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 Exposure Task Force (AHETF) Monograph: 

Closed Cab Airblast Application 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 Airblast 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 separate data evaluation review (DER) memorandum 

(Crowley, 2011 – D381148). The AHETF satisfactorily followed the study protocols and 

satisfied data analysis objectives. EPA considers 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 Division (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 meets the study design objectives outlined in the 

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

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

airblast equipment while wearing the following personal protective equipment (PPE): longsleeved shirts, long pants, shoes, socks, chemical-resistant gloves, and no respirator2

. The 

AHETF data and associated unit exposures are considered superior to the existing closed cab 

airblast applicator dataset.3

 AHETF efforts represented a well-designed, concerted process to 

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

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

improved data handling techniques. 

The primary objective for dermal exposure results (normalized to the amount of active ingredient 

handled) to be accurate within 3-fold at the geometric mean, arithmetic mean and 95th percentile 

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

exposure and the amount of active ingredient handled with 80% statistical power – a key 

assumption in the use of exposure data as “unit exposures” – was not met. Though with lessthan-expected statistical power, regression analysis does not reject proportionality between 

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

of exposure. Additional analyses point to incidental 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 barrier, 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 future 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 airblast applicator scenario “unit exposures” are 

presented in Table 1 below, as well as the PHED value previously used for comparison. 

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

Exposure Route PHED AHETFa

“Best Fit” Geometric Mean Arithmetic Meanc 95th Percentiled

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

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

 Per current EPA policy, dermal unit exposures reflect 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 represent alternative personal protective equipment (e.g., applying 

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

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

addressed in this review. 

3

 Pesticide Handlers Exposure Database (PHED) Scenario 12: Airblast Application, Enclosed Cab (APPL) 

Page 2 of 62 
c

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

d

 95th percentile = GM * GSD^1.645 

2.0 Background 

The following provides background on the AHETF objectives 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 (Agricultural Handlers Exposure Database or AHED) 

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

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

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

airblast, aircraft or boom sprayers) are also key criteria for defining scenarios. AHETFsponsored studies are typically designed to represent individuals wearing long-sleeved shirts, 

long pants, shoes, socks, chemical-resistant gloves as appropriate, and no respirators. In some 

cases, such as the scenario addressed by this monograph, an engineering control (i.e., the closed 

cabs for this scenario) or additional personal protective equipment/clothing may also be a key 

element of the scenario (e.g., certain types of headgear to reduce overhead exposures). 

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

inhalation exposures, which represent the chemical exposure "deposited on or to-the-skin" or “in 

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

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

chemical handled. Mathematically, unit exposures are expressed as exposure normalized by the 

amount active ingredient handled (AaiH) by participants in scenario-specific exposure studies 

(e.g., mg exposure/lb ai handled). Unit exposures are then used generically to predict exposure 

for other chemicals having the same or different application rates. 

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

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

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

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

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

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

Thus, for example, exposure data for spraying one chemical using 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 inhalation exposure are assumed proportional to the 

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

handled, one doubles the exposure. 

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

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

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 accurate within 3-fold. Specifically, the upper and lower 95% 

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

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

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

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

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

exposure, though estimates are provided for reference (see Table 6). 

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

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

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

proposed a log-log regression test to distinguish complete proportionality (slope = 1) 

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

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

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

are performed for informational purposes. 

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

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

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

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

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

sample size perspective. The 25 total participants together with the conditions under which the 

worker handles the active ingredient are referred to as monitoring units (MUs). Within each 

cluster, the AHETF partitions the practical AaiH range handled by the participants in each 

cluster appropriate to a given scenario. In general, the strata of AaiH for any given scenario is 

commensurate with typical commercial production agriculture and HED handler risk 

assessments considerations with respect to amount of area that could be treated in a single work 

day. 

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

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

equipment (PPE): long-sleeved shirts, long pants, shoes, socks, chemical-resistant gloves when 

conducting tasks outside the enclosed cab (e.g., marking treated acreage), and no respirator. 

Dermal and inhalation exposure monitoring was conducted for 24 workers4

 (referred to as 

“monitoring units”, or MUs) applying liquid spray pesticides using closed cab airblast 

equipment. Five separate studies were conducted (references in Table 2 below), each monitoring 

different workers while spraying tree or trellis crops in 5 different 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. 

Page 4 of 62 
airblast equipment would typically be used – citrus in Florida, pecans in Georgia, grapes in 

California, cherries in Michigan, 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, 2010) depict examples of this activity for which the exposure 

data are applicable. 

Figure 1: Closed Cab airblast application in GA pecans 

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

Figure 3: Closed cab airblast application 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 airblast applicator exposure monitoring studies to 

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

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

Page 6 of 62 
by the Human Studies Review Board5

. The following is a timeline of HSRB reviews related to 

this scenario: 

Table 3. Closed Cab Airblast Application 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 [2nd review (1st 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 favorable reviews by the HSRB; however, throughout the 

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

possible. Throughout the review process a number 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 closed cab airblast scenario that were addressed. The following 

summarizes the more substantive scientific HSRB review comments related to this process and 

how the AHETF responded. 

2.2.1 Characterization of non-respondents and responders who declined to 

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

The HSRB was concerned with the inability to evaluate 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 study participants with those eligible nonparticipants via an informal survey of local agricultural experts. 

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

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

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

monitoring was conducted: 

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

applicator; 

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

• # of employed experienced airblast applicators 

• Equipment type 

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

2 vocational agricultural teachers 1 typical in the counties monitored 

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 the 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 the 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 recruitment for occupational pesticide exposure 

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

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

Coordinators for recruitment purposes and employed individuals familiar with survey 

methodology. 

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

HSRB Meeting Reports)

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

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

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

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

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

executed exposure monitoring. 

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

Meeting Report)

Page 8 of 62 
The HSRB recommended that the AHETF better define the recruitment process so as to identify 

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

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

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

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

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

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

multiple employees. 

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

Reports)

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

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

circumstances, such as exiting the cab, opening windows, or non-adherence to label PPE 

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

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

application procedures (i.e., sequence of row treatments) to worker behaviors such as cab 

exits/entrances, brief window openings, and contact with exterior surfaces with bare hands. 

Notable observations and their potential impact on use of the data, including additional statistical 

analyses to evaluate their impact, 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 pesticide applications made to both using closed cab airblast 

applications. The AHETF noted that with consultation with EPA, that the monitoring conducted 

for the array of non-dormant crops would be considered sufficiently adequate for assessment of 

closed cab airblast exposures to dormant crops and hops – as well as other crops not specifically 

monitored. Given the logistical considerations of the sampling design (i.e., the increased 

chances of finding willing participants for more common pesticide applications), HED agrees 

that for the purposes of the generic database, the proposed studies are adequate for assessment of 

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 affect exposure during applications of liquid sprays 

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

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

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

use of solid formulations cannot be made. 

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, including selection of workers and sample representativeness, the HSRB 

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

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

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

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

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

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

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

3.0 Results 

Exposure results were reported in reports for each of the 5 studies and reviewed in Crowley, 

2011 (D381148). The following sections summarize the exposure monitoring results and the 

scenario benchmark statistical analyses presented 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 lbs of active ingredient 

(carbaryl, malathion, or chlorothalonil), spraying 4 to 30 acres in 2 to 9 hours. Dermal exposure 

was measured using 100% cotton “whole body dosimeters” (WBD) underneath normal work 

clothing (i.e., long-sleeved shirt, long pants, socks and shoes), hand rinses (collected at the end 

of the day and during restroom and lunch breaks), and face/neck wipes (adjusted to extrapolate to 

portions of the head covered by protective eyewear, respirators, and/or hair). 

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

residue collection method inefficiencies6

, EPA has directed the AHETF to make adjustments to 

hand and face/neck field study measurements as follows: 

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

average across all workers, no action is required; 

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

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

of a validation study to support the residue collection method; 

• if measured exposure contribution from hands and face/neck represents is greater than 

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

methods is required. 

For these studies, 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 personal air sampling pump and an OSHA Versatile 

Sampler (OVS) tube with a glass fiber filter and Chromosorb 102 solvent. The tube is attached 

to the worker’s shirt collar to continuously sample air from the breathing zone. All samples are 

adjusted as appropriate according to recovery results from field fortification samples. 

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

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

concentration (i.e., ug/L) using a breathing rate of 8.3 liters per minutes (LPM; converted from 

1.0 m3/hr), representing light activities such as mixing/loading light packages (NAFTA, 1998), 

and total work/monitoring time.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 exposure without respiratory protection. 

A summary of the 24 closed cab airblast 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 and 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 [breathing rate (L/min) ÷ average pump flow rate (L/min)] 

Page 11 of 62 
A5 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 grower 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 surrogate 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 62 
Figure 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. The 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 scenarios is for select statistics – the geometric 

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

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

various statistical techniques to evaluate this benchmark. First, both dermal and inhalation unit 

exposures were shown to fit lognormal distributions reasonably well. 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 determined as the maximum of the two ratios of 

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

primary benchmark of 3-fold accuracy for select statistics was met for dermal exposure data 

unadjusted for hand rinse and face/neck wipe method inefficiencies. However, as shown in 

Table 6 below, because the adjustments for hand rinse and face/neck wipe method inefficiencies 

increased the sample variation, fRA values for the 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 

GMS 4.1 1.9 – 8.8 2.2 0.032 0.016 – 0.064 2.0 

GSDS 4.86 3.06 – 7.69 1.6 3.28 2.27 – 4.84 1.5 

GMM 4.1 1.9 – 8.8 2.2 0.033 0.017 – 0.064 2.0 

GSDM 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 -- 

GMS = geometric mean assuming SRS = “exp(average of 24 ln(UE)) values”. 

GSDS = geometric standard deviation assuming SRS = “exp(standard deviation of 24 ln(UE)) values” 

GMM = variance component model-based geometric mean 

GSDM = variance component model-based geometric standard deviation 

ICC = intra-cluster correlation 

AMS 13.3 4.6 – 39.7 3.0 0.057 0.028 – 0.146 2.6 

AMU 14.1 5.2 – 43.4 3.1 0.065 0.029 – 0.154 2.4 

AMM 14.6 5.3 – 46.1 3.2 0.068 0.030 – 0.166 2.4 

AMS = average of 24 unit exposures 

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

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

P95S 70.4 13.3 – 172.6 5.3 0.186 0.071 – 0.576 3.1 

P95U 54.6 18.6 – 158.5 2.9 0.225 0.090 – 0.573 2.5 

P95M 56.4 19.0 – 168.2 3.0 0.240 0.092 – 0.624 2.6 

P95S = 95th percentile (i.e., the 23rd unit exposure out of 24 ranked in ascending order) 

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

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

 Dermal exposure values reflect 50% default adjustment for hands and face/neck measurements. 

3.2.2 Secondary Objective: Testing Proportionality

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

power, complete proportionality from complete independence between dermal exposure and 

amount of active ingredient handled. Based on the AHETF analysis this benchmark was not 

met. 

3.2.2.1 AHETF Analysis

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

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

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

Page 14 of 62 
than 0 – providing support for an independent relationship. Both simple linear regression and 

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

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

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

regression slopes and confidence intervals are summarized in Table 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 

Linearb 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-class 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, suggesting that an independent or a proportional relationship is 

consistent with the data. For inhalation exposure the mixed-effects regression slope is 0.70, with 

95% confidence intervals including 1 while excluding 0, suggesting that a proportional 

relationship is consistent with the data. In terms of the secondary objective, the width of the 

confidence interval for dermal exposure was greater than 1.4, indicating the power to detect 

complete independence from complete proportionality 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 inefficiencies do not alter these conclusions. For 

MEA dermal exposures, the 95% confidence intervals for the log-log regression slope still 

include 0 and 1 and the width of the interval is still greater than 1.4.

3.2.2.2 Additional ICC Considerations (EPA Analysis)

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

was done for exposure to antimicrobial pesticides, EPA conducted additional analysis with 

respect to statistical procedures and the intra-class correlation coefficient (ICC) for the close cab 

airblast applicator dataset. The AHETF statistical analysis for proportionality used the KenwardRogers denominator degrees of freedom (DDF) method to calculate confidence intervals for the 

log-log regression slope8

. 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 degrees of freedom method does not affect the estimated slope and its standard 

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

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

this issue does not impact those calculations.

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 

al10. Table 8 below summarizes the five available methods outlined in the ICF memo. 

Table 8. Summary of SAS Methods for Computing Fixed-Effects DDF in PROC MIXEDa

DDF Method SAS 

Abbreviation Comments 

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

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

Containment contain 

Default method when RANDOM statements are present. Accounts for the 

minimum contribution of the random effects that syntactically contain the fixed 

effects of interest. 

Between-Within bw 

Default method when REPEATED statements are present and RANDOM 

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

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

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

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

unpredictable. 

Satterthwaite / 

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

KenwardRogers kr 

Designed to approximate the denominator degrees of freedom for designs with 

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

simulations suggest better performance than the Satterthwaite method. If a 

covariance parameter has zero variance then this method ignores that 

covariance. a

 RANDOM statement used to define the cluster effect. 

For dermal exposure 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 cluster effect gives 22 error degrees of freedom (24-

2 = 22). If the mixed model and the kr method are used, the estimated cluster variance is zero 

and the kr method also treats the true cluster variance as zero, giving 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 mixed model and the containment method are used, the 

estimated cluster variance is again zero, but the containment 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 cluster variance was estimated to be zero, it does not 

mean it truly is zero. For instance, if the true cluster variance was very close, but not quite, zero 

(e.g., 0.000000001), the kr method would either give 22 or 18 error degrees of freedom 

depending upon whether or not the estimated cluster 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 1-58025-864-6. 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 correct 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 the mixed models where the ICC estimate is nonzero. 

For both the dermal exposure adjusted for potential method inefficiencies and inhalation 

exposure the ICC estimates were not zero, thus the AHETF use of Kenward-Rogers is 

appropriate. For unadjusted dermal exposure data, the ICC estimate is zero for which the 

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

conclusions for the unadjusted dermal exposure with respect to the secondary study objective. 

That is, the 95% confidence interval 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 are 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 proportional or independent relationship, the relationship between 

dermal exposure and the amount of active ingredient handled is inconclusive for this scenario. 

While a proportional relationship seems reasonable for standard exposure scenarios (i.e., if one 

doubles the amount handled, that exposure would 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) appears to be very effective at reducing the opportunity for routine 

exposures and may limit exposure potential to sporadic events such as contacts with exterior 

surfaces during entry or egress from the application vehicle. 

Considering this result, a closer look and additional analyses could result in more appropriate 

uses and interpretation of the data. As discussed in Section 2.2.5, observers recorded both 

typical application procedures as well as events that potentially could affect exposure. Appendix 

A, Table A-3 presents notable detailed observations. For example, AHE56 (GA-pecan) MU A5 

was observed to have a shirt sleeve button missing, 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 approximate 2X margin (1626 ug, method efficiency 

adjusted) – combining to result in the highest 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 the 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 efficiency adjusted) by an approximate 2X margin. 

Because the exposure for these three workers potentially 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 words, 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 the 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 exposure and the amount of active ingredient 

remains inconclusive (i.e., the confidence intervals still include 0). 

Table 9. Dermal Exposure and Amount Handled – 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 (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 

1st and 2nd highest exposures excluded -0.073 -0.80 – 0.65 -0.006 -0.73 – 0.72 

1st, 2nd, and 3rd 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 vehicle 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. The field observations were reviewed to tally the 

number of times workers entered or exited the cab (including at the start and end of the 

workday), and ranged from 4 to 26. Most cab entrances/exits were for routine purposes such as 

lunch or restroom breaks or waiting during tank mixing/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 cleaning or adjusting spray nozzles. 

An analysis similar to the evaluation of the relationship between dermal exposure and amount of 

active ingredient handled was conducted for the number of cab entrances/exits – with the 

hypothesis that those workers who exit the cab more will have higher dermal exposures. When 

including all observations (adjusted for potential method inefficiencies), 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 dermal exposure data). This result appears to 

demonstrate a stronger relationship between dermal exposure and the 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 62 
Table 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 

1st and 2nd highest exposures excluded 0.44 -0.70 – 1.59 0.44 -0.71 – 1.59 

1st, 2nd, and 3rd 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 additional analyses that incidental contacts have a significant impact on 

exposure. This was apparent when comparing 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 consideration of alternate uses of the data from the 

default normalization by amount of active ingredient handled. 

3.3 Data Generalizations and Limitations 

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

discussed and established (Christian, 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 applicators applying any conventional chemical applied 

as a spray using airblast equipment hauled by trucks or tractors with an enclosed cab. However, 

certain limitations need to be recognized with respect to collection, use, and interpretation of the 

exposure data. 

3.3.1 Generic Use in Exposure Assessment 

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

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

long-sleeve shirt, pants, shoes/socks, and chemical resistant gloves. This does not preclude 

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

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

data. 

3.3.2 Applicability of AHETF Data for Volatile Chemicals 

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

exposure to other conventional pesticides used in closed cab airblast equipment, which are 

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

not expected that this dataset would be used to support regulatory decisions 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 analyses 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 exposure ‘models’ will be investigated. However, 

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

ingredient handled as a default condition for the foreseeable future. 

3.3.4 Representativeness and Extrapolation to Exposed Population 

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

certain restrictions necessary for logistical purposes (e.g., selection of major crops that use closed 

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

potential applicators to use certain pesticides due to ensure laboratory analysis of exposure 

monitoring matrices; and requiring selection of workers who normally wear the scenario-defined 

minimal PPE), made the studies comprising this scenario neither purely observational nor 

random to allow for characterization of the dataset as representative of the population of closed 

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

dataset as representative of all closed cab airblast applicators. 

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

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

requirements to avoid non-detect exposures), both of which are valuable for regulatory 

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

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

feasibility, and utility, the resulting dataset is considered a reasonable approximation of expected 

exposure for this population. 

3.4 Conclusions 

HED has reviewed the AHETF Closed Cab Airblast Application scenario monograph and 

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

benchmarks objectives. Conclusions are as follows: 

• Deficiencies in the existing closed cab airblast application scenario dataset (i.e., PHED) 

have been recognized and the need for new data established. 

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

most reliable data for assessing closed cab airblast application exposure. 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 independence between dermal exposure 

and AaiH. 

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

amount of active ingredient handled was not rejected, though additional analyses suggest 

Page 20 of 62 
Page 21 of 62 

incidental contacts with residues 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 Governing Document for a Multi-Year Pesticide 

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

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

AHETF, 2010. Agricultural Handler Exposure Scenario Monograph: Closed Cab Airblast 

Application of Liquid Sprays. Report 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 Agricultural Handler Exposure Task Force 

(AHETF) Closed Cab Airblast Applicator Exposure 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 Chemical Society Symposium Series 273, pp. 341-355. 

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

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

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

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

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

Supplemental Tables 1-4

Page 22 of 62 
Table A-1. Closed Cab Airblast Application – Dermal and Inhalation Exposures 

Study 

ID 

MU 

ID 

AaiH 

(lbs) 

Dermal 

Inner 

WBD 

(μg) 

Hand 

(μg) 

Head 

(μg) 

Total Exposure 

(μg) 

Unit Exposure 

(μg/lb ai) 

Non-MEA MEA Non-MEA MEA Non-MEA MEA Non-MEA ME

AHE55 

(FLcitrus) 

A1 8 139.1 211.2 422 0.89 1.78 351 563 43.9 70

A2 15 47.9 85.09 170 0.89 1.78 134 220 8.9 14A3 24 73.7 402.1 804 7.17 14.40 483 892 20.1 37A4 40 1.0 11.88 23.8 0.27 0.54 13.2 25.3 0.33 0.6A5 75 39.0 50.67 101 0.89 1.78 90.5 142 1.2 1.

AHE56 

(GApecan) 

A1 7.9 40.2 1.67 3.4 0.24 0.48 42.1 44.1 5.3 5.

A2 15.8 31.6 18.93 37.8 0.8 1.60 51.3 71.0 3.2 4.A3 24.6 44.7 7.49 15 0.8 1.60 52.9 61.2 2.2 2.A4 50.5 10.6 31.28 62.6 0.24 0.48 42.1 73.7 0.83 1.A5 75.7 1540 812.8 1626 0.8 1.60 2354 3168 31.1 41

AHE57 

(MIcherry) 

A1 10.8 12.7 7.16 14.4 0.24 0.48 20.1 27.6 1.9 2.

A2 16.9 7.5 0.65 1.3 0.24 0.48 8.4 9.3 0.50 0.5A3 27.6 43.8 9.29 18.6 2.66 5.40 55.8 67.8 2.0 2.A4 40.7 17.5 16.88 33.8 0.24 0.48 34.6 51.8 0.85 1.A5 90.3 61.3 20.62 41.2 0.8 1.60 82.7 104 0.92 1.

AHE58 

(CAgrape) 

A1 63.5 43.8 91.89 184 4.79 9.60 141 237 2.2 3.

A2 34.4 6.7 29.59 59.2 0.80 1.60 37.1 67.5 1.1 2.A3 23.0 4.3 2.63 5.2 0.24 0.48 7.1 10.0 0.31 0.4A4 59.2 9.3 10.67 21.2 2.45 4.80 22.3 35.3 0.38 0.6A5 7.3 115.4 21.31 42.8 0.24 0.48 138 159 18.9 21

AHE59 

(WAapple) 

A1 15.8 1.8 8.61 17.2 0.24 0.48 10.6 19.5 0.67 1.

A2 9.4 0.7 22.35 44.6 0.80 1.60 23.8 46.9 2.5 5.A3 15.9 85.8 114.9 230 0.80 1.60 202 318 12.7 20A4 34.5 15.7 1312 2624 0.80 1.60 1329 2641 38.5 76

Page 23 of 62 
Table A-2. EPA Analysis of AHETF Secondary Objective Analysis using Alternate DDF Methods 

Exposure GSD ICC Parameter DDF Method Estimate 95% CI 

LCL UCL 

Dermal Exposure 

(MEA) 4.7 0.054 

Slope 

residual 0.406 -0.442 1.254 

contain 0.406 -0.453 1.265 

bw 0.406 -0.442 1.254 

satterth 0.406 -0.448 1.260 

kr 0.406 -0.469 1.281 

Intercept 

residual 3.310 0.494 6.127 

contain 3.310 -0.461 7.082 

bw 3.310 0.494 6.127 

satterth 3.310 0.487 6.134 

kr 3.310 0.431 6.189 

Dermal Exposure 

(non-MEA) 4.53 0 

Slope 

residual 0.323 -0.521 1.171 

contain 0.323 -0.532 1.182 

bw 0.323 -0.521 1.171 

satterth 0.323 -0.521 1.171 

kr 0.323 -0.521 1.171 

Intercept 

residual 3.159 0.364 5.953 

contain 3.159 -0.583 6.900 

bw 3.159 0.364 5.953 

satterth 3.159 0.364 5.953 

kr 3.159 0.364 5.953 

Inhalation 

Exposure 3.34 0.220 

Slope 

residual 0.695 0.070 1.320 

contain 0.695 0.061 1.329 

bw 0.695 0.070 1.320 

satterth 0.695 0.063 1.328 

kr 0.695 0.059 1.331 

Intercept 

residual -2.431 -4.580 -0.282 

contain -2.431 -5.300 0.438 

bw -2.431 -4.580 -0.282 

satterth -2.431 -4.584 -0.277 

kr -2.431 -4.591 -0.270 

Page 24 of 62 
Table A-3. Select Closed Cab Airblast Applicator Field Observations 

Study ID MU ID Cab ents./exits (#) Observation 

AHE55 

(FL-citrus) 

A1 4 

1113: “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...” 

A2 16 

1207: “...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...” 

A3 10 

1032: “...exits cab...no gloves worn since no contact with equipment.” 

1144: “...exits cab without gloves...shuts the door...contacting the side edge.” 1214: “...opens door with bare hands...” 

A4 4 

1032: “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.” 

A5 8 1032: “...exits cab wearing gloves. Opens up back of sprayer...” 1529: “...exits cab. Not wearing gloves.” 

AHE56 

(GA-pecan) 

A1 12 

0833: “Donning gloves while exiting cab...” 

0911: “Out of tractor with gloves on...” 0934: “Donned gloves while exiting tractor...” 

A2 14 1119: “...exited cab, donned gloves.” 1149: “Exited cab, wearing fresh gloves...” A3 16 

1137: “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 gloves on...leaning against sprayer” 1347: “Out of cab, gloves on...holds onto...other application equipment.” 

A4 6 

0134: “...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.” 

A5 22 

0640: “Waited inside truck during M/L.” 

0710: “...gets out of the truck to adjust spray pressure while wearing gloves.” 0742: “Rain had started...” 

Page 25 of 62 
Table A-3. Select Closed Cab Airblast Applicator Field Observations 

Study ID MU ID Cab ents./exits (#) Observation 

0835-0843: “Donned clean gloves. Left shirt sleeve cuff is open...states that the buttospray nozzles...reached down into nozzle area with right arm up to arm pit...lean on faspray nozzles” 1017: “...out of truck wearing gloves.” 1025: “...continued to blow out spray nozzles...left shirt sleeve opening near mid-foredosimeter.” 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) 

A1 4 1430: “Arrived at mix/load area and got out of the cab with fresh gloves.” 1528: “Put on clean gloves when he left the tractor...” 

A2 4 0710: “...stayed inside cab...” 0829: “Dons gloves before entering cab and discards gloves outside the cab.” A3 6 

1403: “...shirts sleeve unbuttoned...also unbuttoned...above waist.” 

1534: “...exited the cab with clean gloves on.” 1648: “...drop of spray...landed on his hand...wiped his hand on right pant leg.” 

A4 8 

1534: “Donned nitrile gloves and stood near tractor...” 

1710: “Exited tractor with clean gloves...reentered cab...again exited tractor...inner dhis back...near his waist...” 

A5 6 0630: “...exited tractor with clean gloves.” 0806: “...got out of the cab with clean gloves.” 

AHE58 

(CA-grape) 

A1 14 

1005-1012: “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...” 

A2 8 0948: “Mixer overfilled tank. Two-three gallons spilled at top of tank. Re-entered tra

spill.” 

A3 10 Nothing noteworthy. A4 12 

0330: “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 gl

A5 4 0814: “Turned off water near tank with bare hand...” 0930: “Touched inner dosimeter (cuff) with bare hand.” 

AHE59 

(WA-apple) 

A1 10 

Gen. obs.: “...did not wear the chemical resistant gloves during the mixing/loading pr

0858: “Climbed back into cab, opened door with bare hands.” 0951: “Opened cab door and tied ribbon marker to tree (observer unable to determine during this process).” 

A2 4 Gen. obs.: “The applicator was not observed to handle anything except the door and d

Page 26 of 62 
Page 27 of 62 

Table A-3. Select Closed Cab Airblast Applicator Field Observations 

Study ID MU ID Cab ents./exits (#) Observation 

with bare hand.” 1000: “Arrives at mixing/loading station remained sitting inside cab.” 

A3 12 

0821: “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 glove

A4 26 

Gen. obs.: “Exchange of tractor and sprayer...” 

0822: “Inner dosimeter cuff is observed at right wrist, out from right shirt sleeve. Lontucked into pants. 0833: “Spoke to mixer loader through opened back window of tractor cab. Closed wi1102: “...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 with bare hands, turned off a couple nozzles...” 
Appendix B 

Normal and Lognormal Probability Plots of Dermal and Inhalation Unit Exposures 

Page 28 of 62 
Page 29 of 62 
Appendix C 

SAS Code for “Different Denominator Degrees of Freedom Analysis” (Section 3.2.2.2) 

Note – the code below references the following “.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 Study $10 MU $4; 

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"; 

 ID 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 62 
Run; 

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 Dermal 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 conducted for dermal exposure (MEA 

corrected): If ICC is zero then containment 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 exposure)=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 exposure)=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 highest observations in terms of 

dermal exposure: Study ID: AHE56 ; MUID: A5 ; Study ID: 

AHE59 MUID: A4 ; Study ID: AHE55 MUID: A3

 

Regression model: ln(dermal exposure)=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 exposure)=Intercept + ln(number of 

exits) 52 

Page 38 of 62 
 09:27 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 62 
AHE59 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 

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 " No observation has been removed "; 

Title " Regression 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 62 
 

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/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 

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 " 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 62 
 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; 

*/case 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 

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; 

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 62 
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/ 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; 

*/case 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 

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; 

Page 44 of 62 
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 "; 

/**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";

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 "; 

/**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 62 
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 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 62 
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(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 conducted for dermal exposure without MEA 

correction: If ICC is zero then containment 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 exposure)=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 observation 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 exposure)=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)=Intercept + 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 exposure)=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 highest observations in terms of 

dermal exposure: Study ID: AHE56 ; MUID: 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 exposure)=Intercept + ln(number of 

exits) 56 

Page 52 of 62 
 12:16 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 62 
AHE58 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 

; 

*/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=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)); 

ICC = Vc/V; 

WidthCI = Upper-Lower; 

*ddfmethod="&method";

Keep GSD ICC WidthCI Label Estimate Lower Upper df; 

Run; 

Title " No observation has been removed "; 

Title " Regression 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 62 
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=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 

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 " 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 62 
 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; 

*/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 62 
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"; 

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";

Keep 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 62 
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 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 

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; 

Page 59 of 62 
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 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";

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_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 62 
Page 62 of 62 

*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 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;