Page 1 of 38 UNITED STATES ENVIRONMEN TAL PROTECTION AGENCY WASHINGTON, DC 20460

OFFICE OF CHEMICAL SAFETY AND POLLUTION PREVENTION

MEMORANDUM

DATE: January 3, 2012

SUBJECT: Science Review of the AEATF II Aero sol 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 Regulator y 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 Scien ce Support Branch (RASSB) OPP/Antimicrobials Division (7510P)

TO: Nader Elkassabany, PhD, Branch Chief

Risk Assessment and Scien ce Support Branch (RASSB) OPP/Antimicrobials Division (7510P)

This memorandum presents the EPA/OPP Antimi crobials Division (AD) science review of the human exposure aerosol study submitted by th e Antimicrobial Exposure Assessment Task Force II (AEATF II). The dermal and inhalation exposure data as re presented in this review are acceptable and, subject to the c onsiderations described below, are recommended for use for pesticide handler exposure assessments.Page 2 of 38 EXECUTIVE SUMMARY

This document represents the USEPA, Offi ce 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 data set instead of the Chemical Manufacturers Association (CMA) and/or the Pe sticide Handlers Exposure Database (PHED) datasets to assess exposure for persons using an antimicrobial produc t 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 sc enario does not cover th e subsequent wiping of the aerosol spray solution. The potential exposur e to the subsequent wiping of the sprayed solution can be determined by combining the re sults of this study with the results of the previously AEATF II conducted ready-to-use (R TU) wipe study. This aerosol study did not monitor the activity of wiping beca use there are some products that are labeled to be sprayed-on with no wiping.

Select summary statistics for the “unit expos ures” 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., lowe r leg, upper leg, lower arm, upper arm, etc). Therefore, the analyses of residues on the dosim eters worn by each individual worker allow for the estimation of dermal exposure for th e following 3 clothing configurations:

(1) “Long-Long” or “Long Dermal”= long pant s, long-sleeved shir t, and no gloves; (2) “Long-Short” or “Long Short Dermal”= long pa nts, 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 CM A 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 st atistics reported in Table 1 for the AEATF II data are estimated using the lognormal mixe d model while the CMA and PHED results are empirical estimates (Note: CMA results are in footnote “a” in Table 1).Page 3 of 38 Table 1. Unit Exposures: Aerosol Scenario Exposure

Route Clothing PHED a AEATF II b, c (n=18)

Arithmetic Mean Arithmetic Mean d 95 th Percentile e

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/m 3 /lb ai) Not Available 60.3 (47.2, 77.6) 121 (84.1, 128)

Inhalation (100 μm) Breathing Zone (mg/m 3 /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 antimicro bial products packaged in ae rosol cans. PHED data are based on an insecticide product which was sprayed as a cr ack and crevice treatment. As a comparison, the mean CMA exposure data for aerosols from applying the disinfecta nt 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/m 3 and sampling pumps ran from 1 to 26 minutes).

b AEATF II dermal UE includes correctio ns 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 conduc ted 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 95 th percentile = GM * GSD 1.645

f Inhalation (mg/lb ai) = (air conc (mg/m 3 ) / lb ai) * breathing rate (1 m 3 /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 supe rior to the existing aerosol datasets (i.e., CMA and PHED data ). AEATF II efforts represented a welldesigned, concerted process to collect relia ble 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 derm al and inhalation exposure results are considered scientifically complete. No additional monitoring data are required at this

time.Page 4 of 38 • The data are applicable for assessment of e xposure 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 ingr edient (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. Howeve r, for inhalation exposure, the statistical analysis provides evidence against proportio nality, although inhalati on 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-sp ecific 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 pest icide handler inhalation and/or dermal exposure algorithm: Potential dermal or inhalation exposure = [UE (mg/lb ai or mg/m 3 /lb ai)] x [dermal absorption (%) if applicable] x [max imum label rate (lb ai/gallon or ounces)] x [volume (gallons or ounces)].Page 5 of 38 1.0 Background

The AEATF II is developing a database repres enting inhalation and de rmal exposure during

a number of antimicrobial handler scenarios. A sc enario is defined as a pesticide handling task based on activity (e.g., application) and equipment type (e.g., aeros ol cans, ready-to-use wipes, mop & bucket, pressure treatment of wood facili ties, painting, etc). The AEATF II is monitoring

both inner and outer dosimeters which will allo w the EPA to estimate exposures to various clothing configurations (e.g., long pa nts, long-sleeved shirt or long pants, short-sleeved shirt or short pants, short-sleeved shir ts, plus shoes, socks, and no gloves). Prior to conducting intentional exposure st udies in humans, the protocols ar e 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 va rious horizontal and vertical surfaces such as sinks, shower stalls, walls, et c in hotel rooms using a hand-held pressurized

aerosol can containi ng a formulated antimicrobial product. The application is a spray and leaveon; no wiping of the treated surfaces occurre d. Subjects sprayed as they normally would do. Subjects wore whole body dosimeters (WBD) undern eath long-sleeved shirts, long pants, and no gloves (plus a personal air sample r). The conditions under which the study partic ipants handle the pesticide as they are monitored are referred to as the scenario. Both inner and outer dosimeters were worn by the monitored study part icipants, and both inner and outer dosimeters were analyzed for residues.

1.2 Study Objective

The AEATF II’s study objective is to monitor in halation 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 vari ous surfaces (walls, sinks , toilets, etc) for use in exposure assessments, as “unit exposures”.

“Unit exposure” (UE) is defined as the expect ed external chemical exposure an individual may receive (i.e., "to-the-skin" or “in the breath ing zone”) per weight-unit of chemical handled and is the default data format used in pestic ide handler exposure assessm ents. Mathematically, unit exposures are expressed as "handler" exposure normalized by the amount active ingredient handled by participants in scenar io-specific exposure studies (e.g., mg exposure/lb ai handled). EPA uses these UEs generically to estimate e xposure 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 AE ATF II Governing Document, the criteria can be briefly summarized as follows:Page 6 of 38 • 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. Spec ifically, 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 95 th 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 i ngredient handled. EPA used a log-log regression test to distinguish complete proportiona lity (slope = 1) from comple te independence (slope = 0),

with 80% statistical power, achieved when the width of the 95 th confidence interval of the regression slope is 1.4 or less.

1.3 Protocol Modifications, Amendments, and Deviations

1.3.1 Protocol Modifications Subse quent 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 im precision of the estimates into account. It is possible that collection of additional cluste rs might be considered.”

The HSRB provided written di scussion on a number of “...perceived inadequacies...” of the aerosol review. Shah (2010a and 2010b) responde d 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, sp ray and ejection rates, the size of the particles generated, and th e generation of nonvolatil e active ingredients.” The HSRB

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

exposures to consumers.

EPA acknowledges that ideally it would be mo re accurate to have performance data on all types of antimicrobial products packag ed 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 in dividual applicator behaviors while applying will equally affect exposure such that it may confound the spray nozzle information, and concludes they are sufficien t rationales to accept the use of the product selected. If data generated in the future provide additional inform ation 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 byPage 7 of 38 the AEATF II would need to be based on two stud ies. If a sufficient rationale is available to suggest logical differences in exposure s experienced by the two populations, the need

for two studies could be rati onalized. However, for the aerosol scenario, spraying a prepackaged aerosol can products onto surfaces does not inherently suggest differences (both populations routinely us e this device and no traini ng is provided). Moreover, reading through the study observations (Appe ndix 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 anal yses have been planned. Sample size adequacy cannot be judge d 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 object ive 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 logist ically 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 confiden ce requirements. This would imply that subjects had been unnecessarily used and ex posed 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 rigor ous design process. To inform this approach, the AEATF II is relying on existing EPA guidelines on exposure studies (Series 875 - Occupational and Residen tial Exposure Test Guidelines). These guidelines call for essentially three groups of five observations pe r 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 consu ltation with regulatory agencies, may determine that additional data are require d. At that point, more rigor ous 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 dete rmined after this jo int 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 ofPage 8 of 38 relative 3-fold accuracy for all 3 clothing c onfigurations (Long Dermal, Short Dermal, Long Short Dermal) and for the inhalation exposure (mg/m 3 /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 resear chers need to better clarify the sampling design aspects of the spraying (e.g., wi ll 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 respons es 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 gi ven full cans as well as half full cans, as specified in the protocol. A worker does NO T need to continue spraying a room if the target application is met. Two monitoring even ts 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 ai r 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 shoul d 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 fo llowing EPA discussions are noted. The

available literature studies indicate that the Respicon matches the thoracic and respirable sampling conventions fairly closely while significantly under sa mpling the inhalable convention. These studies generally suggest th at 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 lite rature (summary) and S ection 4.0 (see Figure 10 for a comparison of the results). The minima l 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, a nd 9 of the study report (pages 116, 117, 118) there were no more than 2 MEs per sampling date.Page 9 of 38 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 re spirators; change of study dates; change in personnel including the Study Direct or/Principal Investigator a nd 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 de aling with recruiting subjects and informed consent form; revised procedures for removing subjects’ socks; and re vised sponsors contact information (office location move).

1.3.3 Protocol Deviations

A total of 8 protocol, 2 method, and 8 SOP de viations were noted in the study (study report pages 103-104). The deviatio ns included, but not limited t o, 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 devia tions the reader is referred to the study report. EPA accepts the study author’s conclusion that thes e deviations did not adversely a ffect 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 conducte d in Fresno County, CA. Each of the 3 clusters was a different building. The bu ildings 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 Clor ox Disinfecting Spray (EPA Reg No. 67619- 3). This product also contained 3 othe r Quats, DDAC, ODAC, and DODAC. ADBAC, DDAC, ODAC, and DODAC are in Clor ox Disinfecting Spray at 0.252%, 0.0945%, 0.189%, 0.0945%, respectively. The C14 ADBAC wa s 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 discha rge rates of the cans were measured for both batches of products using triplicate cans from each ba tch 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 produc t is presented below in Figure 1.Page 10 of 38 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 a pproval, first subject enrolled , monitoring dates, etc) is reported on pages 107-108 of the study report.

• Sample Size: The study consisted of 3 clusters an d 6 MEs per cluster for a total of 18 monitoring events (ME). Each ME is a different subject.

• Treatment Solutions: The aerosol cans were obtaine d pre-packaged, no dilution by the researcher was necessary. The nominal per cent 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 C 14 ADBAC sprayed (i.e., 0.124%) were used in normalizing the exposures from th e first batch. Actual amounts of C14 ADBAC sprayed (i.e., 0.104%) were used in normaliz ing 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 sp rayed. The pre-determined number of cans sprayed were:Page 11 of 38 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 incl uded 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 (m 3 ) and the corresponding air concentration (mg/m 3 ). The amount of active ingredie nt (AaiH) sprayed onto surfaces was measured by weighing the cans prior to and subsequent to the m onitoring 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 201 0 mg. The AaiH as well as the spraying times (sampling and spraying) for individua l 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 ea ch 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 (p age 488 of study report) . Typical surfaces sprayed included countertops, railings, shelving, tile walls, sinks, appliances, toilets and tubs, and mirrors. Procedures/observations we re 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 th e study report. The humidity averaged in the 50% range. Temperatures averaged in th e low 70o F range. Th e heating ventilation air conditioning (HVAC) system descriptions su ch as total cubic f eet per minute (CFM), fresh air CFM, room volumes, air changes pe r hour (ACH), etc ar e 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 cont rol samples were all less than the limit of quantification (LOQ), except for one sample, wh ich indicates no background contamination. The exception was for one field control sample in Cluster 3 for the outer dosimeter. This controlPage 12 of 38 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 concurre nt laboratory recovery values range within 70 to 120 percent. Exceptions outside of this range include 3 low leve l fortifications of 131, 136, and 66.7 percent for face/neck, outer dosimeter, and out er dosimeter, respectively. A summary of the overall concurrent laboratory recovery sample s, for each monitoring matrix, by cluster, is reported in the study report starting on page 70 (individual recovery va lues can be viewed starting on page 122). The mean recoveries for all matrices are approx imately 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 for tifications 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 correct ions 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 impac t 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 mon itoring 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 approximate ly in the 90 percent rang e. 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 th e day and during breaks), and face/neck wipes. Because the subjects wore respirators, the resu lts of the face wipes were corrected for the area covered by the respirator (1.43 co rrection factor). The inner a nd 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 anal yzed 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 usi ng the test substance, alkyl dimethyl benzyl ammonium, as the saccharin ate 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 fortifica tion level (1,300 μg). In the second hand removal efficiency study for DDAC (cited in previ ous AEATF II studies) showed aPage 13 of 38 recovery efficiency of 90.3%. The hand measurem ents 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 fortificati on for the ADBAS wipe effici ency. Although the ADBAS study reports the recovery efficiency as 88.2%, a va lue of 89.4% was cited in the aerosol study and used to correct the samples. See Section 3.0 be low for further discussion on the limitations of the removal efficiency studies.

Total dermal exposure is calculated by su mming exposure across all body parts for each individual monitored. The following WBD s ections are summed to calculate the clothing configuration of long pants, long-sleeved shir ts (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 cal culate 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 calcu late 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 ac tive ingredient applied. EPA recalculated the exposures and expressed the re sults as mg/lb ai applied. EPA prefers the normalization by pounds to coincide with the English units re ported 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 a ttached to the worker’s collar to continuously sample air at a target rate of 2.0 Lpm from the breathing zo ne while the Respicon Particle Sampler (3.1 Lpm) was physically attached to th e tube (both samplers were in the breathing zone). Collected residue, per st andard practice, is adjusted for recovery from field fortification samples.Page 14 of 38 The results from the OVS tubes are reported here in as the “total” air concentration monitored (i.e., no sizing of particles). The OVS tube coll ects all particles that co uld physically deposit on the tube when facing downwards with air draw n in by the pump (mimicking nostrils). The results of the Respicon Particle Sampler are repo rted as air concentratio ns 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 concentra tion 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/m 3 /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 assessi ng inhalation risks using an oral toxicological endpoint) the inhalation daily expo sure is calculated as the (air conc (mg/m 3 ) / lb ai) * breathing rate (1 m 3 /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 monitori ng. 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 su mmaries 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 es timate the unit exposure summary statistics, including:

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

• Lognormal simple random sampling model (s ee 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 resu lts (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 incl udes the recorded individua l 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 reco rded 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 obs ervations 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 e xposures 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 observati ons 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 compar ison 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 o verspray onto self; st ands in bathtub to spray; sprays toward self; crouches down and may have over sprayed on lower pants; leans on edge of count er that was already sprayed ; sprays on wall over head while in the shower;” etc. These behaviors may ha ve contributed to the higher exposure. However, none of these behaviors ap pear to be grossly ne gligent. It is of

interest to note that 3 of the 6 inner WB D 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 bath ing. One other possible explanation for the inner residues greater than the outer resi dues (for the upper arm, front torso, and back torso) is that the subject sprayed ove rhead and that spray may have settled down

around the inside of the colla r 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 n on detect samples monitored (other than the controls) were for ~13 percent of the inne r 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 dosim eter values were at l east a factor of 10 lower than the unusually high inner dosimeter values for subject AE18 mentione d above, the impact of the non-detects is negligible.Page 16 of 38Table 2. Aerosol: Summary (Empirical) of Dermal Monitoring Results – Various Clothing Configurations and No Glove Scenario.ClusterSubjectAaiH(lb)SurfaceArea(sqft)Time(min)Samplingand[Spraying]HandsOnly(mg/lbAI)DermalUnitExposures(mg/lbai)Long‐LongLong‐ShortShort‐Short1AE20.00155270732[17]122177261411AE50.002403118457[29]54.188.3142248AE330.002734167363[25]259345443837AE180.00321941052[28]1788509531179AE220.0046742419112[43]75.797.5150325AE290.0052912146119[79]117157250471Mean0.003312142373[37]134286366579Std0.00141779735[22]74.42913073582AE30.00135173148[13]571606688992AE270.002099101442[16]143229399742AE40.002646108446[21]155253404844AE320.003439142268[28]60.2173249479AE100.0038581706108[44]128182272542AE300.0044092408128[38]121249430780Mean0.002967139473[27]196282407730Std0.00114660135[13]1871621571913AE90.00127241725[9]86.5140249492AE210.00209771641[14]54.4100183351AE240.00251384855[21]131301477818AE170.003219111564[22]105204380811AE120.004145161886[29]81.8136242555AE280.0044311459115[42]174240412884Mean0.002946102964[23]105187324652Std0.00121945732[11]42.175.6115216OverallMean0.003075128270[29]145252366653Std0.00120162332[16]118191200257Median0.002976115060[27]122193326648GeoMean0.002836113263[25]12120832460295th%le0.0047662410120[49]3066437281020Page 17 of 38Table 3. Aerosol: Summary (Empirical) of Inhalation Monitoring Results.ClusterSubjectAaiH(lb)SamplingTime(min)AirConcentrationasMeasuredinFieldAirConcentrationNormalizedbyAaiH((mg/m3)/lbAI)Total(mg/m3)<100um(mg/m3)Total<100μm<10μm<2.5μm1AE20.001552320.1280.14382.592.148.417.0AE50.002403570.1240.12651.652.427.68.4AE330.002734630.1220.09544.634.712.95.2AE180.003219520.2940.28491.388.230.214.2AE220.0046741120.1000.09621.420.611.57.5AE290.0052911190.1530.12928.924.415.88.5Mean0.003312730.1540.14653.452.124.410.1Std0.001417350.0710.07028.231.514.14.472AE30.001351480.0430.05631.441.127.710.8AE270.002099420.1180.08956.242.527.613.0AE40.002646460.3020.22111483.561.025.8AE320.003439680.2020.12458.736.117.17.97AE100.0038581080.1880.10448.727.09.654.18AE300.0044091250.1030.06623.414.97.482.98Mean0.002967730.1590.11055.440.825.110.8Std0.001146350.0910.06032.023.319.68.293AE90.001272250.1450.11611491.257.436.8AE210.002097410.1220.10258.248.717.04.52AE240.002513550.1740.10869.243.014.46.19AE170.003219640.2690.17583.654.436.415.1AE120.004145860.1690.12140.829.218.79.90AE280.0044311150.2320.14652.432.915.54.48Mean0.002946640.1850.12869.749.926.612.8Std0.001219320.0550.02826.222.317.212.4OverallMean0.003075700.1660.12859.547.625.311.3Std0.001201320.0710.05428.125.016.18.54Median0.002976600.1490.11954.341.817.98.46GeoMean0.002836630.1510.11953.341.921.39.0695th%le0.0047661200.2950.23011491.358.027.5Page 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 objecti ve of a relative 3-fold accur acy (i.e., K = 3). Using the SAS code originally developed by the Agricultur al Handler Exposure Task Force (AHETF) and independently confirmed by the Health Effect s Division (HED) (and now modified by AD), EPA has determined, and presents, the analysis that th e aerosol study results meet the 3-fold relative accuracy objective. Appendix A provides the detail benchmark analysis which is summarized as

follows:

Benchmark Objective: fo ld Relative Accuracy (fRA)

The benchmark objective for AEATF II scenarios is for select statistics – the geometric mean (GM), the arithmetic mean (AM), and the 95 th percentile (P95) – to be accurate within 3- fold with 95% confidence (i.e ., “fold relative accuracy”). EPA has analyzed the data using various statistical techniques to evaluate this benc hmark. First, to characterize the unit exposures (also referred to as “normalized exposure”), log normal probability plots of dermal and inhalation UEs (adjusted for residue method collection efficien cies) are provided in Fi gures 2 to 5 for the 3 clothing configurations as we ll as inhalation exposure. Thes e 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 re view are natura l 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 7.0log (Normalized Long Dermal Exposure)

Normal Quantiles

Figure 2. Quantile plot of normalized lo ng 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 7.25log (Normalized Short Dermal Exposure)

Normal Quantiles

Figure 3. Quantile plot of normalized shor t 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 7.0lnnrmdermls

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 4.75log (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 th ese 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 esti mates with their respective upper and lower 95% confidence limits. Table 4 below presents the resu lts for the aerosol scenario, for the long pants,

short-sleeved shirt and the inhala tion exposures. The results of the benchmark analysis for the other clothing configurations ar e 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 pres ent confidence intervals computed using a non-parametric bootstrap approach instead of the bootstrap parame tric approach, as suggested by HSRB reviewers of the previous mop study. The pa rametric bootstrap approach assumes that the exposure data were generated from the fitte d 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 configurati ons 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 ex posures 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 di stinguish, with 80% sta tistical power, complete proportionality from complete independence betw een 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/m 3 /lb ai) 95% CI fRA

GM S 324.0 257.5 –

409.1 1.3 53.3 42.3 – 67.2 1.3

GSD S 1.6 1.4 – 1.9 1.2 1.6 1.4 – 1.9 1.2

GM M 324.0 257.5 –

409.1 1.3 53.3 42.3 – 67.2 1.3

GSD M 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

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

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

GM M = variance component model-based geometric mean

GSD M = variance component model-based geometric standard deviation

ICC = intra-cluster correlation

AM S 365.7 285.6 –

466.3 1.3 59.5 46.9 – 76.6 1.3

AM U 366.7 287.2 –

471.1 1.3 60.3 47.2 – 77.4 1.3

AM M 366.7 287.4 –

472.4 1.3 60.3 47.2 – 77.6 1.3

AM S = average of 18 unit exposures

AM U = arithmetic mean based on GM S = GM S *exp{0.5*(ln(GSD S ) 2 }

AM M = variance component model-based arithmetic mean = GM M * exp{0.5*(ln(GSD M ) 2 }

P95 S 953.4 509.9 –

1417.9 1.9 114.2 83.8 – 233.0 2.0

P95 U 734.6 509.1 –

1038.7 1.4 120.7 83.7 – 170.7 1.4

P95 M 734.6 511.6 –

1050.9 1.4 120.7 84.1 – 172.7 1.4

P95 S = 95 th percentile (i.e., estimated as the maximum unit exposure from the 18 unit exposures)

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

P95 M = variance component model-based 95 th percentile = GM M * GSD M 1.645Page 22 of 38 To evaluate the relationship for this scen ario EPA performed regression analysis of log(exposure) and log(AaiH) to determine if th e slope is not significantly different than 1 – providing support for a proportional re lationship – or if the slope is not significantly different than 0 – providing support for an independent rela tionship. If slope is positive, not zero and not 1 then the exposure tends to increase with the Ai aH but not proportionally, so that, for example, doubling the AiaH will not tend to double the expo sure. If the slope confidence interval

excludes both 1 and 0 then the statistical eviden ce 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 ch ange in log lb ai. A slope of one implies that the log of the unit ex posure (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 na ture of the data and were used to evaluate the relationship between exposure and AaiH. Appendix A also pr ovides 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 a nd rejected independence. The estimated slopes were all at least 0.81 (proportionality is when th e slope is one for the und erlying population of all potential janitor exposures). Th e results of the proportionality analysis for the three clothing configurations should be consistent on physic al grounds; either all or none of the clothing configurations should show propor tionality to AaiH. To investigate the proportionality issues further, an alternative model (“re peated measures”) was developed to fit the data from all of the clothing configurations. The r eader is referred to Appendix A and the SAS code for specific details on this repeat ed measures model.

For inhalation exposure, the statistical analyses rejected proportionali ty 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-sleev ed shirt dermal exposure and for inhalation exposure (the two slopes in Figures 6 and 7 are identical; and ther efore, 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 configurat ions 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 esti mated 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) indicat es at least 80% statistical power, which was achieved for the short pants a nd 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 e xposure 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 co ntain 0). The results fo r the inhalation modelsPage 23 of 38 indicate that exposure is not di rectly proportional (1:1) to AaiH (i.e., the confidence interval does not include 1) and suggest that exposure is independent of Ai aH (i.e., confidence interval contains 0). For more details in cluding 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/m 3 ) Mixed 0.43 -0.11 – 0.97 1.08

Figure 6. Mixed and simple linear regression plots for long short dermal exposurePage 24 of 38 Figure 7. Mixed and simple linear regres sion 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 unknow n 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 th e amount of active ingredient.

As an exception, for the short pants and short sleeves clothing confi guration, 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 am ount 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 th e over-prediction will be very

small.

Table 6 gives the minimum amount of active in gredient 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 e xposure calculated using the proportionality model (where the slope of log exposure against log ai is assumed to be one) and using the more generalPage 25 of 38 mixed model (where the slope of log exposure against ai is estim ated). The threshold is where the two predicted means are the same. The proporti onality model uses unit exposures to estimate the exposure for a given amount of active ingredie nt, 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 Expo sure 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/m 3 ) Mixed 0.43 0.00272

*For this case, slope > 1 and so the proporti onality model under-predicts exposure for pounds of active ingredient above th e 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 ex posure using the proportionality and nonproportionality models; threshold value.

3.0 Discussion of Data Gen eralizations and Limitations

The regulatory need for a generic data base of pesticide handlers for antimicrobial pesticide products has been discussed previously (Christi an 2007). This aerosol study was designed to represent the high end of potentia l exposure for workers spraying su rfaces using an antimicrobial product packaged in an aerosol spray can. The study design also incorpor ated random diversity

selection where feasible. Such a study design requires a disc ussion 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 acro ss the country, limits to some degree the statistical generalizati ons of the data. Thus we cannot determine whether these results provide unbiased estimates of e xposure distributions from the sp raying 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 distri bution for these scenario s is independent of the geographic location. The statistical lim itations of the purposive site selection are deemed acceptable by the Joint Regulatory Committee (JRC). It is reasonable to assumePage 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 differe nt from spraying in other types of buildings that have similar HVAC systems. Given a limited set of re sources 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 spendi ng 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, resp ectively) from previously

conducted studies rather than for ADBAC itself. At least 2 uncertainties need to be acknowledged and discussed. First, the solubi lity of the test materials may affect the

removal efficiencies. Accordi ng to the study report (page 61 – 62)... “With the known chemical similarity between the quaternary salt s 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 sol ubility 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 corre ction of face/neck wipe results by a 89.4% removal efficiency.” EPA acknowledges the high removal e fficiencies for hand washes of the various studies available (DDAC and ADBAS), and will wait for the submission of the solubility testing to further comm ent 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 th e 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 underestim ate 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 th e ADBAS wipes were moistened with isopropanol (IPA) while the DDAC study used a 50/50 IPA/water solu tion (as did this aerosol study). Finally, ther e is a potential discrepancy in the ADBAS wipe removal efficiency (88.2% reported in study versus 89.4% used as the corr ection) and EPA will

consult with the AEATF II for clarification. EPA has weighe d the uncertainties of the available removal efficiency da ta against the need for an a dditional study to help clarify hand removal efficiencies. Although EPA view s the removal efficiency studies as having limitations, we are not recommending a new st udy be conducted prior to using the unit exposures generated in this study. EPA cons iderations 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 co nduct 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 acceptabl e 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 thres hold is reviewed by EPA on a case-by-case basis, particularly for those antimi crobial 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 pestic ide application method for the potential for aerosol generation and the available inha lation 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 resu lted from the spraying of the product packaged in an aerosol can generated in this study ar e acceptable to use for clothing configurations of long pants, long-sleeved shirts, and no gloves; long pants, short-sleeved shir ts, 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 reas onably 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 th e subjects, clusters, and sampling dates were chosen were limited and hence might not be re presentative of all Fr esno users of aerosol cans (e.g., those that use aerosol cans but did not vol unteer), buildings (e .g. only hotels were eligible for this study), and tim e periods (e.g., winter versus summ er, 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 pr one 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 inconsiste nt with the proportionality assumption that dermal exposure tends to increase proportio nally with AaiH. The choice of normalizing variable of AaiH is based upon considerati ons of suitability for product labeling and consistency between scenarios. The results fo r inhalation exposure are inconsistent with proportionality. However, for inhalation exposure, the use of this incons istent 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 hea lth protective regulator y decisions. Table 6 above provides for the minimum amount of AaiH for the normalized exposures to be overpredicting exposures (i.e., protec tive 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 st udy and concludes that the AEATF II made the appropriate changes to the protocol propos ed 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 andPage 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. Expos ures occurring while wiping are available in a previously submitte d 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 respir able particles (<2.5 μm). These results are graphically illustrated in Figure 10. EPA will consult with the JRC and inhalation toxicologists to determine which i nhalation exposure monitoring results from this study are best suited to each individual asse ssment (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 revi ew 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 thr ee clothing configurati ons and of inhalation

exposure.

• The data provided 80% statistical power to distinguish complete proportionality or independence between exposure and AaiH fo r 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 ex trapolating the normalized exposure (mg dermal/lb ai or mg/m 3 inhalation/lb ai) to other chemi cal assessments (i.e., using these unit exposures as surrogates to assess other chemicals that handle mo re active ingredient than the threshold).Page 31 of 38 Figure 10. Comparison of ADBAC Air Concen trations Measured Using the OVS Tube and the Respicon Sampler.Page 32 of 38 5.0 References

ACC. 2011. American Chemistry Council, An timicrobial Exposure Assessment Task Force II (AEATF II) Governing Document for a Mult i-year Antimicrobial Chemical Exposure Monitoring Program. Interim Draft Do cument. Version 3. July 8, 2011.

Boatwright, M. (2007). Determinat ion of Removal Efficiency of Didecyl Dimethyl Ammonium Chloride (DDAC) from Hand Surfaces Using Is opropyl Alcohol/Water Wash. Unpublished study prepared by Golden Pacific Laborat ories under Study No. 050211. 341 p. MRID

47214801.

Boatwright, M. (2008). Determ ination 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 AEAT F II Aerosol Scenario Design and Protocol for Exposure Monitoring. Memorandum dated Se ptember 21, 2009 from Timothy Leighton and Cassi Walls, PhD. to Norm Cook, An timicrobials Division, EPA/OPP.

Hackathorn, D.R. and D.C. Eberhart. 1985. Data Ba se 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 Du ring Application of a Liquid Antimicrobial Pesticide Product Using a Pressurized Aero sol Can for Indoor Surface Disinfecting. Unpublished study prepared by Golden Pacifi c 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 InformationPage 36 of 38 Respicon Air Sampler Information

Introduction

The Respicon sampler is a multi-stage, virtual imp actor that traps airbor ne particles onto three

individual filters. The first im pactor 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 samp ling 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 monodisp erse solid particles with aerodynamic diameters ranging from

5 to 68 um. The results indica ted that the Respicon matched th e inhalation convention very well at 0.55 m/sec and oversampled at 1.1 m/sec, matc hed the thoracic conven tion very well except for under-sampling of 5 um particles due to i nner particle loss and ma tched 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 author s mentioned that since the average wind speed in an indoor workspace is 0.3 m/sec (per Baldwin a nd 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 pe rsonal sampling of workers in nickel refining operations. Each worker wore IOM and Respicon sa mplers for a full work shift and a total of 132 pairs of samples were collected. The compar ison 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 th e CALTOOL inhalable resu lts were 1.51 and 1.83 times greater than the Respicon in halable results. This data also suggested that the CALTOOL and IOM results were not greater than the Respic on results when the particle size was less than 10 microns. Analysis of the IO M and Respicon personal sample results indicated that the IOM results are generally higher than the Respicon re sults; 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 respir able 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 pl ant. The test aerosol for the laboratory evaluation consisted fine quartz dust. The laboratory study indicated the Respicon yielded inhalable, thoracic and respirable aerosol le vels that were 50%, 60% and 125% of those measured using the IOM sampler. The field stud y indicated that the Resp icon 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 monodisper sed fluorescence-tagged polymer in either aqueous or powder form with aerodynamic diamet ers 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 wa s close to 100 percent efficient at all of the particle sizes while the Respicon matched the conventional respirable and thoracic convention curves but undersampl ed the inhalable fraction.

Rock et al, 2009. The Respicon sampler was compared to five other aerosol sampler types including Anderson cascade impactors, total su spended particulate (TSP) samplers, PM10 samplers, and laser light scattering particle monitors. This evalua tion 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 ge ometric standard deviation of 2.06 um. The

results indicated that the Re spicon underestimated total su spended 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 th e inhalable convention. These studies generally suggest that the undersampling is consistent and can be addressed by using

correction factors that ra nge 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 Occupationa l Hygiene, Volume 42, Issue 5, pp 303-313, 1998.

Feather and Chen, 2003, Design and Use of a Se ttling Chamber for Sampler Evaluation Under Calm-Air Conditions, Feather, G., Chen, B., Aerosol Science and T echnology, 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 Environm ental Monitoring, Volume 4, pp 657-662, 2002. Li et al, 2000, Evaluation of Six Inhalable Ae rosol 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 Te sting 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 J ournal, Volume 64, pp 312-318, May/June 2003.

Rock et al., 2009, Performance Ev aluation of Six Different Aeroso l 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.