AEATF II Vol. 5 Governing Document for a Multi-Year Antimicrobial Chemical Exposure Monitoring Program Page 1 of 194

Antimicrobial Exposure Assessment 

Task Force II (AEATF II) 

VOLUME 5 

Governing Document 

for a Multi-Year 

Antimicrobial Chemical 

Exposure Monitoring Program 

Interim Draft Document 

February 13, 2008 

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TABLE OF CONTENTS 

1 Introduction................................................................................................................. 4 

1.1 Overview of Antimicrobial Pesticides and Their Benefits ................................. 4 

1.2 Regulatory Need for Estimation of Exposures to Antimicrobial Pesticides....... 8 

1.3 Purpose of the Governing Document................................................................ 10 

2 Specific Objectives of the AEATF II Monitoring Program...................................... 12 

3 AEATF II and BHEDTM ........................................................................................... 14 

4 Regulatory Need for Generic Exposure Data ........................................................... 15 

5 Description of AEATF II Monitoring Program and Scenarios................................. 18 

6 Limitations of Existing Data and Justification for Supplemental or Confirmatory

Data .......................................................................................................................... 20 

7 Alternatives to Additional Human Monitoring......................................................... 26 

8 Ethical Considerations .............................................................................................. 27 

8.1 Subject Recruitment Process............................................................................. 27 

8.1.1 Identification and Recruitment of Potential Subjects ............................... 28 

8.1.2 Exclusion of Vulnerable Group(s)............................................................ 32 

8.2 Informed Consent Process ................................................................................ 33 

8.3 Subject Remuneration....................................................................................... 36 

8.4 IRB Review Process ......................................................................................... 38 

8.5 Additional Efforts to Protect Human Subjects.................................................. 40 

9 Study Benefits........................................................................................................... 41 

9.1 Benefits to Subjects........................................................................................... 42 

9.2 Benefits to Society ............................................................................................ 42 

9.3 Likelihood of Realization of Benefits............................................................... 43 

10 Study Risks ............................................................................................................... 43

10.1 Description of Potential Risks to Subjects........................................................ 43 

10.2 Risks of Heat-Related Illness............................................................................ 44 

10.3 Risks of Exposure to Surrogate Antimicrobial Pesticides ................................ 45 

10.4 Risks of Exposure to Face/Neck and Hand Wash Solution.............................. 45 

10.5 Psychological Risks .......................................................................................... 46 

11 Procedures for Monitoring and Preventing Risk to Subjects.................................... 47 

11.1 Medical Management and Stop Rule................................................................ 48 

11.2 Additional Procedures for Monitoring or Preventing Risk to Subjects ............ 49 

12 Risk Assessment for Anticipated Exposures to Proposed Surrogate Chemicals...... 50 

13 Risk Versus Benefit Comparison.............................................................................. 50 

14 Characterization of Potential Study Participants, Exposure Monitoring Methods and 

Ancillary Information .............................................................................................. 51 

14.1 Subject Characteristics...................................................................................... 51 

14.2 Exposure Monitoring Techniques..................................................................... 52 

14.3 Role of Ancillary Study Information ................................................................ 56 

15 Incorporation of Existing Data into BHEDTM........................................................... 57 

16 Monitoring Event Selection ...................................................................................... 58 

16.1 Predicting Future Generic Exposures with Monitoring Events ........................ 58 

16.2 Determining the Number of Monitoring Events............................................... 60 

16.2.1 The Two-Stage ME Selection Process...................................................... 60 

16.2.2 The Two-Stage Random Sampling Reference Model .............................. 61 

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16.2.3 Benchmark Objective................................................................................ 62 

16.2.4 Sample Size............................................................................................... 63 

16.3 General Diversity Selection Guidelines............................................................ 63 

16.3.1 Stratified Diversity Selection.................................................................... 64 

16.3.2 Diversity Selection of First-Stage Units ................................................... 65 

16.3.3 Diversity Selection of Second-Stage Units............................................... 65 

17 Description and Role of AEATF II Studies.............................................................. 66 

18 Documentation Procedures ....................................................................................... 67 

19 Quality Assurance Procedures .................................................................................. 68 

20 Quality Control Procedures....................................................................................... 68 

21 Reporting Process ..................................................................................................... 70 

22 Scenario Monographs ............................................................................................... 71 

23 Evaluation of Data Adequacy for Completed BHEDTM Scenarios........................... 72 

23.1 Benchmark Adequacy of the Completed Scenario ........................................... 72 

23.2 The Impact of Ignoring Clusters....................................................................... 73 

24 References................................................................................................................. 75 

Appendix A: AEATF II Scoping Document: ................................................................... 79 

Appendix B. Antimicrobial Product Use Sites and Categories ........................................ 92 

Appendix C. Glossary of Terms ..................................................................................... 110 

Appendix D. AEATF II Acceptance Criteria for Existing Studies................................. 119 

Appendix E. Designing Monitoring Events to Predict Future Exposure under 

Antimicrobial Handling Scenarios......................................................................... 124 

Appendix F: Evaluation of Existing PHED Applicator Exposure Data for Hand-Held 

Aerosol Spray......................................................................................................... 141 

Appendix G: List of Current AEATF II Standard Operating Procedures (SOPs).......... 192 

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1 Introduction 

Microorganisms grow anywhere moisture and nutrients are available. 

Antimicrobial pesticides are essential to control microorganisms that otherwise 

would result in economic losses, wasted resources, and human and animal 

illness. Generally, U.S. Environmental Protection Agency (EPA) regulates as 

pesticides those antimicrobials that target microorganism growth on inanimate 

objects. Pesticides and pesticide products are defined by EPA in the Federal 

Insecticide, Fungicide and Rodenticide Act (FIFRA; EPA 1989), 40 CFR (Code of 

Federal Regulations), Section 2(u), Parts 152.3 and 152.15. Other categories of 

chemicals that control microorganisms used as drugs and in human and animal 

food and human personal care and cosmetic products are regulated by the U.S. 

Food and Drug Administration (FDA). Only those categories regulated by EPA 

are discussed here. 

1.1 Overview of Antimicrobial Pesticides and Their Benefits 

Antimicrobials are broadly grouped as “public health” or “nonpublic health” 

depending on whether or not claims are made to control microorganisms 

pathogenic to man and which occur on inanimate objects. However, that simple 

grouping does not adequately identify the uses or benefits of antimicrobial 

pesticides. 

A. Public health antimicrobial pesticides are those that carry claims to control 

on environmental surfaces microorganisms that are pathogenic to man. Claims 

to sanitize, disinfect, or sterilize are considered de facto claims to control 

microorganisms pathogenic to man (including bacteria, fungi, and viruses). 

However, these products are used across a very broad range of use sites and 

applications, including everything from hospital surfaces to home bathrooms, 

from restaurant food processing and handling areas to the home kitchen, from 

municipal drinking water systems and municipal swimming pools to the backyard 

swimming pool, and from commercial or hospital laundries to everyday home 

laundry use. These products are regularly used in homes, offices, schools, 

hospitals, restaurants, food processing facilities, and a large variety of industrial 

facilities as well as farm and animal premises. These products also are used to 

help ensure that the water we drink is not contaminated by pathogenic 

microorganisms. 

In order to obtain an EPA registration, all antimicrobial products that make a 

public health claim must submit efficacy data based on specific application 

instructions and conducted according to strict protocols and must document a 

very high level of performance under stringent conditions. Efficacy testing is a 

requirement and is defined under FIFRA in 40 CFR Part 158.640. These 

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products help to provide protection against food-borne diseases produced by 

Salmonella or E. coli, as well as against pathogens such as Staphylococcus, 

Norovirus, SARS, and HIV/AIDS virus. 

These products include common and well-known chemicals such as hydrogen 

peroxide and sodium hypochlorite, as well as a large number of other 

compounds. The following is a partial list of sites where public health 

antimicrobial pesticides can be used: 

• Hospitals, nursing homes, medical and dental offices, sick 

rooms, and hospices 

• Homeless and emergency shelters, locker rooms, and 

communal living quarters 

• Meat and poultry, seafood, processed food, beverage and dairy, 

and other food storage and processing facilities, agricultural 

premises, animal premises and farms 

• Restaurants, cafeterias, and institutional food services industry 

• Residences, schools, public facilities, senior and child day care 

facilities 

• Public water treatment facilities, personal and emergency water 

treatments 

• Swimming pools, hot tubs, whirlpools, and related facilities 

B. Nonpublic health antimicrobial pesticides are all antimicrobial pesticides 

other than those that claim to control microorganisms pathogenic to man. These 

include antimicrobial pesticides to control, on environmental surfaces, diseases 

pathogenic to animals but not to man (such as hoof and mouth disease, bird flu 

in poultry houses, various diseases in kennels or veterinary facilities). However, 

this grouping also includes a diverse range of products that provide protection 

against microbial degradation, contamination or fouling to inanimate articles, 

substances, systems or processes. Essentially any organic system in the 

presence of moisture is subject to attack by microorganisms, and prevention of 

such attack helps preserve critical resources, extend the useful life of the items, 

minimize disposal, and improve the overall utility of those articles, substances, 

systems, and processes. In many cases, the use of an antimicrobial also can 

minimize or obviate the need for the use of other chemicals or treatments later on 

that can result in greater human or environmental exposure. These 

antimicrobials also are used to improve energy efficiency. Included within this 

category are the following types of antimicrobial pesticides: 

1. Material preservatives: Virtually all water-based products are subject to 

microbial decay. If microbial growth is uncontrolled, the in-service or shelf life of 

manufactured goods is significantly reduced, resulting in economic losses and 

wasted resources. Following is a partial list of products that must be preserved 

to prevent premature deterioration and decay. The need to dispose of spoiled 

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products wastes resources and could increase substantially the burden on the 

environment. 

Latex emulsions 

Paints and coatings 

Pigment dispersions 

Slurries 

Adhesives, caulks and joint compounds 

Printing ink 

Non-clothing textiles (e.g., fire hoses, tarpaulins, cordage, and canvas) 

Leather and suede 

Cotton and wool fabrics 

Paper and package coatings and additives 

Lumber, wood, plywood, particleboard, and other cellulose-derived 

materials 

Plastics, vinyls and polyurethane 

Polymer emulsions 

Detergents, cleaners and other consumer products 

Jet fuel and other petroleum-derived fuels 

Concrete admixtures 

Many of these products would be impractical without antimicrobials to preserve 

them. As an example, latex paints are easier to clean up, have lower odor and 

lower levels of volatile organic carbons than oil and solvent-based formulations. 

However, bacteria can proliferate in the water-based latex medium. Bacterial 

action produces enzymes which can destroy the thickeners in paint overnight. 

Gases resulting from bacterial metabolism not only result in foul odors, but also 

bursting cans, an obvious safety hazard. Incorporating an antimicrobial prevents 

bacterial growth in cans containing latex paint. In fact, antimicrobials made this 

product possible. 

Similar situations exist for a wide variety of products, including water-based 

adhesives, latex emulsions, pigment dispersion, caulking compounds, and 

others. Spoilage of any of these products can result in gas formation, offensive 

odors, color changes, viscosity loss, and pH drift, any one of which may mean 

loss of functionality. These products, which along with paint represent nearly $20 

billion to the US economy, require the use of antimicrobials. 

2. Poultry houses, Egg Producing Facilities, Milking Houses and Other 

Agricultural Premises 

Disinfectants, virucides, fungicides, and sanitizers control or eliminate animal 

pathogens. This has become increasingly important as increased international 

movement has led to the spread of devastating animal diseases such as foot and 

mouth disease, Newcastle disease, and avian flu. 

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3. Water Treatment 

a. Comfort Cooling, HVAC, etc. – Water must be treated to control the 

growth of microorganisms that, left uncontrolled, could reduce efficiency and 

increase energy usage, pit and corrode equipment as well as causing fouling and 

malodors. 

b. Cooling Water Systems -- Water is used for cooling industrial processes 

and as a means of heat exchange. Any industrial processor that produces heat, 

either incidentally, as in, nuclear power plants, other utilities, or those sites 

running heavy equipment, must use coolants to maintain desirable temperature 

ranges and prevent overheating. Otherwise, systems become fouled, equipment 

is damaged, energy consumption increases, and processes fail. For example, 

electrical utilities and many manufacturing facilities use water for process cooling. 

To minimize consumption, the water is cooled and re-circulated. If untreated, 

microbial deposits will form in the system resulting in reduced efficiency yielding 

increased production costs, increased energy consumption, and increased water 

requirements. In extreme cases, biological fouling can compromise the integrity 

of the industrial equipment due to the corrosivity of bacterial waste products. 

c. Industrial Process Waters – Many processes depend upon water as a 

key component of processing (e.g., pulp and paper mills). Treatment is 

necessary to prevent odor, clogging and fouling of systems, protect equipment 

from corrosion, and reduce energy needs and treatment of water prior to 

discharge. Unrestrained microorganism growth in pulp and paper mills interferes 

with paper quality by degrading and staining pulps, causing transparent slime 

spots, decreasing durability, and chemically degrading fibers. 

4. Marine Antifoulants 

Any submerged surface is rapidly fouled with micro- and macro-organisms. On 

ships and boats, fouled bottoms decrease maneuverability and safety, increase 

energy consumption, while reducing speed and performance. Antifoulants 

dramatically decrease the growth of fouling organisms for up to five years. They 

also reduce the spread of invasive alien species, which can have highly 

detrimental economic and environmental impacts and also can pose threats to 

human, animal and plant life. When incorporated into paint on the hull of ships, 

antimicrobials prevent biological deposits. A slime layer only one millimeter thick 

on the hull can reduce speed by 15% and increase fuel costs by more than $1 

million in a single year. Heavier deposits not only cause further speed reductions 

and loss of maneuverability, they can result in corrosion and limit the life of the 

coating, requiring premature dry-docking. With every six-month extension of the 

time between dry-docking for the world’s fleet, the use of antimicrobials results in 

estimated annual cost savings of over $800 million. 

5. Metalworking Fluids 

Metalworking fluids are used at thousands of manufacturing facilities to cool and 

lubricate metal parts being drilled, milled, ground, or otherwise worked. 

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Functioning also to prevent corrosion and flush metal chips from the worksite, the 

fluids are particularly prone to microbial contamination. Microbial growth causes 

offensive odors and plugs equipment lines leading to corrosion and blemishes on 

the finished surfaces, loss of productivity from slowed equipment speeds and 

necessitating more frequent changes of fluid resulting in unnecessary fluid 

replacement costs as well as increased fluid disposal. Antimicrobials are 

essential for optimizing fluid life, fluid functionality, and worker comfort and 

productivity. While this is effectively a material preservative use, it is considered 

separately from the US EPA regulatory perspective. 

6. Wood Treatment 

Wood rots readily and its organic components provide all the nutrients that 

bacteria and fungi require for growth. Failure to control decay organisms will 

result in structural failure, reduced life cycle, and increased disposal 

requirements. The use of wood preservative chemicals protects a significant 

resource (wood) and can extend the useful life of wood products from just 2-5 

years to more than 20 years, resulting in a significant protection of existing 

forests. Treatment with antimicrobials is vital for wood used structurally in 

buildings. Wood treatment chemicals also can be used to prevent growth of 

algae and molds on the surfaces of wood and to prevent the permanent staining 

of cut lumber by microorganisms, i.e., sapstain. Wood treatment antimicrobials 

are used on newly cut wood surfaces, kiln dried wood, milled wood and other 

building materials. A wide variety of seasoned/unseasoned, indoor/outdoor, and 

terrestrial/marine/aquatic wood items and surfaces are treated with wood 

preservatives. The types of wood products include fresh-cut logs or lumber, 

seasoned building materials, utility poles and fence posts and rails (prior to or 

after being placed in service), structural members, structures, dwellings, 

transportation vehicles, crop growing/harvesting/shipping/storage containers, 

lawn furniture, playground equipment, garden/landscape timbers, and log homes. 

1.2 Regulatory Need for Estimation of Exposures to Antimicrobial 

Pesticides 

EPA regulates pesticides under the statutory authority of the Federal Insecticide, 

Fungicide, and Rodenticide Act (FIFRA). Currently, antimicrobials registered 

prior to November 1, 1984, are going through a re-registration process. Starting 

in 2007, all antimicrobials also will be reviewed on a 15-year cycle as part of 

EPA’s “registration review” program. In both programs, EPA must determine 

whether, when used according to the labeled directions, there is a reasonable 

certainty of no harm to humans and no unreasonable risks to the environment. 

In addition, new products, new uses and major amendments to existing 

antimicrobial products must undergo similar review and determination. 

Antimicrobial pesticides are managed by the U.S. EPA’s Office of Pesticide 

Programs (OPP), Antimicrobials Division. 

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EPA has typically based its exposure assessments for antimicrobials on rules of 

thumb, some based on registrant reports of industrial hygiene or other nonpesticide guideline exposure information and some based on EPA-derived 

information. Following passage of the Food Quality Protection Act of 1996, EPA 

produced a number of screening level occupational and residential risk 

assessment guidelines in the form of draft standard operating procedures. 

Although data exist, albeit with limitations, to estimate potential exposures to 

agricultural handlers, i.e., the Pesticide Handlers Exposure Database (PHED; 

EPA 1988), no such database exists for occupational subjects handling 

antimicrobials, outside of limited data (Popendorf et al., 1992) generated by the 

Chemical Manufacturers Association (CMA, now the American Chemistry 

Council or ACC). Further, only limited antimicrobial exposure monitoring data 

exist to support quantitative exposure analyses to this class of pesticides. In the 

past ten years, a few occupational exposure monitoring studies involving 

antimicrobials have been submitted to EPA; however, they have limited utility as 

they cover only certain use patterns. Some of the data generated from these 

studies have a high degree of uncertainty due to low numbers of samples for a 

particular use pattern or insufficient sensitivity in analytical detection. Because 

most antimicrobial pesticides are used in indoor environments, the potential 

exposure from the use of these products could be substantially different from 

handling agricultural pesticides outdoors. Thus, EPA must either to try to use 

agricultural exposure data for industrial and consumer antimicrobial-related 

assessments or use assumptions and predictive models that have not been 

validated to support their exposure assessments and related decision making 

processes. 

In 2001 (letter from Margaret Stasikowski, Director, Health Effects Division to 

Daniel Fay, Valent USA Corporation, 16 March 2001), EPA outlined its 

prospective plans regarding the existing agricultural exposure data contained in 

the Pesticide Handlers Exposure Database (PHED). The letter stated EPA’s 

intention to drastically overhaul PHED version 1.1 because many of the existing 

exposure studies in the database were outdated or scientifically inadequate by 

“today’s standards”. In addition, many exposure scenarios that are being 

assessed by the Agency are under-represented in PHED version 1.1. This is 

particularly apparent with antimicrobial pesticides, where potential exposures are 

very different from those associated with agricultural pesticides. Thus, there is a 

clear need to generate better exposure data to improve the quality of human 

health risks assessments for antimicrobial products. That need has been 

repeatedly identified in regulatory decisions. 

As key elements in the risk assessment process, exposure data allow for 

estimation of absorbed dose, which is then compared to a relevant toxicological 

endpoint from an animal dosing study. The algorithm to calculate the average 

daily dose to a worker is relatively simple. The inputs require an estimate of how 

much chemical is going to be handled during a work shift, a body weight, and a 

measure of exposure potential based upon the activity conducted by the worker 

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(by exposure route). Although there are ranges and uncertainties associated 

with each of these inputs, the measure of exposure for a particular job function or 

activity is where the greatest uncertainty lies. 

The Agency has acknowledged that while the use of existing data and 

assumptions in its human health risk assessment process for antimicrobial 

products is necessary, it would also lead to overly protective labeling, a 

requirement for chemical companies to develop costly product-specific 

confirmatory exposure data, and even the suspension of certain uses. Thus, 

according to EPA, the creation of a consortium to develop generic exposure data 

on occupational activities would be a cost-effective means of generating a large 

amount of high quality credible data (Stasikowski 2001). 

On November 1, 2004, the American Chemistry Council (ACC) Biocides Panel 

established the Antimicrobial Exposure Assessment Task Force II (AEATF II) to 

measure exposure of subjects in mixing and loading operations in industrial 

settings and professionals involved in application of products containing biocides 

in industrial, institutional and residential settings. The AEATF II currently 

consists of forty-three member companies. The purpose of this task force is to 

develop generic exposure data on a broad range of use pattern/application 

method combinations as well as specific post-application exposures (e.g., 

measurements of residue deposition on treated surfaces and the post-application 

transferability of these residues using EPA-recommended environmental 

sampling methods). Each specific set of related tasks, antimicrobial formulations, 

equipment, engineering controls, and worker and/or consumer practices 

considered by AEATF II is termed a scenario. These data obtained for each 

antimicrobial-handling scenario addressed by AEATF II will be used to support 

EPA registration and re-registration of most antimicrobial active ingredients in the 

future. The concept behind a group of companies working together to generate 

jointly-owned data is to reduce individual company’s costs, generate more data 

than would be possible by a single company or even small group of companies 

alone, while providing consistency in design and execution, and obtaining 

coordinated scientific input from appropriate regulatory agencies (U.S. EPA, 

California Department of Pesticide Regulation or CDPR, Health Canada’s Pest 

Management Regulatory Agency or PMRA, and European regulatory authorities). 

1.3 Purpose of the Governing Document 

The purpose of this document is to provide the U.S. EPA, CDPR, PMRA and the 

Human Studies Review Board (HSRB) with a description of the overall scope of 

the AEATF II program, demonstrate the need for additional human exposure 

monitoring data, and explain the proposed methodology for the exposure 

monitoring studies proposed for conduct by the AEATF II. This document also 

describes the plans of the AEATF II to develop a generic database, i.e., the 

Biocide Handlers Exposure Database (BHEDTM). By providing this background 

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information, the AEATF II intends to present a scientifically valid basis for 

conducting the proposed human exposure monitoring studies. 

This draft version of the “Governing Document” focuses on the technical and 

ethical aspects of the AEATF II program. The governing document is being 

submitted to EPA (and other regulatory agencies), and the HSRB, in conjunction 

with each specific study protocol for proposed AEATF II exposure monitoring 

studies. It is anticipated that future versions of this document will be issued to 

incorporate comments and guidance provided by the EPA’s Office of Pesticide 

Programs (OPP) and the HSRB. 

It is important to note that the scientific and ethical aspects of the AEATF II 

program are addressed, more specifically, as part of each proposed protocol 

being submitted to the EPA and HSRB. Thus, this document will be 

supplemented by important study protocol-specific scientific (e.g., study or 

scenario-specific study design and sample size determination) and ethical (e.g., 

how a particular AEATF II study will address recruitment, informing, seeking 

consent, and minimizing risks to study participants) considerations. The ethical 

components of the AEATF II program are based, in part, on recommendations 

made by the National Academy of Sciences Committee on the Use of Third Party 

Toxicity Research with Human Research Participants (e.g., demonstrated need 

for the knowledge to be obtained from intentional human dosing studies, 

justification and documentation of a research design and statistical analysis that 

are adequate to address an important scientific or policy question, an acceptable 

balance of risks and benefits and minimization of risks to participants, equitable 

selection of participants, free and informed consent of participants, review by an 

appropriately constituted IRB or its foreign equivalent) to ensure that AEATF II 

exposure studies, which are to be conducted under EPA Guideline 875 Series A, 

will meet the highest scientific and ethical standards. 

The AEATF II program also addresses relevant feedback provided in the report 

of the HSRB meeting of June 27-30, 2006 (Fisher 2006) which discusses an 

initial review of five study protocols submitted by the Agricultural Handlers 

Exposure Task Force (AHETF), another multi-year exposure monitoring effort. 

The AEATF II program has also been informed by the recent April 18 – 20, 2007 

HSRB meeting (http://www.epa.gov/osa/hsrb/apr-18-20-2007-publicmeeting.htm) and EPA’s “Draft Framework for Developing Best Practices for 

Recruiting, Screening, and Informing Human Subjects, and Obtaining Consent 

for Occupational Exposure Studies with Pesticides” presented at this meeting. 

Furthermore, the AEATF II program incorporates recent recommendations 

provided by the EPA’s Science Advisory Panel (EPA 2007). 

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2 Specific Objectives of the AEATF II Monitoring 

Program 

The primary objective of the AEATF II is to generate handler exposure monitoring 

studies to estimate characterize exposures distributions for a multitude of 

occupational / industrial and consumer exposure scenarios involving 

antimicrobial-containing products. The data from these studies will fill gaps in the 

current antimicrobial exposure dataset and allow for more precise estimations of 

potential dermal and inhalation occupational risks to workers and consumers 

handling products containing antimicrobial agents. The study results will be 

placed into a computer software database (i.e., the Biocide Handlers Exposure 

Database or BHEDTM) allowing the data to be used generically for risk 

assessments of all antimicrobial agents. The AEATF II will exercise the rights 

associated with submission of data under the Federal Insecticide, Fungicide, and 

Rodenticide Act (FIFRA) in connection with BHEDTM. The database will be 

available to both AEATF II company members and regulatory agencies for 

registration and re-registration purposes. 

The AEATF II study program has been designed to cover the most common 

types of occupational and residential handling scenarios involving antimicrobials. 

Initially, EPA identified application methods and use scenarios based on a review 

of antimicrobial product labels and/or Agency areas of interest, in conjunction 

with 12 “Use Site Groups” that EPA has used historically to delineate 

antimicrobials use sites. Some application methods have been combined and 

the following Use Site Groups and 14 application methods/use scenarios have 

been agreed upon by the EPA, Canadian, and California regulatory agencies and 

members of the AEATF II. The EPA Use Site Groups and Application 

Methods/Use scenarios include the following (see Appendices A and B for 

additional explanation and information): 

EPA Use Site Groups 

1. Agricultural Premises and Equipment 

2. Food Handling/Storage Establishments Premises/Equipment 

3. Commercial, Institutional & Industrial Premises/Equipment 

4. Residential and Public Access Premises 

5. Medical Premises and Equipment 

6. Human Drinking Water Systems 

7. Industrial Process Water Systems 

8. Material Preservatives 

9. Antifoulant Coatings 

10. Wood Preservatives 

11. Swimming Pools 

12. Aquatic Areas 

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Application Methods/Use Scenarios 

1. Aerosol Spray 

2. High to Low Pressure Spray 

3. Pour Liquid 

4. Pump Liquid 

5. Pour Solid 

6. Place Solid 

7. Mop 

8. Wipe 

9. Fog 

10. Brush/Roll 

11. Airless Spray 

12. Immerse/Dip/Soak 

13. Pressure Treat 

14. Metalworking Fluid 

Post-application scenarios are still under discussion, with the possibility of 

conducting two studies – one to evaluate exposure (e.g., transferable residue 

measurements) to antimicrobials on soft surfaces (such as carpet) and one to 

evaluate exposure to antimicrobials on hard surfaces (such as countertops or 

wood decking). 

All human subject monitoring studies will be conducted using standard industrial 

hygiene passive dosimetry techniques, consisting of both dermal and inhalation 

monitoring. All Task Force studies will be conducted according to current EPA 

Office of Pesticide Programs’ Harmonized Test Guidelines – Series 875 

Occupational and Residential Exposure Test Guidelines (Series 875 A and B for 

handler and re-entry, respectively) and conducted under Good Laboratory 

Practice standards per 40 CFR Part 160. All monitoring studies will be 

conducted in compliance with all applicable provisions of EPA’s regulations 

providing for the protection of human subjects of research, 40 CFR Part 26. The 

Task Force is designing study protocols that would allow study results to be 

broadly acceptable to both North American and European regulatory authorities. 

Industry-wide generic task forces go through various defined stages, with the 

data generation phase of the task force typically lasting approximately eight 

years. The limit of expenditures for AEATF II is set at $9 million. This is based on 

the assumption that a total of 19 core studies will be conducted, with each study 

containing 15 to 25 sets of individual measurements. The support costs for doing 

this work, such as analytical method development, database construction, legal, 

task force management, etc., are included in the stated dollar amount. This 

sizeable investment by the antimicrobial industry confirms the commitment to 

generate the data needed to accurately assess risks to persons using 

antimicrobial products. 

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3 AEATF II and BHEDTM 

The Antimicrobial Exposure Assessment Task Force II (AEATF II) was 

established to generate data for antimicrobial pesticide exposure scenarios to 

meet EPA data requirements for registration. AEATF II member companies have 

ongoing data requirements resulting from chemical and product-specific existing 

and announced data call-in notices, anticipated re-registration obligations via 

confirmatory data demands, prospective registration review obligations and 

requirements based on registration applications. The member companies 

agreed to jointly develop generic data in support of their respective registration 

obligations since existing data are not adequate. 

The primary AEATF II goal is the collection of worker exposure monitoring data 

and its incorporation into a new generic database that can be used to estimate 

exposure distributions. The database will be a proprietary product of the Task 

Force and will be called BHEDTM (Biocide Handlers Exposure Database). 

BHEDTM will be submitted to EPA and other regulatory agencies, and used by 

those regulators to conduct detailed quantitative exposure assessments to 

support safety determinations for occupational pesticide uses. 

Generic databases were developed over the last twenty years in response to a 

regulatory need to assess the occupational risks associated with a wide range of 

pesticide handling situations. The concept was discussed first in an American 

Chemical Society Symposium in 1984 (Reinert and Severn, 1985; Hackathorn 

and Eberhart, 1985; Honeycutt, 1985) and its development encouraged by a 

FIFRA Scientific Advisory Panel in 1986. In 1992, the Pesticide Handlers 

Exposure Database (PHED) was first released following a joint effort by pesticide 

manufacturers, the EPA, and Canadian regulators (Honeycutt, 1986; Lunchick, 

1994, Reinert, 1986, Leighton and Nielson 1995, Nielson et al. 1995). Since 

then, PHED has been used extensively in a generic manner and has successfully 

supported many occupational risk assessments. However, much of the data in 

PHED are derived from exposure studies that are considered outdated or 

scientifically inadequate by current standards (Stasikowski, 2001). In addition, 

many antimicrobial handler scenarios of interest to EPA are absent or underrepresented in PHED. Other regulatory agencies have expressed similar 

dissatisfaction with the limitations of PHED data. A major purpose of BHEDTM is 

to address deficiencies in existing data such as those included in PHED. And in 

2007, EPA convened another Science Advisory Panel SAP (SAP) to discuss the 

need for new data to replace PHED and the panel agreed with EPA that 

“additional data could significantly improve the Agency’s ability to assess worker 

exposure” (SAP, 2007). A major purpose of the BHEDTM is to address PHED 

deficiencies (and limitations of other existing data sources). 

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Like PHED, BHEDTM will be populated with exposure data for subjects who 

handle antimicrobial pesticides as part of their normal job (occupational) or task 

(consumer), so their participation as subjects in the studies underlying BHEDTM 

will not add appreciably to their typical exposure from handling pesticides. All 

AEATF II studies are designed and conducted in accordance with the latest U.S. 

EPA guidelines for occupational exposure studies. 

The development of BHEDTM is funded and directed by the AEATF II. However, 

an AEATF II Regulatory Agency Advisory Committee (RAAC) has been 

established to promote active participation by interested regulatory agencies. The 

committee is comprised of representatives of the U.S. EPA, the Canadian Pest 

Management Regulatory Agency (PMRA), the California Department of Pesticide 

Regulation (CDPR), and European regulatory authorities. This committee meets 

on an ad hoc basis to review the program progress and provide technical input to 

the AEATF II. 

4 Regulatory Need for Generic Exposure Data 

FIFRA requires the U.S. Environmental Protection Agency to assure that any 

pesticide registered in the United States does not have unreasonable adverse 

effects on subjects handling that pesticide 

(http://www.epa.gov/pesticides/regulating/laws.htm). The Pest Control Products 

Act (PCPA; http://www.pmra-arla.gc.ca/english/legis/pcpa-e.html) requires a 

similar determination by Health Canada. This safety determination is generally 

made by means of quantitative risk assessment and risk management 

procedures. Risk assessments require a detailed evaluation of the toxicity of the 

pesticide and an estimation or measurement of the exposure potential for users 

(and/or amount of pesticide absorbed by the individual as a consequence of its 

use). Exposure or absorbed dose estimates are quantitatively compared to noeffect exposure levels (often from experimental animals) for hazards identified in 

standardized toxicology studies. During the risk evaluation, the likelihood of the 

expression of any toxicological effect on the subjects and a comparison of the 

risks and benefits are considered. This basic paradigm (hazard identification, 

dose-response assessment, exposure assessment, and risk characterization) 

was summarized by the National Academy of Sciences and has become the 

standard for risk assessment by regulatory agencies (NAS, 1983; NAS, 2006). 

More recently, the pesticide handler risk assessment process was fully described 

in a summary document prepared for an EPA SAP review of exposure 

methodologies (EPA 2007). 

The AEATF II database, BHEDTM, is intended to provide the regulatory agencies 

with the handler potential exposure data necessary for them to perform the 

exposure assessment portion of safety determinations. Toxicology data and 

benefit information are product-specific and must be provided by individual 

pesticide product registrants. 

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When estimating exposure to persons who handle pesticides, a major challenge 

to overcome is that several parameters contribute to the likelihood and level of 

exposure. These include things such as handling liquids versus solids, how the 

product is packaged, using open versus closed systems, application with various 

equipment types, how much product is handled, whether or not personal 

protective equipment (PPE) is worn, and whether the worker mixes/loads or 

applies the product or does both. The number of combinations of these 

parameters makes it impossible to generate human exposure data for all 

situations, so a number of simplifying approaches have been adopted. These 

include: 

1) Establishing various ‘scenarios’ (see Appendix A) that cover common 

combinations of these parameters and generating data for those 

scenarios; 

2) Restricting some scenarios to include the higher exposure portions of 

them (e.g., professional janitorial personnel performing mopping tasks 

using higher exposure potential technologies such as string and bucket 

mop systems); 

3) Generating data with subjects wearing minimal PPE; 

4) Using data for one chemical/product as a surrogate for another (similar) 

product; and 

5) Assembling data into a generic database (e.g., BHED) for use as 

surrogate data applicable to many products or for multiple job functions 

performed by one person. 

Since the early 1980’s it has been the consensus of the scientific community that 

the amount of residue that contacts a worker’s clothing and skin, and the amount 

of residue that is available for inhalation, are primarily a function of physical 

rather than chemical factors. That is, the chemical nature of the active ingredient 

in a pesticide product has little influence on the extent of exposure compared to 

physical parameters associated with the use of the product. The physical 

parameters include formulation type (e.g., liquid or granule product), method of 

application, and the way in which a person handles the pesticide during mixing, 

loading and application. Because of this, exposure potential is considered 

“generic” since it is independent of the specific active ingredient (Hackathorn, 

1985; Honeycutt, 1985 and 1986; Reinert, 1985). Generic exposure data may 

therefore be used in lieu of product-specific data for most safety assessments. 

However, some situations, such as exposure to volatile compounds, (e.g., 

chlorine dioxide) require consideration of chemical-specific adjustment factors or 

modeling approaches (e.g., indoor air models). 

The use of generic data enhances the efficiency of regulatory agencies in 

conducting exposure assessments. Rather than relying on individual studies to 

evaluate case-by-case uses of each pesticide product, a single, comprehensive 

database of high quality data applicable to most products can be used. The 

broad applicability of generic data and the resulting efficiency of their use in 

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regulatory safety assessments led to the widespread acceptance of PHED. 

PHED components were created by assembling exposure data from studies that 

had already been conducted and submitted to the EPA. 

Most of the pesticide exposure data available for inclusion in the initial 1992 

version of PHED had been conducted by individual pesticide manufacturers who 

designed their studies to support the registration of a specific product or a group 

of similar products. It was very common for these companies to generate a set 

of exposure data that represented the worst case for exposure potential 

incorporating design features such as the maximum use rate, minimum PPE, and 

minimum engineering controls. If a risk assessment was acceptable for such a 

situation, then it was argued that lower use rates, additional PPE, and additional 

engineering controls would certainly also pass a risk assessment. However, this 

meant it was common for a study to involve 15 or more measurements of 

essentially the same situation where each person handled the same product, in 

the same packaging, in similar amounts, using the same equipment, and for 

similar periods of time. While these studies are useful for product-specific cases, 

they are not always generically useful. Nevertheless, many of these types of 

studies were assembled to form PHED and collectively the database did seem to 

improve the risk assessment process as regulators could often rely on larger 

data sets to estimate potential exposure. 

However, the available studies for inclusion in the PHED, in hindsight, were not 

designed, a priori, to meet the needs of a generic database and thus, have some 

technical limitations. In addition, it is now an older database and many use 

practices have changed. Further, it has limited applicability to most antimicrobial 

pesticide uses. Exposure monitoring methods have also changed. The basic 

passive dosimetry methodology has long been accepted as a standard, 

reproducible procedure that provides accurate and reliable data that does not 

underestimate exposure (Ross et al., 2007). Even though the passive dosimetry 

methodology is still a very sound measure of exposure, there have been some 

improvements. In particular, much of the data in PHED are based on patch 

dosimetry and exposures were often not measured on all body areas for each 

monitoring unit or event, i.e., ME. However, PHED provided reasonable 

estimates of exposure based on the technology of the 1980’s. Today, wholebody garment dosimetry is used instead of patches to improve the ability to 

estimate the distribution of total body exposure. 

There is general consensus among regulatory agencies that the most efficient 

means of generating handler exposure data is to pool technical resources and 

assemble a generic database. This consensus and the extremely limited 

availability of data for antimicrobial pesticides led to the formation of the AEATF II 

in November, 2004. The task force database, BHEDTM, will be designed to 

reflect a logical set of use scenarios with adequate data in each scenario to 

provide reliable estimates of exposure potential and its distribution. Individual 

measurements will involve separate subjects and more diversity in equipment 

and conditions than in PHED, especially for the amount of product handled. 

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5 Description of AEATF II Monitoring Program and 

Scenarios 

The primary purpose of the AEATF II monitoring program is to develop a new 

generation of more accurate and useful information and data on worker and 

consumer exposures to antimicrobials. A secondary purpose is to incorporate 

these data into a generic database (BHEDTM). These data will consist of dermal 

and inhalation exposure estimates derived from monitoring subjects who handle 

pesticides under a variety of circumstances, using various pesticides and 

equipment types. AEATF II refers to each unique handling situation as a 

‘scenario’ and anticipates the database will contain sufficient data to support 

exposure assessments for 14 distinct handling situations, or scenarios (see 

attached “AEATF II Scoping Document” provided as Appendix A; Appendix B 

provides a detailed description of each use site identified in the Scoping 

Document; Appendix C provides a glossary of terms). 

In general, each An antimicrobial scenario is defined as a set of related tasks, 

pesticide formulations, equipment, engineering controls, and worker and/or 

consumer practices. For example, two scenarios of interest are “mopping 

application” and “wiping application.” The scenarios of interest to the Task Force 

fall into two general categories: 

1. Scenarios that are addressed by simulated-condition studies based on 

discrete or segmented tasks (mixing, loading and application methods) 

that can used, separately or in combination, to estimate exposures 

occurring in a variety of use conditions; and 

2. Complex and/or multi-task scenarios that are addressed using in situ (e.g., 

on-site, observational) studies. 

The basic element in both simulated-condition and in situ studies is the 

monitoring unit or monitoring event (ME). Each ME will consist of measuring 

dermal and inhalation exposure potential for a single subject for a time period 

that represents a typical workday. The general approach is to obtain, for each 

scenario, a variety of MEs using different subjects and a diverse set of conditions 

that will reflect current and projected antimicrobial mixing/loading and application 

practices in North America. Diversity in characteristics that are either known or 

assumed to be exposure-related will be emphasized. The measured exposures 

from each scenario-specific set of MEs can then be used to represent the future 

handler-day exposures to arbitrary (i.e. generic) antimicrobial compounds. For 

each scenario, the basic objective always is that the set of MEs adequately 

characterize both the typical and the more extreme exposures expected for a 

single workday. The design of scenario-specific studies and the construction of 

MEs are described in Section 16 and in Appendix E. 

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Collectively, all scenario-specific studies that generate MEs to be included in 

BHEDTM are referred to as the AEATF II monitoring (or testing) program. 

BHEDTM will be used to support North American registrations for existing and 

new pesticide products as required by FIFRA in the United States and the Pest 

Control Products Act (PCPA) in Canada. 

An AEATF II scenario represented by the set MEs in BHEDTM may or may not 

correspond exactly to set of antimicrobial-handling tasks being considered for 

regulatory evaluation. Scenarios that are complex and consist of multiple nonseparable tasks will be addressed by in situ (observational) studies. The MEs 

from these studies can be used directly for regulatory evaluation. In some 

situations, however, the number of possible combinations of tasks would too 

large to address each by a separate scenario. As a result, some AEATF II 

scenarios correspond to single discrete tasks (e.g. mopping, wiping, mixing, etc.) 

that will be addressed by simulated-condition studies. When regulatory interest 

is only in the discrete task (e.g., mopping only) then the scenario in BHETTM is 

directly applicable. However, when interest is in a combination of these tasks 

(e.g. mixing plus mopping) then results from several AEATF II scenarios must be 

combined. 

A single scenario, such as “mopping”, may be defined as a specific task, i.e., the 

mop-based application of a label-specified end-use formulation containing an 

antimicrobial chemical. It is common in institutional settings today that 

automated dispensing systems provide the applicator with ready-to-use mop 

solutions, and the applicator does not mix and load the end-use mop solution in a 

bucket. Therefore, the applicator’s exposure during a single workday in these 

conditions would arise only from the task of application and intermittent disposing 

or emptying the dirty mop bucket solution. The distribution of daily exposures 

under the “mopping” scenario would then adequately describe the handler’s daily 

exposure to the antimicrobial. In other circumstances, however, a mop applicator 

could also be manually mixing and loading the mop solution, i.e., preparing the 

end-use dilution by adding a concentrate to water in a bucket. In these cases, 

the daily exposure for an antimicrobial handler would arise from two discrete 

tasks, i.e., mopping (including dirty mop solution disposal) and mixing/loading of 

mop solution. To provide data for regulatory agencies to address the addition of 

this discrete task (mixing and loading), the AEATF II will conduct separate 

studies of mop application (which would include discrete measurement of 

exposures associated with mopping and dirty mop bucket solution emptying) and 

of mixing and loading via open pouring of liquids. 

At times, user’s of the BHEDTM data may need to consider the distribution of a 

combined exposure from multiple tasks represented by separate scenarios. The 

arithmetic mean of a combined single-day exposure is simply the sum of the 

arithmetic means for each separate task. However, other aspects of the 

combined distribution depend on how exposures for the same individual from 

different tasks are correlated. If the exposures are perfectly correlated (i.e. the 

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correlation is 1) then any percentile of the combined distribution is the sum of the 

percentiles for each task separately. If the same-person-different-task exposures 

are independent, however, then the combined percentiles are less extreme than 

the sum of the separate percentiles. This ‘shrinkage’ of the combined distribution 

is rather minimal and is practically non-detectable if one task’s mean exposure is 

much larger than any of the other tasks mean values. Thus, unless a separate 

estimate of the between-task correlation is available, a practical recommendation 

for most BHEDTM users would be to simply assume that the tasks are maximally 

correlated and add all percentiles. This approach would likely be acceptable in 

the context of regulatory-decision making when relying upon BHEDTM, given the 

overestimation (more conservative) bias associated with summed upperpercentiles. More importantly, the development of normalized exposure data for 

discrete tasks provides the flexibility to construct or assemble and assess multitask exposures and thus, greater utility for a generic exposure database. 

In some instances, there may exist, a discrete task or set of tasks that falls 

outside all the scenarios for which monitoring is planned. This is most often 

because the task is rare or would be expected to give non-detectable exposure 

levels. When reasonable, users of BHEDTM might choose to ignore the task or 

use another scenario as a surrogate for the missing task. For example, in the 

case of mop application, in some cases, a person may pour a concentrated 

formulation containing an antimicrobial into a mop bucket containing water to 

create a label-specified end-use dilution. The exposures (dermal and inhalation) 

that may occur during this liquid pouring task can be addressed with the separate 

“open liquid pouring” study data. The “open liquid pouring” data could be used 

directly as a conservative surrogate for pouring a concentrate into a mop bucket. 

In this example, it is important to adjust the surrogate exposures distribution for 

the amount of active ingredient handled in the specific mop bucket pouring 

situation being assessed. 

6 Limitations of Existing Data and Justification for 

Supplemental or Confirmatory Data 

Since 1992, the EPA has conducted agricultural mixer/loader and applicator 

exposure and risk assessments relying primarily on the exposure data in PHED. 

PHED version 1.01 was initially released in February 1992. It was followed by 

PHED version 1.1 in February 1995. PHED version 1.1 was described by the 

Agency as an incremental improvement over the 1.01 version (Pesticide 

Handlers Exposure Database, User’s Guide Version 1.1, Health Canada, U.S. 

Environmental Protection Agency, American Crop Protection Association, 

February 1995). The forward to Version 1.1 User’s Guide cautions the user that 

the database still has some limitations and should not be considered a panacea 

in estimating pesticide handler exposure. Noting the limitations, the guide states 

that a goal was to release a PHED version 2.0 in 1997. However, no subsequent 

version of PHED has been released. 

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By 2000, the U.S. Environmental Protection Agency began evaluating 

alternatives to PHED. The EPA has outlined its intentions regarding PHED 

(Letter from Margaret Stasikowski, Director, Office of Pesticide Programs, Health 

Effects Division, to Daniel Fay, Valent USA Corporation, 16 March 2001). EPA 

has acknowledged the need to “overhaul” PHED version 1.1 because many of 

the existing exposure studies in the database are outdated or scientifically 

inadequate by “today’s standards”. In addition, many antimicrobial pesticide 

exposure scenarios that are being assessed by the Agency are underrepresented or not even included in PHED version 1.1. 

In summary, PHED suffers from a number of limitations regarding its use as a 

generic exposure database, including: 

• Inadequate number of measurements for one or more body areas; 

• Inadequate quality assurance or quality control data; 

• Use of patch dosimeters instead of whole-body dosimeters; 

• Lack of whole body dermal estimates for subjects (i.e., not all body parts 

monitored for dermal exposure in most studies); 

• Many (>70%) non-quantifiable residues on inner dosimeters; 

• Lack of diversity for test conditions (e.g., same subjects used repeatedly 

or all subjects handling the same amount of product); and 

• Lack of representativeness of test conditions (e.g., equipment or 

procedures that are no longer in common use). 

Issues regarding the adequacy of the data in PHED can be illustrated by reviews 

of the Registration Eligibility Decision (RED) documents issued by EPA as part of 

the recently completed FQPA re-registration process. These documents have 

characterized the existing PHED data as low confidence for the following 

important use patterns. Confidence ratings are based on “number of replicates” 

(quantity) and “QA/QC Grades” (quality). In general, low confidence scenarios 

have fewer than 15 replicates and/or barely acceptable laboratory fortification 

recovery data (or worse). 

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For reference, PHED confidence ratings can be summarized as: 

Confidence 

Rating 

Number of 

Measurements QA/QC Grading 

High >= 15 per body 

part And 

Good laboratory plus good 

field fortification data (or 

better) 

(Grade AB) 

Medium >= 15 per body 

part And 

Moderate laboratory 

fortification data plus 

either poor field fortification or 

moderate storage stability data 

(Grade ABC) 

Low < 15 per body 

part Or 

Barely acceptable (or 

unacceptable) laboratory 

fortification data 

(Grades D or E = All Grades) 

In addition, it should be noted that PHED provides dermal exposure estimates, 

and confidence ratings, for several distinct clothing situations: 

• “no clothes” (i.e., based on outer dosimeters or clothing) 

• single layer of clothing, no gloves (most scenarios) 

• single layer of clothing, with gloves (some scenarios) 

• coveralls over single layer of clothing, with gloves (some scenarios) 

Therefore, PHED can have low confidence for one clothing/PPE situation and 

high confidence for another within an exposure scenario. While protection or 

penetration factors can be used to estimate protected exposure from nonprotected exposure results, or vice versa, this may create additional uncertainty 

for exposure estimates and may not be appropriate for all risk assessments. 

PHED data with potential relevance to antimicrobial scenarios are as follows: 

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PHED Scenario (#) 

Comments re: 

PHED Scenario AEATF II Application Method 

Liquid (3) 

Mixing/Loading (M/L); open 

mixing Pour Liquid 

Pump Liquid (6) M/L; closed mixing Pump Liquid 

Paintbrush/Roller (22) Brush application only Brush / Roll 

Aerosol Spray (10) Application only Aerosol Spray 

High Pressure Spray (19) Application only High Pressure Spray 

High Pressure Spray (35) 

M/L, Liquid, open pouring; and 

application 

Pour Liquid and High Pressure 

Spray 

Lo Pressure Spray (32) 

M/L; Liquid; open pouring; and 

application 

Pour Liquid and Low Pressure 

Spray 

Low Pressure Spray (33) 

M/L; Wettable Powder; and 

application 

Pour Solid and Low Pressure 

Spray 

Low Pressure Spray (18) 

Application only; handwand 

equipment Low Pressure Spray 

Airless Spray (23) Application only; house stain Airless Spray 

Pour Solid (1) M/L; Dry flowable; open mixing Pour Solid 

Pour Solid (2) M/L; Granular; open mixing Pour Solid 

Pour Solid (4) 

M/L; Wettable powder; open 

bag Pour Solid 

In addition to data available in PHED, another source of existing data being used 

by regulatory agencies in the case of antimicrobials is that represented by an 

exposure monitoring program conducted by the CMA (Popendorf et al. 1992). 

On 4 March 1987, a Data Call-In Notice was issued for submission of data for 

antimicrobial pesticide active ingredients. In response, the CMA developed a 

generic biocide exposure assessment protocol and conducted a study, Chemical 

Manufacturers Association Antimicrobial Exposure Assessment Study 

(conducted by Dr. William Popendorf at the University of Iowa; Popendorf et al. 

1992) based on the protocol. The CMA effort originally considered a list of ten 

pesticide active ingredients. This list was reduced to 9, considering several 

criteria. Exposures to seven of these nine chemicals were assessed, as well as 

exposure to zinc chloride, which was used as a surrogate tracer for a process 

and chemical which could not otherwise be assessed. In total, 88 separate MEs 

were obtained for six end-use settings and nine application methods (pour liquid, 

pump, pour solid, place solid, aerosol spray, high pressure spray, low pressure 

spray, mop and wipe) to assess both dermal and inhalation exposures. 

Based on EPA’s review (Mostaghimi 1995), CMA's study met some 

requirements, but was lacking in other areas. Specifically, areas in which the 

Amended Report complied with the procedures specified by the EPA’s dermal 

and inhalation exposure guidelines included the following: 

1. Most of the dermal samples had detection limits low enough to allow 

accurate reporting of the sample, according to EPA guidelines. 

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2. Some of the field recovery data were acceptable; five chemicals had 

acceptable recoveries from gloves, and two chemicals had acceptable 

recoveries from air, and the results were corrected for losses in the field 

using correction factors determined from the recovery data. 

3. The materials used in the analyses were acceptable in most cases and 

were adequate for further analysis. To assess dermal exposure, gauze 

pads were used for dry residues, cotton gloves were used for assessing 

exposure to hands, and placement of dermal pads was found to be 

acceptable. For inhalation exposure, standard flow rates were used for air 

impingers and personal sampling pumps, standard NIOSH factors were 

applied to respirators to estimate reduction of exposure inside the 

respirators; and 

4. Documentation of data collected during laboratory and field operations 

was adequate based on both CMA's description of their data gathering 

efforts and presentation of data provided in Appendix C of CMA’s 

Amended Report (Popendorf et al. 1992). In addition, replicate-specific 

notes were provided for any unusual problems that may have contributed 

to error. 

However, the following areas were found to be lacking: 

1. Good Laboratory Practices, especially in the area of providing quality 

assurance, must be followed more closely; 

2. A majority of extraction efficiencies were below the minimum level 

suggested in the guidelines. Perhaps more importantly, the percent field 

recoveries (which represent the amount recovered under actual conditions 

encountered in the study) of many of the chemicals were lower than the 

minimum needed to assess exposure. Therefore, either different active 

ingredients would need to be used in future studies, or new analytical 

methods to increase recoveries should be employed. 

3. A significant proportion of air monitoring samples were lower than the 

detection limit; and 

4. None of the application method/end use settings had the minimum 

number of replicates (i.e., 15) recommended in EPA’s guidelines. 

The limited number of MEs combined with poor recovery data severely limits the 

conclusions that can be drawn from CMA's study. Therefore, the EPA and other 

regulatory agency reviews indicated that additional data for all application 

method/use setting combinations should be obtained to support more confident 

inferences about exposures in a variety of settings. 

The deficiencies identified by EPA in CMA's report were corroborated by other 

reviewers. First, the California Department of Pesticide Regulation (CA DPR) 

notes that the exposure data cannot be used as generic data for all 

antimicrobials because recoveries were low, precision of the measurements were 

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not established, and CMA did not establish the validity of generalizing the 

information among applications and end-use settings (Powell et al., 1995). 

Canada also reviewed the study and made similar conclusions (Worgan and 

Rozario, 1993). 

In summary, in order to assess potential risks from exposure to antimicrobials, 

EPA has extremely limited data on which to rely. In fact, EPA has repeatedly 

identified that data as inadequate. 

In each of the following example Re-registration Eligibility Decisions issued 

during 2005 and 2006, EPA has stated that “the risk assessment noted 

deficiencies in the surrogate dermal and inhalation exposure data available from 

the Chemical Manufacturers Association (CMA) data base. Therefore, the 

Agency is requiring confirmatory data to support the uses assessed with the 

CMA exposure data within this risk assessment.” 

- PHMB. September 2005. EPA739-R-05-003 

- Benzisothiazoline-3-one. September 2005. EPA739-R-05-007 

- Para-Tertiary-Amylphenol, Potassium Sodium Salt. January 2005. 

EPA738-R-05-001 

- Azadioxabicyclooctane. September 2005. EPA739-R-05-010 

- Chlorine Dioxide and Sodium Chlorite. August 2006. EPA738-R06-007 

- Pine Oil. September 2006. (publication number unavailable) 

- Aliphatic Alkyl Quaternaries (DDAC). August 2006. EPA739-R-06­

008 

- Alkyl Dimethyl Benzyl Ammonium Chloride (ADBAC). August 

2006. EPA7389-R-06-009 

The above list is not exhaustive but is intended to point out that the Agency has 

clearly and repeatedly required additional exposure data for assessing risks from 

occupational and residential uses of antimicrobial pesticides. 

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7 Alternatives to Additional Human Monitoring 

Regulatory agencies are charged with assuring that registered uses of a 

pesticide will not cause unreasonable adverse effects to pesticide handlers. As 

part of such determinations, regulators and risk assessors must be able to 

estimate with confidence likely levels of occupational exposure. Excluding new 

human monitoring studies, the information available to make reliable 

approximations of exposure currently comes primarily from generic data 

contained in PHED and CMA data (Popendorf, 1992), but also from pesticidespecific exposure studies, modeling, and published literature. There is a general 

paucity of published literature relevant to antimicrobial exposure; and the few 

relevant publications were not conducted under GLP, did not typically measure 

whole body exposure, and the raw data for verifying results is generally not 

available. Further, there are no known reliable (validated) models to estimate 

dermal exposure to antimicrobial users. The use of animal data is obviously not 

an option for studies that monitor occupational exposure to individuals engaged 

in their normal work activities. 

Therefore, the only alternative to the conduct of new human monitoring studies 

appears to be: 

• The continued use of the existing information sources (PHED and 

Popendorf et al. 1992, other published literature, and predictive modeling); 

and 

• The acquisitions of additional handler exposure data from other existing 

product-specific studies that meet established acceptance criteria and that 

have generic applicability. 

The limitations of PHED and the other current sources of exposure assessment 

data have been discussed briefly above. The limitations of existing data are 

being evaluated by AEATF II on a scenario-by-scenario basis using acceptance 

criteria (see Appendix D) developed via a consensus process with regulatory 

agencies (U.S. EPA, Health Canada and California EPA). Existing data can also 

inform the design and sample size (see Appendix E for the AEATF II general 

study design approach) for proposed studies, where additional data are 

determined to be needed. Appendix F provides an example evaluation in the 

case of applicator dermal and inhalation exposure data available in PHED for 

hand-held aerosols. This example includes a comparison of the PHED data to 

key acceptance criteria adopted by the AEATF II (see Appendix D). 

Under the first stage of the AEATF II program, and prior to the conduct of 

scenario-specific exposure monitoring studies with human volunteers, the AEATF 

II reviews existing handler exposure data from various sources and considers 

acquiring data that meet established acceptance criteria. A recent SAP (2007) 

evaluated the AEATF II acceptance criteria and concluded: 

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The Panel viewed the selection criteria proposed by AHETF 

and AEATF II to be reasonable for generating exposure data 

for using in exposure assessments, with the following 

caveats. The monitoring duration requirement may be too 

stringent. Some provision to allow the inclusion of data from 

settings where only short-term uses are the norm may need 

to be added. 

Although some useful worker exposure studies may be acquired by AEATF II, 

most of the existing data are not sufficient to meet the generic data needs 

identified in advance by the AEATF II and the Regulatory Agency Advisory 

Committee. While there may be other data that have been submitted to EPA and 

may be suitable for a generic data base, they are proprietary and AEATF II does 

not have access to them. Consequently, at this point, no viable alternatives to 

performing additional human monitoring studies exist for most scenarios. 

It should also be pointed out that pre-requisite studies for AEATF II testing do not 

require research with human subjects. These pre-requisite studies include 

analytical method validations, field recovery validations, and toxicity studies that 

support the registrations of the test materials used. Therefore, the exposure 

measurements (monitoring units or events, i.e., MEs) proposed by this document 

reflect the entirety of human participation. 

8 Ethical Considerations 

All AEATF II studies will be conducted in compliance with the applicable 

requirements of 40 CFR Part 26, EPA’s regulations for “Protections for Human 

Subjects of Research”, and, if they are conducted in California, with the 

applicable requirements of California Code of Regulations Title 3 Section 6710. 

Ethical considerations are scenario and study protocol-specific, however, general 

considerations are discussed below. 

8.1 Subject Recruitment Process 

AEATF II studies require IRB, EPA/HSRB and sometimes CDPR approval of the 

protocol and process before subjects are recruited and the worker exposure 

monitoring study is initiated. The subject recruitment process must be tailored to 

each scenario-specific study. AEATF II studies will typically incorporate the 

elements described in the section as components of the recruitment process. 

Recruitment is conducted by selecting trained or experienced antimicrobial 

chemical handlers from subjects (workers or consumers, depending on the 

scenario and products being studied) identified by personal contact through local 

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service businesses or research organizations. Recruitment materials such as 

advertisements or fliers may be used. The Principal Investigator (Study Director) 

approves the selection of all study sites and subjects, generally after visiting the 

proposed sites and talking to potential volunteers. Informed consent discussions 

are conducted by the Principal Investigator, generally shortly before study 

initiation. 

8.1.1 Identification and Recruitment of Potential Subjects 

It is important to acknowledge that each study protocol must include specifically 

defined processes for the identification and recruitment of potential subjects. 

This protocol specificity includes for example, eligibility criteria. 

Population Base 

In general, AEATF II proposed studies will involve adult subjects that meet 

specified inclusion/exclusion criteria and who will be recruited from the 

professional handler and/or consumer user population in defined geographic 

locations within the United States (U.S.), Canada and possibly, for a few studies, 

from European countries, and defined geographic locations therein. Persons 

with professional training and/or experience will be initially contacted by the Field 

Coordinator using a phone interview script. Recruitment of subjects will be 

through a) word-of-mouth and telephone contact, b) relevant service companies 

or recruitment agencies that have been provided with a flyer that describes the 

study and contains a phone number and name of an AEATF II study contact 

person; or c) direct contact with service providers who are asked if AEATF II may 

have their permission to ask their employees if they might be interested in 

participating in the study independently from their employer where AEATF II 

provides the chemical and use equipment, or at the employer’s place of 

business, if the employer is providing the antimicrobial and use equipment. 

Interested subjects should contact the Field Coordinator directly. 

Enrollment of Alternate Subjects 

Alternate subjects will be enrolled into each study and the number of alternates 

enrolled will depend on an individual study’s objective regarding the number of 

monitoring units (or monitoring events, i.e., MEs). Typically, enrollment of three 

to six alternative subjects is anticipated. All subjects will be informed during the 

interview process that a small number of subjects will be designated as 

alternates and are expected to be present at the test site on a given day, but 

might not participate in that day’s activity. An alternate will be monitored if the 

assigned subject does not present or if the assigned subject drops out for any 

reason. If a subject begins monitoring but stops less than a specified time period 

into a given study, the dosimetry from that subject will not be analyzed and the 

alternate will be used. Dosimetry from any subject that complete a minimum 

specified duration (to be specified in each study protocol) or more will be 

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analyzed and the results assigned to the nearest target task duration interval, 

i.e., subjects who completed as least 20 minutes of a given task (e.g., surface 

wiping), would be assigned to a pre-specified 30 minute target task duration 

group. The alternate subjects not tested the first day will be asked if they are 

available to fully participate the next day. Alternate subjects will be compensated 

for coming to the test site. Alternate subjects will serve as back-up for any 

enrolled subjects who fail to appear on a given day, for subjects that decide to 

withdraw prior or during the test, for female subjects testing positive in the 

pregnancy test, or for any other personal circumstance. 

Inclusion and Exclusion Criteria 

The Principal Investigator is responsible for ensuring that study-specific inclusion 

criteria are met when participants are recruited. The subjects will be asked to fill 

out a demographic questionnaire and asked health-related questions. Females 

will be asked to take a pregnancy test. The responses and results will provide 

the basis for inclusion or exclusion from the study. 

Inclusion 

• Males or females, 18 to 65 years of age 

• In good health 

• Willingness to sign the Informed Consent Form 

• Speak and read English or Spanish (or, if feasible, another 

predominant language selected for a specific study based on the 

associated potential subjects’ demographics) 

Exclusion Criteria 

• Skin conditions on the palmar surface of the hands (e.g., psoriasis, 

eczema, cuts or abrasions) 

• Pregnancy, as shown by a urine pregnancy test 

• Lactation 

• Allergies to household chemical-based products, soaps or isopropyl 

alcohol 

• Declines to sign the Informed Consent Form or the Health 

Questionnaire 

• Does not read and understand English or Spanish (or, if feasible, 

another predominant language selected for a specific study based on the 

associated potential subject’s demographics) 

• Is less than 18 or more than 65 years old 

• Is not in generally good health 

• Severe respiratory disorders (e.g., moderate or severe asthma, 

emphysema) 

• Cardiovascular disease (e.g., history of myocardial infarcts, stroke, 

congestive heart failure or uncontrolled high blood pressure) 

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• Is an employee of the contract laboratories conducting the study, or 

is related by blood or marriage to personnel in the contract 

laboratories. 

Willingness to Participate 

Participants must be freely willing to participate in a study of this type and have 

no interest in the conduct or outcome of the study (e.g., they cannot work for a 

pesticide manufacturer who is a member of AEATF II, or for the Principal 

Investigator or any other sub-investigator, or for any party with a substantial or 

contractual interest in the research, nor can they be relatives of any 

investigators). 

Experience 

Most AEATF II studies will involve only professional workers (e.g., janitorial 

professionals, wood treatment facility workers, machinists at metal working 

shops) with experience specific to the tasks being investigated. This will ensure, 

in the case of subjects, that they have met basic safety trained requirements, as 

dictated by their employer, prior to handling pesticides. Further, if professional 

pesticide products are used in the study, only professional subjects would be 

involved in the study. In some studies, products that could be used by workers 

or consumers will be involved; thus, potential subjects could include consumers 

with relevant experience in performing a scenario-specific task 

Age 

All monitored subjects will be at least 18 years of age, and no older than 65. 

Subjects will be asked for a Government-issued photo-ID to confirm their age. 

Health Status of Participants 

Only subjects who consider themselves to be in good health will be eligible. This 

will be affirmed by the subject’s responses to the health-related questions. The 

subject-provided responses and information help the Study Director to exclude 

subjects who are not in good general health, allergic, mentally ill, cognitively 

impaired, chronically ill, or terminally ill. This will help limit the risks of adverse 

effects due to pesticide handling. 

Work Periods 

All monitoring periods will be designed to represent the typical duration of the 

specific task or activity being monitored during a normal workday. Generally, this 

will involve monitoring periods from 30 minutes to eight hours in length, since 

most AEATF II scenario-specific activities are performed intermittently during the 

work day. Data sources will be identified and evaluated regarding product 

use/usage information to inform study designs with respect to product application 

sites and surfaces, application methods, amounts handled, and duration of tasks 

or work periods. An example source of publicly available professional “habits 

and practices” information is that from the International Sanitary Supply 

Association (ISSA; www.issa.com). ISSA is the leading international trade 

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association for the cleaning industry. ISSA’s worldwide membership includes 

more than 4,800 distributors, manufacturer, building service contractor and inhouse service provider members. ISSA cleaning operations observational 

survey data (time and motion studies) were used to inform AEATF II study 

designs, e.g., predominant mop technology, cleaning task durations, for the mop 

and wipe applicator exposure studies. Another example source of product use 

information, including task duration or work periods, is a proprietary survey 

conducted by the Chemical Specialty Products Association (www.cspa.org), 

Antimicrobial Exposure Joint Venture (AEJV), which has collected data focusing 

on consumer antimicrobial cleaning product use in residential settings. 

Product Label Non-Conformance 

All subjects will be required to perform pesticide handling tasks in conformance 

with the label requirements. BHED® is designed to reflect exposure to workers 

and/or consumers who follow legal and proper handling of pesticides and not 

who misuse the product or otherwise violate the label. In particular, subjects 

must wear the Personal Protective Equipment (PPE) required by the label and 

researchers will remind participants to use that PPE should they be observed not 

wearing the PPE during exposure monitoring. Any subjects who will not follow 

the label requirements during the study will be asked to discontinue their 

participation and their exposure samples will not be collected. A subject will be 

reminded once if found not wearing PPE. A second infraction is grounds for 

subject removal from the study. 

Pregnant or Nursing 

The pregnancy status of all potential female participants will be ascertained 

through the use of a supervised over-the-counter urine pregnancy test conducted 

within 24 hours prior to the initiation of monitoring. Any pregnant subjects will be 

excluded from the study. In addition, women who are nursing will be excluded. 

Speak and Read either English or Spanish 

English and Spanish are by far the most common languages used by 

occupational pesticide handlers in North America. Translators for other 

languages are often difficult to locate where antimicrobial products are used, 

making it difficult to ensure fully informed and fully voluntary consent for speakers 

of other languages. Thus, AEATF II anticipates that it will not enroll participants 

who are not fluent in either English or Spanish. If a language other than English 

or Spanish has high incidence (>15%) amongst potential subjects for a specific 

study, translation of study materials, the availability of translators, and the 

additional language-speaking technical staff person (e.g., present during the 

study’s field phase) will be considered by AEATF II. 

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8.1.2 Exclusion of Vulnerable Group(s) 

AEATF II prohibits most of the vulnerable groups as participants, including: 

people who are ill, cognitively impaired, pregnant, minors, employees or relatives 

of the Principal Investigator, etc. As described above, local site coordinators are 

generally used to locate suitable sites and potential participants – they are 

identified as a research site in applications to IIRB. AEATF II will occasionally 

allow a local site coordinator, or an employee of the site coordinator, to be a 

participant in a study. In this case, the subject must meet all of the criteria listed 

above, including the requirement that he/she be experienced in the particular 

task being monitored. AEATF II will only use such research staff if they also 

have experience handling pesticides in a commercial environment, for example 

as the owner of a separate commercial facility or in a previous job. This group 

(i.e., employees of research site) may be vulnerable to coercion since the local 

site coordinator receives the benefit of payment for his services. 

However, as described in the next section, AEATF II takes special care to 

prevent coercion of these subjects by having their supervisor/employer confirm 

they won’t be coerced and that their participation decision will have no impact on 

their employment or work opportunities. 

AEATF II does not intend to recruit limited or non-readers, however, a fair 

percentage of the workforce (and consumer population) has Spanish as their 

primary language. When AEATF II knows in advance that a Spanish speaker 

may be recruited for a particular study, this potentially vulnerable category will be 

identified in the application to IIRB. AEATF II has procedures in place to deal 

with candidates and subjects who prefer to use Spanish. These procedures are 

discussed in the following section. 

Another potentially vulnerable group that might be part of the target population is 

poor/uninsured (health care insurance) subjects. AEATF II does not intentionally 

recruit these individuals and will not inquire as to the economic or insurance 

status (health care insurance) of potential study participants. Therefore, this 

category will not be identified to the IRB as one that is intended to be recruited. 

As discussed below, the remuneration being offered (generally for just one day of 

participation) is believed to be not high enough to induce otherwise reluctant 

subjects to participate, so the economic status of participants in these studies is 

not a concern. The level of remuneration will be consistent with pay in a 

particular region of the country if there are obvious differences in wages between 

regions. In addition, AEATF II will cover all costs of injury or illness that subjects 

experience because of participating in the study (that are not covered by the 

subject’s or their employer’s insurance). 

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Another potentially vulnerable group that might be part of the target population is 

illegal workers. For example, illegal workers may feel obligated to participate 

(e.g., in order to protect their job) or be reluctant to accept medical treatment. 

Federal laws give employers the responsibility for ensuring their workers are 

legal, but AEATF II does not employ subjects. AEATF II will therefore assume 

workers are legal and will not ask about their status. In addition, should 

researchers become aware of an illegal worker they will not share that 

information. Workers who might be illegal will be protected from coercion 

primarily via the mechanism described below where the Study Director will 

discuss the voluntary nature of study participation with the worker’s 

supervisor/employer. 

8.2 Informed Consent Process 

Two fundamentally different recruitment situations may occur. If a study is being 

conducted on an active worksite where subjects are normally employed to do 

their job, the following preliminaries occur before subjects meet the Principal 

Investigator for studies that occur at normal worksites. When potential sites have 

been selected and potential participants have been identified by the Field 

Coordinator, a flyer describing the study is distributed to potential participants. 

Before any contact with potential subjects the Principal Investigator has a 

discussion with the direct supervisor of each potential participant to ensure that 

the supervisor has no interest in whether the subjects do or do not choose to 

participate, and further, that the supervisor understands that subjects should not 

feel any coercion to participate in the study. The supervisor must confirm there 

will be no adverse impact on a worker who does not volunteer, or withdraws from 

the study, for any reason. This extra care to prevent coercion from employers 

will be documented on a form which the supervisor, business owner, or 

commercial applicator must sign. Prospective volunteers are introduced to the 

Principal Investigator, and their language of choice is determined. Then, each 

volunteer is provided with the supervisor’s signed form, the IRB-approved 

consent form, and a full explanation of the study, its requirements, and any 

potential risks as discussed below for studies conducted away from subjects’ 

normal worksite. This occurs during a confidential and private discussion with 

the Principal Investigator at the worker’s location if the study is being conducted 

at a work site under the worker’s supervisor’s control. 

If subjects are recruited to work at a site not under their supervisor’s control, i.e., 

away from their normal worksite, and not under their supervisor, another 

paradigm is used. The Field Coordinator will be contacted by individuals that 

have been made aware of the study by a flyer posted at their place of 

employment. Using an IRB-approved phone script, the language preference of 

the subject will be identified, and interested potential subjects will be scheduled 

for a meeting with the Principal Investigator or foreign language-speaking 

designee. 

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Interested volunteers will be screened and enrolled into the study based on oneon-one conversation held at the office of the Principal Investigator. A Spanishspeaking technical designee will be available to ensure communication with 

anyone preferring Spanish over English. The Principal Investigator will share 

information on the study design with interested participants, and provide them 

with copies of the IRB approved Informed Consent Form and answer their 

questions. The Principal Investigator will describe the study to the volunteer in 

great detail and encourage each potential subject to ask questions and request 

clarification at any time during this process as well as in all activities that follow. 

The Principal Investigator will provide each potential subject with a copy of the 

product label and MSDS and answer any questions regarding the product to be 

tested. The Principal Investigator will go over the Inclusion and Exclusion Criteria 

for the study and answer any questions that the potential subjects have. They will 

be provided with copies of the Informed Consent Form, the Subject SelfReporting Demographic Form and the State of California Department of Pesticide 

Regulation “Experimental Subject’s Bill of Rights” and encouraged to take them 

home with them to discuss with family and friends. 

The Principal Investigator will explain to potential subjects wishing to remain in 

consideration that they may withdraw from the research study at any time without 

penalty to their compensation. The Principal Investigator will then read the 

“Experimental Subject’s Bill of Rights” to the potential subjects. The amount and 

form of compensation, the potential risks and discomforts and treatment, and 

compensation for injury will be more fully explained and potential subjects 

encouraged to ask questions. If the potential subjects do not have any questions 

and are interested in participating in this research study, they will then be asked 

to sign the Informed Consent Form and then fill out the Subject Self-Reporting 

Demographic Form. The Principal Investigator will check the potential subject’s 

driver license or state-issued identification card to verify age to exclude minors, 

and identity as required by California DPR, and review the package of 

information provided for completeness against the protocol’s inclusion/exclusion 

criteria. The Principal Investigator will retain the final right to refuse participation 

to any potential subject; however, following signing the informed consent form, 

any potential subject not actually monitored will be given the minimum 

compensation. For female subjects, final eligibility will be determined on each 

study day following a pregnancy test. 

Volunteers are advised of their right to withdraw from the study at any time and 

for any reason without jeopardizing their position with their employer. Volunteers 

are also informed during the confidential consenting process that they will receive 

compensation after they volunteer to participate in the study, even if they 

withdraw for any reason or the sponsor does not use them for any reason. 

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AEATF II consent forms are unique to individual studies, but all have very similar 

structure and contain the following: 

• Study title 

• Protocol number 

• Study sponsor 

• Investigator name 

• Study site(s) 

• Study-related phone number(s) 

• Sub-investigators 

• Statement of the purpose of the study 

• List of procedures involving the subjects 

• Detailed list of risks and discomforts 

• Statement regarding disclosure of new findings 

• Statement of benefits of participation 

• Statement of no cost to the subjects for participation 

• Statement of payment for participation 

• Statement of alternatives indicating this is not a medical treatment study 

and the alternative is to not participate in this study 

• Full explanation of confidentiality of worker information 

• Statement of compensation for injury 

• Source of funding 

• Voluntary participation and withdrawal 

• List of resources who may be contacted to obtain answers to questions 

• A section for signatures of the participant and the person conducting the 

informed consent discussion 

During the discussions between potential participants and the Principal 

Investigator, ample time is provided for questions and the Principal Investigator 

will provide any additional information or clarification that is requested. These 

discussions typically take place at the worker’s location, in a private setting, 

generally in a meeting room of a facility or commercial application company. 

Consent is generally obtained within one to three days of actual study conduct, 

but sometimes earlier. If the worker agrees to participate he/she is asked to 

sign and date the informed consent form and the Principal Investigator provides 

a copy of the signed form to the worker. Within 24 hours prior to participation, 

any women who are selected to participate will be asked to take a urine 

pregnancy test (over the counter variety) and will be allowed to participate only 

if the test is negative. This test will be supervised by a female researcher. To 

protect the privacy of the worker, the test results are not revealed to the 

employer or co-subjects. If the worker chooses to proceed with the study then 

a female researcher will confirm the test is negative and record this in the study 

raw data. No positive test results will be documented and the worker will be 

allowed to withdraw from the study without stating a reason (and still receive 

appropriate remuneration). 

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For subjects whose preferred reading language is Spanish, AEATF II obtains an 

IRB-approved translation of the consent form and ensures that a Spanishspeaker familiar with the study protocol is present during the informed consent 

process and during study conduct. In all cases, the Principal Investigator will 

conduct discussions in English, but the participant will sign the version of the 

consent form in his preferred reading language. The Spanish speaker may be 

an employee at the study site, but more typically will be supplied by the 

Principal Investigator and will be brought into the discussion if the subject’s 

preferred language is not English. A Spanish-speaker familiar with the study 

protocol will also be utilized during the study should any issues arise which 

can’t be resolved directly with the worker. 

In all situations, if the Principal Investigator is not comfortable that the worker 

fully understands the discussions and the contents of the consent form, the 

worker will be excluded from consideration to participate in the study. This will 

be ascertained by providing repeated opportunities to ask questions and by 

asking questions of the potential volunteers that would require a response that 

indicates understanding of key issues. For example: 

• Q: When can you withdraw from the study? 

A: Whenever I want. 

• Q: What will your supervisor have you do if you don’t volunteer? 

A: My regular job. 

• Q: What will you wear so we can measure inhalation exposure? 

A: An air pump on my belt. 

• Q: Will researchers tell you how to do your job? 

A: No. 

Subjects’ ability to read and understand either English or Spanish will be 

confirmed by their answers to the Subject Self-Reporting Demographic Form. 

The process for obtaining informed consent is fully documented in each AEATF II 

study protocol. 

8.3 Subject Remuneration 

In almost all cases, AEATF II is studying the potential exposure to pesticides for 

subjects who are performing their usual activities. This would be generally true 

for both scripted and non-scripted activities. Thus, pesticide handling would be 

conducted even if they weren’t participating in the study. 

AEATF II has selected a standard remuneration amount for all AEATF II studies 

and participants since the inconvenience involved is essentially the same for 

participants in all studies. In addition, AEATF II chooses not to offer an hourly 

rate since it prefers that subjects perform their typical tasks and wants to avoid 

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any incentive for subjects to choose a particular task since they could “earn more 

money”. The researchers utilized by AEATF II all have experience in dressing 

and undressing subjects and the complete process takes only about 20 to 30 

minutes throughout the work day. This represents the primary inconvenience 

associated with participation in an AEATF II study. 

While any standard amount of remuneration could represent a very different 

proportion of various subjects’ typical daily pay, fairness suggests that each 

worker should receive the same amount of remuneration since the amount of 

inconvenience is essentially the same. 

AEATF II selected the amount of $100 at the outset of the task force project and 

still believes this is an appropriate amount, although compensation may be 

varied if the local economy requires more or less for cost of living. This is 

equivalent to approximately $15/hour for a full day’s work which is similar to other 

amounts from other studies that have recently been approved by HSRB. AEATF 

II believes that $100 is not sufficiently high as to create undue influence to 

participate in the study, especially since subjects are generally limited to one day 

of study participation. 

Individuals that are not tested including anyone signing the informed consent 

form but not subsequently being monitored will be compensated for their time 

and inconvenience at the rate of $50 per day. Subjects participating in the study 

will be compensated $100 for the single day that they are monitored. The values 

for compensation are based roughly on a day’s wage of $100 and represents 

potential lost time from work, travel time and incidental expenses incurred in 

study participation. Compensation will be in the form of cash at the completion of 

participation. 

Generally, a particular person will be allowed to participate in the study only one 

time. This study design principal provides data for separate exposure 

measurements that reflect different subjects in order to capture variability 

between subjects. However, the same worker could participate more than once 

in a study (or in two studies) as long as the worker performs a different task. For 

example, one person could be monitored for exposure as a mixer/loader on one 

day and as an applicator on another day (assuming the experience and other 

criteria are met). In this case, that person would receive a $100 payment on 

each of those two days. 

It is important to note that subjects who are professional workers and who are 

participating in any AEATF II studies that are being conducted “in situ” (i.e., 

exposure monitoring studies at workplaces such as a metal working shop) are 

“on the job” and will receive their normal salary and all other compensation they 

are due, including compensation for any overtime worked according to local laws. 

This compensation is the responsibility of the worker’s employer and not AEATF 

II. 

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In addition to their normal compensation, AEATF II will provide a payment of 

$100 (U.S. dollars if in the United States or comparable compensation if in 

Canada or Europe) to each worker who volunteers to be monitored for exposure. 

This payment is in appreciation for the extra effort and inconvenience associated 

with subjects participating in the study which includes wearing the inner 

dosimeter (long underwear, requires undressing in a private area); allowing 

researchers to wash their hands and wipe their faces; allowing researchers to 

remove the inner dosimeters at the end of the monitoring period; and wearing a 

personal air sampling pump throughout the workday. 

8.4 IRB Review Process 

AEATF II generally uses the Independent Institutional Review Board (IIRB) in 

Plantation, Florida (www.iirb.com) to review each of its study protocols for ethical 

compliance. Initial review submissions from AEATF II to IIRB typically include 

the following: 

• The submission package to the IRB includes the study protocol and 

appendices including, test substance MSDS, summary of toxicology 

information and estimated risk for exposure anticipated in that study based 

on U.S. EPA’s Registration Eligibility Decision documents (REDs). 

Additionally it includes experimental subjects’ bill of rights, subjects selfreporting demographic form, flyers soliciting prospective subjects and 

scripts used to verbally describe the study to prospective subjects. All 

documents provided to subjects will be translated into another language if 

the subject demographics warrant, i.e., if >15% of the population uses 

another language in any study region. 

• Initial Review Submission Form (IIRB form revised 01-2006; this is part of 

the IRB correspondence file required by 40 CFR 26.1125). 

The IIRB form identifies AEATF II as the sponsor and the Study Director as the 

Principal Investigator. It also identifies study site(s) (generally local site 

coordinator research facilities) and provides details about subject recruitment, 

consent, and payment. Hospitalization procedures are also provided which 

identify the nearest emergency medical facility to the study site(s) and indicate 

that 911 (or other local emergency number) will be used as the primary method 

for obtaining medical attention should any worker experience adverse effects 

during a study. IIRB also maintains files containing the Principal Investigator’s 

curricula vitae and documentation of human subjects training which support the 

submission. 

IIRB will review all new study protocols at regular convened meetings. When 

studies are to be conducted in California, AEATF II will also submit study 

protocols and related information to the California Department of Pesticide 

Regulation (CDPR) for their review and approval under CCR Title 3 Section 

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6710. Any changes required by CDPR will be incorporated into the study 

protocol, which will then be reviewed and approved by IIRB. Only upon receipt of 

IIRB-approved protocol and consent forms will CDPR grant final approval for the 

study to be conducted in California. Further changes in the protocol and 

associated materials may also be required by EPA, and would also lead to rereview by the IIRB. 

All protocol changes (amendments and deviations) shall be reported to the IIRB 

in writing by letter, fax or email. Proposed changes (amendments) deemed 

necessary to eliminate apparent immediate hazards to the human subjects may 

be implemented without prior IIRB approval. All other amendments must be 

reviewed and approved by the IIRB prior to implementation, or as specifically 

instructed by IIRB policy in this regard. Approval will be granted in accordance 

with IIRB policy and procedures, and may be granted by telephone provided it is 

documented in writing in the study raw data. The IIRB may provide expedited 

review of minor changes as defined by 40 CFR Part 26.1110 at its discretion. 

Unplanned changes (deviations) which occur during conduct of the study cannot, 

by definition, be reviewed and approved by the IIRB prior to implementation. 

Deviations will be reported in writing by letter, fax or email as soon as possible 

following the change. The Principal Investigator shall follow written instructions 

provided by the IIRB for prompt reporting to the IIRB, appropriate institutional 

officials, and the EPA of unanticipated problems involving risks to human 

subjects or others. 

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8.5 Additional Efforts to Protect Human Subjects 

The AEATF II takes many steps to ensure the safety of the subjects being 

monitored. As outlined above, protocols and consent forms are approved by an 

IRB (and DPR, if needed), informed consent is obtained for all study participants, 

consent is obtained in writing in the worker’s preferred language, certain 

vulnerable groups are not recruited, pregnant and nursing women are excluded, 

and participants are informed they may withdraw at any time. Other steps that 

AEATF II takes to ensure the safety of study participants are summarized below; 

each study protocol will define “stop criteria” and medical management 

procedures. 

The objective of the AEATF II is to generate data collected under actual use 

conditions and following all label requirements. Subjects are never asked to 

wear less protective clothing than they would ordinarily wear, even if the clothing 

is not required on the product label. In cases where a worker normally wears 

PPE not required by the label, the AEATF II either allows them to wear the extra 

clothing (or equipment) or they are excused from the study, depending on the 

specific goals of the study. The AEATF II may also provide some PPE items 

required on the product label (e.g., protective eyewear) to ensure they meet 

requirements. 

Copies of the material safety data sheet (MSDS) and product label are made 

available to members of the study team and study participants. During the 

consent process, the Principal Investigator will provide these documents for 

review to potential volunteers and will discuss the possible acute toxicity effects 

associated with the pesticide product in the study. AEATF II study participants 

will also be reminded of standard practices that should be followed to reduce 

exposure to pesticides. For example, label-required PPE such as the 

requirement to wear protective eyewear and to remove clothing that get 

drenched with chemical from an accidental spill. 

During study conduct, researchers will ensure compliance with safety 

requirements on the product label. For example, subjects will be reminded to 

use the label-specified PPE and to follow use directions on the label. Each 

worker will be observed by a researcher during the entire monitoring period and 

the Principal Investigator will be present on all days of monitoring. All 

researchers who have interactions with the subjects have completed a course in 

The Protection of Human Research Subjects, e.g., Certified Investigator Training 

Initiative (CITI), which reinforces the ethical requirements of conducting studies 

involving human participants. 

Study participants are asked to wear an extra layer of clothing (whole body inner 

dosimeter) under their normal work attire. Efforts are made to schedule studies 

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during cooler times of the year and/or indoors as much as practical to help 

minimize potential for heat stress. The informed consent form identifies heatrelated illness as a potential health hazard that may be associated with 

participating in the study, so AEATF II is very careful to prevent such illness. 

First, the Principal Investigators and study observers are trained to recognize 

symptoms of possible heat stress. Second, researchers always have plenty of 

water and sports drinks on hand for the subjects who are encouraged to drink 

liquids during the monitoring period. Third, a poster “Controlling Heat Stress 

Made Simple” will be prominently displayed at the study site. Most importantly, 

environmental conditions (temperature and humidity) are continually monitored 

and operating procedures are in place to reduce the possibility that participants 

are subject to heat stress. 

Finally, the Principal Investigators know in advance where to take subjects who 

might be overheated or who have other medical concerns. If any participant is 

injured or experiences illness as a result of being in a study, medical treatment 

will be available at a nearby health care facility. If necessary, AEATF II will 

arrange transportation to receive medical attention. AEATF II will cover the costs 

of reasonable and appropriate medical attention that are not covered by the 

participant’s own insurance or by a third party. Treatment records will not 

become part of the research records for this study. 

9 Study Benefits 

A critical principle of ethical research is that the risks to the subjects must be 

outweighed by the benefits to the subject and to society. To approve proposed 

research with human subjects, an Institutional Review Board must determine that 

“risks to subjects are reasonable in relation to anticipated benefits, if any, to 

subjects, and the importance of the knowledge that may reasonably be expected 

to result” (40 CFR §26.1111(a)(2)). The low incremental risk anticipated for 

pesticide handlers participating as subjects in the AEATF II monitoring program 

are outweighed by the societal benefits expected to be gained from increased 

knowledge of typical exposure levels in representative antimicrobial chemical use 

scenarios. 

A critical principle of ethical research is that the risks to the subjects must be 

outweighed by the benefits of the research to the subjects and to society. The 

Common Rule codifies this principle: “Risks to subjects are reasonable in relation 

to anticipated benefits, if any, to subjects, and the importance of the knowledge 

that may reasonably be expected to result” (DHHS, 2001). If the benefits of 

monitoring pesticide exposure in the field to subjects do not outweigh the risks, 

then the program should not be conducted. AEATF II argues that the risks 

involved with pesticide handlers participating in the monitoring program, who 

expose themselves to pesticides as part of their daily lives, are minimal and are 

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outweighed by the societal benefits gained by knowledge of expected exposure 

levels and by the eventual benefits of safe pesticide use. 

9.1 Benefits to Subjects 

None of the studies in the AEATF II monitoring program will provide direct 

benefits to the study participants. Information from this monitoring program will 

be used to estimate the exposure and health risk to handlers (workers, or for 

some scenarios, consumers) who are involved in mixing, loading and applying 

antimicrobial chemical products. This may lead to safer pesticide handling 

practices that indirectly benefit the participants and other antimicrobial pesticide 

handlers. Individual workers may request their exposure results relative to the 

average and extremes observed in the study. This information may inform them 

that their work practices produce more or less exposure than average and may 

inspire them to modify behavior to reduce their exposure. 

9.2 Benefits to Society 

The AEATF II exposure monitoring program will significantly improve the ability of 

EPA and other regulatory agencies to estimate the risks to professional pesticide 

handlers from handling antimicrobial pesticides. Knowledge gained from the 

monitoring program will be applicable to a variety of antimicrobial pesticides, and 

will be used to assess risks of new pesticides and new uses of registered 

pesticides. Knowledge gained from the monitoring program could also be used 

by EPA to impose stricter safety standards on currently used pesticides, when 

appropriate (Resnick, 2005). 

The data developed in the AEATF II monitoring program will improve the 

scientific basis for EPA’s occupational risk assessments. Worker exposures will 

be measured under realistic conditions. The data collection parameters will 

reflect current antimicrobial practices, equipment, and techniques. Monitoring 

techniques are of high quality and have been standardized for use across the 

AEATF II monitoring program. BHEDTM will become the best available data to 

support assessments of antimicrobial pesticide handler exposure. 

BHEDTM will not repeat the limitations of PHED. In particular, BHEDTM will 

include all data on each individual sampled, not just data on individual body 

parts. Improved estimates of whole-worker exposure, including estimates of the 

potential distribution between subjects, will now be possible. 

To the extent the generic database approach proves successful, it may reduce 

the need for product-specific worker exposure studies conducted by individual 

registrants for new products and uses. 

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9.3 Likelihood of Realization of Benefits 

The collection of worker exposure data that can address the data needs of the 

regulatory community and membership of the AEATF II is considered extremely 

likely. It is also highly likely that regulators and risk assessors will use these data 

extensively. This has been the case for previous FIFRA joint data development 

task forces of many types, including those developing data for generic exposure 

assessment. Regulatory agencies are strongly committed to using generic 

exposure databases as an important component of risk assessments. The use 

of worker exposure data in a generic manner has been generally accepted since 

the concept was discussed and supported by a FIFRA Scientific Advisory Panel 

in 1986. In addition, the successful development and release of PHED in 1992 

and its subsequent use by regulators to support many occupational risk 

assessments strongly suggests that the BHEDTM database will find even greater 

use and its benefit realized. 

10 Study Risks 

Most of the studies designed within the AEATF II program are intended to reflect 

what would be considered “normal” activity patterns for many subjects and 

individuals handling biocidal products. There are some situations in which an 

activity is semi-scripted, within the range of expected practices, to provide 

diversity in parameters that may be related to exposure including the amount of 

active ingredient handled and the duration of tasks. However, in each of the 

proposed studies, careful consideration is given to the potential study-specific 

risks to the individuals involved and what specific efforts will be made to minimize 

or eliminate these risks. General risk considerations are presented in this 

section. Each proposed study protocol will indicate study-specific risk 

considerations and communication of those risks to potential study subjects as 

part of the informed consent process. If a subject is injured as a result of being in 

an AEATF II study, medical treatment will be available from a near-by health care 

facility that knows about the study. The study sponsors (i.e., AEATF II) will cover 

any cost associated with a subject’s medical treatment that is not covered by 

their own insurance or by a third party. 

10.1Description of Potential Risks to Subjects 

The risks of the study can be broadly separated into two general categories, 

those due to potential exposure to an active ingredient and those due to physical 

stress that may arise from certain activities. Each must be considered to 

determine what can be done to avoid unnecessary risks. 

Physical risks can arise from climatic conditions, extra clothing (in the form of 

wearing two complete layers of clothing, the inner and outer dosimeters), and 

exaggerations of normal activities. Some of these aspects can be controlled by 

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location and ventilation, yet other aspects are directly a result of the study design 

and cannot be easily altered without invalidating the data collected. However, 

those aspects that cannot be easily altered should be carefully evaluated, based 

on existing data (e.g., habits and practices or product use survey data) and 

expert opinion, to minimize the exaggeration of activity (e.g., upper-bound for 

total time spent performing a given tasks, such as surface wiping associated with 

cleaning activities during a day) without compromising the study results. The 

exaggeration of activity patterns can lead to concerns related to potential 

ergonomic issues (e.g., fatigue and heat stress). These need to be considered 

on a case by case basis for the application method / study scenario. 

The other aspect of risk is chemical resulting from exposure to an active 

ingredient or solvent used to remove the chemical from skin. This can be 

controlled to some extent by selecting active ingredients that have less toxic 

profiles and have already been evaluated and approved for the application being 

investigated. In addition, preference is for actives that have been evaluated and 

do not require personal protective equipment for their usage in the application. 

This criterion generally assures that the product has been evaluated with very 

health protective assumptions and is approved already for this application. The 

counter to this is that some exaggeration of usage may be required to obtain 

detection of the active ingredient on the dosimeters. An example of this is 

pouring liquids; typically a pouring event is 1 to 5 minutes and the circumstances 

are such that detection of the active ingredient on a dermal dosimeter is unlikely, 

even with extremely low detection limits. Hence to be sure that exposure is 

actually being measured, it is necessary to increase the amount of product 

poured and the length of time that the activity is carried out. For the evaluation it 

may be necessary to pour liquids from several consecutive containers for an 

extended time period. Based on the results of existing studies, it is anticipated 

that the time and amount poured can be scaled to obtain detection on 

dosimeters, but avoid unnecessary potential for exposure. 

10.2Risks of Heat-Related Illness 

Heat stress is the build-up in the body of heat generated by the muscles during 

work and heat coming from the environment. Heat illness (e.g., heat exhaustion 

and heat stroke) can result when the body is subjected to more heat than it can 

accommodate. Weather, workload, clothing/PPE, and lack of worker 

conditioning can increase the risk of a worker experiencing heat-induced 

illnesses. In addition to causing serious physiological conditions, early symptoms 

of heat illness such as dizziness and confusion can lead to an increased risk of 

occupational accidents above that which is already present. Therefore, AEATF II 

takes special care to monitor subjects for signs of heat stress. All study 

observers are trained to recognize signs and symptoms of heat stress, and the 

Principal Investigator (Study Director) and all observers promote drinking water 

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and taking rest breaks. Researchers will stop study participation for subjects 

who experience heat-related illness. 

10.3Risks of Exposure to Surrogate Antimicrobial Pesticides 

Toxicology Hazard to Subjects 

For each specific active ingredient chosen, AEATF II reviews the available 

toxicology data to assure no undue hazard. Government-authored summaries of 

the data are given to an IRB as part of the review package. The surrogate 

chemical / active ingredient selection criteria include selection of active 

ingredients that have low toxicity profiles, with good warning properties and 

reversible effects. This selection process, however, is limited by the fact that the 

active ingredient and the specific product used need to be approved for the 

application method by the US EPA. By making this restriction, there is 

assurance that the product has had at least a screening-level risk assessment 

completed in the past. 

Likelihood of Serious or Irreversible Effects 

All of the applications being evaluated by AEATF II involve short-term exposures, 

in most cases an exposure period of less than 8 hours. Hence, the main 

concerns will be for acute exposure potential. As a result AEATF II focuses on 

reviewing the likely exposures to the active ingredient and assures there is no 

undue risk from acute exposure. Further, constant monitoring of the exposure 

scenario and options for immediate termination by the participant or the study 

observer are included, and every effort is made to avoid injury or over-exposure. 

The subjects selected to participate in a study will be experienced in the use of 

the equipment and types of products involved in that particular study. Any 

subject with known allergic reaction to the product and specific pesticide used in 

the study will be excluded from participating. At high concentration some 

antimicrobial chemicals can produce dermal irritation, but this is not commonly 

seen at the end-use dilutions being handled in AEATF II studies. Any severe 

dermatitis or allergic reactions would result in stopping a subject’s participation in 

the study and providing access to any necessary medical treatment, including 

transportation, if needed, to local medical care facility(ies) identified prior to a 

study’s initiation. 

10.4Risks of Exposure to Face/Neck and Hand Wash Solution 

Risk from irritation due to exposure to the washing solution (e.g., 50% isopropyl 

alcohol and water) used on the face/neck and hands can occur if the subjects 

have existing abrasions. Subjects will be informed prior to the study that the 

washing solution (e.g., alcohol/water mixture) used to rinse their hands and wipe 

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their face and neck may sting, if they have any cuts or abrasions on their hands 

or face. 

At breaks during AEATF II studies and at the termination of the study, the 

subjects will have their hands washed by study personnel using a washing 

solution (e.g., 50% isopropyl alcohol/water). Further, at the end of the study after 

the final wash solution samples are collected, subjects will proceed to wash their 

hands thoroughly with soap and water. 

The Principal Investigator (or designee) will examine subject’s hands at the time 

of each sampling event and note any observed irritation to the skin. Any subject 

showing an adverse skin reaction will be asked to immediately stop further 

participation. The subject’s exposed skin will be gently washed with clean water 

and mild soap to remove the test product, and the area will be gently dried with a 

clean towel. 

10.5Psychological Risks 

Study subjects may find it embarrassing to have a researcher present with them 

while they change from their clothes into and out of the cotton inner and outer 

dermal dosimetry (work) clothing provided by AEATF II. This is necessary to 

make sure that the special dosimeter underwear fits properly, and that it and the 

outer dosimetry clothing doesn’t get contaminated when the test is over. The 

researcher who helps will be of the same sex, and will be the only other person 

with the subject. The subjects will be wearing their own underwear all the time. 

Embarrassment risk from disrobing is expected to be low because the 

researchers are same-sex, and experienced. 

If the subject is female, they might be surprised to learn the results of the 

required pregnancy test on the day of the research. No one but them and one 

female researcher will know those results, and they will not be recorded. 

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11 Procedures for Monitoring and Preventing Risk to 

Subjects 

During all AEATF II studies, the Study Director (Principal Investigator) and the 

field investigators share the responsibility for awareness of heat illness and toxic 

responses in study participants. All such researchers are required to complete 

training on the ethical treatment of test subjects. Prior to study conduct, the 

Principal Investigator will assess the availability of medical assistance in the 

locality of the study and identify appropriate emergency medical facilities that 

may be utilized. This information is included in the Institutional Review Board 

(IRB) application. IRB protocol and consent form approvals are prerequisites to 

submission to EPA or HSRB. During each study, every participant will always 

have a researcher assigned to observe his/her handling practices and this 

“observer” will have the primary responsibility for detecting adverse effects. 

Typically this observer is close enough to the worker to have a conversation. 

Observers are trained to recognize heat stress and are informed of the most 

likely acute effects of overexposure to the pesticide being used in the study. 

Should an adverse reaction occur during an AEATF II study, emergency 

procedures will be implemented. These procedures typically include halting 

subject participation, removing the subject from the offending environment, and 

calling 911 for medical assistance if needed. In addition, AEATF II has an 

adverse effects reporting policy in place to notify EPA of potential new findings as 

required by FIFRA Section 6(a)(2). 

As mentioned above, the primary means of preventing toxic effects is to require 

subjects to wear appropriate clothing and all required PPE. During study 

conduct, observers will remind subjects that PPE must be properly worn when 

handling the pesticide. Non-compliance on the part of the worker will result in 

discontinuing the monitoring for that worker. 

For heat stress, the following procedures will be followed by researchers to 

prevent illness in study participants: 

• Ensure plenty of water and sports drinks are available for the subjects. 

• During worker orientation, remind the subjects of the risk of heat stress, 

suggest they drink some water before they start work, and let them know 

how/where they can get water during the monitoring period. 

• Urge subjects to drink water during the monitoring period and remind them 

that thirst does not give a good indication of how much water a person 

needs to drink. There is no need to take hand washes or stop inhalation 

monitoring during a water break. 

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• Observe subjects during the monitoring period and be aware of the signs 

and symptoms listed below. 

• Require subjects to take rest breaks when early signs or symptoms of 

heat illness are present (e.g., headaches, dizziness, fainting, weakness 

and moist skin, mood changes, mental confusion, upset stomach or 

vomiting; for example, http://www.osha.gov/Publications/osha3154.pdf. 

• Regularly monitor temperature and relative humidity. 

• Stop the monitoring as discussed below. 

11.1Medical Management and Stop Rule 

The subjects will be checked for allergic and irritant skin reactions, particularly 

redness, eczema, itching or pain. The subjects will be asked to immediately 

report any adverse effect including skin reaction to the Principal Investigator (or 

designee). Any subject showing an adverse skin reaction will be asked to 

immediately stop further participation. As noted previously, the subject’s 

exposed skin will be gently washed with clean water and mild soap to remove the 

test product, and the area will be gently dried with a clean towel. 

Since subjects will wear an extra layer of clothing, the risk of heat related illness 

may be increased. The Principal Investigator will discuss the symptoms of heat 

stress with the subjects. Study personnel will monitor subjects for symptoms and 

signs of heat stress, and will monitor the ambient temperature, relative humidity, 

and heat index (HI). Generally, if the HI exceeds 95 degrees Fahrenheit (F) the 

research will be discontinued. 

Study personnel will be instructed to inform the Principal Investigator immediately 

of any skin reactions, heat stress, or other unanticipated adverse effects 

observed or reported during conduct of the study. The medical management 

procedures set forth in AEATF SOP # AEATF 11.C will be implemented for any 

instance where the subject’s work is halted for medical reasons (other than solely 

because of a heat stress index above 95), and for any post-study reports of 

illness, skin reactions or other unanticipated adverse effects. If two or more 

subjects withdraw or are withdrawn from the study for the same medical reasons, 

the study will be suspended until the cause of the withdrawal is fully investigated 

and determined. If two or more subjects develop an adverse skin reaction after 

they leave the study site, all subjects will be contacted by the Principal 

Investigator to determine whether further medical management is appropriate. 

The Principal Investigator will maintain a record of adverse health observations 

and reports, and follow Sponsor, IIRB, EPA and California DPR policies for 

medical event reporting. Sufficient personnel will be present at the study site to 

maintain an appropriate level of technical support, scientific supervision and 

observations relevant to the safety of test subjects. 

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11.2Additional Procedures for Monitoring or Preventing Risk to 

Subjects 

If an OSHA permissible exposure level exists for an inactive ingredient, an 

evaluation will conducted by AEATF II to assure exposure during the study is 

less than the limit. In addition the aim is to keep exposures as low as possible 

while getting less than 40% of measurements below the detection limit (so that a 

majority of measurements are above the detection limit for statistical inferences 

and exposure assessment). This specific aspect is what drives the need for 

preliminary evaluation of existing data to determine adequacy of detection limits. 

Each protocol will address emergency procedures if an adverse reaction occurs. 

Where the study is conducted and what the application method is will influence 

this to a certain degree, but the objective is to have the needed assistance 

available during the conduct of the study. This would include evaluating potential 

physical hazards (i.e., ergonomic concerns) throughout the study as well as 

potential exposures to active ingredients. 

As noted earlier each principal investigator assesses the availability of medical 

assistance in the locality of the study. For example there are certain local 

regulations and practices that need to be accounted for in designing these 

studies as a few of the AEATF II studies could be conducted outside of the US. 

For example, the conduct of an indoor “pour liquid” study is being evaluated for 

conduct in the Netherlands at a contract laboratory facility. Open pouring of 

liquids in indoor settings represents an exposure scenario that could be 

realistically simulated in a laboratory setting. In some cases AEATF II must meet 

local regulations that do not always allow AEATF II to be completely consistent 

across various studies. However, the same objective always exists, to collect 

useful, scientifically defensible data of the highest quality possible, while 

minimizing exposure and protecting subjects. 

As discussed later in this document, scripting in AEATF II studies will be 

minimized and will primarily involve design features that ensure monitoring 

intervals that represent a typical day’s duration (i.e., not excessively short or 

long) and coverage of the practical range for amount of product handled within 

each use scenario. However, study participants will be using familiar equipment 

in a manner that is typical for them. Therefore, AEATF II believes the increased 

risk of heat-related illness in certain conditions is the only added risk that study 

participants will likely encounter. 

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12 Risk Assessment for Anticipated Exposures to 

Proposed Surrogate Chemicals 

AEATF II monitors exposure to subjects who handle commercially available 

antimicrobial pesticide products. In general, useful surrogate chemicals [e.g., 

didecyl dimethyl ammonium chloride (DDAC), CAS No. 7173-51-5] have multiple 

uses (e.g., multiple use sites and application methods), formulation types, 

minimal PPE requirements (i.e., low acute toxicity), and reliable and validated 

analytical methods. AEATF II will utilize extant risk assessments conducted by 

EPA for the antimicrobial pesticide to be used in each study to inform the 

potential for excessive antimicrobial pesticide risk that subjects may experience 

as a result of participation in an AEATF II exposure monitoring study. If a risk 

assessment does not exist for the exposure scenario with that chemical, AEATF 

II will conduct an assessment using EPA-recommended methods. 

13 Risk Versus Benefit Comparison 

By monitoring exposure to professional antimicrobial handlers (or consumers) 

who follow their normal practices, but wear an additional layer of clothing (as an 

inner dosimeter which traps chemical that penetrates the work clothing), AEATF 

II’s monitoring program generally presents a reduced risk to subjects. The risk of 

dermal toxicity is actually reduced and the added risk of heat illness is mitigated 

by a medical management program which emphasizes measures to prevent 

heat-related illness. The potential benefits to antimicrobial workers as a whole 

and to consumers and society in general, for example, in the form of more 

accurate measurements of potential exposure to antimicrobial pesticides to 

inform safety evaluations, versus study-specific risks, will be included in the 

discussion of each study protocol. 

Against the slight risks are balanced substantial benefits. Products containing 

antimicrobial chemicals are used extensively in hospitals, schools, homes, etc. to 

control pathogenic bacteria and viruses known to produce increased morbidity 

and mortality in humans, domestic animals and pets. Measuring exposure of 

subjects in this research study will produce reliable data about the dermal and 

inhalation exposure of subjects and the general population performing these 

tasks. The resulting data will improve the completeness and accuracy of the 

database used by the EPA to assess exposure to these chemicals. The ability to 

accurately predict risk may allow other chemical classes of antimicrobials to also 

be registered based on exposure estimates generated from the data to be 

produced by this study. 

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14 Characterization of Potential Study Participants, 

Exposure Monitoring Methods and Ancillary 

Information 

Appendix A identifies the antimicrobial pesticide handler scenarios that will be 

covered by AEATF II’s generic exposure database, BHEDTM. However, it is 

important to point out several restrictions that will be placed on the subjects to be 

included in those scenarios. 

14.1 Subject Characteristics 

Age of Subjects 

For ethical reasons, all study participants must be at least 18 years of age. 

Health Status of Subjects 

Only subjects who consider themselves to be in good health will be considered 

for participation in AEATF II studies. 

Reproductive Status of Female Subjects 

Women who are nursing or pregnant will be excluded from the study. Nonpregnancy will be confirmed by an over-the-counter urine test just prior to women 

participating as study subjects for studies involving intentional exposure 

Experience of Subjects 

Only subjects who have experience performing the particular task will be allowed 

to participate. 

Monitoring Period Duration 

All monitoring events (MEs) will be designed to represent a normal workday for 

the particular task being monitored. Generally, this will involve monitoring 

periods of at least half of a normal work day to overcome the criticism of early 

exposure studies where many of the sampling regimes monitored subjects for 

only a few minutes. Avoiding very short monitoring intervals will ensure that daily 

exposure estimates are not biased by unusual conditions during that short 

interval. Some work tasks (e.g., mopping) are performed intermittently through a 

work day. When monitoring exposure for such tasks, the work schedule will be 

compressed to obtain the typical duration of exposure or amount of active 

ingredient handled in a normal work day. 

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Product Label Conformance 

All subjects will be required to perform pesticide handling tasks in conformance 

with the label requirements. BHED is designed to reflect exposure to subjects 

who follow legal and proper handling of pesticides and do not misuse the product 

or otherwise violate the label procedures. In particular, subjects must wear the 

PPE required by the label and researchers will remind participants to use that 

PPE should they be observed not wearing the PPE during exposure monitoring. 

If there are any special cases where it is proposed that an AEATF II study data 

set is developed using measurements for a non-pesticidal surrogate chemical, all 

handling will be based on OSHA-specified PPE as identified on the Material 

Safety Data Sheet and/or other workplace safety requirements. 

14.2 Exposure Monitoring Techniques 

Techniques to monitor pesticide handler exposure fall into three main categories. 

These are area (environmental or industrial hygiene) monitoring, passive 

dosimetry measures taken on the individual, and biomonitoring taken from 

individuals. 

Area Monitoring 

The oldest and least accurate exposure monitoring technique to estimate 

individual exposure is area monitoring. This monitoring primarily consists of air 

monitoring in the general vicinity of subjects and sometimes surface monitoring 

of the pesticide in the workplace. This technique is a traditional industrial 

hygiene measure and can be used to monitor the pesticide manufacturing 

workplace to ensure that environmental levels are controlled, but it is not 

particularly useful in quantifying total worker exposure. This method may give a 

reasonable approximation of inhalation exposure potential, but does not allow a 

quantification of dermal exposure. Past monitoring studies have consistently 

demonstrated that the dermal route is the most significant route of exposure to 

pesticide handlers (Wolfe, 1976). 

Passive Dosimetry 

Passive dermal dosimetry taken on subjects consists of (1) patch (e.g., gauze 

pad) dosimetry; (2) whole body garment dosimetry and, (3) hand/face dosimetry 

techniques. Hand washes and patch dosimetry, or the use of whole body 

dosimeters are methods for quantifying the amount of pesticide that contacts the 

skin or clothing of a worker, and provides a measure of external (dermal) 

exposure. The use of whole body dosimeters, which are usually sectioned into 

standard body part areas (e.g., upper arms, lower arms, upper legs, lower legs, 

front and rear torso) prior to extraction and analysis, prevents the need to 

extrapolate from a small patch size to the whole body part. Personal air 

monitoring devices have been used to characterize exposures via the inhalation 

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route by collecting a known volume of air in the breathing zone of the worker and 

analyzing for the mass of pesticide of interest present. 

1. Patch Dosimetry 

Patch dosimetry was first utilized in the mid 1950s for pesticides. With 

patch dosimetry the potential exposure of the subjects' skin and clothing is 

measured using a number of absorbent cloth or paper patches, attached 

to body regions, inside and outside clothing. Placement of patches to 

represent the entire body (head/face/neck, upper and lower arms and 

legs, and front and rear torso) are needed on each individual monitored. 

The surface area covered by the patches represents <10% of the total 

body surface area. After a defined period of exposure, the patches are 

removed and analyzed for pesticide content. The quantity of a pesticide 

on a patch of known area is then related to the area of body region on the 

assumption of uniformity of deposition over that area. Body part surface 

areas can be obtained from standard reference texts and exposure 

guidance documents (EPA 1999). 

The assumption of uniform deposition is probably the principal 

disadvantage of the patch technique. This is illustrated by the 

extrapolation of the value given by half the limit of quantification (LOQ) to 

the total body part; this may give a substantial under or over-estimate of 

exposure. The patch method may give significant under- or overestimates of exposure, depending on whether the patches have captured 

the non-uniform, random deposition of concentrate splashes or spray 

droplets. Individual body region exposure values are then added to give a 

total potential exposure expressed in mg/hour or mg/lb of product handled 

or applied. 

2. Whole Body Dosimetry 

The whole body dosimeter method came into use during the late 

1970s/early 1980s (Abbott et al., 1987). The method involves the use of 

clothing, usually two layers of cotton or cotton/blend material, which act as 

the pesticide collection media. The outer layer of clothing should be 

representative of what the subjects normally wear. The inner layer, 

usually ‘long johns’, represents the skin. The method overcame one of the 

inherent problems of the patch method, i.e. the assumption of uniformity of 

pesticide deposition on the skin and clothing. Exposure of the head is 

assessed by use of a hood or hat preferably made of the same material, 

or a patch attached to a hat. A face wipe technique can also be used, in 

which the skin of the face and anterior and posterior neck is wiped with 

cotton swabs containing a suitable solvent to remove the pesticide 

residues. 

PPE required by the product label are worn over the sampling clothing. 

The selection of sampling clothing should err on the cautious side by 

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utilizing the minimum clothing that might be worn under the prevailing 

conditions. The use of the whole body method overcomes the perceived 

problem of non-uniformity of deposition. Furthermore, extrapolation from 

small target areas to larger body regions is not necessary. For these 

reasons, the method is believed to give a more accurate estimate of 

potential and actual dermal exposure. 

3. Hand Wash 

EPA (1996) reviewed the literature on studies that had included hand 

exposure measurements and concluded that their contribution to total 

exposure ranged from around 40% to 98%, depending upon the 

application method. The methods for measuring hand exposure include 

lightweight absorbent gloves, and swabbing or rinsing the hands in various 

solvents (EPA 1996). Mild detergent solutions can be used in the 

handwash technique, for example ‘Aerosol’ OT. However, AEATF II 

intends to use a wash solution that will have a high solubility for the test 

material. This may result in varying the solvent from study to study. For 

example, in the first 2 studies proposed by AEATF II, 50% 

isopropanol/water will be used to remove a quaternary ammonium 

compound from the skin. All these methods have their advantages and 

limitations and it is difficult to evaluate the accuracy of any procedure. 

The generally held view is that the use of gloves results in a significant 

over-estimation of total dermal exposure, owing to the retention of more of 

the pesticide than would otherwise be retained by the skin. Gloves also 

contain foreign materials such as sizing, which may be co-extracted with 

the pesticide. At low levels of contamination this may cause analytical 

difficulties. However, glove contamination with dirt and grease arising 

from the worker’s activities are a more likely cause of analytical problems. 

It is important to use a solvent that adequately solvates the active 

ingredient. This can be deduced from water and solvent solubility. 

Inevitably there is a loss of standardization of the intervals at which hand 

wash samples are taken. However, it does give some information on the 

extent of hand exposure that might be of value in overall data 

interpretation. 

Measurement of hand exposure through hand washes has become 

standard in exposure monitoring because it is consistent and mimics the 

method of hand decontamination under normal work conditions. Hand 

washing not only provides the best measurement of exposure, it is also 

more accurate than using collection media like cotton gloves. In 

monitoring via hand washes, residue only accumulates on the skin surface 

(as in the real world), rather than on a multi-layer porous medium that, due 

to the permeable nature of the surface, has a far greater capacity to 

accumulate and store residues. However, even hand washes can 

significantly overestimate exposure because most of the residue 

measured as exposure should actually slough off or be washed off the 

skin surface following normal hygiene (washing) during and at the end of a 

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work shift. Further, several lines of evidence suggest that material 

washed from hands is not all bioavailable: 1) The amount washed off in 

well controlled rat dermal absorption studies shows that for most 

compounds the percentage taken into skin does not change from 1–8 

hours post application (Thongsinthusak et al., 1999); 2) Only a fraction of 

the amount applied is dermally absorbed, with an average dermal 

absorption of about 10% for pesticides applied in solution to humans 

(Ross et al., 2000); 3) Much of the pesticide to which subjects are 

exposed can be adsorbed to dust, and in this form it is not as available to 

skin for absorption, thereby further reducing availability (Driver et al., 

1989); 4) Hand washes are taken before breaks, before meals, and at the 

end of shifts so that material washed off early in the day has no 

opportunity for absorption throughout the work day but is counted as part 

of the exposure; and, 5) If gloves are not worn, hands frequently receive 

the highest dose density, and percent dermal absorption is typically 

inversely proportional to dose density (Ross et al., 2001). 

Inhalation Monitoring 

For pesticides that are poorly absorbed via the skin, the inhalation route can 

become the most important route of absorption. An important review of personal 

sampling methodology for field monitoring of airborne pesticides was published 

by Lewis (1976). A personal air sampling method is the most appropriate for the 

determination of potential inhalation exposure of subjects. Several techniques 

are available such as solid adsorbents for vapors and sizing and filter cassettes 

for particles, all attached to battery powered personal sampling pumps. A 

personal sampling technique involving sampling devices located in the breathing 

zone and sampling pumps is preferred, because it is a practical way to get a 

representative sample. Breathing rates for the calculation of inhalation exposure 

from airborne concentration data can be obtained from standard reference texts 

such as EPA’s Exposure Factors Handbook (1999). 

The inhalable fraction (all material capable of being drawn into the nose and 

mouth) is the most biologically relevant fraction to measure. An example of a 

suitable device is the Institute of Occupational Medicine (IOM) personal sampling 

head designed specifically to collect this fraction (Vincent and Mark, 1987). For 

use of this device, a sampling flow rate of 2 L/min is a specific requirement. 

Examples of suitable adsorbent materials for some volatile compounds are 

activated charcoal, `Tenax' and XAD-2 resins mounted in stainless steel or glass 

tubes. The choice of material should be determined by analytical retention 

(trapping efficiency) and extractability studies. Concurrent sampling for 

particulates and vapor can be achieved by mounting the filter sampling head in 

front of the vapor trap in a 'sampling train'. This train allows retention of any 

vapor stripped off the filter on the resin. The material on the filter can be 

analyzed both gravimetrically and/or chemically and an estimate made of the 

pesticide content of the particulate sample. Where use of such a sampling train 

is needed, laboratory validation of the sampling efficacy, particularly of the 

adsorbent resin, is necessary owing to the possibility of stripping material from 

the resin by the relatively high flow rate of 2 L/min. 

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Biomonitoring 

Biomonitoring, also known as biological monitoring, typically uses the amount of 

pesticide (or its metabolites) detected in the urine of exposed individuals to 

obtain an accurate measurement of the total amount of pesticide actually 

absorbed by the worker via all routes (inhalation, dermal, and incidental oral 

ingestion). In order to use biomonitoring quantitatively, one must have primate 

(preferably human) pharmacokinetic data. Although biomonitoring provides total 

absorbed doses (i.e., pesticide levels in the body), it does not explain the 

contributions of each specific exposure pathway, i.e., biomonitoring data cannot 

generally be used generically. However, biomonitoring data prevent the need to 

extrapolate from external dosimetry to internal dose, and can serve as valuable 

validation tools for passive dosimetry. 

Nature of Testing Guidelines 

Regulatory agencies frequently collaborate to make exposure monitoring 

guidelines harmonized. A good example is the Series 875 guidelines of US EPA 

that were designed with multinational input starting with a meeting in The Hague 

in 1992 and punctuated with meetings in Ottawa, Toronto, and Washington, DC 

that culminated with the issuance of OECD and EPA guidelines that are very 

similar (OECD, 1997; EPA, 1997). 

Justification for Passive Dosimetry 

Because it is difficult to isolate and validate particular dermal dosimetry methods, 

the best validation is a comparison of the sum of passive dosimetry methods 

against the biomonitored dose. The data examined in a recent review of both 

proprietary and published studies demonstrated an excellent correlation between 

passive dosimetry and biomonitoring (Ross et al., 2007). Passive dosimetry as a 

measure of dosage appears to be consistent with biomonitoring with no bias, i.e., 

there is no tendency to over or under estimate exposure. This evaluation 

demonstrated that the total absorbed dose (or daily dosage) estimated using 

passive dosimetry for important handler and reentry scenarios is generally similar 

to the measurements for those same scenarios made using human urinary 

biomonitoring methods. Further, this is strongly supported by statistical analysis 

of individual worker passive dosimetry: biomonitoring ratio and variance within 

and between studies. The passive dosimetry techniques currently employed 

yield a reproducible, standard methodology that accurately and reliably quantifies 

exposure and does not underestimate daily absorbed dose. 

14.3Role of Ancillary Study Information 

Every exposure monitoring study collects data that characterizes the 

environmental conditions and behaviors that may have some influence on worker 

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exposure. The environmental data includes temperature, humidity, airflow or 

wind speed and directionality, light levels, detailed descriptions of the equipment 

used to mix/load or apply the chemical, measurements of the amount of chemical 

used dilution rates, water source, chemical source, etc. Behavior of each 

individual monitored including work rate, personal hygiene, neatness or attention 

to detail, personal habits e.g., tobacco, chewing gum, method of using gloves, 

evidence of fatigue, etc. are all recorded either with field notes and/or with 

photography including video. Although most of this information cannot be used 

to quantify individual exposure, it can be extremely useful in understanding how 

conditions/behaviors observed during the study compare with “normal” conditions 

or behaviors, and how any unusual conditions may have contributed to 

differences in exposure. 

15 Incorporation of Existing Data into BHEDTM 

To establish the need for additional exposure monitoring data involving human 

subjects, AEATF II conducted a systematic review of all available, relevant data 

for each scenario proposed for inclusion in the multi-year program. This process 

included publicly available data (e.g., specific data subsets from PHED) and 

proprietary data sources (e.g., CMA study). Each of potential data source has 

the potential to provide scenario-specific exposure data (MEs) and associated 

supporting information for inclusion in BHEDTM. A data evaluation process was 

developed for the evaluation of existing data sources and involves the following 

steps: 

• Development of data acceptability criteria: The existing data 

acceptability criteria addressed general study design and exposure 

monitoring techniques, including the analytical and quality control aspects 

of the studies. They are detailed in Appendix D. 

• Primary review: A process that involved the screening of handler 

exposure data from PHED version 1.1, publicly available data, and 

compensable data owned by AEATF II members. 

• Secondary review: A detailed evaluation of data that passed the 

screening process for acceptability under the acceptance criteria with 

decision records for each study review. 

• Final review: A process that involved guidance by regulatory agencies 

including the U.S. EPA, Health Canada, and California EPA, on 

acceptance of the data for use within BHEDTM. 

Much of the existing data are deemed unsuitable for a generic database due to 

poor QA/QC (generally low or insufficient field fortification recoveries), a 

preponderance of non-quantifiable residues, or the use of testing conditions that 

do not represent current pesticide handling practices in North America. Another 

key technical issue that eliminated some existing data was the decision to 

exclude exposure data for subjects who wore more than a single layer of 

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clothing. This decision was discussed with the Regulatory Agency Advisory 

Committee who agreed that a modern generic database would be most useful if it 

contained exposure data for minimal clothing and PPE situations. Regulators 

prefer to estimate protected exposures (e.g., dermal exposure under coveralls 

plus normal clothing) from unprotected exposure measurements (e.g., dermal 

exposure under just one layer of normal clothing) than vice versa. Therefore, 

BHEDTM has been designed so that clothing/PPE protection factors can be 

estimated by a user from the two dosimetry clothing layers in order to estimate 

protected exposures (typically workers), or consumer (unprotected) exposures. 

16 Monitoring Event Selection 

Each AEATF II antimicrobial-handling scenario will be addressed by one (or 

possibly more than one) study. The purpose of each study is to obtain 

monitoring events (ME) for incorporation into BHEDTM. The specific details of a 

study design will necessarily differ from scenario to scenario. The scenario 

design documents and study protocols will provide the rationale and description 

of the specific ME selection procedures. This section summarizes those aspects 

of the ME design common to all scenarios. Appendix E provides a more 

extensive description of the concepts and procedures for selection and 

construction of monitoring events. 

16.1 Predicting Future Generic Exposures with Monitoring 

Events 

For the purposes of the AEATF II monitoring program, the basic element of a 

scenario is considered to be the handler-day (HD). Each handler-day 

corresponds to a particular worker and the scenario-related activities that he 

performs during a single work day. Regulatory interest for each antimicrobialhandling scenario is centered on predicting occupational exposure under a 

specific set of generic future handler-day conditions. In particular, it is desired to 

characterize exposures resulting from the future use of an arbitrary (and perhaps 

currently non-existent) antimicrobial active ingredient at some arbitrary, but 

quantifiable, amount of active ingredient contact. 

An ME is the basic tool used by the AEATF II Monitoring Program to predict 

exposures. Each ME is a set of scenario-specific handler-day (HD) conditions 

that have been experimentally selected (i.e., chosen, simulated, or constructed) 

to represent expected future HD conditions. Every ME is also monitored to 

obtain a measurement of the actual exposure resulting from the simulated or 

selected HD conditions. 

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An ME can predict future exposure only if the handling conditions of the ME 

represent future HD conditions. Because each ME is expensive, it will be 

possible in practice to construct only a small number (N) of MEs. Obviously, a 

set of only N MEs cannot predict every possible future HD condition. The 

successful use of a small set of MEs to represent the diversity expected among 

future handler-days is aided by three factors: 

1. Dermal and inhalation exposure to the chemicals are considered generic 

(i.e., independent of the particular active ingredient used). This generic 

principle permits use of a small set of surrogate active ingredients to 

predict exposure from other active ingredients. 

2. Exposure is considered to be proportional to the true amount of active 

ingredient contacted by the antimicrobial handler. The exposures 

obtained from MEs are expressed relative to a measurable normalizing 

factor (NF) is expected to be proportional to amount of active ingredient 

contact. Then exposures for any future NF level of interest can be 

predicted by multiplying the normalized exposures by this future level. 

3. Expert knowledge of possible handler conditions expected throughout the 

scenario if obtainable. This permits construction of MEs from HD 

conditions selected (i.e., simulated or chosen) to represent a diverse set of 

future handler-day conditions. 

If the ME conditions can be appropriately chosen then a useful set of MEs can be 

constructed and used as a predictor of future HD exposure in an aggregate 

sense. 

An exposure distribution is a reasonable aggregate description of future handlerday exposures for a scenario. The future HD distribution describes the likely 

exposure that would result if one were to randomly pick a future HD among those 

using ai X when the level of the normalizing factor is HX. There are actually a 

series of predicted exposure distributions, one for each possible value of HX. 

However, since any predicted exposure can be computed from the normalized 

exposure, it is simpler to focus only on the distribution of normalized exposure. 

The complete normalized exposure distribution is rarely needed. Regulatory 

interest most often focused on just two general aspects of this distribution: 

• The middle values such as the arithmetic mean or the median. These 

exposure values tend to characterize average or ‘typical’ exposure levels. 

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• The larger values of exposure possible, such as the 95th percentile of the 

distribution. This aspect better characterize the extreme, one-time, worker 

exposures. 

It is desired that the set of predicted exposures obtained from the set of MEs 

adequately characterize the middle and larger values of the future HD 

distribution. Technically, a set of constructed MEs cannot be a random sample 

from a distribution of future HD conditions. But some random sampling 

interpretation might still be a convenient and reasonable model for how the set of 

predicted exposures from the MEs relate to the future (normalized) exposures for 

an arbitrary antimicrobial. This permits the use of conventional statistics, such as 

means and percentiles, calculated from the observed ME exposures to 

approximate the ‘middle’ and ‘larger’ exposures expected in future HDs. For the 

AEATF II Monitoring Program, confidence in this approximation is improved by 

using a reasonable reference random sampling model (16.2.2) rather than 

assuming just simple random sampling. In addition, diversity selection (16.3), 

using both purposive and random components, is used whenever possible. 

Diversity selection is an attempt to obtain, as much as is practical for small 

sample sizes, a diversity of conditions that are expected to influence exposure, 

either directly or indirectly. This increases the likelihood that the range of 

conditions in the future HD population expected to impact exposure is reflected in 

the ‘pseudo-sample’ of MEs as well. 

16.2 Determining the Number of Monitoring Events 

16.2.1 The Two-Stage ME Selection Process 

Although the details of will vary from scenario to scenario, the ME selection 

process will always have the same general two-stage structure. The first stage 

consists of selecting or constructing specific locations and specifying a range of 

dates for monitoring at each location. Each such local area and range of 

potential monitoring dates is termed a monitoring site. 

The second stage consists of selecting one or more subjects and handling 

conditions within each site and constructing the MEs. For simulated condition 

studies the MEs are created by assigning appropriate subjects to scenario tasks 

under conditions that are expected to exist in the future HD population. For in 

situ studies, appropriate handler-days are selected from among existing subjects 

and conditions that are expected to represent future HD conditions. 

In general, NC sites are selected at the first stage and NM monitoring events 

(MEs) will be obtained within each site at the second stage. When NM is greater 

than one, the set of MEs at the same site is termed a cluster. In general, MEs in 

the same cluster are expected to be more similar than those in different clusters. 

This correlation usually means that the smallest total sample sizes (i.e. total 

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number of MEs) are attainable when there is only a single ME per site. On the 

other hand, there are often substantial overhead costs per site that make multiME sites more efficient. 

16.2.2 The Two-Stage Random Sampling Reference Model 

In the strictest sense, sample sizes can only be determined using statistical 

theory alone when either 

1. There is assumed random, representative sampling from a population and 

the goal is to estimate some characteristic of that population; or 

2. There is assumed randomization of experimental units to treatments and 

the goal is only to compare or to contrast treatments in some manner. 

Only in these two situations will statistical theory predict how increasing sample 

size decreases estimation error. In other experimental situations, sample size 

must be determined using one of the two ‘random’ situations above as a 

reference model. The random reference model is constructed so that it reflects 

the actual situation (i.e., a mixture of random and non-random selection) as 

closely as is practical. 

The sample size that is appropriate for the reference model is then used for the 

actual study design. In a real sense, then, the reference random sampling model 

is used to establish benchmark sample sizes that can satisfy benchmark 

objectives. Although rarely stated explicitly, the use of reference sampling 

models and benchmark objectives are quite common. 

Because all AEATF II scenarios have a two-stage selection structure, they will all 

use the same reference sampling model. For each scenario, two-stage random 

nested (or cluster) sampling is the reference model used for the combination of 

purposive and random two-stage diversity selection that actually occurs. This 

reference model assumes that: 

1. Exposure, normalized by the potential active ingredient contact factor, is 

lognormally distributed with geometric standard deviation GSD. 

2. There are NC clusters (i.e. sites) and NM MEs per cluster. The total 

number of MEs in a scenario is, therefore, N = NC×NM. 

3. The within cluster (i.e., within-site) correlation of (log) normalized exposure 

is equal to ICC. 

The reference sampling model incorporates a two-stage selection structure and 

the potential for correlation within clusters, but ignores any effects of diversity 

selection. The ICC is irrelevant to the future distribution of normalized exposure, 

per se. However, this intra-cluster correlation is a necessary part of the 

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reference sampling model because the MEs are obtained in clusters (i.e. there 

are multiple MEs per site). 

16.2.3 Benchmark Objective 

The benchmark objective for all AEATF II monitoring program scenarios will have 

the general form: 

If there are NC clusters and NM MEs per cluster and the 

underlying lognormal two-stage reference sampling model 

were actually true, then selected parameter estimates will be 

within K-fold of the true values at least 95% of the time. 

If the true parameter of the sampling model is denoted by θ and its sample 

estimate by T, then T is within K-fold of θ whenever 

(1) fRA = Max ( T/ θ, θ / T ) ≤ K 

The quantity fRA is called the fold relative accuracy of T. To satisfy the condition 

(1) above 95% of the time requires that the 95th percentile of fRA, fRA95, be no 

greater than K. If the 2.5th and 97.5th percentiles of the sampling distribution of 

T are denoted T2.5 and T97.5, respectively, then this benchmark objective can also 

be stated as: 

(2) fRA95 = Max ( T97.5 / θ, θ / T2.5 ) ≤ K 

EPA provides guidance to AEATF II on the minimum degree of benchmark 

accuracy needed for regulatory use in particular scenarios. The current 

consensus is that estimates of the geometric mean, the arithmetic mean, and the 

95th percentile should be accurate to within approximately 3-fold of their true 

value. 

It should always be kept in mind, however, that this objective is specified in terms 

of the reference random sampling model. This reference sampling model does 

have the same two-stage nesting structure as the actual ME selection approach. 

The lognormal distribution assumption is also reasonable, robust, and consistent 

with existing data. However, the reference distribution assumes simple random 

sampling at each stage. It does not, and cannot, incorporate the combination of 

purposive and random diversity sampling actually used. As discussed in 

Appendix E, the consequence of diversity sampling is expected to be a tendency 

for the sampling variation of normalized exposure to be overestimated. The 

sample should over-represent extremes and under-represent the more common 

values. Such diversity-oriented data collected for this scenario, but analyzed with 

respect to the two-stage reference distribution, is expected to have minimal bias 

for central tendency. In contrast, upper percentiles of exposure are expected to 

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be, on the average, too large. There is no way to determine the actual 

magnitude of such overestimation. In this case, overestimation of upper 

percentiles is of minimal concern: for practical exposure assessments, 

overestimation of exposures is a conservative practice utilized by regulatory 

agencies. A tendency to both consider and even overestimate upper percentiles 

is consistent with this practice. 

16.2.4 Sample Size 

Under the reference two-stage random sampling model described above, the 

only quantities needed to determine relative accuracy of population parameter 

estimates are reasonable values for GSDnE and ICC. Such values could be 

based on existing exposure data for scenario-specific tasks, surrogate exposure 

data from similar tasks, and/or reasonable assumptions based on subject-matter 

expertise. 

Given values for GSDnE and ICC, fRA95, can be computed for any combination of 

NC and NM and compared with K. Calculation of the 95% percentile of fold 

relative accuracy is complex, however, and is usually best accomplished using 

Monte Carlo simulation methods. When the number of MEs per cluster, Nm, is 

the same for all clusters, a straightforward simulation approach can be used to 

determine fRA95. This procedure is: 

1. Simulate a set of normalized exposure data for Nc clusters and Nm 

monitoring events per cluster using the two-stage reference 

sampling model. 

2. From each set of simulated data, calculate T, the estimate of θ

3. Repeat steps 1 and 2 above M times to get M values of the 

estimate T 

4. From these M T-values calculate T2.5 and T97.5, the 2.5th and 97.5th 

percentiles of T, respectively. 

5. Calculate the 95th percentile of fold relative accuracy, fRA95, using 

formula (2) above. 

The number of simulations, M, should be some large number such as 1,000 or 

10,000. This process can be continued until a combination of NC and NM are 

found that satisfy to benchmark objective. 

16.3 General Diversity Selection Guidelines 

In general, the objective of diversity selection is simply to obtain a diverse set of 

handler-day conditions from among those conditions possible in when an 

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arbitrary ai is used in future scenario-related tasks. These selected HD 

conditions are then used to construct monitoring events. Diversity selection is 

conducted independently at each of the two stages of selection. Thus, a diverse 

set of sites is selected followed by a diverse set of ME conditions within each 

site. 

In the AEATF II Monitoring Program, the term diversity selection is preferred in 

lieu of the phrase diversity sampling. This is to emphasize the fact that the future 

HD conditions used for MEs are selected from either existing or from synthesized 

conditions (or from both). This selection of conditions can employ both 

purposive and random elements. When there are multiple diverse configurations 

available, random selection from among such configurations can reduce the 

likelihood of intentional selection bias. On the other hand, when some possible 

ME configurations are more diverse or more cost effective than others, it might 

be preferable to select these purposively. 

Diversity should always be with respect to characteristics that are expected to 

impact exposure. Diversity selection attempts to create a sample that contains 

as many of the different conditions as possible that exist in the population. If the 

diversifying conditions are associated with exposure, then a diversity sample will 

tend to be more variable with respect to exposure than would a same-sized 

representative sample. In effect it will be analogous to representative sampling 

from a distribution that is more diverse than the actual future one. 

Whenever possible, the characteristics used should be meta-factors. Metafactors are characteristics that indirectly influence a number of other 

characteristics. For example, a worker is a meta-factor because substituting one 

worker for another alters a number of factors (e.g., behavior, physical 

appearance, stamina) that might affect exposure. Other common meta-factors 

are geographic location and time-of-year. Not every characteristic that may 

impact exposure can be, or even should be, considered in diversity selection. 

The number of possible combinations of factors that may impact exposure will 

always greatly exceed the number of planned MEs. Consequently, only a few 

characteristics, preferably meta-factors, can be used effectively in diversity 

selection. 

16.3.1 Stratified Diversity Selection 

As discussed in Appendix E, there are a number of acceptable approaches that 

can be used to achieve diversity among ME handler-day conditions. Among the 

formal methods, stratified diversity selection is often the simplest to implement. 

In this approach available selectable units (e.g., sites, MEs) are partitioned into 

strata based on characteristics likely to impact exposure. Each possible 

selection unit must belong to one and only one stratum. The number of strata 

must be at least as large as the number of units that will be selected. (For 

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example, if there are three units to be selected, then there should be at least 

three strata.) Diversity could be achieved by selecting (purposively or randomly) 

no more than one unit from each stratum. If there are more strata than units to 

be selected, then a subset of the strata should be selected first. This could be 

either purposively (to increase diversity) or randomly (to reduce intentional 

selection bias). 

16.3.2 Diversity Selection of First-Stage Units 

At the first stage of ME selection sites are the ‘selectable’ units. Diversity 

selection of sites means obtaining sites that are different from each other, on the 

average, with respect to some characteristic(s) expected to impact exposure. If 

sites are constructed environments, then they can be built to be different from 

each other with respect to important characteristic(s). If there are a number of 

possible sites available and a set is to be selected (randomly or purposively), 

then stratified diversity selection of sites, based on the important 

characteristic(s), is a feasible approach 

16.3.3 Diversity Selection of Second-Stage Units 

The second stage selection units are the final MEs. The MEs should be 

diversified independently within each selected site. In most cases within-site 

diversity selection of MEs focuses only on two characteristics: subject and 

normalizing factor. 

Handling-day exposures for the same individual (on different days) are expected 

to be correlated since worker characteristics and behaviors are repeated. This 

is not true for different workers. Worker behaviors are expected to have great 

impact on exposure. Consequently, diversity is always increased by simply 

requiring that each ME be constructed using a different individual. 

MEs should also be diverse with respect to the normalization factor that is 

deemed appropriate for the scenario. One feasible approach is to partition the 

possible levels of NF into NM strata and construct one ME from each stratum. In 

some cases (e.g. simulated-condition studies) there is a pool of available workers 

that can be assigned to any NF stratum. If all possible configurations of 

assignment are equivalent, then workers could be randomly allocated to strata. If 

some allocations are non-equivalent (e.g. more cost effective or there are 

scheduling issues) then a purposive assignment of individuals to NF levels might 

be preferable. 

In other cases (e.g. In Situ/observational studies) worker availability depends on 

the particular NF level chosen. Some individuals may only work with higher NF 

levels and some with only lower NF, say. Selection of workers could still be 

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random, although random choice would be within each NF stratum. However, 

when such associations between subject and NF levels exist, purposive 

allocation might result in a more cost effective and practical configuration. 

17 Description and Role of AEATF II Studies 

In the context of the BHEDTM exposure monitoring program a ‘study’ takes on a 

specialized role. It is that component of the program actually conducting MEs in 

accordance with the spirit of Good Laboratory Practice (GLP) standards issued 

by EPA (40 CFR 160). AEATF II studies meet the definition of study in the GLPs 

at 40 CFR §160.3, which reads in pertinent part: “Study means any experiment at 

one or more test sites, in which a test substance is studied in a test system under 

laboratory conditions or in the environment to determine or help predict its effects 

. . . or other characteristics in humans . . . or media.” Each AEATF II study will 

involve conducting MEs under one or a number of scenarios. For example, a 

study might be designed primarily for mopping application, but exposure of the 

mixer/loaders who prepare the mop liquid mixture will be a different study. 

Because it has a very restricted, albeit important, function within the BHEDTM 

program, the study protocol need not contain extensive program information that 

is not relevant to the conduct of its particular MEs. However, each study protocol 

will reference the AEATF II program “governing document.” 

In most cases, AEATF II study timing and location are not dictated by the 

seasonality of work to be performed. However, finding sites and making 

arrangements for the studies is often challenging, particularly in observational 

studies (e.g., subjects at a wood preservative treatment facility) where no 

scripting of subjects’ activities while handling pesticides would be allowed. 

Further, the AEATF II must identify sufficient usage to define and follow a 

representative day of the specific pesticide handling activity for each participant 

(or monitoring unit or event, i.e. ME). Since some studies consist of monitoring 

participants performing activities that are governed by variable schedules, etc., it 

is nearly impossible to provide full protocol details (e.g., specific site, surrogate 

compound, application rate) required by the Good Laboratory Practices 

regulations and still satisfy the review schedule of U.S. EPA and the HSRB, 

which must be done many months before a study can be conducted. In contrast, 

for studies that can be conducted in an experimental manner, wherein a 

surrogate environment (e.g., experimental chamber) can be used and various 

parameters controlled, a more definitive protocol design can be provided. 

All MEs required by the sampling design for most use scenarios can be collected 

in a single study, and most individual AEATF II study protocols will describe a 

single-study and single-year monitoring program designed to generate a range of 

exposure monitoring data for all discrete activities associated with that use 

scenario. An individual protocol typically represents a single, stand-alone study, 

representing MEs performing a single activity. In some cases data from more 

than one study will be combined for a given scenario. 

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Each exposure study is performed in accordance with long-standing EPA 

guidelines for conducting occupational and consumer exposure studies (Durham, 

1962; Wolfe, 1967; WHO, 1975 and 1982, OECD, 1981; NACA, 1985; Chester, 

1993; Worgan, 1995) as described in Series 875: Occupational and Residential 

Exposure Test Guidelines (U.S. EPA, 1986 and 1996) and in accordance with 

U.S. EPA FIFRA Good Laboratory Practice Standards (GLPs), 40 CFR, Part 160 

(U.S. EPA, 1989). These guidelines are consistent with guidelines used in other 

countries such as Canada, Australia, and members of the European Union. 

All of the individual study protocols will have many elements in common (albeit 

with scenario- and study-specific aspects) in order to have consistency and 

uniformity in the data sets. Exposure monitoring protocols differ mainly in the 

specific product used, equipment used, timing of the study, location and activity 

performed. 

18 Documentation Procedures 

Exposure monitoring studies conducted by the AEATF II are designed to 

measure potential exposure to subjects as they perform specific antimicrobial 

pesticide handling activities. As specified in AEATF II Standard Operating 

Procedures (SOPs; see current master list in Appendix G) and each study 

protocol (and as required by GLPs) all aspects of study conduct are fully 

documented. Most of the information collected during each study is entered by 

hand on paper by researchers on standard data forms provided by AEATF II. 

Much of the information that is collected during the study is also entered into the 

generic database, BHEDTM, for use in data analysis and for examination by 

database users in conjunction with data from other AEATF II studies. Information 

about subjects will be recorded under their unique ID code, and not in connection 

with their name or any other identifying information. 

As required by GLPs, all raw data entries are made in ink and are signed and 

dated by the person who entered the data. In addition, data corrections must be 

made by marking a single line through the incorrect information, writing the 

correct information instead, and entering the reason for the change, typically as 

one of a set of standard codes that explains why the correction was made. 

Again, that entry must be initialed and dated by the researcher making the entry. 

Raw data, including viodeography, are collected in a study notebook and study 

file, which is retained indefinitely in AEATF II archives. In addition, a certified 

copy of the data set is made during report writing and report review so that the 

original does not have to be shipped between author and Quality Assurance, and 

in case the original is lost during transit to archives. 

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19 Quality Assurance Procedures 

GLPs require rigorous quality assurance procedures to assure the quality and 

integrity of the data. All aspects of the studies are monitored by appropriate 

quality assurance units (QAUs) while studies are in progress to ensure 

compliance with FIFRA GLP regulations (40 CFR Part 160) and adherence to the 

protocol and relevant Standard Operating Procedures. This generally involves 

three different QAUs: one from the exposure monitoring contractor that conducts 

the study, one from the analytical laboratory that determines the level of 

antimicrobial pesticide residues in samples, and one from AEATF II (the 

sponsor). For each study, the following specific activities, among others, are 

conducted by these QAUs: 

• AEATF II QAU inspects all contract research organizations and 

laboratories prior to use in a study to ensure that those researchers 

operate in compliance with GLPs 

• AEATF II, Field Contractor and Analytical Contractor QAUs each review 

protocols prior to finalization 

• AEATF II QAU performs periodic study inspections, while contractor QAUs 

perform periodic study inspections of their respective (analytical or field) 

portions 

• Field Contractor QAU audits the raw data file in the field and Field Report 

• Analytical Contractor QAU audits the raw analytical data and Analytical 

Report 

• AEATF II QAU reviews and audits the final report which includes the Field 

Report and Analytical Report as appendices 

Each QAU submits an inspection report(s) to the Study Director and AEATF II 

Sponsor’s Representative and any exceptions to full GLP compliance are 

summarized in the Final Report associated with each protocol. 

20 Quality Control Procedures 

In addition to the formal quality assurance efforts discussed above, there are a 

number of important quality control procedures which are followed in order to 

assure that exposure measurements are accurate and precise and to define what 

those exposure measurements represent. These include complete validation of 

all analytical methods; extensive documentation of exactly what the subject does 

while handling the antimicrobial pesticide product; field fortification and control 

samples designed to estimate stability of chemical residues during sampling, 

transit, and storage; laboratory fortification and control samples designed to 

establish efficiency of the analytical methods on a day-to-day basis; and detailed 

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guidelines on the use of calibration curves to determine chemical residues found 

on all sample matrices. 

In the field during each study, a chemist prepares exposure matrix samples that 

are fortified with a known amount of active ingredient. These matrices include 

whole body dosimeters WBD (cotton long underwear and outer work clothing), 

hand wash solvent solution, face/neck wipes moistened with solvent solution, 

and inhalation tubes (referred to as OVS tubes which stands for OSHA Versatile 

Samplers). OVS tubes are fortified in the laboratory by injecting diluted analytical 

grade active ingredient onto the sorbent in the tube while all other matrices are 

typically fortified in the field with a solution or suspension of diluted test 

substance (from individual vials prepared in the laboratory). Each matrix type is 

generally fortified at three levels of active ingredient designed to span the range 

of residues anticipated to be collected from subjects. At each level, triplicate 

samples are fortified. In addition, control samples are prepared for each matrix 

to determine whether background levels of active may be present. Control 

samples serve as a form of negative control. In general, field control and 

fortification samples are collected on at least two days during each study and 

whenever significantly different weather conditions are expected. 

Fortified WBD and OVS tubes are “weathered” in the field since these sample 

types involve collection of residues during the monitoring period. For WBD, this 

involves laying a fortified section of long underwear onto a table in a sunny 

location and covering that sample with a single layer of outer shirt material. 

Fortified shirt material is not overlaid to simulate outer garment weathering. For 

OVS tubes, this involves drawing air through the tube in the same manner as 

done for subjects. Fortified hand wash and face/neck wipe samples are not 

weathered since these samples are collected at specific time points during the 

monitoring period and immediately placed into frozen storage. 

Analysis of field fortification samples provides a “recovery” value which will 

quantify stability of the active ingredient during sample collection (for weathered 

samples), storage in the field, shipment to the laboratory, and storage in the 

laboratory freezer. Therefore, field fortification samples serve as a form of 

positive controls. Field fortification samples are analyzed along with worker 

exposure samples and it is assumed that the worker samples experience similar 

stability as the field fortification samples. Therefore, residues found in worker 

samples are adjusted by appropriate average field fortification results to estimate 

the residues actually collected in the field. These practices are now very 

standard in pesticide exposure monitoring and are discussed in detail in 

internationally accepted testing guidelines. 

Similar quality control procedures are followed in the laboratory, including control 

and fortification samples which are designed to detect background residues, 

monitor the performance of the method, and detect matrix or reagent 

interferences which may be present. These samples serve as a form of positive 

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and negative controls. In addition to the detailed analytical methods for each 

surrogate and each matrix, all analyses must follow detailed AEATF II analytical 

guidelines which specify procedures related to standard curves, extract handling, 

documentation, etc. These procedures are specified in OPPTS Guideline Series 

875. 

21 Reporting Process 

A detailed report is generated for each study, a “final report” in GLP terminology. 

These reports include a text and tabular summary, and detailed appendices 

including a Field Report and an Analytical Report. These reports are formally 

submitted to EPA, CDPR, and PMRA as they are completed. In general, these 

reports detail exactly what was done in the study, the results of analysis of 

residues, and what information will be entered into BHEDTM. However, individual 

study reports which do not represent data for a complete scenario will not include 

an analysis or interpretation of the exposure data generated. 

Field reports document the conduct of exposure monitoring, including: 

• Identification of the location of the study, and the environmental conditions 

during the exposure monitoring period(s) 

• Descriptions of the subjects in the study 

• Description of the test substance and packaging 

• A record of the mixing, loading, and/or application, including a description 

of the subjects, equipment, and worker activities 

• A summary of worker observations identifying any specific occurrences 

that may contribute to unusual worker exposure 

• Descriptions of the work clothing and personal protective equipment worn 

by each worker 

• A detailed summary of the amount of test substance handled or applied 

for each worker 

• A detailed summary of the length of time each worker was monitored 

• A complete description of the field recovery evaluation with a summary of 

specific handling and weathering of all field samples 

• A complete description of collection, handling, storage, and shipping of 

samples. 

• A complete description of the ethical conduct of the field study, including 

all elements required by 40 CFR 26.1303 

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Analytical reports of individual field studies will document the handling and 

analysis of residues in all samples collected in the study, including: 

• Results of analysis (e.g., μg/sample) 

• A detailed description of the analytical instrumentation and methods 

• Example calculations 

• A summary of field and laboratory fortification recovery data 

• Representative chromatograms of control, treated, fortified samples and 

calibration standards 

• A typical standard curve 

Study reports summarize the field and analytical aspects and include calculations 

of adjusted residues found in all collected samples (i.e., adjusted for field 

fortification recovery); total dermal exposure for each worker; and the air 

concentration of active ingredient associated with each worker’s monitoring 

period. Study reports are formatted in accordance with EPA requirements and 

include all required components. 

22 Scenario Monographs 

As part of the documentation supporting BHEDTM, AEATF II will generate 

scenario monographs for the benefit of regulators and other potential database 

users. Each monograph will include a description of the scenario as well as an 

assessment of the data adequacy within that scenario. More specifically, the 

monograph for each scenario will include: 

• Detail definition of the scenario, including any restrictions. 

• Representative use information for AEATF II member products to define 

application methods, rates, use sites, etc. 

• Information about the diversity of work practices (equipment and 

procedures) currently in use in North America 

• Summary of any existing data acquired by AEATF II 

• Scenario design summaries for AEATF II studies 

• Data tables presenting the monitoring data collected for each ME 

• Statistical evaluation of the adequacy of ME data with respect to the 

benchmark objective 

AEATF II may also include in the monograph additional recommendations 

concerning the use of the ME data. Scenario monographs will also be formally 

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submitted to the regulatory agencies when AEATF II determines the data 

collection for a particular scenario is complete. 

23 Evaluation of Data Adequacy for Completed BHEDTM 

Scenarios 

The ultimate purpose of the monitoring program is to make the individual ME 

exposure data from all scenarios available to users of BHEDTM, i.e., to provide a 

generic pesticide handler exposure database. AEATF II will not analyze the 

scenario data for the purposes of exposure characterization or risk assessment 

as part of this data development program. Regulators and other potential users 

of the generic database will conduct such analyses. However, as part of the 

generic database development and documentation activities, AEATF II will 

evaluate how well the collected data meet the pre-specified benchmark 

adequacy objective. In addition, the AEATF II will quantify the impact of ignoring 

clusters and treating the data as a simple random sample. The results of this 

evaluation will be included in the scenario monograph (see section 22). 

Whenever appropriate, AEATF II will include in the monograph additional 

recommendations concerning the use of the ME data. 

23.1 Benchmark Adequacy of the Completed Scenario 

As defined in Section 16.2.3, the benchmark objective for each scenario is that 

selected lognormal-based estimates of the normalized single-day exposure 

reference distribution be accurate to within K-fold, at least 95% of the time. The 

benchmark estimates of interest are the arithmetic mean and the 95th percentile. 

In principle, the value of K could be scenario-specific although the current 

consensus is that for regulatory purposes K=3 is an acceptable default for all 

scenarios. In each scenario design plan will be a brief discussion of why K=3 is 

appropriate for that scenario, or alternatively the rationale for choosing another 

value of K. 

As emphasized in section 16.2 above, it important to keep in mind that, like the 

sample size determination, this statistical adequacy benchmark is relevant only 

within the context of the reference random sampling model defined in Section 

16.2.2. In particular, the monitoring data will be treated as if it were collected as 

a two-stage random sample from an infinite lognormal population. Technically, 

there is no statistical theory that can be applied to non-random samples (or even 

to random samples for which the probability structure is unspecified). Nearly all 

monitoring data used for regulatory purposes is of this type. As has always been 

the case, statistical conclusions based on such data imply the qualification: “to 

the extent that the data can be viewed as deriving from a true random sample.” 

As pointed out in 16.2 above, diversity selection is expected to yield MEs that 

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tend to overestimate the true variation among future exposures. This suggests 

that the estimates of upper percentiles will tend to be overestimated (and lower 

percentiles underestimated) in the resulting monitoring data. With the small 

sample sizes used in this scenario, however, such estimation bias is probably 

trivial relative to ordinary uncertainties due to sampling, whether random or 

purposive. 

This benchmark is also necessarily based on pre-data assumptions about the 

true nature of the exposure variation. It would be unlikely for all these 

assumptions to be exactly satisfied for every scenario. Although slight deviations 

will have little or no impact, large deviations from the assumptions might result in 

data that deviate too far from the benchmark objective. Consequently, it is also 

of value to assess the benchmark requirement using the ME data actually 

obtained. The K-fold benchmark above is specified in terms of the true variation 

structure and the resulting probability that certain characteristics would be 

observed in the data. Once the data are available, however, such probability 

statements are less relevant than confidence statements calculated from the 

actual data. Consequently, evaluation of the benchmark objectives will be based 

solely on confidence intervals. 

To assess this benchmark goal, a 95 percent bound on relative accuracy will be 

calculated from confidence intervals for the arithmetic mean and the 95th 

percentile. For a particular parameter, θ, let T denote its estimate calculated 

from the fit of a cluster sampling (variance component) model to the normalized 

exposure data. Further, let θa and θb denote the upper and lower limits, 

respectively, of a 95% confidence interval for θ. In most cases, the confidence 

interval, (θa, θb), will be a parametric bootstrap percentile interval obtained by 

resampling from a lognormal cluster sampling model. The 95 percent upper 

confidence bound on realized fold relative accuracy (fRA) is then calculated as: 

UCL95(fRA) = Max ( T / θa, θb / T ) 

The values of UCL95(fRA) will then be compared with the pre-specified relative 

accuracy benchmark objective, K. 

23.2 The Impact of Ignoring Clusters 

As described in Section 16 and Appendix E, the AEATF II monitoring design 

involves selecting MEs in two-stages: NC Sites are selected in the first stage and 

a cluster of NM MEs are selected in the second stage. Thus, a cluster is a set of 

MEs obtained in a single study at a particular geographic location (e.g., building) 

over a limited period of time (e.g. several days). Clusters are not a property of 

the future handler-day exposure distribution, per se, but merely necessary 

artifacts of the sampling process. Exposures for MEs in the same cluster are 

likely correlated to some degree. If so, then estimates of parameters should 

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accommodate this correlation. If a user ignores clusters (i.e., assumes the data 

are a simple random sample), then some parameter estimates may be biased 

and the confidence intervals may be too small. On the other hand, for the MEs of 

a particular scenario, such biases may be small and of little practical importance. 

When this is the case, analyses of the data can be simplified considerably. As 

an aid to regulators and other potential BHEDTM users, the impact of ignoring 

clusters will be examined. 

Estimates and confidence intervals for parameters of the normalized exposure 

distribution will be calculated using a model containing random cluster effects. 

From this analysis the variance components and intraclass correlation (ICC) and 

their confidence intervals will be estimated. In addition, the parameter estimates 

will be calculated assuming no cluster effect (i.e., assuming simple random 

sampling). These simplified estimates will be compared to those obtained under 

the cluster-sampling model. The differences, if any, obtained by ignoring clusters 

will then be summarized for the benefit of BHEDTM users. 

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Appendix A: AEATF II Scoping Document: 

American Chemistry Council 

Antimicrobials Exposure Assessment Task Force II (AEATF II) 

Background and Scoping Summary

April 23, 2007 

INTRODUCTION 

On November 1, 2004, the American Chemistry Council Biocides Panel established an 

Antimicrobial Exposure Assessment Task Force II (AEATF II or Task Force) to conduct 

exposure monitoring studies involving the mixing, loading and application of products 

containing antimicrobials or industrial biocides.1

 The Task Force also has planned to 

develop methodologies to assess post-application exposure to applied products 

containing biocides, and will continue to work with EPA, CDPR and PMRA to determine 

the most useful approach, based on current regulatory needs. The Task Force aims to 

design study protocols that will make study results broadly acceptable to both North 

American and European regulatory authorities. The Task Force currently consists of 43 

companies. 

The AEATF II will generate generic exposure data on a broad range of use 

pattern/application methods to support the Registration, Reregistration and Registration 

Review of most antimicrobial active ingredients. Regulatory agencies now conduct most 

risk assessments for antimicrobial uses employing the stringent risk assessment criteria 

evolved from implementing the Food Quality Protection Act of 1996 (FQPA). There is a 

very limited amount of empirical exposure data for antimicrobial uses and EPA and other 

regulators routinely have used highly conservative estimates of exposures to assess 

antimicrobial risks. 

Given the wide use of antimicrobials, developing generic exposure data is the most costeffective and efficient approach to the needs of this industry. Moreover, given the highly 

segmented markets and diverse users of the same or similar antimicrobial products, a 

generic approach is the only practical way that data of the quality required by EPA can 

be generated. To this end, many of the studies are being designed to collect generic 

data that can be applied to the widest possible range of use scenarios. 

Following is a brief overview of the range of antimicrobial use sites, as identified by EPA 

and adopted by the AEATF II, and of the application methods, segmented into separate 

1

 The general terms “antimicrobials and “biocides” are used interchangeably in this 

document. 

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tasks to the extent possible, identified by the AEATF II with the concurrence of EPA, 

PMRA and CDPR. 

EPA ANTIMICROBIAL USE SITE GROUPS 

During preliminary discussions on the conduct of antimicrobial exposure assessment 

studies with EPA, a range of application methods were identified as appropriate for 

covering the 12 broad antimicrobial Use Site Groups that have been used traditionally by 

EPA’s Office of Pesticide Programs (OPP) in regulating antimicrobials. The EPA Use 

Site Groups are set forth in Table 1. 

EPA relies on these groupings to determine data requirements for various antimicrobials 

based on use. To this end, EPA further subdivides the groupings into food and non-food 

categories and indoor and outdoor categories in order to determine mammalian toxicity 

and environmental and ecological toxicity data required to support various uses. The 

Use Site Groups are a helpful way to identify the range of uses for which antimicrobials 

are employed and, in fact, have been used by North American regulators and the Task 

Force to define the scope of the Task Force’s work. There are multiple sites within each 

grouping. Attachment B to this Governing Document is the 1997 listing of individual use 

sites in each EPA Use Site Group. This is the most recent list of use sites that has been 

made available by EPA. 

APPLICATION METHODS FOR STUDIES 

The application methods selected for the studies are the most common methods and 

include the following: pump liquid, pour liquid; pour solid; place solid (collectively 

referred to as mixer/loader activities by EPA); mop, wipe, aerosol spray, spray, 

soak/immerse, fog, brush and roll, airless spray, and pressure treat. These application 

methods are described in detail in Attachment 2, Glossary. 

Table 1 includes a listing of the Use Site Groups and the application methods typically 

associated with each. 

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Table1. 

USE SITE GROUPS APPLICATION METHODS

Agricultural Premises and Equipment 

Food Handling/Storage Establishments 

spray, 

Premises and Equipment 

Commercial, Institutional & Industrial 

spray, Premises and Equipment 

Residential and Public Access Premises 

Medical Premises and Equipment 

Human Drinking Water Systems 

Industrial Process Water Systems 

Material Preservatives 

Antifoulant Coatings 

Wood Preservatives 

Swimming Pools 

Aquatic Areas 

pump, pour liquid, aerosol spray, 

spray, mop, wipe, fog, soak/immerse 

pump, pour liquid, aerosol spray, 

mop, wipe, fog, soak/immerse 

pump, pour liquid, aerosol spray, 

soak/immerse, mop, wipe, fog, 

pump, pour liquid, aerosol spray, 

spray, mop, wipe, fog, soak/immerse 

aerosol spray, spray, mop, wipe, fog, 

soak/immerse 

pump 

pump, pour liquid 

pump, pour liquid, pour solid, place 

solid, spray, soak/immersion, airless 

spray, brush/roll 

 airless spray, brush/roll 

pressure treatment, soak/immersion, 

brush/roll, spray 

pump, pour liquid, pour solid, place 

solid 

pump, pour liquid, pour solid, place 

solid 

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TWO MAJOR GROUPINGS FOR AEATF II STUDIES 

The studies planned by the Task Force fall into two general categories: (1) simulated 

studies based on discrete or segmented tasks (mixing, loading and application methods) 

that can used to estimate exposures occurring in a variety of use scenarios; (2) in situ 

(e.g., on-site, observational) studies for complex and/or multi-task scenarios. 

Studies Based on Segmented Tasks/Application Methods 

AEATF II believes that by reasonably segmenting tasks involved in the application of 

antimicrobials, it will be possible to combine separate tasks as appropriate for various 

determining exposures in a variety of use scenarios. These studies will involve indoor, 

scripted scenarios. This approach will make the generic unit exposure data collected by 

the Task Force useful over the range of antimicrobial use sites and more flexible in utility 

to potentially changing use patterns. To this end, users of the AEATF II study results 

can use information from published and proprietary sources to establish values for other 

exposure factors or variables (e.g., the amount of time spent in a particular task in 

various occupational settings and residences, the average amount (or range) of a given 

product applied) in conjunction with the monitoring data to estimate typical exposures. 

Then the segmented exposure value associated with one discrete task can be combined 

with the values for other tasks that occur in a particular scenario to further estimate the 

exposure that could occur during a typical work day. 

In Situ (Observational) Studies for Complex or Multi-Task Scenarios 

Four of the AEATF II studies are being proposed for conduct in situ, as observational 

studies, given the complex combination of tasks, functions, etc. The unit exposure data 

collected from these studies are not intended to be combined, but instead will be 

representative of similar use scenarios. 

List of AEATF II Planned Studies 

Table 2 includes a list of the studies that the AEATF II plans to conduct, in the order of 

priority currently anticipated. Many of these studies will be conducted with simulated 

exposures. Those that are expected to be conducted as observational studies are so 

noted in the following table. 

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Table 2. 

AEATF II PROPOSED LIST OF STUDIES IN ORDER OF PRIORITY 

A. APPLICATOR/BYSTANDER EXPOSURE RESEARCH 

Mop Study 

Wipe Study 

Wood Pressure Treatment Study (observational) 

Aerosol Spray Study 

Pour Liquid Study 

Metal Working Fluid Study (observational) 

Pour Solid Study 

Brush/Roller Study (observational) 

Airless Spray Study (observational) 

High Pressure to Low Pressure Spray Studies 

Immersion/Dip/Soak Study 

Pump Liquid Study 

Place Solid Study 

Fogging Study 

B. POST-APPLICATION EXPOSURE RESEARCH (approach to be determined) 

Hard Surface 

Soft Surface 

STUDY DESCRIPTIONS 

The following studies are discussed in the order of priority, as currently determined by 

AEATF II in conjunction with US EPA, PMRA and CDPR. 

Mop 

Mopping involves the application of a diluted or ready-to-use antimicrobial solution to a 

floor for disinfection or sanitization.2

 Mopping occurs in the five EPA Use Site Groups 

that involve application of disinfectants and sanitizers to inanimate surfaces: agricultural 

premises and equipment, food handling/storage establishment premises and equipment, 

commercial/institutional/industrial premises and equipment, residential/public access 

premises, medical premises and equipment. The mopping data represent a single or 

discrete task that can be combined with other segments or tasks to estimate exposures 

from combinations of activities that represent typical work days in a variety of occupation 

settings or typical residential use events. The Task Force will simulate mopping 

2

 The terms “disinfect” and “sanitize” and variations of these terms are used as used by EPA in 

regulating antimicrobial pesticides. Each implies a specific level of antimicrobial efficacy, depending on 

the type of application method, and disinfect typically indicates a higher level of antimicrobial efficacy 

than sanitize. These terms do not have the same meanings in other regulatory or non-regulatory contexts. 

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activities, as it will be necessary to establish mopping times sufficient to obtain data, 

while limiting activity to the single task of mopping. This would be impossible in the vast 

majority of real-life occupational settings. Moreover, many of the facilities where 

extensive mopping might occur have legal and ethical considerations that would limit 

availability to the facility to researchers and associated monitors (e.g., food processing 

facilities and hospitals). 

There are numerous types of mopping equipment, including for example, string mops 

used in conjunction with various types of wringer equipment, sponge mops, ready-to-use 

systems with disposable, impregnated mop cloths, etc. AEATF II, based on an 

evaluation of equipment, has concluded that string mops represent a reasonable worst 

case and will use this equipment along with buckets with hand-activated mechanical 

wringers. 

There are data available estimating the amount of time spent mopping in various 

occupational settings (e.g., hospitals, hotels, homes), as well as the amount of time 

spent wiping, spraying, etc. These data can be used to aggregate exposures from 

various functions involving the application of antimicrobials in different occupational and 

residential settings to determine estimated total exposures covering a typical work day or 

residential usage event. 

Wipe 

Wiping involves the application of diluted antimicrobial products to surfaces other than 

floors in the five EPA Use Site Groups involving use of sanitizers and disinfectants on 

inanimate surfaces: agricultural premises and equipment, food handling/storage 

establishment premises and equipment, commercial/institutional/industrial premises and 

equipment, residential/public access premises, medical premises and equipment. The 

primary contact during use of wipes is dermally to the hand. Wiping can occur after 

trigger-spray application, wipe after dipping a wipe, sponge or other material in a 

container, or ready-to-use wipes impregnated with antimicrobial. AEATF II currently is 

planning to conduct two sets of MEs in simulated environments, one covering the use of 

pre-impregnated wipes and another involving use of a trigger spray followed by wiping 

with a dry cotton cloth. Use of wipes in conjunction with full-hand immersion will not be 

employed in this study, because full-hand immersion will be monitored in the 

dip/immersion study. 

As previously noted, the AEATF II will use data from published and proprietary sources 

to establish the amount of time spent in wiping activities in various occupational settings 

and residences in conjunction with the monitoring data to estimate typical exposures. 

The segmented exposure associated with wiping activity can be combined with others to 

further estimate the exposure that could occur during a typical work day in a variety of 

occupational settings. 

Aerosol Spray 

Aerosol spray is another application method employed in the five Use Site Groupings 

where there is application of sanitizers and disinfectants to inanimate surfaces: 

agricultural premises and equipment, food handling/storage establishment premises and 

equipment, commercial/institutional/industrial premises and equipment, residential/public 

access premises, medical premises and equipment. This study under simulated 

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conditions will monitor exposures resulting from spraying from aerosol cans onto 

surfaces, but will not include any wiping activities. To the extent that wiping can occur in 

conjunction with an aerosol product, the AEATF II believes that it will have sufficient data 

from the wipe study to combine with the aerosol study data to estimate exposures from 

such combined activities. These data can be used along with data on work day 

segments to estimate total exposures for various occupational settings and residential 

use scenarios, as needed. 

Whether the aerosol generator is prepackaged in a pressurized can or a refillable can 

with a separate pressurized air supply makes no difference for purposes of monitoring 

exposure, because both systems are producing aerosols using the same principle of 

physics. Only the amount of chemical handled varies by packaging. Thus, refillable 

containers with separate pressurized air supply, which are the higher volume use 

method of application, should be employed for an exposure monitoring study. The 

distance that aerosol generation occurs from the body and the aerosol particle size are 

probably more important determinants of exposure than whether the aerosol is 

dispensed from a can or remotely. Key parameters that are likely to affect exposure 

(e.g., amount of product sprayed, air exchange rate) will be carefully recorded so that 

there is a basis for extrapolating from one use method to another. 

Pressure Treatment of Wood 

This study is not based on an application method, but rather is one type of treatment 

cover by the EPA Use Site Group “Wood Preservation.” In contrast to the majority of 

studies to be conducted by the AEATF II, the data derived from the study of pressure 

treatment of wood cannot be used in conjunction with data from any other Task Force 

study in order to estimate exposure. The data derived from this study will be standalone data applicable only to wood pressure treatment use scenarios. This study is 

being proposed for conduct as an observational exposure monitoring study. Wood 

treatment immersion will not be studied by the AEATF II because the only exposure 

expected would be industrial bystander exposure. Industrial bystander exposure will be 

addressed or characterized in the wood pressure treatment and metalworking fluid 

preservation studies. 

There are at least four GLP-compliant, EPA-accepted studies on pressure treatment of 

wood with the three “heavy duty wood treatment compounds” (chromated copper 

arsenate or CCA, creosote and pentachlorophenol). EPA’s continuing interest in 

exposures resulting from this scenario probably is best explained by the estimate that 40 

percent or more of the annual US volume of pesticide production is consumed in the 

pressure treatment of wood. EPA, therefore, has again requested that industry provide 

these data. 

The AEATF II will rely on the North American regulators to identify tasks of special 

interest for study. Most of the tasks to be monitored are not directly involved in the 

application of the pesticide compounds, but instead involve secondary exposures 

associated with the treatment activities. Active ingredients have not yet been finally 

selected, but likely will reflect the new generation of wood treatment compounds that 

combine organic and metal-based components. 

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Pour Liquid 

Pour liquid data to cover the pouring of registered pesticide products could be used in 

conjunction with the data from a number of other application studies, including: mop, 

wipe, spray, immerse/dip, pump and fog. There are two general types of containers now 

used for pesticides: “glug” and “no-glug”. There are data that suggest “no-glug” 

containers (those with an air bypass that prevents “glugging” and concomitant 

backsplash) produce significantly less exposure to the pourer than do containers without 

this feature, which led EPA to issue its recent regulation requiring certain pesticides to 

be sold in no-glug containers. The AEATF II study intends to generate data comparing 

no-glug containers as a supplement to the existing studies on open pour exposures in 

the PHED database. Moreover, the AEATF II data will cover smaller sized containers 

that are frequently used for antimicrobials, but are not well represented in the PHED 

data. 

Metalworking Fluid Preservative 

EPA has singled out this material preservative use for study because of the Agency’s 

contention that exposures to the preservatives used in MWFs are among the highest of 

all occupational exposures to biocides. A great deal of information exists in the public 

literature with regard to ambient levels of MWFs, but not to the preservatives in the fluid. 

There also are limited data on dermal exposures. Occupational exposures also are 

regulated by the Occupational Safety and Health Administration (OSHA) and have been 

well studied by numerous government and private organizations. However, there are no 

EPA guideline-responsive, GLP data on this particular use. Dermal and inhalation 

exposures will be monitored in the new study for machine operators using the preserved 

fluids, and may also be monitored for others present in the facility (i.e., bystanders). 

This study, therefore, may be used to provide surrogate data to cover a range of 

industrial bystander exposure scenarios, e.g., pulp and paper mills, drift from cooling 

water installations, etc. Size of the aerosol and distance from the source probably could 

determine exposure to persons in the vicinity, and these data will be recorded. It is 

AEATF II’s belief that many of the secondary applicator exposure scenarios will be 

addressed with data generated from the primary applicator exposure studies. Uses such 

as paint, adhesives, caulking, etc. produce exposure to dilute concentrations of 

antimicrobial, and with judgment one can compare the unit exposure from the 

appropriate primary applicator scenario to many of these secondary applicator 

scenarios, i.e., exposure measured where the product makes a pesticidal claim can be 

applied to products that do not. 

Pour Solid 

PHED data clearly show that exposure (both dermal and inhalation) is inversely 

proportional to the particle size (and resulting surface area) of the solid being handled. 

PHED lists results for wettable powders (also applicable to dusts), dry flowables, and 

granulars that are all different solid formulations with increasing particle size and 

decreasing unit exposure in the order listed. The AEATF II expects to conduct a study 

using a dust or wettable powder and a granular formulation to bracket the range of 

particle sizes and resulting exposures currently in PHED. 

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Further, consideration may be given to observations of Heitbrink et al., (1992) regarding 

generation of aerosols during pouring of powders. Pour solid data could be combined 

with data from other application methods, as appropriate to estimate exposures in 

various occupational settings. 

Brush and Roller 

One of the most common applications for industrial biocides is for preservation of paints, 

coatings, caulks, adhesives and similar materials. EPA has historically used exposure to 

paints as the reasonable worst case for exposures to these and similar items. The vast 

majority of these paints and other materials are not themselves pesticides, that is, they 

do make any pesticidal claims. However, some paints and coatings are registered 

pesticides in that they claim to protect the substrates to which they are applied from 

microbial deterioration, e.g., antifoulant coatings and wood stains. 

The Task Force plans to monitor the application of preserved paint where interior spaces 

are being painted. Rollers cover more surface area in a given time period than brushes 

and regulatory agencies assume exposure per unit applied is the same as brushes. 

Published data are currently inadequate to characterize exposure potential from roller 

application, but suggest exposure may be lower than brush application. A combination 

observational study (including both brush and roller methods) is therefore being 

proposed, because typically both methods are used during a typical or normal day of 

paint product use. 

Airless Spray 

Airless spray is used in the application of antifoulant paints, wood preservatives, and 

preserved paints. In fact, the use of airless spray equipment (e.g., Wagner) is growing 

rapidly in the application of paints in many interior commercial and even residential 

settings. The Task Force is proposing to conduct an observational study to monitor 

interior painting events using airless spray equipment. It is important to note that this 

study may differ significantly than others planned by the Task Force because it likely will 

involve the use of both respiratory and dermal personal protective equipment, as 

specified by either or both the spray equipment manufacturer and paint manufacturer (of 

a non-pesticidal paint). 

High Pressure to Low Pressure Spray 

Although distinctions are sometimes made between high and low pressure spraying, two 

variables affect spray particle size: pressure and orifice size. Small particle sizes (<30 

microns mass median diameter) tend to produce higher exposure than larger particles 

because they stay suspended longer in the air and can be produced even with low 

pressure. A nomogram describing the pressure/orifice relationship to particle size would 

be useful and may be prepared during this work. A good deal of the equipment used for 

spray application is hand-held. Thus, another critical determinant of exposure is whether 

the spray emits above or below the shoulder. 

Higher-pressure spray is used with material preservative, wood preservative and 

antifoulant coating uses. Lower pressure spray also may be indicated for these uses, is 

one of the most widely used application methods for sanitizers and disinfectants, and 

includes applications as diverse as trigger sprays and foam generators. However, there 

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are no commonly accepted parameters for what constitutes high or low-pressure sprays. 

Therefore, a large indoor, scripted-activity study (minimum 60 MEs) will be designed to 

account for the range of equipment types typically used in various applications and use 

sites and the range of typical particle sizes that might be generated. The distance from 

the generation source is likely also to be a critical determinant of exposure. 

Data from this work will be used for estimating non-aerosol spray exposures to sanitizers 

and disinfectants in agricultural premises and equipment, food handling/storage 

establishment premises and equipment, commercial/institutional/industrial premises and 

equipment, residential/public access premises, and medical premises and equipment. 

Exposure from non-aerosol spray activity may be combined with exposure from other 

scripted tasks, as appropriate, to estimate total occupational or non-occupational 

exposure for a particular use scenario. The data also will be used for preserved paint, 

antifoulant coatings and wood preservative applications for spray uses that do not 

employ airless spray equipment. Such exposures will not be combined with other 

exposures from other tasks, but instead will be used to estimate typical occupational or 

non-occupational use scenarios. 

Soak and Immerse 

The terms soak and immerse can be used in a variety of contexts for antimicrobial 

application. There are industrial uses involving soaking or immersion (various types of 

wood and anti-sapstain treatment) that are mechanized and may use large volumes of 

antimicrobial but present only a potential for exposure to subjects as bystanders. On the 

other hand, low volume uses, such as dish sanitizers in restaurants and bars, 

commercial or institutional laundry sanitizers, disinfectants for halters and other stable or 

livestock equipment, etc. that are non-mechanized and involve repeated hand 

immersion, may offer more potential for exposure. The latter types of exposure will be 

monitored in a scripted study. The unit exposure data derived from this study may be 

combined with data from other discrete tasks to estimate total workday exposures for the 

agricultural premises and equipment, food handling/storage establishment premises and 

equipment, commercial/institutional/industrial premises and equipment, residential/public 

access premises, and medical premises and equipment Use Site Groups. Use in the 

wood treatment industry that involves primarily bystander exposure could be addressed 

by either the metal working fluid study or some subset of the monitoring units (or 

monitoring events) from the wood pressure treatment study. 

Pump Liquid 

Some of the largest volume uses of antimicrobials involve use of pump systems, e.g., 

application in oilfield and municipal water treatment facilities. However, these systems 

are closed metering systems using dry lock connections, with virtually no opportunity for 

worker exposure. Other examples of closed pump systems include tank truck unloading, 

automatic dispensing systems, metering pump systems for totes and drums, and tubeset 

pumps. Data on these various systems have been shared with EPA, to support the 

AEATF II position that additional data should not be required by EPA for pump liquid 

applications. In any case, there are probably sufficient data in the PHED database to 

cover all antimicrobial use patterns and all product types where closed or similar 

systems are not in place to eliminate or mitigate exposure. An analysis will be 

performed by the AEATF II to further assess the adequacy of the available data. In 

addition, a final decision in part depends on the EPA and the other North American 

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regulators’ final position on whether occupational risk assessments will be required for 

subjects that use closed pump systems. Pump liquid data, if required, could be 

combined with data from other application methods, as appropriate to estimate 

exposures in various occupational settings. 

Place Solid 

Registrants have developed various “place solid” products to reduce the potential for 

exposure to dry flowable products. The most common “solid” delivery systems are 

powders/granulars in sealed, water-soluble bags and “tablets.” EPA is requesting data 

because it has only one data record in the PHED database. However, the AEATF II 

believes that it is unnecessary to require a study with this application method. One of 

the most common antimicrobial pesticide uses employing tablets is application of 

sanitizers or algaecides to swimming pools. To the extent that a home owner might 

apply a single tablet on a weekly or semi-weekly basis, EPA has conceded that 

exposure is likely to be non-detectable. When a professional pool treater or other 

occupational applicator uses tablets, multiple tables may be applied over the course of 

the day. The AEATF II believes in such cases it is appropriate to require occupational 

users to wear chemical-resistant gloves, which again would reduce exposures to nondetect levels. There should be no need for these data and no need to do an 

occupational risk assessment if the requirement for gloves for occupational users 

appears on product labels. Place solid data, if required, could be combined with data 

from other application methods, as appropriate to estimate occupational exposure. 

Fog 

Fogging is used to treat large or irregularly shaped areas. Most fogging is done using 

remote operation, where applicator exposure is negligible. However, there are 

backpacks sold for occupational, indoor antimicrobial fogging applications, and EPA has 

requested data for fogging data using antimicrobials. However, the AEATF II believes 

that to the extent fogging is done using handheld equipment, respiratory and dermal 

PPE should be a standard requirement. Therefore, in either the case of remotely 

controlled foggers or hand-held fogging with PPE requirements, the potential for 

occupational exposure would be negligible. Further, there are no registered residential 

uses for antimicrobial foggers. As a result, the AEATF II believes that the requirement 

for fogging data for antimicrobials should be eliminated. Fogging data, if required, could 

be used for the following EPA Use Site Groups: agricultural premises and equipment, 

food handling/storage establishment premises and equipment, 

commercial/institutional/industrial premises and equipment, residential/public access 

premises, and medical premises and equipment. 

POST-APPLICATION EXPOSURE STUDIES 

Currently EPA uses the Residential Exposure Assessment Standard Operating 

Procedures (SOPs) as codified in SOP 12 (Smegal et al., 2001) and incorporated into 

software tools such as (REx – available at www.infoscientific.com and PIRAT – available 

from http://epa.gov/opptintr/exposure/pubs/piratdl.htm) to estimate potential reentry 

exposures. Dependent on the toxicity of the antimicrobial, use of default exposure 

estimates for post application exposure may be adequate. 

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The typical primary route of post-application exposure is dermal. It is possible to 

generate generic post-application dermal exposure data that would be generally 

applicable to a range of possible exposure scenarios (e.g., dermal contact with sanitized 

floor surfaces; incidental dermal contact with disinfected work surfaces; dermal contact 

with treated articles). Other task forces have developed data to evaluate postapplication exposure (also known as reentry exposure) for environmental surfaces such 

as turf (Outdoor Residential Exposure Task Force; ORETF), and plant foliage surfaces 

(Agricultural Reentry Task Force; ARTF). Post-application exposure monitoring studies 

typically involve concurrent measurement of residues transferred from a treated surface 

as a function of time using a non-human transfer medium (discussed in the following 

paragraph) and the quantity of chemical transferred to full body dosimetry garments 

worn by individuals contacting a treated surface with a known intensity, for specific time 

duration. The dermal exposure rate (e.g., mg/hr) measured using dosimetry garments is 

divided by the concurrently measured surface transferable residues (e.g., mg/cm2

) to 

achieve a generic transfer coefficient (TC). This coefficient, which is also referred to as 

a “contact rate,” is typically expressed in units of cm2

/hr. 

In conjunction with measuring human dermal exposure following contact with a treated 

surface, a post-application exposure study also determines temporal transferable 

residue (measurements taken across time) from the treated surface using a generic 

method (a roller was used by ORETF and leaf washes were used by the ARTF). The 

AEATF II will need to develop or adapt an existing method for measuring transferable 

residues. Transferable residues are chemical-specific (due to different adsorption and 

dissipation characteristics that are unique to each chemical used on a matrix). 

Therefore, chemical-specific transferable residues must be generated by the individual 

registrant. These studies cost approximately 5 to 10% of what a human exposure 

monitoring study costs. 

The AEATF II initially proposed to develop TCs by conducting one study with hard 

surfaces (e.g., floor or countertop) and one with soft surfaces (e.g., textile such as carpet 

or upholstery). EPA has requested that the Task Force defer any further work on postapplication exposures until it has conferred with other North American regulators to more 

clearly determine how it will assess post-application exposures in the future. 

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REFERENCES 

AEATF II (2004a). American Chemistry Council Antimicrobial Exposure Assessment 

Task Force II. Glossary of Terms: Application Methods and Use Patterns. 

AEATF II (2004b). American Chemistry Council Antimicrobial Exposure Assessment 

Task Force II. Justification for Changes to EPA’s Initial List of Application Methods. 

AEATF II (2004c). American Chemistry Council Antimicrobial Exposure Assessment 

Task Force II. Mixer/Loader (Preparation Worker) and Applicator Exposure Monitoring 

Study Design Parameters. 

AEATF II (2004d). American Chemistry Council Antimicrobial Exposure Assessment 

Task Force II. Post Application Exposure Estimates: AEATF II Data Development 

Needs. 

Heitbrink WA, Baron PA, Willeke K (1992). An investigation of dust generation by free 

falling powders. Am. Indust. Hygiene Assoc. J 53: 617-624. 

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Appendix B. Antimicrobial Product Use Sites and 

Categories 

I. Agricultural premises and equipment 

II. Food handling/storage establishment premises and equipment 

III. Commercial, institutional and industrial premises and equipment 

IV. Residential and public access premises 

V. Medical premises and equipment 

VI. Human drinking water systems 

VII. Materials preservatives 

VIII. Industrial processes and water systems 

IX. Antifouling coatings 

X. Wood preservatives 

XI. Swimming Pools 

XII. Aquatic areas 

I. Agricultural premises and equipment 

a. Food area premises and equipment - indirect food contact * 

AGRICULTURAL/FARM PREMISES INDOOR FOOD, 

TERRESTRIAL FOOD 

AGRICULTURAL/FARM 

STRUCTURES/BUILDINGS AND EQUIPMENT 

INDOOR FOOD, 

TERRESTRIAL FOOD 

BARNS/BARNYARDS/AUCTION BARNS INDOOR FOOD 

BEEF/RANGE/FEEDER CATTLE (MEAT) INDOOR FOOD 

BEEHIVES/BEE COLONY (DISEASED/NUISANCE) INDOOR FOOD 

BEEHIVES-EMPTY INDOOR FOOD 

CALVES (MEAT) INDOOR FOOD 

DAIRY CATTLE (LACTATING OR UNSPECIFIED) INDOOR FOOD 

DAIRY CATTLE (NON-LACTATING) INDOOR FOOD 

DAIRY FARM MILK HANDLING 

FACILITIES/EQUIPMENT 

INDOOR FOOD 

DAIRY FARM MILK STORAGE 

ROOMS/HOUSES/SHEDS 

INDOOR FOOD 

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DAIRY FARM MILKING EQUIPMENT INDOOR FOOD 

DAIRY FARM MILKING STALLS/PARLORS INDOOR FOOD 

DAIRY GOATS (LACTATING OR UNSPECIFIED) INDOOR FOOD 

DAIRY GOATS (NON-LACTATING) INDOOR FOOD 

EMPTY CONTAINERS TO BE USED FOR RAW 

AGRICULTURAL COMMODITIES 

INDOOR FOOD 

FISH, FRESHWATER (MEAT) INDOOR FOOD 

FISH, SALTWATER (MEAT) INDOOR FOOD 

FISH HATCHERY BUILDINGS/AREAS (NONAQUATIC) INDOOR FOOD 

FISH ROE (CAVIAR)(MEAT) INDOOR FOOD 

GAME ANIMAL (MEAT) INDOOR FOOD 

GOATS (MEAT) INDOOR FOOD 

HOG/PIG/SWINE (MEAT) INDOOR FOOD 

KIDS (MEAT) INDOOR FOOD 

LAMB (MEAT) INDOOR FOOD 

LIVESTOCK INDOOR FOOD 

MUSHROOM HOUSES-EMPTY 

PREMISES/EQUIPMENT 

GREENHOUSE FOOD, 

INDOOR NON-FOOD 

POTATO SEED PIECE STORAGE 

PREMISES/EQUIPMENT 

INDOOR FOOD, 

TERRESTRIAL FEED 

POULTRY (EGG/MEAT) INDOOR FOOD 

POULTRY (MEAT) INDOOR FOOD 

RABBITS (MEAT) INDOOR FOOD 

SEED HOUSES/STORES/STORAGE 

AREAS/WAREHOUSES 

INDOOR FOOD 

SHEEP (MEAT) INDOOR FOOD 

SHELLFISH (MEAT) INDOOR FOOD 

b. Direct food contact 

ANIMAL DRINKING WATER INDOOR FOOD 

POULTRY DRINKING WATER INDOOR FOOD 

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c. Nonfood area premises and equipment 

AGRICULTURAL/FARM EQUIPMENT/SHOE BATHS 

EGG HANDLING EQUIPMENT (HATCHING) 

EGG HANDLING ROOMS (HATCHING) 

EGG PLANTS/HATCHERIES/BROODER ROOMS/SHOE 

BATHS (HATCHING) 

EGG WASHING TREATMENTS (HATCHING) 

FUR FARM EQUIPMENT/PREMISES 

INDOOR NONFOOD 

INDOOR NONFOOD 

INDOOR NONFOOD 

INDOOR NONFOOD 

INDOOR NONFOOD 

INDOOR NONFOOD 

* Use of a product on sites in this category will be considered a food use and 

registration must be supported by data sufficient to support establishment of a 

tolerance or exemption from the requirement of a tolerance under the Federal 

Food, Drug and Cosmetic Act. The Agency will consider label modifications 

which clarify practices such that use of the product is unlikely to result in 

pesticide residues in food. Uses will be considered nonfood if food is covered or 

removed during application and the treated surfaces are rinsed with potable 

water prior to any contact with food. Registrants are advised to contact the 

Agency if they are uncertain as to whether a proposed application is a food use. 

II. Food handling/storage establishments premises and equipment 

a. Food area premises and equipment - indirect food contact * 

AIRTIGHT STORAGE (FLAT)-EMPTY 

AIRTIGHT STORAGE (SMALL)-EMPTY 

AIRTIGHT STORAGE-EMPTY 

COMMERCIAL SHIPPING CONTAINERS-FEED/FOOD-EMPTY 

COMMERCIAL TRANSPORTATION FACILITIES-FEED/FOODEMPTY 

DAIRIES/CHEESE PROCESSING PLANT EQUIPMENT (FOOD 

CONTACT) 

INDOOR 

FOOD 

INDOOR 

FOOD 

INDOOR 

FOOD 

INDOOR 

FOOD 

INDOOR 

FOOD 

INDOOR 

FOOD 

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DAIRIES/CHEESE PROCESSING PLANT PREMISES (NONFOOD 

CONTACT) 

INDOOR 

FOOD 

DISHWASHING WATER INDOOR 

FOOD 

EATING ESTABLISHMENTS (FOOD CONTACT) INDOOR 

FOOD 

EATING ESTABLISHMENTS EQUIPMENT/UTENSILS (FOOD 

CONTACT) 

INDOOR 

FOOD 

EATING ESTABLISHMENTS FOOD HANDLING AREAS (FOOD 

CONTACT) 

INDOOR 

FOOD 

EATING ESTABLISHMENTS FOOD SERVING AREAS (FOOD 

CONTACT) 

INDOOR 

FOOD 

EGG HANDLING EQUIPMENT (COMMERCIAL) INDOOR 

FOOD 

EGG HANDLING ROOMS (COMMERCIAL) INDOOR 

FOOD 

EGG PACKING PLANTS (COMMERCIAL) INDOOR 

FOOD 

EMPTY CONTAINERS TO BE USED FOR PROCESSED 

FEED/FOOD 

INDOOR 

FOOD 

FEED MILLS/FEED PROCESSING PLANTS INDOOR 

FOOD 

FEED/FOOD STORAGE AREAS-EMPTY INDOOR 

FOOD 

FEED/FOOD TREATMENT-STORAGE/PROCESSING/ INDOOR 

FOOD 

FISH/SEAFOOD PROCESSING PLANT EQUIPMENT (FOOD 

CONTACT) 

INDOOR 

FOOD 

FISH/SEAFOOD PROCESSING PLANT PREMISES (NONFOOD 

CONTACT) 

INDOOR 

FOOD 

FOOD CATERING FACILITIES PREMISES INDOOR 

FOOD 

FOOD DISPENSING EQUIPMENT/VENDING MACHINES INDOOR 

FOOD 

FOOD MARKETING/STORAGE/DISTRIBUTION EQUIPMENT / 

UTENSILS (FOOD CONTACT) 

INDOOR 

FOOD 

FOOD PROCESSING PLANT EQUIPMENT (FOOD CONTACT) INDOOR 

FOOD 

FOOD PROCESSING PLANT NON-FOOD HANDLING AREAS INDOOR 

FOOD 

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FOOD PROCESSING PLANT PREMISES (NONFOOD CONTACT) INDOOR 

FOOD 

FOOD PROCESSING WATER SYSTEMS INDOOR 

FOOD 

FOOD STORES/MARKETS/SUPERMARKETS PREMISES INDOOR 

FOOD 

FOOD/GROCERY/MARKETING/STORAGE/DISTRIBUTION 

FACILITY 

INDOOR 

FOOD 

HANDLING EQUIPMENT INDOOR 

FOOD 

GRAIN/CEREAL/FLOUR BINS-EMPTY INDOOR 

FOOD 

GRAIN/CEREAL/FLOUR ELEVATORS-EMPTY INDOOR 

FOOD 

GRAIN/CEREAL/FLOUR STORAGE AREAS-EMPTY INDOOR 

FOOD 

MEAT PROCESSING PLANT EQUIPMENT (FOOD CONTACT) INDOOR 

FOOD 

MEAT PROCESSING PLANT PREMISES (NONFOOD CONTACT) INDOOR 

FOOD 

MEAT/FISH MARKETS PREMISES INDOOR 

FOOD 

POULTRY PROCESSING PLANT EQUIPMENT (FOOD CONTACT) INDOOR 

FOOD 

POULTRY PROCESSING PLANT PREMISES (NONFOOD 

CONTACT) 

INDOOR 

FOOD 

PROCESSING/HANDLING EQUIPMENT (FOOD CONTACT 

SURFACES 

INDOOR 

FOOD 

b. Direct food contact 

EGG WASHING TREATMENTS (COMMERCIAL) INDOOR FOOD 

FRUIT AND VEGETABLE RINSES INDOOR FOOD 

c. Areas without potential for food contact 

EATING ESTABLISHMENTS (NONFOOD CONTACT) INDOOR NONFOOD 

EATING ESTABLISHMENTS FOOD HANDLING AREAS INDOOR NON­

(NONFOOD CONTACT) FOOD 

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EATING ESTABLISHMENTS FOOD SERVING AREAS 

(NONFOOD CONTACT) 

INDOOR NONFOOD 

EATING ESTABLISHMENTS NON-FOOD AREAS (NONFOOD 

CONTACT) 

INDOOR NONFOOD 

HYDROSTATIC STERILIZER WATER SYSTEMS INDOOR NONFOOD 

TOBACCO PROCESSING PLANT PREMISES/EQUIPMENT INDOOR NONFOOD 

PASTEURIZER/WARMER/CANNERY/RETORT WATER 

SYSTEMS 

INDOOR NONFOOD 

PROCESSING/HANDLING EQUIPMENT (NONFOOD 

CONTACT) 

INDOOR NONFOOD 

* Use of a product on sites in this category will be considered a food use and 

registration must be supported by data sufficient to support establishment of a 

tolerance or exemption from the requirement of a tolerance under the Federal 

Food, Drug and Cosmetic Act. The Agency will consider label modifications 

which clarify practices such that use of the product is unlikely to result in 

pesticide residues in food. Uses will be considered nonfood if food is covered or 

removed during application and the treated surfaces are rinsed with potable 

water prior to any contact with food. Registrants are advised to contact the 

Agency if they are uncertain as to whether a proposed application is a food use. 

III. Commercial, institutional and industrial premises and equipment 

a. Indoor 

AIRTIGHT STORAGE (FLAT)-EMPTY 

AIRTIGHT STORAGE (SMALL)-EMPTY 

AIRTIGHT STORAGE-EMPTY 

CARPETS (COMMERCIAL SANITIZER) 

COMMERCIAL/INSTITUTIONAL/INDUSTRIAL FLOORS 

COMMERCIAL/INSTITUTIONAL/INDUSTRIAL 

PREMISES/EQUIPMENT (INDOOR) 

COMMERCIAL STORAGE/WAREHOUSES PREMISES 

(INDOOR) 

COMMERCIAL TRANSPORTATION FACILITIESINDOOR 

NON-FOOD 

INDOOR 

NON-FOOD 

INDOOR 

NON-FOOD 

INDOOR 

NON-FOOD 

INDOOR 

NON-FOOD 

INDOOR 

NON-FOOD 

INDOOR 

NON-FOOD 

INDOOR 

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NONFEED/NONFOOD NON-FOOD 

DIAPERS (COMMERCIAL LAUNDRY) INDOOR 

NON-FOOD 

DUST MOPS/CLOTHS/TOOL COVERS/DUSTERS 

(LAUNDRY/DRYCLEAN) 

INDOOR 

NON-FOOD 

LAUNDRY (COMMERCIAL) INDOOR 

NON-FOOD 

LAUNDRY (DRYCLEANING) INDOOR 

NON-FOOD 

LAUNDRY EQUIPMENT INDOOR 

NON-FOOD 

MACHINERY (NON-FOOD) INDOOR 

NON-FOOD 

NONFEED/NONFOOD COMMODITIES (TEMPORARY 

STORAGE) 

INDOOR 

NON-FOOD 

NONFEED/NONFOOD CONTAINERS-EMPTY/FULL INDOOR 

NON-FOOD 

NONFEED/NONFOOD STORAGE AREAS-EMPTY/FULL INDOOR 

NON-FOOD 

NONFEED/NONFOOD TREATMENTSSTORAGE/PROCESSING/HANDLING EQUIPMENT INDOOR 

NON-FOOD 

REFUSE/SOLID WASTE TRANSPORTATION 

FACILITIES/HANDLING EQUIPMENT 

INDOOR 

NON-FOOD 

b. Outdoor 

COMMERCIAL/INSTITUTIONAL/INDUSTRIAL 

PREMISES/EQUIPMENT (OUTDOOR) 

TERRESTRIAL 

NON-FOOD 

IV. Residential and public access premises 

a. Indirect food contact * 

HOUSEHOLD/DOMESTIC DWELLINGS INDOOR FOOD, INDOOR 

RESIDENTIAL 

HOUSEHOLD/DOMESTIC DWELLINGS INDOOR 

FOOD HANDLING AREAS 

INDOOR FOOD 

b. Nonfood indoor 

AMPHIBIANS (PET) INDOOR 

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ANIMALS (LABORATORY/RESEARCH) 

BIRDS (PET) 

CATS (ADULTS/KITTENS) (PET) 

CATS (LABORATORY/RESEARCH) 

DOGS (SHOW/MILITARY/SPECIAL) 

DOGS/CANINES (ADULTS/PUPPIES) (PET) 

DONKEYS 

FERRETS (PET) 

FISH (PET) 

FOX 

GOATS (WOOL/ANGORA ANIMAL) 

GREENHOUSE-EMPTY 

HORSES (SHOW/RACE/SPECIAL/PONIES) 

MINK 

MONKEYS (PET) 

MULES (WORK) 

NUTRIA 

RABBITS (FUR ANIMAL) 

RABBITS (PET) 

REPTILES (PET) 

RESIDENTIAL 

INDOOR NONFOOD 

INDOOR 

RESIDENTIAL 

INDOOR 

RESIDENTIAL 

INDOOR NONFOOD 

INDOOR NONFOOD 

INDOOR 

RESIDENTIAL 

INDOOR NONFOOD 

INDOOR 

RESIDENTIAL 

INDOOR 

RESIDENTIAL 

INDOOR NONFOOD 

INDOOR NONFOOD 

INDOOR NONFOOD 

INDOOR NONFOOD 

INDOOR NONFOOD 

INDOOR 

RESIDENTIAL 

INDOOR NONFOOD 

INDOOR NONFOOD 

INDOOR NONFOOD 

INDOOR 

RESIDENTIAL 

INDOOR 

RESIDENTIAL 

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RODENTS (GUINEA 

PIGS/HAMSTERS/GERBILS/MICE/RATS) (PET) 

INDOOR 

RESIDENTIAL 

RODENTS, WILD (CAPTURED FOR SALE) INDOOR NONFOOD 

SHEEP (WOOL ANIMAL) INDOOR NONFOOD 

SHEEP, DESERT BIGHORN INDOOR NONFOOD 

AIR TREATMENTS (COMMERCIAL/HOUSEHOLD) INDOOR 

RESIDENTIAL 

BATHROOM PREMISES/HARD SURFACES INDOOR 

RESIDENTIAL 

CARPETS (HOUSEHOLD SANITIZER) INDOOR 

RESIDENTIAL 

DIAPER PAILS (EMPTY) INDOOR 

RESIDENTIAL 

DIAPERS (HOUSEHOLD/COIN-OPERATED LAUNDRY) INDOOR 

RESIDENTIAL 

DIAPERS (PRESOAK) INDOOR 

RESIDENTIAL 

DOMESTIC/COMMERCIAL NONPOTABLE WATER 

(WATERBED WATER) 

AQUATIC NONFOOD 

RESIDENTIAL, 

FILTERS (AIR/AIR CONDITIONER/FURNACE) INDOOR 

RESIDENTIAL 

HOUSEHOLD TRASH COMPACTOR/FOOD DISPOSAL INDOOR 

RESIDENTIAL 

HOUSEHOLD/DOMESTIC DWELLINGS CONTENTS INDOOR 

RESIDENTIAL 

HOUSEHOLD/DOMESTIC DWELLINGS INDOOR 

NONFOOD HANDLING AREAS 

INDOOR 

RESIDENTIAL 

HOUSEHOLD/DOMESTIC DWELLINGS INDOOR 

PREMISES 

INDOOR 

RESIDENTIAL 

HUMAN BEDDING/MATTRESSES INDOOR 

RESIDENTIAL 

HUMAN CAMPING EQUIPMENT INDOOR 

RESIDENTIAL 

HUMAN DENTURES/TOOTHBRUSHES/MOUTHPIECES INDOOR 

RESIDENTIAL 

HUMAN FACE GEAR INDOOR 

RESIDENTIAL 

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HUMAN FOOTWEAR INDOOR 

RESIDENTIAL 

HUMAN GROOMING INSTRUMENTS 

(BRUSHES,COMBS) 

INDOOR 

RESIDENTIAL 

HUMAN HEADGEAR INDOOR 

RESIDENTIAL 

HUMAN WIGS INDOOR 

RESIDENTIAL 

HUMIDIFIER WATER INDOOR 

RESIDENTIAL 

LAUNDRY (HOUSEHOLD/COIN-OPERATED) INDOOR 

RESIDENTIAL 

PORTABLE/CHEMICAL TOILETS/LATRINE BUCKETS INDOOR 

RESIDENTIAL 

REFUSE/SOLID WASTE CONTAINERS (GARBAGE 

CANS) 

INDOOR 

RESIDENTIAL 

REFUSE/SOLID WASTE SITES (INDOOR) INDOOR 

RESIDENTIAL 

REFUSE/SOLID WASTE TRANSPORTATION 

FACILITIES/HANDLING EQUIPMENT 

INDOOR 

RESIDENTIAL 

RESIDENTIAL FLOORS (ANTIMICROBIALS ONLY) INDOOR 

RESIDENTIAL 

TOILET BOWLS (INTERIOR SURFACES) INDOOR 

RESIDENTIAL 

TOILET TANKS/WATER CLOSETS WATER INDOOR 

RESIDENTIAL 

URINALS (INTERIOR SURFACES) INDOOR 

RESIDENTIAL 

VEHICULAR HOLDING TANKS INDOOR 

RESIDENTIAL 

c. Nonfood indoor/outdoor 

ANIMAL KENNELS/SLEEPING QUARTERS 

(COMMERCIAL) 

HOUSEHOLD/DOMESTIC DWELLINGS 

OUTDOOR PREMISES 

PET LIVING/SLEEPING QUARTERS 

INDOOR NON-FOOD, 

TERRESTRIAL NON-FOOD 

OUTDOOR RESIDENTIAL 

INDOOR RESIDENTIAL, 

OUTDOOR RESIDENTIAL 

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V. Medical premises and equipment 

AIR TREATMENTS (HOSPITAL) 

BARBER/BEAUTY SHOP EQUIPMENT (BARBER 

CHAIR/CABINETS) 

BARBER/BEAUTY SHOP INSTRUMENTS 

(SHAVERS/SCISSORS) 

BIOLOGICAL SPECIMENS (ORGANS/TISSUES/MILK 

SAMPLES) 

CADAVERS AND CASKETS 

CARPETS (HOSPITAL SANITIZER) 

CUSPIDORS/SPITTOONS 

DIAPERS (HOSPITAL LAUNDRY) 

HOSPITAL CONDUCTIVE FLOORS 

HOSPITAL CRITICAL ITEMS (SURGICAL 

INSTRUMENTS/PACEMAKERS) 

HOSPITAL JANITORIAL EQUIPMENT 

HOSPITAL NONCRITICAL ITEMS (BEDPANS/FURNITURE) 

HOSPITAL SEMICRITICAL ITEMS 

(CATHETERS/INHALATION EQUIPMENT 

HOSPITAL/MEDICAL INSTITUTIONS NON-CONDUCTIVE 

FLOORS 

HOSPITALS/MEDICAL INSTITUTIONS CRITICAL PREMISES 

(BURN WARDS, OPERATING ROOM AREA 

HOSPITALS/MEDICAL INSTITUTIONS NONCRITICAL 

PREMISES 

HOSPITALS/MEDICAL INSTITUTIONS PATIENT PREMISES 

HOSPITALS/MEDICAL INSTITUTIONS PREMISES 

(HUMAN/VETERINARY) 

HOUSEHOLD SICKROOMS 

PREMISES/CONTENTS/UTENSILS 

HUMAN WASTE (TYPHOID STOOLS/FECES/URINE) 

INDOOR 

MEDICAL 

INDOOR 

MEDICAL 

INDOOR 

MEDICAL 

INDOOR 

MEDICAL 

INDOOR 

MEDICAL 

INDOOR 

MEDICAL 

INDOOR 

MEDICAL 

INDOOR 

MEDICAL 

INDOOR 

MEDICAL 

INDOOR 

MEDICAL 

INDOOR 

MEDICAL 

INDOOR 

MEDICAL 

INDOOR 

MEDICAL 

INDOOR 

MEDICAL 

INDOOR 

MEDICAL 

INDOOR 

MEDICAL 

INDOOR 

MEDICAL 

INDOOR 

MEDICAL 

INDOOR 

MEDICAL 

INDOOR 

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MEDICAL 

LAUNDRY (HOSPITAL) INDOOR 

MEDICAL 

MORGUES/MORTUARIES/AUTOPSY/EMBALMING 

EQUIPMENT 

INDOOR 

MEDICAL 

MORGUES/MORTUARIES/AUTOPSY/EMBALMING 

INSTRUMENTS 

INDOOR 

MEDICAL 

MORGUES/MORTUARIES/AUTOPSY/EMBALMING ROOM 

PREMISES 

INDOOR 

MEDICAL 

REVERSE OSMOSIS WATER SYSTEM INDOOR 

MEDICAL 

UPHOLSTERY (HOSPITAL/COMMERCIAL) INDOOR 

MEDICAL, 

INDOOR NONFOOD 

VOMITUS INDOOR 

MEDICAL 

VI. Human drinking water systems * 

PUBLIC WATER SYSTEMS 

INDIVIDUAL WATER SYSTEMS 

EMERGENCY WATER SYSTEMS 

WATER PURIFIER UNITS 

INDOOR FOOD 

INDOOR FOOD 

INDOOR FOOD 

INDOOR FOOD 

* Use of a product on sites in this category will be considered a food use and 

registration must be supported by data sufficient to support establishment of a 

tolerance or exemption from the requirement of a tolerance under the Federal 

Food, Drug and Cosmetic Act. The Agency will consider label modifications 

which clarify practices such that use of the product is unlikely to result in 

pesticide residues in food. Uses will be considered nonfood if food is covered or 

removed during application and the treated surfaces are rinsed with potable 

water prior to any contact with food. Registrants are advised to contact the 

Agency if they are uncertain as to whether a proposed application is a food use. 

VII. Materials preservatives 

a. Indoor Food 

ADHESIVES, INDUSTRIAL (INDIRECT FOOD CONTACT 

SURFACES) 

INDOOR 

FOOD 

COATINGS, INDUSTRIAL INDOOR 

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FOOD 

PAINTS (FINGER) INDOOR 

FOOD 

PAPERMAKING (FOOD CONTACT) INDOOR 

FOOD 

PLASTIC-MAKING * INDOOR 

FOOD 

b. Indoor Nonfood 

ADHESIVES, INDUSTRIAL (NONFOOD CONTACT) 

CAULKS 

DIAPERS, DISPOSABLE 

FEATHERS/FELT/FELT PRODUCTS/FURS 

FUELS/OIL STORAGE TANK BOTTOM WATER 

FUELS/OIL (CRUDE) 

HIDES/LEATHER/LEATHER PRODUCTS (SURFACES) 

JANITORIAL PRODUCTS (IN-CONTAINER) 

METALWORKING CUTTING FLUIDS 

PAINTS (IN-CAN) 

PAPER (STORED) 

PAPERMAKING (NONFOOD CONTACT) 

PLASTIC/PVC/VINYL PRODUCTS 

RUBBER PRODUCTS 

SIZES(ING) 

SLURRIES 

SPECIALTY PRODUCTS 

TEXTILES/CORDAGE PRODUCTS 

TEXTILE-/TEXTILE FIBERS-MAKING 

INDOOR NON-FOOD 

INDOOR NON-FOOD 

INDOOR NON-FOOD 

INDOOR NON-FOOD 

INDOOR NON-FOOD 

INDOOR NON-FOOD 

INDOOR NON-FOOD 

INDOOR NON-FOOD 

INDOOR NON-FOOD 

INDOOR NON-FOOD 

INDOOR NON-FOOD 

INDOOR NON-FOOD 

INDOOR NON-FOOD 

INDOOR NON-FOOD 

INDOOR NON-FOOD 

INDOOR NON-FOOD 

INDOOR NON-FOOD 

INDOOR NON-FOOD 

INDOOR NON-FOOD 

b. Indoor/Outdoor Nonfood 

COATINGS, INDUSTRIAL AQUATIC NONFOOD 

INDUSTRIAL, 

INDOOR NONFOOD, INDOOR 

RESIDENTIAL, 

OUTDOOR 

RESIDENTIAL, 

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TERRESTRIAL 

NON-FOOD 

DISPERSIONS/EMULSIONS/SOLUTIONS/SUSPENSIONS INDOOR NONFOOD, 

TERRESTRIAL 

NON-FOOD CROP 

PAINTS (APPLIED FILM) AQUATIC NONFOOD 

RESIDENTIAL, 

INDOOR NONFOOD, 

TERRESTRIAL 

NON-FOOD CROP 

* Use of a product on sites in this category will be considered a food use and 

registration must be supported by data sufficient to support establishment of a 

tolerance or exemption from the requirement of a tolerance under the Federal 

Food, Drug and Cosmetic Act. The Agency will consider label modifications 

which clarify practices such that use of the product is unlikely to result in 

pesticide residues in food. Uses will be considered nonfood if food is covered or 

removed during application and the treated surfaces are rinsed with potable 

water prior to any contact with food. Registrants are advised to contact the 

Agency if they are uncertain as to whether a proposed application is a food use. 

VIII. Industrial processes and water systems 

a. Indoor Nonfood 

PASTEURIZER/CAN WARMER/CANNERY/RETORT 

WATER SYSTEMS 

HYDROSTATIC STERILIZER WATER SYSTEMS 

IMMERSION ULTRASONIC TANK WATER 

LEATHER PROCESSING WATER/LIQUORS 

PHOTO PROCESSING WASH WATER 

RECIRCULATING ELECTRODEPOSITION SYSTEMS 

REVERSE OSMOSIS WATER SYSTEM 

INDOOR NONFOOD 

INDOOR NONFOOD 

INDOOR NONFOOD 

INDOOR NONFOOD 

INDOOR NONFOOD 

INDOOR NONFOOD 

INDOOR NONFOOD 

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b. Aquatic/Outdoor exposure 

AIR CONDITIONER/REFRIGERATION 

CONDENSATE WATER SYSTEMS 

AIR WASHER WATER SYSTEMS 

COAL SLURRY SYSTEMS 

COMMERCIAL/INDUSTRIAL COOLING 

WATER [RECIRCULATING] 

COMMERCIAL/INDUSTRIAL COOLING 

WATER [ONCE-THROUGH] 

DRAINAGE SYSTEMS 

EVAPORATIVE CONDENSER WATER 

SYSTEMS 

INFLUENT WATER FILTRATION SYSTEMS 

TEXTILE MILL WATER SYSTEMS 

INDUSTRIAL SCRUBBING SYSTEM 

INDUSTRIAL WASTEWATER TREATMENT 

SYSTEMS 

LABORATORY EQUIPMENT WATER BATHS 

PULP/PAPER MILL WATER SYSTEMS 

GAS/OIL DRILLING MUDS/PACKER FLUIDS 

[OFFSHORE] 

GAS/OIL DRILLING MUDS/PACKER FLUIDS 

[TERRESTRIAL] 

GAS/OIL PIPELINES MAINTENANCE 

SEWAGE SYSTEMS 

AQUATIC NON-FOOD 

INDUSTRIAL 

AQUATIC NON-FOOD 

INDUSTRIAL 

 AQUATIC NON-FOOD 

INDUSTRIAL 

AQUATIC NON-FOOD 

INDUSTRIAL, INDOOR NONFOOD 

AQUATIC NON-FOOD 

INDUSTRIAL 

AQUATIC NON-FOOD 

INDUSTRIAL 

AQUATIC NON-FOOD 

INDUSTRIAL 

AQUATIC NON-FOOD 

INDUSTRIAL, INDOOR NONFOOD 

AQUATIC NON-FOOD 

INDUSTRIAL 

AQUATIC NON-FOOD 

INDUSTRIAL 

AQUATIC NON-FOOD 

INDUSTRIAL, INDOOR NONFOOD 

AQUATIC NON-FOOD 

INDUSTRIAL 

AQUATIC NON-FOOD 

INDUSTRIAL 

AQUATIC NON-FOOD 

INDUSTRIAL 

TERRESTRIAL NON-FOOD 

AQUATIC NON-FOOD 

INDUSTRIAL, TERRESTRIAL 

NON-FOOD 

AQUATIC NON-FOOD 

INDUSTRIAL 

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c. Environmentally contained 

GAS/OIL RECOVERY INJECTION WATER SYSTEMS INDOOR NON-FOOD 

GAS/OIL FRACTURING FLUID SYSTEMS INDOOR NON-FOOD 

IX. Antifouling coatings 

BOATS/SHIPS HULL/BOTTOM 

CRAB/LOBSTER POTS 

FRESHWATER STRUCTURES/EQUIPMENT 

MARINE STRUCTURES/EQUIPMENT 

WOOD PROTECTION TREATMENT TO WOOD 

ANTIFOULANT 

ANTIFOULANT 

ANTIFOULANT 

ANTIFOULANT 

ANTIFOULANT 

X. Wood preservatives 

a. Heavy duty 

SEASONED WOOD PRESSURE/THERMAL 

TREATMENT 

SEASONED WOOD NONPRESSURE TREATMENT 

(JOINERY) 

SEASONED WOOD NONPRESSURE 

TREATMENT(REMEDIAL) 

UNSEASONED FOREST PRODUCTS TREATMENT 

(SAPSTAIN) 

WOOD 

PRESERVATIVES 

WOOD 

PRESERVATIVES 

WOOD 

PRESERVATIVES 

WOOD 

PRESERVATIVES 

b. Ready to Use 

SEASONED WOOD NONPRESSURE TREATMENT 

(READY-TO-USE) 

WOOD 

PRESERVATIVES 

XI. Swimming Pools 

SWIMMING POOL WATER SYSTEMS AQUATIC NON-FOOD RESIDENTIAL 

XII. Aquatic areas 

AGRICULTURAL DRAINAGE SYSTEMS 

COMMERCIAL FISHERY WATER SYSTEMS 

INTERMITTENTLY FLOODED AREAS/WATER 

AQUATIC FOOD 

AQUATIC FOOD 

AQUATIC FOOD, AQUATIC 

NON-FOOD OUTDOOR 

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IRRIGATION SYSTEMS AQUATIC FOOD 

LAKES/PONDS/RESERVOIRS (WITH HUMAN 

OR WILDLIFE USE) 

AQUATIC FOOD 

ORNAMENTAL PONDS/AQUARIA AQUATIC NONFOOD 

STREAMS/RIVERS/CHANNELED WATER AQUATIC FOOD, AQUATIC 

NON-FOOD OUTDOOR 

LAKES/PONDS/RESERVOIRS (WITHOUT 

HUMAN OR WILDLIFE USE) 

AQUATIC NON-FOOD 

INDUSTRIAL 

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Appendix C. Glossary of Terms 

AMERICAN CHEMISTRY COUNCIL

ANTIMICROBIAL EXPOSURE ASSESSMENT TASK FORCE II 

GLOSSARY of TERMS 

(derived in part from 40CFR Part 158W) 

May 2007 

PART I. ANTIMICROBIAL APPLICATION METHODS 

PART II. EPA ANTIMICROBIAL USE SITE GROUPS 

PART III. GENERAL TERMS 

I. ANTIMICROBIAL APPLICATION METHODS 

AEROSOL SPRAY – A suspension of fine solid or liquid particles in gas that is 

dispensed from a pressurized container. The suspension is relatively stable, that is, the 

particles will remain suspended for a period of time barring an external influence. 

Standard-setting organizations, ISO, ACGIH, and BMRC, have established inspirable 

(able to enter the respiratory system) and reparable (able to enter the alveolar area of 

the lung) levels for aerosols. Generally, particles under 8 to 10 microns are considered 

respirable. One micron particles are considered 95 percent inspirable, 10 micron 

particles are 70 percent inspirable, and 100 micron particles are considered to be 20 

percent inspirable. For example, disinfectant aerosol sprays contain less than 1% 

droplets 10 microns or smaller. Median size typically is 100 microns (mass median 

diameter or mmd), normally distributed. However, government risk assessors usually 

assume that all measured air levels are not only inspirable but also respirable. Thus, the 

only time when it would be critical to distinguish particle size would be if a particular 

chemical had an inhalation toxicology study that showed damage to the alveolar region 

of the lung. 

AIRLESS SPRAY – A spray application that occurs by directly creating pressure to drive 

a liquid out of a nozzle for transfer through the air to a final target surface. Typical 

pressures for paints/coatings are in the range of 5,000 psi with orifice diameters of 

approximately 0.018 inches. Airless spray particles must be large enough to reach the 

target surface and small enough to achieve uniform deposition. 

BRUSH – Application of a liquid material to a surface area, such as a wall, by repeatedly 

inserting a brush (e.g., a paint brush) into a container for loading and then brushing it 

back and forth across the surface to be covered or treated. 

FOGGING – An application that requires a device that generates very small liquid 

particulate for transfer through the air, so it can penetrate into areas difficult to physically 

reach. These devices require some type of mechanical pump to generate the needed 

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pressure to drive the biocide through the nozzles. A "dry fog" has droplets ranging in 

size from 10-15 microns in volume. A "wet fog" or "mist" has droplets ranging in size 

from 30-60 microns in volume. A "fine spray" has droplets larger than 60 microns in 

volume. 

IMMERSION/DIP/ SOAK - Interchangeable terms. Differences in applicator exposure 

potential are attributable primarily to scale, but also may be associated with use patterns 

employing this application method and common industry practices. 

Wood treatment. The American Wood Preservers’ Association (AWPA) 2001 

Standards define dip as “application of a liquid preservative to a wood by immersing the 

wood in the liquid for a short period of time,” typically 3 to 20 minutes. Soaking involves 

leaving the lumber in the solution for a longer period of time (for example, 12-48 hours) 

in an attempt to get the chemical to go below the surface. Typically, these operations 

are large scale, highly automated processes where stacks of wood are mechanically 

lowered into baths. The stacks are inserted, removed and stacked in an automated 

manner with very little human exposure. 

Sanitizer/Disinfectant. Items requiring disinfection or sanitization may be 

immersed in an antimicrobial solution. Examples include flatware, glassware, barber 

and hair salon articles, etc. The length of time immersed has no impact on the amount 

of absorption by non-porous articles. For example, a non-porous material soaked in an 

antimicrobial chemical does not retain the chemical or have any residual antimicrobial 

activity. A good example would be dishes sanitized in sodium hypochlorite by soaking. 

The dishes are sanitized but retain no parent chemical or antimicrobial activity once dry. 

MOP – Application of a liquid material to a large surface area, such as a floor. This can 

be performed by repeatedly inserting an implement made of absorbent material (e.g., 

string mop head) fastened to a handle into a bucket and wiping it back and forth across 

the surface to be treated. Alternatively, “ready-to-use” mop technologies can be used, 

for example, pre-impregnated absorbent materials attached to a “mop head” with a 

handle, or spray delivery systems integrated with the mop head and handle. The readyto-use systems do not require dipping into a bucket. All of these mop technologies 

transfer the antimicrobial product from the liquid formulation to the surface. 

PLACE SOLID – Application of a solid biocide material into a container or final 

application that is accomplished in a single action. The form of the solid may be watersoluble packets, water-permeable containers, tablets, single dose delivery containers, or 

other solid permeable or soluble delivery forms. The application occurs in a single step 

without a continuous flow from one container to another. 

POUR LIQUID – A biocide in a liquid form is poured from a container, either manually or 

with some equipment, into another container or mixing apparatus without the use of 

devices that create a vacuum or pressure (i.e. pumps) to facilitate or force the transfer of 

the liquid. 

POUR SOLID – A solid biocide material (flake, pellet, powder, etc.) is transferred in a 

continuous flow from one container to another, either manually or with the aid of 

equipment. 

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PRESSURE TREATMENT – This is a special application method used in wood 

preservation using vacuum and/or external pressure to drive a biocide product deep into 

a product matrix. . The process involves sealing product within a pressurized container 

(retort) for various lengths of time, pulling a vacuum, and then introducing treating 

solution that is forced into the wood as air pressure is reintroduced into the retort. 

PUMP - Transfer of a liquid antimicrobial from the original container to another by 

pumping as part of the transfer for (1) subsequent use to formulate other pesticides or 

for industrial use or (2) end use applications, such as recirculating water treatment, 

paper mill slimicide application, metalworking fluid preservation, etc. Gravity or hand 

pumps and automated metering pumps are typically used. This type of application is 

anticipated to use hoses and various connection devices to facilitate the transfer in most 

situations. 

ROLL – Application of a liquid material to a surface area, such as a wall, by repeatedly 

inserting a cylindrical device covered with an absorbent material (e.g., a paint roller) into 

a container, and rolling the cylinder back and forth across the surface to be covered or 

treated. 

SPRAY – A spray application occurs when a liquid is forced through an orifice under 

pressure for dispersal to a target object or surface. There is no accepted standard for 

biocide applications that distinguishes between high pressure and low pressure sprays. 

Pressure and orifice size are the variables that impact particle size, which is important 

depending on the specific application. Following are some typical examples of spray 

applications. 

Industrial Use. High pressure sprays are delivered by electric pumps at 

pressures ranging from 500 to 50,000 psi. Droplet size may be less than 10 microns. 

Industrial Use. Low pressure sprays used in industrial applications may be either 

manual or electric at pressures ranging from 1 to 500 psi. Droplet size is usually above 

10 microns. 

Wood Preservation. A high pressure spray nozzle delivers a wide range of 

particle sizes, depending on liquid pressure and nozzle opening. For example, to 

generate a “fog,” typical liquid pressures are 500-1500 psi and nozzle orifices are 0.005 

inch or smaller. High pressure spray delivers low volume of liquid using a higher 

concentration of the chemical. The equipment creates a "fog" or "mist" (low particle size) 

that the lumber passes through. Treated lumber is almost dry to the touch immediately 

after the spray process; there is no dripping. Generally high-pressure systems have a 

vacuum that returns overspray to a holding tank for re-use. This method of application 

results in low exposures via inhalation when a vacuum is used and low exposures for 

dermal contact because lumber is almost dry to touch with a lower opportunity for 

transfer than a wet surface. This method is growing in popularity. 

Wood preservation. Low pressure spray delivers higher volumes of liquid and 

actually "floods" the surface of the wood with a solution that is lower in concentration of 

chemical through larger sized nozzles than a high pressure spray. This results in a 

board that drips as though it were dipped in a bulk dip vat. 

Sanitizer/Food Processing. Low pressure sprays are commonly used for 

sanitizer applications. There are a number of different application devices including: (1) 

hand pressurized (garden type) sprayers. (2) trigger spray bottles; (3) low pressure 

spray or foam devices (typically connected to the water system in the plant and operate 

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end devices or dispensed through a centralized system within a facility); (4) “Cleaning in 

Place” or CIP (automated cleaning/sanitization procedures conducted in large food 

processing establishments; cleaning equipment is actually built in to the food handling 

equipment itself; a complete cycle of pre-cleaning, cleaning, rinsing and finally sanitizing 

is conducted automatically through all areas of the food processing line; typically, these 

operations are completely enclosed; for example, pipes are flushed with the cleaning 

solution; tanks are cleaned and sanitized using a dishwasher type spray arm at the top 

of the tank which sprays the chemical solution onto the tank walls.) 

Textile Treatment. Antimicrobial formulation used in a fabric treatment in which it 

is essentially dispensed in a series of small streams at low pressure onto the surface of 

the fabric, with the excess running off. Aerosol formation is typically low. 

WIPE -- Application of a liquid material to a surface area, such as a counter or wall, by 

use of a small hand held piece of absorbent material (e.g., a sponge or woven or nonwoven fabric) pre-wetted, sprayed or dipped into a container and wiping it back and forth 

across the surface to be treated. Industrial uses of wipes include in-factory kiss rolls, 

dry-wipe lines, doctor bars, or consumer-like wipes used by building restoration and 

maintenance personnel. 

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PART II. ANTIMICROBIAL USE PATTERNS 

AGRICULTURAL PREMISES AND EQUIPMENT - Includes only application of 

disinfectants, sanitizers, fungicides, etc. to reduce or eliminate infectious or other 

undesired microorganisms on inanimate surfaces in farm and livestock premises. (e.g., 

pens, parlors, stalls, barns, etc.), and on equipment, (e.g., forks, shovels, halters, 

feeders, troughs, milking equipment, etc.) 

ANTIFOULANT COATINGS - Antifoulant paints for underwater structures and 

underwater equipment including ship and boat bottoms and hulls, crab and lobster pots, 

and structures and equipment used on fish farms. 

AQUATIC AREAS - Application of antimicrobials to control slime-forming bacteria, fungi 

and algae in lakes, ponds, streams, drainage ditches and other bodies of water. 

COMMERCIAL, INSTITUTIONAL AND INDUSTRIAL PREMISES AND EQUIPMENT – 

Includes only application of disinfectants, sanitizers, fungicides, etc. to reduce or 

eliminate infectious or other undesired microorganisms, on inanimate surfaces, in 

commercial (e.g., hotels, motels, theaters, office buildings, airports, etc.), industrial 

(factories, mills, plants, etc.), and institutional (schools, camps, public offices, prisons, 

etc.) premises. Equipment includes ceilings, doors, doorknobs, fixtures, floors, 

woodwork, walls, and windows. 

FOOD HANDLING/STORAGE PREMISES AND EQUIPMENT – Includes only 

application of disinfectants, sanitizers, fungicides, etc. to reduce or eliminate infectious 

or other undesired microorganisms, on inanimate surfaces, as part of good 

housekeeping or good manufacturing practice programs, in food/feed processing plants 

(e.g., meat, poultry, diary, seafood, beverage, etc.); eating establishments and food 

storage and transportation facilities (e.g., stores, markets, vending machines, trucks, 

shipping containers, etc.) 

HUMAN DRINKING WATER SYSTEMS - Includes application of disinfectants to public 

water systems, including water supplies and components (e.g., pipes, casings, reservoir 

surfaces, filter sands, etc.); individual water systems (homes, farms, institutions, camps, 

industrial facilities, etc.); emergency water systems and water purifier systems (e.g., 

campers, travelers, military, etc.). 

INDUSTRIAL WATER SYSTEMS - Application to commercial and industrial systems 

(e.g., cooling towers, evaporative condensers, air washers, heat exchangers), pulp and 

paper mill systems, gas/oil recovery systems, drainage, wastewater and sewage 

systems, and specialized uses (e.g., immersion ultrasonic tank water, laboratory 

equipment water baths, photo processing water, electro-deposition systems, etc.) 

MATERIAL PRESERVATIVES - Bacteriostats, microbiostats, and fungistats added to 

industrial process intermediate materials (e.g., dispersions, slurries, emulsions, 

solutions, etc.) and resulting products (e.g., paints, coatings, adhesives, textiles, paper, 

etc.) to control growth of slime-forming microorganisms (e.g., papermaking) and prevent 

deterioration or spoilage of material during storage and/or in-use life. 

EPA makes no distinction for purposes of exposure assessment between preserved 

materials (in-can) and exempt treated articles, that is, articles that make a claim of 

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protection for the articles themselves. EPA regards all exposures associated with the 

application of a pesticide to be applicator exposures. As a result, EPA regards the 

application of a preserved material, whether or not it makes a pesticidal claim, as 

secondary application for which it requires exposure data. For example, painter studies 

are required, but EPA does not care whether the paint used in the study make pesticidal 

claims, because it considers use of all paints that contain antimicrobials, even if only for 

in-can preservation, to be secondary pesticide application. As a result, all applications of 

paints and coatings, other than those specific to antifoulants and wood preservatives, 

are included in this category. (However, for purposes of exposure monitoring, there may 

be some possibility of combining replicates from these three use patterns.) Similarly, 

machine operators in contact with metalworking fluids are considered to be secondary 

applicators. 

When exposure to the preserved article occurs following application of the pesticide, as 

is the case with most preserved articles, EPA may be interested in obtaining postapplication exposure data. 

MEDICAL PREMISES AND EQUIPMENT – Includes only application of disinfectants, 

sanitizers, fungicides, etc. to reduce or eliminate infectious or other undesired 

microorganisms, on inanimate surfaces in medical environments. Premises include 

hospitals, clinics, dental and medical offices, veterinaries, nursing homes, “sick rooms,” 

etc. Equipment is limited to non-critical care equipment, that is, equipment that does not 

contact the patient or contacts the patient’s intact skin (e.g., furniture, carts, bedpans, 

telephones, etc.) It is not clear from the EPA definition whether residential “sick rooms” 

are included within this use pattern or the preceding use pattern. 

RECREATIONAL WATER - Antimicrobial treatment of “hydrologically isolated and 

contained manmade bodies of water,” including swimming pools, Jacuzzis and hot tubs. 

Exposure monitoring is limited to the applicator of the antimicrobial to the recreational 

water. Exposure of a bather or swimmer to the antimicrobial is considered postapplication exposure and is determined by use of the EPA’s “Swim Model.” 

RESIDENTIAL AND PUBLIC ACCESS PREMISES – Includes only application of 

disinfectants, sanitizers, fungicides, etc. to reduce or eliminate infectious or other 

undesired microorganisms, on inanimate surfaces in private residences and public 

access areas. (There is no clear distinction between EPA’s use of the terms institutional 

premises and public access premises.) 

SWIMMING POOLS -- see Recreational Water 

WOOD PRESERVATIVES - Preservative treatments for all types of wood, applied by 

pressure or vacuum treatment, remedial applications (i.e., application to utility poles, 

support timbers, etc. while in-service); non-pressure treatments (e.g., joinery and 

millwork), anti-sapstain treatments and ready-to-use coatings. 

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PART III. GENERAL TERMS 

Adequacy of BHEDTM Data - Each handler scenario within BHEDTM will contain 

sufficient monitoring units (MUs; also referred to as monitoring events, i.e., MEs) to 

achieve a pre-determined level of accuracy for statistical descriptors (e.g., mean, 

geometric mean, and 95th percentile) of the distribution of exposure. Generally, the data 

set will be considered accurate if these measures are within 3-fold of the true value and 

the number of MEs collected will be chosen to achieve this level of accuracy. 

AEATF II = Agricultural Handlers Exposure Task Force, L.L.C. - A consortium of 43 

companies that formed a FIFRA joint data development task force to design and develop 

a database of exposure measurements for agricultural subjects during mixing, loading, 

and/or application of pesticides. The exposure data will cover important types of 

mixing/loading systems, application equipment, and formulations. The results will satisfy 

FIFRA data requirements and be used to assess handler, and for some scenarios 

bystander, exposure potential and associated risk assessments for antimicrobial 

pesticide products marketed by AEATF II members. AEATF II was formed in November, 

2004. 

Biomonitoring - Measurement of a pesticide or its metabolite(s) in the body of a 

pesticide handler and the conversion to an equivalent absorbed dose based on 

knowledge of metabolism and pharmacokinetics. This generally includes measurement 

of chemical in blood or urine, but does not include measurement of biological effects 

such as cholinesterase levels. The result is an estimate of total exposure from the 

dermal, inhalation, and oral routes combined. 

Cluster - A set of monitoring units or events (MUs or MEs) from the same scenario 

considered a higher-level sampling unit for the purpose of statistical design and 

analysis. Exposures between MEs from the same cluster (e.g., building location) tend to 

be more similar than those between MEs from different clusters. 

Distribution of Exposure - A statistical description of the probability that a given 

exposure level is attained; derived from a set of monitoring units (or monitoring events) 

within a given scenario and generally described by standard measures such as the 

arithmetic mean, geometric mean, and percentile values. 

Engineering Controls - Equipment or equipment modifications which eliminate or 

reduce exposure to a chemical, such as enclosed cabs, ventilation, or closed transfer 

systems. 

Exposure Monitoring - Using passive dosimetry techniques to measure dermal and 

inhalation exposure to professional, occupational pesticide handlers as they perform 

their typical activities. Researchers will use a variety of pesticide residue collection 

devices (cloth dosimeters, hand washes, face/neck wipes, and sorbent tubes) and 

determine the quantity of active ingredient on each device by chemical residue analysis. 

GLP (Good Laboratory Practice Standards) - Federal regulations (40 CFR 160) that 

prescribe good laboratory practices for conducting studies that support pesticide 

registrations. The standards address the scientific integrity of study conduct and data 

collection, including specific requirements for study management, equipment calibration, 

facilities maintenance, record keeping, reporting, and quality assurance. 

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Handling - Generally refers to mixing, loading, transferring, or applying pesticides. 

However, handling also includes the following common tasks: handling opened 

containers; disposing of pesticides or pesticide containers; and cleaning, adjusting, 

handling, or repairing the parts of mixing, loading, or application equipment that may 

contain pesticide residues. 

IRB (Institutional Review Board) - An independent board that reviews and approves 

study proposals and oversees research to ensure the protection of human subjects who 

volunteer to participate in those studies. IRB responsibilities and authorities are defined 

at 40 CFR Part 26. 

Monitoring Program (or Testing Program) - The testing program consists of all the 

MUs (or MEs) from the studies that will be conducted by AEATF II to monitor exposure 

to antimicrobial pesticide handlers and that will be used to develop a generic database to 

support pesticide registrations. The planned testing program will cover pesticide 

handling and bystander scenarios. 

Monitoring Unit (MU) or Monitoring Event (ME) - All exposure monitoring activities 

pertaining to a single worker for a time period that represents a typical workday, 

including the exposure measurements for the worker involved. A specified number of 

monitoring events (MEs) will be conducted for each scenario to adequately define the 

distribution of exposure expected for that scenario. MEs defined in different scenarios 

may be collected in a single study. 

Passive Dosimetry - Techniques for measuring pesticide exposure to human subjects 

which do not involve invasive collection techniques such as collecting urine or blood. 

AEATF II studies involve whole-body garments that serve to collect dermal residues, 

hand washes to collect hand residues, face/neck wipes to collect residues on the face 

and neck areas, and sorbent tubes to collect air in the breathing zone of a worker. 

Additional cloth dosimeters may be used to measure exposure to the feet or to the head 

area with and without headgear. 

PPE (Personal Protective Equipment) - Devices and apparel that are worn to protect 

the body from contact with pesticides or pesticide residues, including but not limited to 

coveralls, chemical-resistant suits, chemical-resistant gloves, chemical-resistant 

footwear, respiratory protection devices, chemical-resistant aprons, chemical-resistant 

headgear, and protective eyewear (See 40 CFR 170.240). 

Purposive Diversity Sampling - The type of non-random sampling used for each 

scenario in the AEATF II monitoring program. Sampling is purposive because certain 

important conditions are selectively sampled. Diversity (or heterogeneity) sampling 

means that the purposive sampling is targeted to achieve a diversity of major factors that 

are likely to influence exposure, including amount of active ingredient handled, subjects, 

and location. 

Regulatory Agency Advisory Committee (RAAC) - comprised of representatives of 

the U.S. EPA, the Canadian Pest Management Regulatory Agency (PMRA), the 

California Department of Pesticide Regulation (CDPR), and European regulatory 

authorities. This committee meets on an ad hoc basis to review the program progress 

and provide technical input to the AEATF II 

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Scenario - A specific pesticide handling situation that will be represented by data with 

defined common properties; generally a combination of a work task(s), pesticide 

formulation, equipment, engineering controls, and work practices. For example, a 

scenario of interest is ‘mopping indoor surfaces with defined application equipment and 

related tasks, e.g., filling a mop bucket with a defined end-use mop solution using an 

automated dispensing system and disposing of dirty mop solution’. Tasks that are 

common across more than one exposure scenario, e.g., pouring liquids into containers, 

may be specifically addressed in a separate study (e.g., mixing/loading or pouring of 

liquids, such as filling a mop bucket with end-use liquid solution using an automated 

dispensing system, or preparing an end-use mop solution, prior to mopping application). 

Scripting, Scripted Study - Scripting is the partial control of the conditions in a 

particular study. A scripted study is considered to “involve intentional exposure” within 

the meaning of the regulatory definition at 40 CFR 26.1102(i). Subjects are asked to 

conduct their work activities under a set of scripted conditions very similar but not 

identical to those they experience in their normal work activities. Scripted or semiscripted tasks may also refer to repetitive operations performed by workers or 

consumers (e.g., wiping countertops) that are not expected to vary significantly from one 

person (or location) to another. 

Study - A convenient grouping of monitoring units or events (MEs) covered by one 

protocol and one final report. Typically a study will address one or more tasks 

associated with a specifically defined exposure scenario and will be conducted over a 

short period of time (1 to 2 weeks), with one surrogate chemical. Tasks that are 

common across more than one exposure scenario, e.g., pouring liquids into containers, 

may be specifically addressed in a separate study. 

Surrogate Chemical - A pesticide active ingredient which is present in test materials 

which are handled during collection of an monitoring unit or event (MU or ME). AEATF II 

develops validated analytical methods for each surrogate chemical and each exposure 

matrix so residues collected can be determined. AEATF II chooses surrogates which 

have low volatility and are commercially available in suitable formulations and 

packaging. Since exposure to handlers is a generic function, exposure measurements 

from these chemicals are suitable for estimating exposure to other pesticide active 

ingredients. 

Target Population (or Universe) - Each element of the target population is a potential 

task (or set of tasks) performed by a worker under a particular scenario in a day. Each 

element is defined by a set of all conditions that might have any impact at all on that 

worker’s exposure. These conditions include the particular chemical product tested, the 

worker, his behavior, and all relevant environmental conditions. Each ME is assumed to 

be a realization of an element from the target population. 

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Appendix D. AEATF II Acceptance Criteria for Existing 

Studies 

Data Review and Study Acceptance Criteria 

For Inclusion of Existing Antimicrobial Exposure Monitoring Studies In The 

Antimicrobial Exposure Assessment Task Force II (AEATF II) Database 

November 28, 2006 

I. DATA REVIEW PROCESS 

All existing antimicrobial exposure monitoring studies, whether offered by 

a third party for sale or from the public domain, will undergo a 3-step review 

process to determine whether they meet the selection criteria listed in this 

document. In addition to the specific review process outlined below, a continued 

dialogue will take place with U.S. EPA to discuss each exposure scenario to be 

studied and the general study design elements (e.g., worker tasks, equipment, 

locations, replicates) proposed by AEATF II. This discussion will serve to inform 

the existing study review team as to the minimum study design and data 

collection requirements needed for an existing study to be deemed acceptable to 

fulfill a given data requirement. The non-acceptance of a given study, via the 

AEATF II study review process, does not imply that it is not suitable for use by 

any individual member company to fulfill a specific data requirement, only that 

the AEATF II has made the determination that the study will not be purchased for 

broader use by the AEATF II member companies. 

A. Preliminary Review 

1. The preliminary review will be conducted by the study submitter (or a 

designated representative) and provided to AEATF II along with a complete copy 

of the study report at the time the study is submitted to the Task Force for 

consideration. 

2. The AEATF II will consider all studies, including any that are presently in 

PHED version 1.1 or version 2. The studies in PHED version 1.1 are all more 

than 15 years old and are not subject to data compensation requirements. 

However, these data were originally submitted to PHED without attribution to 

compound or company ownership. If any of these data fulfill acceptance criteria, 

they could potentially provide very beneficial information that would complement 

data developed by AEATF II. 

3. Raw data for a study must be made available, if requested. 

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4. A list of potential studies and all preliminary review forms and reports should 

be submitted by dates requested by AEATF II. 

5. The purpose of the preliminary review will be to eliminate the submission of 

studies that clearly do not meet the selection criteria, and to serve as a check on 

the availability and submission of supporting information. 

6. AEATF II will provide an Excel spreadsheet to the submitter for use in 

summarizing the study details and data. 

7. AEATF II will provide a confidentiality agreement to the submitter to protect the 

proprietary nature of the data and the study. 

B. Intermediate Review 

1. The intermediate review will be conducted by a qualified AEATF II contractor, 

hired and trained for this purpose. 

2. The purpose of the intermediate review will be to verify the accuracy of the 

preliminary review and, where necessary, provide a more detailed discussion 

summarizing each specific area of the criteria, including whether each criterion 

was met and possible deficiencies in the study data. 

3. The intermediate review will be evaluated and a determination made as to 

whether the study or any of the data could be used in the AEATF II database. 

Only studies that have met the design considerations will be presented to EPA, 

PMRA and CDPR for final review. 

C. Final Review 

1. A Committee consisting of representatives from the AEATF II and EPA, PMRA 

and CDPR will make the final review and decision on whether a study is 

accepted for purchase. 

2. The intermediate review by the contractor of studies will be made available to 

the Committee and will serve as the basis for the final review. 

3. Studies or portions of studies selected after final review will then be 

considered for purchase by the AEATF II for inclusion in the Task Force 

database. 

4. Reports for studies that the Committee deems not acceptable for the AEATF II 

database will be returned to the submitter with an explanation as to why the 

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study did not meet the selection criteria. Reports of studies that are purchased 

by AEATF II will be placed in the AEATF II archives. 

D. Appeals Process 

Study contributors whose studies are not accepted for possible purchase may 

appeal that decision, but should do so within 30 days of such notification. 

II. STUDY ACCEPTANCE CRITERIA: STUDY DESIGN CONSIDERATIONS 

1. All monitored activities and equipment must be described in detail and 

representative of typical antimicrobial handling practices. 

2. It should be clear that the individuals monitored 1) either are normally 

employed in the mixing/loading and/or application of antimicrobial products or 

pesticide products and handled them comparably, or, 2) if consumers (i.e., nonprofessionals), are applying antimicrobials products by methods they would use 

in the course of their normal activities. . 

3. Appropriate supporting information such as the formulation type, mixing and 

application method, application rate, duration of the work cycle, amount of AI 

handled/replicate, etc. must be available. 

4. The use of protective equipment (PPE) is acceptable but must be part of 

normal work practices. 

5. The study location and environmental/weather conditions during the 

monitoring period should be available. 

6. All elements of a given study may not have been conducted under GLP, but 

must have critical elements of GLP e.g., protocol, final report, and raw data 

available in order to be considered by the AEATF II. 

III. STUDY ACCEPTANCE CRITERIA: EXPOSURE MONITORING 

A. Field Aspects 

1. Field recoveries should have been collected on a site-specific basis for time 

periods and environmental conditions representative of those during collection of 

field activity exposure samples. 

2. Field fortification data should include at least triplicate samples at two rates 

and triplicate samples of controls; however, duplicate samples will be considered 

with justification. 

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3. Dermal exposure monitoring techniques should be specified and should 

include one of the following approaches. Note that glove washes of chemical 

resistant gloves are not an allowable method. 

a) whole-body dosimeters inside and/or outside of typical clothing plus hand 

(cotton gloves can substitute for hand exposure) and head/face exposure 

determinations, 

b) a minimum of 10 patch dosimeters attached inside or outside normal work 

clothing to the chest, back, both upper arms, both lower arms, both upper legs, 

both lower legs, plus hand (cotton gloves can substitute for hand wash) and 

head/face exposure determinations (exceptions for head/face and upper arms 

and upper or lower legs and bilateral measurements will be considered on a case 

by case basis). Conversion and use criteria have been developed by the NAFTA 

harmonization group and should be considered for adaptation of the PHED data. 

c) combination of patches and clothing that are representative of the whole body, 

including hand and head/face exposure determinations. 

4. Inhalation exposure – Inhalation data are required if the vapor pressure of the 

chemical under study is >10-4 mm Hg or if the chemical is used in an 

environment that results in significant volatilization (e.g., around steam pipes or 

in metal working fluids), or if the method produces inspirable aerosols. If data 

were collected, inhalation exposure should have been measured by sampling the 

person’s breathing zone. 

5. Exposure monitoring duration – The monitoring period should be at least half 

of a normal work period duration or mix/load and/or apply at least half of the daily 

amount normally used. 

6. If the exposure monitoring duration does not meet the requirement of item 

number 5, then the number of non-detects/less than LOQ values should account 

for less than 40% for dermal exposure. This cut-off is specified because the 

distribution of exposures can be reasonably extrapolated from a data set with up 

to 40% non-detects. Data sets with ≥50% non-detects produce a degree of 

uncertainty deemed unacceptable for a generic database. 

If the exposure monitoring duration and number of non-detects/less than LOQ 

values do not meet the criteria in items 5 and 6, then the LOQ should be no more 

than 20 ng/cm2 for average dermal exposure (across body part areas) and no 

more than 500 ppb for hand wash solution. The LOQ cutoffs are conservative in 

that if all data were at the LOQ, the resulting calculated exposure (at 1⁄2 LOQ) 

would yield an MOE of ≥100 for a compound with a systemic (absorbed dose) 

NOAEL ≥1 mg/kg. 

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B. Analytical Aspects - QA/QC 

1. Analytical methods should have been validated for each analyte and substrate 

by the performing laboratory including establishment of the method’s working 

concentration range to cover values anticipated in the field studies, determination 

of detector response over a reasonable standard concentration range, and 

determination of the accuracy and precision of the method within the analytical 

environment. 

2. The study should include both field fortification samples and concurrent 

laboratory spikes. 

3. The average recoveries of lab spikes should be between 70-120 percent and 

the precision value (coefficient of variation; CV) should be less than or equal to 

20 percent. 

4. Recovery of field fortification samples should be 50-120% with a C.V. ≤25%. 

5. Exposure samples should have been analyzed in such a manner that the 

stability of each analyte in each substrate was assessed for the entire time period 

from collection to analysis. 

C. Biomonitoring 

Biological monitoring studies will be accepted for further review if they meet the 

selection criteria (excluding passive dosimetry) and there is a primate (human or 

monkey) dermal absorption study for the chemical monitored and 

pharmacokinetic data identifying the major excretory metabolite or parent 

compound. 

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Appendix E. Designing Monitoring Events to Predict 

Future Exposure under Antimicrobial Handling 

Scenarios 

Background 

Future Exposure and Monitoring Events 

An antimicrobial handling scenario is a well-defined worker exposure situation, 

usually characterized by specific antimicrobial handling tasks and equipment. 

For the purposes of the AEATF II monitoring program, the basic element of a 

scenario is considered to be the handler-day (HD). Each handler-day 

corresponds to a particular worker and the scenario-related activities that he 

performs during a single work day. 

Implicitly associated with each HD is a complex set of conditions denoted simply 

by C. The practically infinite number of components of C includes, but is not 

limited to worker behaviors, active ingredient used, amount of chemical 

contacted, surfaces treated, and numerous environmental factors. Some, but 

certainly not all, of these conditions can actually be observed and measured. 

Each particular set of HD conditions, C, results in a particular worker exposure, 

E=E(C). In principle, although always subject to some measurement error, 

handler-day exposures (e.g., dermal or inhalation) can be obtained by actual 

monitoring. 

Regulatory interest for each antimicrobial handling scenario is focused on 

predicting occupational exposure under a specific set of generic future handlerday conditions. In particular, it is desired to characterize exposures resulting 

from the future use of an arbitrary (and perhaps currently non-existent) 

antimicrobial active ingredient assuming some arbitrary, but quantifiable, amount 

of active ingredient contact. 

A monitoring event (or ME) is the basic tool used by the AEATF II Monitoring 

Program to predict exposures under. An ME is a set of scenario-specific 

handler-day conditions that have been experimentally selected (i.e., chosen, 

simulated, or constructed) to represent expected future HD conditions. Each ME 

is monitored to yield a set of exposure measurements. Therefore each ME 

provides a measurement of the actual exposure resulting from the simulated or 

selected HD conditions. The ME will also provide a predicted future exposure if 

the handling conditions of the ME are similar to future HD conditions. 

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The Generic Active Ingredient Principle 

The most obvious handler-day condition is the identity of the active ingredient 

(ai) to be used in the scenario task(s). Every experimental monitoring event must 

use at least one active ingredient. It might seem, therefore, that prediction of 

future exposure to a particular ai would require that every ME use that active 

ingredient. If this were true, a generic exposure database for arbitrary active 

ingredients would not be feasible. 

Fortunately, exposure is not always chemical-specific. For compounds with low 

volatility (which include all the antimicrobials considered), a generally accepted 

generic principle is: 

If all other conditions are the same, the magnitude of exposure 

does not depend on the particular active ingredient used 

More formally, if A1 and A2 are any two active ingredients and if the exposure 

resulting from any active ingredient X under conditions C is denoted by E(X, C), 

then the generic principle is simply: 

(1) E(A1, C) = E(A2, C) = E(C) 

The practical importance of the generic assumption is that it permits an ME 

based on one surrogate chemical to be used to predict HD exposure to other 

active ingredients under the same set of handling conditions. 

Normalized Exposure 

For prediction purposes, it is useful to express handler-day exposure relative to 

the value of a normalizing factor (NF). If H is the value of the normalizing factor 

and C represents all other HD conditions then: 

(2) nE(C) = E(C)/H 

is called normalized exposure. The quantity nE is also often referred to as unit 

exposure because it is viewed as exposure ‘per unit’ of the normalizing factor. 

In the AEATF II Monitoring Program the normalizing factor is always an 

experimentally measurable quantity that is expected to be proportional to the 

potential contact the worker has with active ingredient. Potential ai contact (or 

PaiC) is defined as the amount of active ingredient that a worker is expected to 

come into contact with during a workday. Because it is always expected to be at 

least proportional to PaiC, an AEATF II normalizing factor is a relative measure 

of active ingredient contact. 

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It is generally assumed that under identical conditions exposure is proportional to 

potential ai contact. That is, if P1 and P2 are different amounts of PaiC, and C 

represents other HD conditions, then the proportionality principle states that: 

(3) E(P2, C) / E(P1, C) = P2 / P1 

Exposure may not be directly to proportional to contact in extreme situations 

(e.g., skin saturation). However, such a relationship is expected to hold for the 

levels of active ingredient contact that occur in practice. If the normalizing factor 

is expected to be proportional to PaiC then the proportionality principle 

relationship holds for the NF as well since: 

(4) H2 / H1 = (k·P2)/(k·P1) = P2 / P1 

Thus, under the same conditions, C, exposure for any value, H2, of the 

normalizing factor can be obtained from an ME based on a different value of NF, 

H1 say, since: 

(5) E(H2, C) = (H2/H1)·E(H1, C) = H2·{ E(H1, C)/H1 } = H2·nE(C) 

For most antimicrobial scenarios, a reasonable normalizing factor is the amount 

of active ingredient ‘handled’ (AaiH) by a worker during the workday. 

However, for some scenarios (e.g., the pump liquid scenario) a worker might 

actually process (i.e., ‘handle’) a large amount of active ingredient, but may have 

the opportunity to contact only a small fraction of this amount. In such cases, 

there may be other measures of PaiC that are more appropriate than AaiH. 

It is important to note that the term normalized (or unit) exposure is not always 

defined as a relative measure of ai contact. A familiar example occurs in studies 

of exposure to agricultural reentry workers. Here, exposure, E, is often 

normalized by duration of the reentry activities to give exposure per hour worked. 

Unless the concentration of active ingredient on treated foliage is always 

constant, the normalizing factor ‘hours worked’ is not expected to be proportional 

to contact with ai. However, the quantity: 

(6) D = (hours worked) x (dislodgeable foliar ai residue) 

is expected to be proportional to the amount of ai contacted by the reentry 

worker. In agricultural reentry studies, the quantity E/D is often called the 

transfer coefficient (TC) and corresponds to normalized exposure as defined by 

AEATF II. 

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Using MEs to Predict Future Exposure 

The generic and proportionality principles together imply that, under the same 

conditions, the normalized exposure from any ME can be used to obtain a 

predicted future HD exposure (pE) for any arbitrary chemical, X, at any 

arbitrary level of the normalizing factor, HX, simply as: 

(7) pE(HX, C) = HX·nE(C) 

Although this relationship permits a single ME to predict exposure for a broader 

range of future handler-days, it cannot describe all future exposures. Even when 

HX is specified, the number of possible ‘other conditions’ (i.e., different Cs), is 

extremely large. (Although some of these Cs are more likely than others to occur 

in the future HD population.) Because each ME is expensive, it will only be 

possible in practice to construct MEs having a limited set of Cs. This set of N 

MEs will then be used to obtain a set of N predicted exposures using (7): 

pE(HX , C1) = HX·nE(C1) 

pE(HX , C2) = HX·nE(C2) 

pE(HX , C3) = HX·nE(C3) 

●

●

●

pE(HX , CN) = HX·nE(CN) 

Obviously, a set of only N predicted exposures cannot cover every possible 

future HD condition. Nor is it reasonable to expect that a small set of N MEs will 

provide experimental material to develop statistical models for exposure as a 

function of C. In fact, only some, but by no means all, of the components of each 

C can be controlled or measured when constructing MEs. The unknown 

components might have the biggest impact on exposure. 

Nevertheless, this set of pEs will need to be sufficient to allow regulatory issues 

to be addressed in a practical manner. If some components of C that can be 

controlled are chosen appropriately, then a useful set of MEs can still be 

constructed. In this case the resulting set of pEs will be used as a predictor of 

future HD exposure in an aggregate sense. 

The Future Exposure Distribution 

An exposure distribution provides a natural aggregate description of future 

handler-day exposures for a scenario. It is the most common way to think about 

the set of exposures resulting from all possible future HD conditions. The future 

HD distribution (Figure 1) describes the likely exposure that would result if one 

were to randomly pick a future HD among those using ai X when the level of the 

normalizing factor is HX. 

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Middle Exposure Levels 

Larger Exposure 

Levels Distribution of Future 

Exposures 

Exposure 

Figure 1. The distribution of future handler-day exposures 

The complete exposure distribution is rarely considered. Regulatory interest is 

most often focused on two general aspects of this distribution: 

• The middle values such as the arithmetic mean or the median. These 

exposure values tend to characterize average or ‘typical’ exposure levels. 

• The larger values of exposure possible, such as the 95th percentile of the 

distribution. This aspect better characterizes the extreme, one-time, 

worker exposures. 

Obviously the exposures can vary proportionally with the value of the normalizing 

factor, H. Therefore there are actually a series of predicted exposure 

distributions, one for each possible value of HX. Since any predicted exposure 

can be computed from the normalized exposure, it is simpler to focus only on the 

distribution of normalized exposure. 

The Set of MEs as a Pseudo-Random Sample 

The set of predicted exposures obtained from the constructed set of MEs should 

adequately characterize the middle and larger values of the future HD 

distribution. This might appear simple, since it is quite common practice to treat 

any set of values as a random sample from a distribution. Thus, one could treat 

the set of pEs as a simple random sample from the future exposure distribution. 

This cannot be strictly the case since exposure is a function of the HD conditions, 

C, and the likelihood of the various C’s in a future HD population (for X and HX) 

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are not known in advance. Therefore the C’s for the MEs cannot be randomly 

chosen with probability proportional to the future population frequencies. 

But some random sampling interpretation might still be a convenient and 

reasonable model for how the set of predicted exposures from the MEs might 

relate to the future exposures for an arbitrary ai and HX. Confidence in this 

approximation is increased by using a nested reference random sampling model 

rather than assuming just simple random sampling. In addition, diversity 

selection (using both purposive and random components) is used whenever 

possible. This increases the likelihood that the range of conditions in the future 

HD population expected to impact exposure is reflected in the ‘pseudo-sample’ of 

MEs as well. 

Diversity Selection 

Diversity selection is an attempt to make the set of MEs (and resulting pEs) 

more useful for regulatory purposes when they are treated as random sample. 

Often some factors that are likely to influence exposure are known or can at least 

be reasonably hypothesized. Diversity selection results from any procedure that 

improves the chance that different MEs differ with respect to such factors. It is 

an attempt to obtain, as much as is practical for small sample sizes, a diversity of 

conditions that are expected to influence exposure, either directly or indirectly. 

With a small set of MEs, it is more practical to construct MEs that differ that to 

reproduce the (unknown) frequencies of selected future HD conditions. 

In the AEATF II Monitoring Program, the term diversity selection is preferred in 

lieu of the phrase diversity sampling. This is to emphasize the fact that the 

future HD conditions used for MEs are selected from either existing or from 

synthesized conditions (or from both). This selection of conditions can employ 

both purposive and random elements. When there are multiple diverse 

configurations available, random selection from among such configurations can 

reduce the likelihood of intentional selection bias. On the other hand, when 

some possible ME configurations are more diverse or more cost effective than 

others, it might be preferable to select these purposively. 

Diversity selection attempts to create a sample that contains as many of the 

different conditions as possible that exist in the population. If the diversifying 

conditions are associated with exposure, then a diversity sample will tend to be 

more variable with respect to exposure than would a same-sized representative 

sample. In effect it will be analogous to representative sampling from a 

distribution that is more diverse than the actual future one (Figure 2). As a result, 

a diversity selection sample should tend to have more extreme exposures (both 

higher and lower) and fewer exposures ‘in the middle’. Thus, a diversity 

selection sample will tend to estimate central tendencies of the exposure 

distribution better than it will either upper or lower percentiles. To the extent that 

the diversifying conditions are associated with exposure, diversity selection will 

tend to under-predict lower percentiles and over-predict upper percentiles. (This 

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effect is illustrated by envisioning a normal, or even lognormal, distribution 

compared with its most extreme ‘diversity selection’ counterpart, a uniform 

distribution covering a similar range.) For regulatory purposes the important 

aspects of the distribution of exposures are the central tendencies and the upper 

percentiles. In addition, overestimation of these characteristics is less of a 

problem than underestimation. 

Diversity Selection Tends 

True Distribution of 

Future Exposures 

to Produce Less ‘Middle’ 

and More ‘Extreme’ 

Values 

Normalized Exposure 

Figure 2. Diversity selection tends to make the distribution of future handler-day 

exposure appear more diverse than it is. 

Design Objectives and Sample Size 

The Two-Stage ME Selection Process 

Obtaining potential workers and scenario-specific handling-day conditions 

needed to create monitoring events is a complicated process. Of necessity, the 

specific selection process used will vary from scenario to scenario. This is 

especially true for the two major categories of AEATF II studies: simulatedcondition studies and in situ studies. Simulated-condition MEs are created 

synthetically whereas in situ MEs must be located from among existing handlerday conditions in facilities willing to participate. 

However, as shown in Table 1, the ME construction process for both types of 

studies can be envisioned as occurring in two successive stages of selection. 

The first stage consists of selecting or constructing specific locations and 

specifying a range of dates for monitoring at each location. Each such local area 

and range of potential monitoring dates is termed a monitoring site. For in situ 

studies, a site might consist of a particular wood-treating facility during one 

particular week. In contrast, a site in a simulated-condition study might be a 

week-long period of monitoring activities in a particular leased office building. 

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Table 1 

The general two-stage structure for selecting MEs in AEATF II studies 

Study Type Used for Scenario: 

First Stage Units 

(Monitoring Sites): 

Second Stage 

Units 

(Monitoring 

Events): 

Simulated-Condition 

Synthetic environments 

constructed (or vacant 

facilities leased) 

expressly for the 

purpose of the study 

Monitoring events (ME) 

constructed at the sites 

using subjects selected 

from a volunteer pool 

In Situ (Observational) 

Existing facilities where 

scenario-related tasks 

occur and that agree to 

participate in the study 

Monitoring events (ME) 

selected for observation 

from among those 

occurring at the site 

The second stage consists of selecting one or more subjects and handling 

conditions within each site and constructing the MEs. For simulated-condition 

studies the MEs are created by assigning appropriate subjects to scenario tasks 

under conditions that are expected to exist in the future HD population. For in 

situ studies, appropriate handler-days are selected from among existing subjects 

and conditions that are also expected to represent future HD conditions. 

In general, NC sites are selected at the first stage and NM monitoring events will 

be obtained within each site at the second stage. When NM is greater than one, 

the set of MEs at the same site is termed a ‘cluster’. In general, MEs in the same 

cluster are expected to be more similar than those in different clusters. This 

correlation usually means that the smallest total sample sizes (i.e. total number 

of MEs) are attainable when there is only a single ME per site. On the other 

hand, there are often substantial overhead costs per site that make multi-ME 

sites more efficient. 

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The Two-Stage Random Sampling Reference Model 

In the strictest sense, sample sizes can only be determined using statistical 

theory alone when either 

1. There is assumed random, representative sampling from a population and 

the goal is to estimate some characteristic of that population; or 

2. There is assumed randomization of experimental units to treatments and 

the goal is only to compare or to contrast treatments in some manner. 

Only in these two situations will statistical theory predict how increasing sample 

size decreases estimation error. In other experimental situations, sample size 

must be determined using one of the two ‘random’ situations above as a 

reference model. The random reference model is defined so that it reflects the 

actual situation (e.g., a mixture of random and non-random selection) as closely 

as is practical. The sample size that is appropriate for the reference model is 

then used for the actual study design. In a real sense, then, the reference 

random sampling model is used to establish benchmark sample sizes that can 

satisfy benchmark objectives. Although rarely stated explicitly, the use of 

reference sampling models and benchmark objectives are quite common. 

Because all AEATF II scenario studies have a two-stage selection structure, they 

all assume the same reference sampling model. For each scenario, two-stage 

random nested (or cluster) sampling is the reference model used for the 

combination of purposive and random two-stage diversity selection that actually 

occurs. This reference model assumes that: 

1. Exposure, normalized by the potential active ingredient contact factor, is 

lognormally distributed with geometric standard deviation GSD. 

Equivalently, the logarithm of normalized exposure is normally distributed 

with standard deviation Log(GSD). 

2. There are NC clusters (i.e. sites) and NM MEs per cluster. The total 

number of MEs in this scenario is, therefore, N=NC×NM. 

3. The within cluster (i.e., within-site) correlation of log normalized exposure 

is equal to ICC. 

The reference sampling model incorporates a two-stage selection structure and 

the potential for within-cluster correlation, but ignores any effects of diversity 

selection. Thus, for determining sample sizes, normalized exposures, nE, are 

assumed to follow the nested variance component model 

(8) Log ( Eij / Hij ) = Log nEij = Log GMnE + Qi + Wij 

where 

Eij = the exposure obtained for ME j in cluster i 

Hij = the value of the normalizing factor for worker for ME j in cluster i 

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nEij = the exposure for ME j in cluster i normalized by NF 

GMnE = the population geometric mean for normalized exposure 

Qi = a random effect of cluster i 

Wij = a random effect of ME j within cluster i 

The random effects Qi and Wij are normally distributed with means 0 and 

variances VQ and Vw, respectively. 

The population variance of log nE is then equal to V = VQ + Vw and the square 

root of V is the true population standard deviation, SD. The quantity GSDnE = 

antilog (SD) is the true population geometric standard deviation of normalized 

exposure. The intra-cluster correlation (i.e., the intraclass correlation due to 

clusters) is defined as 

(9) ICC = VQ / V = 1 – Vw / V 

The ICC is irrelevant to the future distribution of normalized exposure, per se. 

However, this intra-cluster correlation is a necessary part of the reference 

sampling model because the MEs are obtained in clusters (i.e. there are multiple 

MEs per site). 

Relative Accuracy and Fold Relative Accuracy 

The benchmark objective of the AEATF II monitoring program will be to achieve 

adequate relative accuracy of selected parameter estimates if the reference 

sampling model described above were used. This benchmark target can be 

stated more precisely as: 

If there are NC clusters and NM MEs per cluster and the 

underlying lognormal two-stage reference sampling model 

were actually true, then selected parameter estimates will be 

within K-fold of the true values at least 95% of the time. 

If θ denotes the distributional parameter of interest and T is the estimate of that 

parameter obtained from monitoring data, then the relative accuracy of T is 

defined simply as: 

(10) RA(T|θ) = T/θ

Satisfying the benchmark objective above requires that there be at least a 95% 

chance that T/θ is between 1/K and K. More formally this is stated as: 

(11) Prob {1/K ≤ RA(T|θ) ≤ K} ≥ 0.95 

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It is more convenient, however, to consider relative accuracy expressed as a ‘fold 

relative difference’. This is because statements such as “T is within K-fold of θ” 

are more intuitive than the formulation given in (11). The ‘fold relative accuracy’, 

fRA, is defined as: 

(12) fRA(T|θ) = Max{ RA(T|θ), 1/RA(T|θ) } = Max ( T/ θ, θ / T ) 

Then, statement (11) is equivalent to 

(13) Prob { fRA(T|θ) ≤ K } ≥ 0.95 

and simply says that the estimate, T, will be within K-fold of the true parameter, 

θ, at least 95% of the time. The 95th percentile of fRA, fRA95, is the specific foldaccuracy value that satisfies (13). Consequently, the benchmark adequacy goal 

reduces to requiring that: 

(14) fRA95 ≤ K 

If we denote the 2.5th and 97.5th percentiles of the sampling distribution of T by 

T2.5 and T97.5, respectively, then the 95th percentile of fold relative accuracy can 

also be calculated from 

(15) fRA95 = Max ( T97.5 / θ, θ / T2.5 ) 

Benchmark Objective for Antimicrobial Scenarios 

The default benchmark objective for all antimicrobial scenarios in the AEATF II 

Monitoring Program is that a sample from the hypothetical reference sampling 

distribution above be of adequate size to describe selected measures of the 

(normalized) exposure distribution with a pre-determined level of accuracy. EPA 

provides guidance to AEATF II on the minimum degree of accuracy needed for 

regulatory use in particular scenarios. The current consensus is that estimates of 

the geometric mean, the arithmetic mean, and the 95th percentile generally 

should be accurate to within approximately 3-fold of their true value. 

It should always be kept in mind, however, that this objective is specified in terms 

of the reference random sampling distribution. This reference sampling model 

does have the same two-stage nesting structure as the actual sampling 

approach. The lognormal distribution assumption is also reasonable, robust, and 

consistent with existing data. However, the reference distribution assumes 

simple random sampling at each stage. It does not, and cannot, incorporate the 

combination of purposive and random diversity sampling actually used. 

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As noted above, the consequence of diversity sampling is expected to be a 

tendency for the sampling variation of normalized exposure to be overestimated. 

The sample should tend to over-represent extremes and under-represent the 

more common values. Such diversity-oriented data collected for this scenario, 

but analyzed with respect to the two-stage reference distribution, is expected to 

have minimal bias for central tendency. In contrast, upper percentiles of 

exposure are expected to be, on the average, too large. There is no way to 

determine the actual magnitude of such overestimation. For regulatory 

purposes, however, overestimation of upper percentiles is of minimal concern: 

for practical exposure assessments, overestimation of exposures is a 

conservative practice utilized by regulatory agencies. A tendency to both 

consider and even overestimate upper percentiles is consistent with this practice. 

Parameter Estimates Used for Benchmark Objectives 

As defined above, relative accuracy applies to the particular quantity, T, that is 

used to estimate the reference distribution parameter θ. Thus, it is important to 

consider which types of estimates of the geometric mean, arithmetic mean, and 

95th percentile are used to evaluate fRA95. The relative accuracies could differ 

depending on the particular estimates used. 

There are often multiple choices for the parameter estimates. The estimators 

can be broadly grouped into either empirical or parametric. Empirical estimates 

are the commonly-used statistics available in spreadsheet programs. They do 

not (explicitly) assume any distribution. However, they can sometimes require 

simple random sampling for greatest efficiency. Parametric estimates 

incorporate the fact that the reference distribution is lognormal and could also 

account for cluster sampling being used. 

The most straightforward parameter is the geometric mean (GMnE). In the 

balanced case, the simple empirical estimate of GMnE can be calculated by 

averaging the log-transformed normalized exposures and then taking the antilog 

of this value. In this case, the empirical and parametric estimates of GMnE are 

identical. If the number of MEs per cluster varies, however, one could consider 

geometric means with different degrees of weighting by cluster size. The 

arithmetic mean can also be calculated empirically by summing up the 

normalized exposures and dividing by the total number of MEs. Again, when the 

cluster sizes differ, other types of weighted empirical arithmetic means exist. In 

the unbalanced case, neither the weighted nor the unweighted estimates of GMnE 

or AMnE are universally best. Consequently, for the purposes of sample size 

determination, the simple (and most common) versions of the empirical 

geometric and arithmetic means seem preferable. Empirical percentiles could, 

theoretically be calculated in the conventional manner. However, when there is 

cluster sampling and the number of MEs are not large, empirical estimates of the 

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extreme upper (or lower) percentiles are not especially efficient. The parametric 

percentiles (see below) are preferred in this case. 

Parametric estimates are those closely aligned with the sampling model used. In 

this case one uses the fit to the variance component model described in (8) 

above to get estimates for the geometric mean (GMnE) and the total geometric 

standard deviation (GSDnE). To estimate the arithmetic mean (AMnE) and 95th 

percentile (nE95) one could then use the lognormal relationships: 

AMnE = GMnE × Exp { 1⁄2 (logeGSDnE)

2

 } 

(16) 

nE95 = GMnE × Exp { Z95 logeGSDnE } 

where Z95 is the 95th percentile of the standard normal distribution. For simplicity, 

these will be labeled the ‘parametric cluster sampling estimates’. 

It can be argued that few if any users of the AEATF II monitoring data will choose 

to (or be able to) fit variance component models to the data. They will probably 

ignore the sampling model and use more conventional estimates. In this case 

empirical estimates of GMnE and AMnE defined above would probably be used. 

Potential data users might also be less inclined to use empirical percentiles, 

especially with smaller sample sizes. The lognormal percentile estimate of nE95 

in (16) above would then still be used but perhaps with the mixed model GSDnE 

estimate replaced with the more conventional GSDnE (i.e., the back-transformed 

simple standard deviation of log exposures.) For convenience, estimates that 

assume lognormality but not cluster sampling will be labeled ‘simple random 

sampling parametric percentiles’. 

Any or all of the above estimators could be evaluated. However, for the 

purposes of determining sample sizes, it is recommended that focus be on the 

following estimators: 

• GMnE – simple empirical estimate 

• AMnE – simple empirical estimate 

• nE95 – parametric cluster sampling estimate 

The Determination of Sample Size 

As stated above, the benchmark adequacy goal reduces to requiring that the 

95% percentile of fold relative accuracy, fRA95, be less than or equal to K. Under 

the reference two-stage random sampling model described above, the only 

quantities needed to determine relative accuracy of population parameter 

estimates are reasonable values for GSDnE and ICC. Such values could be 

based on existing exposure data for scenario-specific tasks, surrogate exposure 

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data from similar tasks, and/or reasonable assumptions based on subject-matter 

expertise. 

Given these values, fRA95, can be computed for any combination of the NC and 

NM. Calculation of the 95% percentile of fold relative accuracy is complex and is 

usually best accomplished using Monte Carlo simulation methods. When the 

number of MEs per cluster, Nm, is the same for all clusters, the geometric mean, 

fRA95 can be calculated directly from the GSDnE and ICC as: 

nE N exp 1.96ln 

where N is the total number of MEs (i.e., N=Nc×Nm). For parameters other than 

the geometric mean, a straightforward simulation approach can be used to 

determine fRA95. This procedure is: 

1. Simulate a set of normalized exposure data for Nc clusters and Nm 

monitoring units per cluster using the reference sampling model 

defined in (8) above. 

⎧

⎨

⎩

2. From each set of simulated data, calculate T, the estimate of θ

3. Repeat steps 1 and 2 above M times to get M values of the 

estimate T 

4. From these M T-values calculate T2.5 and T97.5, the 2.5th and 97.5th 

percentiles of T, respectively. 

5. Calculate the 95th percentile of fold relative accuracy, fRA95, using 

formula (15) above. 

The number of simulations, M, should be some large number such as 1,000 or 

10,000. This process can be continued until a combination of NC and NM are 

found that satisfy to benchmark objective. 

General Guidelines for Diversity Selection of Monitoring Events 

Although diversity selection is simple in theory, practical implementation is often 

complex and usually scenario specific. This section presents diversity selection 

procedures and recommendations that apply generally to all scenarios. 

Scenario-specific design documents and study protocols will provide details of 

ME selection and construction. 

As described previously, the objective of diversity selection is to obtain a diverse 

set of handler-day conditions from among those conditions possible when an 

arbitrary ai is used in future scenario-related tasks. These selected HD 

conditions are then used to construct monitoring events. Diversity selection is 

⎫

⎬

⎭

ICC 1− ICC (17) fRA95 = GSD +

c N

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done independently at each stage of ‘sampling’. Thus, a diverse set of sites is 

selected followed by a diverse set of ME conditions within each site. 

Diversity should always be with respect to characteristics (i.e. particular 

components of C) that are expected to impact exposure. Whenever possible, the 

characteristics used should be meta-factors. Meta-factors are characteristics 

that indirectly influence a number of other characteristics. For example, a 

worker is a meta-factor because substituting one worker for another alters a 

number of factors (e.g., behavior, physical appearance, stamina) that might affect 

exposure. Other common meta-factors are geographic location and time-of-year. 

Not every characteristic that may impact exposure can be, or even should be, 

considered in diversity selection. The number of possible combinations of factors 

that may impact exposure will always greatly exceed the number of planned 

MEs. Consequently, only a few characteristics, preferably meta-factors, can be 

used effectively in diversity selection. 

Diversity Selection Approaches 

There are a number of ways to achieve diversity among ME handler-day 

conditions. The most straightforward approach is to purposively select MEs that 

appear to be sufficiently different with respect to the characteristics of interest. 

Documentation for such direct purposive selection should include the 

characteristics considered and how much these characteristics differ among the 

ME. Although flexible, and likely to achieve a set of MEs with a great amount of 

diversity, this approach is subjective and, therefore, difficult to reproduce. 

More formal approaches are also possible. A general, albeit quite sophisticated, 

approach is to define diversity scores for each possible configuration of the 

characteristics of interest. A total configuration score might be defined as a 

function of the dissimilarity between possible pairs of units. Then, one simply 

selects (or synthetically constructs) those configurations that result in the 

greatest diversity score. If multiple configurations have the highest score, a 

random selection among these is possible. While achieving diversity in an 

objective and reproducible manner, this approach is quite complex and difficult to 

implement. 

A formal approach that is both common and simple to implement is stratified 

diversity selection. In this approach available selectable units (e.g., sites, MEs) 

are partitioned into strata based on characteristics likely to impact exposure. 

Each possible selection unit must belong to one and only one stratum. The 

number of strata must be at least as large as the number of units that will be 

selected. For example, if there are three units to be selected, then there should 

be at least three strata. Diversity could be achieved by selecting (purposively or 

randomly) no more than one unit from each stratum. If there are more strata 

than units to be selected, then a subset of the strata should be selected first. 

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This could be either purposively (to increase diversity) or randomly (to reduce 

intentional selection bias). 

Stratified diversity selection is similar to the method of stratification used in 

population sampling. Unlike the case with stratified sampling, however, diversity 

is increased by selecting only a single unit from each stratum. There is no 

attempt to sample or to weight results in proportion to stratum size. In fact 

stratum size in the future HD population is usually unknown. 

Diversity Selection of First-Stage Units 

At the first stage of ME selection, sites are the ‘selectable’ units. As defined 

previously, site is considered a particular experimental location and timeframe for 

monitoring. Diversity selection of sites means obtaining sites that are different 

from each other, on the average, with respect to some characteristic(s) expected 

to impact exposure. Thus, there should be little surprise if exposure, on the 

average, differs between sites. If sites are constructed environments, then they 

can be built to be different from each other with respect to important 

characteristic(s). If there are a number of possible sites available and a set is to 

be selected (randomly or purposively), then stratified diversity selection of sites, 

based on the important characteristic(s), is a feasible approach 

Diversity Selection of Second-Stage Units 

Ultimately the second stage selection units are the final MEs. The MEs should 

be diversified independently within each selected site. In most cases within-site 

diversity selection of MEs focuses only on two characteristics: subject and 

normalizing factor. 

Handling-day exposures for the same individual are expected to be correlated. 

That is, many components of C relating to same worker will be identical, even on 

different days. In contrast, different individuals are less correlated. Worker 

behaviors are expected to have great impact on exposure. Consequently, 

diversity is increased by simply requiring that each ME be constructed using a 

different individual. 

MEs should also be diverse with respect to the normalization factor that is 

deemed appropriate for the scenario. One feasible approach is to partition the 

possible levels of NF into strata and construct one ME from each NF stratum. 

In some cases (e.g. simulated-condition studies) there is a pool of available 

workers that can be assigned to any NF stratum. If all possible configurations of 

assignment are equivalent, then workers could be randomly allocated to strata. If 

some allocations are non-equivalent (e.g. more cost effective or there are 

scheduling issues) then a purposive assignment of individuals to NF levels might 

be preferable. 

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In other cases (e.g. In Situ/observational studies) worker availability depends on 

the particular NF level chosen. Some individuals may only work with higher NF 

levels and some with only lower NF, say. Selection of workers could still be 

random, although random choice might be restricted to within each NF stratum. 

However, when such associations between subject and NF levels exist, 

purposive allocation might result in a more cost effective and practical 

configuration. 

Rationale for Using the Normalizing Factor in Diversity Selection 

It might at first appear pointless to select MEs with differing levels of the 

normalizing factor when diversity of normalized exposures is desired. If the NF is 

approximately proportional to the amount of potential ai contact, then normalized 

exposure should be independent of the levels of NF. Consequently, it should 

make no difference whether all MEs are at the same level or at different levels of 

the NF. 

This would be true if the value of the normalizing factor had no impact on the 

other ME conditions selected. However, this might not be true. Because amount 

of active ingredient contact is so important to exposure, it is likely that the NF is 

also a meta-factor. That is, it is not unreasonable to expect that different values 

of the NF will be naturally associated with specific sets of handler-day conditions, 

C. Suppose, for example, that the normalizing factor for wiping application 

exposure was correlated with duration of task. It is conceivable that some worker 

behaviors (e.g. fatigue) and diversity of surfaces wiped might be different for 

shorter duration MEs than for longer duration MEs. By insuring that the levels of 

NF are varied, the set of MEs indirectly captures diversity in those components of 

C that are naturally associated with the normalizing factor. 

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Appendix F: Evaluation of Existing PHED Applicator 

Exposure Data for Hand-Held Aerosol Spray 

The Pesticide Handlers Exposure Database (PHED, version 1.1) contains two 

studies involving the monitoring of dermal and inhalation exposure during the use 

of a pressurized aerosol container. The current HED PHED Surrogate Guide 

uses the data only from Study 521 for aerosols (Scenario 10). The two studies 

were accessed in PHED by subsetting the applicator file for method of 

application equal to aerosol can. The two studies were identified as Study 456 

and 521 and both involved the application of an insecticide. A general review of 

these studies is provided in the “PHED SCENARIO DISCUSSION” section of this 

report. This is followed by the “ACCEPTANCE CRITERIA EVALUATION” 

section of this report, wherein the following information and “data acceptance 

criteria” were evaluated: 

1. Meta information: Scenario (e.g., hand held aerosols) study(ies) code(s), 

total number of replicates or monitoring events, range of AaiH, dermal and 

inhalation sampling durations, average limits of quantitation (LOQ’s) for 

dermal and inhalation exposure, percentage of samples with undetectable 

residues body area. 

2. Results of selected AEATF II/AHETF data acceptance criteria applied to 

the scenario-specific study data set(s): 

a. Identification of the number of monitoring events classified in PHED 

as A and B grade; A and B grade data indicate compliance with the 

criteria: the average recoveries of lab spikes should be between 70­

120 percent and the precision value (coefficient of variation; CV) 

should be less than or equal to 20 percent AND recovery of field 

fortification samples should be 50-120% with a C.V. +/-25%; 

b. The number monitoring events where non-detects/less than LOQ 

values account for less than 40% of dermal exposure; and 

c. The number of monitoring events with a minimum of 10 dermal 

patch dosimeters attached inside or outside normal work clothing to 

the chest, back, both upper arms, both lower arms, both upper legs, 

both lower legs, plus hand (cotton gloves can substitute for hand 

wash) and head/face exposure determinations. 

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PHED SCENARIO DISCUSSION 

Method of Application 

PHED Study 456 involved the application of one 15-ounce aerosol can of 

insecticide per house in each of 15 houses in Kansas City, Missouri. Three study 

volunteers treated five houses each. The aerosol cans contained 1% of the 

active ingredient under study, equivalent to 4.33 grams a.i. per can. Applicators 

held the aerosol can in one hand and sprayed the contents of the container into 

cracks and crevices, along baseboards, under sinks, behind appliances, and in 

other areas were insects would be expected to hide. 

PHED Study 521 involved the application of one 16-ounce aerosol can of 

insecticide per house in each of 15 houses in Vero Beach, Florida. Five study 

volunteers treated three houses each. The aerosol cans contained 1% active 

ingredient, equivalent to 4.54 grams a.i. per can. Applicators held the aerosol 

can in one hand and sprayed the contents into cracks and crevices, along 

baseboards, under sinks, behind appliances, and in other areas where insects 

were expected to hide. 

Exposure Monitoring Methodology 

Both Study 456 and Study 521 used gauze patches for dermal exposure 

monitoring. Volunteers wore a single layer of clothing that included a longsleeved shirt, long pants, and shoes. The dermal gauze patches were in holders 

with an open diameter of 5.6 cm, and were placed under the single layer of 

clothing on the participant’s upper arms, forearms, chest, back, thighs, and lower 

legs. These dosimeters provide dermal exposure estimates under a single layer 

of clothing. 

A second set of dermal dosimeters were placed outside the clothing, but so as 

not to occlude the inner dosimeters. The outer dermal dosimeters were also 

placed on the upper arms, forearms, chest, back, thighs, and lower legs. This 

set-up provides dermal dosimetry data needed to estimate actual dermal 

exposure for a variety of clothing possibilities ranging from long-sleeved shirt and 

long pants to short pants and a short-sleeved shirt. Head exposure was 

monitored by the placement of a gauze dosimeter on a ball cap just above the bill 

of the hat. 

Hand exposure was monitored using hand-rinses. Applicator’s hands were 

rinsed at the completion of application in each house. Each hand was rinsed 

separately using 200 mL of ethanol and each hand was rinsed twice in a total of 

400 mL of ethanol. The total rinsate from the four rinses (two for each hand) 

were combined for analysis. Volunteers in Study 456 wore chemical-resistant 

gloves during all 15 replications; the results of this study represent hand 

exposure under protective gloves. Volunteers in Study 521 wore no gloves, and 

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the results represent exposure to unprotected hands. Taken together, both 

studies support a comparison of protected and unprotected hand exposure 

during aerosol can use. 

Inhalation exposure was monitored using personal air samplers with the 

sampling cassette placed on the collar near the participant’s breathing zone. Air 

was drawn through the sampling cassettes at a flow rate of 1 liter per minute. 

The air pumps were sampled before and after each monitoring period. 

PHED Data Quality Grades 

Data in PHED are graded for quality based on analytical quality assurance. The 

data are assigned to one of five grades, A through E, based on the recovery of 

the active ingredient from fortified field samples, fortified laboratory samples, and 

storage stability samples. Grade A and B data meet the minimum analytical 

quality assurance requirements as described in EPA’s Subdivision U of the 

Pesticide Assessment Guidelines. Grade A and B data have laboratory 

recoveries of 80% to 110% with a coefficient of variation of 25% or less. The 

field recoveries are between 50% and 120%. Grade C data have laboratory 

recoveries of 70% to 120% with a coefficient of variation of 33% or less. Grade 

C data must also have either field recoveries of 30% to 120%, or the data must 

have storage stability recoveries of 50% to 120%. Grade D data have laboratory 

recoveries of 60% to 120% with a coefficient of variation of 33% or less. Field 

recovery or storage stability data are not required for a grade D classification. 

Grade E data do not meet any of these standards. To support the registration of 

a pesticide, the data subset should contain a minimum of 15 replications for each 

body area except the feet. The data should also meet the minimum analytical 

quality assurance requirements of grades A or B. The Health Effects Division 

uses the number of replicates in a scenario and the data quality grades to rate 

the PHED data for a given scenario as high confidence, medium confidence, or 

low confidence data. 

Study 521 contains 15 replications of hand, dermal, and inhalation data that were 

graded A. The hand data in Study 456 were graded A, but the dermal and 

inhalation data were graded C. The HED PHED Surrogate Guide lists aerosol 

application as Scenario 10. Because of the grading, Scenario 10 lists only the 15 

replicates from Study 521 and gives them a high confidence rating. 

Study 456 Dermal Exposure 

The dermal exposures from Study 456 are presented below. The dermal 

exposure is presented both as the “no clothing” scenario and as long-sleeved 

shirt and long pants to permit analysis of multiple clothing scenarios. The 

exposure data are presented in micrograms and were not normalized by the 

amount of a.i. handled. As previously noted, all replicates in Study 456 involved 

handling 4.33 grams of active ingredient. 

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 << Specifications >> 

Subset Specifications for STUDY456.APPL 

With Study Code Equal to 456 

Subset originated from AEROSAL.APPL 

With Application Method Equal to 4 

Subset originated from APPL.FILE 

 SUMMARY STATISTICS FOR CALCULATED DERMAL EXPOSURES 

SCENARIO: No clothing (total deposition) 

PATCH DISTRIB. MICROGRAMS 

LOCATION TYPE Median Mean Coef of Var Geo. Mean Obs. 

HEAD (ALL) Lognormal 288.6 435.5867 90.9793 209.1935 15 

NECK.FRONT Lognormal 14.25 30.07 126.9042 11.7512 15 

NECK.BACK Lognormal 13.86 20.5847 80.8261 13.0283 15 

UPPER ARMS Lognormal 776.97 1129.177 125.0676 

508.4372 15 

CHEST Lognormal 337.25 711.6567 126.9042 278.1117 

15 

BACK Lognormal 447.3 664.3233 80.826 420.4601 15 

FOREARMS Lognormal 362.395 714.9083 137.7725 276.778 

15 

THIGHS Lognormal 255.94 328.0107 97.1166 210.1775 15 

LOWER LEGS Lognormal 86.87 137.564 90.5475 97.689 15 

FEET 0 

HANDS 0 

TOTAL DERM: 2025.6265 2583.435 4171.8814 2025.6265 

95% C.I. on Mean: Dermal: [-29183.6113, 37527.3741] 

 Number of Records: 15 

Data File: APPLICATOR Subset Name: STUDY456.APPL 

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The dermal exposure to all body areas had a lognormal distribution. The total 

geometric mean dermal exposure was 2.03 mg/replicate. Note that this dermal 

exposure represents exposure outside the clothing and because of the use of 

protective gloves, there are no data for the hands. 

The dermal exposure under a single layer of clothing and protective gloves is 

presented below. 

 SUMMARY STATISTICS FOR CALCULATED DERMAL EXPOSURES 

SCENARIO: Long pants, long sleeves, gloves 

PATCH DISTRIB. MICROGRAMS 

LOCATION TYPE Median Mean Coef of Var Geo. Mean Obs. 

HEAD (ALL) Lognormal 288.6 435.5867 90.9793 209.1935 15 

NECK.FRONT Lognormal 14.25 30.07 126.9042 11.7512 15 

NECK.BACK Lognormal 13.86 20.5847 80.8261 13.0283 15 

UPPER ARMS Other 43.65 43.65 0 43.6502 15 

CHEST Other 53.25 53.25 0 53.2503 15 

BACK Other 53.25 53.25 0 53.2503 15 

FOREARMS Other 18.15 18.3113 3.4121 18.3022 15 

THIGHS Other 57.3 57.3 0 57.3003 15 

LOWER LEGS Other 35.7 37.128 9.5542 36.9913 15 

FEET 0 

HANDS Other 5 15.7067 152.4528 9.0385 15 

TOTAL DERM: 500.273 583.01 764.8374 505.7561 

95% C.I. on Mean: Dermal: [-5623.8395, 7153.5143] 

 Number of Records: 15 

Data File: APPLICATOR Subset Name: STUDY456.APPL 

The dermal exposure to the head and neck are the same as in the “no clothing” 

scenario because they are based on the same outer dosimeters. The dermal 

exposure to the upper arms, chest, back, and thighs is based on residue levels 

below the limit of quantification and therefore are based on half of the LOQ as 

per PHED and HED guidelines. It can be determined that all 15 observations for 

these body areas were below the LOQ because the coefficient of variation is 0. 

The forearms had only one study participant with detectable residues and then 

only to the right forearm dosimeter. The lower legs had one replication with 

detectable residues to both dosimeters and two additional replicates with 

detectable residues to the left lower leg dosimeter. The dermal dosimeter LOQ 

in Study 456 was 0.03 μg/cm2

. The hand wash LOQ was 10 μg per sample. The 

total dermal exposure for an individual wearing long pants, a long-sleeved shirt, 

and protective gloves is 0.50 mg based on the PHED “best fit” guideline. 

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Study 521 Dermal Exposure 

The potential dermal exposure under the “no clothing scenario is presented 

below. 

 << Specifications >> 

Subset Specifications for STUDY521.APPL 

With Study Code Equal to 521 

Subset originated from AEROSAL.APPL 

With Application Method Equal to 4 

Subset originated from APPL.FILE 

 SUMMARY STATISTICS FOR CALCULATED DERMAL EXPOSURES 

SCENARIO: No clothing (total deposition) 

PATCH DISTRIB. MICROGRAMS 

LOCATION TYPE Median Mean Coef of Var Geo. Mean Obs. 

HEAD (ALL) Lognormal 419.9 697.6927 124.9606 410.258 15 

NECK.FRONT Lognormal 21 71.499 217.76 25.1292 15 

NECK.BACK Lognormal 21.34 26.1235 80.9252 19.9517 15 

UPPER ARMS Lognormal 1063.896 1320.4028 69.3397 999.3333 15 

CHEST Lognormal 497 1692.143 217.7599 594.7233 15 

BACK Lognormal 688.7 843.0777 80.9252 643.8954 15 

FOREARMS Lognormal 752.62 800.2295 82.2052 488.1726 15 

THIGHS Lognormal 223.47 483.612 124.2601 299.811 15 

LOWER LEGS Lognormal 169.456 627.3363 177.4108 248.3608 15 

FEET 0 

HANDS Lognormal 1100 1210.2667 51.0748 1056.7992 15 

TOTAL DERM: 4786.4345 4957.382 7772.3832 4786.4345 

95% C.I. on Mean: Dermal: [-60276.6132, 75821.3796] 

 Number of Records: 15 

Data File: APPLICATOR Subset Name: STUDY521.APPL 

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The total potential exposure under the “no clothing scenario” is 4.79 mg/replicate. 

All body areas had a lognormal distribution. Because hand exposure is included 

in the Study 521 data the hand exposure must be subtracted from the total 

potential exposure to permit a direct comparison with Study 456. The geometric 

mean hand exposure was 1.057 mg and the dermal exposure excluding the 

hands is 3.73 mg/replicate. The total potential dermal exposure in Study 456 

was 2.03 mg/replicate. 

The actual dermal exposure to an individual wearing long pants and a long 

sleeved shirt is presented below. 

 SUMMARY STATISTICS FOR CALCULATED DERMAL EXPOSURES 

SCENARIO: Long pants, long sleeves, no gloves

 PATCH DISTRIB. MICROGRAMS 

LOCATION TYPE Median Mean Coef of Var Geo. Mean Obs. 

HEAD (ALL) Lognormal 419.9 697.6927 124.9606 410.258 15 

NECK.FRONT Lognormal 21 71.499 217.76 25.1292 15 

NECK.BACK Lognormal 21.34 26.1235 80.9252 19.9517 15 

UPPER ARMS Other 59.073 59.073 0 59.075 15 

CHEST Other 72.065 72.065 0 72.0675 15 

BACK Other 72.065 72.065 0 72.0675 15 

FOREARMS Other 24.563 27.3783 39.8253 26.2578 15 

THIGHS Other 77.546 77.546 0 77.5487 15 

LOWER LEGS Other 48.314 48.314 0 48.3157 15 

FEET 0 

HANDS Lognormal 1100 1210.2667 51.0748 1056.7992 15 

TOTAL DERM: 1865.7641 1915.866 2362.0232 1867.4703 

95% C.I. on Mean: Dermal: [-14926.1143, 19650.1607] 

 Number of Records: 15 

Data File: APPLICATOR Subset Name: STUDY521.APPL 

The exposure pattern under a single layer of clothing in Study 521 is similar to 

that reported in Study 456. All covered body areas except the forearms had 

residue levels on all dosimeters that were below the study’s LOQ. Among the 

forearm dosimeters there was one replicate with quantifiable residues on the left 

forearm dosimeter. The limit of quantification in Study 521 was 0.0406 μg/cm2 

for the dermal dosimeters and 100 μg/replicate for the hand rinses. The dermal 

exposure under a single layer of clothing from Study 521 can be compared to 

exposure in Study 456 by subtracting the 1.058 mg hand exposure from the total 

dermal exposure of 1.866 mg. The total dermal exposure excluding the hands in 

Study 521 is 0.809 mg/replicate compared to the total dermal exposure excluding 

the hands of 0.495 mg/replicate. 

By combining the dermal exposure data from Studies 456 and 521 it is possible 

to estimate the reduction in exposure attributable to the use of protective gloves. 

Although EPA does not routinely consider the use of protective gloves for 

homeowner uses of aerosols, the use of protective gloves may possibly be 

considered for occupational uses of aerosol containers. 

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Combined Dermal Exposure Estimate 

The combined dermal exposure data based on Studies 456 and 521 are 

presented below. The first scenario represents exposure when wearing a longsleeved shirt, long pants, and no gloves. The second scenario represents 

exposure when wearing a long-sleeved shirt, long pants, and protective gloves. 

The difference between the two estimates provides insight into the protection 

provided by chemical-resistant gloves. 

 << Specifications >> 

Subset Specifications for AEROSAL.APPL 

With Application Method Equal to 4 

Subset originated from APPL.FILE 

 SUMMARY STATISTICS FOR CALCULATED DERMAL EXPOSURES 

SCENARIO: Long pants, long sleeves, no gloves

 PATCH DISTRIB. MICROGRAMS 

LOCATION TYPE Median Mean Coef of Var Geo. Mean Obs. 

HEAD (ALL) Lognormal 353.6 566.6397 119.763 292.9561 30 

NECK.FRONT Lognormal 20.1 50.7845 223.2098 17.1842 30 

NECK.BACK Lognormal 18.095 23.3541 80.9408 16.1226 30 

UPPER ARMS Other 51.3615 51.3615 15.2708 50.7803 30 

CHEST Other 62.6575 62.6575 15.2708 61.9485 30 

BACK Other 62.6575 62.6575 15.2708 61.9485 30 

FOREARMS Other 22.5665 22.8448 38.8679 21.922 30 

THIGHS Other 67.423 67.423 15.2709 66.66 30 

LOWER LEGS Other 48.314 42.721 14.5118 42.276 30 

FEET 0 

HANDS Lognormal 1100 1210.2667 51.0748 1056.7992 15 

TOTAL DERM: 1698.0421 1806.775 2160.7103 1688.5974 

95% C.I. on Mean: Dermal: [-10432.2269, 14753.6475] 

 Number of Records: 30 

Data File: APPLICATOR Subset Name: AEROSAL.APPL 

 SUMMARY STATISTICS FOR CALCULATED DERMAL EXPOSURES 

SCENARIO: Long pants, long sleeves, gloves

 PATCH DISTRIB. MICROGRAMS 

LOCATION TYPE Median Mean Coef of Var Geo. Mean Obs. 

HEAD (ALL) Lognormal 353.6 566.6397 119.763 292.9561 30 

NECK.FRONT Lognormal 20.1 50.7845 223.2098 17.1842 30 

NECK.BACK Lognormal 18.095 23.3541 80.9408 16.1226 30 

UPPER ARMS Other 51.3615 51.3615 15.2708 50.7803 30 

CHEST Other 62.6575 62.6575 15.2708 61.9485 30 

BACK Other 62.6575 62.6575 15.2708 61.9485 30 

FOREARMS Other 22.5665 22.8448 38.8679 21.922 30 

THIGHS Other 67.423 67.423 15.2709 66.66 30 

LOWER LEGS Other 48.314 42.721 14.5118 42.276 30 

FEET 0 

HANDS Other 5 15.7067 152.4528 9.0385 15 

TOTAL DERM: 646.2429 711.775 966.1503 640.8367 

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95% C.I. on Mean: Dermal: [-6835.8234, 8768.124] 

 Number of Records: 30 

Data File: APPLICATOR Subset Name: AEROSAL.APPL 

Based on the PHED analysis the use of protective gloves reduces the dermal 

exposure from 1.7 mg/aerosol can to 0.65 mg/aerosol can, where an aerosol can 

is 15 to 16 ounces. 

Inhalation Exposure 

PHED was used to estimate the inhalation exposure potential for Studies 456 

and 521 separately and combined. PHED requires the user to select a breathing 

volume. Past analysis by OPP’s Health Effects Division HED assumed a 

breathing volume of 29 liters/minute. For this analysis a breathing volume of 

approximately 17 liters/minute was used (U.S. EPA Exposure Factors Handbook; 

http://www.epa.gov/ncea/efh/). 

The inhalation exposure from Study 456 is as follows: 

 << Specifications >> 

Subset Specifications for STUDY456.APPL 

With Study Code Equal to 456 

Subset originated from AEROSAL.APPL 

With Application Method Equal to 4 

Subset originated from APPL.FILE 

SUMMARY STATISTICS FOR INHALATION EXPOSURES 

EXPOSURE 

DISTRIB. 

TYPE 

Lognormal 

NANOGRAMS 

Median Mean Coef of Var Geo. Mean 

27523.8095 26580.3734 49.7805 22356.8193

Obs. 

15 

95% C.I. on Geo. Mean: [5704.4462, 87620.6649] 

Number of Records: 15 

Data File: APPLICATOR Subset Name: STUDY456.APPL 

The inhalation exposure in Study 456 was 22 μg/replicate or aerosol can. The 

inhalation exposure is 1.3% of the dermal exposure when gloves are not worn. 

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The inhalation exposure from Study 521 is as follows: 

 << Specifications >> 

Subset Specifications for STUDY521.APPL 

With Study Code Equal to 521 

Subset originated from AEROSAL.APPL 

With Application Method Equal to 4 

Subset originated from APPL.FILE 

SUMMARY STATISTICS FOR INHALATION EXPOSURES 

EXPOSURE 

DISTRIB. NANOGRAMS 

TYPE Median Mean Coef of Var Geo. Mean 

Other 7456.1404 10594.0506 61.6887 9345.3422 

Obs. 

15 

95% C.I. on Geo. Mean: [3720.0299, 23477.0745] 

Number of Records: 15 

Data File: APPLICATOR Subset Name: STUDY521.APPL 

The inhalation exposure in Study 521 was 9.3 μg/replicate. The inhalation 

exposure is similar to the exposure monitored in Study 456 and represents 0.5% 

of the dermal exposure when gloves are not worn. 

The combined inhalation exposure from both studies is presented below. 

 << Specifications >> 

Subset Specifications for AEROSAL.APPL 

With Application Method Equal to 4 

Subset originated from APPL.FILE 

SUMMARY STATISTICS FOR INHALATION EXPOSURES 

DISTRIB. NANOGRAMS 

TYPE Median Mean Coef of Var Geo. Mean Obs. 

EXPOSURE Other 13379.6296 18587.212 70.4015 14454.4846 30 

95% C.I. on Geo. Mean: [3433.5175, 60850.7529] 

Number of Records: 30 

Data File: APPLICATOR Subset Name: AEROSAL.APPL 

The inhalation exposure is 14 μg/aerosol can when the container is 15 to 16 

ounces. 

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Conclusions 

PHED contains two studies that monitored dermal and inhalation exposure 

during the application of the entire contents of one container per replicate. EPA’s 

PHED Surrogate Guide (Scenario 10) uses the data only from Study 521. 

Study 456 provides data for hands protected by gloves while Study 521 presents 

data for unprotected hands. This permits an estimate of exposure reduction 

when gloves are worn (for scenarios where protective glove use is reasonable.) 

A significant limitation of these existing data is that the use conditions for both 

studies, including the number of cans applied per replicate and the AaiH, were 

very similar. This prevents evaluating any potential relationship between 

exposure and either the number of cans used or the AaiH. PHED study 521 

provides MUs that meet key AEATF II acceptance criteria, but additional 

monitoring events should be considered to provide a wider range of amount of 

formulation sprayed (AaiH) under conditions relevant to antimicrobial aerosol 

product use. 

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Attachment 1. Individual Replicate Dosimeter Residue Levels 

 << AEROSAL.APPL >> 

Total AI Total 

Record Applied US Gal 

I.D. (lb) Sprayed 

0456*B*4 .0094 .1200 

0456*B*5 .0094 .1200 

0456*C*1 .0094 .1200 

0456*C*2 .0094 .1200 

0456*C*3 .0094 .1200 

0456*C*4 .0094 .1200 

0456*C*5 .0094 .1200 

0521*B*02 .0100 .1250 

0521*D*02 .0100 .1250 

0456*A*01 .0094 .1200 

0456*A*02 .0094 .1200 

0521*A*02 .0100 .1250 

0521*C*01 .0100 .1250 

0521*A*03 .0100 .1250 

0521*C*02 .0100 .1250 

0521*C*03 .0100 .1250 

0521*E*01 .0100 .1250 

0521*E*02 .0100 .1250 

0521*E*03 .0100 .1250 

0521*A*01 .0100 .1250 

0456*A*3 .0094 .1200 

0456*A*4 .0094 .1200 

0456*A*5 .0094 .1200 

0456*B*1 .0094 .1200 

0456*B*2 .0094 .1200 

0456*B*3 .0094 .1200 

0521*B*01 .0100 .1250 

0521*D*01 .0100 .1250 

0521*D*03 .0100 .1250 

0521*B*03 .0100 .1250

 << AEROSAL.APPL >> 

Air Smpl Air Quan. Air Air 

Record Time Limit Volume Amount 

I.D. (min) (ug) (l) (ug) 

0456*B*4 28.0 .1000 30.8 3.0300 

0456*B*5 18.0 .1000 19.4 1.3900 

0456*C*1 15.0 .1000 16.2 .8900 

0456*C*2 21.0 .1000 22.6 1.2500 

0456*C*3 19.0 .1000 19.7 1.3700 

0456*C*4 26.0 .1000 27.0 .5800 

0456*C*5 13.0 .1000 13.5 .7800 

0521*B*02 28.0 1.0000 33.0 ND 

0521*D*02 19.0 1.0000 22.4 ND 

0456*A*01 30.0 .1000 31.5 1.7000 

0456*A*02 28.0 .1000 29.4 .2200 

0521*A*02 33.0 1.0000 38.9 ND 

0521*C*01 32.0 1.0000 34.9 ND 

0521*A*03 31.0 1.0000 35.3 ND 

0521*C*02 27.0 1.0000 31.9 1.6143 

0521*C*03 32.0 1.0000 36.5 ND 

0521*E*01 31.0 1.0000 33.8 1.3600 

0521*E*02 32.0 1.0000 37.8 ND 

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0521*E*03 36.0 1.0000 41.0 ND 

0521*A*01 28.0 1.0000 30.5 ND 

0456*A*3 33.0 .1000 34.6 2.6700 

0456*A*4 30.0 .1000 32.7 2.0300 

0456*A*5 25.0 .1000 27.2 2.6000 

0456*B*1 23.0 .1000 25.0 2.6200 

0456*B*2 30.0 .1000 32.7 2.2500 

0456*B*3 31.0 .1000 34.1 1.8900 

0521*B*01 27.0 1.0000 29.4 ND 

0521*D*01 26.0 1.0000 28.3 1.6000 

0521*D*03 22.0 1.0000 25.1 ND 

0521*B*03 18.0 1.0000 20.5 ND 

 << AEROSAL.APPL >> (H)P 

Outside Inside Dermal Avg Hand 

Record Both Hands Both Hands Smpl Time Quan. Limit 

I.D. (ug) (ug) (hrs) (ug/sq cm) 

0456*B*4 ND .470 10.0000 

0456*B*5 ND .310 10.0000 

0456*C*1 ND .250 10.0000 

0456*C*2 21.6000 .350 10.0000 

0456*C*3 97.6000 .320 10.0000 

0456*C*4 ND .430 10.0000 

0456*C*5 ND .220 10.0000 

0521*B*02 430.0000 .470 100.0000 

0521*D*02 981.0000 .320 100.0000 

0456*A*01 ND .500 10.0000 

0456*A*02 ND .470 10.0000 

0521*A*02 1240.0000 .550 100.0000 

0521*C*01 1750.0000 .530 100.0000 

0521*A*03 1070.0000 .520 100.0000 

0521*C*02 645.0000 .450 100.0000 

0521*C*03 2690.0000 .530 100.0000 

0521*E*01 1530.0000 .520 100.0000 

0521*E*02 299.0000 .530 100.0000 

0521*E*03 1060.0000 .600 100.0000 

0521*A*01 1100.0000 .470 100.0000 

0456*A*3 22.4000 .550 10.0000 

0456*A*4 24.8000 .500 10.0000 

0456*A*5 ND .420 10.0000 

0456*B*1 19.2000 .380 10.0000 

0456*B*2 ND .500 10.0000 

0456*B*3 ND .520 10.0000 

0521*B*01 1150.0000 .450 100.0000 

0521*D*01 2010.0000 .430 100.0000 

0521*D*03 1440.0000 .370 100.0000 

0521*B*03 759.0000 .300 100.0000 

Note: Avg. Hand Quantification is μg/sample.

The 100 μg LOQ for study 521 was erroneously entered into PHED as 100 μg. The correct value is 10 μg. 

 << AEROSAL.APPL >> (H)Page 1 (V 

Uppr Arm Uppr Arm Uppr Arm Uppr Arm Avg Dermal 

Record In Rght In Left Out Rght Out Left Quan. Limit 

I.D. (ug/sq cm) (ug/sq cm) (ug/sq cm) (ug/sq cm) (ug/sq cm) 

0456*B*4 ND ND .2880 .2050 .0300 

0456*B*5 ND ND .3250 .2090 .0300 

0456*C*1 ND ND .0340 .0140 .0300 

0456*C*2 ND ND .0180 .0280 .0300 

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0456*C*3 ND ND ND ND .0300 

0456*C*4 ND ND .0650 .1610 .0300 

0456*C*5 ND ND .0170 .0320 .0300 

0521*B*02 ND ND .1770 ND .0406 

0521*D*02 ND ND .9990 .4340 .0406 

0456*A*01 ND ND 1.1970 .1930 .0300 

0456*A*02 ND ND .7630 .0730 .0300 

0521*A*02 ND ND .5030 .9990 .0406 

0521*C*01 ND ND .1400 .3760 .0406 

0521*A*03 ND ND .1360 .0830 .0406 

0521*C*02 ND ND .4300 .7920 .0406 

0521*C*03 ND ND .7430 .9660 .0406 

0521*E*01 ND ND .4710 .1790 .0406 

0521*E*02 ND ND .7880 .3670 .0406 

0521*E*03 ND ND .1560 .3000 .0406 

0521*A*01 ND ND .9700 .0620 .0406 

0456*A*3 ND ND .1970 .8200 .0300 

0456*A*4 ND ND .9730 .3980 .0300 

0456*A*5 ND ND .0780 .1390 .0300 

0456*B*1 ND ND .7500 .2530 .0300 

0456*B*2 ND ND 1.3070 2.5330 .0300 

0456*B*3 ND ND .2180 .3230 .0300 

0521*B*01 ND ND .7110 ND .0406 

0521*D*01 ND ND .7630 1.4940 .0406 

0521*D*03 ND ND .1830 .1830 .0406 

0521*B*03 ND ND .1020 .0650 .0406 

 << AEROSAL.APPL >> 

Forearm Forearm Forearm Forearm 

Record In Rght In Left Out Rght Out Left 

I.D. (ug/sq cm) (ug/sq cm) (ug/sq cm) (ug/sq cm) 

0456*B*4 ND ND .1860 1.0100 

0456*B*5 ND ND .1830 1.3750 

0456*C*1 ND ND .0180 .0240 

0456*C*2 .0190 ND .0330 .0500 

0456*C*3 ND ND ND ND 

0456*C*4 ND ND .0750 .0370 

0456*C*5 ND ND .0460 .0800 

0521*B*02 ND ND ND ND 

0521*D*02 ND ND ND 1.3400 

0456*A*01 ND ND 2.8240 .5200 

0456*A*02 ND ND .4640 .1350 

0521*A*02 ND ND .3290 .6740 

0521*C*01 ND ND .2740 .9700 

0521*A*03 ND ND 1.2630 .1420 

0521*C*02 ND ND .5810 1.5790 

0521*C*03 ND ND 1.4290 .3690 

0521*E*01 ND ND .4300 1.2300 

0521*E*02 ND ND .8930 .2900 

0521*E*03 ND ND .0510 .1920 

0521*A*01 ND ND 4.1410 .2540 

0456*A*3 ND ND .3060 .7240 

0456*A*4 ND ND .0960 .2350 

0456*A*5 ND ND .0370 .5380 

0456*B*1 ND ND 1.3150 .7400 

0456*B*2 ND ND .6510 5.3930 

0456*B*3 ND ND .2000 .4000 

0521*B*01 ND ND .4590 .4140 

0521*D*01 ND .0900 .9180 1.1000 

0521*D*03 ND ND .1730 .2090 

0521*B*03 ND ND .0550 ND 

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 << AEROSAL.APPL >> 

Chest Chest Back Back 

Record In Left Out Rght In Rght Out Left 

I.D. (ug/sq cm) (ug/sq cm) (ug/sq cm) (ug/sq cm) 

0456*B*4 ND .4940 ND .1650 

0456*B*5 ND .2270 ND .4470 

0456*C*1 ND .0130 ND .0200 

0456*C*2 ND ND ND .0230 

0456*C*3 ND .0190 ND .1210 

0456*C*4 ND ND ND .0270 

0456*C*5 ND ND ND .0280 

0521*B*02 ND ND ND ND 

0521*D*02 ND .1310 ND .0910 

0456*A*01 ND .1310 ND .1260 

0456*A*02 ND .2610 ND .0900 

0521*A*02 ND .0850 ND .2960 

0521*C*01 ND .1370 ND .2510 

0521*A*03 ND ND ND .1440 

0521*C*02 ND .4790 ND .8440 

0521*C*03 ND .3900 ND .3480 

0521*E*01 ND .4990 ND .1640 

0521*E*02 ND .3170 ND .3170 

0521*E*03 ND .1120 ND .3090 

0521*A*01 ND 4.1820 ND .1940 

0456*A*3 ND .3880 ND .3190 

0456*A*4 ND .0340 ND .3690 

0456*A*5 ND .0190 ND .0930 

0456*B*1 ND .3710 ND .4000 

0456*B*2 ND .9100 ND .3300 

0456*B*3 ND .0950 ND .2490 

0521*B*01 ND .2610 ND .1170 

0521*D*01 ND .3560 ND .1970 

0521*D*03 ND .1400 ND .1260 

0521*B*03 ND ND ND .1440 

 << AEROSAL.APPL >> 

Thigh Thigh Thigh Thigh 

Record In Rght In Left Out Rght Out Left 

I.D. (ug/sq cm) (ug/sq cm) (ug/sq cm) (ug/sq cm) 

0456*B*4 ND ND .1570 .1650 

0456*B*5 ND ND .1250 .0780 

0456*C*1 ND ND ND ND 

0456*C*2 ND ND .0150 .0140 

0456*C*3 ND ND .0240 .0270 

0456*C*4 ND ND ND .0210 

0456*C*5 ND ND ND ND 

0521*B*02 ND ND ND ND 

0521*D*02 ND ND .0820 ND 

0456*A*01 ND ND .3070 .1510 

0456*A*02 ND ND .1560 .0520 

0521*A*02 ND ND .0850 .0970 

0521*C*01 ND ND .1430 .1470 

0521*A*03 ND ND .0450 .0610 

0521*C*02 ND ND .2410 .3380 

0521*C*03 ND ND .1300 1.1040 

0521*E*01 ND ND .1060 .0630 

0521*E*02 ND ND .0590 .0530 

0521*E*03 ND ND .0730 .0530 

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0521*A*01 ND ND .0500 .0670 

0456*A*3 ND ND .1600 .4260 

0456*A*4 ND ND .0670 .0800 

0456*A*5 ND ND .0310 .0440 

0456*B*1 ND ND .1240 .0490 

0456*B*2 ND ND .0730 .0610 

0456*B*3 ND ND .0490 .0450 

0521*B*01 ND ND .0650 ND 

0521*D*01 ND ND .1190 .3890 

0521*D*03 ND ND ND .0860 

0521*B*03 ND ND ND ND 

 << AEROSAL.APPL >> 

Shin Shin Shin Shin 

Record In Rght In Left Out Rght Out Left 

I.D. (ug/sq cm) (ug/sq cm) (ug/sq cm) (ug/sq cm) 

0456*B*4 .0170 .0240 .1270 .1270 

0456*B*5 ND ND .0590 .0400 

0456*C*1 ND ND .0130 .0140 

0456*C*2 ND ND .0260 .0440 

0456*C*3 ND ND .0290 .0380 

0456*C*4 ND ND ND .0170 

0456*C*5 ND ND ND ND 

0521*B*02 ND ND .1220 ND 

0521*D*02 ND ND .0490 ND 

0456*A*01 ND ND .2060 .0380 

0456*A*02 ND ND .0800 .0310 

0521*A*02 ND ND .1220 ND 

0521*C*01 ND ND 1.8640 .0500 

0521*A*03 ND ND .1120 .2550 

0521*C*02 ND ND .5360 .2680 

0521*C*03 ND ND 2.8060 .6210 

0521*E*01 ND ND .0860 .0500 

0521*E*02 ND ND .0590 .0450 

0521*E*03 ND ND .0430 ND 

0521*A*01 ND ND ND .0690 

0456*A*3 ND .0190 .0110 .1500 

0456*A*4 ND ND ND .0200 

0456*A*5 ND ND .0220 ND 

0456*B*1 ND .0180 .0810 .0250 

0456*B*2 ND ND .0750 .3130 

0456*B*3 ND ND .0330 .0400 

0521*B*01 ND ND ND .1120 

0521*D*01 ND ND .1860 .1100 

0521*D*03 ND ND ND .1600 

0521*B*03 ND ND ND ND 

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ACCEPTANCE CRITERIA EVALUATION: 

PHED SCENARIO 10: AEROSOL APPLICATION (APPL) 
AEATF II Vol. 5 Governing Document for a Multi-Year Antimicrobial Chemical Exposure Monitoring Program 

Study Code 456: Handheld Aerosols Task: Evaluate handheld aerosols (specifically study code 456) from the applicator file of the Pesticide 

Database. The following criteria are reviewed to determine if the study code is complete enough for fu1. Are the dermal grades (covered/uncovered) for all study codes listed as “A” or “B”? No 2. Are the airborne grades for all study codes listed as “A” or “B”? No 3. Are the hand grades for all study codes listed as “A” or “B”? No 4. Is the percentage of non-detect values for all body part depositions less than 40% when determining whole body exposures? Total Depositi

Sin

gle L

a

y

er Clothin

g

5. Are the 10 selected body parts (head, neck (front and back), both upper arms, 

both forearms, chest, back, upper legs, lower legs, and hands) available to determine whole body exposures? Total Deposition/No 

Traditional - Substitution method Total Deposition/Norhandled Traditional - Substitution method Single layer clothing long sleeve/long panprotection/ No normalization _ Traditional - Substitution method 

Page 158 of 194 
AEATF II Vol. 5 Governing Document for a Multi-Year Antimicrobial Chemical Exposure Monitoring Program 

Single layer clothing long sleeve/long panprotection/ Normalized by lb ai h_ Traditional - Substitution method 

Summary: Number of Replicates: 15 Range of lbs AI Handled: 9.40E-03 lbs ai - 9.40E-03 lbs ai Range of inhalation sampling durations: 13 minutes – 33 minutes Range of dermal sampling durations: 0.22 hrs – 0.55 hrs Average Dermal LOQ: 3.02 ug/cm2 Average Inhalation LOQ: 10.0 ug Inhalation Rate/Minute: 16 L/minute Non-Detect Handling: Half LOQ Values Hand Protection: 

No 

Head Protection: 

No 

Page 159 of 194 
AEATF II Vol. 5 Governing Document for a Multi-Year Antimicrobial Chemical Exposure Monitoring Program 

Section 1: Clothing Layer: Total Deposition Table 1.1 is a listing of Non-Detect Counts of total deposition. For each column, the number of non-denumber of replicates with a value is listed. Head Neck (front/ back) Upper Arms Right Left Chest Right Left Back Right Left Forearm s Right Left 1/15 4/15=27% 0/0=NA% 1/15=7% 3/15=20% 0/0=NA% =7% Thighs Right Left Lower Legs shin right shin left calf right calf left ankle right ankle left 3/15=20% 2/15=13% 0/0=NA% 3/15 =20% 2/15=13% 0/0=NA% 0/0=NA% 0/0=NA% 0/0

0/0=NA% 1/15=7% 0/0=NA% 0/15 = 0% 1/15 =7% Table 1.1. Non-Detect Counts of body part values (total deposition) 

Percentage Non-Detects: 21/165 = 13% 

F

Page 160 of 194 
AEATF II Vol. 5 Governing Document for a Multi-Year Antimicrobial Chemical Exposure Monitoring Program 

1a. No Normalization – Total Deposition Table 1.2 is a listing of all replicates with a study code of 456. The exposure values are results of no ntotal deposition. No replicates had a whole body exposure using the traditional exposure evaluation mSubstitution Body Method resulted in all 15 replicates having a whole body exposure value. Replicat e Sampl e Time (hrs) Whole Body Exposure (Tradition al Method) (ug) Whole Body Exposure (Substitutio n Body Method) (ug) Inhalatio n Exposur e (ug) Average Dermal Quantification Limit (ug/cm2) Average Hand Quantificatio n Limit (ug/cm2) Airborne Grade 

U

456-A-3 0.55 - 7.58e+03 4.07E+0 4 3.0e-02 10 C 456-A-4 0.50 - 4.57e+03 2.98E+0 4 3.0e-02 10 C 456-A-5 0.42 - 1.76e+03 3.82E+0 4 3.0e-02 10 C 456-A-1 0.50 - 8.45e+03 2.59E+0 4 3.0e-02 10 C 456-A-2 0.47 - 4.53e+03 3.35E+0 4 3.0e-02 10 C 456-B-1 0.38 - 7.09e+03 3.85E+0 4 3.0e-02 10 C 456-B-2 0.50 - 1.81e+03 3.30E+0 4 3.0e-02 10 C 456-B-3 0.52 - 3.20e+03 2.75E+0 4 3.0e-02 10 C 456-B-4 0.47 - 5.61e+03 4.41E+0 3.0e-02 10 C 

Page 161 of 194 
AEATF II Vol. 5 Governing Document for a Multi-Year Antimicrobial Chemical Exposure Monitoring Program 

4 2.06E+0 C 

456-B-5 0.31 - 5.90e+03 3.0e-02 10 4 1.32E+0 C 

456-C-1 0.25 - 343 3.0e-02 10 4 1.85E+0 C 456-C-2 0.35 - 449 3.0e-02 10 4 2.11E+0 C 456-C-3 0.32 - 1.05e+03 3.0e-02 10 4 8.92E+0 C 456-C-4 0.43 - 723 3.0e-02 10 4 1.20E+0 C 456-C-5 0.22 - 470 3.0e-02 10 4 Table 1.2. Exposure values using no normalization and total deposition. Table 1.3 displays the body parts and their corresponding geometric mean exposure values in ug usin

Substitution Method. Head Neck (front) Neck (back) Upper Arms Chest Back Forearm s Thighs Low

Le

209 11.8 13.0 508 278 420 277 210 97

Table 1.3. Body parts and corresponding geometric mean exposure values (ug) 

Page 162 of 194 
AEATF II Vol. 5 Governing Document for a Multi-Year Antimicrobial Chemical Exposure Monitoring Program 

Figure 1.1 shows a log-log graph of whole body exposures vs. total lbs AI handled. This graph is a resbody part method, no normalization and total deposition. No data exists for the traditional body part mFigure 1.1 

Whole Body Exposure vs. Total Lbs AI Handled (not normalized, total deposition) 

Page 163 of 194 
AEATF II Vol. 5 Governing Document for a Multi-Year Antimicrobial Chemical Exposure Monitoring Program 

1b. Normalized by Lb AI Handled – Total Deposition. Table 1.4 is a listing of all replicates with a study code of 456. These values are based on normalizatiHandled and total deposition. No replicates had a whole body exposure using the traditional exposure The Substitution Body Method resulted in all 15 replicates having a whole body exposure value. Replicate Sample Time (hrs) Whole Body Exposure (Traditional Method) (ug) Whole Body Exposure (Substitution Body Method) (ug) Inhalation Exposure (ug) Average Dermal Quantification Limit (ug/cm2) Average Hand Quantification Limit (ug/cm2) Airborne Grade 

456-A-3 0.55 - 8.06e+05 7.87E+06 3.0e-02 10 C 456-A-4 0.50 - 4.86e+05 6.34E+06 3.0e-02 10 C 456-A-5 0.42 - 1.88e+05 9.74E+06 3.0e-02 10 C 456-A-1 0.50 - 8.99e+05 5.51E+06 3.0e-02 10 C 456-A-2 0.47 - 4.82e+05 7.64E+06 3.0e-02 10 C 456-B-1 0.38 - 7.54e+05 1.07E+07 3.0e-02 10 C 456-B-2 0.50 - 1.93e+06 7.03E+06 3.0e-02 10 C 456-B-3 0.52 - 3.40e+05 5.66E+06 3.0e-02 10 C 456-B-4 0.47 - 5.97e+05 1.00E+07 3.0e-02 10 C 456-B-5 0.31 - 6.27e+05 7.30E+06 3.0e-02 10 C 

456-C-1 0.25 - 3.65e+04 5.61E+06 3.0e-02 10 C 456-C-2 0.35 - 4.78e+04 5.63E+06 3.0e-02 10 C 456-C-3 0.32 - 1.12e+04 7.08E+06 3.0e-02 10 C 456-C-4 0.43 - 7.69e+04 2.19E+06 3.0e-02 10 C 456-C-5 0.22 - 5.00e+04 5.89E+06 3.0e-02 10 C 

Table 1.4. Exposure values normalized by lbs AI handled and total deposition. 

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AEATF II Vol. 5 Governing Document for a Multi-Year Antimicrobial Chemical Exposure Monitoring Program 

Table 1.5 displays the body parts and their corresponding geometric mean exposure values in ug usinSubstitution Method. Head Neck front Neck back Upper Arms Chest Back Forearm s Thighs Low

Le

5.58E0 3.13E03 3.47E03 1.36E05 7.41E0 1.12E05 7.38E04 5.60E04 2.60

4 4 Table 1.5. Body parts and corresponding geometric mean exposure values (ug)

Page 165 of 194 
AEATF II Vol. 5 Governing Document for a Multi-Year Antimicrobial Chemical Exposure Monitoring Program 

Figure 1.2 shows a log-log graph of whole body exposures vs. total lbs AI handled. This graph is a resbody part method, normalized by lb ai handled and total deposition. No data exists for the traditional bFigure 1.2 Whole Body Exposure vs. Total Lbs AI Handled (normalized, total deposition) 

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AEATF II Vol. 5 Governing Document for a Multi-Year Antimicrobial Chemical Exposure Monitoring Program 

Section 2. Clothing Layer: Long Sleeves, Long Pants, No gloves. Table 2.1 is a listing of Non-Detect Counts with a clothing layer of long sleeves, long pants and no glovcolumn, the number of non-detect values and the number of replicates with a value is listed. Head Neck (front/ back) Upper Arms Right Left Chest Right Left Back Right Left Forearms Right Left Thighs Right Left Lower Legs shin righ

shin left calf righ

calf left ankle rig

ankle lef

14/15 15/15=100 15/15=100 =93% 0/0=NA% 15/15=100 14/15 =93% % 12/15=80

4/15=27% 15/15=100% % 15/15=100 % 15/15=100 15/15=100 0/0=NA% 

15/15=100% 15/15=100 % 0/0 = NA % 0/0=NA% % % 0/0=NA% 0/0=NA% Table 2.1. Non-Detect Counts of body part values (permeable layer clothing – long sleeves, long pant

Percentage Non-Detects: 179/195 = 92% 

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AEATF II Vol. 5 Governing Document for a Multi-Year Antimicrobial Chemical Exposure Monitoring Program 

2a. No Normalization – Permeable Clothing Table 2.2 is a listing of all replicates with a study code of 456. The exposure values are results of no npermeable clothing - long pants, long sleeves and no gloves. No replicates had a whole body exposurtraditional exposure evaluation method. The Substitution Body Method resulted in all 15 replicates havexposure value. Replicate Sample Time (hrs) Whole Body Exposure (Traditional Method) (ug) Whole Body Exposure (Substitution Body Method) (ug) Inhalation Exposure (ug) Average Dermal Quantification Limit (ug/cm2) Average Hand Quantification Limit (ug/cm2) Airborne Grade 

456-A-3 0.55 - 1.51e+03 4.07E+04 3.0e-02 10 C 456-A-4 0.50 - 761 2.98E+04 3.0e-02 10 C 456-A-5 0.42 - 552 3.82E+04 3.0e-02 10 C 456-A-1 0.50 - 1.23e+03 2.59E+04 3.0e-02 10 C 456-A-2 0.47 - 1.20e+03 3.35E+04 3.0e-02 10 C 456-B-1 0.38 - 630 3.85E+04 3.0e-02 10 C 456-B-2 0.50 - 1.55e+03 3.30E+04 3.0e-02 10 C 456-B-3 0.52 - 590 2.75E+04 3.0e-02 10 C 456-B-4 0.47 - 713 4.41E+04 3.0e-02 10 C 456-B-5 0.31 - 913 2.06E+04 3.0e-02 10 C 

456-C-1 0.25 - 297 1.32E+04 3.0e-02 10 C 456-C-2 0.35 - 302 1.85E+04 3.0e-02 10 C 456-C-3 0.32 - 578 2.11E+04 3.0e-02 10 C 456-C-4 0.43 - 298 8.92E+04 3.0e-02 10 C 456-C-5 0.22 - 298 1.20E+04 3.0e-02 10 C 

Table 2.2. Exposure values using no normalization with permeable clothing – long sleeves, long pants

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Table 2.3 displays the body parts and their corresponding geometric mean exposure values in ug usinSubstitution Method. Head Neck front Neck back Upper Arms Chest Back Forearm s Thighs Low

Le

209 11.8 13.0 43.7 53.2 53.2 18.3 57.3 37

Table 2.3. Body parts and corresponding geometric mean exposure values (ug) 

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Figure 2.1 shows a log-log graph of whole body exposures vs. total lbs AI handled. This graph is a resbody part method, no normalization with permeable layer clothing consisting of long sleeves, long pantNo data exists for the traditional body part method. Figure 2.1 Whole Body Exposure vs. Total Lbs AI Handled (not normalized, permeable single layer closleeves, long pants, no gloves) 

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2b. Normalized by Lb AI Handled – Permeable Clothing. Table 2.4 is a listing of all replicates with a study code of 456. These values are based on normalizatiHandled with permeable single layer clothing (long sleeves, long pants, no gloves). No replicates had exposure using the traditional exposure evaluation method. The Substitution Body Method resulted in having a whole body exposure value. Replicate Sample Time (hrs) Whole Body Exposure (Traditional Method) (ug) Whole Body Exposure (Substitution Body Method) (ug) Inhalation Exposure (ug) Average Dermal Quantification Limit (ug/cm2) Average Hand Quantification Limit (ug/cm2) Airborne Grade 

456-A-3 0.55 - 1.61e+05 7.87E+06 3.0e-02 10 C 456-A-4 0.50 - 8.10e+04 6.34E+06 3.0e-02 10 C 456-A-5 0.42 - 5.87e+04 9.74E+06 3.0e-02 10 C 456-A-1 0.50 - 1.31e+04 5.51E+06 3.0e-02 10 C 456-A-2 0.47 - 1.28e+04 7.64E+06 3.0e-02 10 C 456-B-1 0.38 - 6.71e+04 1.07E+07 3.0e-02 10 C 456-B-2 0.50 - 1.65e+05 7.03E+06 3.0e-02 10 C 456-B-3 0.52 - 6.27e+04 5.66E+06 3.0e-02 10 C 456-B-4 0.47 - 7.59e+04 1.00E+07 3.0e-02 10 C 456-B-5 0.31 - 9.72e+04 7.30E+06 3.0e-02 10 C 

456-C-1 0.25 - 3.16e+04 5.61E+06 3.0e-02 10 C 456-C-2 0.35 - 3.21e+04 5.63E+06 3.0e-02 10 C 456-C-3 0.32 - 6.15e+04 7.08E+06 3.0e-02 10 C 456-C-4 0.43 - 3.17e+04 2.19E+06 3.0e-02 10 C 456-C-5 0.22 - 3.17e+04 5.89E+06 3.0e-02 10 C 

Table 2.4. Exposure values normalized by lbs AI handled with permeable clothing – long sleeves, long 

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Table 2.5 displays the body parts and their corresponding geometric mean exposure values in ug usinSubstitution Method. Head Neck front Neck back Upper Arms Chest Back Forearm s Thighs Low

Le

5.58E0 3.13E03 3.47E03 1.16E04 1.42E0 1.42E04 4.88E03 1.53E04 9.86

4 4 Table 2.5. Body parts and corresponding geometric mean exposure values (ug)

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Figure 2.2 shows a log-log graph of whole body exposures vs. total lbs AI handled. This graph is a resbody part method, normalized by lb ai handled with permeable layer clothing – long sleeves, long pantdata exists for the traditional body part method. Figure 2.2 Whole Body Exposure vs. Total Lbs AI Handled (normalized, permeable single layer clothinlong shirt, no gloves) 

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Attachment A: Body Part Substitutions. Table A.1 shows a listing of the 10 body parts inspected and the list of possible body part substitutions was recorded. There are 2 methods described. The traditional body part method is used in the DOS vwhile the body part substitution method is an alternative used for a possible web based version of PHEBody Part Traditional PHED Substitution Body Part SubstitutHead Back, Chest Shoulders Neck (front and bacNeck (front) Chest Shoulders, Upper ANeck (back) Back Shoulders, Upper AUpper Arms None Back, Chest; If no results, ForeaForearms None Chest, Upper Arms

If no results: Should

Chest None Shoulder, Upper ar

If no results: Head Back None Shoulders, Upper AIf no results: Head Thighs None Shin, Calf Lower Legs None Hip, Thigh 

Hands None Forearms 

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Draft – Do not cite or quote Study Code 521: Handheld Aerosols Task: Evaluate handheld aerosols (specifically study code 521) from the applicator file of the Pesticide Database. The following criteria are reviewed to determine if the study code is complete enough for fu1. Are the dermal grades (covered/uncovered) for all study codes listed as “A” or “B”? 2. Are the airborne grades for all study codes listed as “A” or “B”? 3. Are the hand grades for all study codes listed as “A” or “B”? 

Yes 

4. Is the percentage of non-detect values for all body part depositions less than 40% 

when determining whole body exposures? Total Depositi Single Layer Clothing

5. Are the 10 selected body parts (head, neck (front and back), both upper arms, 

both forearms, chest, back, upper legs, lower legs, and hands) available to determine whole body exposures? Total Deposition/No 

Traditional - Substitution method Total Deposition/Norhandled Traditional - 

Yes 

Substitution method Single layer clothing long sleeve/long panprotection/ No normalization _ Traditional - 

Yes 

Substitution method 

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Single layer clothing long sleeve/long panprotection/ Normalized by lb ai h_ Traditional - Yes Substitution method 

Summary: Number of Replicates: 15 Range of lbs AI Handled: 0.01 lbs ai - 0.01 lbs ai Range of inhalation sampling durations: 18 minutes – 36 minutes Range of dermal sampling durations: 0.30 hrs – 0.60 hrs Average Dermal LOQ: 4.06e-02 ug/cm2 Average Inhalation LOQ: 1.00 ug Average Hand LOQ: 100 ug/ cm2 Inhalation Rate/Minute: 16 L/minute Non-Detect Handling: Half LOQ Values Hand Protection: 

No 

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Section 1: Clothing Layer: Total Deposition Table 1.1 is a listing of Non-Detect Counts of total deposition. For each column, the number of non-denumber of replicates with a value is listed. Head Neck (front/ back) Upper Arms Right Left Chest Right Left Back Right Left Forearms Right Left 15/15=100 2/15 Thighs Right Left Lower Legs shin right shin left calf right calf left ankle right ankle left 3/15=20% 4/15=27% 0/0=NA% 4/15 =27% 5/15=33% 0/0=NA% 0/0=NA% 0/0=NA% 

1/15=7% % 0/15=0% 3/15=20% 0/0=NA% =13% 0/0

15/15=100 2/15=13% 0/0=NA% 0/15 = 0% 2/15 

% =13% Table 1.1. Non-Detect Counts of body part values (total deposition) 

Percentage Non-Detects: 56/210 = 27% 

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1a. No Normalization – Total Deposition Table 1.2 is a listing of all replicates with a study code of 521. The exposure values are results of no ntotal deposition. No replicates had a whole body exposure using the traditional exposure evaluation mSubstitution Body Method resulted in all 15 replicates having a whole body exposure value. Replicat e Sampl e Time (hrs) Whole Body Exposure (Tradition al Method) (ug) Whole Body Exposure (Substitutio n Body Method) (ug) Inhalatio n Exposur e (ug) Average Dermal Quantification Limit (ug/cm2) Average Hand Quantificatio n Limit (ug/cm2) Airborne Grade 

U

521-A-1 0.47 2.18E+04 N/A 7.34E+0 3 4.06E-02 100 A 521-A-2 0.55 6.57E+03 N/A 6.78E+0 3 4.06E-02 100 A 521-A-3 0.52 3.65E+03 N/A 7.02E+0 3 4.06E-02 100 A 521-B-1 0.45 4.88E+03 N/A 7.34E+0 3 4.06E-02 100 A 521-B-2 0.47 4.59E+03 N/A 6.78E+0 3 4.06E-02 100 A 521-B-3 0.30 1.99E+03 N/A 7.02E+0 3 4.06E-02 100 A 

521-C-1 0.53 8.15E+03 N/A 7.34E+0 3 4.06E-02 100 A 521-C-2 0.45 1.14E+04 N/A 2.19E+0 4 4.06E-02 100 A 521-C-3 0.53 1.72E+04 N/A 7.02E+0 4.06E-02 100 A 

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3 2.35E+0 521-D-1 0.43 1.02E+04 N/A 4 A 

4.06E-02 100 6.78E+0 521-D-2 0.32 5.21E+03 N/A 3 A 4.06E-02 100 7.02E+0 521-D-3 0.37 3.84E+03 N/A 3 A 4.06E-02 100 2.00E+0 521-E-1 0.52 6.83E+03 N/A 4 A 4.06E-02 100 6.78E+0 521-E-2 0.53 6.17E+03 N/A 3 A 4.06E-02 100 7.02E+0 521-E-3 0.60 4.10E+03 N/A 3 A 4.06E-02 100 Table 1.2. Exposure values using no normalization and total deposition. Table 1.3 displays the body parts and their corresponding geometric mean exposure values in ug. Head Neck front Neck back Upper Arms Chest Back Forearm s Thighs Low

Le

410 25.1 20.0 999 595 644 488 300 24

Table 1.3. Body parts and corresponding geometric mean exposure values (ug) 

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Figure 1.1 shows a log-log graph of whole body exposures vs. total lbs AI handled. This graph is a resnormalization and total deposition. Figure 1.1 

Whole Body Exposure vs. Total Lbs AI Handled (not normalized, total deposition) 

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1b. Normalized by Lb AI Handled – Total Deposition. Table 1.4 is a listing of all replicates with a study code of 521. These values are based on normalizatiHandled and total deposition. Replicate Sample Time (hrs) Whole Body Exposure (Traditional Method) (ug) Whole Body Exposure (Substitution Body Method) (ug) Inhalation Exposure (ug) Average Dermal Quantification Limit (ug/cm2) Average Hand Quantification Limit (ug/cm2) Airborne Grade 

521-A-1 0.47 2.18E+06 N/A 7.34E+05 4.06E-02 100 A 521-A-2 0.55 6.57E+05 N/A 6.78E+05 4.06E-02 100 A 521-A-3 0.52 3.65E+05 N/A 7.02E+05 4.06E-02 100 A 521-B-1 0.45 4.88E+05 N/A 7.34E+05 4.06E-02 100 A 521-B-2 0.47 4.59E+05 N/A 6.78E+05 4.06E-02 100 A 521-B-3 0.30 1.99E+05 N/A 7.02E+05 4.06E-02 100 A 

521-C-1 0.53 8.15E+05 N/A 7.34E+05 4.06E-02 100 A 521-C-2 0.45 1.14E+06 N/A 2.19E+06 4.06E-02 100 A 521-C-3 0.53 1.72E+06 N/A 7.02E+05 4.06E-02 100 A 521-D-1 0.43 1.02E+06 N/A 2.35E+06 4.06E-02 100 A 521-D-2 0.32 5.21E+05 N/A 6.78E+05 4.06E-02 100 A 521-D-3 0.37 3.84E+05 N/A 7.02E+05 4.06E-02 100 A 

521-E-1 0.52 6.83E+05 N/A 2.00E+06 4.06E-02 100 A 521-E-2 0.53 6.17E+05 N/A 6.78E+05 4.06E-02 100 A 521-E-3 0.60 4.10E+05 N/A 7.02E+05 4.06E-02 100 A 

Table 1.4. Exposure values normalized by lbs AI handled and total deposition. 

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Table 1.5 displays the body parts and their corresponding geometric mean exposure values in ug. Head Neck front Neck back Upper Arms Chest Back Forearm s Thighs Low

Le

3.00E+0 4.10E+0 5.95E+0 4.88E+0 2.51E+03 2.00E+03 9.99E+04 6.44E+04 2.48E

4 4 4 4 Table 1.5. Body parts and corresponding geometric mean exposure values (ug)

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Figure 1.2 shows a log-log graph of whole body exposures vs. total lbs AI handled. Figure 1.2 Whole Body Exposure vs. Total Lbs AI Handled (normalized, total deposition) 

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Section 2. Clothing Layer: Long Sleeves, Long Pants, No gloves. Table 2.1 is a listing of Non-Detect Counts with a clothing layer of long sleeves, long pants and no glovcolumn, the number of non-detect values and the number of replicates with a value is listed. Head Neck (front/ back) Upper Arms Right Left Chest Right Left Back Right Left Forearms Right Left Thighs Right Left Lower Le

shin rig

shin lef

calf righ

calf lef

ankle rig

ankle le

15/15 =100% 15/15=100 15/15=100 15/15=1

15/15=100% % 0/0=NA% 15/15=100 15/15 % % 

1/15=7% 15/15=100% 15/15=100 15/15=100 % =100% 15/15=100 0/0=NA

% % 0/0 = NA 14/15=93% % 0/0=NA

0/0=NA0/0=NA

Table 2.1. Non-Detect Counts of body part values (permeable layer clothing – long sleeves, long pantPercentage Non-Detects: 180/210 = 86% 

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2a. No Normalization – Permeable Clothing Table 2.2 is a listing of all replicates with a study code of 521. The exposure values are results of no npermeable clothing - long pants, long sleeves and no gloves. Replicate Sample Time (hrs) Whole Body Exposure (Traditional Method) (ug) Whole Body Exposure (Substitution Body Method) (ug) Inhalation Exposure (ug) Average Dermal Quantification Limit (ug/cm2) Average Hand Quantification Limit (ug/cm2) Airborne Grade 

521-A-1 0.47 2.13E+03 N/A 7.34E+03 4.06E-02 100 A 521-A-2 0.55 2.26E+03 N/A 6.78E+03 4.06E-02 100 A 521-A-3 0.52 1.61E+03 N/A 7.02E+03 4.06E-02 100 A 521-B-1 0.45 1.98E+03 N/A 7.34E+03 4.06E-02 100 A 521-B-2 0.47 4.24E+03 N/A 6.78E+03 4.06E-02 100 A 521-B-3 0.30 1.35E+03 N/A 7.02E+03 4.06E-02 100 A 

521-C-1 0.53 2.79E+03 N/A 7.34E+03 4.06E-02 100 A 521-C-2 0.45 1.91E+03 N/A 2.19E+04 4.06E-02 100 A 521-C-3 0.53 4.95E+03 N/A 7.02E+03 4.06E-02 100 A 521-D-1 0.43 2.78E+03 N/A 2.35E+04 4.06E-02 100 A 521-D-2 0.32 1.59E+03 N/A 6.78E+03 4.06E-02 100 A 521-D-3 0.37 2.07E+03 N/A 7.02E+03 4.06E-02 100 A 

521-E-1 0.52 2.40E+03 N/A 2.00E+04 4.06E-02 100 A 521-E-2 0.53 1.54E+03 N/A 6.78E+03 4.06E-02 100 A 521-E-3 0.60 1.84E+03 N/A 7.02E+03 4.06E-02 100 A 

Table 2.2. Exposure values using no normalization with permeable clothing – long sleeves, long pants

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Table 2.3 displays the body parts and their corresponding geometric mean exposure values in ug. Head Neck front Neck back Upper Arms Chest Back Forearm s Thighs Low

Le

410 25.1 20.0 59.1 72.1 72.1 26.3 77.5 48

Table 2.3. Body parts and corresponding geometric mean exposure values (ug) 

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Figure 2.1 shows a log-log graph of whole body exposures vs. total lbs AI handled. This graph is a resbody part method, no normalization with permeable layer clothing consisting of long sleeves, long pantFigure 2.1 Whole Body Exposure vs. Total Lbs AI Handled (not normalized, permeable single layer closleeves, long pants, no gloves) 

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2b. Normalized by Lb AI Handled – Permeable Clothing. Table 2.4 is a listing of all replicates with a study code of 521. These values are based on normalizatiHandled with permeable single layer clothing (long sleeves, long pants, no gloves). Replicate Sample Time (hrs) Whole Body Exposure (Traditional Method) (ug) Whole Body Exposure (Substitution Body Method) (ug) Inhalation Exposure (ug) Average Dermal Quantification Limit (ug/cm2) Average Hand Quantification Limit (ug/cm2) Airborne Grade 

521-A-1 0.47 2.13E+05 N/A 7.34E+05 4.06E-02 100 A 521-A-2 0.55 2.26E+05 N/A 6.78E+05 4.06E-02 100 A 521-A-3 0.52 1.61E+05 N/A 7.02E+05 4.06E-02 100 A 521-B-1 0.45 1.98E+05 N/A 7.34E+05 4.06E-02 100 A 521-B-2 0.47 4.24E+05 N/A 6.78E+05 4.06E-02 100 A 521-B-3 0.30 1.35E+05 N/A 7.02E+05 4.06E-02 100 A 

521-C-1 0.53 2.79E+05 N/A 7.34E+05 4.06E-02 100 A 521-C-2 0.45 1.91E+05 N/A 2.19E+06 4.06E-02 100 A 521-C-3 0.53 4.95E+05 N/A 7.02E+05 4.06E-02 100 A 521-D-1 0.43 2.78E+05 N/A 2.35E+06 4.06E-02 100 A 521-D-2 0.32 1.59E+05 N/A 6.78E+05 4.06E-02 100 A 521-D-3 0.37 2.07E+05 N/A 7.02E+05 4.06E-02 100 A 

521-E-1 0.52 2.40E+05 N/A 2.00E+05 4.06E-02 100 A 521-E-2 0.53 1.54E+05 N/A 6.78E+05 4.06E-02 100 A 521-E-3 0.60 1.84E+05 N/A 7.02E+05 4.06E-02 100 A 

Table 2.4. Exposure values normalized by lbs AI handled with permeable clothing – long sleeves, long 

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Table 2.5 displays the body parts and their corresponding geometric mean exposure values in ug. Head Neck front Neck back Upper Arms Chest Back Forearm s Thighs Low

Le

7.75E+0 7.21E+0 2.63E+0 4.10E+04 2.51E+03 2.00E+03 5.91E+03 7.21E+03 4.83E

3 3 3 Table 2.5. Body parts and corresponding geometric mean exposure values (ug)

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Figure 2.2 shows a log-log graph of whole body exposures vs. total lbs AI handled. This graph is a resmethod, normalized by lb ai handled with permeable layer clothing – long sleeves, long pants, no gloveFigure 2.2 Whole Body Exposure vs. Total Lbs AI Handled (normalized, permeable single layer clothinlong shirt, no gloves) 

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Attachment A: Body Part Substitutions. Table A.1 shows a listing of the 10 body parts inspected and the list of possible body part substitutions was recorded. There are 2 methods described. The traditional body part method is used in the DOS vwhile the body part substitution method is an alternative used for a possible web based version of PHEBody Part Traditional PHED Substitution Body Part SubstitutHead Back, Chest Shoulders Neck (front and bacNeck (front) Chest Shoulders, Upper ANeck (back) Back Shoulders, Upper AUpper Arms None Back, Chest; If no results, ForeaForearms None Chest, Upper Arms

If no results: Should

Chest None Shoulder, Upper ar

If no results: Head Back None Shoulders, Upper AIf no results: Head Thighs None Shin, Calf Lower Legs None Hip, Thigh Hands None Forearms 

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Appendix G: List of Current AEATF II Standard 

Operating Procedures (SOPs) 

Chapter 1 – Administration 

AEATF II-1A.0 Organizational Structure 

AEATF II-1B.0 Personnel Responsibilities 

AEATF II-1C.0 Study Director Selection 

AEATF II-1D.0 Inspection of AEATF II Facilities/Data 

Chapter 2 – Protocols 

AEATF II-2A.0 Study Authorization and Approval 

AEATF II-2B.0 Study Number Assignment 

AEATF II-2C.0 Protocols 

Chapter 3 – Standard Operating Procedures 

AEATF II-3A.0 SOP Preparation, Approval, Maintenance, and Distribution 

AEATF II-3B.0 Use of AEATF II and Contractor SOPs 

Chapter 4 – Study Reports 

AEATF II-4A.0 Study Report Preparation 

AEATF II-4B.0 Final Report Issue 

Chapter 5 – Quality Assurance Unit 

AEATF II-5A.0 QA Personnel Administration 

AEATF II-5B.0 AEATF II QAU Responsibilities 

AEATF II-5C.0 QAU Records 

AEATF II-5D.0 QA Master Schedule 

AEATF II-5E.0 Protocol and Amendment Review 

AEATF II-5F.0 Inspection/Audit Types and Frequency 

AEATF II-5G.0 Study Inspections 

AEATF II-5H.0 Data Audits 

AEATF II-5I.0 Facility Inspections 

AEATF II-5J.0 Report Audits 

AEATF II-5K.0 Inspection Report Distribution 

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Chapter 6 – Archives 

AEATF II-6A.0 Storage of Raw Data 

AEATF II-6B.0 Access to Archived Data 

AEATF II-6C.0 Specimen and Wet Sample Storage 

Chapter 7 – Test, Control and Reference Substance 

AEATF II-7A.0 Test, Reference, and Control Substance Receipt and 

Shipment 

AEATF II-7B.0 Test, Control and Reference Substance Labeling 

AEATF II-7C.0 Disposal of Test, Control, and Reference Substances 

AEATF II-7D.0 Test, Control, and Reference Substance Chain of Custody 

AEATF II-7E.0 Test and Reference Substance Analyses 

Chapter 8 – Matrix Samples 

AEATF II-8A.1 Whole Body Sampling – Inner Dosimeters 

AEATF II-8B.1 Hand Wash Samples 

AEATF II-8C.1 Dermal Face/Neck Wipe Samples 

AEATF II-8D.0 Collection of Air Samples Using OVS Tubes 

AEATF II-8E.0 Fortification of Matrix Samples 

AEATF II-8F.0 Sample Identification 

AEATF II-8G.1 Whole Body Sampling – Outer Dosimeters 

Chapter 9 – Documentation 

AEATF II-9A.0 Body Surface Areas 

AEATF II-9B.1 Field Fortification Adjustment Factors 

AEATF II-9C.0 Numerical Formatting and Handling 

AEATF II-9D.0 Analytical Method Number Assignment 

AEATF II-9E.0 Raw Data Collection 

AEATF II-9F.0 Data Corrections 

AEATF II-9G.0 Raw Data Handling 

AEATF II-9H.0 Preparation of True Copies 

Chapter 10 – Field Study Procedures 

AEATF II-10A.0 Rotameter Calibration 

AEATF II-10B.0 Packing, Handling, and Shipping of Samples 

AEATF II-10C.0 Worker and Study Observations 

AEATF II-10D.0 Application Equipment Operation Verification 

AEATF II-10E.0 Worker Sample Collection Sequence 

AEATF II-10F.0 GPI Electronic Digital Flow Meter 

AEATF II-10G.0 Personal Air Sampling Pump Calibration 

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Chapter 11 - Human Subject Management 

AEATF II-11A.0 - Pregnancy Testing 

AEATF II-11B.0 - Heat Stress 

AEATF II-11C.0 - Emergency Procedures 

AEATF II-11D.0 - Reportable Findings 

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