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UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

WASHINGTON, DC 20460 

OFFICE OF CHEMICAL SAFETY AND POLLUTION PREVENTION 

 

MEMORANDUM

DATE: January 3, 2012 

SUBJECT: Science Review of the AEATF II Aerosol Human Exposure Monitoring Study. 

PC Code(s): Not Applicable (NA) DP Barcode(s)/No(s): NA 

Decision No.: NA Registration No(s).: NA 

Petition No(s).: NA Regulatory Action: Human Health 

Risk Assess Type: Surrogate Handler 

Exposure Data 

Case No(s).: NA 

TXR No.: NA CAS No(s): NA 

MRID No(s).: 48659001 40 CFR: None 

 

FROM: Tim Leighton, Environmental Scientist 

Risk Assessment and Science Support Branch (RASSB) 

OPP/Antimicrobials Division (7510P) 

TO: Nader Elkassabany, PhD, Branch Chief 

Risk Assessment and Science Support Branch (RASSB) 

OPP/Antimicrobials Division (7510P) 

 

This memorandum presents the EPA/OPP Antimicrobials Division (AD) science review of 

the human exposure aerosol study submitted by the Antimicrobial Exposure Assessment Task 

Force II (AEATF II). The dermal and inhalation exposure data as represented in this review are 

acceptable and, subject to the considerations described below, are recommended for use for 

pesticide handler exposure assessments. 
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EXECUTIVE SUMMARY

This document represents the USEPA, Office of Pesticides Program, Antimicrobials 

Division (AD) review of the Antimicrobial Exposure Assessment Task Force II (AEATF II) 

aerosol study. The aerosol study investigators monitored inhalation and dermal exposures to 18 

workers spraying surfaces using a 19 ounce aerosol can. EPA confirms that the data meet the 

study design objective outlined in the AEATF II Governing Document and are considered the 

most reliable data for assessing exposures from spraying surfaces with an aerosol can. The 

reader is referred to Section 3.0 for a discussion on the data limitations and use of the data as 

surrogate. 

EPA intends to use this AEATF II aerosol dataset instead of the Chemical Manufacturers 

Association (CMA) and/or the Pesticide Handlers Exposure Database (PHED) datasets to assess 

exposure for persons using an antimicrobial product while spraying surfaces with an aerosol can. 

The exposure data in the AEATF II aerosol scenario represent the application of an antimicrobial 

product pre-packaged in an aerosol can. The scenario does not cover the subsequent wiping of 

the aerosol spray solution. The potential exposure to the subsequent wiping of the sprayed 

solution can be determined by combining the results of this study with the results of the 

previously AEATF II conducted ready-to-use (RTU) wipe study. This aerosol study did not 

monitor the activity of wiping because there are some products that are labeled to be sprayed-on 

with no wiping. 

Select summary statistics for the “unit exposures” normalized to pounds active ingredient 

handled are presented in Table 1 for inhalation exposure as well as for 3 clothing configurations. 

Each worker wore both inner and outer whole body dosimeters (WBD) that were sectioned and 

analyzed separately for each body part (e.g., lower leg, upper leg, lower arm, upper arm, etc). 

Therefore, the analyses of residues on the dosimeters worn by each individual worker allow for 

the estimation of dermal exposure for the following 3 clothing configurations: 

 

(1) “Long-Long” or “Long Dermal”= long pants, long-sleeved shirt, and no gloves; 

(2) “Long-Short” or “Long Short Dermal”= long pants, short-sleeved shirt, and no gloves; and 

(3) “Short-Short” or “Short Dermal” = short pants, short-sleeved shirt, and no gloves. 

For comparison, results from the earlier CMA and PHED studies for aerosols are also 

presented. It has been EPA’s practice to use the PHED dataset for the aerosol can scenario rather 

than the CMA aerosol can data. The summary statistics reported in Table 1 for the AEATF II 

data are estimated using the lognormal mixed model while the CMA and PHED results are 

empirical estimates (Note: CMA results are in footnote “a” in Table 1). 
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Table 1. Unit Exposures: Aerosol Scenario 

Exposure 

Route Clothing PHEDa AEATF IIb, c (n=18) 

Arithmetic Mean Arithmetic Meand 95th Percentilee

Dermal 

(mg/lb ai) 

Long pants/long-sleeves, 

no gloves 

190 

(n=15) 

248 

(185, 339) 

552 

(359, 845) 

Long pants/short-sleeves, 

shoes/socks, no gloves Not Available 367 

(287, 472) 

735 

(511, 1050) 

Short pants/short-sleeves, 

shoes/socks, no gloves 

220 

(n=15 hands, 30 body) 

661 

(537, 820) 

1220 

(894, 1670) 

Inhalation 

(Total) 

Breathing Zone 

(mg/lb ai) f

1.3 

(n=15) 

25.1 

(19.8, 32.0) 

49.4 

(34.8, 69.8) 

Breathing Zone 

(mg/m3/lb ai) Not Available 60.3 

(47.2, 77.6) 

121 

(84.1, 128) 

Inhalation 

(100 μm) Breathing Zone 

(mg/m3/lb ai) NA 

48.1 

(37.2, 63.0) 

99.5 

(67.9, 145) 

Inhalation 

(10 μm) 

25.5 

(18.9, 35.1) 

57.4 

(37.0, 88.7) a

 PHED data has been used to assess exposures to antimicrobial products packaged in aerosol cans. PHED data are 

based on an insecticide product which was sprayed as a crack and crevice treatment. As a comparison, the mean 

CMA exposure data for aerosols from applying the disinfectant in motels, dental offices, etc, using 19 oz cans (plus 

one 24 oz can) of a 0.1% ai product indicate the dermal UE (no glove scenario) of 144,000 mg/lb ai of which hand 

exposure was only 922 mg/lb ai (n=6) and inhalation UE of ~6,000 mg/lb ai (n=8; where all air samples were ND; 

LOD ranged from 0.294 to 74.8 mg/m3

 and sampling pumps ran from 1 to 26 minutes). 

 

b

AEATF II dermal UE includes corrections for removal efficiencies of 90.3% for hands and 89.4% for face/neck. 

 

c

 Lower and upper 95% confidence intervals reported in “( )”; 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 Appendix A. 

d

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

e

 95th percentile = GM * GSD1.645 

f

 Inhalation (mg/lb ai) = (air conc (mg/m3) / lb ai) * breathing rate (1 m3/hour) * spray duration (hours/day) 

The following important points with respect to these data are noted: 

• The AEATF II data and associated unit exposures are considered superior to the existing 

aerosol datasets (i.e., CMA and PHED data). AEATF II efforts represented a welldesigned, concerted process to collect reliable 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 AEATF II study report containing dermal and inhalation exposure results are 

considered scientifically complete. No additional monitoring data are required at this 

time. 
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• The data are applicable for assessment of exposure to non-volatile pesticides. The cutoff 

for volatility is reviewed on a case-by-case basis (rule of thumb is that <E-4 mmHg @ 

20° C is considered non-volatile). 

• The statistical analysis provides evidence of direct proportionality (1:1) between dermal 

exposure and pounds of active ingredient (ai) handled (i.e., the confidence intervals for 

the slope of log exposure against log ai include 1 but not zero in Table 10 below), and the 

analysis shows that dermal exposure tends to increase with pounds of ai handled (AaiH) 

as described in Section 2.4 below. However, for inhalation exposure, the statistical 

analysis provides evidence against proportionality, although inhalation exposure tends to 

increase with pounds of ai handled (AaiH). 

To assess the risks resulting from aerosol spray exposures, EPA will combine appropriate 

unit exposure (UE) values with chemical-specific inputs (e.g., maximum labeled application 

rates, dermal absorption rates, and toxicological endpoints of concern) and default inputs (e.g., 

high end area treated or volume applied) in the standard pesticide handler inhalation and/or 

dermal exposure algorithm: Potential dermal or inhalation exposure = [UE (mg/lb ai or mg/m3

/lb 

ai)] x [dermal absorption (%) if applicable] x [maximum label rate (lb ai/gallon or ounces)] x 

[volume (gallons or ounces)]. 

 
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1.0 Background 

The AEATF II is developing a database representing inhalation and dermal exposure during 

a number of antimicrobial handler scenarios. A scenario is defined as a pesticide handling task 

based on activity (e.g., application) and equipment type (e.g., aerosol cans, ready-to-use wipes, 

mop & bucket, pressure treatment of wood facilities, painting, etc). The AEATF II is monitoring 

both inner and outer dosimeters which will allow the EPA to estimate exposures to various 

clothing configurations (e.g., long pants, long-sleeved shirt or long pants, short-sleeved shirt or 

short pants, short-sleeved shirts, plus shoes, socks, and no gloves). Prior to conducting 

intentional exposure studies in humans, the protocols are reviewed by the Human Studies 

Review Board (HSRB). The HSRB reviewed this aerosol exposure study protocol in October 

2009. 

1.1 Aerosol Scenario Defined 

The aerosol scenario in this study is defined as spraying various horizontal and vertical 

surfaces such as sinks, shower stalls, walls, etc in hotel rooms using a hand-held pressurized 

aerosol can containing a formulated antimicrobial product. The application is a spray and leaveon; no wiping of the treated surfaces occurred. Subjects sprayed as they normally would do. 

Subjects wore whole body dosimeters (WBD) underneath long-sleeved shirts, long pants, and no 

gloves (plus a personal air sampler). The conditions under which the study participants handle 

the pesticide as they are monitored are referred to as the scenario. Both inner and outer 

dosimeters were worn by the monitored study participants, and both inner and outer dosimeters 

were analyzed for residues. 

1.2 Study Objective 

The AEATF II’s study objective is to monitor inhalation and dermal exposures to be used 

as inputs in exposure algorithms to predict future exposures to persons spraying surfaces with an 

antimicrobial product packaged in an aerosol can. Dermal and inhalation exposure monitoring 

was conducted while study participants sprayed various surfaces (walls, sinks, toilets, etc) for use 

in exposure assessments, as “unit exposures”. 

“Unit exposure” (UE) is defined as the expected external chemical exposure an individual 

may receive (i.e., "to-the-skin" or “in the breathing zone”) per weight-unit of chemical handled 

and is the default data format used in pesticide handler exposure assessments. Mathematically, 

unit exposures are expressed as "handler" exposure normalized by the amount active ingredient 

handled by participants in scenario-specific exposure studies (e.g., mg exposure/lb ai handled). 

EPA uses these UEs generically to estimate exposure for other chemicals having the same or 

different application rates. 

Criteria for determining when a scenario is considered complete and operative have been 

developed (Christian 2007). Outlined in the AEATF II Governing Document, the criteria can be 

briefly summarized as follows: 
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• The AEATF II’s objective for this study design is to be 95% confident that key statistics 

of normalized dermal exposure are accurate within 3-fold. Specifically, the upper and 

lower 95% confidence limits should be no more than 3-fold (K=3) higher or lower than 

the estimates for each the geometric mean, arithmetic mean, and 95th percentile dermal 

unit exposures. To meet this objective AEATF II proposed an experimental design with 

a 3 cluster by 6 monitoring events (MEs). 

• EPA also plans to use the data to evaluate the presumption of proportionality between 

exposure and amount of active ingredient handled. EPA used 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. 

 

1.3 Protocol Modifications, Amendments, and Deviations 

1.3.1 Protocol Modifications Subsequent to EPA/HSRB Review 

EPA required one science-based modification to the aerosol protocol (EPA 2009). The 

EPA review of the protocol noted that the AEATF II needs “...to indicate the course of action if 

the benchmark accuracy goals (i.e., k=3) are not achieved.” The AEATF II responded at the 

time of the protocol review that they “...will, in consultation with regulatory agencies, determine 

the best course of action to take. This may mean the development of guidance for the use of these 

data that takes the increased imprecision of the estimates into account. It is possible that 

collection of additional clusters might be considered.”

The HSRB provided written discussion on a number of “...perceived inadequacies...” of the 

aerosol review. Shah (2010a and 2010b) responded to the HSRB inquiries on the behalf of the 

AEATF II in letters to EPA (see Appendix B for letters). The HSRB issues and EPA responses 

and discussion are summarized below: 

• Use of the results – The HSRB noted that “...other variables can affect rates of 

exposure, including different nozzle sizes, spray and ejection rates, the size of the 

particles generated, and the generation of nonvolatile active ingredients.” The HSRB 

comment also stated it is unclear if occupational exposures could be used to estimate 

exposures to consumers. 

EPA acknowledges that ideally it would be more accurate to have performance data on 

all types of antimicrobial products packaged in aerosol cans (e.g., particle sizes 

generated, ejection rates, etc). However, EPA agrees with the AEATF II’s pre-testing 

provided in the aerosol protocol, along with the fact that individual applicator behaviors 

while applying will equally affect exposure such that it may confound the spray nozzle 

information, and concludes they are sufficient rationales to accept the use of the product 

selected. If data generated in the future provide additional information about the effects 

of ejection rates on exposure, additional limitations can be considered by comparing 

ejection rates measured in this study (~1 gram/second). To directly compare and evaluate 

occupational exposures to those of consumers, most of the scenarios to be monitored by 
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the AEATF II would need to be based on two studies. If a sufficient rationale is available 

to suggest logical differences in exposures experienced by the two populations, the need 

for two studies could be rationalized. However, for the aerosol scenario, spraying a prepackaged aerosol can products onto surfaces does not inherently suggest differences 

(both populations routinely use this device and no training is provided). Moreover, 

reading through the study observations (Appendix L starting on page 474 of the study 

report) supports the idea that workers are not more skilled or careful compared to a 

consumer when applying a product from an aerosol can. 

• Sample size & analysis – The HSRB noted that “...no statistical analyses have been 

planned. Sample size adequacy cannot be judged without a statistical analysis plan. 

...additional subjects may need to be enrolled.” 

EPA agreed with the AEATF II Governing Document (ACC, 2011) which provides the 

following description of sample size adequacy: “In an ideal situation the determination 

of sample size would be based on an objective statistical approach. This approach would 

leverage existing data to estimate the variability that must be accounted for to specified 

confidence requirements. Such an approach was used for the initial few studies of the 

AEATF II. However, as the AEATF II began to work on implementing additional studies 

it became evident that either such data did not exist or that it was not relevant to current 

practices and methodologies. Attempts to use existing data from poor quality or only 

marginally relevant studies produced sample sizes that were logistically impractical to 

implement and unaffordable for the task force to complete. There is also concern that 

post collection analysis of newly collected data would indicate more samples had been 

collected than required to meet the confidence requirements. This would imply that 

subjects had been unnecessarily used and exposed in the data collection process. 

As a result, the determination was made that a new, relevant, high quality “base set” of 

data needs to be created prior to applying a more statistically rigorous design process. 

To inform this approach, the AEATF II is relying on existing EPA guidelines on exposure 

studies (Series 875 - Occupational and Residential Exposure Test Guidelines). These 

guidelines call for essentially three groups of five observations per group. It is the 

intention of the AEATF II to collect 15 to 20 MEs per scenario to create a base-case data 

set. The exact number will depend on the number of levels of key factors that are 

considered likely to impact exposure. 

It is anticipated in some cases that after the base case is collected no additional data 

collection will be necessary as the data will be sufficient to meet regulatory needs. In 

other situations, the task force, in consultation with regulatory agencies, may determine 

that additional data are required. At that point, more rigorous statistical methods as 

used in the first few studies, and outlined in Appendix E [of the Governing Document],

may be applied. The exact steps will be determined after this joint evaluation, and with 

consideration for how the data will be used.” 

EPA has analyzed the aerosol scenario to see if the data meet the relative 3-fold accuracy 

goal (i.e., K = 3) for determining sample size. See Section 2.4 below for details of the 

analysis of the relative 3-fold accuracy. In summary, the aerosol data met the goal of 
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relative 3-fold accuracy for all 3 clothing configurations (Long Dermal, Short Dermal, 

Long Short Dermal) and for the inhalation exposure (mg/m3

/lb ai and mg/lb ai). On this 

basis, EPA concludes that the sample sizes used were adequate according to the 

previously established criteria.

 

• Application of spray – The HSRB noted that the researchers need to better clarify the 

sampling design aspects of the spraying (e.g., will the subjects need to keep spraying in a 

room if the room has been treated before the target number of cans are sprayed? What is 

the sampling interval between sites? Empty apartments or motel rooms? Effect of 

exhaust vents on exposure? 

The AEATF II provided the following responses in a letter submitted by Shah (2010a): 

“We will clarify in the protocol the target numbers of cans sprayed in each location by 

each worker on each day. Workers will be given full cans as well as half full cans, as 

specified in the protocol. A worker does NOT need to continue spraying a room if the 

target application is met. Two monitoring events can occur within the same building, but 

not the same room within ONE DAY. The next day the same room could be used. 

Regarding the use of apartments rather than motel rooms, we have surveyed the Fresno 

area and have found a number of motels with full kitchens. We will be using 

hotels/motels exclusively for the study and will not be using apartments.” A description 

of the effect of exhaust vents on exposure is not apparent in the AEATF II’s 

documentation. EPA notes that the air exchange rates are 0.6 to 2 ACH. 

• QA/QC improvements – The HSRB indicated that more thought was needed on the air 

monitoring methods such as equipment used, differences in orifice diameters and flow 

rates, because these parameters could affect aerosol collection efficiencies. HSRB also 

noted that minimal fortification levels should be increased to 2x to 4x the LOQ. Other 

variables were also mentioned by the HSRB: are surfaces wet or dry during treatment? 

Did subjects accidentally wipe surfaces as this action is not part of this study? Limit the 

number of workers to 2 for any given day in the same location. 

In response to the HSRB discussions the following EPA discussions are noted. The 

available literature studies indicate that the Respicon matches the thoracic and respirable 

sampling conventions fairly closely while significantly under sampling the inhalable 

convention. These studies generally suggest that the under sampling is consistent and can 

be addressed by using correction factors that range from 1.5 to 1.8. The reader is referred 

to Appendix C for further details of the literature (summary) and Section 4.0 (see Figure 

10 for a comparison of the results). The minimal field fortification levels were modified 

by the AEATF II in the study; increased to 4x the LOQ as suggested. The surfaces were 

dry prior to the application events. The study observations did not make note of any 

subjects accidentally wiping surfaces as part of a routine for spray & wipe (as opposed to 

spray & leave-on). Finally, as evident in Tables 7, 8, and 9 of the study report (pages 

116, 117, 118) there were no more than 2 MEs per sampling date. 
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1.3.2 Protocol Amendments 

The study report (page 103) lists 4 protocol amendments. The amendments included the 

offering to the subjects the voluntary use of respirators; change of study dates; change in 

personnel including the Study Director/Principal Investigator and QA personnel; included a GLP 

analysis of the test substance and formulated product; lengthened the storage stability time from 

6 to 18 months; revisions to the appendices dealing with recruiting subjects and informed 

consent form; revised procedures for removing subjects’ socks; and revised sponsors contact 

information (office location move). 

1.3.3 Protocol Deviations 

A total of 8 protocol, 2 method, and 8 SOP deviations were noted in the study (study 

report pages 103-104). The deviations included, but not limited to, order in which socks were 

removed from subjects, subject in “fair” health was enrolled but not selected when the criteria 

was “good” health; not all of the field fortification samples were analyzed; order in which cans 

were sprayed for two MEs were switched; some of the laboratory QC samples were out of the 

range; extraction solvent expired (date) but used for some dosimeters; etc. For a detailed 

description of the protocol deviations the reader is referred to the study report. EPA accepts the 

study author’s conclusion that these deviations did not adversely affect the outcome of the study. 

1.4 Material & Methods 

The following is a summary of the field aspects of the study: 

• Study Location: The aerosol spray study was conducted in Fresno County, CA. Each of 

the 3 clusters was a different building. The buildings were all hotels (Marriott, Piccadilly 

Inn, and Hilton). Photos and schematics of the hotels and rooms are located in 

Appendices I, J, and K starting on page 460 of the study report. 

• Pesticide Tested: The test substance monitored was n-alkyl dimethyl benzyl ammonium 

chloride (ADBAC), CAS number 68424-85-1. ADBAC was formulated in a product 

known as Clorox Commercial Solutions Clorox Disinfecting Spray (EPA Reg No. 67619-

3). This product also contained 3 other Quats, DDAC, ODAC, and DODAC. ADBAC, 

DDAC, ODAC, and DODAC are in Clorox Disinfecting Spray at 0.252%, 0.0945%, 

0.189%, 0.0945%, respectively. The C14 ADBAC was quantified in the analytical phase 

of the study. ADBAC consists of 50% C14, 40% C12, and 10% C16. 

• Test System: The aerosol cans each contained 19 ounces (538 grams) of product (page 

36 of study report). The discharge rates of the cans were measured for both batches of 

products using triplicate cans from each batch and each can sprayed three times for 10 

seconds. The discharge rates averaged 0.991 and 1.19 g/second for the two batches of 

products. A photo of the aerosol product is presented below in Figure 1. 
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 Figure 1. Aerosol Can Used In Study. 

• Sequence of Events: A table listing the chronological order of key events for the study 

(e.g., test site selection, IIRB approval, first subject enrolled, monitoring dates, etc) is 

reported on pages 107-108 of the study report. 

• Sample Size: The study consisted of 3 clusters and 6 MEs per cluster for a total of 18 

monitoring events (ME). Each ME is a different subject. 

• Treatment Solutions: The aerosol cans were obtained pre-packaged, no dilution by the 

researcher was necessary. The nominal percent active ingredient for the C14 ADBAC is 

0.126%. The measured percent of C14 ADBAC in the two batches of products was 

0.124% and 0.104%. Nominal amounts of C14 ADBAC sprayed (i.e., 0.124%) were 

used in normalizing the exposures from the first batch. Actual amounts of C14 ADBAC 

sprayed (i.e., 0.104%) were used in normalizing the exposures from the second batch. 

• Duration & AaiH: For each of the 3 clusters, the MEs were randomly assigned to 1 of 

the 6 purposively selected number of cans sprayed. The pre-determined number of cans 

sprayed were: 
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o 1 to 1.5 cans 

o 1.5 to 2 cans 

o 2 to 2.5 cans 

o 2.5 to 3 cans 

o 3 to 3.5 cans 

o 3.5 to 4 cans 

The total sampling times for Cluster 1 ranged from 32 to 119 minutes; for Cluster 2 times 

ranged from 42 to 125 minutes; and for Cluster 3 times ranged from 25 to 115 minutes. 

The actual time spent spraying (i.e., finger on the nozzle) was measured with a stop 

watch and recorded. The sampling time included movement of the subject within the 

room, moving from one room to the next, changing cans, breaks, etc. The sampling 

duration was used to calculate the volume of air sampled (m3

) and the corresponding air 

concentration (mg/m3

). The amount of active ingredient (AaiH) sprayed onto surfaces 

was measured by weighing the cans prior to and subsequent to the monitoring event. The 

AaiH for Cluster 1 ranged from 704 to 2400 mg; for Cluster 2 it ranged from 613 to 2000 

mg; and for Cluster 3 it ranged from 577 to 2010 mg. The AaiH as well as the spraying 

times (sampling and spraying) for individual MEs are reported in Table 2 below. 

• Spraying Procedures: Appendix L on pages 474 to 507 of the study report records the 

observation notes taken during each ME. The workers were generally allowed to spray 

as they would normally do. Note: In Cluster 2 subject number AE4 was told to lighten 

up the spray pattern by the study director (page 488 of study report). Typical surfaces 

sprayed included countertops, railings, shelving, tile walls, sinks, appliances, toilets and 

tubs, and mirrors. Procedures/observations were that the subjects would shake the spray 

can and spray, roughly 6 to 12 inches above the various surfaces using random/erratic 

spray patterns, zig-zag patterns, from left to right, and from top to bottom. Subjects used 

a light to heavy spray pattern, sometimes until surfaces were visibly dripping wet and at 

other times not uniformly wet. 

• Environmental Conditions: Environmental conditions (humidity and temperature) are 

reported for the MEs on pages 116 to 118 of the study report. The humidity averaged in 

the 50% range. Temperatures averaged in the low 70o F range. The heating ventilation 

air conditioning (HVAC) system descriptions such as total cubic feet per minute (CFM), 

fresh air CFM, room volumes, air changes per hour (ACH), etc are reported on pages 119 

and 120 of study report. ACH ranged from 0.6 to 2.0. Lighting levels were not 

measured. 

2.0 Results 

2.1 QA/QC Recovery Results 

Controls: The non-fortified field and laboratory control samples were all less than the limit of 

quantification (LOQ), except for one sample, which indicates no background contamination. 

The exception was for one field control sample in Cluster 3 for the outer dosimeter. This control 
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sample indicated 38.2 μg/sample. The LOQs for the various matrices are air sampling tubes 25 

ng/sample, neck/face wipe 100 ng/sample, WBD sections 3 μg/sample, and hand wash 0.05 

μg/sample (1.0 ng/mL). 

Laboratory Recoveries: Most of the concurrent laboratory recovery values range within 70 to 

120 percent. Exceptions outside of this range include 3 low level fortifications of 131, 136, and 

66.7 percent for face/neck, outer dosimeter, and outer dosimeter, respectively. A summary of the 

overall concurrent laboratory recovery samples, for each monitoring matrix, by cluster, is 

reported in the study report starting on page 70 (individual recovery values can be viewed 

starting on page 122). The mean recoveries for all matrices are approximately in the 90 to 100 

percent range. 

Field Recoveries: Most of the individual field fortified recovery values range within 70 to 120 

percent. Exceptions include 2 low level fortifications of 757 and 295 percent for the outer 

dosimeters in Cluster 3 (see pages 88 and 158 of study report). Both of these values were 

considered to be outliers and not used for corrections or reported in mean recoveries based on the 

laboratories SOPs. According to the study report (page 88), “The Study Director determined 

that these recoveries did not impact the study as the residues in the subject outer dosimeters in 

these sets were 9 to 166 times higher than the low fortification level.” A summary of the overall 

field fortified recovery samples, for each monitoring matrix, by cluster, is reported starting on 

page 73 of the study report (individual recovery values can be viewed starting on page 128). The 

mean recoveries for all matrices are approximately in the 90 percent range. All exposure/field 

matrices were corrected for the field fortified recovery results. 

2.2 Calculating Unit Exposures 

Dermal Unit Exposure: Dermal exposure is measured using 100% cotton inner and 

outer whole body dosimeters (WBD). The inner WBDs were worn underneath normal work 

clothing (i.e., long-sleeved shirt and long pants). The normal work clothing worn over the inner 

WBDs were also analyzed and reported as outer dosimeters. In addition, dermal exposures also 

included hand washes (collected at the end of the day and during breaks), and face/neck wipes. 

Because the subjects wore respirators, the results of the face wipes were corrected for the area 

covered by the respirator (1.43 correction factor). The inner and outer WBDs are sectioned and 

analyzed by body part (i.e., upper and lower arms, front and rear torso, and upper and lower 

legs). All of the inner WBD sections were analyzed because all of the outer dosimeter sections 

tested above the LOQ. All samples are adjusted as appropriate according to recovery results 

from field fortification samples. 

Dermal exposures to the hands and face/neck are also corrected for sampling efficiency 

(see study report pages 61 to 63 for equations). A removal efficiency study (previously 

conducted study purchased by the AEATF II) for wipes (4x4 inch 6-ply dressing sponges, 

moistened with 5 mL isopropanol) was performed using the test substance, alkyl dimethyl benzyl 

ammonium, as the saccharinate salt (ADBAS, CAS 39387-42-3) (Boatwright 2008, MRID 

48656201). The hand wipe removal efficiency for ADBAS is 88 ± 1.71% for the low 

fortification level (70 μg) and 90.5 ± 3.88% for the high fortification level (1,300 μg). In the 

second hand removal efficiency study for DDAC (cited in previous AEATF II studies) showed a 
Page 13 of 38 

recovery efficiency of 90.3%. The hand measurements in this aerosol study were corrected for 

the 90.3% DDAC removal efficiency while the face/neck wipes were corrected using the results 

of the low level fortification for the ADBAS wipe efficiency. Although the ADBAS study 

reports the recovery efficiency as 88.2%, a value of 89.4% was cited in the aerosol study and 

used to correct the samples. See Section 3.0 below for further discussion on the limitations of 

the removal efficiency studies. 

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

individual monitored. The following WBD sections are summed to calculate the clothing 

configuration of long pants, long-sleeved shirts (Long-Long) plus face/neck wash and hand 

wash: 

• inner lower and inner upper arms, 

• inner front and inner rear torso, and 

• inner lower and inner upper legs. 

The following WBD sections are summed to calculate the clothing configuration of long 

pants, short-sleeved shirts (Long-Short) plus face/neck wash and hand wash: 

• outer and inner lower arm, 

• inner upper arm, 

• inner front and inner rear torso, and 

• inner lower and inner upper leg. 

The following WBD sections are summed to calculate the clothing configuration of short 

pants, short-sleeved shirts (Short-Short) plus face/neck wash and hand wash: 

 

• outer and inner lower arm, 

• inner upper arm, 

• inner front and inner rear torso, 

• inner upper leg, and 

• inner and outer lower leg. 

Dermal unit exposures (i.e., mg/lb ai handled) are calculated by dividing the summed total 

exposure by the amount of active ingredient handled. The AEATF II’s study report normalized 

the dermal exposures by milligrams (mg) of active ingredient applied. EPA recalculated the 

exposures and expressed the results as mg/lb ai applied. EPA prefers the normalization by 

pounds to coincide with the English units reported on pesticide labels (e.g., pounds, ounces). 

Inhalation Exposure: Inhalation exposure is measured using a personal air sampling pump 

and an OSHA Versatile Sampler (OVS) tubes with a separate pump that was used to run the 

Respicon Particle Sampler. The OVS tube was attached to the worker’s collar to continuously 

sample air at a target rate of 2.0 Lpm from the breathing zone while the Respicon Particle 

Sampler (3.1 Lpm) was physically attached to the tube (both samplers were in the breathing 

zone). Collected residue, per standard practice, is adjusted for recovery from field fortification 

samples. 
Page 14 of 38 

The results from the OVS tubes are reported herein as the “total” air concentration monitored 

(i.e., no sizing of particles). The OVS tube collects all particles that could physically deposit on 

the tube when facing downwards with air drawn in by the pump (mimicking nostrils). The 

results of the Respicon Particle Sampler are reported as air concentrations with 50 percent cut 

size diameter of 100 μm, 10 μm, and 2.5 μm. As per the Respicon sampler’s brochure, 

http://www.ajabrams.com/pdf/tsi/resp_bro.pdf, the following was used by EPA to 

report/calculate the air concentration results for the inhalable, thoracic, and respirable sized 

particulates: 

• Inhalable (100 μm) = results of 100 μm + 10 μm + 2.5 μm filters; 

• Thoracic (10 μm) = results of 10 μm + 2.5 μm filters; 

• Respirable (2.5 μm) = results of 2.5 μm filter. 

Inhalation unit exposures (i.e., mg/m3

/lb ai handled) are calculated by dividing the air 

concentrations by the amount of ai handled. When the need arises for the unit inhalation 

exposures to be expressed in units of mg/lb ai (e.g., when assessing inhalation risks using an oral 

toxicological endpoint) the inhalation daily exposure is calculated as the (air conc (mg/m3

) / lb 

ai) * breathing rate (1 m3

/hour) * aerosol spray duration (hours/day). 

2.3 Dermal and Inhalation Exposure Results 

Results -- A summary of the dermal results of the 18 MEs is presented in Table 2 for the 

clothing configurations of long pants and long sleeved-shirts (Long-Long), long pants and shortsleeved shirts (Long-Short), and short pants and short-sleeved shirts (Short-Short). Table 3 

reports the results for the inhalation monitoring. These tables report the results for each 

individual worker along with empirical statistical summaries of each cluster and overall 

exposures. Note: The recommended unit exposures in this review are based on the results of the 

lognormal mixed model, not the empirical summaries provided in Tables 2 and 3. The 

individual ME results are reported for others to analyze (if desired) and because the empirical 

results are easily understood. 

Appendix A provides statistical models to estimate the unit exposure summary statistics, 

including: 

• Empirical simple random sampling model (see Appendix A, Tables 1 and 2); 

• Lognormal simple random sampling model (see Appendix A, Tables 4 through 11); and 

• Lognormal mixed model (see Appendix A, Table 3 for a summary, and Appendix A, 

Tables 4 through 11 for detailed results). 

The results of the lognormal mixed model have been selected to best represent the summary 

statistics for the unit exposures (for summary results of recommended unit exposures see Table 1 

above, which is taken from Appendix A, Table 3). For a detailed discussion of the lognormal 

mixed model calculations and results (along with a discussion of the HSRB-suggested quadratic 

models) the reader is referred to Appendix A. 
Page 15 of 38 

Observations -- This aerosol study includes the recorded individual participant activities by 

observers. The study report indicates... “There were always three to four study personnel 

following the subject during a given monitoring event”, with one being the “observer” (page 41 

of study report). Detailed observations recorded during each ME capturing the notable events 

that occurred during the aerosol can applications can be viewed in the study report’s Appendix L 

starting on page 474. Although a review of these observations indicate that subjects occasionally 

touched or brushed against treated surfaces or had overspray near body (e.g., page 478 of study 

report) or wiped hands on body (e.g., page 486, 492), these types of exposures are expected 

based on the task and are not considered outliers in the data. The following observations are 

highlighted: 

• Cluster 2, AE3 – This subject received the highest hand exposure in the study (571 

mg/lb ai). According to the observations recorded (page 486 of study report), 

“...liquid is observed pooling on subject’s spray hand and on spray nozzle of can; 

possibly due to subject’s finger or can malfunction.”

• Cluster 1, AE18 – This subject received the highest dermal exposure in the study 

(850, 953, and 1179 mg/lb ai for Long-Long, Long-Short, and Short-Short, 

respectively). See Table 2 below for a comparison of these results to the other MEs. 

The observation notes for AE18 (page 479 of study report) indicate that this subject 

“...stood close to surfaces with possible overspray onto self; stands in bathtub to 

spray; sprays toward self; crouches down and may have over sprayed on lower 

pants; leans on edge of counter that was already sprayed; sprays on wall over head 

while in the shower;” etc. These behaviors may have contributed to the higher 

exposure. However, none of these behaviors appear to be grossly negligent. It is of 

interest to note that 3 of the 6 inner WBD sections are higher than the corresponding 

outer WBD. This is perplexing and did not happen in any of the other MEs. The 

study authors surmised that the subject had residues on their skin prior to the study, 

perhaps by bathing in a product containing ADBAC (page 24 of study report). EPA 

is not aware of any ADBAC products for bathing. One other possible explanation for 

the inner residues greater than the outer residues (for the upper arm, front torso, and 

back torso) is that the subject sprayed overhead and that spray may have settled down 

around the inside of the collar getting more residues on these inner WBD sections. 

This ME is not considered an outlier in the analysis. Nonetheless, EPA ran the 

models again without subject AE18 and the resulting unit exposures did not change 

more than ~10 percent. 

Impact of Non-detects -- All of the inhalation monitors, hand samples, and dermal outer 

WBD sections were above the LOQ. The only non detect samples monitored (other than the 

controls) were for ~13 percent of the inner WBD sections (i.e., 14 of the 108 inner WBD 

samples were non detect). Samples with results less than the limit of quantification (LOQ) are 

included in the calculation of total exposure as 1⁄2 LOQ. Since the number of non-detects was 

small, and since all of those non-detect inner dosimeter values were at least a factor of 10 lower 

than the unusually high inner dosimeter values for subject AE18 mentioned above, the impact of 

the non-detects is negligible. 
Page 16 of 38 

Table 2. Aerosol: Summary (Empirical) of Dermal Monitoring Results – Various Clothing Configurations and No GlCluster Subject AaiH (lb) Surface Area (sq ft) Time (min) Sampling and [Spraying] Hands Only (mg/ lb AI)

Derm

Long

1

AE2 0.001552 707 32 [17] 122 1

AE5 0.002403 1184 57 [29] 54.1 88

AE33 0.002734 1673 63 [25] 259 3AE18 0.003219 410 52 [28] 178 8AE22 0.004674 2419 112 [43] 75.7 9AE29 0.005291 2146 119 [79] 117 1

Mean 0.003312 1423 73 [37] 134 2

Std 0.001417 797 35[22] 74.4 2

2

AE3 0.001351 731 48 [13] 571 6

AE27 0.002099 1014 42 [16] 143 2

AE4 0.002646 1084 46 [21] 155 2

AE32 0.003439 1422 68 [28] 60.2 1AE10 0.003858 1706 108 [44] 128 1AE30 0.004409 2408 128 [38] 121 2

Mean 0.002967 1394 73 [27] 196 2

Std 0.001146 601 35 [13] 187 1

3

AE9 0.001272 417 25 [9] 86.5 1

AE21 0.002097 716 41 [14] 54.4 1AE24 0.002513 848 55 [21] 131 3AE17 0.003219 1115 64 [22] 105 2AE12 0.004145 1618 86 [29] 81.8 1AE28 0.004431 1459 115 [42] 174 2

Mean 0.002946 1029 64 [23] 105 1

Std 0.001219 457 32 [11] 42.1 75

Overall

Mean 0.003075 1282 70 [29] 145 2

Std 0.001201 623 32 [16] 118 1

Median 0.002976 1150 60 [27] 122 1

GeoMean 0.002836 1132 63 [25] 121 2

95th %le 0.004766 2410 120 [49] 306 6
Page 17 of 38 

Table 3. Aerosol: Summary (Empirical) of Inhalation Monitoring Results.

Cluster Subject AaiH (lb) Sampling Time (min) Air Concentration as Measured in Field Air Concentration Normalized b

Total (mg/m3) <100 um (mg/m3) Total <100 μm <

1

AE2 0.001552 32 0.128 0.143 82.5 92.1 

AE5 0.002403 57 0.124 0.126 51.6 52.4 

AE33 0.002734 63 0.122 0.095 44.6 34.7 AE18 0.003219 52 0.294 0.284 91.3 88.2 AE22 0.004674 112 0.100 0.096 21.4 20.6 AE29 0.005291 119 0.153 0.129 28.9 24.4 

Mean 0.003312 73 0.154 0.146 53.4 52.1 

Std 0.001417 35 0.071 0.070 28.2 31.5 

2

AE3 0.001351 48 0.043 0.056 31.4 41.1 

AE27 0.002099 42 0.118 0.089 56.2 42.5 

AE4 0.002646 46 0.302 0.221 114 83.5 

AE32 0.003439 68 0.202 0.124 58.7 36.1 AE10 0.003858 108 0.188 0.104 48.7 27.0 AE30 0.004409 125 0.103 0.066 23.4 14.9 

Mean 0.002967 73 0.159 0.110 55.4 40.8 

Std 0.001146 35 0.091 0.060 32.0 23.3 

3

AE9 0.001272 25 0.145 0.116 114 91.2 

AE21 0.002097 41 0.122 0.102 58.2 48.7 AE24 0.002513 55 0.174 0.108 69.2 43.0 AE17 0.003219 64 0.269 0.175 83.6 54.4 AE12 0.004145 86 0.169 0.121 40.8 29.2 AE28 0.004431 115 0.232 0.146 52.4 32.9 

Mean 0.002946 64 0.185 0.128 69.7 49.9 

Std 0.001219 32 0.055 0.028 26.2 22.3 

Overall

Mean 0.003075 70 0.166 0.128 59.5 47.6 

Std 0.001201 32 0.071 0.054 28.1 25.0 

Median 0.002976 60 0.149 0.119 54.3 41.8 

GeoMean 0.002836 63 0.151 0.119 53.3 41.9 

95th %le 0.004766 120 0.295 0.230 114 91.3 
Page 18 of 38 

2.4 Evaluation of Scenario Benchmark Objective 

 Benchmark Objective -- The data from the study has been analyzed to see if the aerosol 

scenario meets the AEATF II objective of a relative 3-fold accuracy (i.e., K = 3). Using the SAS 

code originally developed by the Agricultural Handler Exposure Task Force (AHETF) and 

independently confirmed by the Health Effects Division (HED) (and now modified by AD), EPA 

has determined, and presents, the analysis that the aerosol study results meet the 3-fold relative 

accuracy objective. Appendix A provides the detail benchmark analysis which is summarized as 

follows: 

 Benchmark Objective: fold Relative Accuracy (fRA) 

The benchmark objective for AEATF II 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”). EPA has analyzed the data using 

various statistical techniques to evaluate this benchmark. First, to characterize the unit exposures 

(also referred to as “normalized exposure”), lognormal probability plots of dermal and inhalation 

UEs (adjusted for residue method collection efficiencies) are provided in Figures 2 to 5 for the 3 

clothing configurations as well as inhalation exposure. These plots support the assumed 

lognormal distributions for the normalized exposure. Note: The figure titles are provided both 

above and below the graphs because they were cut and pasted as file images. Also note that all 

logarithms defined in this review are natural logarithms. 

Quantile plot normalized long dermal exposure data with a lognormal distribution

Normalized by Pounds Active Ingredient Handled

-2 -1 0 1 2

4.0

4.5

5.0

5.5

6.0

6.5

7.0

log (Normalized Long Dermal Exposure)

Normal Quantiles

Figure 2. Quantile plot of normalized long dermal exposure data with a lognormal 

distribution normalized by pounds of Active Ingredient handled. 
Page 19 of 38 

Quantile plot normalized short dermal exposure data with a lognormal distribution

Normalized by Pounds Active Ingredient Handled

-2 -1 0 1 2

5.50

5.75

6.00

6.25

6.50

6.75

7.00

7.25

log (Normalized Short Dermal Exposure)

Normal Quantiles

Figure 3. Quantile plot of normalized short dermal exposure data with a lognormal 

distribution normalized by pounds of Active Ingredient handled. 

Quantile plot normalized long short dermal exposure data with a lognormal distribution

Normalized by Pounds Active Ingredient Handled

-2 -1 0 1 2

4.5

5.0

5.5

6.0

6.5

7.0

lnnrmdermls

Normal Quantiles

Figure 4. Quantile plot of normalized long short dermal exposure data with a lognormal 

distribution normalized by pounds of Active Ingredient handled. 
Page 20 of 38 

Quantile plot normalized inhalation conc exposure data with a lognormal distribution

Normalized by Pounds Active Ingredient Handled

-2 -1 0 1 2

3.00

3.25

3.50

3.75

4.00

4.25

4.50

4.75

log (Normalized Inhalation Conc Exposure)

Normal Quantiles

Figure 5. Quantile plot of normalized total inhalation exposure data with a lognormal 

distribution normalized by pounds of Active Ingredient handled.

 Next, EPA calculated estimates of the GM, AM and P95 based on three different 

calculation methods: 

• Empirical estimates; 

• Assuming a lognormal distribution and a simple random sample (SRS); and, 

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

The 95% confidence limits for each of these estimates were obtained by generating 

10,000 parametric bootstrap samples. Then, the fRA for each was determined as the maximum 

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

confidence limits. Table 4 below presents the results for the aerosol scenario, for the long pants, 

short-sleeved shirt and the inhalation exposures. The results of the benchmark analysis for the 

other clothing configurations are reported in Appendix A Table 4 (long pants, long-sleeved shirt) 

and Appendix A, Table 5 (short pants, short-sleeved shirt). 
Page 21 of 38 

Tables 4, 5, 6 and 8 of Appendix A also present confidence intervals computed using a 

non-parametric bootstrap approach instead of the bootstrap parametric approach, as suggested by 

HSRB reviewers of the previous mop study. The parametric bootstrap approach assumes that the 

exposure data were generated from the fitted lognormal mixed model. The non-parametric 

bootstrap approach assumes that the data were generated using a simple random sample from 

each cluster. The parametric and non-parametric bootstrap confidence intervals were similar for 

these clothing configurations and inhalation exposure. 

The benchmark of 3-fold accuracy for dermal and inhalation unit exposures has been met 

for all 3 clothing configurations and inhalation exposures for all 3 statistical models, using both 

the parametric and non-parametric bootstrap methods. 

Presumption of Proportionality -- EPA evaluated the presumption of proportionality 

between exposure and amount of active ingredient handled (AaiH). EPA tested proportionality 

using a statistical benchmark to be able to distinguish, with 80% statistical power, complete 

proportionality from complete independence between exposure and amount of active ingredient 

handled. 

Table 4: Results of Primary Benchmark Analysis for Long Pants, Short-sleeved Shirt and Inhalation. 

Dermal Exposure Inhalation Exposure 

Statistic Unit Exposure Estimate 

(mg/lb ai) 95% CI fRA Unit Exposure Estimate (mg/m3/lb ai) 95% CI fRA 

GMS 324.0 257.5 – 

409.1 1.3 53.3 42.3 – 67.2 1.3 

GSDS 1.6 1.4 – 1.9 1.2 1.6 1.4 – 1.9 1.2 

GMM 324.0 257.5 – 

409.1 1.3 53.3 42.3 – 67.2 1.3 

GSDM 1.6 1.4 – 2.0 1.2 1.6 1.4 – 2.0 1.2 

ICC 0.0 0.0 – 0.4 NA 0.0 0.0 – 0.4 NA 

GMS = geometric mean assuming SRS = “exp(average of 18 ln(UE)) values” 

GSDS = geometric standard deviation assuming SRS = “exp(standard deviation of 18 ln(UE)) values” 

GMM = variance component model-based geometric mean 

GSDM = variance component model-based geometric standard deviation 

ICC = intra-cluster correlation 

AMS 365.7 285.6 – 

466.3 1.3 59.5 46.9 – 76.6 1.3 

AMU 366.7 287.2 – 

471.1 1.3 60.3 47.2 – 77.4 1.3 

AMM 366.7 287.4 – 

472.4 1.3 60.3 47.2 – 77.6 1.3 

AMS = average of 18 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 953.4 509.9 – 

1417.9 1.9 114.2 83.8 – 233.0 2.0 

P95U 734.6 509.1 – 

1038.7 1.4 120.7 83.7 – 170.7 1.4 

P95M 734.6 511.6 – 

1050.9 1.4 120.7 84.1 – 172.7 1.4 

P95S = 95th percentile (i.e., estimated as the maximum unit exposure from the 18 unit exposures) 

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

1.645

P95M = variance component model-based 95th percentile = GMM* GSDM 1.645
Page 22 of 38 

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

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

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

than 0 – providing support for an independent relationship. If slope is positive, not zero and not 

1 then the exposure tends to increase with the AiaH but not proportionally, so that, for example, 

doubling the AiaH will not tend to double the exposure. If the slope confidence interval 

excludes both 1 and 0 then the statistical evidence rejects both proportionality and independence 

and shows that the exposure tends to increase with the AiaH but not proportionally. Note: the 

slope measures the change in log mg dermal exposure for each unit change in log lb ai. A 

slope of one implies that the log of the unit exposure (mg/lb AI) is equal to a constant plus a 

random error, so that the unit exposure has the same mean for any amount of ai, and thus 

the mg dermal exposure is proportional to the lb ai. 

A simple linear regression, a mixed-effect regression, and a more complex “repeated 

measures” model (see Appendix A page 47 for more details) were used to analyze the data to 

take into account the clustered nature of the data and were used to evaluate the relationship 

between exposure and AaiH. Appendix A also provides an analysis of the proportionality for 

each of the three clothing configurations for each scenario. The statistical analyses of all three 

clothing configurations showed proportionality and rejected independence. The estimated slopes 

were all at least 0.81 (proportionality is when the slope is one for the underlying population of all 

potential janitor exposures). The results of the proportionality analysis for the three clothing 

configurations should be consistent on physical grounds; either all or none of the clothing 

configurations should show proportionality to AaiH. To investigate the proportionality issues 

further, an alternative model (“repeated measures”) was developed to fit the data from all of the 

clothing configurations. The reader is referred to Appendix A and the SAS code for specific 

details on this repeated measures model. 

For inhalation exposure, the statistical analyses rejected proportionality and did not reject 

independence. The estimated slope was 0.4. 

The resulting regression slope and confidence intervals are summarized in Table 5 and in 

Figures 6 and 7 for the long pants, short-sleeved shirt dermal exposure and for inhalation 

exposure (the two slopes in Figures 6 and 7 are identical; and therefore, only show on the graph 

as one slope/line). To calculate the confidence intervals, the Kenwood-Rogers method was used 

to estimate the denominator degrees of freedom for the repeated measures models and would 

have been used for the mixed-effect regressions with a non-zero estimated ICC. However, in the 

aerosol study, the mixed effects models for the three clothing configurations and the inhalation 

exposure had a zero estimated ICC. Following comments from HSRB reviewers of the mop 

study analyses, we used the containment method to estimate the denominator degrees of 

freedom in those cases, since when the estimated ICC is zero, the Kenwood-Rogers method 

ignores the uncertainty of the estimated ICC and produces a confidence interval that is too 

narrow. 

Note that a confidence interval width of 1.4 (or less) indicates at least 80% statistical 

power, which was achieved for the short pants and short sleeves dermal exposure, for the long 

pants and short sleeves dermal exposure, and for inhalation exposure, and almost achieved 

(width = 1.47) for the long pants and long sleeves dermal exposure. For the dermal models, the 

slopes are positive and the results indicate that exposure is directly proportional (1:1) to AaiH 

(i.e., the confidence interval includes 1) and also indicate that exposure is not independent of 

AaiH (i.e., the confidence interval does not contain 0). The results for the inhalation models 
Page 23 of 38 

indicate that exposure is not directly proportional (1:1) to AaiH (i.e., the confidence interval does 

not include 1) and suggest that exposure is independent of AiaH (i.e., confidence interval 

contains 0). For more details including results for other exposure measures and other normalizing 

variables, the reader is referred to Appendix A, Tables 13, 13b, and 13c. 

Table 5. Results of Analysis of Proportionality for Dermal and Inhalation Exposure.

Exposure 

Route Clothing Model Slope 

Confidence 

Interval 

Confidence 

Interval 

Width 

Dermal (mg) Long pants 

and long 

sleeves 

Mixed 0.87 0.08 – 1.54 1.47 

Short pants 

and short 

sleeves 

Mixed 1.01 0.47 – 1.55 1.07 

Long pants 

and short 

sleeves 

Mixed 0.87 0.25 – 1.48 1.24 

Inhalation 

(mg/m3) 

 Mixed 0.43 -0.11 – 0.97 1.08 

Figure 6. Mixed and simple linear regression plots for long short dermal exposure
Page 24 of 38 

Figure 7. Mixed and simple linear regression plots for inhalation exposure. 

Threshold of AaiH for Over- or Under-Predicting Exposure – The mixed model regresses the 

log exposure against the log lb ai with an unknown slope. The proportionality model is the mixed 

model where the slope of log exposure against log lb ai is assumed to equal to 1. It is shown in 

Appendix A that if the mixed model formulation is correct and the estimated regression slope is 

less than one, then the mean exposure will be over-predicted if the proportionality model is 

extrapolated to high levels of the amount of active ingredient and the exposure will be underpredicted at low levels of the amount of active ingredient. 

As an exception, for the short pants and short sleeves clothing configuration, the slope was 

higher than one, and in this case the mean exposure will be under-predicted if the proportionality 

model is extrapolated to high levels of the amount of active ingredient and the exposure will be 

over-predicted at low levels of the amount of active ingredient. However, since the estimated 

slope for the short pants and short sleeves clothing configuration is only slightly greater than one, 

the two models predict nearly the same mean exposures and the over-prediction will be very 

small. 

Table 6 gives the minimum amount of active ingredient handled for which the proportionality 

model will over-estimate the expected exposure (under-estimate if the slope is greater than 1). 

Figures 8 and 9 show the statistical models and thresholds for the long pants and short sleeves 

and inhalation exposures. These figures display the 18 measured exposure values (6 in each 

cluster) together with the predicted mean exposure calculated using the proportionality model 

(where the slope of log exposure against log ai is assumed to be one) and using the more general 
Page 25 of 38 

mixed model (where the slope of log exposure against ai is estimated). The threshold is where 

the two predicted means are the same. The proportionality model uses unit exposures to estimate 

the exposure for a given amount of active ingredient, and this is a “conservative” overestimate, 

compared to the more general mixed model, when the amount of active ingredient is higher than 

the threshold. 

Table 6. Minimum Pounds of Active Ingredient for Which Normalized Exposure Model Over-Predicts 

Dermal and Inhalation Exposure.

Exposure 

Route Clothing Model Slope Threshold Level (lb AiaH) 

Dermal (mg) Long pants 

and long 

sleeves 

Mixed 0.81 0.00295 

Short pants 

and short 

sleeves 

Mixed 1.01 0.00174* 

Long pants 

and short 

sleeves 

Mixed 0.87 0.00297 

Inhalation 

(mg/m3

) 

 Mixed 0.43 0.00272 

*For this case, slope > 1 and so the proportionality model under-predicts exposure for pounds of 

active ingredient above the threshold. Since the slope is only 1.01, the under-prediction is small. 
Page 26 of 38 

Figure 8. Predicted means for exposure with long pants and short sleeves using the 

proportionality and non-proportionality models; threshold value. 
Page 27 of 38 

Figure 9. Predicted means for inhalation exposure using the proportionality and nonproportionality models; threshold value. 

3.0 Discussion of Data Generalizations and Limitations 

The regulatory need for a generic data base of pesticide handlers for antimicrobial pesticide 

products has been discussed previously (Christian 2007). This aerosol study was designed to 

represent the high end of potential exposure for workers spraying surfaces using an antimicrobial 

product packaged in an aerosol spray can. The study design also incorporated random diversity 

selection where feasible. Such a study design requires a discussion of how the data can be 

generalized and the limitations of the results. The following items are provided to characterize 

the results of this sampling effort: 

(1) The study purposively selected Fresno, CA, as the study location. This selection criterion, 

rather than a random selection of sites across the country, limits to some degree the 

statistical generalizations of the data. Thus we cannot determine whether these results 

provide unbiased estimates of exposure distributions from the spraying of an aerosol can in 

locations other than Fresno, CA, and it is not possible to use these data to estimate the 

potential bias or the geographic variability. To generalize these results to the whole country 

requires an assumption that the exposure distribution for these scenarios is independent of 

the geographic location. The statistical limitations of the purposive site selection are 

deemed acceptable by the Joint Regulatory Committee (JRC). It is reasonable to assume 
Page 28 of 38 

that the mechanics of spraying an aerosol can onto surfaces inside hotels in Fresno are not 

substantially different than spraying an aerosol can onto surfaces inside other hotels 

throughout the country. It is also reasonable to assume that the spraying inside hotels would 

also not be substantially different from spraying in other types of buildings that have similar 

HVAC systems. Given a limited set of resources for the overall AEATF II monitoring 

program, the assumption that indoor spraying of surfaces using an aerosol can does not vary 

geographically was sufficiently reasonable to forgo the random site selection (of all 

buildings in the country) in favor of spending the limited resources to monitor additional 

distinctly different scenarios (e.g., pouring of liquids, painting, metal working fluids, 

pressure treatment of wood, etc). 

(2) The removal efficiencies for the hand wash and face/neck wipes were both conducted using 

quaternary ammonium products (i.e., DDAC and ADBAS, respectively) from previously 

conducted studies rather than for ADBAC itself. At least 2 uncertainties need to be 

acknowledged and discussed. First, the solubility of the test materials may affect the 

removal efficiencies. According to the study report (page 61 – 62)... “With the known 

chemical similarity between the quaternary salts used as in the removal efficiency studies 

and the test material for this study, it is expected that the 89.4% dermal removal efficiency 

for ADBAS via wiping will apply to ADBAC as well. In the same manner, the 90.3% 

removal of DDAC via washing should apply to ADBAC also. In an ongoing study at Golden 

Pacific Laboratories, the solubility of ADBAS, ADBAC and DDAC is being evaluated in 

IPA as well as IPA/water (50/50) solvents (GPL Study No. 110388). Preliminary results of 

these experiments show solubility of both salts of ADBA as well as the chloride salt of DDA 

in these solvents to be much greater than levels observed in subject samples. This complete 

solubility data will support the measured high removal efficiencies reported by Boatwright 

(above), and validate the planned correction of hand wash results from this study for a 

90.3% removal efficiency and correction of face/neck wipe results by a 89.4% removal 

efficiency.” EPA acknowledges the high removal efficiencies for hand washes of the 

various studies available (DDAC and ADBAS), and will wait for the submission of the 

solubility testing to further comment on the solubility results. 

Second, the sampling (wash and wipe) procedures may also affect the removal efficiency. 

The DDAC removal efficiency study used a hand wash procedure less vigorous then the 

AEATF II SOP (which may tend to overestimate the correction factor). The dry time of the 

DDAC on the hands was 30 minutes compared to the average sampling time of 70 minutes 

in this aerosol study (which may tend to underestimate the correction factor). Of note is that 

the ADBAS wipe efficiency is 88.2 percent while the DDAC wipe efficiency was ~60 

percent. Contributing to this difference may be that the ADBAS wipes were moistened with 

isopropanol (IPA) while the DDAC study used a 50/50 IPA/water solution (as did this 

aerosol study). Finally, there is a potential discrepancy in the ADBAS wipe removal 

efficiency (88.2% reported in study versus 89.4% used as the correction) and EPA will 

consult with the AEATF II for clarification. EPA has weighed the uncertainties of the 

available removal efficiency data against the need for an additional study to help clarify 

hand removal efficiencies. Although EPA views the removal efficiency studies as having 

limitations, we are not recommending a new study be conducted prior to using the unit 

exposures generated in this study. EPA considerations were given to the low dermal 

absorption of these chemicals; to the potential minor changes to the unit exposures based on 

recovery efficiencies; the time and resources necessary to conduct a new removal efficiency 

study; and the use of additional human test subjects. 
Page 29 of 38 

(3) The data generated in this study are acceptable to use as surrogate for assessing other 

chemicals considered to have low volatility (i.e., vapor pressures less than ~1E-4 mmHg @ 

20oC). This “rule-of–thumb” for the vapor pressure threshold is reviewed by EPA on a 

case-by-case basis, particularly for those antimicrobial pesticides with vapor pressures that 

are near to this threshold. For example, for those chemicals with vapor pressures of ~1E-4 

mmHg, EPA reviews the pesticide application method for the potential for aerosol 

generation and the available inhalation toxicity data to see if the toxicity studies were 

performed as a gas or with an aerosol. 

(4) The data generated in this study are acceptable to use as surrogate to assess pesticide labeled 

uses of spraying surfaces using an aerosol can. 

 

(5) The dermal exposures that resulted from the spraying of the product packaged in an aerosol 

can generated in this study are acceptable to use for clothing configurations of long pants, 

long-sleeved shirts, and no gloves; long pants, short-sleeved shirts, and no gloves; as well as 

short pants, short sleeved-shirts, and no gloves. 

(6) The small sample size by itself does not cause statistical limitations since the confidence 

intervals for the summary statistics were reasonably narrow (in most cases meeting 2-fold 

relative accuracy or better). More important is the fact that the original sets of subject 

participants, locations, and dates from which the subjects, clusters, and sampling dates were 

chosen were limited and hence might not be representative of all Fresno users of aerosol 

cans (e.g., those that use aerosol cans but did not volunteer), buildings (e.g. only hotels were 

eligible for this study), and time periods (e.g., winter versus summer, night versus day, etc.). 

In other words, the most significant limitation is that these data were not derived from a 

stratified random sample of MEs even though the statistical analyses made that assumption. 

At a minimum this increases the uncertainty of the estimates (so the calculated confidence 

intervals are too narrow) and there may also be some bias (e.g., study participants not in the 

volunteer pool might be more or less prone to exposure than the selected group). 

(7) EPA will continue using exposures normalized by AaiH as a default condition. The results 

of the aerosol study scenario are not inconsistent with the proportionality assumption that 

dermal exposure tends to increase proportionally with AaiH. The choice of normalizing 

variable of AaiH is based upon considerations of suitability for product labeling and 

consistency between scenarios. The results for inhalation exposure are inconsistent with 

proportionality. However, for inhalation exposure, the use of this inconsistent assumption of 

direct proportionality of exposure to AaiH when extrapolating to the high end of AaiH – the 

EPA regulates on the high end of AaiH – tends to overestimate the exposure, resulting in 

conservative risk assessments and human health protective regulatory decisions. Table 6 

above provides for the minimum amount of AaiH for the normalized exposures to be overpredicting exposures (i.e., protective of human health). Data will continue to be collected 

by the AEATF II to add to the knowledge base of normalized exposures. 

4.0 Conclusions 

EPA has reviewed the AEATF II aerosol study and concludes that the AEATF II made 

the appropriate changes to the protocol proposed by the EPA and HSRB and has executed the 

study successfully. The protocol deviations that occurred and were reported on have not 

adversely impacted the reliability of these data. The EPA recommends that the inhalation and 
Page 30 of 38 

dermal UE generated in this aerosol study be used provided the data are used within the 

boundaries set forth in this review. 

The following is a summary of our conclusions. 

• The AEATF II data for inhalation and dermal exposures represent reliable data for 

assessing the spraying of surfaces using an aerosol can. This scenario does not include 

the subsequent wipe with a rag. The “wiping” portion of this type of an application does 

not always occur; some products are “leave on” applications. Exposures occurring while 

wiping are available in a previously submitted AEATF II study. Alternative data sources 

or special circumstances will be considered on a case by case basis. 

• The inhalation exposure monitoring results are available as total particles, inhalable 

particles (<100 μm), thoracic particles (<10 μm), and respirable particles (<2.5 μm). 

These results are graphically illustrated in Figure 10. EPA will consult with the JRC and 

inhalation toxicologists to determine which inhalation exposure monitoring results from 

this study are best suited to each individual assessment (e.g., a chemical that is an irritant 

causing no systemic toxicity might be best represented by the results of the inhalable 

particles). EPA will also continue to review the potential correction factors for the 

inhalable particle results (i.e., ranging from 1.5 to 1.8) as noted in Appendix C. Any 

advice from the HSRB on the Respicon results will also be considered 

• Estimates of the GM, AM, and P95 were shown to be accurate within 3-fold with 95% 

confidence for all the analyses of the three clothing configurations and of inhalation 

exposure. 

• The data provided 80% statistical power to distinguish complete proportionality or 

independence between exposure and AaiH for both dermal and inhalation routes of 

exposure. 

• A direct proportionality (1:1 relationship) between dermal exposure and AaiH was 

established. 

• A direct proportionality (1:1 relationship) between inhalation exposure and AaiH was not 

established but the trend of inhalation exposure increases as AaiH increases. 

• Additionally, Table 6 provides a threshold that is the minimum AaiH value where 

exposure will be over-estimated when extrapolating the normalized exposure (mg 

dermal/lb ai or mg/m3

 inhalation/lb ai) to other chemical assessments (i.e., using these 

unit exposures as surrogates to assess other chemicals that handle more active ingredient 

than the threshold). 
Page 31 of 38 

Figure 10. Comparison of ADBAC Air Concentrations Measured Using the OVS Tube and 

the Respicon Sampler.
Page 32 of 38 

5.0 References 

ACC. 2011. American Chemistry Council, Antimicrobial Exposure Assessment Task Force II 

(AEATF II) Governing Document for a Multi-year Antimicrobial Chemical Exposure 

Monitoring Program. Interim Draft Document. Version 3. July 8, 2011. 

Boatwright, M. (2007). Determination of Removal Efficiency of Didecyl Dimethyl Ammonium 

Chloride (DDAC) from Hand Surfaces Using Isopropyl Alcohol/Water Wash. Unpublished 

study prepared by Golden Pacific Laboratories under Study No. 050211. 341 p. MRID 

47214801. 

Boatwright, M. (2008). Determination of Removal Efficiency of Alkyl Dimethyl Benzyl 

Ammonium Saccharinate (ADBAS) from Hand Surfaces Using Isopropyl Alcohol Dressing 

Sponges. Unpublished study prepared by Golden Pacific Laboratories under Study No. 050198. 

421 p. MRID 48656201. 

EPA, 2009. Science and Ethics Review of AEATF II Aerosol Scenario Design and Protocol for 

Exposure Monitoring. Memorandum dated September 21, 2009 from Timothy Leighton and 

Cassi Walls, PhD. to Norm Cook, Antimicrobials Division, EPA/OPP. 

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

MRID 48659001. Testman RJ and Boatwright MT. (2011) A Study for Measurement of 

Potential Dermal and Inhalation Exposure During Application of a Liquid Antimicrobial 

Pesticide Product Using a Pressurized Aerosol Can for Indoor Surface Disinfecting. 

Unpublished study prepared by Golden Pacific Laboratories, LLC, under, GPL Report No. 

070270 and AEATF II Project ID AEA04. 1851 p. 

Christian, M. 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. 

Popendorf W, Selim M, and Kross, B. 1990. Chemical Manufacturers Association Antimicrobial 

Exposure Assessment Study. MRID No. 41412201. 
Page 33 of 38 

Appendix A 

Statistical Review of the AEATF II Aerosol Study 

(To be included as a separate electronic file) 
Page 34 of 38 

Appendix B 

AEATF II Letters in Response to HSRB Protocol Review Comments 

(To be included as two separate electronic files) 
Page 35 of 38 

Appendix C 

Respicon Air Sampler Information 
Page 36 of 38 

Respicon Air Sampler Information 

Introduction 

The Respicon sampler is a multi-stage, virtual impactor that traps airborne particles onto three 

individual filters. The first impactor stage separates out and collects particles smaller than 2.5 

um. The second stage collects particles below 10 um while the third stage collects the remaining 

particles. The filters from the three stages can be added to together as follows to represent the 

inhalable, thoracic and respirable sampling conventions for airborne aerosols: 

• Inhalable (50% cutoff at 100 um) : Stages 1 + 2 + 3 

• Thoracic (50% cutoff at 10 um): Stages 1 + 2 

• Respirable (50% cutoff at 2.5 um): Stage 1 

Literature Study Test Results 

Li et al, 2000. The Respicon sampler was compared to five other inhalable aerosol samplers 

including the IOM sampler. This evaluation was conducted in a wind tunnel operated at 0.55 

and 1.1 m/sec and the samplers were oriented at 0, 90 and 180 degrees to the wind. The test 

aerosol was generated as monodisperse solid particles with aerodynamic diameters ranging from 

5 to 68 um. The results indicated that the Respicon matched the inhalation convention very well 

at 0.55 m/sec and oversampled at 1.1 m/sec, matched the thoracic convention very well except 

for under-sampling of 5 um particles due to inner particle loss and matched the respirable 

convention very well except for under-sampling of the 1.6 micron particles which was due to 

non-uniformity of the test particles. The authors mentioned that since the average wind speed in 

an indoor workspace is 0.3 m/sec (per Baldwin and Maynard, 1998), the result at 0.55 m/sec is 

more relevant. 

Koch et al, 2002. The Respicon sampler was compared to the IOM sampler and a stainless steel 

mannequin (i.e. the CALTOOL) that is designed to simulate human breathing. The comparison 

to IOM sampler was done using side by side personal sampling of workers in nickel refining 

operations. Each worker wore IOM and Respicon samplers for a full work shift and a total of 132 

pairs of samples were collected. The comparison to CALTOOL was done using side by side 

area samples in workplace environments or a test chamber. Regression analysis of the area 

sample resulted indicated that the IOM and the CALTOOL inhalable results were 1.51 and 1.83 

times greater than the Respicon inhalable results. This data also suggested that the CALTOOL 

and IOM results were not greater than the Respicon results when the particle size was less than 

10 microns. Analysis of the IOM and Respicon personal sample results indicated that the IOM 

results are generally higher than the Respicon results; however, some of this difference might be 

due to known vulnerability of the IOM sampler to contamination by non-airborne material. 
Page 37 of 38 

Linnainmaa et al, 2003. The Respicon sampler was compared to seven other inhalable aerosol 

samplers including two IOM samplers (with or without foam filters for respirable aerosols). This 

evaluation was conducted under laboratory conditions in a chamber with an airflow of 0.1 m/sec 

or under field conditions in a talc production plant. The test aerosol for the laboratory 

evaluation consisted fine quartz dust. The laboratory study indicated the Respicon yielded 

inhalable, thoracic and respirable aerosol levels that were 50%, 60% and 125% of those 

measured using the IOM sampler. The field study indicated that the Respicon yielded inhalable, 

thoracic and respirable levels that were 75%, 60% and 100% of the IOM results. 

Feather and Chen, 2003. The Respicon sampler was compared to the IOM sampler in a small 

chamber that was designed to maintain calm air conditions where the air velocities do not exceed 

20 cm/sec. This velocity was chosen based on the study by Baldwin and Maynard, 1998 of wind 

speeds in the workplace. The test aerosol consisted of monodispersed fluorescence-tagged 

polymer in either aqueous or powder form with aerodynamic diameters of 2.0, 6.1, 16.4, 30.7 

and 69.7 um. The test aerosol was dispersed into top of the chamber and allowed to settle to the 

bottom where it was collected by isokinetic reference probes and the IOM and Respicon 

samplers. The results indicated that the IOM sampler was close to 100 percent efficient at all of 

the particle sizes while the Respicon matched the conventional respirable and thoracic 

convention curves but undersampled the inhalable fraction. 

Rock et al, 2009. The Respicon sampler was compared to five other aerosol sampler types 

including Anderson cascade impactors, total suspended particulate (TSP) samplers, PM10 

samplers, and laser light scattering particle monitors. This evaluation was conducted in a 

controlled test chamber (wind speed not reported) using poly-disperse fly ash with a mass 

median aerodynamic diameter of 11.77 um and a geometric standard deviation of 2.06 um. The 

results indicated that the Respicon underestimated total suspended PM by 43 percent when 

compared to the TSP samplers. The Anderson samplers had 17% and 85% more thoracic and 

respirable mass, respectively, than the Respicon and this was determined to be from particle 

bounce in the Anderson samplers. 

Conclusion 

The literature studies indicate that the Respicon matches the thoracic and respirable sampling 

conventions fairly closely while significantly undersampling the inhalable convention. These 

studies generally suggest that the undersampling is consistent and can be addressed by using 

correction factors that range from 1.5 to 1.8. 
Page 38 of 38 

References: 

Baldwin and Maynard, 1998, A Survey of Wind Speeds in Indoor Workplaces, Baldwin, P.E.J., 

and Maynard, A.D., Annuals Occupational Hygiene, Volume 42, Issue 5, pp 303-313, 1998. 

Feather and Chen, 2003, Design and Use of a Settling Chamber for Sampler Evaluation Under 

Calm-Air Conditions, Feather, G., Chen, B., Aerosol Science and Technology, Volume 37, pp 

261-270, 2003 

Koch et al, 2002, Evaluation of the Respicon as a Personal Inhalable Sampler in Industrial 

Environments, Koch, W., Dunkhorst, W., Lodding, H., Thomassen, Y., Skaugset, N., Nikanov, 

A., and Vincent, J., Journal Environmental Monitoring, Volume 4, pp 657-662, 2002. 

Li et al, 2000, Evaluation of Six Inhalable Aerosol Samplers, Li, S-N, Lundgren, D., RovellRixx, D., AIHA Journal, Volume 61, pp 506-516, July/August 2000. 

Linnainmaa et al, 2003, Laboratory and Field Testing of Particle Size-Selective Sampling 

Methods for Mineral Dusts, Linnainmaa, M. Teikari, M., Laitinen, J., Kalliokoski, P., Vincent, 

J., Tiitta, P., and Raunemaa, T., AIHA Journal, Volume 64, pp 312-318, May/June 2003. 

Rock et al., 2009, Performance Evaluation of Six Different Aerosol Samplers in a Particulate 

Matter Generation Chamber, Park, J-M, Rock, J., Wang, L., Seo, Y-C, Bhatnager, A., Kim, S., 

Atmospheric Environment, Volume 43, pp 280-289, 2009.