast equipment. Second, dermal and i nhalation exposure are a ssumed 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 occupati onal handler exposure was based on criteria reviewed by HED and presented to the Human Studies Review Board (HSRB) for determining

Page 3 of 62

### 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 accurat e within 3-fold. Specifical ly, the upper and lower 95%
confidence limits should be no more than 3-fo ld 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 suffici ent number of monitore d individuals. Note
that this “fold re lative accuracy” ( f

RA) objective does not apply to normalized inhalation exposure, though estimates are provided for reference (see Table 6 ).

• The secondary objective is to evaluate the assumption of proportionality between exposure and amount of active ingr edient handled (AaiH) in or der to be able to use the AHETF data generically across application ra tes. To meet this objective, the AHETF proposed a log-log regression te st 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 regressi on 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 a nd secondary objectives described above and contain costs, the AHETF developed a study desi gn employing a ‘cluster’ st rategy. Each cluster is defined by a region. Typically, these regions are defined by a few c ontiguous 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. The 25 total participants together wi th the conditions under which the worker handles the active ingredient are referre d to as monitoring units (MUs). Within each cluster, the AHETF partitions the practical Aa iH range handled by the participants in each cluster appropriate to a given scen ario. In general, the strata of AaiH for any given scenario is commensurate with typical commercial pr oduction agricultu re 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 liqui d spray pesticides using airblast sprayers hauled by trucks or tractors with en closed cabs while wearing th e following personal protective equipment (PPE): long-sleeved shirts, long pants, shoes, socks, chemical-resistant gloves when conducting tasks outside the encl osed cab (e.g., marking treated acreage), and no respirator. Dermal and inhalation exposure monitoring was conducted for 24 workers 4 

### (referred to as “monitoring units”, or MUs) applying liquid sp ray pesticides using closed cab airblast equipment. Five separate studi es were conducted (references in Table 2 below), each monitoring different workers while spraying tr ee or trellis crops in 5 differe nt states in the U.S. where

4 One worker in study AHE59 (WA-apple) was not monitored, reducing the originally planned total from 25 to 24.
See Section 3.1 and 3.2.

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### airblast equipment would typically be used – citrus in Florida, pecans in Georgia, grapes in California, cherries in Michiga n, and apples in Washington.

Table 2. AHETF Closed Cab Airblast Applicator Studies Study ID Study Title AHE# EPA MRID

AHE55 48289601 Determination of Dermal and Inhalation Exposure to Workers During Airblast Applications of Liquid Sprays Using Closed Cab Equipment in Florida Citrus

AHE56 48289602 Determination of Dermal and Inhalation Exposure to Workers During Airblast Applications of Liquid Sprays Using Closed Cab Equipment in Georgia Pecans

AHE57 48303501 Determination of Dermal and Inhalation Exposure to Workers During Airblast Applications of Liquid Sprays Using Closed Cab Equipment in Michigan Stone Fruit

AHE58 48289604 Determination of Dermal and Inhalation Exposure to Workers During Airblast Applications of Liquid Sprays Using Closed Cab Equipment in California Trellis Crops

AHE59 48303502 Determination of Dermal and Inhalation Exposure to Workers During Airblast Applications of Liquid Sprays Using Closed Cab Equipment in Washington Pome Fruit



### The figures below (from AHETF, 20 10) depict examples of this activity for which the exposure data are applicable.

Figure 1: Closed Cab airblast application in GA pecans

Page 5 of 62Figure 2: Closed Cab airblast application in WA apples

Figure 3: Closed cab airblast applic ation with 2-row tower sprayer with electrostatic nozzles in CA grapes



### 2.2 HSRB Review and Comments

The ability of the EPA to use the closed cab ai rblast 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 Pa rt 26), including review

Page 6 of 62

### by the Human Studies Review Board 5 

### . The following is a timeline of HSRB reviews related to
this scenario:

Table 3. Closed Cab Airblast Applic ation Scenario – HSRB Review Timeline

Date HSRB Review
June 2006 AHE56 (GA-pecan ) Protocol (at that time titled “AHE38”)

June 2008 Closed Cab Airblast Application Scenario Design AHE55 (FL-citrus) Protocol AHE56 (GA-pecan) Protocol [2 n d review (1 s t review in June 2006)]

October 2008 Revised Closed Cab Airblast Application Scenario Design AHE57 (MI-cherry) Protocol AHE58 (CA-grape) Protocol AHE59 (WA-apple) Protocol



### Execution of the field studies followed favorab le reviews by the HSRB; however, throughout the review process, numerous comments and sugge stions were noted and incorporated when possible. Throughout the review process a num ber of significant comments and suggestions were noted by the HSRB which were incorporated into the research whenever possible. Appendix D of the AHETF scenario monograph (AHETF, 2010) outlines both scientific and
ethical issues related to the cl osed 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-respondent s and responders who declined to participate (11/14/08 and 12/30/08 HSRB Meeting Reports)

The HSRB was concerned with the inability to eval uate study participants against the universe of closed 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 st udy participants with those eligible nonparticipants via an informal surv ey of local agricultural experts.

The surveyed experts were asked to evaluate how the selected employers and equipment compares to the local population of airblast appl icators, using the following characteristics to determine whether they were typical of othe r growers/applicators in the area where the monitoring was conducted:

• Whether the participant was the grower, em ployed 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 5 studies were not atypical of the population of closed cab airblast applicators. EPA believes that

5 http://www.epa.gov/osa/hsrb/

Page 7 of 62

### this methodology, however, could be improved for future AHETF studies, perhaps via a more systematic database compilation of the inform ation obtained during the recruitment phase. A summary of the findings is provided in Table 3 below.

Table 4. Synopsis of Experts Used to Evaluate the Representativeness of Monitored Workers Study ID Recruited Responded Response

AHE55 (FL-citrus) 5 USDA agricultural extension agents 4 All agreed that the study participants were typical in the counties monitored 1 vocational agricultural teacher 0
-- AHE56 (GA-pecan) 4 USDA agricultural extension agents 3 All agreed that the study participants were typical in the counties monitored 2 vocational agricultural teachers 1
AHE57 (MI-cherry) 5 USDA agricultural extension agents 5 All agreed that the study participants were typical in the counties monitored

AHE58 (CA-grape) 5 USDA agricultural extension agents 5 4 of 5 agreed that th e study participants were typical in the counties monitored 1 of 5 did not feel knowledgeable enough

to comment

AHE59 (WA-apple) 5 USDA agricultural extension agents 4 3 of 4 agreed that th e study participants were typical in the counties monitored 1 of 4 did not agree – farm sizes were low and newer spray technologies were not
included 1 commissioner from Washington Apple Commission 0
--



### 2.2.2 Documented Survey Implementation Expertise (11/14/08 and 12/30/08 HSRB Meeting Reports)

Given the admittedly difficult attempts at re cruitment for occupational pesticide exposure monitoring studies, the HSRB advised the AHETF to employ individuals with expertise in survey implementation. As a result, the AH ETF 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 th e 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 mon itored. This was accurately reflected in the executed exposure monitoring.

2.2.4 Better Characterization of the Re cruitment Process (11/14/08 HSRB Meeting Report)

Page 8 of 62

### The HSRB recommended that the AHETF better define the recruitment process so as to identify the individuals or organizations contacted. Spec ifically, this arose fr om 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 recruitmen t-related standard operating pr ocedures (SOPs). Among other issues, for the purposes of contacting growers on th e “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 closed cab airblast applicator behavior under normal circumstances, such as exiting the cab, ope ning windows, or non-adherence to label PPE requirements. Per protocol, the AHETF employed observers who recorded applicator behavior throughout the workday. All manner of behavi ors were captured, from specifics of the application procedures (i.e., sequence of row tr eatments) to worker behaviors such as cab exits/entrances, brief window openings, and contact with exterior surfaces with bare hands. Notable observations and their pot ential impact on use of the data , including additi onal statistical analyses to evaluate their imp act, are presented in Section 3.3.

2.2.6 Exclusion of Monitoring Exposure During Applications to Dormant Crops and Hops

The HSRB expressed concern with the exclusion of monitoring exposure for both dormant crops as well as hops, despite pestic ide applications made to bot h using closed cab airblast applications. The AHETF noted that with cons ultation with EPA, that the monitoring conducted for the array of non-dormant crops would be cons idered sufficiently adequate for assessment of closed cab airblast exposures to dormant crops and hops – as well as othe r crops not specifically monitored. Given the logistical considerations of the samp ling design (i.e., the increased chances of finding willing partic ipants for more common pesticid e applications), HED agrees that for the purposes of the gene ric database, the proposed studies are adequate for assessment of closed 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 aff ect exposure during applications of liquid sprays if a solid formulation were to clog nozzles a nd require cleaning by the applicator. In these studies, it is apparent that wo rkers did interact with the sp ray nozzles; however, since all formulations used in these studies were liquid co ncentrates, attribution of these interactions to
use of solid formulations cannot be made.

Page 9 of 62

### 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, includi ng selection of workers and sample representativeness, the HSRB outlined an alternative sampling approach, which included a more robust approach at identifying and recruiting potential participan ts. With respect to recruitment procedures such as developing the universe of potential participants, writing recruitment letters, employing experienced interviewers, and comparing participant characteris tics with those of th e applicator population, the AHETF believes, and EPA agrees that, they ha ve followed the fundamental principles of the HSRB recommendations. The changes in recrui tment procedures also satisfied the EPA requirement to incorporate “random elements” whenever feasible in the sampling process.

3.0 Results

Exposure results were reported in reports for ea ch of the 5 studies and reviewed in Crowley, 2011 (D381148). The following sections summarize the exposure monitoring results and the scenario benchmark statistical analyses pres ented in the AHETF scenario monograph (AHETF, 2010).

3.1 Exposure Monitoring and Calculations

Monitored on actual days of work, participants handled between 7 to 90 lb s of active ingredient (carbaryl, malathion, or chloroth alonil), spraying 4 to 30 acres in 2 to 9 hours. Dermal exposure was measured using 100% cotton “whole body do simeters” (WBD) underneath normal work clothing (i.e., long-sleeved shirt, long pants, socks and shoes), ha nd rinses (collected at the end of the day and during restroom and lunch breaks), and face/neck wipes (adjusted to extrapolate to portions of the head covered by protective eyewear, respirators, and/or hair).

Additionally, as presen ted at a June 2007 HSRB meeting, in order to account for potential
residue collection me thod inefficiencies 6 

### , EPA has directed the AHETF to make adjustments to hand and face/neck field study measurements as follows:

• if measured exposures from hands, face a nd neck contribute less than 20% as an average across all workers, no action is required;

• if measured exposure contribution from hands and face/neck represents between 20% and 60% of total, the measurements sha ll 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 represen ts is greater than 60%, a validation study demonstrating the efficiency of the residue collection methods is required.

For these studies, the measurements fell in the second category and hand rinse and face/neck wipe measurements have been adjusted upward by 50% (i.e., multiplied by 2).

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

Page 10 of 62

### Inhalation exposure is measured using a persona l 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 samp le air from the breathing zone. All samples are adjusted as appropriate accord ing to recovery results from field fortification samples.

Total dermal exposure was calculated by summ ing exposure across all body parts for each
individual monitored. Total inha lation 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 m 3 

### /hr), representing light activities such as mixing/loading light p ackages (NAFTA, 1998), and total work/monitoring time. 7 

### 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. Results represent dermal exposure while wearing a long-sleeved shirt, pants, shoes/socks and chemical-resistant gloves and inhalation e xposure without respir atory protection.

A summary of the 24 closed cab airbla st applicator MUs is provided in Table 5 below, with data plots shown in Figures 4 and 5. More detailed exposure data are provided in Appendix A, Table A-1. For dermal exposure, both hand rinse a nd face/neck wipe method efficiency adjusted (MEA) data and unadjusted results are presented. Note for inhalation exposure, there were a total of 23 (rather than 25) measurements due to unknown sampling time invalidating MU A2 in AHE59 (WA-apple). All field measurements were adjusted by their corresponding field fortification recovery values.

Table 5. Closed 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 Non-MEA MEA

AHE55 A1 FL Orange 4.6 12 8 43.9 70.4 0.151

A2 FL Tangerine 4.3 6 15 8.9 14.7 0.0396
A3 FL Orange 4.6 9 24 20.1 37.2 0.245

A4 FL Orange 4.9 20+ 40 0.33 0.63 0.0115
A5 FL Orange 7.8 30+ 75 1.2 1.9 0.0187

AHE56 A1 GA Pecan 4.4 12 7.9 5.3 5.6 0.0188

A2 GA Pecan 4.8 18 15.8 3.2 4.5 0.0628
A3 GA Pecan 5.9 30 24.6 2.2 2.5 0.0759

A4 GA Pecan 4.4 20 50.5 0.83 1.5 0.002
A5 GA Pecan 6.5 30 75 .7 31.1 41.8 0.0254

AHE57 A1 MI Cherry 2 4 10.8 1.9 2.6 0.0144

A2 MI Cherry 3.6 7.5 16.9 0.50 0.55 0.0078
A3 MI Cherry 4 12 27.6 2.0 2.5 0.0042

A4 MI Cherry 4.25 19.75 40.7 0.85 1.3 0.0173
A5 MI Cherry 4.7 24 90.3 0.92 1.2 0.0177

AHE58 A1 CA Grape 5.75 35 63.5 2.2 3.7 0.0557

A2 CA Grape 5 20 34.4 1.1 2.0 0.186
A3 CA Grape 4.25 16 23.0 0.31 0.43 0.0187

A4 CA Grape 6 35 59.2 0.38 0.60 0.0855

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

Page 11 of 62A5 CA Grape 3.1 15 7.3 18.9 21.8 0.0414

AHE59 A1 WA Apple 5 8.5 15.8 0.67 1.2 0.0840

A2 WA Apple 3.9 6 9.4 2.5 5.0

-- a

A3 WA Apple 5.3 12 15.9 12.7 20.0 0.0848

A4 WA Apple 9.4 23.5 34.5 38.5 76.6 0.0347

A5 Worker monitoring planned but not executed because:

• On the scheduled monitoring day, only one field studies team was available and was already

monitoring another worker

• A replacement worker from another eligible grow er was not monitored because the grower did

not need to spray the surrogate chemical (carbaryl)

• An identified backup grower did not have a cab with fully-functional air conditioning

• The application window for su rrogate chemical (carbaryl) ended before recruitment was

possible.

a Air sampling pump time unknown – sample is invalid.

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

Page 12 of 62Figure 5: Inhalation Unit Exposures (ug/lb ai)



### 3.2 Evaluation of Scenario Benchmark Objectives

The AHETF monograph details the extent to which the closed cab airblast applicator scenario meets objectives described in Section 2.1. Th e monograph states that while the primary objective was met, the secondary objective was not. EPA (OPP/HED/CEB) has independently
confirmed these results.

3.2.1 Primary Objective: fold Relative Accuracy (fRA)

The primary benchmark objective for AHETF scenario s is for select statis tics – 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 techniques to evaluate this benchmark. First, both dermal and inhalation unit exposures were shown to fit lognormal distribu tions reasonably well. Normal and 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

Page 13 of 62

### bootstrap samples. Then, the fRA for each was dete rmined as the maximum of the two ratios of the statistical point estimates with their respective upper and lo wer 95% confidence limits. The primary benchmark of 3-fold accuracy for select statistics was met for dermal exposure data unadjusted for hand rinse and face/neck wipe me thod inefficiencies. However, as shown in
Table 6 below, because the adjustments for hand rins e and face/neck wipe method inefficiencies increased the sample variation, fRA values for th e adjusted dermal unit exposures are slightly larger than those for the unadjusted dermal unit exposures – and in some cases, causes the fRA to rise slightly above the 3-fold accuracy threshold. Despite this result, HED considers the primary objective satisfied. Note, though not applicable to the benchmark, the fRA values for inhalation are also presented and generally are below 3-fold.

Table 6. Closed Cab Airblast Application Scenario – Results of Primary Benchmark Analysis Dermal Unit Exposure (ug/lb ai) a Inhalation Unit Exposure (ug/lb ai)
Statistic Estimate 95% CI fRA Estimate 95% CI fRA

GM S 4.1 1.9 – 8.8 2.2 0.032 0.016 – 0.064 2.0
GSD S 4.86 3.06 – 7.69 1.6 3.28 2.27 – 4.84 1.5
GM M 4.1 1.9 – 8.8 2.2 0.033 0.017 – 0.064 2.0
GSD M 4.92 3.08 – 7.99 1.6 3.36 2.29 – 5.14 1.5

ICC 0.10 0.00 – 0.48
-- 0.24 0.00 – 0.63
-- GM S = geometric mean assuming SRS = “exp(average of 24 ln(UE)) values”.
GSD S = geometric standard deviation assuming SRS = “exp(standard deviation of 24 ln(UE)) values”
GM M = variance component model-based geometric mean
GSD M = variance component model-based geometric standard deviation
ICC = intra-cluster correlation

AM S 13.3 4.6 – 39.7 3.0 0.057 0.028 – 0.146 2.6

AM U 14.1 5.2 – 43.4 3.1 0.065 0.029 – 0.154 2.4

AM M 14.6 5.3 – 46.1 3.2 0.068 0.030 – 0.166 2.4

AM S = average of 24 unit exposures
AM U = arithmetic mean based on GM S = GM S *exp{0.5*(ln(GSD S )^2}
AM M = variance component model-based arithmetic mean = GM M * exp{0.5*(ln(GSD M )^2}
P95 S 70.4 13.3 – 172.6 5.3 0.186 0.071 – 0.576 3.1
P95 U 54.6 18.6 – 158.5 2.9 0.225 0.090 – 0.573 2.5
P95 M 56.4 19.0 – 168.2 3.0 0.240 0.092 – 0.624 2.6
P95 S = 95 th percentile (i.e., the 23 r d unit exposure out of 24 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 ^1.645

a Dermal exposure values reflect 50% default ad justment for hands and face/neck measurements.



### 3.2.2 Secondary Objective: Testing Proportionality

The secondary objective of AHETF studies is to be able to di stinguish, with 80% statistical power, complete proportionality from complete independence between dermal exposure and amount of active ingredient handled. Based on the AHETF analysis this benchmark was not

met.

3.2.2.1 AHETF Analysis

To evaluate the relationship fo r this scenario the AHETF perfo rmed regression analysis of ln(exposure) and ln(AaiH) to de termine if the slope is not si gnificantly different than 1 – providing support for a proportional re lationship – or if the slope is not significantly different

Page 14 of 62

### than 0 – providing support for an independent relationship. Both simple linear regression and mixed-effect regression were performed to eval uate the relationship between dermal exposure (both standard and adjusted for exposure me thod collection inefficiencies) and AaiH. A confidence interval of 1.4 (or le ss) indicates at least 80% stat istical power. The resulting regression slopes and confidence intervals are summarized in Table 7

.

Table 7. Summary Results of log-log Regression Slopes

Model Dermal Exposure Inhalation Exposure Standard (non-MEA) a MEA

Est. 95% CI CI
Width Est. 95% CI CI
Width Est. 95% CI CI
Width Simple
Linear b 0.32 -0.52 – 1.17 1.69
-- -- -- 0.67 -0.02 – 1.36 1.39

MixedEffects 0.32 -0.52 – 1.17 1.69 0.41 -0.47 – 1.28 1.75 0.70 0.06 – 1.33 1.27

a Note because the correlation estimate (i.e., the “intra-cla ss correlation”, or ICC) is 0, the slope estimates for the simple linear regression and the mixed model are identical.

b Confidence intervals based on a simple linear regression are only valid if between-MU correlations are absent.



### For dermal exposure, the slope of the mixed- effects regression is 0.32 with 95% confidence intervals including both 0 and 1, s uggesting that an independent or a proportional relationship is consistent with the data. For inhalation exposur e the mixed-effects regr ession slope is 0.70, with 95% confidence intervals including 1 while excluding 0, suggesti ng that a proportional relationship is consistent with the data. In te rms of the secondary objective, the width of the confidence interval for dermal exposure was gr eater than 1.4, indicating the power to detect complete independence from complete propor tionality was less than 80%. The AHETF suggests, and EPA concurs, that this may be the result of the range of AaiH may being small relative to the range in exposure observed.

Adjustments for hand rinse and face/neck wipe ine fficiencies do not alter these conclusions. For MEA dermal exposures, the 95% confidence inte rvals for the log-log regression slope still
include 0 and 1 and the width of th e interval is st ill greater than 1.4.

3.2.2.2 Additional ICC Considera tions (EPA Analysis)

Considering discussions from a meeting of the HSRB in October 2010, where a similar analysis was done for exposure to antimicrobial pestic ides, EPA conducted additional analysis with respect to statistical procedures and the intra-cl ass correlation coefficient (ICC) for the close cab airblast applicator dataset. The AHETF statistical analysis fo r proportionality used the KenwardRogers denominator degrees of freedom (DDF) method to calculate confidence intervals for the log-log regression slope 8 

### . 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 non-MEA dermal exposure (ICC estimates for both inhalation and MEA-dermal exposure are non- zero). Under contract with EPA/OPP, ICF,

8 Note that the choice of denominator de grees 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 th e normalized exposure summary statistics (arithmetic mean, 95 th percentile, etc.), this issue does not impact those calculations.

Page 15 of 62

### Inc. investigated the alternate approaches to calculate the DDF for a similar set of exposure monitoring studies conducted by the Antimicrobial Exposure Assessment Task Force II (AEATF-II) 9 

### . 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 10



### . Table 8 below summarizes the five available methods outlined in the ICF memo.

Table 8. Summary of SAS Methods for Co mputing 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 effect s 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 not adjusted for potential measurement collection inefficiencies (nonMEA), there are 5 workers in 4 of the clusters – AHE59 (WA-apple) had only 4 monitored workers. A simple linear model without the clus ter effect gives 22 error degrees of freedom (24- 2 = 22). If the mixed model and the kr method ar e used, the estimated cl uster variance is zero and the kr method also treats the true cluster variance as zero, givi ng 22 error degrees of freedom. This ignores the fact that the cluster variance was estimated, so there should be some uncertainty in that parameter. If the mixe d model and the containment method are used, the
estimated cluster variance is again zero, but the containmen t method includes 4 degrees of
freedom for the cluster covariance (5 – 1 = 4 clusters), giving 18 error degrees of freedom and a slightly wider confidence interval.

The containment method is better in this case because the method accounts for the uncertainty in the estimated cluster effects – though the cluste r variance was estimated to be zero, it does not mean it truly is zero. For instance, if the true cl uster variance was very close, but not quite, zero (e.g., 0.000000001), the kr method would either gi ve 22 or 18 error degrees of freedom depending upon whether or not the estimated clus ter variance is zero. The containment method,

9 “Additional statistical issues for the AEATF Mop Study Statistical Review for HSRB”. Contract No.: EP-W-06-
091.

10 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 1-58 025-864-6. SAS Institute, Cary, NC 27513.

Page 16 of 62

### on the other hand, would give 18 error degrees of freedom in either case. If the true and
estimated cluster variances are zero, then the kr method is co rrect and the containment method is incorrect. However, since it is impossible to know for certain the true cluster variance, the
recommended methods are the containment method for the mixed models when the ICC estimate is zero, and the Kenward-Rogers method for th e mixed models where the ICC estimate is nonzero.

For both the dermal exposure adjusted for pot ential method inefficiencies and inhalation exposure the ICC estimates were not zero, t hus the AHETF use of Kenward-Rogers is appropriate. For unadjusted dermal exposure da ta, the ICC estimate is zero for which the “containment” method is recommended. Using this method, however, doe s not alter the overall conclusions for the unadjusted dermal exposure w ith respect to the secondary study objective. That is, the 95% confidence inte rval still includes both 0 and 1 (-0.532 – 1.18) – with a width of 1.71, very slightly larger than the 1.69 reported by the AHETF using the Kenward-Rogers method. Additional details for this analysis ar e provided as Appendix A, Table A-2. The SAS code for this analysis is provided in Appendix C.

3.2.3 Applicator Behavior and Evaluation of Exposure Determinants (EPA Analysis)

Based on the analysis in Section 3.2.2.1, which showed regression slope confidence intervals failing to reject either a pr oportional or independent relati onship, the relationship between dermal exposure and the amount of active ingredient handled is inco nclusive for this scenario. While a proportional relationship seems reasonable for standard exposure scenarios (i.e., if one doubles the amount handled, that exposure woul d double as well seems plausible), a weak relationship for this scenario is not surprising. The engineering control defining this scenario (i.e., closed tractor cabs) appear s to be very effective at re ducing the opportun ity for routine exposures and may limit exposure po tential to sporadic events su ch as contacts with exterior surfaces during entry or egress fr om the application vehicle.

Considering this result, a closer look and additional analyses could result in more appropriate uses and interpretation of the data. As di scussed in Section 2.2.5, observers recorded both typical application procedures as well as events that potentially could a ffect exposure. Appendix A, Table A-3 presents notable detailed observa tions. For example, AHE56 (GA-pecan) MU A5
was observed to have a shirt sleeve button missi ng, exposing his forearm skin (i.e., the upper body dosimeter at the forearm) while cleaning and reaching into the airblast sprayer. This worker had the highest inner body measurement by an approximate 10X margin (1540 ug) and the second highest hand measurement by an appr oximate 2X margin (1626 ug, method efficiency adjusted) – combining to result in the highe st total dermal exposure measurement by an approximate 2X margin (3168 ug). The next highest dermal exposure measurement (2641 ug) was for AHE59 (WA-apple) MU A4. This worker was frequently observed not wearing chemical-resistant gloves while contacting surfaces outside th e enclosed cab. Likely not coincidentally, this worker had the highest hand exposure measurement by an approximate 1.5X margin (2624 ug, method efficiency adjusted). The third highest total dermal exposure measurement (892 ug), was substantially lower than the first two, but was also observed touching

Page 17 of 62

### exterior surfaces with bare hands. This worker , AHE55 (FL-citrus) MU A3, in turn had the third highest hand measurement (804 ug, method efficien cy adjusted) by an ap proximate 2X margin.

Because the exposure for these three workers pot entially result from incidental contacts, a sensitivity analysis was conducted to determine whether the results showing an inconclusive relationship between dermal exposure and the amount of active ingredient handled would change
if these workers were excluded. In other wo rds, did these unusual observations obscure a proportional relationship between dermal exposure and amount of active ingredient handled?
Table 9 below presents the regression slopes for th e initial case (where all observations were included) and the results when each of the top 3 exposures is excluded. After excluding these exposures, the relationship between dermal e xposure and the amount of active ingredient remains inconclusive (i.e., the confidence intervals still include 0).

Table 9. Dermal Exposure and Amount Handled – Se nsitivity of Regression Slopes to Data Exclusion

Model Exposure Data Configuration Dermal Exposure
Standard (non-MEA) MEA

Est. 95% CI Est. 95% CI

MixedEffects All observations included (same as Table 7) 0.32 -0.52 – 1.17 0.41 -0.47 – 1.28 Highest exposure excluded 0.027 -0.78 – 0.84 0.13 -0.71 – 0.97

1 s t

and 2 n d highest exposures excluded -0.073 -0.80 – 0.65 -0.006 -0.73 – 0.72

1 s t , 2 n d , and 3 r d highest exposures excluded -0.079 -0.74 – 0.58 -0.023 -0.69 – 0.65 Detailed results including regression plots, as well as the SAS 9.2 code, are provided in Appendix D.



### Another behavior, more quantifiable than those descriptive events just discussed, that could impact exposure is entering and exiting the vehi cle hauling the airblast equipment, potentially contacting with pesticide residues as they climb on the machinery. This factor was also analyzed for its potential effect on dermal exposure. Th e field observations were reviewed to tally the
number of times workers entered or exited th e cab (including at the start and end of the workday), and ranged from 4 to 26. Most cab entran ces/exits were for rou tine purposes such as lunch or restroom breaks or waiting during tank mi xing/loading. For those getting out of the cab many times, they appeared to do so for a number of reasons including marking sprayed rows, fixing malfunctioning rigs, or cleani ng or adjusting spray nozzles.

An analysis similar to the eval uation of the relations hip between dermal exposure and amount of active ingredient handled was conducted for the number of cab entran ces/exits – with the hypothesis that those workers who exit the cab mo re will have higher dermal exposures. When including all observations (adjus ted for potential method inefficien cies), the results show a loglog regression slope of 1.29 with 95% confidence intervals excluding a slope of 0 and including a slope of 1 (results are similar for unadjusted de rmal exposure data). This result appears to demonstrate a stronger relationshi p between dermal exposure and th e number of times a worker
exits and enters the cab than that with the amount of active ingredient handled. The same sensitivity analysis as previously done does not substantially change this conclusion either. In fact, the model fits best with all observations, with confidence intervals including zero, indicating a weaker relationship, when the highest exposures are excluded. Table 10 below
summarizes these results.

Page 18 of 62Table 10. Dermal Exposure and # Cab Entrances/Exits – Sensitivity of Regression Slopes to Data Exclusion

Model Exposure Data Configuration Dermal Exposure
Standard (non-MEA) MEA

Est. 95% CI Est. 95% CI

MixedEffects All observations included 1.26 0.24 – 2.27 1.29 0.13 – 2.44 Highest exposure excluded 0.92 -0.18 – 2.02 0.97 -0.15 – 2.10

1 s t

and 2 n d highest exposures excluded 0.44 -0.70 – 1.59 0.44 -0.71 – 1.59

1 s t , 2 n d , and 3 r d highest exposures excluded 0.33 -0.67 – 1.33 0.32 -0.70 – 1.33 Detailed results including regression plots, as well as the SAS 9.2 code, are provided in Appendix D.



### It appears based on these additiona l analyses that incidental cont acts have a significant impact on exposure. This was apparent when compari ng the field observations with the corresponding exposure measurements and potentially confirmed via the analysis of the relationship between dermal exposure and cab entrances/exits – more cab exits increases the likelihood for exposure
via contacts with exterior surface residues. EPA intends to further evaluation the implications of this for exposure assessment, including considera tion of alternate uses of the data from the default normalization by amount of active ingred ient handled.

3.3 Data Generalizations and Limitations

The need for an upgraded generic pesticide handler exposure database has been publicly discussed and established (Christi an, 2007). No existing closed cab airblast applicator exposure
data met AHETF criteria for inclusion in an updated database, thus the 5 exposure monitoring studies were proposed to complete the closed cab airblast applicator scenario. The data will be used generically to assess exposure for appli cators applying any conventional chemical applied as a spray using airblast equipmen t hauled by trucks or tractors with an enclosed cab. However, certain limitations need to be recognized with re spect 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 an y crop using any type of closed cab airblast equipment, while wearing a long-sleeve shirt, pants, shoes/so cks, and chemical resistant gl oves. This does not preclude additional consideration or use of acceptable av ailable 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 closed cab airblast equipment, which are generally chemicals of low volatility. Since they ar e not typically used in airblast sprayers, it is not expected that this dataset would be used to support regulatory deci sions for high volatility pesticides (e.g., fumigants).

3.3.3 Use of “Unit Exposures”

Page 19 of 62

### As previously shown, statistical analyses provide only limited, at best minimal, support for use of the exposure data normalized by the amount of active ingredient handled. In fact, a closer
look at the data and additional an alyses may potentially show that episodic, incidental exposures may ultimately have the most consequence on exposure than other factors. As a result,
alternative uses of the data and/or additional ex posure ‘models’ will be investigated. However,
HED will continue to recommend use of the e xposure data normalized by the amount of active ingredient handled as a default c ondition for the foreseeable future.

3.3.4 Representativeness and Extrapol ation to Exposed Population

Targeting and selecting specific monitoring charac teristics (i.e., “purposiv e sampling”) as well as certain restrictions necessary for logistical purpo ses (e.g., selection of majo r crops that use closed cab airblast application methods to ensure a large pool of potential a pplicators; 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 datase t as representative of the population of closed cab airblast applicators. Thus, it is important to recognize these limitations in considering this dataset as representative of all cl osed cab airblast applicators.

It appears however, that the dataset has captu red routine behavior as well as limiting the likelihood of “low-end” exposures via certa in 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 in corporated into the recr uitment 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 Closed Cab Ai rblast Application s cenario monograph and concurs with the technical analysis of the data as well as the evalua tion of the statistical benchmarks objectives. Conc lusions are as follows:

• Deficiencies in the existing closed cab airbla st application scenario dataset (i.e., PHED) have been recognized and the n eed for new data established.

• The AHETF data developed and outlined in th e monograph and this re view represent the most reliable data for assessing closed cab airb last application exposur e. Alternative data sources or special circumstances will be considered on a case by case basis.

• Per stated objectives, estimates of the GM, AM, and P95 were shown to be accurate within 3-fold with 95% confidence, however , the data did not provide 80% statistical power to distinguish complete proportionality or independ ence between dermal exposure
and AaiH.

• The assumption of proportionality between bot h dermal and inhalation exposure and the amount of active ingredient handled was not rejected, though additional analyses suggest

Page 20 of 62Page 21 of 62 

### incidental contacts with residue s exterior to the enclosed cab may impact exposure more than the amount of active ingredient handled. As a result, HED will consider alternative uses of the data, but 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 Gove rning Document for a Multi-Year Pesticide Handler Worker Exposure Monitoring Program . Version Number: 1. April 7, 2008. Agricultural Handlers Exposure Ta sk Force (AHETF). [MRID 47172401]

AHETF, 2010. Agricultural Handler Exposure Scen ario Monograph: Closed Cab Airblast Application of Liquid Sprays. Repor t Number AHE1005. December 2, 2010. [MRID 48314201]

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 Ag ricultural Handler Exposure Task Force (AHETF) Closed Cab Airblast ApplicatorExposure Monitoring Studies: AHE55, AHE56, AHE57, AHE58, AHE59. D381148. January 7, 2011

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

Human Studies Review Boar d Meeting Report – June 24-25, 2008. EPA-HSRB-08-03. November 14, 2008. http://www.epa.gov/osa/hsrb /files/june2008hsrbfi nalreport111408.pdf

Human Studies Review Boar d 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 (D PR), California EPA, HSM-98014, April 24, 1998.

### Appendix A Supplemental Tables 1-4

Page 22 of 62Table A-1. Closed Cab Airblast Application – Dermal and Inhalation ExposuresStudyIDMUIDAaiH(lbs)DermalInhalationInnerWBD(μg)Hand(μg)Head(μg)Total Exposure(μg)Unit Exposure(μg/lb ai)Total(ug)UnitExposure(μg/lb ai)Non-MEAMEANon-MEAMEANon-MEAMEANon-MEAMEAAHE55(FLcitrus)A18139.1211.24220.891.7835156343.970.41.210.151A21547.985.091700.891.781342208.914.70.590.0396A32473.7402.18047.1714.4048389220.137.25.880.245A4401.011.8823.80.270.5413.225.30.330.630.460.0115A57539.050.671010.891.7890.51421.21.91.410.0187AHE56(GApecan)A17.940.21.673.40.240.4842.144.15.35.60.150.0188A215.831.618.9337.80.81.6051.371.03.24.50.990.0628A324.644.77.49150.81.6052.961.22.22.51.870.0759A450.510.631.2862.60.240.4842.173.70.831.50.100.002A575.71540812.816260.81.602354316831.141.81.920.0254AHE57(MIcherry)A110.812.77.1614.40.240.4820.127.61.92.60.160.0144A216.97.50.651.30.240.488.49.30.500.550.130.0078A327.643.89.2918.62.665.4055.867.82.02.50.120.0042A440.717.516.8833.80.240.4834.651.80.851.30.710.0173A590.361.320.6241.20.81.6082.71040.921.21.600.0177AHE58(CAgrape)A163.543.891.891844.799.601412372.23.73.540.0557A234.46.729.5959.20.801.6037.167.51.12.06.390.186A323.04.32.635.20.240.487.110.00.310.430.430.0187A459.29.310.6721.22.454.8022.335.30.380.605.060.0855A57.3115.421.3142.80.240.4813815918.921.80.300.0414AHE59(WAapple)A115.81.88.6117.20.240.4810.619.50.671.21.330.0840A29.40.722.3544.60.801.6023.846.92.55.0Sample invalidA315.985.8114.92300.801.6020231812.720.01.350.0848A434.515.7131226240.801.601329264138.576.61.200.0347Page 23 of 62Table A-2. EPA Analysis of AHETF Secondary Objective Analysis using Alternate DDF MethodsExposureGSDICCParameterDDF MethodEstimate95% CIDFLCLUCLWidthDermal Exposure(MEA)4.70.054Sloperesidual0.406-0.4421.2541.7022contain0.406-0.4531.2651.7218bw0.406-0.4421.2541.7022satterth0.406-0.4481.2601.7119.55kr0.406-0.4691.2811.7519.55Interceptresidual3.3100.4946.1275.6322contain3.310-0.4617.0827.544bw3.3100.4946.1275.6322satterth3.3100.4876.1345.6521.10kr3.3100.4316.1895.7621.10Dermal Exposure(non-MEA)4.530Sloperesidual0.323-0.5211.1711.6922contain0.323-0.5321.1821.7118bw0.323-0.5211.1711.6922satterth0.323-0.5211.1711.6922kr0.323-0.5211.1711.6922Interceptresidual3.1590.3645.9535.5922contain3.159-0.5836.9007.484bw3.1590.3645.9535.5922satterth3.1590.3645.9535.5922kr3.1590.3645.9535.5922InhalationExposure3.340.220Sloperesidual0.6950.0701.3201.2521contain0.6950.0611.3291.2717bw0.6950.0701.3201.2521satterth0.6950.0631.3281.2717.51kr0.6950.0591.3311.2717.51Interceptresidual-2.431-4.580-0.2824.3021contain-2.431-5.3000.4385.784bw-2.431-4.580-0.2824.3021satterth-2.431-4.584-0.2774.3120.25kr-2.431-4.591-0.2704.3220.25Page 24 of 62Table A-3. Select Closed Cab Airblast Applicator Field ObservationsStudy IDMU IDCab ents./exits (#)ObservationAHE55(FL-citrus)A141113: “Worker in cab while mix/load is performed.”1231: “...brushes up against treated foliage due to narrow rows”1322: “...again brushing up against treated foliage.”1422: “...again brushing up against treated foliage...”A2161207: “...exits cab with gloves on, then turns valve...”1232: “...exits cab with gloves on, picks up a plastic pipe...”1245: “...exits cab after putting gloves on...”1412: “...exits cab wearing gloves...”A3101032: “...exits cab...no gloves wornsince no contact with equipment.”1144: “...exits cab without gloves...shuts the door...contacting the side edge.”1214: “...opens door with bare hands...”A441032: “Mixing completed. Worker did not exit cab.”1124: “Worker remains inside cab as water was added, then test substance.”1213: “Puts gloves on, then exits cab.”A581032: “...exits cab wearing gloves. Opens up back of sprayer...”1529: “...exits cab. Not wearing gloves.”AHE56(GA-pecan)A1120833: “Donning gloves while exiting cab...”0911: “Out of tractor with gloves on...”0934: “Donned gloves while exiting tractor...”A2141119: “...exited cab, donned gloves.”1149: “Exited cab, wearing fresh gloves...”A3161137: “Climbed out of truck with gloves on...”1141: “Climbed up on flat bed...”1322: “...donned clean gloves, exited cab.”1335: “Out of truck and puts on gloves to jump start the sprayer engine.”1340: “Exited cab with gloveson...leaning against sprayer”1347: “Out of cab, gloves on...holds onto...other application equipment.”A460134: “...at mixing/loading site. Stays inside cab...”0300: “Exited tractor with gloves donned.”0346: “Waits inside the cab...during M/L.”0405: “Donned gloves and exited cab.”0447: “Donned gloves before exiting tractor.”A5220640: “Waited inside truck during M/L.”0710: “...gets out of the truck to adjustspray pressure while wearing gloves.”0742: “Rain had started...”Page 25 of 62Table A-3. Select Closed Cab Airblast Applicator Field ObservationsStudy IDMU IDCab ents./exits (#)Observation0835-0843: “Donned clean gloves.Left shirt sleeve cuff is open...states that the button popped off...clean outspray nozzles...reached down into nozzle area with rightarm up to arm pit...lean on fan casing...clean the topspray nozzles”1017: “...out of truck wearing gloves.”1025: “...continued to blow out spray nozzles...left shirt sleeve opening near mid-forearm, exposing thedosimeter.”1059: “With only left glove on, climbed on flat-bed near sprayer...”1230: “...left lower sleeve was soiled and the edge of the right lower sleeve was soiled.”AHE57(MI-cherry)A141430: “Arrived at mix/load area and got out of the cab with fresh gloves.”1528: “Put on clean gloveswhen he left the tractor...”A240710: “...stayed inside cab...”0829: “Dons gloves before entering cab and discards gloves outside the cab.”A361403: “...shirts sleeve unbuttoned...also unbuttoned...above waist.”1534: “...exited the cabwith clean gloves on.”1648: “...drop of spray...landed on his hand...wiped his hand on right pant leg.”A481534: “Donned nitrile gloves and stood near tractor...”1710: “Exited tractor with clean gloves...reentered cab...again exited tractor...inner dosimeter is exposed onhis back...near his waist...”A560630: “...exited tractorwith clean gloves.”0806: “...got out of thecab with clean gloves.”AHE58(CA-grape)A1141005-1012: “A1 stepped out of tractor...donned face mask and glove and goggles.”1035: “...got out to adjust nozzles.”1122: “A1 occasionally opened back window of cab to give instructions to mixer.”1315: “Donned glove, goggles. Turned off nozzles...”A280948: “Mixer overfilled tank. Two-three gallons spilled at top of tank. Re-entered tractor. Did not step inspill.”A310Nothingnoteworthy.A4120330: “Entered field for bathroom break. Refused hand wash.”0338: “Adjusted agitation...after donning gloves.”0345: “...got out of cab...to check pressure and agitation...Wore safety glasses and gloves.”A540814: “Turned off water near tank with bare hand...”0930: “Touched inner dosimeter (cuff) with bare hand.”AHE59(WA-apple)A110Gen. obs.: “...did not wear the chemical resistant gloves during the mixing/loading process...”0858: “Climbed back into cab, opened door with bare hands.”0951: “Opened cab door and tied ribbon marker to tree (observer unable to determine whether gloves wornduring this process).”A24Gen. obs.: “The applicator was not observed to handle anything except the door and door handle...Door openedPage 26 of 62Page 27 of 62Table A-3. Select Closed Cab Airblast Applicator Field ObservationsStudy IDMU IDCab ents./exits (#)Observationwith bare hand.”1000: “Arrives at mixing/loading station remained sitting inside cab.”A3120821: “Put on chemical resistant gloves and goes to airblast sprayer...”0828: “Remained in cab during loading.”0956: “Opened truck door with bare hands...”1209: “Exited tractor cab and wiped nose with bare hand.”1248: “Placed gloved hand on lever...Touched another valve on spray tank with gloved hand.”A426Gen. obs.: “Exchange of tractor and sprayer...”0822: “Inner dosimeter cuff is observed at right wrist,out from right shirt sleeve. Long sleeve shirt is nottucked into pants.0833: “Spoke to mixer loader through opened back window of tractor cab. Closed window”1102: “...exited cab and with bare hands picked up a marker from ground...”1320: “Exited cab, walked back to sprayer and with bare hands, turned lever...”1432: “Walked to front of spray tank and with bare hand turned lever...”1500: “Opened rear window...”1549: “...walked back to sprayer and withbare hands, turned off a couple nozzles...”

### Appendix B

Normal and Lognormal Probability Plots of Dermal and Inhalation Unit Exposures

Page 28 of 62Page 29 of 62

### Appendix C SAS Code for “Different Denominator Deg rees of Freedom Analysis” (Section 3.2.2.2)

Note – the code below references the follow ing “.csv” files to be housed in the same folder/directory:

“CCAB-QAd-Final – 11-8-10.csv” “CCAB-with MEA-QA’d_FINAL 6-10-10 - EPA vers 6-15-10.csv”

1. Name file with code below as “Eval_CCAB_HED.sas”

Options LS = **93** PS = **59** NoDate NoNumber FormDLIM = '-' Mprint ;

Title1 "CCAB Scenario: Benchmark Evaluation Analyses" ;

LibName Here '.' ;
%Let DataSet=CCABData;

** Identify Excel spreadsheets with data (saved in CSV format) **;

FileName _DermInh "CCAB-QAd-Final - 11-8-10.csv" ; FileName _MEADerm "CCAB-with MEA-QA'd_FINAL 6-10-10 - EPA vers 6-15-10.csv" ;

*** Read in the non-MEA dermal and the inhalation data ***;

**Data** DermInh;
Infile _DermInh Delimiter = ',' lrecl = **300** dsd FirstObs = **64** ;

Length MUID $ **8** Study $ **10** MU $ **4** Town $ **40** Crop $ **16** ; Length c1-c26 cInh cNrmInh $ **16** ;

InFormat MonDate mmddyy10. ;

Format MonDate mmddyy10. ;

Input Study MU AaiH

c1-c5 Derm NrmDerm
c6-c8 cInh cNrmInh c9-c14 MonDate c15-c26 Crop;

If Study= ' ' then delete ;

*--> Create an MU ID variable from Study and MU code --; MUID = Trim(Study) || "-" || Trim(MU);

*--> Put nearest town and state together --; Town = Trim(c17) || " " || Trim(c15);

*--> Handle missing inhalation exposures --;

If cInh= "---"
then Inh=

**.** ;
else Inh=Input(cInh, **16.** );

If cNrmInh= "---"
then NrmInh=

**.** ;
else NrmInh=Input(cNrmInh, **16.** );

Keep Study MUID MonDate Crop Town AaiH Derm NrmDerm Inh NrmInh ;

Page 30 of 62**Run** ;

*** Read in the MEA Dermal data ***;

**Data** MEADerm; Infile _MEADerm Delimiter = ',' lrecl = **300** dsd FirstObs = **33** ;

Length MUID $ **8** tudy $ **10** MU $ **4** ; S Length c1-c5 $ **16** ;

Input Study MU AaiH c1-c5 Derm_MEA NrmDerm_MEA;

If Study= ' ' then delete ;

MUID = Trim(Study) || "-" || Trim(MU);

Drop MU c1-c5;
Keep MUID Derm_MEA NrmDerm_MEA;

**Run** ;

*** Combine all exposure data into a single SAS dataset ***;

**Proc Sort**  Data =DermInh;
By MUID;

**Run** ;

**Proc Sort**  Data =MEADerm;
By MUID;

**Run** ;

**Data** &DataSet;
Merge DermInh MEADerm;
By MUID;

**Run** ;

** List the dataset for documentation purposes **;

**Proc print**  data =&DataSet;
Title2 "Listing of Dataset &DataSet" ;

I D Study MUID;

**Run** ;

** Save as a permanent SAS dataset for possible future use **;

**Data** Here. &DataSet;

Set &DataSet;

**Run** ;

*** Perform benchmark objective evaluations ***;

%Include "Macro_ObjEval_REG.sas" ; % ObjEvalREG (&DataSet,Study,MUID,AaiH,Derm,NrmDerm);

Page 31 of 62% ObjEvalREG (&DataSet,Study,MUID,AaiH,Inh,NrmInh);

% ObjEvalREG (&DataSet,Study,MUID,AaiH,Derm_MEA,NrmDerm_MEA);

*EndSAS;



### 2. Name file with code below as “Macro_ObjEval_REG.sas”

**%Macro** ObjEvalREG(Dset,Clus,MU,AaiH,Exp,NrmExp);

*---> Extract Needed Data ---;

Data AnaSet;

Set &Dset;
Clus=&Clus;

MU=&MU;
Exp = &Exp; If Exp=

**.** then delete;
AaiH = &AaiH;
NrmExp = &NrmExp; LnAaiH = Log(AaiH); LnExp = Log(Exp); LnNrmExp = Log(NrmExp); Keep Clus MU Exp AaiH NrmExp LnAaiH LnExp LnNrmExp;

Run;

Proc Sort Data=AnaSet; By Clus MU;
Proc Print Data=AnaSet NoObs Label; By Clus; ID Clus; Var MU AaiH Exp NrmExp;

Run;

proc datasets library=work;
delete misc1 misc2;

run;

%do meth= **1** %to **5** ;

%if &meth= **1** %then %let method=residual;
%if &meth= **2** %then %let method=contain;
%if &meth= **3** %then %let method=bw;
%if &meth= **4** %then %let method=satterth;
%if &meth= **5** %then %let method=kr;

Proc Mixed Data=AnaSet Method=REML;
Title2 "Mixed Model Regression of Ln Exposure on Ln " ; Class Clus; Model LnExp = LnAaiH / DDFM = &method;

Random Clus;
Estimate 'Intercept' Intercept **1** / CL ;
Estimate 'Slope' LnAaiH **1** / CL ;

ODS Output CovParms=CovOut Estimates=EstOut(keep=Label Estimate Lower Upper df);

Page 32 of 62Run; Proc Transpose Data=CovOut Out=Covout(drop=_Name_ rename=(Clus=Vc Residual=Vw));

ID CovParm;
Var Estimate;

Run;

data covout;

set covout ;

run;

*---> Additional Calculations from Regression ---;
Data Misc; length ddfmethod $ **20** ; Merge CovOut EstOut;

V = Vc+Vw; GSD = exp(Sqrt(V));
ICC = Vc/V; WidthCI = Upper-Lower;

ddfmethod= "&method" ; Keep GSD ICC WidthCI Label Estimate Lower Upper ddfmethod df;

Run;

proc Print data=misc;

run;

proc append base=misc2 data=misc;

run;

%end ;

proc export data=misc2 file= "F:\AHTEF Monograph\CCAB study\mixed model with only linear term.xls" dbms=excel2000 replace;

sheet= "Mixed Linear model Exp &Exp" ;

run;

**%Mend** ObjEvalREG;

*==========================================================;

Page 33 of 62

## Appendix D

Results and SAS 9.2 Code for:

Regression Sensitivity to D ermal Exposure Data Exclusion

and

Statistical Evaluation of “Cab Entrances/Exits”

Page 34 of 62

### 1 – MEA-Dermal Exposure Data
Results and SAS code



## Note: The following analyses were c onducted for dermal exposure (MEA corrected): If ICC is zero then contai nment method was used for calculating 95% C.I for slope and intercept, else KR method was used.

A. Use of all observations (no exclusions).

Regression model: ln(dermal expo sure)=Intercept + ln(number of Exits)

Obs Label Estimate DF Lower Upper GSD ICC WidthCI

1 Intercept 1.8549 18.5 -0.7082 4.4180 3.95169 .004764165 5.12624 2 Slope_ln(number_exits) 1.2853 20.2 0.1316 2.4390 . . 2.30735



# B. Removing the observation with maximum dermal exposure: Study ID: AHE56 ; MUID:
A5.

Regression model: ln(dermal exposure)=Intercept + ln(Ai)

16 09:27 Tuesday, December 14,
2010

Obs Label Estimate DF Lower Upper GSD ICC WidthCI

1 Intercept 4.0466 20.3 1.3275 6.7658 4.12162 0.085456 5.43824 2 Slope_ln(ai) 0.1322 18.9 -0.7060 0.9703 . . 1.67638

Page 35 of 62

### Regression model: ln(dermal exposure)=Intercept + ln(number of exits) 21
09:27 Tuesday, December 14,

2010

Obs Label Estimate DF Lower Upper GSD ICC WidthCI

1 Intercept 2.4112 20.3 -0.07201 4.8943 3.71350 0.10173 4.96634 2 Slope_ln(number_exits) 0.9730 21 -0.1513 2.0973 . . 2.24864



# C. Removing the first and second highest observations in terms of dermal exposure:

Page 36 of 62

# Study ID: AHE56 ; MUID: A5 ; Study ID:
AHE59 MUID: A4

Regression model: ln(dermal exposure)=Intercept + ln(Ai)

29 09:27 Tuesday, December 14,
2010

Obs Label Estimate DF Lower Upper GSD ICC WidthCI

1 Intercept 4.3180 19.6 1.9769 6.6591 3.39500 0.11084 4.68224 2 Slope_ln(ai) -0.00569 18.7 -0.7315 0.7201 . . 1.45155



### Regression model: ln(dermal expo sure)=Intercept + ln(number of exits)

09:27 Tuesday, December 14,
2010

Obs Label Estimate DF Lower Upper GSD ICC WidthCI

1 Intercept 3.3912 19.7 0.9295 5.8529 3.33272 0.12368 4.92339 2 Slope_ln(number_exits) 0.4417 20 -0.7081 1.5915 . . 2.29952

Page 37 of 62

### D. Removing the first three highes t observations in terms of dermal exposure: Study ID: AHE56 ; MU ID: A5 ; Study ID: AHE59 MUID: A4 ; Stud y ID: AHE55 MUID: A3

Regression model: ln(dermal ex posure)=Intercept + ln(Ai)

09:27 Tuesday, December 14,
2010

Obs Label Estimate DF Lower Upper GSD ICC WidthCI

1 Intercept 4.2580 4 1.4308 7.0851 3.00727 0 5.65434 2 Slope_ln(ai) -0.02250 15 -0.6922 0.6472 . . 1.33932



### Regression model: ln(dermal expo sure)=Intercept + ln(number of exits) 52

Page 38 of 6209:27 Tuesday, December 14,
2010

Obs Label Estimate DF Lower Upper GSD ICC WidthCI

1 Intercept 3.5449 4 0.7824 6.3075 2.97011 0 5.52507 2 Slope_ln(number_exits) 0.3150 15 -0.6952 1.3251 . . 2.02026



### SAS Code

/* MEA corrected */

**data** monograph; input StudyID $ MUID $ ai number_cab_exits Total_residue Cluster

; datalines ;
AHE55 A1 8 4 563 1

AHE55 A2 15 16 220 1

AHE55 A3 24 10 892 1

AHE55 A4 40 4 25.3 1

AHE55 A5 75 8 142 1

AHE56 A1 7.9 12 44.1 2

AHE56 A2 15.8 14 71 2

AHE56 A3 24.6 16 61.2 2

AHE56 A4 50.5 6 73.7 2

AHE56 A5 75.7 22 3168 2

AHE57 A1 10.8 4 27.6 3

AHE57 A2 16.9 4 9.3 3

AHE57 A3 27.6 6 67.8 3

AHE57 A4 40.7 8 51.8 3

AHE57 A5 90.3 6 104 3

AHE58 A1 63.5 14 237 4

AHE58 A2 34.4 8 67.5 4

AHE58 A3 23 10 10 4

AHE58 A4 59.2 12 35.3 4

AHE58 A5 7.3 4 159 4

AHE59 A1 15.8 10 19.5 5

Page 39 of 62AHE59 A2 9.4 4 46.9 5

AHE59 A3 15.9 12 318 5

AHE59 A4 34.5 26 2641 5

;

*/case 1 No observation is deleted*/;

**data** monograph;

set monograph; ln_ai=log(ai); ln_number_exits=log(number_cab_exits); ln_ total_residue=log(Total_residue);

**run** ;

**proc mixed**  data =monograph;

class cluster;
model ln_total_residue=ln_ai/ ddfm =kr;
random cluster;
Estimate 'Intercept' Intercept **1** / CL ; Estimate 'Slope_ln(ai)' ln_ai **1** / CL ;

ODS Output CovParms=CovOut

Est imates=EstOut(keep= Label Estimate Lower Upper df);

**Run** ;

**Proc Transpose**  Data =CovOut

Out =Covout(drop=_Name_ rename=(Cluster=Vc Residual=Vw));

ID CovParm;

Var Estimate;

**Run** ;

**data** covout;

set covout ;

**run** ;

*--- > Additional Calculations from Regression ---;

**Data** Misc; *length ddfmethod $ 20;
Merge CovOut EstOut;

V = Vc+Vw; GSD = exp(Sqrt(V));
ICC = Vc/V; WidthCI = Upper-Lower;

*ddfmethod="&method";

Kee p GSD ICC WidthCI Label Estimate Lower Upper df;

**Run** ; Title " No observation has been removed " ; Titl " Re e gression model: ln(dermal exposure)=Intercept + ln(Ai)" ;

**proc Print**  data =misc;

**run** ;

**proc mixed**  data =monograph;

class cluster;
model ln_total_residue=ln_ai/ outp =pred1 outpm =pred2;
random cluster;

Label ln_total_residue= "log dermal Exposure(ug)" ln_ai= "log Pounds of Active ingredient handled" ;

**run** ;

Page 40 of 62Title "Mixed Effect Regression plot " ; /**scatter plot and the average regression line **/

**proc sgplot**  data =pred2 noautolegend ;

scatter y =ln_total_residue x =ln_ai/ group =cluster;
series y =pred x = ln_ai/ group =cluster ;

**run** ;

**proc mixed**  data =monograph;

class cluster;

model ln_total_residue=ln_number_exits/ ddfm =kr;
random cluster;
Estimate 'Intercept' Intercept **1** / CL ; Estimate 'Slope_ln(number_exits)' ln_number_exits **1** / CL ;

ODS Output CovParms=CovOut

Est imates=EstOut(keep= Label Estimate Lower Upper df);

**Run** ;

**Proc Transpose**  Data =CovOut

Out =Covout(drop=_Name_ rename=(Cluster=Vc Residual=Vw));

ID CovParm;

Var Estimate;

**Run** ;

**data** covout;

set covout ;

**run** ;

*--- > Additional Calculations from Regression ---;

**Data** Misc; *length ddfmethod $ 20;
Merge CovOut EstOut;

V = Vc+Vw; GSD = exp(Sqrt(V));
ICC = Vc/V; WidthCI = Upper-Lower;

*ddfmethod="&method";

Kee p GSD ICC WidthCI Label Estimate Lower Upper df;

**Run** ; Title " No observation has been removed " ; Title " Regression model: ln(dermal exposure)=Intercept + ln(number of Exits)" ;

**proc Print**  data =misc;

**run** ;

**proc mixed**  data =monograph;

class cluster;
model ln_total_residue=ln_number_exits/ outp =pred1 outpm =pred2;
random cluster;

Label

Page 41 of 62ln_total_residue= "log dermal Exposure(ug)" ln_ number_exits= "log number of cab exits" ;

**run** ;

Title "Mixed Effect Regression plot " ; /**scatter plot and the average regression line **/

**proc sgplot**  data =pred2 noautolegend ;

scatter y =ln_total_residue x =ln_number_exits/ group =cluster;
series y =pred x =ln_number_exits;

**run** ;

*/ca se 2 deleting one observation*/;

**data** monograph1;

set monograph;
If total_residue= **3168** then delete ;

**run** ;

**proc mixed**  data =monograph1;

class cluster;

model ln_total_residue=ln_ai/ ddfm =kr;
random cluster;
Estimate 'Intercept' Intercept **1** / CL ; Estimate 'Slope_ln(ai)' ln_ai **1** / CL ;

ODS Output CovParms=CovOut

Est imates=EstOut(keep= Label Estimate Lower Upper df);

**Run** ;

**Proc Transpose**  Data =CovOut

Out =Covout(drop=_Name_ rename=(Cluster=Vc Residual=Vw));

ID CovParm;

Var Estimate;

**Run** ;

**data** covout;

set covout ;

**run** ;

*--- > Additional Calculations from Regression ---;

**Data** Misc; *length ddfmethod $ 20;
Merge CovOut EstOut;

V = Vc+Vw; GSD = exp(Sqrt(V));
ICC = Vc/V; WidthCI = Upper-Lower;

*ddfmethod="&method";
Keep GSD ICC WidthCI Label Estimate Lower Upper df;

Page 42 of 62**Run** ; Title " Removing the highest observation ranked by dermal exposure" ; Title1 " Regression model: ln(dermal exposure)=Intercept + ln(Ai) " ;

**proc Print**  data =misc;

**run** ;

**proc mixed**  data =monograph1;

class cluster;
model ln_total_residue=ln_ai/ outp =pred1 outpm =pred2;
random cluster;

Label ln_total_residue= "log dermal Exposure(ug)" ln_ai= "log Pounds of Active ingredient handled" ;

**run** ;

Title "Mixed Effect Regression plot " ; /**s atter c plot and the average regression line **/

**proc sgplot**  data =pred2 noautolegend ;

scatter y =ln_total_residue x =ln_ai/ group =cluster;
series y =pred x = ln_ai ;

**run** ;

**proc mixed**  data =monograph1;

class cluster;

model ln_total_residue=ln_number_exits/ ddfm =kr;
random cluster;
Estimate 'Intercept' Intercept **1** / CL ; Estimate 'Slope_ln(number_exits)' ln_number_exits **1** / CL ;

ODS Output CovParms=CovOut Estimates=EstOut(keep= Label Estimate Lower Upper df);

**Run** ;

**Proc Transpose**  Data =CovOut

Out =Covout(drop=_Name_ rename=(Cluster=Vc Residual=Vw));

ID CovParm;

Var Estimate;

**Run** ;

**data** covout;

set covout ;

**run** ;

*---> Additional Calculations from Regression ---;

**Data** Misc; *length ddfmethod $ 20;
Merge CovOut EstOut;

V = Vc+Vw; GSD = exp(Sqrt(V));
ICC = Vc/V; WidthCI = Upper-Lower;

*ddfmethod="&method";
Keep GSD ICC WidthCI Label Estimate Lower Upper df;

**Run** ; Title " Removing the highest observation ranked by dermal exposure" ;

Page 43 of 62Title " Regression model: ln(dermal exposure)=Intercept + ln(number of exits)

" ;

**proc Print**  data =misc;

**run** ;

**proc mixed**  data =monograph1;

class cluster;
model ln_total_residue=ln_number_exits/ outp =pred1 outpm =pred2;
random cluster;
Label ln_total_residue= "log dermal Exposure(ug)" ln_ number_exits= "log number of cab exits" ;

**run** ;

Title "Mixed Effect Regression plot " ; /**scatter plot and the average regression line **/

**proc sgplot**  data =pred2 noautolegend ;

scatter y =ln_total_residue x =ln_number_exits/ group =cluster;
series y =pred x =ln_number_exits;

**run** ;

*/ca se 3 deleting two observations*/;

**data** monograph2;

set monograph;

If total_residue= **3168** or total_residue= **2641** then delete ;

**run** ;

**proc mixed**  data =monograph2;

class cluster;

model ln_total_residue=ln_ai/ ddfm =kr;
random cluster;
Estimate 'Intercept' Intercept **1** / CL ; Estimate 'Slope_ln(ai)' ln_ai **1** / CL ;

ODS Output CovParms=CovOut

Est imates=EstOut(keep= Label Estimate Lower Upper df);

**Run** ;

**Proc Transpose**  Data =CovOut

Out =Covout(drop=_Name_ rename=(Cluster=Vc Residual=Vw));

ID CovParm;

Var Estimate;

**Run** ;

**data** covout;

set covout ;

**run** ;

*--- > Additional Calculations from Regression ---;

**Data** Misc; *length ddfmethod $ 20;
Merge CovOut EstOut;

V = Vc+Vw; GSD = exp(Sqrt(V));
ICC = Vc/V;

Page 44 of 62WidthCI = Upper-Lower;

*ddfmethod="&method";
Keep GSD ICC WidthCI Label Estimate Lower Upper df;

**Run** ; Title " Removing two highest observation ranked by dermal exposure" ; Title " Regression model: ln(dermal exposure)=Intercept + ln(Ai) " ;

**proc Print**  data =misc;

**run** ;

**proc mixed**  data =monograph2;

class cluster;
model ln_total_residue=ln_ai/ outp =pred1 outpm =pred2;
random cluster;
Label ln_total_residue= "log dermal Exposure(ug)" ln_ai= "log Pounds of Active ingredient handled" ;

**run** ;

Title "Mixed Effect Regression plot " ; /**s atter c plot and the average regression line **/

**proc sgplot**  data =pred2 noautolegend ;

scatter y =ln_total_residue x =ln_ai/ group =cluster;
series y =pred x = ln_ai ;

**run** ;

**proc mixed**  data =monograph2;

class cluster;

model ln_total_residue=ln_number_exits/ ddfm =kr;
random cluster;
Estimate 'Intercept' Intercept **1** / CL ; Estimate 'Slope_ln(number_exits)' ln_number_exits **1** / CL ;

ODS Output CovParms=CovOut Estimates=EstOut(keep= Label Estimate Lower Upper df);

**Run** ;

**Proc Transpose**  Data =CovOut

Out =Covout(drop=_Name_ rename=(Cluster=Vc Residual=Vw));

ID CovParm;

Var Estimate;

**Run** ;

**data** covout;

set covout ;

**run** ;

*---> Additional Calculations from Regression ---;

**Data** Misc; *length ddfmethod $ 20;
Merge CovOut EstOut;

V = Vc+Vw; GSD = exp(Sqrt(V));
ICC = Vc/V; WidthCI = Upper-Lower;

Page 45 of 62*ddfmethod="&method";

Kee p GSD ICC WidthCI Label Estimate Lower Upper df;

**Run** ; Title " Removing two highest observations ranked by dermal exposure" ; Title " Regression model: ln(dermal exposure)=Intercept + ln(number of exits)

" ;

**proc Print**  data =misc;

**run** ;

**proc mixed**  data =monograph2;

class cluster;
model ln_total_residue=ln_number_exits/ outp =pred1 outpm =pred2;
random cluster;

Label ln_total_residue= "log dermal Exposure(ug)" ln_ number_exits= "log number of cab exits" ;

**run** ;

Title "Mixed Effect Regression plot " ; /**s atter c plot and the average regression line **/

**proc sgplot**  data =pred2 noautolegend ;

scatter y =ln_total_residue x =ln_number_exits/ group =cluster;
series y =pred x =ln_number_exits;

**run** ;

*/case 4 deleting three observations*/;

*---> Additional Calculations from Regression ---;

**data** monograph3;

set monograph;

If total_residue= **3168** or total_residue= **2641** or total_residue= **892** then delete ;

**run** ;

**proc mixed**  data =monograph3;

class cluster;

model ln_total_residue=ln_ai/ ddfm = contain ; random cluster;
Estimate 'Intercept' Intercept **1** / CL ; Estimate 'Slope_ln(ai)' ln_ai **1** / CL ;

ODS Output CovParms=CovOut Estimates=EstOut(keep= Label Estimate Lower Upper df);

**Run** ;

**Proc Transpose**  Data =CovOut

Out =Covout(drop=_Name_ rename=(Cluster=Vc Residual=Vw));

ID CovParm;

Var Estimate;

**Run** ;

**data** covout;

set covout ;

**run** ;

*---> Additional Calculations from Regression ---;

**Data** Misc; *length ddfmethod $ 20;

Page 46 of 62Merge CovOut EstOut;

V = Vc+Vw; GSD = exp(Sqrt(V));
ICC = Vc/V; WidthCI = Upper-Lower;

*ddfmethod="&method";
Keep GSD ICC WidthCI Label Estimate Lower Upper df;

**Run** ; Title " Removing all three highest observation ranked by dermal exposure" ; Title " Regression model: ln(dermal exposure)=Intercept + ln(Ai) " ;

**proc Print**  data =misc;

**run** ;

**proc mixed**  data =monograph3;

class cluster;
model ln_total_residue=ln_ai / ddfm = contain outp =pred1 outpm =pred2;
random cluster;

Label ln_total_residue= "log dermal Exposure(ug)"

ln_ ai= "log Pounds of active ingredient handled" ;

**run** ;

Title "Mixed Effect Regression plot " ; /**scatter plot and the average regression line **/

**proc sgplot**  data =pred2 noautolegend ;

scatter y =ln_total_residue x =ln_ai/ group =cluster;
series y =pred x =ln_ai;

**run** ;

**proc mixed**  data =monograph3;

class cluster;

model ln_total_residue=ln_number_exits/ ddfm = contain ; random cluster;
Estimate 'Intercept' Intercept **1** / CL ; Estimate 'Slope_ln(number_exits)' ln_number_exits **1** / CL ;

ODS Output CovParms=CovOut Estimates=EstOut(keep= Label Estimate Lower Upper df);

**Run** ;

**Proc Transpose**  Data =CovOut

Out =Covout(drop=_Name_ rename=(Cluster=Vc Residual=Vw));

ID CovParm;

Var Estimate;

**Run** ;

**data** covout;

set covout ;

**run** ;

*---> Additional Calculations from Regression ---;

**Data** Misc; *length ddfmethod $ 20;
Merge CovOut EstOut;

V = Vc+Vw; GSD = exp(Sqrt(V));

Page 47 of 62ICC = Vc/V; WidthCI = Upper-Lower;

*ddfmethod="&method";
Keep GSD ICC WidthCI Label Estimate Lower Upper df;

**Run** ; Title " Removing all three highest observation ranked by dermal exposure" ; Title " Regression model: ln(dermal exposure)=Intercept + ln(number of exits)

" ;

**proc Print**  data =misc;

**run** ;

**proc mixed**  data =monograph3;

class cluster;
model ln_total_residue=ln_number_exits/ ddfm = contain outp =pred1
outpm =pred2;
random cluster;

Label ln_total_residue= "log dermal Exposure(ug)" ln_ number_exits= "log number of cab exits" ;

**run** ;

Title "Mixed Effect Regression plot " ; /**s atter c plot and the average regression line **/

**proc sgplot**  data =pred2 noautolegend ;

scatter y =ln_total_residue x =ln_number_exits/ group =cluster;
series y =pred x =ln_number_exits;

**run** ;

Page 48 of 62

### 2 – Non-MEA-Dermal Exposure Data



## Note: The following analyses were conduct ed for dermal exposure without MEA correction: If ICC is zero then contai nment method was used for calculating 95% C.I for slope and intercept, else KR method was used.

A. Use of all observatio ns (no exclusions).

Regression model: ln(dermal expo sure)=Intercept + ln(number of Exits) 11 12:16 Tuesday, December 14,
2010

Obs Label Estimate DF Lower Upper GSD ICC WidthCI

1 Intercept 1.5073 4 -1.4722 4.4868 3.82025 0 5.95903 2 Slope_ln(number_exits) 1.2570 18 0.2404 2.2736 . . 2.03325



### B. 

# Removing the observati on with maximum dermal

exposure: Study ID: AHE56 ; MUID: A5.

Regression model: ln(dermal exposure)=Intercept + ln(Ai)

22 12:16 Tuesday, December 14,
2010

Obs Label Estimate DF Lower Upper GSD ICC WidthCI

1 Intercept 3.9652 4 0.5016 7.4287 3.86635 0 6.92701 2 Slope_ln(ai) 0.02617 17 -0.7832 0.8355 . . 1.61867

Page 49 of 62

### Regression model: ln(dermal expo sure)=Intercept + ln(number of exits) 30 12:16 Tuesday, December 14,
2010

Obs Label Estimate DF Lower Upper GSD ICC WidthCI

1 Intercept 2.1227 19.4 -0.2847 4.5301 3.49880 0.017230 4.81472 2 Slope_ln(number_exits) 0.9151 20.7 -0.1849 2.0150 . . 2.19992



### C. 

# Removing the first and second highest observations in terms of dermal exposure: Study ID: AHE56 ; MUID: A5 ; Study ID: AHE59
MUID: A4

Page 50 of 62

### Regression model: ln(dermal exposure)=Interc ept + ln(Ai)

38 12:16 Tuesday, December 14,
2010

Obs Label Estimate DF Lower Upper GSD ICC WidthCI

1 Intercept 4.1303 19.6 1.7995 6.4612 3.27514 0.035975 4.66168 2 Slope_ln(ai) -0.07255 19 -0.7991 0.6540 . . 1.45304



### Regression model: ln(dermal exposu re)=Intercept + ln(number of exits) 43 12:16 Tuesday, December 14,
2010

Obs Label Estimate DF Lower Upper GSD ICC WidthCI

1 Intercept 2.9928 19.2 0.5561 5.4295 3.21497 0.049222 4.87340 2 Slope_ln(number_exits) 0.4423 19.9 -0.7034 1.5881 . . 2.29150

Page 51 of 62

### D. Removing the first three high est observations in terms of dermal exposure: Study ID: AHE56 ; MU ID: A5 ; Study ID: AHE59 MUID: A4 ; Study ID: AHE55 MUID: A3

Regression model: ln(dermal exposure)=Intercept + ln(Ai)

51 12:16 Tuesday, December 14,
2010

Obs Label Estimate DF Lower Upper GSD ICC WidthCI

1 Intercept 4.0446 4 1.2454 6.8438 2.97471 0 5.59843 2 Slope_ln(ai) -0.07868 15 -0.7417 0.5844 . . 1.32608



### Regression model: ln(dermal expo sure)=Intercept + ln(number of exits) 56

Page 52 of 6212:16 Tuesday, December 14,
2010

Obs Label Estimate DF Lower Upper GSD ICC WidthCI

1 Intercept 3.1282 4 0.3920 5.8644 2.93944 0 5.47238 2 Slope_ln(number_exits) 0.3279 15 -0.6726 1.3284 . . 2.00099



### SAS Code

/* No_ MEA correction is made */

**data** monograph; input StudyID $ MUID $ ai number_cab_exits Total_residue Cluster

; datalines ;
AHE55 A1 8 4 351 1

AHE55 A2 15 16 134 1

AHE55 A3 24 10 483 1

AHE55 A4 40 4 13.2 1

AHE55 A5 75 8 90.5 1

AHE56 A1 7.9 12 42.1 2

AHE56 A2 15.8 14 51.3 2

AHE56 A3 24.6 16 52.9 2

AHE56 A4 50.5 6 42.1 2

AHE56 A5 75.7 22 2354 2

AHE57 A1 10.8 4 20.1 3

AHE57 A2 16.9 4 8.4 3

AHE57 A3 27.6 6 55.8 3

AHE57 A4 40.7 8 34.6 3

AHE57 A5 90.3 6 82.7 3

AHE58 A1 63.5 14 141 4

AHE58 A2 34.4 8 37.1 4

AHE58 A3 23 10 7.1 4

AHE58 A4 59.2 12 22.3 4

Page 53 of 62AHE58 A5 7.3 4 138 4

AHE59 A1 15.8 10 10.6 5

AHE59 A2 9.4 4 23.8 5

AHE59 A3 15.9 12 202 5

AHE59 A4 34.5 26 1329 5

;

*/ca se 1 No observation is deleted*/;

**data** monograph;

set monograph; ln_ai=log(ai); ln_number_exits=log(number_cab_exits); ln_ total_residue=log(Total_residue);

**run** ;

**proc mixed**  data =monograph;

class cluster;
model ln_total_residue=ln_ai/ ddfm = contain ; random cluster;
Estimate 'Intercept' Intercept **1** / CL ; Estimate 'Slope_ln(ai)' ln_ai **1** / CL ;

ODS Output CovParms=CovOut

Est imates=EstOut(keep= Label Estimate Lower Upper df);

**Run** ;

**Proc Transpose**  Data =CovOut

Out =Covout(drop=_Name_ rename=(Cluster=Vc Residual=Vw));

ID CovParm;

Var Estimate;

**Run** ;

**data** covout;

set covout ;

**run** ;

*--- > Additional Calculations from Regression ---;

**Data** Misc; *length ddfmethod $ 20;
Merge CovOut EstOut;

V = Vc+Vw; GSD = exp(Sqrt(V));
ICC = Vc/V; WidthCI = Upper-Lower;

*ddfmethod="&method";

Kee p GSD ICC WidthCI Label Estimate Lower Upper df;

**Run** ; Title " No observation has been removed " ; Titl " Re e gression model: ln(dermal exposure)=Intercept + ln(Ai)" ;

**proc Print**  data =misc;

**run** ;

**proc mixed**  data =monograph;

class cluster;
model ln_total_residue=ln_ai/ ddfm = contain outp =pred1 outpm =pred2;
random cluster;

Label ln_total_residue= "log dermal Exposure(ug)"

Page 54 of 62ln_ai= "log Pounds of Active ingredient handled" ;

**run** ;

Title "Mixed Effect Regression plot with average lines " ; /**scatter plot and the average regression line **/

**proc sgplot**  data =pred2 noautolegend ;

scatter y =ln_total_residue x =ln_ai/ group =cluster;
series y =pred x = ln_ai ;

**run** ;

**proc mixed**  data =monograph;

class cluster;

model ln_total_residue=ln_number_exits/ ddfm = contain ; random cluster;
Estimate 'Intercept' Intercept **1** / CL ; Estimate 'Slope_ln(number_exits)' ln_number_exits **1** / CL ;

ODS Output CovParms=CovOut

Est imates=EstOut(keep= Label Estimate Lower Upper df);

**Run** ;

**Proc Transpose**  Data =CovOut

Out =Covout(drop=_Name_ rename=(Cluster=Vc Residual=Vw));

ID CovParm;

Var Estimate;

**Run** ;

**data** covout;

set covout ;

**run** ;

*--- > Additional Calculations from Regression ---;

**Data** Misc; *length ddfmethod $ 20;
Merge CovOut EstOut;

V = Vc+Vw; GSD = exp(Sqrt(V));
ICC = Vc/V; WidthCI = Upper-Lower;

*ddfmethod="&method";

Kee p GSD ICC WidthCI Label Estimate Lower Upper df;

**Run** ; Title " No observation has been removed " ; Title " Regression model: ln(dermal exposure)=Intercept + ln(number of Exits)" ;

**proc Print**  data =misc;

**run** ;

**proc mixed**  data =monograph;

class cluster;

Page 55 of 62model ln_total_residue=ln_number_exits/ ddfm = contain outp =pred1
outpm =pred2;
random cluster;
Label ln_total_residue= "log dermal Exposure(ug)" ln_number_exits= "log number of cab exits" ;

**run** ;

Title "Mixed Effect Regression plot with average lines " ; /**s atter c plot and the average regression line **/

**proc sgplot**  data =pred2 noautolegend ;

scatter y =ln_total_residue x =ln_number_exits/ group =cluster;
series y =pred x =ln_number_exits;

**run** ;

*/case 2 : 1 observation is deleted*/;

**data** monograph1;

set monograph;

If total_residue= **2354** then delete ;

**run** ;

**proc mixed**  data =monograph1;

class cluster;

model ln_total_residue=ln_ai/ ddfm = contain ; random cluster;
Estimate 'Intercept' Intercept **1** / CL ; Estimate 'Slope_ln(ai)' ln_ai **1** / CL ;

ODS Output CovParms=CovOut Estimates=EstOut(keep= Label Estimate Lower Upper df);

**Run** ;

**Proc Transpose**  Data =CovOut

Out =Covout(drop=_Name_ rename=(Cluster=Vc Residual=Vw));

ID CovParm;

Var Estimate;

**Run** ;

**data** covout;

set covout ;

**run** ;

*---> Additional Calculations from Regression ---;

**Data** Misc; *length ddfmethod $ 20;
Merge CovOut EstOut;

V = Vc+Vw; GSD = exp(Sqrt(V));

Page 56 of 62ICC = Vc/V; WidthCI = Upper-Lower;

*ddfmethod="&method";
Keep GSD ICC WidthCI Label Estimate Lower Upper df;

**Run** ; Title " Removing the highest observation ranked by dermal exposure" ; Title1 " Regression model: ln(dermal exposure)=Intercept + ln(Ai) " ;

**proc Print**  data =misc;

**run** ;

**proc mixed**  data =monograph1;

class cluster;
model ln_total_residue=ln_ai/ ddfm = contain outp =pred1 outpm =pred2;
random cluster;

Label ln_total_residue= "log dermal Exposure(ug)" ln_ ai= "log Pounds of Active ingredient handled" ;

**run** ;

Title "Mixed Effect Regression plot with average lines " ; /**scatter plot and the average regression line **/

**proc sgplot**  data =pred2 noautolegend ;

scatter y =ln_total_residue x =ln_ai/ group =cluster;
series y =pred x = ln_ai ;

**run** ;

**proc mixed**  data =monograph1;

class cluster;

model ln_total_residue=ln_number_exits/ ddfm =kr;
random cluster;
Estimate 'Intercept' Intercept **1** / CL ; Estimate 'Slope_ln(number_exits)' ln_number_exits **1** / CL ;

ODS Output CovParms=CovOut Estimates=EstOut(keep= Label Estimate Lower Upper df);

**Run** ;

**Proc Transpose**  Data =CovOut

Out =Covout(drop=_Name_ rename=(Cluster=Vc Residual=Vw));

ID CovParm;

Var Estimate;

**Run** ;

**data** covout;

set covout ;

**run** ;

*---> Additional Calculations from Regression ---;

**Data** Misc; *length ddfmethod $ 20;
Merge CovOut EstOut;

V = Vc+Vw; GSD = exp(Sqrt(V));
ICC = Vc/V; WidthCI = Upper-Lower;

Page 57 of 62*ddfmethod="&method";

Kee p GSD ICC WidthCI Label Estimate Lower Upper df;

**Run** ; Title " Removing the highest observation ranked by dermal exposure" ; Title " Regression model: ln(dermal exposure)=Intercept + ln(number of exits)

" ;

**proc Print**  data =misc;

**run** ;

**proc mixed**  data =monograph1;

class cluster;
model ln_total_residue=ln_number_exits/ ddfm =kr outp =pred1 outpm =pred2;
random cluster;

Label ln_total_residue= "log dermal Exposure(ug)" ln_ number_exits= "log number of cab exits" ;

**run** ;

Title "Mixed Effect Regression plot with average lines " ; /**s atter c plot and the average regression line **/

**proc sgplot**  data =pred2 noautolegend ;

scatter y =ln_total_residue x =ln_number_exits/ group =cluster;
series y =pred x =ln_number_exits;

**run** ;

*/case 3 two observations have been deleted*/;

**data** monograph2;

set monograph;

If total_residue= **2354** or total_residue= **1329** then delete ;

**run** ;

**proc mixed**  data =monograph2;

class cluster;

model ln_total_residue=ln_ai/ ddfm =kr;
random cluster;
Estimate 'Intercept' Intercept **1** / CL ; Estimate 'Slope_ln(ai)' ln_ai **1** / CL ;

ODS Output CovParms=CovOut Estimates=EstOut(keep= Label Estimate Lower Upper df);

**Run** ;

**Proc Transpose**  Data =CovOut

Out =Covout(drop=_Name_ rename=(Cluster=Vc Residual=Vw));

ID CovParm;

Var Estimate;

**Run** ;

**data** covout;

set covout ;

**run** ;

*---> Additional Calculations from Regression ---;

**Data** Misc; *length ddfmethod $ 20;

Page 58 of 62Merge CovOut EstOut;

V = Vc+Vw; GSD = exp(Sqrt(V));
ICC = Vc/V; WidthCI = Upper-Lower;

*ddfmethod="&method";
Keep GSD ICC WidthCI Label Estimate Lower Upper df;

**Run** ; Title " Removing two highest observation ranked by dermal exposure" ; Title " Regression model: ln(dermal exposure)=Intercept + ln(Ai) " ;

**proc Print**  data =misc;

**run** ;

**proc mixed**  data =monograph2;

class cluster;
model ln_total_residue=ln_ai/ outp =pred1 outpm =pred2;
random cluster;

Label ln_total_residue= "log dermal Exposure(ug)" ln_ ai= "log Pounds of Active ingredient handled" ;

**run** ;

Title "Mixed Effect Regression plot with average lines " ; /**scatter plot and the average regression line **/

**proc sgplot**  data =pred2 noautolegend ;

scatter y =ln_total_residue x =ln_ai/ group =cluster;
series y =pred x = ln_ai ;

**run** ;

**proc mixed**  data =monograph2;

class cluster;

model ln_total_residue=ln_number_exits/ ddfm =kr;
random cluster;
Estimate 'Intercept' Intercept **1** / CL ; Estimate 'Slope_ln(number_exits)' ln_number_exits **1** / CL ;

ODS Output CovParms=CovOut

Est imates=EstOut(keep= Label Estimate Lower Upper df);

**Run** ;

**Proc Transpose**  Data =CovOut

Out =Covout(drop=_Name_ rename=(Cluster=Vc Residual=Vw));

ID CovParm;

Var Estimate;

**Run** ;

**data** covout;

set covout ;

**run** ;

*--- > Additional Calculations from Regression ---;

**Data** Misc; *length ddfmethod $ 20;
Merge CovOut EstOut;

Page 59 of 62V = Vc+Vw; GSD = exp(Sqrt(V));
ICC = Vc/V; WidthCI = Upper-Lower;

*ddfmethod="&method";
Keep GSD ICC WidthCI Label Estimate Lower Upper df;

**Run** ; Title " Removing two highest observations ranked by dermal exposure" ; Title " Regression model: ln(dermal exposure)=Intercept + ln(number of exits)

" ;

**proc Print**  data =misc;

**run** ;

**proc mixed**  data =monograph2;

class cluster;
model ln_total_residue=ln_number_exits/ outp =pred1 outpm =pred2;
random cluster;

Label ln_total_residue= "log dermal Exposure(ug)" ln_number_exits= "log number of cab exits" ;

**run** ;

Title "Mixed Effect Regression plot with average lines " ; /**s atter c plot and the average regression line **/

**proc sgplot**  data =pred2 noautolegend ;

scatter y =ln_total_residue x =ln_number_exits/ group =cluster;
series y =pred x =ln_number_exits;

**run** ;

*/case 4 three observations have been deleted*/;

*---> Additional Calculations from Regression ---;

**data** monograph3;

set monograph;

If total_residue= **2354** or total_residue= **1329** or total_residue= **483** then delete ;

**run** ;

**proc mixed**  data =monograph3;

class cluster;

model ln_total_residue=ln_ai/ ddfm = contain ; random cluster;
Estimate 'Intercept' Intercept **1** / CL ; Estimate 'Slope_ln(ai)' ln_ai **1** / CL ;

ODS Output CovParms=CovOut Estimates=EstOut(keep= Label Estimate Lower Upper df);

**Run** ;

**Proc Transpose**  Data =CovOut

Out =Covout(drop=_Name_ rename=(Cluster=Vc Residual=Vw));

ID CovParm;

Var Estimate;

**Run** ;

**data** covout;

set covout ;

**run** ;

Page 60 of 62*--- > Additional Calculations from Regression ---;

**Data** Misc; *length ddfmethod $ 20;
Merge CovOut EstOut;

V = Vc+Vw; GSD = exp(Sqrt(V));
ICC = Vc/V; WidthCI = Upper-Lower;

*ddfmethod="&method";

Kee p GSD ICC WidthCI Label Estimate Lower Upper df;

**Run** ; Title " Removing all three highest observation ranked by dermal exposure" ; Titl " Re e gression model: ln(dermal exposure)=Intercept + ln(Ai) " ;

**proc Print**  data =misc;

**run** ;

**proc mixed**  data =monograph3;

class cluster;
model ln_total_residue=ln_ai / ddfm = contain outp =pred1 outpm =pred2;
random cluster;

Label ln_total_residue= "log dermal Exposure(ug)" /*ln_number_exits="log number of cab exits"; */ ln_ ai= "log Pounds of active ingredient handled" ;

**run** ;

Title "Mixed Effect Regression plot with average lines " ; /**scatter plot and the average regression line **/

**proc sgplot**  data =pred2 noautolegend ;

scatter y =ln_total_residue x =ln_ai/ group =cluster;
series y =pred x =ln_ai;

**run** ;

**proc mixed**  data =monograph3;

class cluster;

model ln_total_residue=ln_number_exits/ ddfm = contain ; random cluster;
Estimate 'Intercept' Intercept **1** / CL ; Estimate 'Slope_ln(number_exits)' ln_number_exits **1** / CL ;

ODS Output CovParms=CovOut Estimates=EstOut(keep= Label Estimate Lower Upper df);

**Run** ;

**Proc Transpose**  Data =CovOut

Out =Covout(drop=_Name_ rename=(Cluster=Vc Residual=Vw));

ID CovParm;

Var Estimate;

**Run** ;

**data** covout;

set covout ;

**run** ;

*---> Additional Calculations from Regression ---;

**Data** Misc;

Page 61 of 62Page 62 of 62 *length ddfmethod $ 20;
Merge CovOut EstOut;

V = Vc+Vw; GSD = exp(Sqrt(V));
ICC = Vc/V; WidthCI = Upper-Lower;

*ddfmethod="&method";

Kee p GSD ICC WidthCI Label Estimate Lower Upper df;

**Run** ; Title " Removing all three highest observation ranked by dermal exposure" ; Title " Regression model: ln(dermal exposure)=Intercept + ln(number of exits)

" ;

**proc Print**  data =misc;

**run** ;

**proc mixed**  data =monograph3;

class cluster;
model ln_total_residue=ln_number_exits/ ddfm = contain outp =pred1
outpm =pred2;
random cluster;

Label ln_total_residue= "log dermal Exposure(ug)" ln_number_exits= "log number of cab exits" ;

**run** ;

Title "Mixed Effect Regression plot with average lines " ; /**s atter c plot and the average regression line **/

**proc sgplot**  data =pred2 noautolegend ;

scatter y =ln_total_residue x =ln_number_exits/ group =cluster;
series y =pred x =ln_number_exits;

**run** ;