# Antimicrobial Exposure Assessment Task Force II (AEATF II)

VOLUME 5

Governing Document for a Multi-Year Antimicrobi al Chemical Exposure Monitor i ng Program

Interim Draft Document

February 13, 2008

Page 1 of 194

### TABLE OF CONTENTS
1 Introduction

................................................................................................................. 4 1.1 Overview of Antim icrobial Pe sticides and Their Benefits

................................. 4 1.2 Regulatory Need for Est i mation of Exposures to An tim icrobi al Pesticides

....... 8 1.3 Purpose of t he Governing Document

................................................................ 10 2 Specific Objectives of the AEATF II Monitoring Program

...................................... 12 3 AEATF II and BHEDTM

........................................................................................... 14 4 Regulatory Need for Ge neri c Expos ure Data

........................................................... 15 5 Des c ription of AEATF II Mon itoring Program and Scenarios

................................. 18 6 Lim itations of Existing Dat a and Jus tif ication for Supplemental or Confirmatory Dat a

.......................................................................................................................... 2 0 7 Alternatives to Addition a l Human Monitoring

......................................................... 26 8 Ethical Considerations

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

............................................................................. 27 8.1.1 Identification and Recrui tmen t of Potential Subjects

............................... 28 8.1.2 Exclusion of Vulnerable Group(s)

............................................................ 32 8.2 Informed C onsent Process

................................................................................ 33 8.3 Subject Remuneration

....................................................................................... 36 8.4 IRB Review Process

......................................................................................... 38 8.5 Additional Efforts to Pr otect Human Subjects

.................................................. 40 9 Study Benefits

........................................................................................................... 41 9.1 Benefits to Subjects

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

............................................................................................ 42 9.3 Likelihood of Realiza tion of Benefi ts

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

............................................................................................................... 43 10.1 Des c ription of Potentia l Risks to Subjects

........................................................ 43 10.2 Risks of Heat-Relat ed Il lness

............................................................................ 44 10.3 Risks of Exposure to Surrogate Antim i c robi al Pesticides

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

.............................. 45 10.5 Psy c hological 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 Prevent i ng Risk to Subjects

............ 49 12 Risk Assessment for Antic ipated Exposures to Proposed Surrogat e Chem i c als

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

.............................................................................. 50 14 Charact eri z ation of Poten tial Study Par ticipants, Exposure Monitoring Methods and Ancillary Information

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

...................................................................................... 51 14.2 Exposure M onitoring Techniques

..................................................................... 52 14.3 Role of Ancillar y Stud y Information

................................................................ 56 15 Incorporation of Exis ting Dat a into BHEDTM

........................................................... 57 16 Monitoring Event Selection

...................................................................................... 58 16.1 Predicting Future Generi c Expos ures with Monitoring Events

........................ 58 16.2 Det e rm ining the Num b er of Monitoring Events

............................................... 60 16.2.1 The Two-Stage ME Selection Proces s

...................................................... 60 16.2.2 The Two-Stage Random Sam p ling Reference Model

.............................. 61

216.2.3 Benchmark Objective

................................................................................ 62 16.2.4 Sam ple Size

............................................................................................... 63 16.3 General Diversity Se lection Guidelines

............................................................ 63 16.3.1 Stratified Divers ity Selection

.................................................................... 64 16.3.2 Diversity Se lection of First-Stage Units

................................................... 65 16.3.3 Diversity Se lection of Second-Stage Units

............................................... 65 17 Des c ription 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 M onographs

............................................................................................... 71 23 Evaluation of Data Adequacy fo r Completed BHEDTM Scenarios

........................... 72 23.1 Benchmark Adequacy of the Com pleted Scenari o

........................................... 72 23.2 The Im pact of Ignoring Clusters

....................................................................... 73 24 References

................................................................................................................. 7 5 Appendix A: AEATF II Scoping Document:

................................................................... 79 Appendix B. Antim icrobial Produc t Use Sites and Categories

........................................ 92 Appendix C. Glossary of Term s

..................................................................................... 110 Appendix D. AEATF II Acceptance Criteria fo r Existing Studies

................................. 119 Appendix E. Designing Monitoring Events to Predict Futu re Exposure under Antim icrobi al Handling Scenarios

......................................................................... 124 Appendix F: Evaluation of Ex isting PHED Applicator E xpos ure Data fo r Hand-Held Aerosol Spray

......................................................................................................... 141 Appendix G: List of Cur r ent AEATF II St andard Operating Procedures (SOPs)

.......... 192

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## 1 Introduction

Microorganisms grow any where moisture and nutrients are av ailable

. Antimicrobial pesticides are essential to c ontrol microorganism s that otherwise would result in economic losses, wa sted resources, and human and anima l illness. Generally, U.S. Environmental Protection Ag ency (EPA) regulates as pesticides those antimicrobials that ta rget microorganism gr owth on inanimate objects. P e sticides a nd pesticid e pr oducts are defined by EPA in the Fed e ral Insecticide, Fungicide and Rodenticide Act (FIFRA; EPA 1989), 40 CF R (Code of Federal Regulations), Sect ion 2(u), Parts 152.3 and 152. 15. Other categories of chemicals that control mi croorganisms used as drugs and in human and animal food and human personal c a re and cosmetic products are regulat ed by the U.S. Food and Drug Administration ( F DA). Only those cat egories regulated by EPA are discussed here.



### 1.1 Overview of Antimi crobi al Pestici des and Their Benefits

Antimicrobials are br oadly grouped as “p ublic healt h ” or “nonpublic health” depending on whether or not claims ar e made to control microorganisms pathogenic to man and which oc cur on inanimate obje cts. However, that simple grouping does not adequately identify the uses or benef its of antimicrobia l pesticides.

A. Public health antim icr obial pestic i des are those that carry claims to control on environmental surfaces microorganism s that are pathogenic to man. Claims to sanitize , disinfect, or steriliz e are con sidered de facto claims to control microorganisms pathogenic to man (includi ng bacteria, fungi, and viruses). Howev e r, 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 proce ssing and handling areas t o the home kitchen, f r om municipal drinking water systems and munic i pal s wimming pools t o the back yard swimming pool, and f r om comm ercial or hospital laun dries to everyday home laundry us e. These products are regular ly used in homes, offices, schools, hospitals, restaurants, food processing facili ties, and a large variety of industrial facilities as well as farm and animal premis es. These products also are use d to help ensure that the water we drin k is not contami nated by pathogenic microorganisms.

In order to obtain an EPA reg i stration, a ll antimicrobial prod ucts that make a public health claim must submit effica cy data based on specific application instructions and conducted according to strict protocols and must document a very high level of performance under strin gent conditions. Effic a cy testing is a requirement and is defined under FIFRA in 40 CF R Part 158.640. T hese

4products help to provide protection agai ns t food-borne diseases produced by Salmone lla o r E

. c o l i , as well as against pathogens such as Staphyloc o ccus , Norovirus , SARS, and HIV/AIDS virus.

These products include common and well-k nown 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, m edical and dental offices, sick rooms, and hospices

• Homeless and emergency shelters, locker rooms, and communal living quarters

• Meat and poultry, seafood, proc essed food, bever age and dairy, and other food storage an d pr ocessing facilities, agricultural premises, animal premises and farms

• Restaurants, cafeterias, and institutional food services industry

• Residences, schools, public fac i lities, senior and child day care facilities

• Public wat e r treatment facilit ies, personal a nd emergency water treatments

• Swimming pools, hot tubs, whir lpools, and related facilities

B. Nonpublic health an timicrobial pesticides are all antimicrobial pesticides other than those that clai m to control microorganisms pathogenic to man. These include antimicrobial pesticides to contro l, on environmental surfaces, diseases pathogenic to animals but not to man (such as hoof and mouth disease, bird flu in poultry houses, various dis eas es in k ennels or veter i nary facilities). However, this grouping also inc l udes a div e rse range of product s that prov ide protection against microbial degradation, contamination or fouli ng to inanimate articles, substances , systems or processes. Es sentially any organic system in the presence of moisture is subject to atta ck by microorganisms, and prevention of such attack helps preserve critical resource s, extend t he useful life of the items, minimize d i sposa l , an d improve the overal l utility of those articles, substances, systems, a nd processes. In ma ny 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 gr eater 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 mi crobial growth is unc ontrolled, the in-service or shelf life of manufactured goods is significantly reduced, resulting in economic losses and wasted resources. Following is a partial lis t of products that must be preserved to prevent premature deterioration and de c a y. The need to dis p ose of spoiled

5products wastes resources and could increa se substantially the burden on the environment.

Latex emulsions Paints and coatings Pigment dispersions Slurries Adhesiv es, caulks and joint compounds Printing ink Non-clothing textiles (e.g., fire hos es, tarpaulins, cordage, and canvas) Leather and suede Cotton and wool fabrics Paper and package coatings and additives Lumber, wood, ply w ood, particleboard, and other cellulos e -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 produc ts would be impracti cal without antimicrobi als to preserve them. As an example, la tex paints are easier to clean up, have lower odor and lower levels of volatile organic c a rbons than oil and s o lvent-bas ed formulations. Howev e r, bacteria can proliferate in t he water-based latex medium. Bacterial action produces enz ymes whic h can destroy the thi ckeners in paint overnight. Gases resulting from bacterial metabolism not only result in foul odors, but also bursting cans, an obvious safety hazard. Incorporating an antimic robial prev ents bacterial gr owth in cans containing latex paint. In fact, antimicrobials made this product possible.

Similar situations exist for a wi de va riety of products, including water-based adhesiv es, latex emulsions, pigment dis persion, c aulking compounds, and others. Spoilage of any of these products can result in gas formation, offensiv e odors, color changes, viscosity loss, and pH drift, any one of which may mean loss of func tionality. These products, which along with paint represent nearly $20 billion to the US economy, require the use of antimicrobials.

2. Poultr y houses, Egg Producing Fac ilitie s, Milk ing Houses and Other Agricultural Premises Disinfectants, virucides, fungicides, and sani tizers control or eliminate animal pathogens. This has become increasingly im portant as increas ed international movement has led to the spread of devasta ting animal diseases s u ch as foot and mouth disease, Newc astle disease, and avian flu.

63. Water Tre a tment

a. Comfort Cooling, HVAC, etc . – Water must be treated to control the growth of microorganisms that, left unc ontrolled, cou l d reduce efficiency and increase energy usage, pi t and c o rrode equipment as well as causing fouling and malodors.

b. Cooling Water Systems -- Water is use d f o r cooling industrial processes and as a means of heat exchange. Any i ndustrial processor that produces heat, either incidentally, a s in, nucle ar power plants, other utilities, or those sites running heavy equipment, mu st use coolants to maintain des irable temper ature ranges and prevent ov erheat ing. Otherwise, systems become fouled, equipment is damaged, energy consumption increases , and processes fail. For example, electrical utilities an d many manufacturing facilities us e water for process co olin g. To minimiz e consumption, the water is cooled and re-circulat ed. If untreated, microbial deposits will form in the system resulting in reduced eff i ciency yielding increased production costs, increased energy consumpti on, and increased water requirements. In extreme cases, biologic a l fouling can compromise the integrity of the industrial equipment due to the corro sivity of bacterial waste products.

c. Industrial Process Waters – Many proc esses de p end upon water as a key component of processing (e.g., pul p and p a per mills). Treatment is necessary to prevent odor, clogging and fouling of systems, protect equipm ent from corrosion, and reduc e energy needs and treatment of water prior to discharge. Unrestrained microorganism gr o w th in pulp and paper mills interferes with paper quality by degrading and staining pulps, causing transparent slime spots, decreasing dur ability, and chemically degrading fibers.

4. Marine Antifoulants Any submerged surface is rapidly fouled with micro- and macro-organisms. O n ships and boats, fouled bo ttoms decrease maneuverability and s a fety, increase energy consumption, while reducing s p eed and performance. Antifoulants dramatically decrease the growth of fouli ng organisms for up to five years. They also reduc e the spread of invasive alien species, which c a n have highly detrimental economic and env ironmental im pacts and also can pose threats to human, animal and plant life. When incorporated into paint on the hull of ships, antimicrobials prevent biolog ical deposits. A sl ime lay e r only one millimeter thick on the hull can reduc e speed by 15% and increase fuel costs by more than $1 million in a single year

. Heavier deposits n o t only cau se further speed reduc tions and loss of maneuverability, they can result in corrosion and limit the life of the coating, requiring prematur e dry-docking. With every six-month extens ion of the time between dry-docking for the world’s flee t, the use of antimic robials results in estimated annua l cost savings of over $800 million.

5. Metalw ork i ng Fluids Metalworking fluids ar e used at thousands of manufacturing facilities to cool and lubricate metal parts being drilled, mi lled, ground, or ot herwise worked.

7Functioning also to pr event corrosion and flush metal chips from the worksite, the fluids are particularly prone to microbial c o ntamination. Microbial growth causes offensive odors and plugs equ ipment lines leading to c o rrosion and blemis hes on the finished surfaces, loss of pr oductivi ty from slowed equ ipment speeds and necessitating more frequent changes of fl uid resulting in unnecessary fluid replacement costs as well as increased fluid disposal. Antimicrobials are essential for optimizing fluid lif e, flui d functionality, and wo rker comfort and productivity. While this is effectively a mate rial preserv a tive use, it is considered separately from the US EPA reg u latory perspective.

6. Wood Treatment Wood rots readily and its organic comp onents provide all t he nutrients that bacteria and fungi require for growth. Failure to control decay organisms will result in structural failure, reduc ed life cycle, and inc r eased disposal requirements. The use of wood preserva tive chemicals protec ts a significant resource (wood) and can extend the usef ul life of wood produ cts from just 2-5 years to more than 20 years, resulting in a significant protection of existing forests. Treatment with antimicrobials is vital fo r wood us ed structurally in buildings. Wood treatment chemicals also can be used to prevent growth of algae and molds on the su rfaces of wood and to prev ent the per manent stainin g of cut lumber by microorganism s, i.e., sapstain. Wood treatment antimicrobia l s are used o n newly cu t wood sur f aces, k iln dried wo od , milled wo od and oth er building materials. A wide variet y of seasoned/unsea soned, indoor/outdoor, and terrestrial/marine/aquatic wood it ems and surfaces are treated with wood preservatives. The types of wood produc ts include f r esh-cut logs or lumber, seasoned building m a terials, utility poles a nd fence p o sts and rails (prior to or after being placed in service) , structur al members, structures, dwellings, transportati on vehicles, crop growing/har vesting/shipping/storage containers, lawn furniture, playground equipment, garden/ landscape timbers, and log homes.



### 1.2 Regulatory Need for Estimati on of Exposures to Antimicrobial Pesticides EPA reg u la tes pesticid e s under th e statutory authority of the Feder al Insecticide, Fungic i de, and Rodenticide Act (FIFRA). Currentl y, antimicrobials registered prior to November 1, 1984, are going through a re-reg i st ration process. Starting in 2007, all antimicrobials also will be revi ewed on a 15-year c ycle as part of EPA’s “registration review” program. In both programs, EPA must determine whether, when us ed according to the labe led directions, there is a reasonable certainty of no harm to humans and no unr easonable risks to the environment. In addition, new products, new uses and major amendments to exis ting antimicrobial product s mu st undergo similar review and determination. Antimicrobial pesticides are m anaged by the U.S. EPA’s Office of Pesticide Programs (OPP), Antimicr obials Divis ion.

8EPA has ty pically bas ed its exposure asse s sments for antimicrobials on rule s of thumb, some based on regist rant reports of industr ial hygiene or other nonpesticide guide lin e exp osure information and some based on EPA-d erived information. Following pa ssage of the Food Quality Protection Act of 1996, EPA produced a number of screeni ng level occupatio nal and r e sidential risk assessment guidelines in the f o rm of draft standard operating procedu r es. Although data exist, albeit wit h limitations , to estimate potential exposures to agricultural handlers, i.e., the Pestici de Handlers Exposure Database (PHED; EPA 1988), no suc h databas e exists f o r occupational s ubjects handling antimicrobials, outside of limited data (Popendorf et al., 1992) generated by the Chemical Manufacturers Assoc i atio n (CMA, now the Americ an Chemistry Counc il or ACC). Further, only limited antimicrobial expos ure monitoring data exist to support quantitative exposure analy ses to this class of pesticides. In the past ten years, a few occupational exposure monitoring s t udies inv o lving antimicrobials hav e been subm itted to EPA; however, they have limited utility as they cover only certain use patterns. Some of the data gener ated from these studies have a high degree of uncertainty due to low numbers of samples for a particular use pattern or insufficient sens itivity in analytical detection. Because most antimicrobial pestici des are used in indoor e n vironments, the potential exposure from the use of these products could be s ubstantially different from handling agricultural pesticides outdoors. Thus, EPA must either to try to use agricultural expos ure data for industria l and consumer antimicrobial-related assessments or use assumptions and pr edictive models that have not been validated to support their expos u re assessments and related decision making processes.

In 2001 (letter from Margaret Stasikowsk i, Director, Health Effects Div ision to Daniel Fa y, Valent USA Co rporat ion, 16 March 2001), EPA outline d its prospective plans regarding the existing agricultural expos ure data contained in the Pesticide Handlers Expos ure Dat abas e (PHED). The letter stated E PA’s intention to drastically ov erhaul PHED vers ion 1.1 bec ause many of the exis ting exposure s t udies in the database were outdated or scientific ally 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. T h is is particularly apparent with anti microbial pes ticides, where potential exposur es are very different from those associat ed with agr icultural pesticides. Thus, there is a clear need to generate better expos ure data to improve the quality of human health risks assessments for a n timicrobial products. That ne ed has been repeatedly identifie d 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 algor ithm to calculate the aver age daily dose to a worker is relatively simp le. The inputs require an estimate of how much chemical is going to be handled dur ing a work shift, a body weight, and a measure of exposure potentia l based upon the activity conducted by the worker

9(by exposure route). Al though there are ranges and uncertainties associated with each of these inputs, t he measure of exposure for a particular job function or activity is where the greatest uncertainty lies.

The Agency has acknowl edged that while the u se of existing data and assumptions in its human health risk as sessment process for antimicrobial products is necessary, it would also le ad 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 cons ortium to develop gen eric expos ure data on occupational activ i ties would be a c o st -effective means of generating a large amount of high quality credible data (Stasi kowski 2001).

On November 1, 2004, t he American Chemistry Counc il (ACC) Biocides Panel established the Antimicrobial Exposure Assessment Task Force II (AEATF II) to measure expos ure of subjects i n mixi ng and loading operations in indust r ial settings and professionals involv ed in appli cation of products containing bioc ides in industrial, instituti onal 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 br oad range of use pattern/applic ation method combinations as well as specif ic post-application exp o sures (e.g., measurements of residue depos ition on tr eated surfaces and the post-applicat ion transferability of the se residu es using EPA-recommended environm ental sampling methods). Each specific set of related tasks, antimicrobial formulat i ons, equipment, engineer ing controls, and work er and/or consumer practi ces considered by AEAT F II is termed a scenario

. These data obtained for each antimicrobial-hand ling sc enario addressed by AEATF II will be u sed to sup p ort EPA registration and re-registration of most ant imicrobial active in gredients in the future. The concept behind a group of companies working together to generate jointly-owned data is to reduce individual c o mpany’s costs, generate more data than would be poss i ble by a s i ngle com pany or even small gr oup of companies alone, while providing consistency in design and exec ution, and obtainin g coordinate d scientific input from appr opriate regulatory agencies (U.S. EPA, California Department of Pesticide Regu lation or CDPR, Health Canada’s Pest Management Regulatory Agency or PMRA, and European regulat o ry authorities).



### 1.3 Purpose of the Governi ng Document The purpos e of this document is t o pr ovide t he U.S. EPA, CDPR, PMRA a n d the Human Studies Review Board ( H SRB) wit h a descrip tion of the overall scope of the AEATF II program, demonstrate the need for addition al h u man exp o sure monitoring data, and explain the pr oposed methodology for the exposure monitoring studies proposed for conduct by the AEATF II. Th is document also describes the plans of the AEATF II to develop a generic dat abase, i.e., the Biocide Handlers Exposure Database (BHEDTM). By pr oviding t h is background

10information, the AEATF II intends to pres ent a scientifically valid bas i s for conducting the proposed human exposure monitoring studies.

This draft version of the “Governing Doc u ment” 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 HS RB, in conjunc t ion with each specific st udy protoc ol fo r proposed AEATF II exposure monit o ring studies. It is anticipated that future vers ions of this document will be issued to incorporate comments and guidance provid ed by the EPA’s Office of Pesticide Programs (OPP) and the HSRB.

It is important to note that the scientific and ethica l aspects of the AEAT F II program are addressed, more specifica lly, as part of each proposed protocol being submitted to the EPA and HSRB

. Thus, this doc ument will be supplemented by im portant study protocol-specific scientific (e.g., study or scenario-s pecific study design and sample size determination) and ethical (e.g., how a par ticular AEATF II study will ad dress recruitment, informing, seeking consent, and minimiz i ng risks to study parti cipants) considerations. The ethical component s of the AEATF II program are based, in part, on recommendations made by the National Academy of Sciences Committee on the Us e of Third Party Toxicity Research wit h Human Research Participant s (e.g., demonstrated need for the knowledge t o be obtained from intentional human dosing studies, justification and documentation of a resear ch design and statistical analys is that are adequate to address an im portant scientific or po lic y question, an acceptable balanc e of risks and benefits and minimization of risks to participants, equitable selection of participants, free and informed c onsent of participants, review by an appropriate ly constituted IRB or its fore ign equiva lent) to ensure that AEATF II exposure studies, which are to be conduc ted under EPA Guidel ine 875 Series A, will meet the highest scientific and ethical standards.

The AEAT F II program also addresses rele vant feedback provided in the r e port of the HSRB meeting of June 27-30, 2006 (Fisher 2006) whic h discusses an initia l revie w of five st udy protocols submitted by the Agricultural Handler s Exposure Task Force (AHETF) , another multi- year exposure monitoring effort. The AEAT F II program has also been inform ed by the recent April 18 – 20, 2007 HSRB meeting ( http://www.epa.gov/osa/hsr b/apr-18-20-2007 -publicmeeting.htm ) and EPA’s “Draft Framework for Developing Bes t Practices for Recruiting, Screening, and Informing Hum an Subjects, and Obtaining Consent for Occupational Exposure Studies with Pe sticides” presented at this meeting. Furthermore, the AEATF II program inco rporates recent recommendations provided b y the EPA’s Science Advisory Panel (EPA 2007).

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## 2 S pecific Objectives of t he AEA TF II Monitoring Progra m

The primary objective of t he AEATF II is to generate ha ndler exposure monitoring studies to estimate characterize exposures distributions for a multitude of occupational / industrial and cons umer exposure scenarios inv o lving antimicrobial-contain i n g products. The data f r om these studies will fill g a ps in the current antimicrobial expos ure dataset and allow for more precise estimations o f potential dermal and inhalation occupatio nal risks to workers and cons umers handling products containing antimicrobi al agents. The st udy results will be placed into a computer software database (i.e., the Biocide Handlers Exposure Database or BHEDTM) allo wing the data to be used generically for risk assessments of all an timicrobial agents. The AEATF II will e xe r cise the rights associated with submission of data under the Federal In secticide, Fungic i de, and Rodentic ide Act (FIFRA) in connection with BHEDTM. The database will be available t o both AEATF II company members and regulatory agencies for registration and re-registration purposes.

The AEAT F II study program has been de signed to cover the most common types of occupational and resi dential handling scenarios in volv ing antimicrobials. Initially, EPA identified application met hods and use sc enarios bas ed on a rev i ew of antimicrobial product l abels and/or Agency areas of interest, in conjun ction with 12 “Use Site Groups” that EPA has used historically to delineate antimicrobials use sit e s. Some applic ation methods have b e en combined and the following Use Sit e Groups and 14 app lication methods/use scenarios have been agreed upon by the EPA, Canadian, and California regul ator y agencies and members of the AEATF II. The EPA Use Site Groups and Application Methods/Use scenarios include the follo wing (see Appendic es A and B for additional explanation and information):

EPA Use Sit e Groups 1. Agricultu r al Premises and Equipment 2. Food Handling/Stora ge Establish m ents Premises/Equipment 3. Commercial, Institutional & Industrial Premises/Equipment 4. Residential and Public Access Premises 5. Medical Premises and Equipment 6. Human Drinking Wa ter Systems 7. Industrial Process W ater Systems 8. Material Preservative s 9. Antifoula nt Coatings 10. Wood Preservatives 11. Swimming Pools 12. Aquatic Areas

12Application 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. Pressur e Treat 14. Metalworking Fluid

Post-applic ation scen arios are still under discussio n, with the possibilit y of conducting two studies – one to evaluat e ex posure (e.g., trans ferable residue measurements) to antimicrobials on soft surfaces (such as carpet) and one to evaluate expos ure to antimicrobials on har d surfaces (such as countertops or wood deck i ng).

All human subject monitoring studies will be conducted using standard industrial hygiene passive dos imetry techniques, co nsisting of both dermal and inhalation monitoring. All Task Force studies will be conducted according to current E PA Office of Pesticide Programs’ Harmoni zed Test Guidelines – Series 875 Occupational and Residential Exposure Test Guidelines (Serie s 875 A and B for handler and re-entry, respectively) and conducted under Good Laboratory Practice standards per 40 CF R Part 160. All monitoring s t udies will be conducted in compliance with all applic abl e prov isions of EPA’s regulations providing for the protection of human subjec ts of research, 40 CFR Part 26. The Task Force is designing study protocols that would allow study results to be broadly acc eptable to both North American and European regulatory authorities.

Industry-wide generic task forces go thro ugh various defined stages, with the data generation phas e of the task force typically la sting approximately eight years. The limit of exp enditures for AEATF I I is set at $9 million. This is base d o n the assumption that a total of 19 core studies will be conducted, with each study containing 15 to 25 sets of individual measur ements. The support costs for doing this work, such as analytical met hod dev elopment, database construction, legal, task force management, etc., are included in the stated dollar amount. This sizeable investment by t he antimicrobial industry c onfirms the commitment to generate the data needed to accurately assess risks to persons using antimicrobial products.

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## 3 AEA TF I I and BHEDTM

The Antim i crobial Expos ure Assessment Task Force II (A EATF II) was established to generate data for antimicr obial pestic i de exposure scenarios to meet EPA data requir e ments fo r registration. AEATF II member companies hav e ongoing data requirements resu lting from chemical and product-specific exis ting and announced data call-in not ices, antici pated re-registration obligations via confirmatory data demands, prospective registration review obligations and requirements based on registration appli cations. The member companies agreed to j o intly develop generic data in su pport of their respective registration obligations since existi ng data are not adequate.

The primary AEATF II goal is the collectio n of worker exposure monitoring data and its inc o rporation into a new generic dat abase that can be used to estimate exposure distributions. The database wi ll be a proprietary product of the Tas k Force and will b e calle d BHE D TM (Biocide Hand le rs Exposu r e Database). BHEDTM will be subm itted to EPA and ot he r regulatory agencies

, and used by those regulators to conduct detailed quantitative exposure assessments to support safety determi nations for occupational pesticide uses.

Generic databases were developed over t he last twenty years i n response to a regulatory need to assess the occupationa l risks associated with a wide range of pesticide handling sit uations. The conc ept was discussed first i n an American Chemical Society Sy mposium in 1984 (Reinert and Severn, 1985; Hackathor n and Eberhart, 1985; Honeycutt, 1985) and its development encouraged by a FIFRA Scientific Adv i sory Panel in 19 86. In 1992, the Pesticide Handler s Exposure Database ( P HED) was first releas ed following a joint effort by pesticide manufacturers, the E PA, and Canadian r egulators (Honeycutt, 1 9 86; Lunchick, 1994, Reinert, 1986, Leighton and Nielson 1995, Nielson et al. 1995). Since then, PHED has been used ext ensively in a generic manner and has succ essfully supported many occupat ional ris k assessments. However, much of the data in PHED are derived from expos ure studies that are considered outdated or scientifically inadequate by current standar ds (Stasikowski, 2001). In addition, many antimicrobial h andler sce narios of interest to EPA are a b sent or underrepresented in PHED. Other regulat ory agencies have expressed similar dissatisfaction with the limitations of PH ED data. A major purpose of BHEDTM is to address deficienc ies in existing data su ch as those included in PHED. And in 2007, EPA convene d another Science Advis o ry Pane l SAP (SAP) to discuss the need for new data to replace PHED an d the panel agreed with EPA that “additional data could signifi c antly improve t he Agency’ s ability to assess worker exp o sure” (SAP, 20 0 7 ). A major purpose of the BHEDTM is to address PHED deficienc ies (and limitations of other existing data sources).

14Like PHED, BHEDTM will be populated with exposure data for subjects who handle antimicrobial pesticides as part of their normal j ob (occupa tional) or t a sk (consumer), so their participation as s ubject s in the studies underlying BHEDTM will not add appreciably to their typical exposure from handling pesticides

. All AEAT F II studies are design ed a nd conducted in accordance with the latest U.S. EPA gu ide l ines for occupationa l e x pos ure studies.

The develo p ment of B H EDTM is f unded an d directed by the AEAT F II. However, an AEATF II Regulatory Agency Adv i s o ry Committee (RAAC) has been established to promote active participat ion by interested regulatory agencies. The committee is compris ed of representatives of the U.S. EPA, the Canadian Pest Management Regulat ory Agency (PMRA), the California Department of Pesticide Regu lation (CDPR), a nd E u r opean regulatory authorities. T h is c o mmittee meets on an ad hoc bas is to review the program progress and provide technical input to the AEATF II.



## 4 Regulatory Need for Generic Exposure Dat a

FIFRA requires the U.S. Envir onmental Protection A gency to assure that any pesticide registered in the Unit ed St ates does not have unreas onable adv erse effects on subjects ha ndling that pesticide ( http://www.epa.gov/pesticides/regulatin g/la ws.htm ). The Pest Control Products Act (PCPA; http://www.pmra-arla.gc.ca/englis h/legis / pcpa-e.html ) requires a similar determination by Health Canada. This safety determination is generally made by means of quant itative risk assessment and risk management procedures

. Risk assessments require a deta iled evaluation of the toxicity of the pesticide and an estimation or measurement of the expo sure potential for users (and/or amount of pesticide abs orbed by t he indiv i dual as a consequenc e of its use). Exposure or absorbed dose estima tes are quantitatively c o mpared to noeffect exposure levels (often from experim ental animals) for haz ards identif i ed in standardiz ed toxicology studies

. During the risk eva l uati on, the likelihood of the express i on of any toxicolo gical effect on the subjects and a com parison of the risks and benefits are consider ed. This bas ic paradigm (hazard identification, dose-response asses sment, exposure as sessment, and risk characterization) was summarized by the National Academy of Sciences and ha s become the standard for risk assessment by regulator y agencies (NAS, 1983; NAS, 2006). More recently, the pesticide handler risk assessment process was fully described in a s u mmary document prepared for an EPA SAP review of exposure methodolo g ies (EPA 2007).

The AEAT F II database, BHEDTM, is inten ded to prov ide the regulatory agencies with the handler pot ential exposure data necessary for them to perform the exposure assessment portion of safety determinations. Toxicology data and benefit inf o rmation are product-specific and must be provided by indiv i dual pesticide pr oduct registrants.

15When estimating ex posure to persons wh o handle pesticides, a major challenge to overcome is that s e veral par ameters contribute to the likelihood and lev e l of exposure. These include things such as handling liquids versus solids, how the product is packaged, using open versus closed systems, application with var i ous equipment types, how much product is handled, whether or not personal protective equipment (PPE) is worn, and whether t he worker mixes/loa d s or applies the product or does both. The number of co mbinations of these parameters makes it impossible to generate human exposur e data for all situations, so a number of simplifyi ng appr oaches ha ve been adopted. T hese include:

1) Establishing various ‘scenarios ’ (see Appendix A) t hat cover common combinations of these parameter s and generating data for those scenarios; 2) Restricting some scenarios to incl ude the higher exposure por tions of them (e.g., professional janitori al personnel performing mopping tasks using higher expos ure potential tec hnologies such as string and bucket mop syste m s); 3) Generating data with subj ects wearing minimal PPE; 4) Using data for one chemical/pr oduct as a surrogate for another (similar) product; and 5) Assembling data int o a gener ic databas e (e.g., B H ED) for use as surrogate data applic able to many product s or for multiple job f unctions performed by one person.

Since the early 1980’s it has been the cons ensus of the scientif ic community that the amount of residue that contacts a worker ’s clot hing and skin, a nd the amount of residue that is available for inhalati on, are primarily a function of physical rather than chemical factors. That is, t he chemical nature of the active ingr edient in a pesticide product has little influence on the extent of exposure compared to physical parameters associated with the use of the product. The phys i cal parameters include formulation type (e.g., liquid or granule product), method of application, and the way in whic h a perso n handles th e pesticide during mixing, loading and applic ation. Because of this, exposur e potential is considered “generic” since it is independen t of the specific activ e ingredient (Hackathorn, 1985; Honeycutt, 1985 and 1986; Reinert, 1985). G eneric exposure data may therefore be used in lieu of product-specific data for most safety assessments. Howev e r, some situations, such as ex posure 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 e fficiency of regulatory agencies in conducting exposure assessment s. Rather than relying on indiv i dual studies to evaluate c a se-by-case uses of each pes ticide product, a single, comprehensive database of high quality dat a applicable to most products can be used. The broad applicability of generic data and the resulting efficiency of their use in

16regulatory safety assessments led to the wid espread accept ance of PHED. PHED com ponents were created by assem b lin g expos u re data from studies that had alread y been conducted and s ubmitted to the EPA.

Most of the pesticide expos ure data available for inclus ion in the initia l 19 92 version of PHED had been co nducted by individual pe sticide manufacturers who designed their studies to support the regist ration of a specific product or a group of similar products. It was very comm on for these companies to generate a set of expos ure data that r epresented the worst case for exposure potential incorporating des ign f eatures such as the max i mum use rate , minimum PPE, and minimum engineering controls. If a risk assessment was acceptable for such a situation, then it wa s argued that lower use rates, additional PPE, and ad ditional engineering controls w ould certainly als o pass a risk assessment. However, this meant it was common for a study to involve 15 or more measurements of essentially the same s i t uation where each person ha ndled the same product, in the same packaging, in similar amount s, using the same equipment, and for similar periods of time. While these studi es are useful for pr oduct-specific cases, they are not always generically useful. Nevertheless

, many of these type s of studies wer e assembled to form PHED and collectiv ely the database did seem to improve the risk assessment process as regulators could often rely on larger data sets to estimate potential exposure.

Howev e r, the available studies for inclus ion in the PHED, in hindsight, were not designed, a priori , to meet the needs of a gener ic database and t h us, have some technical limitations. In addition, it is now an older database and many use practices have changed. Further, it has lim ited app lic a b ility to most antimicrobia l pesticide uses. Exposure monitoring me thods have also changed. The basic passive dosimetry methodology has lo ng been accepted as a standard, reproducible procedure that provides a ccur a te and reliable data that does not underestimate exposure (Ross et al., 2007). Even though the passive dos i metry methodology is still a very sound meas ure of exposure, t here have been some improvements. In particular, much of the data in PHED are based on patch dosimetry and expos ures were often not measured on all body areas for each monitoring unit or event, i.e., ME. However, PHED provided reasonable estimates of exposur e based on the tech nology of the 1980’s. Today, wholebody garment dosim etry is used instead of patches to improv e the ability to estimate the distribution of total body exposure.

There is general c o nsensus am ong regula tory agencies that the most efficient means of generating handler ex pos ure data is to po ol technical resources and assemble a generic database. This c onsensus and the ex tremely limited availability of data for antim icrobial pestic i des led to the formation of the AEATF II in November, 2004. The task force dat abase, BHEDTM, will be des i gned to reflect a logical s e t of use scenario s with adequate data in ea ch scenario t o provide reliable estim a tes of exposure pot ential and its distribut ion. Individual measurements will in volve sepa rate subjects and more diversity in equipm ent and conditions than in PHED, es pecially for the amount of product handled.

17

## 5 Description of AEA TF II Monitoring Program and Scenarios

The primar y purpose of the AEATF II monitoring progr a m is to d e velop a n e w generation of more accurate and usef ul information and data on worker and consumer exposures to antimicrobials. A secondary purpose is to incorporat e these data into a gene ric database (BHEDTM ). These data will co n s ist of dermal and inhalation exposure estimates derived fr om monitoring subject s who handle pesticides under a variety of circumst ances, using various pesticides and equipment types. AEAT F II refers to each un iq ue hand lin g situation as a ‘scenario’ and anticipates the database will contain su fficient data to support exposure assessments for 14 distinct handling situations , or scenarios (see attached “AEAT F II Scopin g Document” pr ovided a s Appen dix A; Appe ndix B provides a detailed descrip tion of each use site identified in the Scoping Document; Appendix C provides a glossary of terms).

In general, each An antimicrobi al scenario is defined as a set of related tasks, pesticide f o rmulations, equipm ent, engin eering controls, and worker and/or consumer practices. For example, two scenarios of interest are “mopping application” and “wipin g application.” The scenarios of interest to the Task Force fall into two general categories:

1. Scenarios that are addr essed by simulat ed-conditio n studies based on discrete or segmented tasks (mixing, loading and application methods) that can used, separately or in co mbination, to estimate exposures occurring in a variety of use conditions; and

2. Complex and/or multi-task sc enarios that ar e addressed using in s i tu (e.g., on-site, observational) studies.

The bas ic element in bot h s i mulated-condition and in s i tu studies is the monitoring unit or monitoring e vent (ME ). Each ME will co nsist of measuring dermal and inhalation exposure potential for a single subject for a time period that represents a typical workday. The general ap proach is to obtain, for each scenario, a variety of MEs using different subjects and a diverse set of conditions that will reflect current and pr ojected antimicrobia l mixi n g /loading a nd application practices in North America. Diversity in characteristics that are either known or assumed to be expos ure-related will b e emphasize d. The measu r ed expos u r es from each scenario-s pecific set of MEs can then be used to represent the future handler-day exposures to arbitrary (i.e . generic) antimicrobial c o mpounds. For each scenario, the basic objective always is that the set of M E s ade quately characterize both the typical and the more extreme exposures expected for a single workday. The design of s cenario-spe cific studies and the construction of MEs are described in Secti on 16 and in Appendix E.

18Collectively , all scenario-specific studi es that generate MEs to be included in BHEDTM are referred to as the AEATF II monitoring (or testing) program. BHEDTM will be use d to support North Amer ican registrations for existing and new pesticide product s as requir ed by FI F R A in the United States and the Pest Control Products Act (PCPA) in Canada.

An AEATF II scenario represented by the set MEs in BHED TM may or may not correspond exactly to set of antimicr obial-handling tas ks being c onsidered for regulatory evaluation. Sc enarios that are co mplex and cons ist of multiple nonseparable tasks will be addressed by in si t u (observational) studies. The MEs from these studies can be used directly for regulatory evaluation. In some situations, however, the number of possi ble combinations of tasks would too large to address each by a separate sc enario. As a result, some AEATF II scenarios c o rrespond to single dis crete tasks (e.g. mopping, wiping, mixing, etc.) that will be addressed by simulat ed-conditi o n studies. When regu latory interest is only in the discrete task (e.g., mopping only) then t he scenario in BHET TM is directly applicable. Howev e r, when intere st is in a combinat ion of these tasks (e.g. mixing plus mopping) then results from several AEATF II scenarios must be combined.

A single sc enario, suc h 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 chemic al. It is common in institutional s e ttings today that automated dispens ing systems provide t he applicator with ready-to-use mop solutions, and the applicator does not mi x and load the end-use mop solution in a bucket. Therefore, the applicator’s ex pos ure 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 expos u res under the “mopping” scenario would then adequately descr ibe the handler’s daily exposure t o the antim icrobial. 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 expos ure for an antimicrobial handler would arise from two discrete tasks, i.e., mopping (includi ng dirty mop solution dispos al) and mixing/loading of mop solution. To provide data for r egulatory agencies to address the addition of this discrete task (mi xing and loadin g ), the AEAT F II will con duct separate studies of mop application (which wo uld include discret e measurement of exposures associated with mopping and dirt y mop bucket solution emptying) and of mixing and loading via open pouring of liquids.

At times, user’s of the BHED TM data may need to consider the distribution o f a combined exposure from multiple tasks represented by separate scenarios. The arithmetic mean of a comb ined single-day exposure is simply t he sum of the arithmetic means for each separate ta sk. Howev e r, other aspects of the combined distribution depend on how exposures for the same indiv i dual from different ta sks are correlated. If the exposures are perfectly corre lated (i.e. the

19correlation is 1) then any percentile of t he combined dis t ribution is the sum of the percentiles for each task separately. If the same-person-diffe rent-task exposures are independent, however, t hen the combined percentiles are les s e x treme than the sum of the separate percentiles. This ‘s hrinkage’ of the combined distribution is rather mi nimal and is practically non-det ectable if one task’s mean expos u re is much larger than any of the other tasks mean values. Thus, unless a separa te estimate of the between-task correlation is available, a practical recommendation for most BHED TM users would be to simply assu me that the tasks are maximally correlated and add all percentiles. This approach would lik ely be acceptable in the context of regulat ory-decisio n making when relying upon BHED TM , giv en the overestimation (more conservative) bias associated with s u mmed upperpercentiles. More importantly, the devel opment of normalized expos ure data for discrete tasks provides the flexibility to construct or assemble and ass e s s multitask expos ures and thus, gr eater utility for a generic e xposure database.

In some instances, th ere may exist, a di screte task o r set of ta sks that fa lls outside all the scenarios for which monitori ng is planned. This is most often because the task is rare or would be ex pec ted to give non-detectable expos u re levels. When reasonable, users of BHED TM might choose to ignore the task or use another scenario as a surrogate for th e missing t a sk. For example, in the case of mop application, in some cases, a person may pour a concentrated formulation containing an antimicrobial in to a mop bucket cont aining water to create a label-specified end-us e dilution. T he exposur es (dermal and inhalat ion) that may occur during this liquid pouring tas k can be address ed with the separate “open liquid pouring” study dat a. The “open liquid pouri ng” data could be used directly as a conservative surrogate for pouring a concentrate into a mop bucket. In this example, it is important to adj ust the surrogate exposu re s distribution for the amount of active ingredient handled in the specific mop bucket pouring situation being assess ed.



## 6 Limit a tions of Existing Dat a and Justification for Supplement a l or Confirmatory Dat a

Since 19 9 2, the EPA has con ducted agric ultural mixer/load er and applic ator exposure and risk ass e ssments relying prim arily on the exposure data in PHED. PHED vers ion 1.01 was initially releas ed in February 1992. It was followed by PHED version 1.1 in February 1995. PH ED version 1.1 was described by the Agency as an incremental improvement over the 1.01 version (Pesticide Handlers Expos ure Database, User’s Gu ide Version 1.1, Health Canada, U.S. Environmental Protection Agency, Amer ican Crop Protection Association, February 1995). The forward to Version 1.1 User’s Guide cautions the user that the databa se still h as some limit ations and should not be conside r ed a pana c ea in estimating pesticide handler exposure. Noting the limitations, the guide st ates that a goal was to release a PHED versi on 2.0 in 1997. However, no subsequen t version of PHED has been released.

20By 2000, the U.S. Environmental Protection Agency began evalu a ting alternatives to PHED. The EPA has outlined its int entions regarding PHED (Letter from Margaret Stasik ows ki, Director, Office of Pesticide Pr ograms, Health Effects Division, to Daniel F a y, Val ent USA Corporation, 16 March 2001). EPA has acknowledged the need to “overhaul” PHED vers i on 1.1 because many of the existing exposur e studies in the database are outdated or scientifically inadequate by “today ’s standar ds”. In addi tion, m any antimicrobial pes ticide exposure scenarios that are being ass e ssed by the Agency are underrepresented or not even included in PHED v e rsion 1.1.

In summary, PHED s u ffers from a number of limitations regarding its use as a generic exposure database, including:

• Inadequate number of m easurements for one or more body areas;

• Inadequate quality assurance or quality control data;

• Use of patch dosimeters inst ead of whole-body dosimeters;

• Lack of whole body dermal estimates for subjects (i.e., not all body parts monitored for dermal expos ure 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 c an be illustrated by reviews of the Registration Eligibility Dec i sion (RED) documents issued by EPA as pa rt of the recently completed FQPA re-registra tion process. These documents have characterized the existing PHED data as low confidence for the following important use patterns. Confidence rati ngs are based on “num ber of replicat es” (quantity) and “QA/QC Grades” (quality). In general, low confidence scenarios have fewer than 15 r eplic ates and/or barel y acceptable laborat ory fortificati o n recovery data (or worse).

21For reference, PHED confidenc e ratings can be summarized as:

Confidence Rating Number of Measurements QA/QC Gr ading

High >= 15 per body part And Good labor atory 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 acc eptable (or unacceptable) laborat ory fortification data (Grades D or E = All Grades)

In addition, it should be not ed that PHED provides de rmal exposure estimates, and confidence ratings, for severa l 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 confi dence for one clothing/PPE situation and high confidence for another within an expo sure scenario. While protection or penetration factors can be us ed to es timate protected exposure from nonprotected expos ure results, or vice vers a, this may create additional uncertaint y for exposure estimates and may not be appropriate for all risk assessments. PHED data with potential relevanc e to ant imicrobial scenarios are as follows:

22PHED Scenario (#) C o mments re: PHED Scen a rio AEAT F II Application Method

Liquid (3) Mixing/Loadi ng (M/L ); ope n mixing Pour Liq u id Pump Liqui d (6) M/L; closed mixing Pump Liqui d Paintbru sh/ R oller (22) Brush appli c a t ion only Brush / Roll
Aeros o l Spray (10) Applic ation only Aerosol Spray High Pressu re Spray (19 ) Applicatio n o n ly High Pressu re Spray

High Pressu re Spray (35 ) M/L, Liquid, open po urin g; and appli c ation Pour Liq u id a nd Hig h Pre s sure Spray

Lo Pre s s ure Spray (32 ) M/L; Liquid; open po urin g; and appli c ation Pour Liq uid a nd Lo w Pre s sure Spray

Low Pr es su re Spray (33 ) M/L; Wettabl e Powde r ; an d appli c ation Pour Solid an d Low Pressu re Spray

Low Pr es su re Spray (18 ) Applicatio n o n ly; handwan d equipm ent Low Pressu re Spray Airless Spray (23) Applicatio n o n ly; house stain Airless Spray Pour Solid (1) M/L; Dry flowable; ope n mixing Pour Solid Pour Solid (2) M/L; Granul ar; open mixing Pour Solid

Pour Solid (4) M/L; Wettabl e powder; op en bag Pour Solid

In addition to data available in PHED, another source of ex isting data being us ed by regulatory agencies in the case of ant i m icrobials is that represented by an exposure monitoring progr am conducted by the CMA (P opendorf et al. 1992). On 4 March 1987, a Data Call-In Notice was issued for submission of data for antimicrobial pestic i de active ingredi ents. In respons e, the CMA developed a generic biocide expos ure assessment protocol and co nducted a study, Chemica l Manufacturers Association Antim i cr obial Exposure Assess ment Study (conducted by Dr. William Pope n dorf at the University of Iowa; Popendorf et al

. 1992) based on the protocol. The CMA effort originally consider ed a list of ten pesticide active ingredients. This lis t was reduced to 9, considering se veral criteria. Exposures to seven of these ni ne chemicals were assessed, as well as exposure to zinc chloride, which was us ed as a surrogate tracer for a process and chemical which c ould not ot herwise be assessed. In total, 88 separate MEs were obtained for six end-us e settings and nine applic ati on methods (pour liquid, pump, pour solid, place solid, aerosol spra y, high pressure spray, low pressure spray, mop and wipe) to assess both dermal and inhalation exposur e s.

Based on EPA’s r e view (Mostaghi mi 1995), CMA's study met some requirements, but was lacking in other areas. Specific ally, areas in which the Amended Report complied wit h the proc edures specifi ed by the EPA’s dermal and inhalation exposur e guidelines included the following:

1. Most of the dermal samples had detection limits low enough to allow accurate reporting of the samp le, according to EPA guidelines.

232. Some of the field recovery data were ac ceptable; five chemic als had acceptable recoveries from gloves, and two chemic als had ac ceptable recoveries from air, and the results were corrected fo r losses in the field using correction factors determi ned from the recovery data.

3. The materials used in the analyses were acceptable in most cases and were adequate for further analy sis. To as sess dermal exposure, gauze pads were used for dry residues , cott on gloves were used for assessing exposure to hands, and plac ement of dermal pads was found to be acceptable. For inhalation expos ure, standard flow rates were used for air impingers and personal sa mpling pumps, standard N IOSH factors were applied to respirators to estimate reduction of expos ure inside the respirators; and

4. Documentation of dat a collected during laborat ory and field operations was adequate based on bot h CMA's descr iption of their data gathering efforts and presentation of data pr ovided in Appendix C o f 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

.

Howev e r, the following areas were found to be lacking:

1. Good Laboratory Practices, especia lly in the area of providing quality assurance, must be fo llowed more closely;

2. A majority of extrac tion efficiencies wer e below the minimum level suggested in the guid elines. Perhaps more importantly, the percent field recoveries (which repr esent the am ount recovered under actual conditions encountered in the st udy) of many of the c hemicals were lower than the minimum needed to assess exposure. Th erefore, 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 samp les were lower than the detection limit; and

4. None of the application method/end use settings had the minimum number of replic ates (i.e., 15) recommended in EPA’s gu idelines.

The limited number of MEs combined with poor recovery data severely limits the conclus i ons that can be draw n from CMA's study. Ther efore, the EPA and other regulatory agency reviews indicated that additional data for all applic ation method/use setting combinations should be obtained to support more confident inferences about exposures in a variety of settings.

The defic iencies ident ified by EPA in CMA' s report we re corroborated by other reviewers. First, the California Depar tment of Pesticide Regulat i on (CA DPR) notes that the exposure data cannot be used as generic data for all antimicrobials becaus e recoveries were lo w, precision of the measurements were

24not established, and CMA did not estab lish the v a lidity of generalizin g the information among applic ations and end-use settings (Powell et al., 1995). Canada also reviewed the study and m ade similar conclus i ons (Worgan and Rozario, 1993).

In summary, in order to assess potential risks from exposure to antimicrobials, EPA has e x tremely limited data on which to rely. In fact, EPA has repeatedly identified that dat a as i nadequate.

In each of the following exam ple Re-regi stration Eligib ility Decisions iss u ed during 2005 and 2006, EPA has stat ed that “the risk assessment noted deficienc ies in the sur r ogate dermal and inhalation exposure dat a available from the Chemical Manufacturers A ssociati on (CMA) data base. Therefore, the Agency is requiring c onfirmatory data to support the uses assessed with the CMA exposure data within this risk assessment.”

- PHMB. September 2005. EPA73 9 -R-05-003

- Benzisoth i azoline-3-one. September 2005. EPA739- R-05-007

- Para-Tertiary-Amylphenol, Potassium Sodium Salt. January 2005. EPA7 38-R-05-001

- Azadioxabicyclooctane. Sept ember 2005. EPA739-R-05-010

- Chlorine Dioxide and Sodium Ch lorite. August 2006. EPA7 38-R06-007

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

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

­ 008

- Alkyl Dimethyl Benzyl Ammoni um Chloride ( A DBAC). 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 requir ed additional exposure data for assessing risks from occupational and residential uses of antimicrobial pesticides.

25

## 7 Alternatives to Additional Human Monitoring

Regulatory agencies are charged with a ssuring that registered uses of a pesticide will not cause unr easonable adver se effects to pesticide handlers. As part of such determinations, regulator s and risk assessors must be able to estimate with confidence lik ely levels of occ upational expos u re. Exc l uding ne w human monitoring studies, the informa tion available to make reliable approximations of expos ure c u rrently comes primarily from generic data contained in PHED and CMA data (P opendorf, 1992), but also from pesticidespecific exposure studies, modeling, and publis hed lit erature. T here is a general paucity of published literature relevant to antimicrobial e x posure; and the few relevant publications were not conduct ed under GLP, did not typically meas ure whole body exposur e, and the raw data fo r verifying results is generally not available. Further, there are no known reliable (validated) models to estimate dermal ex posure to antimicr obial users. The use of animal data is obviously not an option f o r studies that monitor occupat ional exposure to individuals engaged in their normal work activities.

Therefore, the only alt e r native to the conduct of new human monitoring studies appears to be:

• The continued use of the existi ng infor m ation sources (PHED and Popendorf et al. 1992, other published lit erat ure, and predictive modeling); and

• The acquis itions of addi tional handler exposure data from other existing product-specific studies that meet est ablished acceptance criteria and that have gener ic applicability.

The limitations of PHED and the other cur r ent sources of expos ure assess ment data have been discussed briefly above. T he limitations of existing data are being ev aluated by AEATF II on a scenario-b y-scenario basis using accepta n ce criteria (see Appendix D) dev eloped v i a a consens us process with regulatory agencies (U.S. EPA, Health Can ada and Califor nia EPA). Existing data ca n also inform the design an d sample size (see Appendix E f o r the AEATF II general study design approach) for proposed st udies, where additional data ar e determined to be needed. Appendix F prov ides an example evaluation in the case of applicator dermal and inhalation expos ure data available in PHED for hand-held aerosols. This ex ample includes a comparison of the PHED data t o key acceptance criteria adopted by the AEATF II (see A ppendix D).

Under the first stage of the A EATF II program, and prior to the conduct of scenario-s pecific exposure monitoring st udies with human volunteers, the A EAT F II reviews existing handler exposure dat a from various sources and considers acquiring data that meet established ac c e ptance criteria. A recent SAP ( 2007) evaluated the AEAT F II acceptan ce criteria and conclu ded:

26The Panel viewed the selectio n criteria proposed by AHET F and AEAT F II to be reasonable for generating exposur e data for using in exposur e assessments, with the following caveats. The monitoring dur ation requirement may be too stringent. Some provision to al low the inclusion of dat a from settings where only short-term uses are the norm may need to be added.

Althoug h some useful worker exposure studies may be acquire d by AEAT F II, most of the existing data are not su fficient to meet t he generic data needs identifie d in advance by the AEATF II and the Re gulatory Agency Adv i sory Committee. While there may be other dat a that have be en submitted to EPA an d may be suitable for a generic d a ta base, the y are proprietary and AEAT F II doe s not have access to them. Cons equently, at this point, no v i able alternatives to performing additional human monitoring st udies exist for most scenarios.

It should also be pointed out that pre-requisite studies for AEATF I I testing do not require research with human s ubjects. These pre-requisite studies inc l ude analytical method validations, field recov e ry validations, and toxicity studies tha t support the registrations of the test mate rials 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 c onducted in compliance with t h e applicable requiremen t s of 40 CFR Part 26, EPA’s regulatio ns for “Protecti ons for Human Subjects of Research”, and, if they are conducted in California, with the applicable requirements of Calif ornia Code of Regulations Title 3 Section 6710. Ethical considerations are scenar io and st udy protocol-specific, however, general considerations are discussed below.



### 8.1 Subject Recruitment Process

AEAT F II studies requ ire IRB, EPA/HSRB a nd sometimes CDPR approva l o f the protocol and proces s before subjects are recruited and the worker exposure monitoring study is init iated. The subject recruitment process must be tailored to each scen a rio-specific study. AEAT F II st udies will typically incorporate the elements described in the section as com ponents of the recruitm ent process.

Recruitment is conducted by selecting trained or experienced antimicrobial chemical handlers from subjects (wor kers or consumers, depending on th e scenario and products being studied) iden tified by personal cont act through loca l

27service businesses or research organizati ons. Recruitment materials such as advertisements or flier s may be used. The Principal Investigator (Study Director) approves the selection of all study sites and subjects, generally after visiting the proposed s i tes and talking to potential volu nteers. Informed consent discuss ions are conducted by the Principal Invest igator, generally shortly before study initiation.



### 8.1.1 Ide n tificat ion a n d R ecruitm e n t of P o te ntial S ubj ects

It is important to acknowle dge that each study protocol must include spec ific ally defined pr ocesses for the identification and recruitment of potential subjects. This protocol specific ity includes fo r example

, eligib ility c r iteria.

Population Base In general, AEATF II proposed studies will involv e adult subjects that meet specified inclusion/exclus ion criteria and who wi ll be recruited from the professional handler and/ or consumer user populat ion 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 profes sional traini ng and/or experience wil l be initially c ontacted by the F i eld Coordin a tor using a phone interview scrip t. Recruitment of subjects will b e through a) word-of-mouth and telephone contact, b) relevant serv ice companies or recruitment agenc ies that hav e been prov ided with a flyer that describes the study and contains a phone number and nam e of an AEATF II study contact person; or c) direct cont act with service providers who are asked if AEATF II may have their permission to ask their employ ees if they might be interested in participating in the study independently fr om their employer where AEATF II provides t he chemic al and us e equipmen t, or at the employer’s plac e of business, if the employer is prov iding the antimicrobi al and use equipment. Interested subjects should contac t t he Field Coordinator directly.

Enrollment of Alternate Subjects Alternate subjects will be enrolle d into each study and the number of alternates enrolled will depend on an indiv i dual study ’s objective regarding t he number of monitoring units (or monitoring events, i.e ., MEs). Typically, enrollment of three to six alternative subj ects 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 alternat e will be m o nitored if the assigned subject does not present or if the assigned subject drops out for any

reason. If a subject begins monitoring but s t ops 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. Do simet r y from any subject that complete a minim u m specified d uration (to be specif ied in eac h study protocol) or more will be

28analyzed 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 as signed to a pre-spec ified 30 minute target task duration group. The alternate subjects not tested the first day will be asked if they ar e available to fully participate the next day. Alt e rnate s ubjects will be compensated for coming to the test site. Alternat e subjects will serve as back-up for any enrolled subjects who fail to appear on a given day, f o r subjects that decide to withdraw prior or during the test, for fe male subjects testing positive in the pregnancy test, or for any other personal circ umstance.

Inclusion and Exclusion Criteria The Principal Investigator is respo n sible for ensuring that study-specific inclus ion criteria are met when participant s are recruited. The s ubjects will be asked t o fill out a demographic questionnaire and ask ed health-related questions. Females will be ask ed to take a pregnanc y test. The response s and results will provide the basis for inclusion or exclus ion from the study.

Inclusion

• Males or females, 18 to 65 years of age

• In good health

• Willingness to sign the Informed Consent F o rm

• Speak and read Englis h or Spani s h (or, if feasible, another predominant language selected for a specific study based on the associated potential su bjects’ demographics)

Exclusion Criteria

• Skin conditions on the palmar surf ace of the hands (e.g., psoriasis, eczema, cuts or abrasi ons)

• Pregnancy, as shown by a urine pregnancy test

• Lactation

• Allergies to household chemical-bas ed products, soaps or isopropyl alcohol

• Declines to sign the Informed Consent Form or the Health Questionnaire

• Does not read and understand Englis h or Spanish (or, if feasible, another predominant language selected for a specific study based on the associated potential su bject’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)

• Cardiovasc ular diseas e (e.g., hist ory of myocardial infarcts, stroke, congestive heart failure or uncontrolled high blood pres sure)

29• Is an employee of the contract la boratories conducting the study, or is related by blood or marriage to personnel in the contract laboratories.

Willingness to Participate Participant s must be freely willing to parti cipate in a s t udy 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 fo r the Principal Investigator or any other s ub-inv e stigator, or for any party with a substantial or contractual interest in the research , nor can they be relatives of any investigator s).

Experienc e Most AEA TF II studies will inv o lve on ly pr ofessional workers (e.g., janitorial profession als, woo d treatm ent facility workers, machinists at metal wo rking shops) with experienc e specific to the tasks being inv e stigated. This will ens ure, in the c a se of subjects, that they have met basic safet y 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 involv ed in the study. In some studies , products that could be used by workers or consumers will be involv ed; thus, potential subjects could include consumers with relevant experience in perfo rming a scenario-specific task

Age All monitor ed subjects will be a t least 18 years of age, and no older than 65. Subjects will be asked for a Government-i ssued photo-ID to confirm their age.

Health Status of Part icipants Only subjects who consider themselves to be in good health will be eligible. This will be affirmed by the subject’s responses t o the healt h -related questions. The subject-provided responses and informati on help the Study Director to exclude subjects who are not in good g eneral hea lt h, allergic , mentally ill, cogn itiv ely impaired, c h ronically ill, or terminally ill. Th is will he lp limit the ris ks of adverse effects due to pesticide handling.

Work Periods All monitoring periods will be d e s igned to r epresent the typical duration of the specific tas k or activity being monitor ed during a normal workday. Generally, this will involve monitoring periods from 30 mi nutes to eight hours in length, since most AEAT F II scenario-specific a ctivities are performed intermittently during t h e work day. Data sources will be i dentified and ev aluated regarding pr oduct use/usage information to inform study desig ns with respect to product applic ation sites and s u rfaces, application methods , amounts handled, and duration of tasks or work periods. An example s ource of public ly available professional “habits and practices” information is that from the International Sanitary Supply Associatio n (ISSA; www.issa.co m ). ISSA is the leading international trade

30associatio n for the cleanin g ind u stry. ISSA’s worldwide membership inc l udes more than 4,800 dist ributors, manufacturer, building service co ntractor and inhouse service provider members. ISSA clean ing operation s observationa l survey data (time and motion studies) were used to inform AEATF II study designs, e. g., predominant mop technology , cleaning t a sk durations, for the mop and wipe applic ator expos ure studies. Another example s ource of product use information, including task duration or wo rk periods, is a proprietary survey conducted by the C hemical Spec ialty Products Association ( www.cspa. org ), Antimicrobial Exposur e Joint Venture (AEJ V), which has collected data focusing on consumer antimicrobial cleaning pr oduct use in residential settings.

Product Label Non-Conformance All subjects will be required to perf orm pesticide handling tasks in conformanc e with the label requi rements. B H ED ® is designed to reflec t ex posure to workers and/or consumers who follow legal and proper handling of pesticides and not who misus e the product or otherwise violat e the label. In particular, subjects must wear the Personal Protective E quipment (PPE) requir ed by the label an d researchers will remin d participa nts to us e that PPE sh ould they b e observed not wearing the PPE during exposu re monitoring. Any su bjects who will not follow the label requirements during the study will be as ked to dis c ontinue t heir participation and their expos ure samples will n o t be c o lle cted. A subject will be reminded once if found not wear ing PPE. A second infraction is grounds for subject removal from the study.

Pregnant or Nursing The pregn ancy status of all po tential female particip a nts will b e ascertained through the use of a s upervised over-the- counter urine pregnan cy test conducted within 24 h ours prior to the initiation of monitoring. Any pregnant subjects will b e
exc l uded from the study. In addition, women who are nursing will be excluded.

Speak and Read either English or Spanish English and Spanis h are by far t he most common languages us ed by occupational pestic i de handler s in Nort h America. Translators for other languages are often difficult to locate where antimicrobial products are used, making it difficult to ensure fully inform ed and fully volunt ary consent for speakers of other la nguages. Thus, AEATF II anticipat es that it will not e n roll participants who are not fluent in either Englis h or Spanish. If a language other than English or Spanish has high incidenc e (>15%) amongs t potential subjec ts for a specific study, translation of study materials, the availab ilit y 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.

31

### 8.1.2 Exclusi on of Vul n er able Group(s)

AEAT F II prohibits most of the vulnerab le groups as particip ants, inclu d ing : people wh o are ill, co gnitive ly 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 partici pants – they are identifie d a s a research site in applicat ion s to IIRB. AEAT F II will occas i onally allow a loc a l site coordinator, or an em ployee of the site coordinator, to be a participant in a study. In this case, the s ubject must meet all of the criteria listed above, inc l uding the require ment that he/she be experi enced in the partic ular task bein g monitored

. AE ATF II will on ly us e s u ch research staff if they also have exper ience handling pestic i des in a commercial environment, for example as the owner of a separate commercial fac ility 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.

Howev e r, as describ ed in the next sect io n, AEATF II takes specia l care to prevent coercion of t hese subjects by having their supervisor/employer co nfirm they won’t be coerced and that their par ticipation dec ision will hav e no impac t on their employment or w o rk opportunities.

AEAT F II does not intend to recruit lim ited or non-readers, however, a fair percentage of the workforce (and consum er population) has Spanish as t heir primary language. When AEAT F II knows in advanc e that a Spanish speaker may be recruited for a particular study, this potentially vulnerab le category will be identified in the application to II RB. AEAT F II has pr ocedures in place to deal with candidates and s ubjects who prefer to use Spanis h

. These procedures are discussed in the following section.

Another potentially vulnerabl e group that might be part of the target population is poor/unins ured (health care insu rance) s ubjects. AEATF II does not intentio nally recruit these indiv i duals and will not i nquire as to the economic or insurance status (health care insurance) of potentia l study participants. Therefore, this category will n o t be id entified to the IRB as one that is intended t o be recruited. As discuss ed below, the remuneration being offered (generally for just one day of participation) is believed to be not high enough to in duce otherwise reluctant subjects to participate, so the economic st atus of participants in these studies is not a concern. The level of remunerat ion will b e consist ent with pay in a particular region of the country if there are obvious differences in wages bet w een regions. In additio n , AEATF II will cover all c o sts of injury or illnes s that subjects experience because of participating in th e study (that are not covered by the subject’s or their employer’s insur ance).

32Another potentially vulnerabl e group that might be part of the target population is illegal workers. For exampl e, 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 respon sibility for ensuring t heir worke r s are lega l, but AEAT F II does not employ s ubjects. AEA TF 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 n o t share that information. Workers who might be illegal will be protected from coercion primarily via the mechanism de scribed be low wh ere the Study Director will discuss t he voluntary nature of study participation wit h the worker’s supervisor/employer.



### 8.2 Informed Consent Process

Two fundamentally dif f erent re cruitment situations may o ccur. If a study is being conducted on an active worksite where s ubjects are normally employed to do their job, the following pre liminaries occur before subj ects meet the Principa l Investigator for studies that occur at no rmal 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 participa n ts. Before any contact with potential subjec ts the Principal Inves t igator has a discussion with the dir e ct s upervisor of eac h pot ential participant to ensure t hat the supervisor has no interest in whether t he subjects do or do not choos e to participate, and further, that the supervisor understands that subjects should not feel any coercion to participate in the st udy. The supervisor must confirm ther e will be no adverse impact on a w o rker who does not v o lunteer, or withdraws from the study, for any reason. This extra ca re to prevent coercion from employ ers will be documented on a form which t he supervis o r, busine ss owner, or commercial applic ator must sign. Prospec tive volunteers are introduced to the Principal Investigator, and their language of choice is determined. Then, each volunteer is provided with the super visor’s signe d form, the IRB-approved consent form, and a full explanation of t he study, its requirements, and any potential risks as dis cussed below for st udies c onducted away from subjects’ normal worksite. This occurs during a confidential and priv ate discussion with the Principal Investigator at the worker’s location if the study is being c onducted at a work si te 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 nor mal works i te, and not under t heir supervisor, anot her paradigm is used. The Fi eld Coordinator will be cont acted by individuals that have been made aware of the study by a flyer p o sted at their place of employment. Using an IRB-approved p hone script, the la nguage preference of the subject will be identified, and interested potential s ubjects will be scheduled for a meeting with the Principal Inve stigator or fore ign language-speaking designee.

33Interested volunteers will be screened and enrolled into the study based on oneon-one conversation held at the office of t he Principal Investigator. A Spanishspeaking t e chnical designee will be av ai lable to ensure communication wit h anyone pr eferring Spanish ov er English. The Princ i pal Investigator will s hare information on the study design with inte rested participants, and provide them with copies of the IRB approv ed In formed Consent Form and answer their questions. The Princ i pal Investigator will describe the study to t he volunteer in great detail and encourage eac h potential s ubject 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 eac h potential subj ect with a copy of the product label and MSDS and answer any questions regarding the product to be tested. The Principal Investi gator will go ov er the Inclus ion and Exclusion Crit eria for the study and answer any questions that the potential subjects have. They wil l be provided with copies of the Info rmed Consent Form, the Subject Self

Reporting Demographic Form a nd the Stat e of Ca lifor nia Department of Pesticide Regulation “Experimental S ubjec t’s Bill of Rights” and encouraged to take them home with them to discuss with family and friends.

The Principal Investigator will e x p l ain to potential su bjects wishing to remain in consideration that they may withdraw from the research study at any time without penalty to their compensation. The Prin cipal Inves t igator will then read the “Experime n t al Subject’s Bill of Rig h ts” to the potential s ubjec ts. The amount and form of compensation, the potential ri sks and discomforts and treatment, and compensation for injury will be more fully explained and potential s u bjects encouraged to ask questions. If the potent ial subjects do not have any ques t ions and are interested in participating in this research study, t hey will then be asked to sign the Informed Consent F o rm and then fill out the Subject Self-Reporting Demographic Form. The Princ i pal Investi gator will c heck the potential subject’s driver lic ense or state-issued identificati on c a rd to verif y age to exclude minors, and identit y as required by California DPR, and re view the package of information provided for completeness agains t the protocol’s inc l usion/exclusion criteria. The Princ i pa l Invest igator will retain the final right to refuse participation to any potential subject; however, follo wing signing the informed consent form, any potential subject not actually monitored will be given the minimum compensation. For female subjects, fi nal 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 po sition with t heir employer. Volunt eer s are also inf o rmed during the conf idential consenting process that they will receive compensation after they volunt eer to participate in the study, even if they withdraw for any reason or the sponsor does not use them for any reason.

34AEAT F II consent forms are unique to indivi dua l studies, but all have very simila r 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 pur pose of the study

• List of procedures inv o lving the subjects

• Detailed lis t of risks and discomforts

• Statement regarding discl osure of new findings

• Statement of benefit s of participation

• Statement of no cost to t he subjects for parti cipation

• Statement of paym ent 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 confident iality of worker i n formation

• Statement of compensation for injury

• Source of funding

• Voluntary partici pation and withdr awal

• List of resources who may be cont acted to obtain ans wers to questions

• A section for signatur es of the part icipant and the person conducting the informed consent disc ussion

During the discuss ions between potentia l participants and the Principal Investigator, ample time is prov ided for questions and the Pr incipal Investigator will provide any additional information or cl arification that is requested. Thes e discussions typically take place at the wo rker’s location, in a private setting, generally in a meeting room of a facility or commerci a l app lication compan y. Consent is generally obtained within one to three days of actual study conduct, but sometimes earlier. If the worker agrees to parti cipate he/she is asked to sign and date the informed cons ent form and the Principal Invest igator provides a copy of the signed form to the worker. Within 24 hours prior to participation, any women who are selected to participa te will be asked to take a urine pregnancy test (over the counter variety) and will b e allowed to participate only if the test is negative. This test will be supervised by a female researcher. To protect the privacy of the work er, the test results are not rev ealed to t h e employer or co-subjects. If the worker chooses to proc eed with the study the n a female researcher will c onfirm the test is n egative an d record this in the stu d y raw data. No positive test results will be d o cument ed and the worker will be allo wed to withdraw from the study witho ut stating a reason (an d still rece iv e appropriate remunerati on).

35For subjects whos e pr eferred reading la nguage is Spanish, AEAT F II obtains an IRB-approved translation of the consent form and ensures that a Spanis hspeaker familiar with the st udy protocol is present during the informed consent process and during study conduct. In all cases, the Principal Investigator will conduct discussions in Englis h, but the par ticipant will sign the v e rsion of t he consent form in his pr eferred reading la nguage. The Span ish speaker may be an employ ee at the study site, but more typically will be su pplied by the Principal Investigator and will be brought into the discussion if the subject’s preferred languag e is not Englis h. A Spanish-speake r familiar with the study protocol will a l so be utiliz e d dur ing the stu d y shou ld any issu es arise which can’t be resolved directly with the worker.

In all situations, if the Principal Investi gator is not comfort able that the worker fully under stands the discussions and the contents of the consent form, the worker will be exc l uded from consideration to participate in the study. This will be ascertained by providing repeated oppor tunities to ask questions and by asking questions of the potential volunteer s that would require a response tha t indic a tes understanding of key issues. For example:

• Q: When can you withd raw from the study? A: Whenever I want.

• Q: What w ill your sup e rvisor have you do if you don’t volunteer? A: My regular job.

• Q: What w ill you wear so we can measure inhalatio n 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 S panish will be confirmed by their answers to the Su bject Self-Repo r ting Demographic F o rm. The proces s for obtaining informed consent is fully doc umented in each AEAT F II study protocol.



### 8.3 Subject Remuneration

In almost all cases, AEATF II is studyi ng the potential exposure t o pesticides for subjects who are performing their usual ac tivities. This would be generally t r ue for both scripted and non-scrip ted activities. Thus, pe sticide handling would be conducted even if they weren’t participating in the study.

AEAT F II has selecte d a standa rd remunerat ion amou nt for all AEATF II studie s and participants sinc e the inconvenience in volv ed is essentially the same for participants in all studies. In addition, AEAT F II choos es not to offer an hourly rate since it prefers th at subjects per form th eir typical tasks and wants to avoid

36any incentive for subjects to choose a particular task since they could “earn more money”. The researc hers utiliz e d by AEAT F II all hav e e x perie n c e in dress i ng and undressing s ubjects and t he 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 r e present a very different proportion of various subjects’ typical da ily pay, fairness suggests that each worker should receive the same amount of remuneration since t he amount of inconvenience is essentially the same.

AEAT F II selected the amount of $100 at th e outset of the task force project and still be liev es this is an approp riate amou nt, althoug h compen sation ma y be varied if the local ec onomy requires more or less for cost of li ving. This is equivalent to approximately $15 /hour for a full day ’s work which is similar to other amounts from other studies that have re cently 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 lim ited to one day of study participation.

Individuals that are not tested including anyone s i gning the inf o rmed consent form but not subsequently being monitor ed will be co mpensated for their ti me and inconv enienc e at the rate of $50 per day. Subjects participating in the st udy will be compensated $100 for t he single day t hat they are monitored. The values for compensation are based roughly on a day’s wag e of $100 and represents potential lost time from wo rk, travel time and incide ntal expenses incurred in study participation. Compensat ion will be in the form of ca sh at the complet i on of participation. Generally, a particular person will be allowed to partici pate in the st udy only one time. This study design principal pr ovides data for separate exposure measurements that reflect different subj ects in or der to capture variability between subjects. However, the same worker could participate more than onc e in a study (or in two studies) as long as the worker performs a different task. For example, one person could be m onitored for expos ure as a mixer / loader on one day and as an applic ator on another day (assuming the experience and other criteria are met)

. In this case, that person would rec e ive a $100 payment on each of those two days.

It is important to note that subjects who are professional worker s and who are participatin g in any AEAT F 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 com pensation for any overtime worked according to loc a l laws. This compensation is the responsibility of the worker’s empl oyer and not AEAT F II.

37In additio n to their n o rmal compensati on, AEAT F II will provid e a payme nt of $100 (U.S. dollars if in the United Stat es or comparable co mpensation if in Canada or Europe) to each work er who vo lunteers to be monitored for exposure. This payment is in appreciation f o r t he extra effort and inconvenience assoc i ated with subjects participating in the st udy whic h includes wearing the inner dosimeter (long underwear, requires undr essing 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

AEAT F II generally u ses the Independ ent Inst itutional Rev i ew Board (IIRB) in Plantation, Florida (www.iirb. com) to review each of its study protocols for ethica l complianc e. Initial review submissions from AEATF II to IIRB typically include the followin g :

• The submission pack age to the IRB includes the study protocol and appendices including, test substanc e MSDS, summary of toxicology information and estimated risk for exposure anticipated in that study based on U.S. EPA ’s Re gistration Elig ib ility Decis i on documents (REDs). Additionally it includes ex perimental subjects’ bill of rights, subjects selfreporting demographic form, flyers so lic iting prospective subjects and scripts used to verbally describe the study to prospective subjects. All documents provided t o 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 Submis sion F o rm (IIRB fo rm 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 s ponsor and the Study Director as the Principal I n vestigator. It also identif ies study sit e (s) (generally local site coordinator research facilities) a nd prov id e s details a bout subje ct recruitment, consent, and payment. Hospitalization procedures are also provided which identify the nearest emergency medical facilit y to the study site(s) and indicate that 911 (or other local emergen cy number ) will be us ed as the primary method for obtaining medical attention should an y worker experience adverse effects during a study. IIRB also maintains file s c ontaining the Principal Investigator’s curricula vitae and documentation of human subjects training which support the submission.

IIRB will re view all ne w study protocols at regular convened me etings. When studies ar e to be c onducted in California, AEATF II will a l s o submit study protocols and related information to t he California Department of Pesticide Regulation (CDPR) for their review and approval under CCR Title 3 Section

386710. Any changes required by CDPR will be inc o r porated into the study protocol, which will then be reviewed and a pproved by IIRB. Only upon rec e ipt of IIRB-approved protoc ol and consent forms will CDPR grant final approval for the study to be conduct ed in California. Further changes in the protocol and associated materials may also b e required by EPA, a nd wou l d a l so lea d to rereview by the IIRB.

All protocol changes (amendments and dev ia tions) shall be reported to the IIRB in writing by letter, fa x or email. Propos ed c hanges (amendments) deemed necessary to eliminat e apparent immediat e hazards to the human subjects may be implemented without prior IIRB approv al

. All ot her am endments must be reviewed and approv ed by the IIRB prior to implementation, or as specific ally instructed by IIRB policy in this regard. Approval will be granted in accordance with IIRB policy and procedures, and may be gr anted by telephone provide d it is documente d in writing in t he study raw dat a. The IIRB may pr ovide expedited review of minor changes as defined by 40 CFR Part 26.1110 at its discretion.

Unplanned changes (deviations) which occ u r during conduct of the study cannot, by definition, be reviewed and approved by the IIRB prior to i m plementation. Deviations will be reported in writing by letter, fax or email as soon as poss i ble following the change. The Pr incipal Investigator shall follow written instructions provided by the IIRB for pr ompt reporting to the IIR B, appropriate institution a l 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 AEAT F II takes many steps to ens ur e the safety of the subjects being monitored. As outlined above, protocol s and consent f o rms are approved by an IRB (and DPR, if needed), informed consent is obtained for all study participants, consent is obtained in writing in t he worker’s preferred language, certain vulnerable groups are not recruited, pregnant and nursing women are excluded, and participants are informed they may withdr aw at any time. Other steps that AEAT F II takes to ensure the safety of study participa nts are summarized belo w ; each study protocol will define “stop criteria” and medic a l management procedures

. The objective of the AEAT F II is to generate data collected u n d er actual use conditions and following all label requirem ents. Subjects are never asked to wear less protective clothing than they w ould 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 la bel, th e AEATF II ei ther allo ws them to wear the e x tr a clothing (or equipment ) or they are ex cused from the study, depending on the specific goals of the study. The AEA TF I I may also provide s o me PPE items required on the product label (e.g., protec tive eyewear) to ensure they meet requirements.

Copies of the material safety data sheet ( M SDS) and pr oduct label are m ade available to members of the study t eam and study participants. During the consent pr ocess, the Princ i pal Invest igator will prov ide these documents for review to potential v o lunteer s and will disc uss the pos sible acute toxicity effects associated with the pesticide product in the study. AEATF II study participants will also be reminded of standard practices that sh ould be followed to r educe 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 st udy conduct, researchers will ensure compliance with safety requiremen t s on the product lab e l. For example, subjects will b e reminde d to use the la bel-specifie d PPE an d to follow use directions on th e label. Each worker will be observed by a res earcher during the en tire monitoring period and the Principal Investigator will be present on all da ys of monitoring. All researchers who hav e interactions with the subjects have completed a course in The Protection of Human Rese arch Subjects, e.g., C e rtif ied Investigator Training Initiative (CITI), which reinforces the ethical requirements of conducting studies involv ing human participants.

Study participants are asked to wear an extr a layer of clothing (whole body inner dosimeter) under their normal work attire. E fforts are made to schedule studies

40during cooler times of the year and/or indoors as much as practical to help minimize potential for heat stress. T he informed consent form identifies heatrelated illness as a potential health hazard that may be associated wit h participating in the st udy, so AEATF II is ve ry careful to prevent such illness. First, the Principal I n vestigators and stud y 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 liqu i ds during the monitoring pe riod. Th ird, a poster “Controlling Heat S t ress Made Sim p le” will be prominently display e d at the study site. Most importantly, environmental conditions (t emperature and humidity) ar e continually monitored and operating procedures are in place to reduce the po ssibility that participants are subject to heat stress.

Finally, the Princ i pal Investigators know in advance where to tak e subjects who might be overheated or who have other medica l conc erns. If any participant is injured or exp erience s illn ess as a result of being in a study, medical treatment will b e ava ilab l e at a nearby h ealth care facility. If necessary, AEATF II will arrange transportation to receive medical at t ention. AEATF II will cover the costs of reasonable and appropriate medical att ention that are not covered by the participant’s own ins u rance or by a th ird party. Treatment records will not become part of the research recor d s for this study.



## 9 S t udy Benefit s

A critical principle of ethi cal research is that the ri sks to the subjects must be outweighed by the benefits to the subject and to soc i et y. To approve proposed research with human subjects, an Instituti onal Rev i ew Board must determine that “risks to subjects are reasonable in relation to anticipated benef its, if an y, to subjects, and the importance of the kno wledge that may reas onably be expected to result” (40 CFR §26.1111(a)(2)). T he low incremental risk anticipated for pesticide handlers participating as subj ects in the AEATF II monitoring progr a m are outweighed by the societal benefits expected to be gained from increased knowledge of typical expos ure levels in r epresentative antimicr obial chemical use scenarios.

A critical principle of ethi cal research is that the ri sks to the subjects must be outweighed by the benefit s of the research to the subjects and to society. The Common Rule codifies this principle: “Risks t o subjects are reas onable in relation to anticipated benefits

, if any , to subjects, and the im portance of the knowle dge that may reasonab ly be e x p e cted to re sult ” (DHHS, 2001). If the be nefits o f monitoring pesticide exposure in the field to subjects d o not outweigh the risks, then the program should not be conduct ed. AEAT F II argues that the risks involv ed with pesticide handlers participat ing in the monitoring program, who expose themselves to pesticides as part of their daily lives, are minimal a n d are

41outweighed by the societal benefits gained by knowledge of expected exposur e levels and by the eventual benef its of safe pesticide us e.



### 9.1 Benefits to Subjects

None of the studies in the AEATF II m onitoring p r ogram will provide direct benefits to the study participants. Informa tion from this moni toring program will be used to estimate the exposure and health risk to handlers (workers, or for some scenarios, cons umers) who are in v o lved in mixing, l oading and apply i ng antimicrobial chemical pr oducts. This may lead to safer pesticide handling practices that indirect ly benefit the parti cipants and other antim icrobial pes ticide handlers. Individual worker s may request their exposure results relative to the average and extremes observed in the study

. This infor m ation may inform them that their w o rk practices produce mo re or less expos ure than average and may inspire them to modify behavior to reduce their expos u re.



### 9.2 Benefits to Society

The AEAT F II exposure monitoring program wi ll s i gn ific antly impr ove the ability of EPA and other regulat ory agencies to estimate the risk s to professiona l pesticid e handlers from handling antimic robial pesticides. Knowledge gained from the monitoring program will be ap plic able to a v a riety of an timicrobial pesticides, and will be used to assess risks of new pesti cides and new uses of registered pesticides. Knowledge gained from the monitoring progr am could also be us ed by EPA to impose stricter safety standar ds on currently used pesticides, whe n appropriate (Resnick, 2005).

The data developed in t he AEATF II monitoring pr ogram will improv e the scientific basis for EPA’s occupational ri sk assessments. Worker ex posures will be measured under realistic conditions. The data collection parameters will reflect current antimicrobial prac tices, equipment, and techniques. Monitoring techniques are of high quality and hav e been standardized for use across the AEAT F II monitoring program. BHEDTM will become the best a vaila ble da ta to support assessments of antimicrobial pestic i de handler exposure.

BHE D TM will not rep eat the limitations o f PHED. In particular, BHEDTM will include all data on each individual sampl ed, not just data on i ndividual body parts. Improved estimates of w hole-worker exposure, includi ng estimates of the potential distribution between sub j ects, will n ow be poss i ble.

To the extent the generic database appr oach proves successful, it may re duc e the need for product-specific worker expos ure studies conducted by indiv i dual registrants for new products and uses.

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### 9.3 Likelihood of Re alization of Benefits

The collect ion of work er exposur e data that can address the dat a needs of the regulatory community and membership of the AEATF II is considered extremely like l y. It is also hig hly like l y that regulators a nd ri sk assessors will use these data extens ively

. This has been the case for previous FIF R A joint data development task forces of many types, including thos e developing data fo r generic exposure assessment. Regulatory agencies are st rongly com m itted to using generic exposure databases as an important co mponent of risk assessments. The us e of worker expos ure data in a generic manner has been generally accepted since the concept was discussed and s upported by a FIFRA Scientific Advisory Panel in 1986. In addition, t he succes sful development and release of PHED in 1992 and its s ubsequent use by regulators to suppo rt many occupational risk assessments strongly suggests that the BHEDTM database will find even gr eater use and its benefit realized.



## 10 S t udy Risks

Most of the studies desi gned within the AEATF II program are intended to reflect what would be considered “normal” acti v i ty pattern s for m any subjects and indiv i duals handling biocidal products. T here are some situations in which an activity is semi-scripted, within the r ange of e x pected practices, to provide diversity in parameters that may be related to expos ure inclu d ing the amount of active ingr edient handled and t he duratio n of tasks. However, in each of the proposed studies, careful consideration is given to the potential study-specific risks to the individuals involv ed and what specific effort s will be m ade to minimize or eliminat e these risks. Gen e ral risk considerations are presented in this section. Each proposed st udy protocol will indicate st udy-specific risk considerations and c o mmunication of thos e risks to potential st udy subjects as part of the informed consent process. If a s ubject is injured as a result of being in an AEATF II study, medica l tr eatment will b e available fr om a near-by health care facility that knows abo ut t he study. The study sponsors (i.e., AEATF II) will cover any cost associated with a subj ect’s medi cal treatment that is not cover ed by their own insurance or by a third party.



### 10.1 Description of Potential Risks to Subjects

The risks of the study can be broadly se parated int o two general categories, those due t o potential exposure to an active ingredient and t hose due to phy sical stress that may arise from certain acti vities. Each must be considered t o determine what can be done to avoid unnec essary risks.

Physical risks can arise from cli m atic c onditions, extr a clothing (in the form of wearing two complete layers of clothi ng, the inner an d out er dosimeters), and exaggerations of normal activities. Some of these aspects can be controlled by

43location and ventilation, yet other aspects ar e directly a result of the study design and cannot be easily altered without inv a li dating the data collec ted. However, those aspects that cannot be easily altere d should be carefully ev aluated, based on existing data (e.g., habits and practi ces or produc t use surv ey data) and expert opinion, to minimize the exagger at ion of activity (e.g., upper-bound for total time spent performing a giv en tasks, such as surface wiping associated with cleaning activities during a day) without compromising the study results. The exaggeration of activity patterns can le ad to concerns related to potentia l ergonomic issues (e.g., fatigue and heat st ress). 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 exposur e to an active ingredient or solvent used to remove t he chemical from skin. This ca n be controlled to some extent by selecting active ingredients that have less toxic profiles and have already been evaluated and approved for the applic ation being investigated. In addition, preference is for actives that have been evaluated and do not require personal protec tive equipm ent for their usage in the application. This criterion generally assures that the product has been evaluated with very health prot ective assumptions and is appr ov ed already for this applicatio n. T h e counter to this is that some exagger ation of usage may be required to obtain detection of the acti ve ingredient on the dosimeters. An example of this is pouring liquids; typically a pouring event is 1 to 5 mi nutes and the circumstances are such that detection of the active in gredient on a dermal dosimeter is unlikely

, even with extremely low detection limits. Hence to be sure t hat exposure is actually being measured, it is necessary to incr ease the amount of produc t poured and the length of time t hat the activity is carried ou t. For the evaluation it may be necessary to pour liquids from several cons ecutive 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.



### 10.2 Risks 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 illn ess (e.g., heat ex haustion and heat stroke) can result when the body is subjected to more heat than it can accommodate. Weather, workload, c l othing/PPE, and lack of worker conditioning can inc r ease the risk of a worker experiencing heat-induced illnesses. I n addition t o causing s e rious phys i ological conditions, early symptoms of heat illn ess such a s dizziness and confu s ion can le ad to an in creased risk of occupational acc i dent s 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 si gns and symptoms of heat stress, and the Principal Investigator (Study Director) and all observer s promote drinking water

44and taking rest breaks. Researchers will stop study participation for subjects who experience heat-related illness.



### 10.3 Risks of Exposure to Surr ogate Antimicrobial Pesticides

Toxicology Hazard to Subjects

For each specific ac tive ingred ient chose n , AEATF II reviews the available toxicology data to assure no undue hazar d. Government-authored summaries of the data are given to an IRB as part of the review package. The surrogate chemical / active ingredient s e lection criteria include selection of active ingredients that have low to xicity profiles, with goo d warning properties and reversible effects. Th is selection process, however, is limited by t h e fact that the active ingredient and the spec ific pr oduct 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-lev e l risk assessment completed in the past.

Likelihood of Serious or Irreversible Effects

All of the applic ations bei ng evaluated by AEATF II in v o lve short-term expos ures, in most cases an expos ure period of less than 8 hours. Hence, the main concerns will be for acute ex po sure potential. As a result AEAT F II focuses on reviewing the likely expos ures to the acti ve ingredien t and assures there is no undue risk from acute expos ure. Further , constant monitoring of the expos ure scenario and options for immediate terminat ion by the participa nt or the study observer are included, and every effort is ma de to avoid injury or over-exposure. The subjects selected to participate in a study will be experie nc ed in the use of the equipment and types of products invo lved in that particular study. Any subject with known allergic reaction to the product and specific pesticide us ed in the study will be excluded from participat ing. At high concentration some antimicrobial chemic als can produce derma l irritation, but this is not commonly seen at the end-use dilutions being h andled in AEATF II studies. Any sever e dermatitis or allergic r eactions would result in stopping a subjec t’s participation in the study and providing access to any necessary medical treatment, including transportati on, if needed, to local medical c a re facility(ies) identified prior to a study’s initiation.



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

Risk from irritation due to expos u re to t he washing solution (e.g., 50% isopr opyl alcohol and water) used on the face/ne ck and hands can occur if the s ubjects have e xisting abrasio ns. Subje cts will be informed prior to the study that the washing solution (e.g., alcohol/water mix t ure) used to rinse their hands and wipe

45their face and neck may sting, if they hav e any cuts or abrasions on their ha nds or face.

At breaks during AEATF II studies and at the termination of the study, the subjects will h a ve their hands washed by study personnel us ing a was hing solution (e. g ., 50% isopropyl alcohol/water). Further, at the end of the study after the final wash solutio n samples are collect ed, subjects will proce ed 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 ev ent and note any obser ved irritation to the skin. Any subject showing an adverse skin reaction will be asked to immediately stop further participation. The subject’s exposed sk in will b e g ently washed with c l ean water and mild s oap to rem o ve the tes t product, and the area will be gently dried with a clean towel.



### 10.5 Psychological Risks

Study subj ects 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 (w ork) clothing provided by AEAT F II. This is necessary to make sure that the specia l dos imeter underwear fits pr operly, and that it and the outer dosimetry clothing doesn’t get cont aminated when the test is over. The researcher who helps will be of the same s e x, an d will be the only other person with the s u bject. The subjects will be w earing their own underwe ar 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 pr egnancy test on the day of the research. No one but them and one female researcher will know those resu lts, and they will not be recorded.

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## 11 Procedures for Monitoring and Preventing Risk to Subject s

During all AEAT F II studies, the Study Direct or (Principal Inves t igator) and the field investigators share t he responsibility for awareness of heat illness and toxic responses in study partici pants. All such researcher s are required to complete training on the ethic a l treat ment of test subjects. Prior to study conduct, t h e Principal I n vestigator will asses s the avai lability of medical assistance in the locality of the study and identif y appropriate emergency medical facilities that may be utilized. This information is incl ud ed in the Institutional Revie w Board (IRB) application. IRB protocol and co nsent form approvals are prerequis i tes to submission to EPA or HSRB. During eac h study, every partici pa nt will a lwa ys have a researcher assigned to observe his/her handling practices and this “observer” will have the primary respons ibility for detecting adverse ef fects. Typically t h is obs erver is close enough to the worker to have a conversation. Observers are trained to recognize heat stress and ar e informed of the most likely acute effects of overexposure to the pesticide being used in the study

. Should an adverse reaction occu r during an AEATF II study, emergency procedures will be implement ed. These procedures typically include halting subject participation, removing the subj ect from the offending env ironment, and calling 911 for medical a ssistance if needed. In addition, AEATF II has an adverse effects reporting policy in place to n o tify EPA o f potential new find ing s as required by FIFRA Section 6(a)(2).

As mentioned above, t he prim ary means of preventing toxi c effects is to require subjects to wear ap propriate clothi ng a n d all requ ired PPE. During st udy conduct, observers will remind s ubjects that PPE must be properly worn wh en handling th e pesticide

. Non-compliance o n the part of the work er will res ult in discontinuing the monitoring for that worker.

For heat stress, the following procedures will be followed by r e searchers to prevent illn ess in study participan ts:

• Ensure plenty of water and sports drinks are available for the subjects.

• During wor ker orientation, remind the subjects of the risk of heat stress, suggest they drink some water before they start work, and let them kno w how/where they can get water during the monitoring per iod.

• Urge subjects to drink water during the monitoring period and remind them that thirst does not give a good indic a tion of how much water a person needs to drink. Ther e is no need to take hand washes or stop inhalation monitoring during a water break.

47• Observe subjects during the monitori ng period and be aw are of the signs and symptoms listed below.

• Require subjects to take rest br eaks when early signs or symptoms of heat illn es s are present (e.g., headach e s , dizziness

, fainting, weakness and moist skin, mood changes, mental confusion, upset stomach or vomiting; for example, http://www.osha.gov/Public ations/ o sha315 4.pdf.

• Regularly monitor temperat ure and relativ e humidity.

• Stop the monitoring as discussed below.



### 11.1 Me dical Ma nage ment and Stop Rule

The subjects will be checked for allergic a nd irritant skin reactions, particularly redness, eczema, itching or pa in. The su bjects will be asked to immedia t ely report any adverse effect including skin r eac tion to the Principal Investigator (or designee). Any subject showing an adver se skin reaction will be asked to immediately stop further par ticipation. As noted previously, the subject’s exposed sk in will be gently washed with clean water and mild soap to remove the test product, and the area will be gently drie d with a clean towel.

Since subj ects will wear an extra lay e r of c l othing, the risk of heat related illness may be inc r eased. The Princ i pal Investigat or will disc uss the sy mptoms of heat stress with the subjects. St udy personnel will monitor subjects for symptoms and signs of heat stress, and will monitor the ambient temper ature, relative humidity, and heat index (HI). Generally, if the HI exceeds 95 degrees Fahrenheit (F) the research will be discontinued.

Study pers onnel will be instructed to info rm the Princ i pal Investigator immediately of any skin reactions, heat stress, or other unanticipated adverse effects observed or reported duri ng conduct of the study. The medical management procedures set forth in AE ATF 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 advers e e ffects. If two or more subjects withdraw or are withdrawn from the study for the same medical reasons, the study will be sus pended until the caus e 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 subjec ts will be contacted by the Principal Investigator to determi ne whether furt her medical management is appropriate.

The Principal Investigator will maintain a record of adverse health observation s and reports, and follow Spo n s o r, IIRB, EPA an d California DPR p olic ies for medical ev ent reporting. Sufficient pers onnel will be p r esent at the study site to maintain an appropriate level of tec hnical support, scientific supervision and observations relevant to the safety of test subjects.

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### 11.2 Additional Procedures for M onitoring or Preventing Risk to Subjects

If an OSHA permissible expos ure level ex ists for an inactive ingredie n t, an evaluation will conduc ted by AEATF II to assure exp o sure 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 measuremen ts below the detection limit (so that a majority of measurements ar e above the detection limit for stati stical inferences and expos ure assessment). This specific aspect is what drives the need for preliminary evaluation of ex isting data to determine adequacy of detection limits.

Each protocol will address emergency proce dures if an adverse reaction occurs. Where the study is conduct ed an d what the applicatio n method is will influe nce this to a certain degr ee, but the objec tive is to hav e the needed assist ance available during the c onduct of the study. This would inclu de evaluating potential physical hazards (i.e., ergonomic concer ns) throughout the study as well as potential exposures to active ingredients.

As noted e a rlier each principa l investigat or assesses the availa b ility of medica l assistance in the loc a lity 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 out side 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 t h at could be realistically simulated in a laborat ory setti ng. In some cases AEAT F II must meet local regu lations that do not always allo w AEAT F II to be completely consistent across various studies. However, the sa me objective always exists, to collect useful, scientifically defens ible data of the highes t quality possible, while minimizing exposure and protecting subjects.

As discus sed later in this document, scripting in AEATF II studies will be minimized and will primarily involve design featur es that ensure monitoring intervals that represent a typical day’s dur ation (i.e., not excess ively short or long) and coverage of the practical r ange for amount of pr oduct handled within each use s cenario. However, study partici pants will b e usin g fa miliar e quipment in a manne r that is typical for them. Therefore, AEAT F II believes the increased risk of heat-related illness in certain condit ions is the only ad ded risk that study participants will likely e n counter.

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## 12 Risk Assessment for Anticip a ted Exposures to Proposed Surrogate Chemicals

AEAT F II monitors e x pos ure to subjects who hand l e commercially ava i lable antimicrobial pesticide products. In gener al, useful surrogate chemicals [e.g., didecy l dim e thyl ammonium chloride ( DDAC), CAS No. 7173-51-5] have multiple uses (e.g., multiple use sites and app lic ation methods), formulation types, minimal PPE requirements (i.e., low ac ute toxic i ty), and reliable and validated analytica l methods. AEAT F II will utiliz e extant risk assessments conducted by EPA for t he antimic robial pesticide to b e used in each stu d y to infor m the potential for excess ive antimicrobial pesti cide risk that subjects may experienc e as a result of particip ation in an AEAT F II exp o sure monitori ng study. If a risk assessment does not exist for the expos ur e scenario with that c h emical, AEAT F II will cond uct an assessment using EPA-rec o mmended methods.



## 13 Risk V e rsus Benefit Comp arison

By monitoring exposure to professional ant imicrobial handlers (or consumers) who follow their normal practices, but w ear an additional layer of clothing (as an inner dosimeter which traps chemical t hat penetrates the work clothing), AEATF II’s monitoring program generally presents a reduced risk to subjects. The ri sk of dermal toxicity is actually reduc ed and t he added risk of heat illness is mitigated by a medical management program whic h emphasizes measures to prevent heat-related illness. The potential benefit s to antimic robial work ers as a whole and to consumers and society in general, for example, in the form of more accurate measurements of potential expo sure to antimicrobi al pestic id es to inform safety evaluations, versus study-specific risks, will be included in the discussion of each study protocol.

Against the slight ris ks are balanced subs tantial benefits. Pr oducts containing antimicrobial chemicals are used extens ively in hospitals, schools, homes, etc. to control pat hogenic bacteria and viruses known to pro duce increased morbidity and mortality in humans, domestic animals and pets. Measuring exposure of subjects in this research study wil l produc e reliable data about the dermal and inhalation exposure of subjects and the general populatio n performing these tasks. The resulting data will improve th e completeness and accuracy of the database u s ed by the EPA to ass e ss exposu r e to these chemical s. The ability to accurately predict risk may allow other ch emical c l ass e s of antim icrobials to also be registered based on exposure estima tes generated from the data to be produced by this study.

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## 14 Characterization of Potential S t udy Particip ant s , Exposure Monitoring Methods and Ancillary Information

Appen dix A identifies the antimicrobial pes ticide hand ler scenarios that will be covered by AEATF I I ’s generic expos ure database, BHEDTM. However, it is important to point out severa l res t rictions 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 parti cipants 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 studi es.

Reproductive Status of Female Subjects

Women who are nursing or pregnant will be exc l uded from the study. Nonpregnancy will be c onfirmed by an over-the-c ounter urine test just prior to women participating as study subjects for st udies involvin g intentional exposure

Experienc e of Subjects

Only subjects who have experience perfo rming the particular task will be a llo we d to participate.

Monitoring Period Dur ation

All monitoring events (MEs) will be desig ne d to represent a normal workday for the particular task being monitored. G enerally, 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 sa mpling regimes monitored subjects for only a few minutes. Avoiding very short mo nitoring int e rvals will ensure that daily exposure estimates are not biased by unusual conditions during that short interval. Some work t a sks (e.g., mopping) are performed intermittently through a work day. When monitoring ex posure for such tasks, t he work s chedule will be compressed to obtain the typical duratio n of exposure or amount of activ e ingredient handled in a normal work day.

51Product Label Confor mance

All subjects will be required to perf orm pesticide handling tasks in conformanc e with the label requirements. BHED is designed to refl ect 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 requ ired by the label an d researcher s will remind participa nts to use that PPE s hou ld they be observed n o t wearin g the PPE d u ring e x p o sure 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 b ase d on OSHA-specified PPE as identified o n the Materia l Safety Data Sheet and/or other wo rkplace safety requirements.



### 14.2 Expos ur e M o nitoring Tec hniques

Techniques to monitor pesticide handler expos ure fall into three main categories. These are area (environmental or indus trial hygiene) monitoring, pas siv e dosimetry measures taken on the indi vidual, and biomonitoring taken from indiv i duals.

Area Monitoring The oldest and leas t accurate exposure monitoring technique to estimate indiv i dual expos ure is area monitoring. Th is monitoring primarily consists of air monitoring in the general vic i nity of s ubjects and sometimes surface monitoring of the pes ticide in t he workplace. This techni que i s a tr aditional industrial hygiene measure and can be used to monitor the pesticide manufacturing workplace to ensure that environmental le vels ar e controlled, but it is not particularly useful in quantifying t o tal work er exposure. This method may giv e a reasonable approximation of inhalation expo sure potential, but does not allow a quantification of dermal expos ure. Past monitoring studies have cons istently demonstrated that the dermal route is the mo st significant route of exposur e to pesticide handlers (Wolfe, 1976).

Passive Dosim e try Passive dermal dosimetry taken on s ubjects consists of (1) patch ( e.g. , gauze pad) dosimetry; (2) w hole body garment dos imetry and, (3) hand/face dosimetry techniques. Hand washes and patch dos imetry, 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 provid es a measure of external (dermal) exposure. The use of whole body dosimet ers, which are usually sectioned int o standard body part areas ( e.g. , upper arms, lower arms, upper legs, lower legs, front and rear torso) prior to extrac tion and analy sis, prevents the need to extrapolate from a small patch size to the who l e body part. Personal air monitoring devices have been us ed to char acterize exposures via the inhalation

52route by collecting a known volume of ai r in the breathing zone of the worker and analyz ing for the mass of pesticide of interest present.

1. Patch Dosimetry Patch dosimetry was first utilized in the mid 1950s fo r pesticide s. With patch dosimetry the potentia l exposure of the subjec ts' skin and c l othing is measured using a number of absorbent cloth or paper patches, a ttached to body regions, inside and outside clot hing. Placement of patches to represent the entire body (head/face/neck, upper and lower arms and legs, and f r ont and rear torso) are needed on eac h individ ual monitored. The surface area cov e red by the patc hes represents <10% of the total body surface area. After a defined per iod of expos ure, the patches are removed and analyzed for pestic i de c ont ent. The quantity of a pesticide on a patch of known area is then relat ed to the area of body regio n on the assumption of uniformity of depositi on over that area. Body part surface areas can be obtained fr om standard reference texts and exposure guida nce d o cuments (EPA 199 9).

The assumption of uniform depositio n is probably the principal disadva ntage of the patch techniqu e. This is illustrated by the extrapolation of the v a lue given by half the limit of quantification ( L OQ) to the total body part; this may give a s ubstantial under or over-estimate of exposure. The patc h method may giv e significant under- or overestimates of exposure, depending on w het her the patches have captured the non-uniform, random depos ition of concentrate splashes or spray droplets. Individual body region exposure v alues are then added to give a total potential exposure expressed in mg/hour or mg/lb of product handled or applied.

2. Whole Body Dosim e try The whole body dosimeter method ca me into use during the lat e 1970s/early 1980s (Abbott et al., 1987). The method involves t he use of clothing, us ually two layers of cotton or cotton/blend ma terial, whic h act as the pesticide collection media. The outer layer of clothing s hould be representative of what the subjects normally wear. The inner layer, usually ‘long johns’, repr esents the skin. The method overcame one of the inherent pr oblems of t he patch m e thod, i. e. the assumption of uniformity of pesticide deposition on the skin and c l othing. Ex pos ure 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 c an also be used, in which the skin of the face and anter ior and posterior neck is wiped wit h cotton swabs containing a suitable solv ent to remove the pesticide residues.

PPE requir ed by the product lab el are wo rn over the sampling clothing

. The selection of sampling clothing should err on the cautious side by

53utiliz ing th e minimu m clothing that might be worn under the prevailing conditions. The use of the whole body method ov ercomes the perceived problem of non-uniformity of deposition. F u rthermore, extrapolat ion from small target areas to la rger body regions is not necessary. For these reasons, the method is believed to gi ve a more accurate estimate of potential and actual dermal exposure.

3. Hand Wash EPA (19 9 6 ) reviewed the literature on stu d ies that h ad inc l ude d han d exposure measurements and concluded that their contribution to total exposure ranged from around 40% to 98%, de pending upon the application method. The methods fo r measuring han d exposure include lightweight absorbent gloves , and swabbing or rinsing the hands in various solvents (EPA 1 996 ). Mild deter gent solutions c an be us ed in th e handwash techniq ue , for exam ple ‘A eros ol’ OT. Howev e r, AEAT F II intends to use a was h solution t hat wil l ha ve a high solub ility fo r the test material. This may result in varying the s o lvent from study to study. For

ex ample, in the first 2 st udies proposed by AEATF II, 50% isopropan o l /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 eval uate the accuracy of any procedu r e.

The generally held view is that the use of gloves res u lts in a s i gnifican t over-estimation of total dermal expos ure, ow i n g to the retenti o n of more of the pesticide than wo uld otherwise be retained by the skin. Gloves als o contain for e ign materials such as si zing, which may be co-extracted with the pesticide. At low levels of cont amination this may cause analytical difficulties. However

, glove c o ntam ination with dirt and grease arising from the worker’s activities are a more lik ely cause of analytical problems. It is important to use a solvent that adequately s o lvates the active ingredient. This can be ded uced from water and solvent solub ility. Inevitably there is a loss of standardizati on of the intervals at which han d wash sam p les are taken. However, it does give som e information on the extent of hand exposure that might be of value in ov erall data interpretation.

Measurement of hand exposure through hand wash es has become standard in exposure monitoring because it is cons istent and mimics the method of hand dec ontamination under normal work conditions. Hand washing not only provides the best meas urement of exposure, it is also more accurate than using collectio n media like cotton gloves. In monitoring via hand washes, residue on ly 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 surfac e, has a far greater capacity to accumulate and stor e residues

. Howev e r, even hand was hes can signific antly overestimate exposu re bec ause most of the residue measured as expos ure should actually slough off or be washed off the skin surface following normal hygiene (w as hing) during and at the end of a

54work shift. Further, several lines of evidence suggest that material washed from hands is not all bioavail able: 1) The amount washed off in well controlled rat dermal absorpti on 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 pestici des a pplied in s olution to humans (Ross et al. , 2000); 3) Much of the pesti cide to which subjects are exposed c an be adsorbed to dust, and in this form it is not as available t o skin for absorption, thereby furt her reducing availability (Driver et al.

, 1989); 4) H and 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 t he work day but is counted as part of the exposure; and, 5) If gloves are not worn, hands frequently receive the highes t dose density, and percent dermal absorption is typically inversely pr oportional t o dose density (Ross et al., 2001).

Inhalation Monitoring For pesticides that are poor ly absorbed via the skin, t he inhalation rout e can become the most important route of absor pt ion. An important review of personal sampling methodology for field monitoring of airborne pesticides was published by Lewis ( 1976). A personal air sampling method is t he most appropriate fo r the determination of potential i nhalation expos ure of subjects. Several techniques are available such as solid adsor bents for vapors and sizing and filter cassettes for particles, all attached to battery powered personal sampling pumps. A personal s a mpling technique inv o lving sam p ling dev ic es located in the breat h ing zone and sampling pumps is preferred, bec ause it is a practical way to get a representative sample. Breathing rates fo r the calculation of inhalation exposure from airborne concent ration data can be obtained from standard reference texts such as EPA’s Exposure Fa ctors Handbook (1999).

The inhalable fraction (all material c apable of being drawn into the nose and mouth) is the most biologic ally r e levant fraction to m easure. An example of a suitable device is the Inst itute of Occupational Medici ne (IOM) personal sampling head des igned spec ifically to collect this fraction (Vinc ent and Mark, 1987). For use of this device, a sampling flow rate of 2 L/min is a spec ific requirement.

Examples of suitable adsorbent material s for some volatile c o mpounds are activated charcoal, `Tenax' and XAD-2 resi ns mounted in stainless steel or glas s tubes. The choice of material shoul d be determined by analy t ical 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 's ampling tr ai n'. Th is train allows retention of any vapor stripped off the filter on the resin. The mate rial on the f ilter can be analyzed both gravim etrically and/or chemically and an estimate made of the pesticide c ontent 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 t he possibility of stripping material from the resin by the relatively hi gh flow rate of 2 L/min.

55Biomonitoring Biomonitoring, also known as biologic a l monitoring, typically uses the amount of pesticide ( o r its metabolites) detected in the urine of expos ed individuals to obtain an accurate measurement of t he total amount of pesticide actually absorbed by the worker via all routes (i nhalation, dermal, and incidental oral ingestion). In order t o use biomonitori ng quantitative l y, 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 spec ific expos ure pathway, i. e., biomonitoring data cannot generally be used generically

. However, biomonitoring data prev ent the need to extrapolate from external dos imetry to internal dose, and can ser ve as valuable validation tools for passive dosimetry.

Nature of Testing Guid elin es

Regulatory agencies frequently collabor at e to make expos ure monitoring guidelines harmonized. A good example is the Seri es 875 gu idelines of US EPA that were designed with multinational in put starting wit h a m eeting in The Hague in 1992 and punctuat ed with meetings in O ttawa, Toronto, and W a shington, DC that culminated with the i ssuanc e of OECD and EPA gu idelines that are very similar (OECD, 1997; EPA, 1997)

. Justification for Passive Dosimetry

Because it is difficult to isolate and validate particular dermal dos imetry methods, the best v a lidation is a co mparison of the sum of passive dosimetry methods against the biomonitored dose. The data ex amined in a recent review of both proprietary and publis hed studies demonstrat ed an excellent correlation between passive dosimetry and biomonitoring (Ros s et al., 2007). Passive dosimetry as a measure of dosage appears to be consistent with biomonitoring with no bias, i.e.

, there is no tendenc y to over or under estimate expos ure. This ev alu a tion demonstrated that the total absorbed dos e (or daily dosage) estimated using passive dosimetry for import ant handler and reentry scenar ios is generally similar to the measurements for those same s c enarios made us ing human urinar y biomonitoring methods. Furt her, this is strongly s upport ed by statistical analysis of individual worker passive dosimetr y: bi omonitoring ratio and variance with i n and between studies. The pas sive dos imet ry techniques currently emplo yed yield a repr oducible, standard methodology t hat accurately and reliably quantifies exposure and does not underestimate daily absorbed dose.



### 14.3 Role of Ancillary Study Information

Every exposure monitoring study coll ects data that characterizes the environmental conditions and behaviors that may have some influence on worker

56exposure. The envir onmental data include s temperature, humid ity, airflo w or wind speed and directionality, light levels , detailed desc r iptions of t he equipm ent used to mix/load or apply the chemical, measurements of the amount of chemical used dilution rates, water source, chem ical source, etc. Behavior of eac h indiv i dual monitored including work rate , personal hy giene, neatness or attention to detail, personal habits e.g., tobacco , chewing gum , method of using gloves, evidenc e of fatigue, etc. are all record ed either with field notes and/or with photography including vi deo. Although mos t of this inf o rmation cannot be used to quantify indiv i dual expos ure, it can be extremely us ef ul in understanding how conditions/behaviors observed during the st udy compare with “normal” conditions or behaviors, and how any unusual conditio n s may have contributed to differences in expos ure.



## 15 Incorporation of Existing Dat a into BHEDTM

To establis h the need for additional ex pos ure monitoring data involving human subjects, AEATF II conducted a systematic revi ew of all availabl e, relevant data for each scenario proposed for inclusion in the multi-year program. This process included public ly available data (e.g., specific data subsets from PHED) and proprietary data sour ces (e.g., CMA study ). Each of potential data sourc e has the potential to provide scenario-specific exposure data (MEs) and associated supporting information for inclusion in BH EDTM. A data evaluation process was developed for the evaluation of existing da ta sources and involv es the following steps:

• Developm ent of data acceptability criteria : The existing data acceptabilit y criteria addressed general study design and expos ure monitoring techniques, including the anal ytic al and quality control aspects of the studies. They ar e detailed in Appendix D.

• Primary review : A process that involved the screening of handler exposure data from PHED v e rsion 1.1, pub licly available data, and compensable data owned by AEATF II members.

• Second ary review : A detailed evaluation of data that passed the screening process for acceptab ility u nder the acce ptance criteria with decision records for each study review.

• Final review : A process that involved gu idance by r egulatory agencies inclu d ing the U.S. EPA, Hea l th Can a d a , and California EPA, on acceptance of the data for use within BHEDTM.

Much of the existing dat a are deemed uns uitable fo r a generic database due to poor QA/QC (generally low or insufficient field fortification recoveries), a preponderance of non-quantifiable residues, or the us e of testing conditions that do not repr esent current pesticide handling practices in North America. Another key technical issue t hat eliminated so me existing data was the decision to

exc l ude exposure dat a for subjects who wore more than a single layer of

57clothing. This decision was discussed with the Regulatory Agency Adv i sory Committee who agreed that a modern generic database would be most useful if it contained exposure data for mi nimal cl othing and PPE situat ions. Regulators prefer to estimate protected exposure s (e.g., dermal exposu re under coveralls plus normal clothing) from unprotect ed exposure measurements (e.g., dermal exposure under just one layer of normal cl othing) than vice versa. Therefore, BHEDTM has been designed s o that clot hing/PPE protection factors can be estimated by a user from t he two dosimetry clothing layers in order to esti mate protected expos ures (typical ly workers), or consumer (unprotected) exposures

.



## 16 Monitoring Event Selection

Each AEATF II antimicrobial -handling sc enario will be addressed by o ne (or possibly more than one) study. The pu rpose 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 doc u ments and study protocols will provide the rationale and description of the spec ific ME selection proc edures. This section s u mmarizes those aspects of the ME design c o mmon to all scena rios. Appendix E p r ovides a more extens ive description of the concepts and procedur es for selectio n and construction of monitoring events.



### 16.1 Predicting Fut ure Generic Expos ur es with Monitori ng Events

For the purposes of the AEAT F II moni toring program, the bas ic element of a scenario is consider ed to be the handler-day (HD). Each handler-day corresponds to a particular wor ker and the scenario-related activities that he performs during a single work day. R egulatory interest for each antimicrobialhandling s c enario is centered on predic t ing occupational exposure under a specific set of generic future handler-day condi tions. In particular, it is desired to characterize exposures resulting from the future use of an arbitrary (and perh aps currently non-existent) antimicrobial acti v e ingredient at some arbitrary, but quantifiable, amount of active ingredient contact.

An ME is the basic tool used by t he AEATF II Monitoring Program to predict exposures. Each ME is a set of scenar io-specific handler-day (HD) conditions that have been exper imentally s e lected (i.e., chosen, simulated, or constructed) to represent expected future HD conditio ns. Every ME is als o monitore d to obtain a measurement of t he actual expos ure resulti ng from the simulated or selected HD conditions.

58An ME can predict f u ture exposure onl y if the handling conditions of the ME represent future HD conditions. Bec ause each ME is expens ive, it will be possible in practice to construct only a small number ( N ) of MEs. Obviously, a set of only N MEs c annot 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 c onsidered 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 ot her active ingredients.

2. Exposure is consider ed to be proportional to the true amount of active ingredient contacted by the antimicrobial ha ndler. The exposures obtained from MEs are expressed re lative to a measurable normalizing factor (NF) is expected to be proporti onal to amount of active ingredien t
contact. Then exposures for any futu re NF level of interes t can be predicted by multiplying the normalized exposures by this future lev e l.

3. Expert knowledge of possible handler conditi ons expected throughout the scenario if obtainable. This permits construction of MEs from HD conditions selected (i. e ., simulated or c hosen) to represent a div e rse set of future handler-day conditions.

If the ME conditions can be appr opriately c hosen then a useful set of MEs can be constructed and used as a pr edictor of future HD exposure in an aggr egate

sense.

An expos ure distribution is a reasonable aggregate description of future handlerday expos ures for a scenario. The future HD distribution descr ibes the lik ely exposure that would r e sult if one were to randomly pic k a future HD among those using ai X when the level of the normalizin g factor is HX. There are actually a series of predicted exposure distributi ons, one for each possible value of HX. Howev e r, since any predicted expos ure can be computed from the normalized exp o sure, it is simpler to focus only on the distribution of normalized exposure.

The complete normalized expos ure distri bution is rarely needed. Regulatory interest most often focused on just tw o general aspects of this distri bution:

• The middle values s u ch as the arithmetic mean or the median. Thes e exposure values tend to characterize average or ‘typical’ exposure levels.

59• The larger values of expos ure possible, suc h as the 95th percentile of the
distribution. This aspect better char acterize the extrem e, one-time, worker
exposures.

It is desired that the set of predicted exposures obtained from the set of MEs adequately characterize the middle and la rger values of the future HD distribution. Technically, a set of c onstructed MEs cannot be a random sample from a distribution of future HD co nditions. But some random samplin g interpretation might still be a con v enient an d reasonab le model for how the set of predicted expos ures from the MEs relate to t he future (normalized) ex posures for an arbitrary antimicrobial. This permits t he use of conventional statistics, such as means and percentiles, calculated fr om the obs erved ME exposures to approximate the ‘middle’ and ‘larger’ expos u res expec te d in future HDs. For the AEAT F II Monitoring Program, confidence in this appr oximation is improved by using a reasonable reference random sa mpling 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 pos sible. Diversity s e lection is an attempt to obtai n, as much as is practical for small sample sizes, a diversity of conditions that are expec t ed to influence exposure, either directly or indirectly. T h is increas es the lik e lihood that the range of conditions in the future HD population expec t ed to impact expos ure is reflected in the ‘pseudo-sample’ of MEs as well.



### 16.2 Determining the Number of M onitoring Events

16.2. 1 The T w o- Stage ME Selection Process

Althoug h 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 s pec ific locations and s pecif ying 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 selecti ng one or more subjects and handling conditions within eac h site and constructi ng the MEs. For simul a ted condition studies the MEs are c r eated by assigni ng appropriate subjects to scenario tasks under conditions that are expec ted to exist in the future HD population. F o r in situ studies , appropriate handler-days are selected from among existing subjects and conditions that are expected to represent future HD conditions.

In general, N C sites are selected at the first stage and N M monitoring ev ents (MEs) will be obta i ne d within ea ch si te at the seco nd stage. Wh en N M is gr eater 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 si milar than those in different clusters. This correlation usually means that the sm allest total sample sizes (i.e. total

60number of MEs) are attainabl e when there is only a s i ngle ME per site. On the other hand, there are often substantial over head costs per site that make multiME sites m o re efficient.



### 16.2. 2 The T w o- Stage Random Sa mpling R e fere nc e Model

In the strictest sense, sample sizes can only be det ermined using statistical theory alone when either

1. There is as sumed random, represent ative s a mpling from a popu lation and the goal is to estimate some char acteristic of that population; or

2. There is assumed random ization of experimental unit s to treatments and the goal is only to compare or to c ontrast treatments in some manner.

Only in these two s i tuati ons will statistical theory pr edict how incr easing sample size decreases estim a tion error. In other experimental situat ions, sample size must be determined using one of the two ‘random’ situations above as a reference model. The random reference model is c o nstructed so that it refl ects 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 m odel is used to establis h benchmark samp le sizes that can satisfy benchmark objectives. Although rarely stated exp lic itly, the use of reference sampling models and benchmark objecti ves are quite common.

Because a l l AEATF II scenarios have a two

- stage sele ction structure, they will a l l use the same reference sampling model

. For each s cenario, two-stage random nested (or cluster) sampling is t he refer ence 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 pot ential activ e ingred ient contact factor, is lognormally distributed with geomet ric standard deviation GSD.

2. There are N C clusters (i.e. sites) and N M MEs per cluster. The total number of MEs in a scenario is, therefore, N = N C ×N M

. 3. The within cluster (i.e., within-site ) correlation of (log) normalized exposure is equal to ICC.

The reference sampling model incorporat es a two-stage selection structure and the potential for correlation within cluste r s, but ignores any effects of diversity selection. The ICC is irrelevant to the future distribution of normalized exposure, per se. Howev e r, this intra-cluster correlation is a necess a ry part of the

61reference sampling model bec ause the MEs are obtained in clusters (i.e. there are multiple MEs per site).



### 16.2. 3 Benchm ark Objecti v e

The bench m ark objective for all AEAT F II m onitoring program scenarios will have the general form:

If there are N C

clusters and N M

M E

s 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 valu es 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 ac cura cy of T. To satisfy the condition (1) above 95% of the time requires that the 95 th perc entile of fRA

, fRA 95 , be no greater than K. If the 2. 5th and 97.5th percentiles of t he sampling distribution of T are denoted T 2.5 and T 97.5 , respectively, then this benchmark objective can als o be stated as:

(2) fRA 95 = M a x ( T 97.5 / θ

, θ / T 2.5 ) ≤ K

EPA provides guid a n ce to AEATF II on the minimum degree of ben chmark accuracy needed for regulatory use in par ticular scenarios. The current consensus is that estimates of the geomet ric mean, the arithmetic mean, and the 95 th percentile should be accurate to withi n approximately 3-fold of their true value.

It should always be kept in mind, however, that this objective is specified in te rms of the reference random sampling model

. This reference sampling model does have the s a me two-stage nesting structure as the actual ME selection approach. The lognor mal distribution assumption is also reasonable, robus t, and consistent with existing data. However, the refe rence distribution assumes simple random sampling at each stage. It does not, and cannot, incor porate t he combination of purposive and random diversity sampling actually used. As discus sed in Appendix E, the cons equence of diversity sampling is expected to be a tendency for the sampling variation of normalized exposure to be overestimated. The sample should over-represent ex tremes and under-represent the more common values. Such diversity

- oriented data co llected for this s cenario, but analyzed with respect to the two-stage reference distribut ion, is expected to have minimal bias for central tendency. In contrast, upper per centiles of exposure a r e expected to

62be, on the average, too large. Ther e 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 conser vative practice utiliz e d by regulatory agencies. A tendency to both consider and even overestima te upper percentiles is consistent with this practice.



### 16.2. 4 Sam p le Size

Under the reference two-stage random sampling model described above, the only quantities needed to determine relati ve accuracy of population parameter estimates are reasonable values for GSD nE and ICC. Such values could be based on existing exposure data for scenario-specific tasks, surro gate expos ur e data from similar tasks , and/or reasonable assumptions based on subject-matter expertise.

Given values for GSD nE and ICC, fRA 95 , can be computed for any combination of N C and N M and compared with K. Calc ulation of the 95% percentile of fold relative ac curacy is complex, however, and is usually best accomplished using Monte Carlo simulation methods. W hen the number of MEs per cluster, N m , is the same for all clust e rs, a straightforwar d simulation approach c a n be used t o determine fRA 95

. This procedure is:

1. Simulate a set of nor malized exposure dat a for N c c l usters and N m monitoring events per cluster using the two-stage reference sampling m odel.

2. From each set of simul a ted data, calculate T, the estimate of θ

3. Repeat st eps 1 and 2 above M times to get M values of t he estimate T

4. From these M T-values calculate T 2.5 and T 97.5 , the 2.5 th and 97.5 th percentiles of T, respectively.

5. Calculate the 95 th per centile of fold relative accuracy, fRA 95 , usi ng formula (2) above.

The number of simulations, M, should be some large number such as 1,000 or 10,000. This proces s can be cont inued until a combination of N C and N M are found that satisfy to benchmark objective.



### 16.3 General Diversity Selection Guidelines

In general, the objective of diver sity select ion is s i mply to obtain a diverse s e t of handler-day conditions from among thos e conditio n s possible in when an

63arbitrary ai is us ed in future sc enario- r elated tasks. These selected HD conditions are then used to construct moni toring 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 eac h site.

In the AEATF II Monitoring Progr am, the term diversity se lection is preferred in lieu of the phrase div e rsity sampling. This is to emphasize the fact that the future HD conditions used f o r MEs are selected from either existing or from synthesized conditions (or from both). This se lection of c onditions can employ both purposive and random elements. When there are multiple diver s e configurations available, random selection from among such configurations can reduce the likelihood of intentional se lection bias. On the ot her hand, when some possible ME configurations are more diverse or mo re cost effective t han others, it might be preferable to select these purposively.

Diversity should always be with respect to characteristics that are expected to impact e x p o sure. Div e rsity selec t ion atte mpts to create a s a mple that contains as many of the different conditions as possi ble that exist in the population. If the diversifying conditions are assoc i ated with expos ure, then a divers ity sample will tend to be more vari able with respect to exposure than wo uld a same-si z ed representative sample . In effect it will be a nalog ous to representative samplin g from a distribution that is more diverse than the actual future one.

Whenever possible, the charac teristics used should be meta-factors. Metafactors are characteristics that i ndirectly influenc e a number of other characteristics. For example, a worker is a meta-facto r bec ause substituting one worker for another alters a number of factors (e.g., behavior, physical appearanc e, stamina) that mi ght affect exposure. Ot her 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 diver sity selection. The number of possible combinations of factors that may impact exposur e will always greatly exceed the number of pl anned MEs. Consequently, only a fe w characteristics, preferably meta-factors , can be used effectively in diver s ity selection.



### 16.3. 1 Stratified Diver s ity Selection

As discuss ed in Appendix E, there are a number of acceptabl e approaches that can be used to achiev e diversity am ong ME hand ler-day condi tions. Among the formal methods, strati fied diversity selecti on is often the simples t to implement. In this approach available select able units (e.g., sites, MEs) ar e p a rtitioned into strata based on characteristics likely to impact expos ure. Each possib l e 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

64example, if there are thr ee units to be selected, then there should be at least three strata.) Diversity could be achiev ed by selecting ( p ur posively or random ly) no more than one unit from each stratum. If there are mo re strata than units to be selected, then a s ubset of the strata should be selected first. This c ould be either purposively (to increase diversit y) or randomly (to reduce intentional selection bias).



### 16.3. 2 Diver sity Selection of First-St a ge Units

At the first stage of ME se lection sites are the ‘selec table’ units. Diversity selection of sites means obtaining sites t hat are different from each other, on the average, with respect to some characteri stic(s) expected to im pact exposure. If sites are constructed environments, then they can be built to be different from each other with res p ect to important charac teristic(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 im portant characteristic(s), is a feasible approach



### 16.3. 3 Diver sity Selection of Second-Stage Units

The second stage selection units are the final MEs. The MEs should be diversified independently wit hin each selec t ed site. In most cases within- site diversity selection of MEs focuses onl y on two characteristics: subject and normalizin g factor.

Handling-day exposur es for the same indi vid ual (on different days) are expect ed to be correlated since worker characteri stics and behaviors are repeated. This is not true for different workers. Work er behaviors ar e expected to have great impact on expos ure. Consequently, divers ity is always increased by simply requiring that each ME be construc ted using a different i ndividual.

MEs should also be diverse with respec t to the normalization factor that is deemed appropriate for the sc enario. One feasible appr oach is to partition the possible levels of NF into N M strata and construct one ME fr om each stratum. In some cases (e.g. simulated-condition studie s) there is a pool of available workers that can be assigned to any NF stratum. If all possible c o nfigurations of assignment are equiv a lent, then workers could be randomly allocated to strata. If some allocations ar e non-equivalent (e.g . more cost effective or there are scheduling issues) then a purposive assignm ent of indiv i duals to NF levels might be preferable.

In other cases (e.g. In Situ/observati onal studies) worker availab ility depen ds o n the particular NF level chosen. Some i ndiv i duals may only work with higher NF levels an d some with only lo wer NF, say. Selection of workers could still be

65random, although random choice would be within eac h NF stratum. However, when suc h assoc i ations between subject and NF le v e ls exist, purposive alloc a tion might result in a more cost effective and practical configuration.



## 17 Description and Role of AEA T F II S t udies
In the context of the BHEDTM expos ure monitoring pr ogram a ‘study’ takes on a specialized role. It is t hat component of the program actually conducting MEs in accordanc e with the spirit of Good Laborat ory Practice (GLP) standards is sued by EPA (40 CFR 160). AEATF II studies meet the definition of study in the GLPs at 40 CF R §160.3, which reads in pertinent part: “Study means any experiment at one or more test sites, in which a test substa nce is studied in a test system un der laboratory conditions or in the environment to determine or help pr edict its effects . . . or other characteristics in humans . . . or media.” Ea ch AEATF II study will involv e conducting M E s under one or a num ber of scenarios. For examp l e, a study might be des igned primar ily for mopp ing app lic ation, but exposure of the mixer/loaders who prepare the mop liqui d mixture will be a different study. Because it has a very restricted, al beit important, function within the BHEDTM program, the study pr otocol need not cont ain extensiv e program information that is not relevant to the conduct of its parti cular MEs. However, each study protocol will reference the AEATF II progr am “governing docum ent.”

In most cases, AEATF II study timing a nd location are not dictated by the seasonality of work to be performed. Howev e r, finding s i tes and m a king arrangements for the studies is often cha llenging, particular ly in observational studies (e.g., subjects at a wood preser vative treatment facility) where no scripting of subjects’ activities while handling pes ticides would be allowed. Further, the AEATF II must identify suffi cient usage to define and follow a representative day of the s pecific pesticide handling ac tivity for each participant (or monitoring unit or event, i.e. ME). Sinc e some studies c onsis t of monitoring participants performing activities t hat ar e gov erned by v a riable 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 s a tisfy the revi ew sch edule of U.S. EPA and the HSRB, which must be done m any months before a study can be conducted. In cont rast, for studies that can be conducted in an experim ental manner, wherein a surrogate environment (e.g., experiment al chamber) can be used and various parameters controlled, a more definitiv e protocol design can be provided.

All MEs required by t he sampling des ign for most use scenarios c a n be collected in a single study, and most individual AEATF II study protoc ols will describe a single-study and single-year monitoring pr ogram designed to gener ate a range of exposure monitoring data for all discret e activities associated with that use scenario. An indiv i dual pr otocol typically represents a single, stand-alone study, representing MEs performing a single activ i ty. In some cases data from more than one study will be combined for a given scenario.

66Each exposure study is performed in accordance wit h long-standing EPA guidelines for conducting occupational and c onsumer expos ure studies (Durham, 1962; Wolf e, 1967; W H O, 1975 and 1982, OECD, 1981; NA CA, 1985; Chester, 1993; Worgan, 1995) as described in Seri es 875: Occupationa l and Res i dential Exposure Test Guidelines (U.S. EPA, 1986 and 199 6) and in accordanc e with U.S. EPA FIFRA Good Laborat ory Practice Standards (G LPs), 40 CFR, Part 160 (U.S. EPA, 1989). Th ese gu ide l ines are c o ns istent with gu ideline s used in o t her countries such as Canada, Australia, and members of the Eu ropean Union.

All of the individual study protocols wil l hav e many elements in common (albeit with scenario- and study-specific aspects ) in order to have consistency and uniformity in the data sets. Exposure m onitoring pr otocols differ mainly in the specific pr oduct used, equipment used, timi ng of the s t udy, location and activity performed.



## 18 Document ation Procedures

Exposure monitoring studies c onduct ed by the AEATF II are designed to measure potential exposure to subjects as they perfo rm specific antimicrobia l pesticide handling ac tivities. As spec ified in AEAT F II Standard Operating Procedures (SOPs; see current master list in Appendix G) and each s t udy protocol (and as required by GLPs) a ll aspects of study conduct are fully documented. Most of the information co llec ted 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 dur ing the study is also entered into the generic database, BHEDTM, for use in data analysis and for examination by database users in conj unction wit h data from other AEATF II studies. Information about subjects will b e recorded under their unique ID co de, and not in connection with their name or any other identifying information.

As required by GLPs , all raw data entri es are made in in k and are signed and dated by the person who enter ed the data. In addition , data corrections must be made by marking a single line through t he incorrect information, writing the correct information ins t ead, and entering th e reason f o r the change, typically as one of a set of standard code s that explains why the correction was m ade. Again, that entry must be in itialed and dated by the resear cher making the entry.

Raw data, including viodeograph y, are collected in a s t udy notebook and study file, whic h is retained indef initely in AEAT F II archives. In addition, a c e rtified copy of the data set i s made during report writing and report re view so that the original does not have to be shipped bet ween author and Quality Assurance, and in case the original is lost during transit to archives.

67

## 19 Quality Assurance Procedures

GLPs require rigorous quality assurance procedures to assure the quality and integrity of the data. A ll aspect s of the studies ar e monitored by appropriate quality assurance units (QAUs) while studies are in progress to ensure complianc e with FIFRA GLP regulations (4 0 CFR Part 160) and adherence t o the protocol and relevant St andard Operating Procedures

. This generally inv o lves three different QAUs: one from the expos ur e monitoring contractor that conducts the study, one from the analyt ical laboratory that determines the leve l of antimicrobial pesticid e residues in samples, and one from AEAT F II (the sponsor). For each study, the following specific acti vities, among others, are conducted by these QAUs:

• AEAT F II QAU in spects all contract research organizat i ons an d laboratories prior to us e in a study to ensure that those researchers operate in complianc e with GLPs

• AEATF II, Field Contractor and Analytic al Contractor QAUs each review protocols prior to finalization

• AEAT F II QAU performs periodic study in spections, while contractor QAUs perform periodic study inspections of thei r respective (analytical or field) portions

• Field Contractor QAU audits the raw dat a file in the field and Field Report

• Analytical Contractor QAU audits the raw analytic al data and Analytica l Report

• AEAT F II QAU revie w s and au dits the final report which inc l ud es the Field Report and Analytical Report as appendices

Each QAU submits an inspection report(s) to the Study Director and AEAT F II Sponsor’s Representative and any exce ptions to full GLP complianc e are summarized in the Final Report associated with each protocol.



## 20 Quality Control Procedures

In addition to the formal q uality assurance efforts di scussed abov e, there are a number of important quality control procedures whic h are followed in order to assure that exposure meas urements are ac curate and pr ecise and to define what those exposure meas urements represent. These include comple te validation of all analytic al methods ; extensive document ation of exactly what the subject does while handling the antimicrobial pesticide product; field fortification and control samples d e signe d to estimate stability of chemical residues d u ring sam p lin g, transit, and storage; laboratory fortific ation and control samples des i gned to establish efficiency of t he analytic a l methods on a day-to-day basis; and detailed

68guidelines on the use of calib ration curves t o determine chemical residues f o und on all samp le matrices.

In the field during eac h study, a chemist prepares exposure ma trix samples that are fortified with a k nown am ount of active ingredient. These matrices include whole body dosimeters WBD (cotton l ong underwear and outer work clothing), hand wash solvent s o lution, fac e /neck wi pes moistened with solvent solution, and inhalation tubes (referred to as OVS tubes which stands for OSHA Versatile Samplers). OVS tubes are fortified in t he laboratory by inject ing diluted analy tical grade activ e ingredient onto the sorbent in the tube while all other matrices are typically fortified in the field with a solut i on or suspension of diluted test substance (from individual v i als prepared in the laboratory). Each matrix t ype is generally fortified at three levels of active ingredient designed to span the range of residues anticipated to be c o llected from subjects. At each level, triplicate samples are fortified. In addition, c ontrol samples are prepared for each matrix to determi ne whether background levels of active may be present. Control samples s e rve as a form of negative c o nt rol. In general, field c ontrol and fortification samples are collect ed on at least two days during each study and whenev er signific antly different w eather conditions are expected.

Fortified WBD and O VS tubes are “weathered” in the fi eld s i nc e these sample types involve collection of residues durin g the monitoring period. For WBD, this involv es laying a fortified section of long underwear onto a table in a s unny location and covering that sample with a single lay e r of outer shirt material. Fortified shirt material is not overlaid to s i mulate outer garm ent weathering. For OVS tubes , this involves drawing air th rough 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 pl aced int o frozen storage.

Analysis of field for t ification samples provides a “r ecovery” v a lue which will quantify stability of the acti ve ingredient during sample collection (for weathered samples), storage in the field, shipment to the laboratory, and storage in the laboratory freezer. Therefor e, field fortification samples serve as a form of positive controls. Fi eld fortificati on samples are analyzed along with worker exposure s a mples and it is assumed that the worker s a mples experience similar stability as the field fortification samples. Therefore, residues found in wor ker samples are adjusted by appropriate average fi eld fortification res u lts to estimate the residues actually colle cted in the field. These practices are now very standard in pesticide exposur e monitoring and ar e discuss ed in det ail in internationally accept ed testing guidelines.

Similar quality control procedures are follo wed in the laborator y, including control and fortification sam p les which are designed to det ect background residues, monitor the performance of the me thod, and detect matrix or reagent interferences which may be present. These samples serve as a form of positive

69and negative controls

. In addition to th e detailed analytical methods for each surrogate and each matrix, al l analyses must follow detai led AEATF II analytica l guidelines which spec ify pr ocedures 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 fo r each study, a “final repor t” in GLP terminology. These reports include a text and t abular summary, and det ailed appendic e s including a Field Report and an Analytic al Report. These reports are formally submitted to EPA, CDPR, and PMRA as they are com p leted. In general, these reports detail exactly what was done in t he study, the results of analysis of residues, and what inf o rmati on will be entered in to BHEDTM. Howe ver, individual study reports which do not represent data for a complete scenario will not include an analys is or interpretation of the exposure data generated.

Field reports document the conduct of expos ure monitoring, including:

• Identification of the lo cation of the study, and t he environmental conditions during the exposure monitoring period(s)

• Descriptions of the s ubjects 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 re covery e valuation with a summary of specific handling and weather ing of all field samples

• A complete description of collecti on, handling, storage, and shipping of samples.

• A complete description of the ethica l conduct of the field study, including all elements required by 40 CFR 26.1303

70Analytical reports of individual field studies will document the handling and analys is of residues in all sample s collected in the study, including:

• Results of analys is (e.g., μg/sample)

• A detailed description of the analyt ical instrumentation and methods

• Example c a lculations

• A summary of field and laborat ory fortification recovery data

• Represent ative chromatograms of c ontrol, treated, fortified samples and calibration standards

• A typical standard curve

Study reports summarize t he field and analytical aspect s and inc l ude calculations of adjusted residues found in all collect ed samples (i.e., adjusted for field fortification recovery); total dermal expos ure for each worker; and the air concentration of active ingredient a ssociated with each work er’s monitoring period. Study reports are formatted in accordance with EPA requirements and include all r equired components.



## 22 Scenario Monographs

As part of the doc ument ation supporting BHED TM , AEATF II will generate scenario monographs for the benefit of regulators and ot her potential database

users. Each monograph will inc l ude a d e s c ri ption of the scenario as well a s an assessment of the data adequacy within t hat scenario. More specifically, the monograph for each scenario will include:

• Detail definition of the scenar io, including any restrictions.

• Represent ative use information for AEAT F II member products to define application methods, rate s, use sites, etc.

• Information about the div e rsity of work practices (equipment and procedures ) currently i n use in North America

• Summary of any existing data acquired by AEATF II

• Scenario d e sign summaries for AEAT F 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

AEAT F II may also inclu de in the mon ograph a d d itiona l re commendations concerning the use of the ME data. Scenario monographs wi ll als o be formally

71submitted to the regulatory agencie s when AEATF II determines the data collection for a particular scenario is complete.



## 23 Evaluation of Dat a Adequacy f or Completed BHEDTM Scenarios

The ultimate purpose of the monitoring pr ogram is t o make the individual ME exposure data from all scenarios available to users of BHEDTM, i.e., to provide a generic pe sticide han dler expos ure database. AEATF II will not analyze th e scenario data for the purposes of exposu re characterization or risk assess ment as part of this data development program

. Regulators and ot her potential users of the generic datab ase will conduct such analyses. However, as part of the generic database development and doc u m entation activities, AEATF I I will evaluate how well the co llected data meet the pre-specified benchmark adequacy objective. In add ition, the AEATF II will qua ntify the impact of ign o ring clusters and treating the dat a as a simple r andom sample

. The results of this evaluation will be include d in the scenario monograph (see section 22). Whenever appropria te, AEATF II will in cl ude in t he monog raph add i tional 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 s i ngle-day exposure reference distribution be accurat e 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 s cenario-specific although the current

consensus is that for regulatory purpos es K=3 is an acceptable default for all scenarios. In each scenario des ign plan wi ll be a brief discussion of why K=3 is appropriate for that scenario, or alternatively the rationale for choosing an other value of K.

As emphas ized in sec t ion 16.2 above, it im portant to keep in mind that, like the sample siz e determination, this statisti cal adequacy benchmark is relevant only within the context of the reference r andom sampling model defined in Sec t ion 16.2.2. In particular, the m onitoring data will be treated as if it were collected as a two-stage random sample from an infini te lognormal population. Technically, there is no statistical theor y 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 f o r regulatory purposes is of this t ype. As has always been the case, statistical conclu sions based on such data impl y the qualific ation: “to the extent t hat the data c an be viewed as deriving from a true random sample.” As pointed out in 16. 2 above, diversity se lection is expected to yield MEs that

72tend to ov erestimate the true variation among future expos ur es. This suggests that the estimates of upper percentiles will tend to be overestimated (and lower percentiles underestimated) in the resulting monitor i ng data. With the small sample siz e s used in this scenario, howev er, such estimation bias is probably trivial relative to ordinary uncertainti es due to sampling, whether random or purposive.

This benchmark is also necess a rily 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 ev ery scenario. Although slight dev iations will hav e little or no im pact, large deviations from the assumptions might result in data that deviate too far fr om the benchmark objective. Consequently, it is also of value to assess the benchmark requi rement using the ME data ac tually obtained. The K-fold benchmark above is s pecified in terms of the true variation structure and the resulti ng probability that certain characteristics would be observed in the data. Once the data ar e available, however, such probabilit y statements are less relevant than confidence statem ents calculated from the actual data. Consequently, evaluation of the benchmar k objectives will be based solely on c onfidence intervals.

To assess this benchmark goal, a 95 perce nt bound on relative accuracy will be calculated from confidence interval s for the arithmetic mean and the 95 th percentile. For a particular par ameter, θ , let T denote its es timate calculated from the fit of a cluster sampling (var iance c o mponent) model to the normalized exposure data. Further, let θ a and θ b denote the upper and lower limits, respectively, of a 95% confidence interval for θ

. In most cases, the confidence interval, ( θ a , θ b ), will be a parametric bootstrap percentile interval obta ined by resampling from a lognormal cluster sampling model. The 95 percent upper confidence bound on realiz ed fold relative accuracy ( fR A ) is then calculated as:

UCL 95 ( fRA ) = Ma x ( T / θ a , θ b / T )

The values of UCL 95 ( fRA ) will then be c o mpared 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 AEA TF II monitoring design involv es selecting MEs in two-stages: N C Sites are selected in the first stage and a cluster of N M MEs are selected in the second stage. Thus, a cluster is a set of MEs obtained in a single study at a parti cular geographic locati on (e.g., building) over a limited period of time (e.g. severa l days). Clusters are not a property of the future handler-day exposure distribution, per s e , but merely neces sary artifacts of the sampling proces s. Expo sures for ME s in the same cluster are likely correlated to some degree. If so , then estimates of parameters should

73accommodate this correlation. If a user i gnores clusters (i.e., assumes the data are a simple random sample), then some parameter estimates may be bias ed and the confidence intervals may be too sma ll. On the other hand, for the ME s of a particular scenario, such biases may be sm all and of little practical importance. When this is the case, anal yses of the data can be s i mp lified considerab ly. As an aid to regulators a nd other potential BHED TM user s, the impact of ignoring clusters will be examin ed.

Estimates and confidence intervals for par ameters of the normalized exposure distribution will be c a lculat ed using a model containing random cluster effects. From this analysis the variance c o m ponents and intraclass co rrelation (ICC) and their confidence intervals will be estimat ed. In addition, t he parameter estimates will be c a lculated as suming no cluster e ffect (i.e., assuming simple random sampling). These s i m p lified estimates wil l be compar ed to those obtained under the cluster-sampling model. The differenc es , if any, obtained by ignoring clusters will then be summarized fo r the benefit of BHED TM users.

74

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Ross, J.H., Driver, J.H., Cochran, R. C., Thongsinthusak, T. and Krieger, R.I. 2001. Could Pesticide Toxico logy Studies Be More Re lev ant to Occupational Risk Asses sment? Ann. Occup. Hygiene 45 (Suppl 1): 5-17.

Scientific Advisory Panel (SAP) Meeting 2007. Memorandum from M. Christian t o A. Lindsay, Acting Director, EP A Office of Pesticide Programs, “Transmitta l of Meeting Minutes of the FIFR A Scientific Advisory Pane l Meeting Held January 9 - 12, 2007 on the Review of Worker Expo sure Assessment Methods”, April 2, 2007.

Stasikowsk i, Margaret, Director of the Health Effects Divis ion (EPA)

, Memorandum to Mr. Dan Fay on behalf of agricultural chemical producers (March 16, 2001).

Thongsinthusak T, Ross JH, Saiz S, and Krieger R 1999. Estimation of Dermal Absorption Using the Exponential Saturati on Model. Reg. Toxicol. Pharmacol. 29: 37-43.

Wolfe, H. R., Durham, W.F. 1967. Arms trong, J. Exposure of Subjects to Pesticides, Arch. Environ Health, 14: 622-633.

Wolfe, H.R. (1976). Field exposur e to airborne pesticides. In Air Pollution fro m Pesticides and Agric u ltural Processes (R. E. Lee, Ed.), pp. 137-161. CRC Pr ess, Boca Raton, FL.

Worgan, J. and S. Rozario. Exposure A sses s ment Section. Re: Review of CMA Antimicrobial Exposur e Study . September 24. (1993).

Worgan, J. P., and S. Rozario. Pestic ide Exposure Assessment: Past, Present and Future, In: Methods of Pesticide Expo s u re Assess ment, Editor, Curry, P.B., Plenum Press New York, (1995).

World Health Organiz a tion, Survey of Exposure to Organophos phate Pesticides in Agriculture, Standard protocol, VBC/75.9, WHO, Geneva (1975).

World Health Organization, Field Surve ys of Exposure to Pesticides", Standard Protocol, VBC/82.1, WHO, Geneva (1982).

78

## Appendix A: AEA TF II Scoping Document :

American Chemistry Council Antimicrobials Exposure Assessment Task Force II (AEATF II)

Backgroun d and Scoping Summary

April 23, 20 07

INTRODUCTION

On No vemb er 1, 2004, the American Chemistry Council Biocides Panel established an Antimicrobial Exposure Assessment Task Force II (AEATF I I or Task Force) to conduct exposure monitoring st udies involving the mixing, loading and applicat ion of prod ucts containing a ntimicrobials or industria l biocides. 1 The Task Force also h as planned to develop methodologie s to a sse ss post-app lication exposure to a pplied prod ucts containing biocides, and will continue to wo rk with EPA, CDPR and PMRA to determine the most useful approach, based on current reg ulatory needs. The Task Force aims to design stud y protocols that will make study results broadl y acceptabl e to both North American and European regulatory authorities. The Task F orce current ly consists o f 43 companies.

The AEAT F II will generate generic exposure data on a broad range of use pattern/application meth ods to supp ort the Registration, Rer egistration a nd Registrat i on Review of most antimicrobial active ingredients. Regulatory agencies no w conduct most risk a ssessments for antimicrobial uses emplo y ing the stringent risk a ssessment criteria evolved fro m i m plemen ting the Food Quality Protection Act of 1996 (F QPA). There is a very limited amount of empirical exposure dat a f or antimicrobial uses and EPA and other regulators r outinely have used high ly conservative estimates of exposures to asse ss antimicrobia l risks.

Given the wi de use of an timicrobials, developing generic exposure data is the most costeffective an d efficie nt a pproach to t he needs of this indu stry. Moreover, given the highly segmented markets and diverse users of the same or similar antimicrobial prod ucts, 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 bein g designed to collect ge neric data that ca n be applied to the widest possible r ange of use scenarios.

Following is a brief overview of the range of ant imicrobial u s e sites, as ide ntified by EPA and adopted by the AEATF II, and of the application methods, segmented into sepa rat e

1 The general ter m s “anti m i crobials and “bio cides” are used interc hangeably in this docum ent.

79tasks to the extent possible, iden tif i ed by the AEATF II with the concurrence of EPA, PMRA and CDPR.

EPA ANTI MICROBIAL USE SITE GROUP S

During preliminary discussions on the conduct of antimicrobial exposure assessmen t studies with EPA, a ra nge of application methods were identified as appropriate for covering the 12 broad antimicrobial Use Site Groups that ha ve been used traditionally b y 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 dat a requirements for various antimicrobials based on use. To t his end, EPA further subdivides the groupings int o food and non-f ood categories a nd indoor and outdoor categories in order to d etermine mammalian to xicity and environmental and ecologica l t oxicity data required to support various uses. The Use Site Groups are a helpful way to identify th e range of u s es for which antimicrobials are employe d and, in fa ct, have be en used by North American regulato r s and the T ask Force to define the scope of the Tas k Force’s work. There are multiple sites within each grouping. Attachment B to this Go verning Document is the 1997 listing of individual use sites in each EPA Use Site Group. This is the most recent list of use sit es that has been made availa ble by EPA.

APPLICATI O N METHODS FOR ST UDIES

The applica tion methods sele cted f or the stu dies are the most common methods and include the following: pump liquid, pour liqu i d; pour solid; place solid (collectively referred to as mixer/loader activities by EPA); mop, wipe, aerosol spray, spray, soak/immerse, fog, bru s h and roll, airless spra y, and pressure treat. These application methods are described in detail in At tachment 2, Glossary.

Table 1 includes a list i n g of the Use Site Grou ps and the application methods typ i cally associat ed with each.

80Table1.

USE SITE GROUP S APPLICATI O N METHODS

Agricultural Premises and Equipment

Food Handling/Storage Establishments spray , Premises and Equipment

Commercial, Institutiona l & Industrial spray, Premises and Eq uipment

Residential and Public Access Pre m ises

Medical Premises and Equipment

Human Drin king Water Systems

Industrial Pr ocess Water Systems

Material Preservatives

Antifoulant Coatings

Wood Preservatives

Swimming Pools

Aquatic Areas pump, pour liquid, aer osol spray, spray, mop, wipe, fog, soak/immerse

pump, pour liquid, aer osol spray,

mop, wipe, fog, soak/im m erse

pump, pour liquid, aer osol spray, soak/immerse, mop, wipe, fog,

pump, pour liquid, aer osol spray, spray, mop, wipe, fog, soak/immerse

aerosol spra y, spray, mo p, wipe, fog, soak/immerse

pump

pump, pour liquid

pump, pour liquid, pour solid, place solid, spray, soak/immersion, airless spray, brush/roll

airless spra y, brush/roll

pressure treatment, soak/immersion, brush/roll, spray

pump, pour liquid, pour solid, place solid

pump, pour liquid, pour solid, place solid

81TWO MAJOR GROUPI NGS F O R A EATF II ST UDIES

The studie s planned by the Task F orce fall int o two general categorie s: (1) simulated studies based on discre te or segmented tasks ( m ixing, loading and appli c ation methods) that can used to estimate exposures occurring in a variety of use sce narios; (2) in sit u (e.g., on-site , observational) studie s for complex and/or multi-task scenarios.

Studies Based on Segmented Tasks/Application Methods

AEATF II believes that by reasonably segment ing tasks in volved in the applicatio n of antimicrobia l s, it will be possible to combine separate tasks as appropriate for various determining exposures in a variety of use scenarios. These studies will involve in door, scripted sce narios. This approach will make the generic unit exposure data collecte d by the Task Force useful over the rang e of antimicrobial use sit es and more flexible in utility to potentially changing use patterns. To this end, users of the AEATF II study results can use info rmation fro m published and proprietary sources to establish values for o ther exposure factors or variables (e.g., the amoun t of time spent in a particular task in various occupational se ttings and r esidences, t he average amount (or range) of a g i ven product app lied) in conjunction with the monitor i ng data to estimate typical exposur es. Then the se gmented exposure value associate d with one d i screte ta sk can be combined with the values for ot he r tasks that occur in a p articular sce nario to furt her estimate the exposure that could occur during a t y pical work day.

In Situ (Observational) Studies for Complex or Multi-Task Scenarios

Four of the AEATF II st udies are being propos ed for conduct in situ, as observational studies, giv en the complex combination of tasks, functions, etc. The unit exposure dat a collected fr om these studies are not intende d to be co mbined, but instead will be representative of similar use scenar ios.

List of AEATF II Planned Studies

Table 2 includes a list o f the studies that the AEATF II plans to conduct, in the order of priority currently anticip ated. Man y of these studies will b e conducte d with simulated exposures. Those that are expect ed to be conducted as observational studies a r e so noted in the following tab l e.

82Table 2.

AEATF II P R OP OSE D LIST OF ST UDIES I N ORDER OF P R IORIT Y

A. APPLICAT OR/BY S TA NDER E X P O SURE RE SEARCH

Mop Study Wipe Study Wood Pressure Treatment Study (ob s ervational) Aerosol Spray Study Pour Liquid Study Metal Working Fluid Stu dy (observat i onal) Pour Solid Study Brush/Roller Study (observational) Airless Spra y Study (obs ervational) High Pressure to Low Pressure Spra y Studies Immersion/ Dip/Soak Study Pump Liquid Study Place Solid Study Fogging Study

B. POST-APPLICATION EXPOSURE RESEARCH (approach t o be determined)

Hard Surface Soft Surface

STUDY DE SCRIPTI O N S

The followin g studies ar e discussed in the orde r of priority, as currently determine d by AEATF II in conjunctio n with US EPA, PMRA an d CDPR.

Mop

Mopping involves the ap plication of a diluted or ready-to-use antimicrob i al so lution t o a floor for disinfection or sanitizat i on. 2 Mopping occurs in the five EPA Use Site Groups that involve application of disinf ecta nts and sanitizers to ina nimate surfa c es: agricult ural premises and equipment, food handling/storage establishme nt premises and equipment, commercial/institutio nal/industrial pr emises and equipment , residential/public access premises, medical premises and equipment. The mopping data rep r esent a sin gle or discrete task that can b e combined with other segments or tasks to estimate exposures from combinations of a c t i vities that r epresent typical work da ys in a variety of occupa tion settings or typical resi dential use events. The Task Force will simulate mopping

2 The term s “di sinfect” a n d “s anitize” and variations of these term s are use d as use d by EPA i n regulating a n timicrobial pesticides. Each i m plies a specific level of a n tim i crobial effi cacy, depe nding on the type of application m e thod, and disinfect ty pically indicates a higher le vel of a n tim i crobial efficacy

t h an sani t i ze. These t e rm s do n o t ha ve t h e s a m e m eani ngs i n ot her re gul at ory or n o n

- r eg ul at ory c o nt ext s

.

83activities, a s it will be necessary t o establi s h mopping times sufficie nt to obtain data, while limitin g activity to the single ta sk of mopping. This would be impo ssible in the vast majority of real-life occupationa l settings. Moreo v er, man y of the facilitie s where extensive mopping might occur ha ve legal and ethical co nsiderations that would limit availability to the facil i ty to research ers and associated monitors (e.g., f ood processing facilit ies and hospital s ).

There are numerous types of mop ping equip m ent, including for exa m ple, string mops used in conjunction with various typ es of wringer equipment, sponge mo ps, ready-to-use systems wi th disposable, impregnated mop cloths, etc. AEATF II, based on an evaluation of equipment, has conclu ded that string mops represent a reasonable worst case and will u s e thi s equipment along with buckets wit h hand-acti vated mechanical wringers.

There are data available estimating the amount of time spent mo pping in va rious occupationa l settin gs (e .g., hospita ls, hotels, h omes), as well as th e amount of time spent wiping, spraying, etc. These data can be used to aggregate exposures from various functions involving the application of ant imicrobials in different o ccupationa l and residential settings to determine estimated total exposures covering a t y pical work day or residential u s age event.

Wipe

Wiping involves the app lication of diluted antimicrobial prod ucts to surf aces other t han floors in the five EPA Use Site Groups involving use of sanitizers and disinfect ant s on inanimate surfaces: agricultural premises and equip m ent, food handling/storage establishme nt premises and equipment, comme rc ial/in stitutional/industrial premises and equipment, residential/p ublic access premis es, medical premises and equipment. The primary contact during use of wipe s is dermally to the ha nd. Wip i ng can o ccur after trigger-spra y application, wipe after dipping a wipe, sponge or other materia l in a container, or ready-to-u s e wipes impregnated with antimicrobial. AEATF II currently is planning to conduct two sets of MEs in simulated environments, one covering the u s e of pre-impregnated wipes and another involving use of a trigg er spray followed by wi ping with a dry cotton cloth. Use of wipes in con j un ction with fu ll-hand immersion will n ot be employed i n this stud y, because full-hand immersion will be monitored in the dip/immersion study.

As previously noted, the AEATF II will use data f r om published and proprietary sources to establish the amount of time spen t in wiping a c tivities in various occu pational sett ings and residen ces in conjunction with the monitoring data to estimate typical exposures. The segmented exposure associate d with wipin g activity ca n be combin ed with othe rs to further estimate the e x posure that could occur during a typ i cal work day in a variety of occupationa l setting s.

Aerosol Spray

Aerosol spr ay is anoth er applicat io n method employed in the five Use Site Groupings where there is application of sa nitizers and disinfect an ts to inanimate surfaces: agricultural premises an d equipment, food hand ling/storage establishme nt premises and equipment, commercial/institutio nal/industrial pre m ises and equipment, residential/ p ublic access pre m ises, medical premises and eq uipment. This study under simulated

84conditions will monitor exposures resulti ng f r om sprayi ng from aerosol can s onto surfaces, bu t will not in cl ude any wiping activitie s

. To the extent that wipi ng can occu r in conjunction with an aerosol product, the AEATF I I believes that it will have sufficient dat a from the wip e study to combine with the aer osol study data to estimate exposures from such combined activitie s

. These data can b e used alo ng with data on work day segments to estimate total exposures for various occupat ional setting s and resid entia l

use scenar ios, as need ed.

Whether the aerosol generator is prepackaged i n a pressurized can or a refillable can with a sepa rate pressur i zed air su p ply makes no difference for purpose s of monitoring exposure, because bot h systems are producing aerosols using the same principle of physics. Only the amo unt of chemical handl ed varies by packaging. Thus, refillable containers with separa te pressurized air supp ly, which are the high er volume use method of application

, should be employed for an exposure monitoring study. The distance tha t aerosol ge neration occurs from the body and t he aerosol particle size are probably more important determinants of exposure tha n whether the aerosol is dispensed f r om a can or remotely. Key para m eters that are likely t o affect exposure (e.g., amount of product sprayed, air exchange rate) will be carefully recorded so that there is a ba sis for extra polating fro m one use method to another.

Pressure Treatment of Wood

This study is not base d on an application meth od, but rath er is one type of treat men t cover by th e EPA Use Site Group “Wood Preservation.” In contrast t o the majority of studies to b e conducted by the AEATF II, the data derived from the study of pre ssure treatment of wood cannot be used in conjunctio n with data from any ot her Task Force study in ord er to est i mate exposure. The data derived fro m this stud y will be st andalone data applicable o nly to wood pressure tr eatment use scenarios. This stud y is being propo sed for con duct as an observational exposure monitoring study. Wood treatment immers ion will not be st udied by th e AEATF II because the only expo sure expected would be in du strial bystan der exposure. Industr ial bystander exposure will be addressed or characterized in the wood pres sure treatment and me talworking fluid preservation studies.

There are at least four GLP-compli ant, EPA-accepted studies on pressure treatme nt of wood with the three “ heavy duty wood treatment compounds” (chr omated copper arsenate or CCA, creosote and pentachloro phenol). E PA’s contin uing interest in exposures resulting fro m this scen ario probably is best ex plained by t he estimate that 40 percent or more of the annual US volume of pesticide pr oduction is consumed in the pressure treatment of wood. EPA, t herefore, has again requested that industry provide these data.

The AEATF II will rely on the North American regulators to identify tasks of spe c ial interest for study. Most of the tas ks to be monitored are not directly involved in the application of the pe st icide compounds, but instead in volve secondary expo sures associat ed with the treatment activities. Active ingredients have not yet been finally selected, bu t likely will r eflect the n ew generation of wood treatment compounds that combine organic and metal-based co mponents.

85Pour Liquid

Pour liquid data to cover the pouring of registered pesticide products could be used in conjunctio n with the data from a number of other applicatio n studies, including: m op, wipe, spray, immerse/dip, pump and fog. There are two general types of containers now used for pesticide s: “ glug” and “no-glug”. There are data that suggest “no- glug” containers (those with an air bypass that prevents “glugging” and conco m itant backspla sh) produce sig nificantly less exposure to the pourer than do containers with out this feature, which led EPA to issu e its recent regulation requiring cer tain pesticides to be sold in no-glug containers. The AEATF II study intends to generate data comparing no-glug con tainers as a supplement to the exist i ng studie s on open po ur exposures in the PHED d atabase. Moreover, the AEATF II data will cov er smaller sized containers that are frequently used for antimicrobials, b ut are not well represent ed in the P H ED data.

Metalworking Fluid Preservative

EPA has singled out this material preservati ve use for study because of the Agency’s contention t hat exposures to the preservatives used in MW Fs are amo ng the highest of all occupational exposu r es to b i ocides. A gr e at deal of in formation e x ists in the public literature with regard to ambient levels of MWF s , but not to the preservatives in the fluid. There also are limited data on der mal exposur es. Occup ational exposures also are regulated by the Occupa tional Safety and Health Administration (OSHA) and have been well studied by numerou s governme nt and privat e 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 preserve d fluids, and may also be monitored for others pr esent in the facility (i.e., bystanders).

This study, therefore, may be used to provide surrogat e data to cover a range of industrial bystander exposure scen arios, e.g., pulp and p aper mills, drift from cooling water installations, et c. Size of the aerosol and distance fro m the source probably could determine exposure to persons in t he vicinity, and these data will be recorded. It is AEATF II’s belief that many of th e secondar y applicator exposure scenarios will be addressed with data ge nerated from the pri m a r y applicator exposure st udies. Uses such as paint, a dhesives, caulking, et c. produce exposure to dilute co ncentrations of antimicrobia l , and with judgment one can compare the unit exposure from the appropriate primary a pplicator scenario to many of these se condary applicator scenarios, i. e., exposure measured where the product makes a pe sticidal claim can be applied to pr oducts that do not.

Pour Solid

PHED data clearly sh ow that exposure (bot h dermal and inhalatio n) is inversely proportional to the particle size (an d resulting surface area) of the so lid being hand led. PHED lists results for wettable po wders (also applicab le to dusts), dr y flowables, and granulars that are all different solid formulations with increasing p article size and decreasing unit exposure in the ord er listed. T he AEATF II expects to conduct a study using a dust or wettable powder and a granular formulation to bracket the range of particle size s and result ing exposures currently in PHED.

86Further, con s ideration may be given to observations of Heitbrink et al., ( 1992) regarding generation of aerosols during pouring of powders. Pour solid data co uld be combined with data from other a pplication methods, as appropriat e to estimate exposures in various occupational se ttings.

Brush and Roller

One of the most common applications for indu strial b i ocide s is for preservation of p aints, coatings, ca ulks, adhesives and simi lar material s. EPA has h i storica lly used exposure to paints as the reasonable worst case for expos ures to these and similar items. Th e vast majority of t hese paints and other materials are not themselves pesticid es, that is, they do make any pesticida l claims. However, s ome paints and coatin gs are registere d pesticide s in that they claim to pro tect the sub s trates to which they ar e applied fr om microbial de terioration, e .g., antifoula nt coatings and wood stains.

The Task F orce plans t o monitor the applicatio n of preserved paint where interior spaces are being p ainted. Rollers cover more surface area in a giv en time period than bru s hes and regulat ory agencies assume e x posure per unit applie d is the sa me as brushes. Published d ata are curr ently inadequate to cha r acterize exposure pote ntial from roller application

, but suggest exposure may be lower than brush applicatio n. A combination observational study (including bo th brush a nd roller methods) is therefore being proposed, b ecause typically both methods ar e used during a typical or normal day o f paint product use.

Airless Spray

Airless spra y is used in the applica tion of antif oulant paint s, wood preservatives, and preserved paints. In fa ct, the use of airless sp ray equipme nt (e.g., Wagner) is growing rapidly in the applicatio n of paints in many int erior commercial and even residentia l settings. T he Task Fo rce is prop osing to co nduct an o bservational study to monitor interior pain ting events using airle s s spray equipment. It is important to note that this study may d i ffer sign ificantly than ot hers planne d by the Task Force be cause it like l y will involve the use of bo th respirato r y and dermal person al protect i ve equipment, as specified by either or bo th the spray equipment manufacturer and paint manufacturer (of a non-pesticidal paint).

High Pressure to Low Pressure Spra y

Although distinctions are sometimes made betwe en high and low pressure spraying, two variables affect spray p article size : pressure and orifice size. Small particle size s (<30 microns mass median diameter) tend to produ ce higher e x posure than larger par ticles because th ey stay suspended lon ger in the air and can be produced even wit h low pressure. A nomogram describing t he pressure/ orifice relationship to pa rticle size would be useful an d may be prepared during this work. A good de al of the equ ipment used for spray application is hand-held. Thus, another critical determinant of exp osure is whether the spray emits above or below the shoulder.

Higher-pressure spray is used with materi al preservative, wood preservative and antifoulant coating uses. Lower pressure spray also may be indicated f o r these use s , is one of the most widely used application meth ods for sa nitizers and d i sinfecta nts, and includes ap plication s as diverse as t r igger sprays and foam generators. Howe ver, t here

87are no commonly acce pted parameters for what constitute s high or low-pressure sprays. Therefore, a large indoo r, scripted-a c tivity study (minimum 60 MEs) will be designe d to account for the range of equipment types typically used in various applications and use sites and th e range of t y pical particle size s that might be generated. Th e distance fr om the generation source is likely also t o be a critical determinant of exposure.

Data from th is work will be used for estimating non-aerosol spray exposu r es to saniti zers and disinfe c tants in agricultural premises and equipment, food handling/ storage establishme nt premises and equipment, comme rc ial/in stitutional/industrial premises and equipment, residential/p ublic acce ss prem ises, and medical premises and equipment. Exposure from non-aerosol spra y activity ma y be combined with exp o sure from other scripted tasks, as app ropriate, to estimate total occupa tional or n on-occupational exposure for a particula r use scena rio. The data also will be used for preserved paint, antifoulant coatings an d wood pre s ervative a pplication s f or spray uses that do not employ airless spray equipment. Such ex posures will n ot be comb ined with o ther exposures from other tasks, but inst ead will be used to esti mate typical occupation al or non-occupat ional use scenarios.

Soak and Immerse

The terms soak and immerse can be used in a variety of contexts for antimicr obial application

. There are industrial u s es involving soaking or immersion (various types of wood and a nti-sapstain treatment) t hat are mec hanized and ma y use large volumes of antimicrobia l but present only a pote ntial for exp osure to sub j ects a s byst anders. On the other hand, low volume uses, su ch as dish sanit i zers in re staur ants and b ars, commercial or institution al laundry sanitizers, disinfectants fo r halters and other stable or livestock e quipment, etc. that a r e non-me chanized a nd involve repeated hand immersion, may offer more potentia l for exposu r e. The l att er types of exposure will be monitored in a scr ipted study. The unit ex posure data de rived from this study may be combined with data from other discre t e tasks to e s timate total workday exposures for t he agricultural premises an d equipment, food hand ling/storage establishme nt premises and equipment, commercial/institutio nal/industrial pre m ises and equipment, residential/ p ublic access pre m ises, and medical premises and equipment Use Site Groups. Use in the wood treatment industry that involves pr imarily bystander expos ure could be addressed by either the metal working fluid study or so me subset of the monitoring units (or monitoring events) from t he w ood pressure treatment study.

Pump Liquid

Some of th e largest volume uses of antimicrobials involve use of pump syste m s, e.g. , application i n oilfield an d municipal water treatment faciliti es. However, these systems are closed metering systems using dry lock connections, with virtually no opportunity for worker expo sure. Other examples of cl osed pump systems in clude tank truck unloading, automatic dispensing systems, mete ring pum p s y stems for t otes and drums, and tub eset pumps. Data on these various systems have been shared with EPA, to support the AEATF II p osition that additional d ata should not be required by EPA for pump li quid application s

. In an y case, there are probably s ufficient dat a in the PHED database to cover all antimicrobial use patter ns and a ll product types where closed or similar systems are not in place to e liminate or mit i gate exposure. An analysis will be performed by the AEAT F II to further assess t he adequacy of the available data. In addition, a f i nal decision in part depends on the EPA an d the other North Ame r ican

88regulators’ f i nal posit ion on whether occupat iona l risk asse ssments will be required for subjects th at use clo s ed pump systems. Pump liquid data, if req uired, could be combined with data from other application methods, as appropria te to estimate exposures in various occupational settings.

Place Solid

Registrants have develo ped various “place so lid” products to reduce t he potential for exposure to dry flowable products. The mo st common “solid” delivery systems are powders/granulars in sealed, water-soluble bags and “tablets.” EPA is requesting data because it has only on e data reco rd in the P H ED datab ase. However, the AEATF II believes tha t it is unne cessary to re quire a stud y with this a pplication method. One of the most common ant imicrobial p esticide uses employing tablets is applicatio n of sanitizer s or algaecide s to swimmi ng pools. To the extent that a ho me owner might apply a single tablet on a weekly or semi-weekly basis, EPA has conceded t hat exposure is likely to b e non-detectable. Whe n a professional pool t r eater or ot her occupationa l applicator uses tablets, multiple tables may be applied over the course of the day. The AEATF II believes in such cases it is appropr iate to require occupational users to wear chemical- r esistant glo v es, which again would reduce exp osures to n ondetect levels. There should be no need fo r these d at a and no need to do an occupationa l risk asse ssment if t he require ment for gloves for o ccupationa l users appears on product labels. Place solid data, if required, could be combined with data from other application m ethods, as a ppropriate t o estimate occupationa l exposure.

Fog

Fogging is used to treat large or irregularly shaped areas. Most fogging is done using remote operation, where applicat or exposure is neglig ible. However, there are backpacks sold for o ccu pational, ind oor antimicr obial fogg in g applicatio ns, and EP A has requested d ata for fogging data using antimicrobials. However, the AEATF II believes that to the extent fogging is done using handheld equipment, respiratory and d ermal PPE should be a standard requirement. Therefore, in either the case of remotely controlled f oggers or hand-held f ogging with PPE requirements, the potential for occupationa l exposure would be negligible. Fu rther, there are no registered reside ntial uses for ant imicrobial foggers. As a resu lt, 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/stor age establishment premises an d equipment

, commercial/institutio nal/industrial pr emises and equipment , residential/public access premises, and medical premises and equipment.

POST-A PP LICATION E XPOS URE STUDIE S

Currently EPA uses the Residential Ex posure Assessment Stan dard Operating Procedures (SOPs) as codified in SOP 12 (Smegal et al. , 2001) and incorporate d into software tools such as ( R Ex – avail able at www.infoscie ntifi c.com and PIRAT – avail able from http://epa.gov/opptintr/exposure/pubs/pirat dl.htm ) to estimate potential ree ntry exposures. Dependent on the toxicity of th e antimicrobia l , use of d efault exposure estimates fo r post application exposure may be adequate.

89The typical primary ro ute of post-application exposure is dermal. It is possib l e to generate generic post

- application dermal exposure dat a that would be gene rally applicable t o a range of possib l e ex posure scen arios (e.g., dermal cont act with sanitized floor surfaces; incident al dermal contact with disinfect ed work surfaces; dermal contact with treated articles). Other task forces h ave develo ped data t o evaluate postapplication exposure (also known a s reentry exposure) for environment al surface s such as turf (Outdoor Residential Exposure Task Force; ORETF), and plant foliage surfa c es (Agricultural Reentry Ta sk Force; A R TF). Post-application exposure monitoring stu dies typically involve concurr ent measurement of residues tran sferred from a treated su rface as a fun c tio n of time u s ing a non- human transfer mediu m (discusse d in the following paragraph) and the quantity of chemical transferred to full body do simetry garments worn by individuals cont acting a tre ated surface with a kno w n intensity, for specific time duration. T he dermal exposure rate (e.g., mg/hr) measured using dosim etry garmen ts is divided by the concurre ntly measured surface transferable residues (e.g., mg/cm 2 ) to achieve a g eneric transf er coefficien t (TC). Thi s coefficient, which is a l so referred to as a “contact ra te,” is typically expressed in units of cm 2 /hr.

In conjunct i on with me asuring hu man dermal exposure following co ntact with a treated surface, a post-applica tion exposu r e study also determines temporal transfera ble residue (measurements taken across time) from the treat ed surface using a generic method (a roller was used by ORETF and leaf washes were used by t he ARTF). The AEATF II will need to develop or a dapt an exi s ting method for measu r ing transfer able residues. T r ansferable residues ar e chemical- s pecific (du e to differen t adsorption and dissipat ion characterist ics that are unique to each che m ical used on a mat r ix). Therefore, chemical-spe cific tran sfer able residue s must be generated by the individual registrant. These studies cost ap proximat ely 5 to 10% of what a human exp osure monitoring study costs.

The AEATF II initially proposed to develop T C s by conducting one study with hard surfaces (e. g., floor or countertop) and one with soft surface s (e.g., textile such as carpet or upholster y). EPA ha s requested that the Task Force de fer any furth er work on postapplication exposures until it has co nferred with other North American re gulators to more clearly determine how it will asse ss post-applica tion exposures in the fut ure.

90REFER ENC ES

AEATF II (2004a). American Chemistry Cou nc il Antimicrobial Exposure Assessment Task Force I I. Glossary of Terms: Application M ethods and Use Patterns.

AEATF II (2004b). American Chemistry Cou nc il Antimicrobial Exposure Assessment Task Force I I. Justif ication for Changes to EPA’s Initial List of Application Methods.

AEATF II (2004c). American Chemistry Cou n cil Antimicrobial Exposure Assessment Task Force II. Mixer/Lo ader (Preparation Worker) and Applicator Exposure Monitoring Study Design Parameters.

AEATF II (2004d). American Chemistry Cou nc il Antimicrobial Exposure Assessment Task Force II. Post A pplication Exposure Estimates: AEATF II Data Development Needs.

Heitbrink W A , Baron PA, Willeke K (1992). An investigati on of dust g eneration b y free falling powd ers. Am. Indust. Hygiene Assoc. J 53 : 617-624.

91

## Appendix B. Antimicrobial Product Use Sites and Categories

I. Agricultural pr emises and equipment

II. Food handling/storage establis hment premises and equipment

III. Commercial, institutional and industrial premises and equipment

IV. Residential and public access premises

V. Medical pr emises 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 eq uipmen t - indirect food contact *

AGRICULT URAL/F A RM PREMISES INDOOR FOOD, TERREST RIAL FOOD
AGRICULT URAL/F A RM STRUCTURES/B U ILDING S AND EQUIPMENT INDOOR FOOD, TERREST RIAL FOOD
BARNS/BARNYARDS/AUCTION BARNS INDOOR FOOD
BEEF/RANGE/FEEDER CATTL E (MEAT) INDOOR FOOD
BEEHIVES/BEE COL O NY (DISEASE D/NUI SANCE) INDOOR FOOD
BEEHIVES-EMPTY INDOOR FOOD CALVES (MEAT) INDOOR FOOD DAIRY CATTLE (LACTATING OR UNSPECIFIED) INDOOR FOOD DAIRY CATTLE (NON-LACT A TING) INDOOR FOOD
DAIRY FA RM MILK HANDLING FACILITIES/EQUIPMENT INDOOR FOOD

DAIRY FARM MILK S T ORAGE ROOMS/HOUSES/SHEDS INDOOR FOOD

92DAIRY FARM MILKING EQUIPMENT INDOOR FOOD
DAIRY FARM MILKING STALL S /PARLORS INDOOR FOOD DAIRY GOATS (LACTATING OR UNSPECIFIED) INDOOR FOOD DAIRY GOATS (NON-LACTA TING) INDOOR FOOD
EMPTY CONTAINE RS TO BE USE D FOR RAW AGRICULT URAL CO MMODITIES INDOOR FOOD

FISH, FRESHWATER (MEAT) INDOOR FOOD FISH, SAL TWATER (MEAT) INDOOR FOOD
FISH HAT CHE RY B U ILDINGS/ARE AS (NONAQUATIC) INDOOR FOOD

FISH ROE (CAVIAR)(MEAT) INDOOR FOOD GAME ANIMAL (MEAT) INDOOR FOOD GOATS (M EAT) INDOOR FOOD HOG/PIG/SWINE (M EAT) INDOOR FOOD KIDS (MEAT) INDOOR FOOD LAMB (MEAT) INDOOR FOOD
LIVESTOCK INDOOR FOOD
MUSHRO OM HOUSES-EMPTY PREMISES/EQUIPMENT GREENHOUSE FOOD, INDOOR NON-FOOD
POTATO SEED PIECE STORAGE PREMISES/EQUIPMENT INDOOR FOOD, TERREST RIAL 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

93c. Nonfood area premises and equipment

AGRICULT URAL/F A RM EQUIPMENT/ SHOE BATHS

EGG HANDLING EQUIPMENT (HATCHING)

EGG HANDLING ROOMS (HATCHING)

EGG PLANTS/HAT CHERIES/BROODER ROOMS/SH OE BATHS (HATCHING)
EGG WASHING TREATMENTS (HATCHING)

FUR FARM EQUIPMENT/PR EMISES INDOOR NONFOOD
INDOOR NONFOOD
INDOOR NONFOOD
INDOOR NONFOOD
INDOOR NONFOOD
INDOOR NONFOOD

* Use of a product on sites in t h is cate gory will be c onsidered a food us e and registration must be supported by data suff icient to support establishment of a tolerance or exemption from the requirement of a to lerance under the Federal Food, Drug and Cos m etic Act. The Agen cy will co nsider lab e l modifications which clarify practices such that use of the produc t is unlikely to result in pesticide residues in food. Uses will be considered nonfood if fo od is c overed or removed during application and the treat ed surfaces are rinsed with pot able water prior to any contact with food. Re gistrants are advised to contact the Agency if they are uncertain as to whet her a proposed application is a food use.

II. Food handling/storage establishm ents premises and equipment

a. Food area premises and eq uipmen t - indirect food contact *

AIRTIGHT STORAGE (FLAT)-EMPTY

AIRTIGHT STORAGE (SMALL)- E MPTY

AIRTIGHT STORAGE-EMPTY

COMMERCIAL SHIP PING CONTAINE RS-FEED/FOOD-EMP TY

COMMERCIAL TRANSPORT ATION FACILITIES-FEED/FOODEMPTY
DAIRIES/CHEESE PROCESSING PLANT EQUIPMENT (FOOD CONTACT ) INDOOR FOOD
INDOOR FOOD
INDOOR FOOD
INDOOR FOOD
INDOOR FOOD
INDOOR FOOD

94DAIRIES/CHEESE PROCESSING PL ANT PREMISES (NONFOOD CONTACT ) INDOOR FOOD
DISHWASHING WATER INDOOR FOOD EATING ESTABLISHMENTS (FOOD CONTACT) INDOOR FOOD
EATING ESTABLISHMENTS EQUI PMENT/UTENSILS (FOOD CONTACT ) INDOOR FOOD
EATING ESTABLISHMENTS F OOD HANDLING AREAS (FOOD CONTACT ) INDOOR FOOD
EATING ESTABLISHMENTS F OOD SERVING AREAS (FOOD CONTACT ) INDOOR FOOD EGG HANDLING EQUIPMENT (COMMERCIAL) INDOOR FOOD EGG HANDLING ROOMS (C OMMERCIAL) INDOOR FOOD EGG PACKING PLANTS (COMMERCIAL) INDOOR FOOD
EMPTY CONTAINERS TO BE USED FOR PROCESSED FEED/FOOD INDOOR FOOD
FEED MILLS/FEED PROCESSING PLANT S INDOOR FOOD
FEED/FOOD STORAGE AREAS-EMPTY INDOOR FOOD
FEED/FOOD TREATMENT-STOR AGE/PROCESSING/ INDOOR FOOD FISH/SEAFOOD PROCESSING PLANT EQUIPMENT (FOOD CONTACT ) INDOOR FOOD
FISH/SEAFOOD PROCESSING PL ANT PREMISES (NONFOOD CONTACT ) INDOOR FOOD
FOOD CATERING FACILITIES PREMISES INDOOR FOOD
FOOD DISPENSING EQUIPMENT/ VENDING MACHINES INDOOR FOOD
FOOD MA RKETING/STORAGE/D ISTRIBUTION EQUIPMENT / UTENSILS (FOOD CONTACT) INDOOR FOOD
FOOD PROCESSING PLANT EQUIPMEN T (FOOD CONTACT ) INDOOR FOOD
FOOD PROCESSING PLANT NON-FOOD HANDLING AREAS INDOOR FOOD

95FOOD PROCESSING PLANT PREMISES (NONFOOD CONTACT) INDOOR FOOD
FOOD PROCESSING WATER SYST EMS INDOOR FOOD
FOOD STORES/MARKET S/SUPERMA RKETS PREMISES INDOOR FOOD
FOOD/GR OCERY/MARKETING/S TORAGE/DISTRIBUTION FACILITY INDOOR FOOD
HANDLING EQUIPMENT INDOOR FOOD
GRAIN/CE REAL/FLO UR BINS-E MPTY INDOOR FOOD
GRAIN/CEREAL/FLO UR ELEVATORS-EMPTY INDOOR FOOD
GRAIN/CEREAL/FLO UR STORAGE AREAS-EMPTY INDOOR FOOD
MEAT PROCESSING PLANT EQUIPMEN T (FOOD CONTACT ) INDOOR FOOD
MEAT PROCESSING PLANT PREMISES (NONFOOD CONTACT) INDOOR FOOD
MEAT/FISH MARKETS PREMISES INDOOR FOOD
POULTRY PROCESSING PLANT EQUIPM ENT (FOOD CONTA C T) INDOOR FOOD POULTRY PROCESSING PLANT PREMISES (NONF OOD CONTACT ) INDOOR FOOD
PROCESSING/HANDLING EQ UIPMENT (FOOD CONTACT SURF ACE S INDOOR FOOD

b. Direct food contact

EGG WASHING TREATMENTS (CO MMERCIAL) INDOOR FOOD
FRUIT AND VEGETA B LE RINS ES INDOOR FOOD

c. Areas without potenti al for food contact

EATING ESTABLISHMENTS (NONFOOD CONTACT ) INDOOR NONFOOD
EATING ESTABLISHMENTS F OOD HANDLING AREAS INDOOR NON­ (NONFOOD CONTA C T) FOOD

96EATING ESTABLISHMENTS F OOD SERVING AREAS (NONFOOD CONTA C T) INDOOR NONFOOD
EATING ESTABLISHMENTS NON- FOOD AREAS (NONFOOD CONTACT ) INDOOR NONFOOD
HYDROSTATIC STERILIZER WA TER S Y S TEMS INDOOR NONFOOD
TOBACCO PROCESSING PLANT PREM I SES/EQUI PMENT INDOOR NONFOOD
PAST EURIZER/WARMER/CANNERY/RET ORT WATER SYST EMS INDOOR NONFOOD
PROCESSING/HANDLING EQUIPMENT (NONFOOD CONTACT ) INDOOR NONFOOD

* Use of a product on sites in t h is cate gory will be c onsidered a food us e and registration must be supported by data suff icient to support establishment of a tolerance or exemption from the requirement of a to lerance under the Federal Food, Drug and Cos m etic Act. The Agen cy will co nsider lab e l modifications which clarify practices such that use of the produc t is unlikely to result in pesticide residues in food. Uses will be considered n onfood if food is c o vered or removed during application and the treat ed surfaces are rinsed with pot able water prior to any contact with food. Re gistrants are advised to contact the Agency if they are uncertain as to whet her a proposed application is a food use.

III. Comme rcial, institut ional and industria l premises and equipment

a. Indoor

AIRTIGHT STORAGE (FLAT)-EMPTY

AIRTIGHT STORAGE (SMALL)- E MPTY

AIRTIGHT STORAGE-EMPTY

CARPET S (COMMERCIAL SANI TIZER)

COMMERCIAL/INSTITUTIONAL /INDUSTRI A L FLOORS

COMMERCIAL/INSTITUTIONAL /INDUSTRI A L PREMISES/EQUIP MENT (INDOOR)
COMMERCIAL STORAGE/WAREHOUSES PREMISES (INDOOR)
COMMERCIAL TRA N SPORT A T ION FACILITIESINDOOR NON-FOOD
INDOOR NON-FOOD
INDOOR NON-FOOD
INDOOR NON-FOOD
INDOOR NON-FOOD
INDOOR NON-FOOD
INDOOR NON-FOOD
INDOOR

97NONFEE D/NONFOOD NON-FOOD
DIAPE RS (COMMERCIAL LAUNDRY) INDOOR NON-FOOD
DUST MOPS/CLOTHS/TOOL C O VERS/DUSTERS (LAUNDRY /DRYCL EA N) INDOOR NON-FOOD LAUNDRY (COMMERCIAL) INDOOR NON-FOOD LAUNDRY (DRYCLEA NING) INDOOR NON-FOOD
LAUNDRY EQUIPMENT INDOOR NON-FOOD MACHINERY (NON-FOOD) INDOOR NON-FOOD
NONFEE D/NONFOOD COMMO DITIES (TEMPORARY STORAGE) INDOOR NON-FOOD
NONFEE D/NONFOOD CONTAI NERS-EMPTY/FUL L INDOOR NON-FOOD
NONFEE D/NONFOOD STORA G E AR EAS-EMPTY/FULL INDOOR NON-FOOD
NONFEE D/NONFOOD TREA TMENTSSTORAGE/PROCESSING/HANDLING EQUIPMENT INDOOR NON-FOOD
REFUSE/SOLID WASTE TRANSPORTATION FACILITIES/HANDLI NG EQUIPMENT INDOOR NON-FOOD

b. Outdoor

COMMERCIAL/INSTITUTIONAL /INDUSTRI A L PREMISES/EQUIPME NT (OUTDOOR) TERREST RIAL NON-FOOD

IV. Residential and public access premises

a. Indirect food contact *

HOUSE H OLD/DOMESTIC DWE LLING S INDOOR FOOD, INDOOR RESIDENT IAL
HOUSE H OLD/DOMESTIC DWE LLINGS INDOOR FOOD HANDLING AREAS INDOOR FOOD

b. Nonfood indoor

AMPHIBIANS (PET) INDOOR

98ANIMALS (LABORAT O RY/RES EARCH)

BIRDS (PE T )

CATS (ADULTS/KITT ENS) (PET )

CATS (LA B ORATORY/RES EARCH)

DOGS (SHOW/MILIT A RY/SPECIAL)

DOGS/CANINES (ADULTS/PUPP IES) (PET)

DONKE Y S

FERRETS (PET)

FISH (PET )

FOX

GOATS (W OOL/ANGORA ANIMAL)

GREENHOUSE-EMPTY

HORSE S (SHOW/RA CE/SP E CI AL/PONIE S)

MINK

MONKEYS (PET)

MULES (WORK)

NUTRIA

RAB B ITS (FUR ANIM AL)

RABBITS (PET)

REPTIL ES (PET) RESIDENT IAL
INDOOR NONFOOD
INDOOR RESIDENT IAL
INDOOR RESIDENT IAL
INDOOR NONFOOD
INDOOR NONFOOD
INDOOR RESIDENT IAL
INDOOR NONFOOD
INDOOR RESIDENT IAL
INDOOR RESIDENT IAL
INDOOR NONFOOD
INDOOR NONFOOD
INDOOR NONFOOD
INDOOR NONFOOD
INDOOR NONFOOD
INDOOR RESIDENT IAL
INDOOR NONFOOD
INDOOR NONFOOD
INDOOR NONFOOD
INDOOR RESIDENT IAL
INDOOR RESIDENT IAL

99RODENT S (GUINEA PIGS/HAMSTERS/GERBILS/MICE/RATS) (PET) INDOOR RESIDENT IAL
RODENT S

, WILD (CAPTURED F O R SALE) INDOOR NONFOOD SHEEP (WOOL ANIM AL) INDOOR NONFOOD SHEEP, DESERT BIGHORN INDOOR NONFOOD AIR TREATMENTS (COMMERCI AL/HOUSEHOLD) INDOOR RESIDENT IAL
BATHROOM PREMISES/HARD SURF ACES INDOOR RESIDENT IAL
CARPET S (HOUSEHOLD SANI TIZER) INDOOR RESIDENT IAL DIAPER PAILS (EMPTY) INDOOR RESIDENT IAL DIAPERS (HOUSEHOLD/COIN-OPER AT E D LAUNDRY) INDOOR RESIDENT IAL
DIAPE RS (PRE SOAK ) INDOOR RESIDENT IAL
DOMESTIC/COMMERCIAL NO NPOTAB L E WATER (WATERBED WATER) AQUATIC NONFOOD RESIDENT IAL, FILTERS (AIR/AIR CONDIT IONER/FURNACE) INDOOR RESIDENT IAL
HOUSE H OLD TRA S H COMPACTOR/ FOOD DISPOSAL INDOOR RESIDENT IAL
HOUSE H OLD/DOMESTIC DWE LLI NGS CONTENT S INDOOR RESIDENT IAL
HOUSE H OLD/DOMESTIC DWE LLINGS INDOOR NONFOOD HANDLING AREAS INDOOR RESIDENT IAL
HOUSE H OLD/DOMESTIC DWE LLINGS INDOOR PREMISES INDOOR RESIDENT IAL
HUMAN BEDDING/MATTRESSES INDOOR RESIDENT IAL HUMAN CAMPING EQUIPMENT INDOOR RESIDENT IAL
HUMAN DENTURE S /TOOTHBRUSHES/MOUTHPIE CES INDOOR RESIDENT IAL
HUMAN FACE GEAR INDOOR RESIDENT IAL

100HUMAN FOOTWEAR INDOOR RESIDENT IAL
HUMAN GROOMING INSTRUMENTS (BRUS HE S,COMBS) INDOOR RESIDENT IAL
HUMAN HEADGEAR INDOOR RESIDENT IAL
HUMAN WIGS INDOOR RESIDENT IAL
HUMIDIFIER WATER INDOOR RESIDENT IAL LAUNDRY (HOUSEHOLD/COIN-OPERATED) INDOOR RESIDENT IAL
PORTABL E /CHEMICAL TOILETS/ LATRINE BUCKET S INDOOR RESIDENT IAL
REFUSE/SOLID WASTE CO NTAINERS (GARBAGE CANS) INDOOR RESIDENT IAL
REFUSE/SOLID WASTE SIT ES (INDOOR) INDOOR RESIDENT IAL
REFUSE/SOLID WASTE TRANSPORTATION FACILITIES/HANDLI NG EQUIPMENT INDOOR RESIDENT IAL
RESIDENT IAL FLOORS (ANTIM ICROBIALS ONLY) INDOOR RESIDENT IAL TOILET BOWLS (IN T ERIOR SURFACES) INDOOR RESIDENT IAL
TOILET TANKS/WATER CLOSETS WATER INDOOR RESIDENT IAL
URINAL S (INTERIOR SURF ACE S ) INDOOR RESIDENT IAL
VEHICULAR HOLDING TANKS INDOOR RESIDENT IAL

c. Nonfood indoor/outdoor

ANIMAL KENNELS/SLEEPING QUARTERS (COMMERCIAL)
HOUSE H OLD/DOMESTIC DWE LLINGS OUTDOOR PREMISES
PET LIVING/SLEEPING QUARTERS INDOOR NON-FOOD, TERREST RIAL NON-FOOD
OUTDOOR RESIDENTIAL

INDOOR RESIDENTIAL, OUTDOOR RESIDENTIAL

101102V. Medical premises and equipment

AIR TREATMENTS (HOSPITAL )

BARBER/BEAUT Y SHOP EQUIPMENT (BARBER CHAIR/CABINETS)
BARBER/BEAUT Y SHOP INSTRUMENTS (SHAV E RS /SCISSORS) BIOLOGICAL SPECIMENS (ORGANS/TISSUES/MILK SAMPLES)
CADAVERS AND CASKET S

CARPET S (HOSPITAL SANITIZER)

CUS PIDORS/SPITT OONS

DIAPE RS (HOSPITAL LAUNDRY )

HOSPITAL CONDUCTIVE FLOORS

HOSPITAL CRITICAL ITEMS (SURGICAL INSTRUM ENTS/PACEMAKERS)
HOSPITAL JANITORIAL EQUIPMENT

HOSPITAL NONCRITICAL ITEMS ( BEDP A N S/FURNITURE)

HOSPITAL SEMICRITICAL ITEMS (CATHE TE RS/INHAL A TION EQUIPMENT
HOSPITAL / MEDICAL INSTIT UTIONS NON-CONDUCT IVE FLOORS
HOSPITAL S /MEDICAL INSTIT UTIONS CRITICAL PREMISES (BURN WA RDS, OPE R ATING ROOM AREA
HOSPITAL S /MEDICA L INSTITUTIONS NONCRITICA L PREMISES
HOSPITAL S /MEDICAL INSTITUTIONS PATIENT PREMISES

HOSPITAL S /MEDICAL INSTITUTIONS PREMISES (HUMAN/VETERINARY)
HOUSE H OLD SICKROOMS PREMISES/CONTENTS/UT ENSILS
HUMAN W ASTE (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

103MEDICAL LAUNDRY (HOSPITAL) INDOOR MEDICAL
MORGUES/MORTUARIES/ AUTOPSY/EMBALMING EQUIPMENT INDOOR MEDICAL
MORGUES/MORTUARIES/ AUTOPSY/EMBALMING INSTRUM ENTS INDOOR MEDICAL
MORGUES/MORTUARIES/AU TOPSY/EMBALMING ROOM PREMISES INDOOR MEDICAL
REVERSE OSMOSIS WATE R SYSTEM INDOOR MEDICAL
UPHOLS T E RY (HOS PITAL/COMMERCIAL) INDOOR MEDICAL,INDOOR NONFOOD
VOMITUS INDOOR MEDICAL

VI. Human drinking w a ter systems *

PUBLIC WATER SYSTEMS
INDIVIDUAL WATER SYST EMS
EMERGENCY WATER SYST EMS
WATER PURIFIER UNITS INDOOR FOOD
INDOOR FOOD
INDOOR FOOD
INDOOR FOOD

* Use of a product on sites in t h is cate gory will be c onsidered a food us e and registration must be supported by data suff icient to support establishment of a tolerance or exemption from the requirement of a to lerance under the Federal Food, Drug and Cos m etic Act. The Agen cy will co nsider lab e l modifications which clarify practices such that use of the produc t is unlikely to result in pesticide residues in food. Uses will be considered nonfood if fo od is c overed or removed during application and the treat ed surfaces are rinsed with pot able water prior to any contact with food. Re gistrants are advised to contact the Agency if they are uncertain as to whet her a proposed application is a food use.

VII. Materials preservatives

a. Indoor Food

ADHESIVE S, INDUST R IAL (I NDIRECT FO OD CONTACT SURF ACE S ) INDOOR FOOD COATINGS, INDUST R IAL INDOOR

104FOOD PAINTS (FINGER) INDOOR FOOD
PAP ERMA KING (FOOD CONTACT) INDOOR FOOD
PLASTIC-MAKING * INDOOR FOOD

b. Indoor Nonfood

ADHESIVE S, INDUST R IAL (NONFOOD CONTACT)
CAUL KS
DIAPE RS, DISPOSA B L E
FEAT HERS/FELT/FELT PRODUC TS/FURS
FUELS/OIL STORAGE TANK BOTTO M W A TER FUELS/OIL (CRUDE)
HIDES/LE ATHE R/LE ATHE R PRODUCT S ( S URF ACE S ) JANITORIAL PRODUCTS (IN-CO NTAINE R)
METALWORKING CUTTING FLUIDS PAINTS (IN-CAN)
PAP ER (S TORED) PAPERMAKING (NONFOOD CO NTACT)
PLAS TIC/P V C/VINYL PRODUCT S
RUB BER P R ODUCT S SIZES(ING)
SLURRIES
SPECIALTY PRODUCTS
TEXTILES/CORDAGE PRODUCTS
TEXTILE-/TEXTILE F I BERS-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, INDUST R IAL AQUATIC NONFOOD INDUST R IAL,INDOOR NONFOOD, INDOOR RESIDENT IAL, OUTDOOR RESIDENT IAL,

105TERREST RIAL NON-FOOD
DISPERSIONS/EMULSIONS/SOLUTION S/SUSPENSIONS INDOOR NONFOOD, TERREST RIAL NON-FOOD CROP PAINTS (A PPLIED FI LM) AQUATIC NONFOOD RESIDENT IAL,INDOOR NONFOOD, TERREST RIAL NON-FOOD CROP

* Use of a product on sites in this ca tegory will be cons idered a food use and registration must be supported by data su ffi cient to support establishment of a tolerance or exemption from the require ment of a tolerance under the Federal Food, Drug and Cosmetic Act. The Agency will consid er label modifications which clarify practices such that use of the product is unlikely to result in pesticide re sidues in fo od. Uses will be c ons idered nonfood if food is covered or removed during applic ation and the treat ed 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 whet her a proposed application is a food use.

VIII. Industrial processes and w a ter sy stems

a. Indoor Nonfood

PAST EURIZER/CAN WARM ER/ C ANNERY/RETORT WATER SYSTEMS
HYDROSTATIC STERILIZER WA TER S Y S TEMS

IMMERSION ULTRASONIC TA NK WATER

LEAT HER PROCE S S I NG WATER/LIQUORS

PHOTO PROCESSING WASH WATER

RECIRCUL ATING ELECTRODEPOS ITION SYST EMS

REV E RS E OSMOSIS WATER S YSTEM INDOOR NONFOOD
INDOOR NONFOOD
INDOOR NONFOOD
INDOOR NONFOOD
INDOOR NONFOOD
INDOOR NONFOOD
INDOOR NONFOOD

106b. Aquatic/Outdoor e x posure

AIR CONDITIONER/REFRIGERATION CONDENSATE WATER SYST EMS
AIR WASHER WATER SYST EM S

COAL SLURRY SYST EMS

COMMERCIAL/INDUSTRIAL COOLING WATER [RECIRCULATING]

COMMERCIAL/INDUSTRIAL COOLING WATER [ONCE-THROUGH]
DRAINAGE SYST EMS

EVAPORATIVE CONDE NSER WATER SYST EMS
INFLUENT WATER FILTRATION SYSTEMS

TEXTILE MILL WATER SYSTEM S

INDUST R IAL SCRUB B ING SYS TEM

INDUST R IAL WASTE W ATER T R EATME NT SYST EMS

LABORAT O RY EQUIPMENT WATER B A T H S

PULP/PAPER MILL WATER SYST EMS

GAS/OIL D R ILLING M UDS/PACKER FLUIDS [OFFSHORE]
GAS/OIL D R ILLING M UDS/PACKER FLUIDS [TERRE ST RIAL]
GAS/OIL P I PELINES MAINTENA NCE

SEWAGE SYST EMS AQUATIC NON-FOOD INDUST R IAL
AQUATIC NON-FOOD INDUST R IAL
AQUATIC NON-FOOD INDUST R IAL
AQUATIC NON-FOOD INDUST R IAL, INDOOR NONFOOD
AQUATIC NON-FOOD INDUST R IAL
AQ UATIC NON-FOOD INDUST R IAL AQUATIC NON-FOOD INDUST R IAL
AQUATIC NON-FOOD INDUST R IAL, INDOOR NONFOOD
AQUATIC NON-FOOD INDUST R IAL AQUATIC NON-FOOD INDUST R IAL
AQUATIC NON-FOOD INDUST R IAL, INDOOR NONFOOD
AQUATIC NON-FOOD INDUST R IAL AQUATIC NON-FOOD INDUST R IAL
AQUATIC NON-FOOD INDUST R IAL
TERREST RIAL NON-FOOD

AQUATIC NON-FOOD INDUST R IAL, TERRESTRIA L NON-FOOD
AQ UATIC NON-FOOD INDUST R IAL

107c. Environmentally contained

GAS/OIL R E COVERY INJECTION WA TER SYSTEMS INDOOR NON-FOOD
GAS/OIL F R ACTURI NG FLUID SYSTEMS INDOOR NON-FOOD

IX. Antifouling coatin gs

BOATS/SHIPS HULL/ BO TTOM
CRAB/LOBSTER POT S
FRESHWATER ST RUCTURES/ EQUIPMENT
MARINE S TRUCTURES/EQ UIPMENT
WOOD PROTECTION TREATMEN T TO WOOD ANTIFOULANT
ANTIFOUL ANT
ANTIFOUL ANT
ANTIFOULANT
ANTIFOULANT

X. Wood preservatives

a. Heavy d u t y

SEASONED WOOD PRESSURE/THERM AL TREATMENT
SEA SONE D WOOD NONPRES S URE TRE A TMENT (JOINERY)
SEA SONE D WOOD NONPRES S URE TREATMENT(REMEDIAL)
UNSEASO NED FOREST PRODUCTS T R EATMENT (SAPST AIN) WOOD PRESERVATIVES
WOOD PRESERVATIVES
WOOD PRESERVATIVES
WOOD PRESERVATIVES

b. Read y t o Use

SEA SONE D WOOD NONPRES S URE TRE A TMENT (READY-TO-USE) WOOD PRESERVATIVES

XI. S w imming Pools

SWIMM I NG POOL W A TER SYSTEMS AQUATIC NON-FOOD RESIDE NTIAL

XII. Aquatic areas

AGRICULT URAL DRA I NAGE SY STEMS
COMMERCIAL FISHERY WAT E R SY STEMS
INTERMITTENTLY FLOODED AREAS/ WATER AQUATIC FOOD
AQUATIC FOOD AQUATIC FOOD, AQUATIC NON-FOOD OUTDOOR

108IRRIGATION SYST EMS AQUATIC FOOD LAKES/PONDS/RESERVOIRS (WITH HUMAN OR WILDLIFE USE) AQUATIC FOOD

ORNAMENTAL PONDS/AQUA RI A AQUATIC NONFOOD
STREAMS/RIVERS/CH ANNEL E D WATER AQUATIC FOOD, AQUATIC NON-FOOD OUTDOOR LAKES/PONDS/RESERVOIRS (WITHOUT HUMAN OR WILDLIFE USE) AQUATIC NON-FOOD INDUST R IAL

109

## Appendix C. Glossary of T e rms

AME R ICA N CHE MISTR Y COUNCIL ANTIMI C ROBIAL EXP OSURE AS SESS MENT TASK FORCE II

G LO S SARY of TERMS (derived in part from 4 0 CFR Part 158W)

Ma y 2007

PART I. ANTIMI C R O BIAL APP L ICATION METH OD S

PART II. EPA A N TIMICROBIAL USE SITE GR OUP S

PART III. GENERAL TERMS

I. ANTIMIC ROBI AL AP PLICATION METH OD S

A E RO SO L SPR AY – A suspension of fine solid or liq uid particle s in gas th at is dispensed fr om a press urized conta i ner. The suspension is relatively stable, that is, the particles will remain su spended for a period of time barring an external influe nce. Standard-setting organizations, ISO, ACGI H, and BMRC, have estab lished inspir able (able to enter the respiratory syste m ) and rep arable (able to enter the alveolar area of the lung) levels for aero s ols. Generally, particle s under 8 to 10 microns are con s ide r ed respirable. One micron particle s are conside r ed 95 percent insp ira ble, 10 micron particles ar e 70 perce nt insp irable , and 100 micron particles are co nsidered to be 20 percent inspirable. For exa m ple, disinfect ant aerosol sprays contain less than 1% droplets 10 microns or smaller. Median size typically is 100 micro ns (mass m edian diameter or mmd), normally distributed. However, government risk assessor s u s ually assume that all measured air levels are not only inspirable b ut also respirable. Thus, the only time when it would be critical to distinguish particle size would be if a particular chemical ha d an inhalation toxicology study that showed damage to the alveolar region of the lung.

AIRLES S S PRAY – A spray application that occurs by directly creating pressure to d r ive a liquid out of a no zzle for transf er through t he air to a final target surface. Typical pressures f or paints/co atings are in the range of 5,000 p s i with orifice diameters of approximate l y 0.018 inches. Airless spray part i cles must be large enough to reach the target surface and small enough to a c hieve unifo rm deposition.

BRUS H – Application of a liquid mat erial to a sur face area, such as a wa ll, by repeat edly inserting a brush (e.g., a paint brush) into a co ntainer for loading and then brushing it back and for th across th e surface to be covered or treated.

FOGGING – An application that r equires a d evice that generates very small liquid particulate f or transfer through the air, so it can penetrate into areas difficult to physically reach. These devices require some type of mechanical p ump to gen erate the needed

110pressure to drive the bi ocide through the nozzles. A "dry fog" has droplets ranging in size from 1 0-15 microns in volume. A "wet fog " or "mist" h as droplet s ranging in size from 30-60 microns in volume. A "fine spray" has drople t s larger tha n 60 micron s in volume.

I M

M

E

RSI O N/DIP/ SOA K - Intercha ngeable ter m s. Differences in app licator expo sure potential are attributable primarily to scale, but also may be a ssociate d with use patterns employing this application method and common industry practices.

Wood treatment. The American Wood Preservers’ Association (A WPA) 2001 Standards d efine dip as “application of a liquid pr eservative to a wood by immersing the wood in the liquid f or a short period of time,” typ i cally 3 to 2 0 minutes. Soaking inv olves leaving the lumber in th e solution fo r a longer period of time (for exa m ple, 12-48 hours) in an attem pt to get th e chemical t o go below the surface. Typically, these opera tions are large scale, high ly automated processe s where stacks of wood are mechanically lowered into baths. The stacks ar e inserted, removed and stacked i n an automated manner with very little human expos ure.

Sanitizer/Disinfectant. Items requiring disinf ection or sanitization may b e immersed i n an antimicrobial solu t i on. Exa m ples inclu de flatware, glassware, barber and hair salon articles, etc. The le ngth of time immersed has no impact on the amount of absorptio n by non-po rous articles. For example, a no n-p orous material soa k ed in an antimicrobia l chemical d oes not re ta in the chemical or have any residu al antimicro bial activity. A good examp l e would be dishes sani tized in sodiu m h y pochlorite by soaking. The dishes are sanitize d but retain no parent ch emical or antimicrobial a c tivity once dry.

MO P – Application of a liquid material to a large surface area, such as a floor. This can be performe d by repeat edly insertin g an implement made of absorbent material (e.g., string mop head) faste ned to a handle into a bucket and wiping it back and forth across the surface to be treated. Alternatively, “read y-to-use” mop technologies can be used, for example , pre-impregnated absorbent materials atta ched to a “ m op head” with a handle, or spray delivery systems integrated wit h the mop h ead and ha ndle. The readyto-use syst ems do not require dip ping into a bucket. All of these mop technolo gies transfer the antimicrobia l product fro m the liquid formulation to the surface.

PLACE S O LID – Application o f a solid biocide material into a co ntainer or f i nal application t hat is acco mplished in a single a c tion. The for m of the solid may be watersoluble packets, water-permeable containers, ta blets, sing le dose delivery containers, or other solid p ermeable or soluble d elivery forms. The applica tion occur s in a single st ep without a co ntinuous flo w from one container to another.

POUR LIQUID – A bio c ide in a liquid form is po ur ed from a container, e i ther manually o r with some equipment, into another container or mixing apparatus without the u s e of devices that create a vacuum or pressure (i.e . pumps) to fac ilitate or force the transfer of the liquid.

POUR S O L ID – A solid biocide ma terial (flake, pellet, powder, etc.) is transferred in a continuous flow from one containe r to anothe r, either manually or with the aid of equipment.

111PRES SURE TREAT M

E

N T – This is a spe c ial application method used in wood preservation using vacuum and/or e x ternal pressure to drive a biocide product deep into a product matrix.

. The process involves sealin g product within a pressurized conta i ner (retort) for various len gths of t i me, pulling a vacuum, and then intr oducing tre ating solution that is forced int o the wood as air pressure is reintro duced into t he retort.

PUM P - Transfer of a liquid antimicrobial from the orig inal contain er to anoth er by pumping as part of the transfer for (1) subsequ ent use to f ormulate other pesticid es or for industria l use or (2) end use application s , such as recirculating w ater treatment, paper mill slimicide app lication, met alworking fl uid preservation, etc. Gravity or h and pumps and automated metering pu mps are t y p i cally used. This type of applicatio n is anticipated t o use hoses and various connection devices to f acilitate th e transfer in most situation s

.

ROLL – Ap plication of a liquid material to a su rface area, such as a wall, by repeatedly inserting a cylindrical de vice covered with an ab sorbent material (e.g., a paint roller) into a container, and rolling the cylinder back and fo rth across t he surface t o be covere d or treated.

SPRAY – A spray application o ccur s when a liquid is forced through an orifice u nder pressure for dispersal to a target object or surface. There is no accept ed standard for biocide app lications that distingu ish es between high pressur e and low p r essure spr ays. Pressure and orifice size are the variables t hat impact particle size, which is impo rtant depending on the specific application. Following are some typical examples of spray application s

.

Industrial U s e. High pressure sprays are delivered by electric p umps at pressures ra nging from 500 to 50,000 psi. Drople t size may be less than 10 microns. Industrial Use. Low pressure sprays used in indu strial applica tions may b e either manual or e l ectric at pre ssures ra nging from 1 to 500 psi. Droplet size is usually abo ve 10 microns. Wood Preservation. A high pressure spray nozzle de livers a wide r ange o f particle size s, dependin g on liquid pressure and nozzle opening. For exa m ple, to generate a “fog,” typical liquid pressures are 50 0-1500 psi and nozzle orifices are 0.005 inch or smaller. High pressure spray delivers low volu me of liquid using a higher concentratio n of the che m ical. The equipment creates a "fo g" or "mist" (low particle size) that the lumber passes t hrough. Treated lumber is almost dry to the tou c h immediat ely after the spray process; there is no dripping. Generally hi gh-pressure systems ha ve a vacuum that returns overspray to a holding tank for re-use. This method of applicat ion results in lo w exposure s via inhalation when a vacuum is u s ed and low exposures for dermal contact because lumber is almost dry to touch with a lowe r opportunity for transfer tha n a wet surface. This m ethod is gro w ing in pop ularity. Wood preservation. Lo w pressure spray delive r s higher vo lumes of liquid and actually "flo ods" the sur face of the wood with a solution tha t is lower in concentratio n of chemical thr ough larger sized no zzles than a high pressu re spray. This results in a board that d r ips as thou gh it were dipped in a bu lk dip vat. Sanitizer/Fo od Processing. Low pressure sprays are commonly used for sanitizer ap plication s

. There are a number of different ap plication de vices includ ing: (1) hand pressurized (gard en type) sprayers. (2) trigger spra y bottles; (3) low pressure spray or foam devices (typically connected to th e water system in the plant and operate at 20 -100 psi; antimicro bial is in ject ed into the water stream and spra yed; either hose112end devices or dispense d through a centralized sy stem within a facility); (4) “Cleaning in Place” or CIP (automated cleaning/sanitization procedu res conduct ed in large food processing establishme nts; cleanin g equipment is a c tually built in to t he food han dling equipment itself; a comp lete cycle of pre-cleanin g, cleaning, rinsing and f i nally sanitizing is conducte d automatically through all areas of the food processing line; typically, th ese operations are completely enclose d; for example, pipes a r e flushed with the cle aning solution; ta nks are clea ned and sanitized usin g a dishwasher type spray arm at the top of the tank which sprays the chemical solut i on onto the tan k walls.) Textile Treatment. Anti microbial formulation used in a fabric treatment in which it is essent ially dispensed in a series of small streams at low pressure on to the surface of the fabric, w i th the excess running o ff. Aerosol f ormation is t y pically low.

WIPE -- Ap plication of a liquid mat erial to a su rface area, such as a counter or wall, by use of a sm all hand held piece of a bsorbent material (e.g., a spong e o r woven or nonwoven fabri c ) pre-wetted, sprayed o r dipped into a container and wiping it back and forth across the surface to b e treated. I ndustrial u s es of wipes include in-f actory kiss rolls, dry-wipe lin es, doctor bars, or consumer-like wipes used by building restoration and maintenance personnel.

113PART II. ANTIMI C ROBIAL USE PATTERNS

AGRI CULT URAL P R E M

ISE S A ND E Q UIP M E

NT - I ncludes on ly application of disinfect ants, sanitizers, fungicide s, etc. to re duce or eliminate infectious or o ther undesired microorganisms on inan imate surfaces in farm a nd livestock premises. ( e.g., pens, parlo rs, stalls, b arns, etc.), and on e quipment, (e.g., forks, shovels, ha lters, feeders, tro ughs, milkin g equipment, etc.)

ANTIFOUL ANT C O A T INGS - Antifoulant paints for underwater structure s and underwater equipment including sh ip and boat bottoms and hulls, crab and lobster pots, and structur es and equipment used on fish farms.

AQU ATIC AREA S - Application of antimicrobials to contro l slime-forming bacteria, fungi and algae in lakes, pond s, streams, drainage dit c hes and ot her bodies o f water.

CO MM E

RCI AL, INSTIT UTIONAL AND INDUST RIAL PRE M

ISES AND EQUIP M E

N T – Includes onl y

application of disin fectants, sa nitizers, fun gicides, etc. to reduce or eliminate in fectious or other und esired micr oorganisms, on inan imate surface s , in commercial (e.g., hotels, motels, theaters, office buildin gs, airports, etc.), industrial (factories, mills, plants, etc.), and institutio nal (schools, ca mps, public offices, pri s ons, etc.) premises. Equipment includes ceili n gs, doors, doorknobs, fixtures, floors, woodwork, walls, and windows.

FOOD HA NDLIN G/ST ORA G E P R E M

I SES AND E Q UI P M E

N T – Includes only application of disinfe c ta nts, sanit i ze rs, fungicide s, etc. to re duce or eliminate infectious

or other u ndesired microorganisms, on in animate su rfaces, a s 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 transportati on facilitie s (e.g., stores, markets, vending machines, tru cks, shipping co ntainers, etc.)

HUM A N DRINKING WATER SYST E

M

S

- Includes applicat ion of disinf ectants to p ublic water systems, includin g water sup plies and co mponents (e.g., pipes, casings, reservoir surfaces, filt er sands, et c.); individu al water systems (home s , farms, institutions, ca mps, industrial fa ciliti es, etc.) ; emergenc y wate r sys tems and water purifier systems (e .g., campers, travelers, military, etc.).

INDUST R IA L WATER SYSTE M

S - Applicatio n to commercial and industrial systems (e.g., coolin g towers, evaporative condensers, air washers, heat exchangers), pulp and paper mill systems, g as/oil recovery sy ste m s, drainage, wastewater and sewage systems, and specia lized uses ( e.g., immers ion ultra s o nic tank water, labora tory equipment water baths, photo proce ssing water, electro-dep osition syste m s, etc.)

M

A TE RIAL PRE SERV A TIVES - Bacteriostat s, microbiostats, and fungistats added to industrial p r ocess inte rmediate materials (e.g., disper sions, slurr i es, emulsions, solutions, et c.) and resu lting product s (e.g., pain ts, coatings, adhesives, textiles, pap er, etc.) to cont rol growth o f slime-forming microorganisms (e.g. , papermaking) and pre v ent deterioration or spoilage of material during storag e and/or in- use life.

EPA makes no distinction for purposes of exposure assessment between preserved materials (in-can) and exempt trea ted article s , that is, art i cles that make a claim of

114protection for the articles themselves. EPA re gards all exposures associated with the application of a pesticide to be a pplicator exposures. As a result, EPA regard s the application of a preser ved materia l , whether or not it makes a pe sticidal claim , as secondary application fo r which it re quires exposure data. For exampl e, painter st udies are required, but EPA d oes not care whether the paint used in the study make pesticidal claims, because it considers use of all paints that contain antimicrobials, even if onl y for in-can prese r vation, to b e secondary pesticide a pplication. As a result, all applicat io ns of paints and coatings, ot her than those specif ic to antifoula nts and wood preservatives, are included in this cate gory. (However, for purposes of exposure monitoring, there ma y be some possibility of combining replicates fro m these three use patt erns.) Similarly, machine operators in contact with metalworking fluids are considered t o be secon dary applicators.

When expo sure to the preserved a r ticle occur s following application of the pesticid e, as is the case with most preserved articles, EPA ma y be in terested in obtaining postapplication exposure data.

M

E

DI CAL PRE M

ISES AND E Q UI P M E

NT – Includes on ly applicatio n of disinf e c tants, sanitizer s , f ungicides, etc. to reduce or eliminate infectious or other undesired microorganisms, on inanimate surfaces in medical environments. Premises include hospitals, clinics, denta l and medical offices, veterinaries, n ursing homes, “sick roo m s,” etc. Equipment is limited to non-critical care e quipment, that is, equip m ent that d oes not contact the patient or contacts the patient’s in t act skin (e. g., furniture, carts, bedp ans, telephones, etc.) It is not clear from the EPA definition w hether residential “sick r ooms” are included within this u s e pattern o r the preceding use patte rn.

RECR EATI ONAL WA TER - Antimicrobial tr eatment of “hydrologically isolate d and contained manmade bodies of wate r,” including swimming p ools, Ja cuzzis and hot tu bs. Exposure monitoring is limited to the applicator of the antimicrobial to the recreational water. Exposure of a bather or swimmer t o the antimicrobial is considered p ostapplication exposure and is determined by use of the EPA’s “Swim Mod el.”

RESIDENTI A L AND P UBLIC ACCESS PRE M

I SES – I n cludes only applicat io n of disinfect ants, sanitizers, fungicide s, etc. to re duce or eliminate infectious or o ther undesired microorganisms, on inanimate surfaces in private residences and public

access areas. (There is no clear distincti on betw een EPA’s u s e of the terms institutional premises and public a c cess premise s .)

SWI M

M

I NG POOL S -- see Recreational Water

WOOD P R ESERVATI VES - Pres ervative tre atments for all types of wood, applied by pressure or vacuum treatment, remedial app lications (i.e. , application to utility poles, support timbers, etc. while in-ser vice); non-pressure tr eatments (e.g., joinery and millwork), anti-sapstai n treatments and ready-to-use coating s

.

115PART III. GENERAL TERMS

Adequac y of BHEDTM Data - E ach handler scenari o within BHEDTM will co ntain sufficien t monitoring units (MUs; al so referred to as monit oring events, i.e., MEs) to achieve a pre-determined level o f accuracy for statist i cal descr ipto rs (e.g., mean, geometric mean, and 95 th percentile) of the distribution of ex posure. Ge nerally, the data set will be considered accurate if these measures are within 3-fold of the true valu e and the number of MEs colle cted will be chosen to a c hieve this l evel of accu racy.

AEATF II = Agricultura l 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 appli c ation of pesticide s. The exposure data will cover imp ortant types of mixing/loading systems, applica tion equipment, and formulations. The r esults will satisfy FIFRA data requirements and be used to a ssess handler, and for some sce narios bystander, exposure potential an d asso ciate d risk assessments f or antimicr obial pesticide pr oducts marketed by AEATF II me mb ers. AEATF II was formed in Nove mber, 2004.

Biomonitoring - Meas urement of a pesticid e or its me tabolite(s) in the body of a pesticide h andler and the conve r sion to an equivalent absorbed dose based on knowledge of metabolism and pharmacokinetics. This g en erally includ es measuremen t of chemical in blood or urine, but does not include measurement of biologica l effects such as ch olinesterase levels. The result is an estimate of total exposure from the dermal, inhalation, and o r al routes co mbined.

Cluster - A set of mon i toring units or events (MUs or MEs) from the same scenario considered a higher-level sampling unit for th e purpose of stat istical design a nd analysis. E x posures between MEs from the same cluster ( e.g., buildin g location) t end to be more similar than tho s e between MEs from di fferent clust ers.

Distributio n of Exposure - A s tatistica l de scription of the probability that a given exposure level is attain ed; derived from a se t o f monitoring units (or monitoring events) within a given scenario and generally descr ib ed by standard measures such as the arithmetic mean, geometric mean, and percentile values.

Engineering Controls - Equipment or equip m ent modifications which eliminat e or reduce exposure to a chemical, such as enc losed cabs, ventilation, or closed tr ansfer sy stem s.

Exposure Monitoring

- Using passive dosimetry techniques to mea s ure dermal and inhalation e x posure to professional, occupatio nal pesticid e handlers as they perform their typical activities. Researchers will use a variety of pesticide r esidue colle ctio n devices (clo th dosimeters, hand washes, face /neck wipes, and sorbent tubes) an d determine the quantity of active ingre dient on each device by chemical re sidue analysis.

GLP (Good Laboratory Practice Standards) - Federal regulations ( 40 CFR 16 0) that prescribe g ood laborat ory practices for con ducting stu dies that support pest i cide registrations. The stan dards address the scien tific integrity of study co nduct and data collect ion, in cluding spe c ific requ ire m ents for study manage ment, equipment calibration, facilit ies mai ntenance, re cord keepin g, reporting, and quality assurance.

116Handling - Generally refers to mixing, loadin g , transferring, or applying pesticides. However, handling also includes the follow i ng common tasks: h andling op ened containers; disposing o f pesticides or pesticid e container s; and clea ning, adjust i ng, handling, or repairing t he parts of mixing, loading, or application equip m ent that may contain pest i cide residu es.

IRB (Institutional Review Board) - An indepe ndent board that reviews and appro v es study proposals and oversees rese arch to ens ure the protection of hu man subject s who volunteer to participate in those stud ies. IRB responsibilities and authorities are defin ed 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 m onitor exposure to antimicrobial pesticid e handlers and that will be used to develop a gen eric database to support pe sticide regi strations. The planne d testing p r ogram will cover pesticide handling an d bystander scenarios.

Monitoring Unit (MU) or Monito ri ng Event (ME)

- All e x posure mo nitoring activities pertaining t o a single worker for a time period that rep r esents a t y pical workday, including th e exposure measureme nts for the worker involved. A specified number of monitoring events (MEs) will be conducted fo r each scen ario to adequately define the distribution of exposure expected for that scen ario. MEs defined in d i fferent scen arios may be collected in a single study.

Passive Dosimetry - T echniques f or measuring pesticide exposure to human subject s which do not involve invasive collection techn i ques such as colle ctin g urine or blood. AEATF II studies involve whole-body garment s that serve to colle ct dermal residues, hand washes to collect hand residu es, face/ne c k wipes to collect residu es on the fa ce and neck areas, and sorbent tubes to collect air in the breathing zone of a worker. Additional cloth dosimet ers may be used to mea s ure exposu r e to the fee t or to the h ea d area with and without headgear.

PPE (Perso nal Protective Equipment) - Devices and app arel that are worn to protect the body fro m contact with pesticide s or pesticid e residues, including bu t not limited to coveralls, chemical-re sistant suit s, chemica l -resistant gloves, ch emical-resistant footwear, respiratory protection de vices, chemical-resistan t aprons, chemical-resistant headgear, and protective eyewear (See 40 CFR 170.240).

Purposive Diversity Sampling - The type of non-rando m sampling used for e ach scenario in the AEATF II monitoring program. Sampling i s purposive because certain important conditions ar e sele ctively sampl ed. Diversity (or heterog eneity) samplin g means that the purposive sampling is targeted to achieve a diversity of major factors t hat are likely to influence exposure, including amount of active ingredient ha ndled, subje c ts, and location

. Regulatory Agenc y Advisory Co mmittee (RAAC) - comprised of representatives of the U.S. EPA, the Canadian Pest Management Regulatory Agen cy (PMRA), the California Department of Pesticide R egulation (CDPR), and Euro pean regulatory authorities. This committee meets on an ad ho c basis to r eview the program progress and provide technical input to the AEATF II

117Scenario - A specific p esticide han dling situat i on that will be represented by data with defined co mmon prop erties; gen erally a co mbination of a work t ask(s), pe st icide formulation, equipment, engineerin g controls, and work p r actices. F or example, a scenario of interest is ‘mopping indo or surfaces with defined application equipment and related tasks, e.g., fillin g a mop bu cket with a defined end -use mop solution usin g an automated dispensing system and disposing of dirty mop solution’. Tasks that are common across more th an one exp osure scena rio, e.g., pouring liquids into containers, may be specifica lly add ressed in a separate st udy (e.g., mixing/loading or pouring of liquids, such as fi lling a mop bucket with end- use liqu i d solution usin g an automated dispensing system, or preparing an e nd-use mop solution, pr ior to mopping applicatio n).

Scripting, Scripted Stud y - Scri pting is the partial control of the conditions in a particular st udy. A scripted study is considere d to “involve intentional exposure” within the meaning of the regulatory definition at 40 CFR 26.110 2(i). Subjects are asked to conduct the i r work activities under a set of scripted con ditions very similar but not identical to those they experience in their normal work a c tivities. Scripted or semiscripted ta sks 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 a nother.

Stud y - A convenient grouping of monitoring units or ev ents (MEs) covered by one protocol an d one final report. Typically a study will address one or more tasks associat ed with a specifically defin ed exposure scenario and will be conducted o v er a short period of time (1 to 2 weeks), with on e surrogate chemical. Tasks that are common across more th an one exp osure scena rio, e.g., pouring liquids into containers, may be spe c ifica lly addressed in a separate stud y.

Surrogate Chemical

- A pesticide active ingr edient which is present in test mat erials which are handled durin g collect ion of an monitoring unit or e v ent (MU or ME). AEAT F II develops validated analytical methods for each surrogate chemical and each exposure matrix so re sidues collected can be deter mined. AEATF II chooses surrogates which have low volatility and are commercially available in suitable f ormulations and packaging. Since exposure to handlers is a gen eric function , exposure measureme nts from these chemicals a r e suitable for esti mating exposure to other pesticide active ingredients.

Target Population (or Universe) - Each element of the ta rget population is a pote ntial task (or set of tasks) performed by a worker under a particular scenari o in a day. Each element is defined by a set of all conditions t hat might h ave any impact at all o n that worker’s exposure. The se condit i on s include th e particular chemical pr oduct tested , the worker, his behavior, and all relevant environme n tal conditio ns. Each ME is assumed to be a realizat ion of an ele m ent from t he target po pulation.

118

## Appendix D. AEA TF II Accept a nce Criteria for Existing S t udies

Data Review and Stud y Accept a nce Criteria For Inclusion of Existing Antimicrobial Exposure Monitoring Studies In The Antimicrobial Exposure Assessment Task Force II (AEAT F II) Database

November 28, 2006

I. DATA REVIEW PROCESS

All e x istin g antimicrob i al exposur e monitoring studi es, whether offered by a third party for sale or from the pub lic domain, will undergo a 3-step r e view process to determine whether they meet the selection criter ia listed in this document. In addition to the specific re view process outlined be low, a continued dialogue will take plac e with U.S. EPA to discuss each exposure scenario to be studied and the general study design el em ents (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 d e sign and data collection r equirement s needed for an existi ng study to be deem ed acceptable to fulfill a giv en data re quirement. The n on- acceptance of a give n study, via the AEAT F II study revie w process, does not impl y that it is not suit able for us e by any indiv i d ual member company to fulfill a specific da ta 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. Prelimin ary Review

1. The pr eliminary review will be conducted by t he study submitter (or a designated representative) and pr ovided to AEATF II along with a c o mplete 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, includ ing any that are presently in PHED vers ion 1.1 or version 2. The st udies in PHED versio n 1.1 are all more than 15 years old and are not subject to data compensatio n requirements. Howev e r, these data were originally subm it ted to PHED without attribution to compound or company ownership. If any of these data fulfill a cce ptance criteria, they could potentially prov ide ver y beneficial information that would complement data developed by AEATF II.

3. Raw data for a study must be made available, if requested.

1194. A list of potential st udies and all prelimin ary review forms and reports should be submitted by dates requested by AEAT F II.

5. The purpose of the preliminary review will be to e liminate the submission of studies that clearly do not meet the sele ction criteria, and to serve as a chec k on the availability and submission of supporting information.

6. AEATF II will pr ovide an Exc e l s p re adsheet to the subm itter for use in summarizing the study details and data.

7. AEATF I I will provid e a confide ntiality agr eement 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 qua lifie d A E A TF 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 discus sion summarizing each specific ar ea of the criteria, includ ing whether each criterion was met and possible deficienc ies in the study data.

3. The intermediate review will be evaluat ed and a determination made as to whether the study or any of the data could be used in the AEATF II database. Only studie s that have met the design cons iderations will be pre s ented to EPA, PMRA and CDPR for fi nal review.

C. Final Review

1. A Committee consis ting of representat ives from the AEATF II an d EPA, PM RA and CDPR will make the final review and decis ion on whether a study is accepted for purchase.

2. The intermediate review by the contract or 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 s e le c t ed after final revi ew will then be considered for purchase by the AEATF II for inclusion in the Task Force database.

4. Reports for studies t hat the Committee deems not acceptable for the AEATF I I database will be returned to the submitter with an expl anation as to why the

120study did not meet the selection criteria. Reports of studies that are purchased by AEATF II will be pla c ed in the AEAT F II archives.

D. Appeals Process

Study contributors whose studies are not accepted for possible purchase may appeal that decision, but should do so wit hin 30 days of such notification.

II. STUDY ACCEPT ANCE CRITERI A: STUDY DESIGN CONSIDERATIONS

1. All monitored activities and equipm ent must be describ ed in detail and representative of typical antim icrobial handling practices.

2. It should be clear that the indiv i duals monitor ed 1) either are normally employed in the mixing/load ing and/or applicatio n of antimicrobial products or pesticide products and handled them com parably, or, 2) if consumers (i.e., nonprofessionals), are applying ant imicrobial s 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, a pplic ation rate, durat i on of the work cycle, amount o f AI handled/replicate, etc. mu st be available.

4. The use of protective equip m ent (PPE) is acceptable but must be part of normal work practices.

5. The study location and environmenta l/weather conditions during the monitoring period should be available.

6. All elements of a gi ven study may not have been c onducted 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 ACCEPT ANCE CRITERI A: EXPOSURE MONITORING

A. Field As pects

1. Field recoveries s hould have been c o llec ted on a site-specific basis for time periods and environmental cond itions representative of those during collection of field activit y exposure samples.

2. Field fortification data should include at least triplicate samples at two rates and triplicate samples of controls; howev er, duplicate samples will be considered with justification.

1213. Dermal expos ure monitoring techni ques should be specified and s hou ld include one of the following approaches. Note that glove was hes of chemical resistant gloves are not an allowable method.

a) whole-body dos imeters inside and/or ou tside of typical c l ot hing plus hand (cotton gloves can substitute for hand expos ure) and head/ face exposure determinations,

b) a minimum of 10 patch dos imeters at tached inside or outside normal work clothing to the chest, back, both upper a rms, both lower arms, both upper legs, both lower legs, plus hand (cotton gloves can substitute for hand wash) and head/face exposure determinations (excep tions for head/face and upper arms and upper or lower legs and bilateral m easurements will be cons idered on a case by case basis). Conversion and use crit eria have been developed by the NAFTA harmonization group and should be consider ed for adaptation of the PHED data.

c) combination of patches and clothing t hat are representativ e of the whole body, including hand and head/face exposure determinations.

4. Inhalation expos ure – Inha lation 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 environme n t that results in sign ificant volatiliz ation (e. g ., around steam pipe s or in metal working fluids), or if the method produces ins p irable aerosols. If dat a were collec t ed, 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 durati on or mix/load and/or apply at least half of the daily amount normally used.

6. If the expos ure monitoring duration does not meet the requirement of item number 5, then the number of non-detects/less than LOQ values should acc ount for less than 40% for dermal expos ure. This cut-off is specified becaus e the distribution of expos ures 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 monit o ring duration and number of non-det ects/less than LOQ values do not meet t he criteria in items 5 and 6, t hen the LOQ should be no more than 20 ng/cm2 for average der mal expos ure (across body par t areas) and no more than 500 ppb for hand was h solution. The LOQ cuto ffs are conservative in that if all data were at the LOQ, the resulting calcu l ated exposur e (at 1⁄2 LOQ) would yield an MOE of ≥ 100 for a compound with a systemic (absorbed dose) NOAEL ≥ 1 mg/kg.

122B. Analytic al Aspects - QA/QC

1. Analytic al methods should have been va lidated for each analy t e and substrate by the performing laboratory including es tablis hment of the method’s wor king concentration range to cover values antic ipat ed in the fi eld studies, determination of detector response over a r easonable standard concentration range, and determination of the accuracy and precis ion of the met hod wit hin the analytical environment.

2. The study should incl ude both field fortification samples and concurrent laboratory spikes.

3. The average recoveries of lab spik es should be between 70-120 percent and the precision value (coefficient of variat ion; 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. Expos ure samples should have been a nalyzed in such a manner that the stability of each ana lyte in e a ch s ubstrat e was assesse d for the entire time perio d from collection to analy sis.

C. Biomonitoring

Biologic al monitoring studies will be accepted for further review if they meet the selection criteria (excluding pass i ve dos imet ry) and there is a primate (human or monkey) dermal absorption study fo r the chemical monitored and pharmacokinetic data identifying the major excretory metabolite or par ent compound.

123

## Appendix E. Designing Monitoring Event s to Predict Future Exposure under Antimicrobial Handling Scenarios

Backgrou nd

Future Exposure and Monitoring Events

An antimic robial handling sc enar io is a well-defined worker exposure situation, usually characterized by specific ant imicrobial handling tasks and equipment. For the purposes of the AEATF II monito ri ng program, the basic element of a scenario is consider ed to be the handler-day (HD). Each handler-day corresponds to a particular wor ker and the scenario-related activities that he performs during a single work day.

Implicitly associated with each HD is a co mplex set of conditio n s denoted simply by C

. The practically infinite number of components of C includes, but is not limited to worker behaviors, active i ngredient used, amount of chemical contacted, surfaces treat ed, and numerous environmenta l factors. Some, but certainly not all, of t hese conditions can actually be obser ved and measured.

Each partic u lar set of HD conditions, C , results in a particular worker expos u re, E=E( C ). In principle, although always s ubject to some measurement error, handler-day exposures (e.g., dermal or inhalation) can be obtained by a c tual monitoring.

Regulatory interest for each antimi crobial handling scenario is focused on predicting occupational exposur e under a specific set of generic future handlerday conditions

. In particular, it is desired to characterize exposures resulting from the future use of an arbitrar y (and perhaps currently non-existent) antimicrobial active ingredi ent as suming some arbitrar y, but quantifiable, amount of active ingredient contact.

A monitoring event (or ME) is the basic to ol used by the AEAT F II Monitoring Program to predict exposures under. An ME is a set of scenario- specific handler-day conditions that hav e been experimentally selected (i.e., chosen, simulated, or constructed) to represent expected futu re HD conditions. Each ME is monitor ed to y i eld a set of exposure measurem ents. Therefore each ME provides a measurement of the actual exposure result ing from the simulated or selected HD cond ition s

. The ME will als o provide a pr edicted future exp o sure if the handling conditions of the ME are similar to future H D conditions.

124The Generic Active Ingredient Principle

The most obvious handler-day c ondi tion is the identity of the active ingredi e nt (ai) to be used in the scenario task(s). Every experimental monitoring event must use at least one active ingredient. It mi ght seem, therefore, that prediction of future exposure to a particu lar ai would r equire that every ME use that activ e ingredient. If this were true, a generic exposure database for arbitrary a ctive ingredients would not be feasible.

Fortunately, exposure is not always chemical-specific. For compounds with low volatility (which include all the antimicrobials consid ered), a generally acc e pted 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 A 1 and A 2 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(A 1 , C ) = E(A 2 , C ) = E( C )

The practical importance of the generic assumption is that it permits an ME based on one surro gate chemical to be used to predict HD expos ure to other active ingredients under the sa me set of handling conditions.

Normalized Expos ure

For prediction purpos es, it is useful to express handler-day expo s u re 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 a l s o often referred to as unit exposure because it is viewed as exposure ‘ per unit’ of the normalizing factor.

In the AEATF II Monitoring Program t he normalizing factor is alway s an experimentally measurable quantity that is expected to be proportional to the potential c ontact the worker has with activ e 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 nor malizing factor is a relative measure of active ingredient contact

.

125It is generally ass u med that under identical c onditions expos ure is proportional to potential ai contact. That is, if P 1 and P 2 are different am ounts of PaiC, and C represents other HD c onditions, then the proportionality principle states that:

(3) E(P 2 , C ) / E(P 1 , C ) = P 2 / P 1
Exposure may not be directly to proporti onal to contact in extreme situations (e.g., skin saturation). However, such a relationship is expected to hold for the levels of ac tive ingredient contact that o ccur in practice. If the normalizing factor is expected to be proportional to Pa iC then the proportionality principle relationship holds for the NF as well sinc e:

(4) H 2 / H 1 = (k ·P 2 )/(k ·P 1 ) = P 2 / P 1

Thus, under the same conditions, C , expos ure for any value, H 2 , of the normalizing factor can be obtained from an ME based on a different value of NF, H 1 say, since:

(5) E(H 2 , C ) = (H 2 /H 1 )·E(H 1 , C ) = H 2 ·{ E(H 1 , C )/H 1 } = H 2 ·n E( C )

For most antimicrobial scenarios, a r easonable normalizing factor is the amount of active ingredient ‘handled’ (AaiH) by a worker during the workday. Howev e r, for some scenarios (e.g., t he pump liquid scenario) a worker might actually pr ocess (i.e., ‘handle’) a large amount of active i ngredient, but may have the opportunity to contact only a small frac tion of this amount. In such c a ses, there may be other measures of PaiC that are more appropriate than AaiH.

It is important to note that the term no rmalized (or unit) exposure is not always defined as a relative measur e of ai c ontact. A familiar example occurs in st udies of exposure to agric ultural reentry wo rkers. Here, exposure, E, is often normalized by duration of the reentry activities to give exposure per hour worked. Unless the concentration of active i ngredient on treat ed foliage is alway s constant, the normalizing factor ‘hours wo rked’ is not exp ect ed t o be propo r tional to contact with ai. However, the quantity:

(6) D = (hours worked) x (dis lodgeable foliar ai residue)

is expected to be proportional to the am ount of ai contact ed by the reentry worker. In agricultural reentry studies , t he quantity E/D is often called the transfer coefficient (TC) and corresponds to norma liz ed exposure as defined by AEAT F II.

126Using 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 us ed to obtain a predicted future HD exposure (pE) for any arbitrary chemical, X, at any arbitrary level of the normalizing f a ctor, H X , s i mply as:

(7) pE(H X , C ) = H X ·nE( C )

Although this relationship permits a singl e ME to predict exposure for a broader range of future handler -days, it cannot descr ibe all future exposures. Even when H X is specified, the number of possibl e ‘other conditions’ (i.e., different C s ) , is extremely large. (Although some of these C s are more likely than others to occur in the future HD pop ulation.) Because e ach ME is expe nsive

, it will on ly be possible in practice to construct MEs having a limited set of C s. This set of N MEs will th en be used to obtain a set of N predicted e x p o sures usin g (7):

pE(H X , C 1 ) = H X ·nE( C 1 ) pE(H X , C 2 ) = H X ·nE( C 2 ) pE(H X , C 3 ) = H X ·nE( C 3 )

●

●

● pE(H X , C N ) = H X ·nE( C N )

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 wil l provide experimental material t o develop statistical models for exposure as a function of C

. In fact, only some, but by no m eans all, of the components of each C can be controlled or measured w hen constructing MEs. The unk nown component s might have the bigge st impact on expos ure.

Nevertheless, this set of pEs will need to be sufficient to allow regulatory is sues to be addressed in a practica l manner. If some components of C that c an be controlled are chosen appropriately, t hen a useful set of ME s can still be constructed. In this case the resulti ng se t of pEs will be use d as a predic t or of future HD expos ure in an aggregate sense.

The Future Expos ure Distribution

An expos ure distribution provides a natural aggre gate description of future handler-day exposures for a scenario. It is the most common way to think about the set of expos ures resulting from all possible future HD conditions. The futur e HD distribution (Figur e 1) de scribes the lik ely exposure that would result if one were to randomly pick a future HD among those us ing ai X w hen the lev e l o f the normalizin g factor is H X

.

127

### M i ddl e E

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v

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L ar ger E x p o s u r e Le v

e l s D i s t r i but i on of F ut ur e Ex

p o s u r e s



### Ex p o s u r e Figure 1. The distribution of future handler-day exposures

The complete expos ure distribution is rare ly consider ed. Regulatory interest is most often focused on two general aspects of this distri bution:

• The middle values s u ch as the arithmetic mean or the median. Thes e exposure values tend to characterize average or ‘typical’ exposure levels.

• The larger values of expos ure possible, suc h as the 95th percentile of the
distribution. This as pect better c haracterizes the extreme, one-time, worker exposures.

Obviously t he expos ures can var y proportio nally with t he value of the normalizing factor, H. Therefore there are act ually a series of predicted exposure distributions, one for each possible value of HX. Since any predicted exposure can be computed from the norma lize d e x po sure, it is si mpler to focus only on the distribution of normalized exposur e

. The Set of MEs as a Pseudo-Random Sample

The set of predicted expos ures obtained fr om the cons tructed set of MEs should adequately characterize the middle and la rger values of the future HD distribution. This might appear simple, sinc e 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 ex pos ure distribution.

This cannot be strictly the case since ex pos ure is a function of the HD cond itions, C , and the likelihood of the various C ’s in a f u ture HD population (for X and H X )

128are not known in adv ance. Therefore the C ’s for the MEs cannot be rand omly chosen wit h probability proportional to the future population frequencies.

But some random sampling interpreta tion might s t ill b e a convenient and reasonable model for how the set of pr edicted expos ures from the MEs might relate to the future exposu res for an arbitrary ai and H X

. Co nfidence in this approximation is increased by us ing a nested reference random sampling mode l rather than assuming just simple ran dom sampling. In addition, div e rsity selection ( u sing both purposive and r andom components) is used whenever possible. This increases t he lik elihood that the range of conditions in the f u ture HD population expected to impa ct expos ure is reflect ed in the ‘pseudo-sample’ of MEs as well.

Diversity S e lection

Diversit y 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 infl uence exposure are known or can at l east be reasonably hypothesized. Diversity selection result s from any procedure that improves the chanc e that different MEs diffe r with res pect to such factors. It is an attempt to obtain, as much as is practi cal f o r small sample sizes, a diversity of conditions that are expected to influence expos ure, either directly or indirectly. With a small set of MEs, it is more prac tical to construct MEs that differ that to reproduce the (unknown) frequencies of selected future HD conditions.

In the AEATF II Monitori ng Program, the term diversity selectio n is preferred in lieu of the phrase diversit y sampling . This is to emphasize the fact that the future HD conditions used for MEs are s e lected from either existing or from synthesized conditions (or from both). This selection of conditions can employ both purposive and random elements

. When t here are multiple diverse configurations available, random selection from among such c onfigurations can reduce the likelihood of int entional 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 s e lection att e mpts 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 expos ure, t hen a divers ity 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 samplin g from a distribution that is more diverse than the ac tual future one (Figur e 2). As a result, a div e rsity selection s a mple sh o u ld ten d to have mor e extreme exposures (both higher and lower) and fewer expos ures ‘i n the middle’. Thus, a diversity selection s a mple will tend to estimate central tendencies of the exposure distribution better than it will either upper or lower perc entile s. To the extent that the diversif ying condit i ons are associated with e x p o s u re, diversity selectio n will tend to under-predict lower percentiles a nd over-predict upper per centiles. ( This

129effect is illustrated by envisio ning a no rmal, or even logn ormal, distributio n compared with its most extreme ‘diversi ty selection’ counterpart, a uniform distribution covering a similar r ange.) For regulatory purpos es the important aspects of the distribution of exposu res are the central tendencies and the upper percentiles. In addition, overestimation of these characteristics is less of a problem than underes timation.

D iv e r s it y S

e le c t io n T e n d s

T

r ue D i s t r i bu t i on of F

ut ur e E

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pos ur e s t o P r o duc e L e s

s ‘ M i d dl e ’

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### N o rm a l i z e d E x p o s u re Figure 2. Diversity selection tends to ma ke the distribution of future handler-day exposure appear more diverse than it is.

Design Objectives and Sample Size

The Two-Stage ME Selection Pr ocess

Obtaining potential workers and scenar io-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 sc enario. T h is is especially true for the two major categories of AEATF II studies: simulatedcondition studies and in situ studies. Simulated-c ondition MEs are created synthetically whereas in s i tu MEs must be located from am ong existing handlerday conditions in facilities willing t o participate.

Howev e r, as shown in Table 1, the ME construction process for both types of studies can be envisioned as occ u rring in tw o successive stag es of selection

. The first stage consists of selecting or constructing specific locations and specifying a range of dates for monitoring at each loca tion. Each s u ch loc a l area and range of potential monito ring dates is termed a monitoring site

. For in situ studies, a site might consist of a parti cular wood-treating facility during one particular week. In contrast, a site in a simulated-c ondition study might be a week-long period of monitoring activities in a particular leased office building.

130Table 1 The general two-stage structure for se lecting MEs in AEATF II studi es

Stud y T y p e Used for Scenario:

First Stage Units (Monitoring Sites):

Second St age Units (Monitor i n g

Events): Simulated-Condition Synthetic environments constructed (or vacant facilities le ased) express l y for the purpose of the study Monitoring events (ME ) constructed at the sites using subjects selected

from a volunteer pool In Situ (Observational)
Existing fac ilities wher e 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 selecti ng one or more subjects and handling conditions within eac h site and constructi ng the MEs. For s i mulated-condition studies the MEs are c r eated by assigni ng appropriate subjects to scenario tasks under conditions that are expec ted to exist in the future HD population. F o r 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, N C sites are selected at the first stage and N M monitoring events will be obtained within each site at the second stage. When N M 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 t han 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 onl y a single ME per site. On the other hand, there are often subst antial overhead costs per si te that make multi-ME sites more efficient.

131The Two-Stage Random Sampling Reference Model

In the strictest sense, sample sizes can only be det ermined using statistical theory alone when either

1. There is as sumed random, represent ative s a mpling from a population and the goal is to estimate some char acteristic of that population; or

2. There is assumed random ization of experimental unit s to treatments and the goal is only to compare or to c ontrast treatments in some manner.

Only in these two s i tuations will statistical t heory predict how in cr easing sample size decreases estim a tion error. In other experimental situat ions, 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 mi xture of random and non-random selection) as closely as is practical. The s a mple size that is appropriate for the reference model is then used for the actual study design. In a real s ense, then, the reference random sampling model is used to establis h benchmark sampl e sizes tha t can satisfy benchmark objectives

. Although rarely stated explic itly, the use of reference sampling m odels and benchmar k objectives are quite common.

Because all AEATF II scenario st udies have a two-stage selecti on structure, they all assume the same reference sampli ng model. For each scenario, two-stage random nested (or cluster) sampling is the reference model used for the combination of purposive and random two-st age diver sity selection that actually

occurs. This reference model assumes that:

1. Exposure, normalized by the pot ential activ e ingred ient contact factor, is lognormally distribut ed with geom etric standar d dev iation GSD. Equivalently, the logarithm of norma lized e xposure is normally dis t ributed with standard deviation Log(GSD).

2. There are N C clusters (i.e. sites) and N M MEs per cluster. The total number of MEs in this sc enario is, therefore, N=N C ×N M

. 3. The within cluster (i.e., within-si te) correlation of log normalized expos ure is equal to ICC.

The reference sampling model incorporat es a two-stage selection structure and the potential for within-cluster correlation, but ignores any effects of diversity selection. Thus, for dete rmining sample sizes, norma lized expos u res, nE, are assumed to follow the nested variance component model

(8) Log ( E ij / H ij ) = Log nE ij = Log GM nE + Q i + W ij

where E ij = the exposure obtained for ME j in cluster i H ij = the value of the normalizing f a ctor for worker for M E j in cluster i

132nE ij = the expos ure for ME j in cluster i normalized by NF GM nE = the population geometric m ean for normalized exposure Q i = a random effect of cluster i W ij = a random effect of ME j within cluster i

The random effects Q i and W ij are normally distributed with means 0 and variances V Q and V w , respectively.

The population variance of log nE is then equal to V = V Q + V w and the square root of V is the true popul ation standard deviation, SD. The quantity GSD nE = antilog (SD) is the true population geom etric standard deviation of normalized exposure. The intra-cluster correlation (i.e., the intr aclass correlation due to clusters) is defined as

(9) ICC = V Q / V = 1 – V w / V

The ICC is irrelevant to the future dist ribution of normalized exp o sure, per se. Howev e r, this intra-cluster correlation is a necess a ry part of the reference sampling model bec ause the MEs are obtained in clusters (i.e. t here are multiple MEs per site).

Relativ e Accuracy and Fold Relative Accuracy

The benc h m ark objective of the AEAT F II m onitoring program will be to ach i eve adequate r e lative acc u racy of selected par ameter estimates if the reference sampling model des cribed above were us ed. This benchmark target can be stated more precisely as:

If there are N C

clusters and N M

M E

s 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 valu es at least 95% of the time.

If θ denotes the distributional pa rameter of interest and T is the estimate of that parameter obtained from monitoring data, then the relative acc u racy of T is defined sim p ly as:

(10) RA (T| θ ) = T/ θ

Satisfying the benchm ark objective above r equires tha t 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

133It is more convenient, however, to consider relative acc u racy expressed as a ‘fold relative difference’. This is because st atements such as “T is within K-fold of θ ” are more intuitive than the formulation give n in (11). The ‘fold relative accur a cy’, 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-f o ld of the t r ue parameter, θ , at least 95% of the time. The 95 th percentile of fRA

, fRA 9 5 , is the specific foldaccuracy v a lue that s a tisfies (13). Consequently, the benchmark adequacy goal reduces to requiring that:

(14) fRA 95 ≤ K

If we denot e the 2.5th and 97.5th percentiles of the sampli ng dist ribution of T by T 2.5 and T 97

. 5 , respectively, then the 95 th per centile of fold relative accuracy can also be calculated from

(15) fRA 95 = Ma x ( T 97.5 / θ

, θ / T 2.5 )

Benchmark Objective for Antimicrobial Scenarios

The default benchmark objective for all ant imicrobial scenarios in the AEATF II Monitoring Program is that a sample from the hypothetical refer e nce 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 g u idanc e to AEAT F II on the minimum degree of accuracy neede d for regulatory use in particular sc enarios. The c u rrent consensus is that estimates of the geometric mean, the ar ithmetic mean, and the 95 th percentile generally should be accurate to within appr oximat ely 3-fold of thei r true value.

It should always be kept in mind, however, that this objective is specified in terms of the reference random sampling distri but ion. This reference sampling m odel does hav e the same two-stage nesting structure as the actual sampling approach. The lognormal distribution assu mption is also reasonable, robust, and consistent with existing data. Howeve r, the reference distribution ass u mes simple random sampling at each stage. It does not, and cannot, incorporate the combination of purposive and random diversity sampling actually used.

134As noted above, the consequence of di v e rsity sampling is expected to be a tendency for the sampling variation of norma lized exposure to be overestimated. The sample should t end to over-repres ent ex tremes and under-represent the more common values. Such diversity- oriented data collected f o r this scenario, but analyz ed with res pect to the two-stage reference distribution, is expected to have minimal bias f o r central tendency. In contrast, upper percentiles of exposure are expected to be, on the aver age, too large. There is no way to determine the actual magnit ude 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 b y r egulatory agencie s. A tendency to both consider and even overestimate upper percent iles is c onsistent with this practice.

Parameter Estimates Used for Benchmark Objectives

As defined above, relative accur a cy applies to the particular quant ity, T, that is used to estimate the refer ence distribution parameter θ

. Thus, it i s important to consider which types of estimates of t he geometric mean, arithmetic mean, and 95 th percentile are used to evaluate fRA 95

. The relative accuracies could differ depending on the particular estimates used.

There are often multiple choic e s for the p a rameter e s timates. The estimators can be broadly grouped into either empirical or p a rame tric. Empirical estimates are the commonly-used statis tics available in spreadsheet programs. They do not (explic itly) assume any distribution. However, they can sometimes require simple random sampling for greatest e fficiency

. Parametric estimates incorporate the fact that the reference distribution is lognormal and could also account for cluster sampling being used.

The most straightforward param eter is the geomet ric mean (GM nE )

. I n t h e balanc ed c a se, the simple empirical estim a te of GM nE can be calculated by averaging t he log-transformed normalized expos ures and then taking the antilog of this value. In this case, the empirical and parametric estimates of GM nE are identical. If the number of MEs per cluster varies, ho wever, one could consider geometric means wit h different degrees of weighting by cluster size. The arithmetic mean can also be calculat ed empirica lly by summing up the normalized exposures and dividing by the to tal number of MEs. Again, when the cluster sizes differ, other types of weight ed empirical arithmetic means exist. In the unbalanced cas e , neither the weighted nor the un weighted estimates of GM nE or AM nE 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. Empi rical percentiles could, theoretically be calculated in the conventional manner. However, when ther e is cluster sampling and the number of MEs are not large, empirica l estimates of the

135extreme upper (or lower) perc entiles are not especially e fficient. The parametric percentiles (see below) are pr e f er red in this case.

Parametric estimates are those closely al igned with the sampling model used. In this case one uses t he fit to the vari ance compone nt model described in (8) above to get estimates for the geometric mean (GM nE ) and the total geometric standard deviation (GSD nE ). To estimate the arithmetic mean (AM nE ) and 95 th percentile (nE 95 ) one could then use the lognormal relationships:

AM nE = GM nE × Ex p { 1⁄2 (log e GSD nE ) 2 } (16) nE 95 = GM nE × Ex p { Z 95 log e GSD nE }

where Z 95 is the 95 th percentile of the standard normal distribution. For simplicity, these will be labeled the ‘parametri c cluster sampling estimates’.

It can be argued that f e w if any u sers of the AEAT F II m onitoring data will ch oose to (or be able to) fit variance component models to the data. They will probably ignore the sampling model and use more c onventional estimates. In this case empirical estimates of GM nE and AM nE defined above would probably be used.

Potential data users might also be less inclined to use empirical percentiles, especially with smaller samp le sizes. The lognormal percentile estimate of nE 95 in (16) abo ve would then still b e used but perhaps with the mixe d model GSD nE estimate replaced with t he more conventional GSD nE (i.e., the back-transfor m e d simple standard deviation of log exposure s.) For convenience, estimates that assume lognormality but not cluster samp lin g will b e labe led ‘simple ran dom sampling parametric p e rcentiles’.

Any or all of the above estimators co uld be ev aluated. Howev e r, for the purposes of determining sample sizes, it is recommended that f o cus be on the following estimators:

• GM nE – simple empiric a l estimate

• AM nE – simple empiric a l estimate

• nE 95 – parametric cluster sampling estimate

The Determination of Sample Size

As stated above, the benc hmark adequac y goal reduces to requiring that the 95% percentile of fold relative ac curacy, fRA 95 , be les s than or equal to K. Under the reference two-stage random samp ling model described above, the only quantities needed to determine relative accuracy of population para m eter estimates are reasonable values for GSD nE and ICC. Such values could be based on existing exposure data for scenario-specific tasks, surro gate expos ur e

136data from similar tasks , and/or reasonab le assumptions based on subject-matter expertise.

Given thes e values, fRA 95 , can be comput ed for any combination of the N C and N M

. Calculation of the 95% per c entile of fold relative a ccuracy is comple x a n d is usually best accomplished us ing Monte Carlo simulation methods. When the number of MEs per cluster, N m , i s the same for all clus ters, the geometric mean, fRA 95 can be calculated directly from the GSD nE and ICC as:

nE N exp 1

. 96 ln

where N is the total num ber of MEs (i.e., N=N c ×N m ). For para meters oth e r than the geometric mean, a straight forward simulation approach c a n be used to determine fRA 95

. This procedure is:

1. Simulate a set of nor malized exposure dat a for N c c l usters and N m monitoring units per cluster us ing the ref e rence sampling model defined in ( 8 ) above. ⎧ ⎨ ⎩

2. From each set of simul a ted data, calculate T, the estimate of θ

3. Repeat st eps 1 and 2 above M times to get M values of t h e estimate T

4. From these M T-values calculate T 2.5 and T 97.5 , the 2.5 th and 97.5 th percentiles of T, respectively.

5. Calculate the 95 th per centile of fold relative accuracy, fRA 95 , usi ng formula (15) above.
The number of simulations, M, should be some large number such as 1,000 or 10,000. This proces s can be cont inued until a combination of N C and N M are found that satisfy to benchmark objective.

General Guidelines for Diversity Selectio n of Monitoring Events

Although diversity selection is simple in theor y, practical implementation is often complex and usually scenario s pecific. This section presents diversity selection procedures and recommendations that apply generally to all scenarios. Scenario-s pecific design documents and study protocols will pr ovide details of ME selection and construction.

As described previously, the objective of di versity selection is to obtain a diverse set of handler-day c onditions from among those conditions pos sible when an arbitrary ai is us ed in future sc enario- r elated tasks. These selected HD conditions are then used to construct moni toring events. Diversity selection is ⎫ ⎬ ⎭ ICC 1

− ICC (17) fRA 95 GSD + = N

c

137done 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 s hould always be with respect to characteristics (i.e. particular component s of C ) that are expected to impact ex pos ure. Whenev er possible, the characteristics used should be meta-facto rs

. Meta-factors are characteristics that indirectly influence a num ber of other characteri stics. For example, a worker is a meta-factor because substituting one worker for another alters a number of factors (e.g., behav ior, physical appear ance, stamina) that might affect exposure. Other common meta-factors ar e geographic location and time-of-year. Not every characteristic that may impac t expos ure can be, or even should be, considered in diversity sele ction. The number of possibl e combinations of factors that may i m pact exposure will always greatly exceed the number of planned MEs. Consequently, only a few characteri stics, preferably meta-factors, can be used effectively in diversity selection.

Diversity Selection Approaches

There are a number of ways to achi ev e diversity among ME handler-day conditions. The most straightforward appr oach is to purposively select MEs that appear to be sufficiently different with respec t to the characteristics of interest. Documentation for such direct purpos ive selection should include the characteristics considered and how much these characteristics differ among the ME. Although flexible, and likely to achiev e a set of MEs with a great amount of diversity, this approac h is subjective and, therefore, difficult to reproduce.

More formal approac hes are als o possible. A general, albeit quit e sophistic ated, 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 poss i ble pairs of units. Then, one simply selects (or synthetically constructs) thos e configurations that result in the greatest diversity score. If multiple configurations have the highest score, a random selection am ong these is possible. While achiev ing diversity in an objective and reproducible m anner, this approach is quite complex and difficult to implement.

A formal approach that is both comm on and simple to implement is stratified diversit y selection

. In this approach available sele ctable units (e.g., sites, MEs) are partitioned into st rata based on charac teristics likely to impact expos u re. Each poss i ble selection unit must bel ong t o one a n d only one stratum. The number of strata must be at leas t as lar ge as the number of units that will be selected. For example, if there are three units to be s e lected, then there s hould be at least three strata. Diversity coul d be achieved by selecting (purposively or randomly) no more than one unit from each st ratum. If there are more strata than units to be selected, t hen a subset of the strata should be selected first

.

138This could be either purposively (to incr ease 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 s t ratified sampling, however, diversity is increased by selec t ing only a single unit from each stratum. There is no attempt to sample or to we ight results in proportion to stratum size. I n fact stratum size in the future HD population is usually unk nown.

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 respec t to some characteristic(s) ex pected to impact exposure. Thus, there should be little surprise if exposure, on the average, differs between sites. If site s are c onstructed 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 avai lable and a s e t is to be selected (randomly or purposively), then st ratified diversity selection of s i tes, based on the important characterist ic(s), is a feasible approach

Diversity Selection of Second-St age Units

Ultimately the second stage se lection units are the final MEs. The MEs should be div e rsified independently within each sele cted site. In most cases within-site diversity selection of MEs focuses onl y on two characterist ics: subject and normalizin g factor.

Handling-day exposur es for the same indi v i dual are expected to be correlated. That is, many components of C relating to s ame worker will b e ide ntical, even on different days. In contrast, different individuals are less correlated. Worker behaviors are expect ed to have great impact on expos ure. Consequently, diversity is increased by simply requiri ng t hat each ME be constructed using a different individual.

MEs should also be diverse with respec t to the normalization factor that is deemed appropriate for the sc enario. One feasible appr oach is to partition the possible levels of NF into strata and c onstruct 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 c onfigurations of assignment are equiv a lent, then workers could be randomly allocated to strata. If some allocations ar e non-equivalent (e.g . more cost effective or there are scheduling issues) then a purposive assignm ent of indiv i duals to NF levels might be preferable.

139In other cases (e.g. In Situ/observati onal studies) worker availab ility depen ds o n the particular NF level chosen. Some i ndiv i duals may only work with higher NF levels an d some with only lo wer NF, say. Selection of workers could still be random, although random choic e might be re stricted to within each NF stratum. Howev e r, when suc h associat ions between subject and NF levels exist, purposive allocation might result in a more cost effective and practical configuration.

Rationa le for Using the Normalizing Factor in Diversity Selectio n

It might at first appear pointles s to sele c t MEs with differing levels of the normalizing factor when diversity of normaliz ed exposur es is des ired. 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 shou ld make no difference whether all MEs are at t he same level or at different levels o f the NF.

This would be true if the va lue of the normalizing fact or had no impact on the other ME conditions selected. However, this might not be true. B e cause am ount of active ingredient contact is so important to ex pos ure, it is likel y that the NF is also a meta-factor. That is, it is not unreas onable to ex pect that different values of the NF will be naturally assoc i ated with s pecific sets of handler-day cond itions, C

. Suppose, for example, that the nor maliz ing factor for wiping applicat ion exposure was correlated with duration of tas k

. It is conceivable that some worker behaviors (e.g. fatigue) and div e rsity of surfaces wiped might be different for shorter duration MEs than for lo nger duration MEs. By insuring th at the lev e ls of NF are varied, the set of MEs indirectly captures divers ity in those component s of C that are naturally as sociat ed with the normaliz ing factor.

140

## Appendix F: Evaluation of Existing PHED Applicator Exposure Dat a for Hand-Held Aerosol S pray

The Pestic ide Handlers Expos ure Databas e (PHED, version 1.1) contains two studies inv o lving the monitoring of derma l and inhalation expos ure during the use of a pressurized aerosol container. The current HED PHED Surrogate Guide uses the data only from Study 521 for aer osols (Scenario 10). The two studies were accessed in PHED by subsetting the applicator file for metho d of application equal to aerosol can. The tw o studies were ident ified as Study 456 and 521 and both involved the app lication of an insecticide. A general review of these studies is provided in the “ P HE D S C ENA R IO DISCUS SION” section of this report. This is followed by t he “ACCEPTANCE CRITERIA EVAL UATION” section of this report, wherein the following info rmation and “data acceptanc e criteria” were evaluated:

1. Meta information: Scenario (e.g ., hand held aerosols) study(ies) code(s), total number of replic ates or monito ring ev ents, range of AaiH, dermal and inhalation sampling durations, average limits of quantitation (LOQ’s) for dermal and inhalation ex posure, percentage of sa mple s with und e tectable 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 t he number of monitoring ev ents classif i ed in PHED as A and B grade; A and B grade data indic a te compliance with t h e criteria: the average recoveries of lab spikes should be between 70­ 120 percent and the precision value (coef ficient of variation; CV) should be less than or equal to 20 per cent 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 acc ount for less than 40% of dermal exposure; and

c. The number of monitoring events with a minimum of 10 dermal patch dosimeters attached ins i de or outside normal work clothing t o the chest, back, both upper arms, bot h lower arms, both upper legs, both lower legs, plus hand (cotton gloves can substit u te for hand wash) and head/face exposu re determinations.

141

## PHED SCENARIO DISCUSSION

Method of Application

PHED Study 456 involved the applic at ion 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. T he 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 spray ed the contents of the container into cracks and crevices, along baseboards, under sinks, behi nd applianc es, and in other areas were insects would be expected to hide.

PHED Study 521 involved the applic at ion 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. Th e aerosol cans contained 1% active ingredient, equivalent to 4.54 grams a.i. per can. Ap plicators held the aerosol can in one hand and sprayed the cont ents into cracks and crevices, along baseboards, under sinks, behind applianc es , and in other areas where insects were expec t ed to hide.

Exposure Monitoring Methodology

Both Study 456 and Study 521 used gauze pat ches for dermal exposure monitoring. Volunteers wore a single lay e r of clothing that inc l uded a longsleeved shirt, long pants, and shoes. Th e de rmal gauze patches were in holders with an open diameter of 5.6 c m , and were placed under the single layer of clothing on the participant’s upper arms, forearms, chest, back, thighs, and lower legs. T hes e dosimeters provide dermal ex posure estimates under a single layer of clothing.

A second set of der mal dos im eters were plac ed outside th e clothing, but so as not to occlude the inner dos im eters. The outer derma l dosim eters were also placed on the upper arms, forearms, chest, back, thighs, and lower legs. This set-up provides der mal dosim etry dat a needed to estimate actual dermal exposure f o r a variety of clothing possibilities ranging from long-sleeved shirt and long pants to short pants and a short- sl eeved shir t. Head exposure was monitored by the plac ement of a gauze dos imeter on a ball cap just above the bill of the hat.

Hand exposure was monitored using ha nd-rinses. Applicator’s hands were rinsed at the complet i on 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 protec tive glov es. Vol unteer s in Study 521 wore no glov es, a n d

142the results represent expos ure to unpr otected hands. Taken together, both studies support a comparison of pr otected and unprotected hand expos ure during aerosol can use.

Inhalation exposure was monit o red us ing personal air sam p lers with the sampling c a ssette placed on the collar near the participant’s br eat hing zone. Air was drawn through the sampling cassettes at a flow rate of 1 liter per minute. The air pumps were sampled bef ore and after each monitoring per iod.

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 t he recovery of the active ingredient from fortified field samples, fortified laboratory samples, and storage stability samples. Grade A and B data meet the minimum analy t ical quality assurance requirements as descr ibed in EPA’s Subdivis ion U of the Pesticide Assessment Guidelines. Grade A and B data have la boratory recoveries of 80% to 110% with a coeffici ent of variation of 25% or less. The field recov e ries are between 50% and 120%. Grade C data have laboratory recoveries of 70% to 120% with a coefficien t of variati on of 33% or less. Grade C data must also hav e either field recoveri es of 30% to 120%, or the data must have storage stability recove ries of 50% to 120%. Gr ade D data have labor atory recoveries of 60% to 120% with a coefficien t of variation of 33% or less. Field recovery or storage stability dat a are not required for a grade D classific a tion. Grade E data do not meet any of these standards. To support the registration o f a pesticide, the data subset should contai n a minimum of 15 replications for each body area except the feet. The data should also me et the minimum analytica l quality assurance requirement s of grades A or B. The Health Effects Division uses the number of replic ates in a sc enario and the data quality grades to rate the PHED data for a given scenario as hi gh confidence, medium confidence, or low confidence data.

Study 521 contains 15 replic ations of hand, dermal, and i nhalation 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 Surr ogate Guide lists aerosol application as Scenario 10. Because of the grading, Sc en ario 10 lists only the 15 replicates from Study 521 and gives them a high confidence rating.

Study 456 Dermal Exposure

The dermal expos ures from S t udy 456 are presented below. The dermal exposure is presented both as the “no clothing” scenario and as long-slee ved shirt and long pants to permit analysis of multiple clothing scenarios. The exposure data are presented in micr ograms and were not normalized by the amount of a.i. hand led. As prev iously noted, all replic ates in Study 456 inv o lved handling 4. 33 grams of active ingredient.

143<< Specifications >> Sub s et Spe c ificatio ns for STUDY4 56.A PPL

With Study Code Equal to 456 Subs et originated from AEROSAL.APPL With Application Method E qual to 4 Subs et originated from APPL.FILE

SUM M ARY STAT ISTICS FOR CALCULATE D DERMAL EXPOSURES SCENARI O: No cl othing (t otal depo sitio n) PATCH DISTRIB. M I CROGRAMS LOCATION TYPE Median M ean Coef of Var Geo. Mean Obs. HEAD (AL L ) Logn orm a l 288.6 435.58 67 90.9793 209.19 35 15 NECK.F RO NT Logno rmal 14.25 30.07 126.9042 11.751 2 15 NECK.BACK Logn orm a l 13.86 20.5847 80.8261 13.028 3 15 UPPER ARM S Lognorm a l 776.97 1129.177 125.0676 508.43 72 15 CHEST Logno rmal 337.25 711.656 7 126.904 2 278.11 17 15 BACK Logn orm a l 447.3 664.323 3 80.826 420.46 01 15 FOREA R MS Logno rmal 362.395 714.908 3 137.772 5 276.778 15 THIG HS Logno rmal 255.94 328.01 07 97.1166 210.17 75 15 LOWE R LEG S Logno rmal 86.87 137.56 4 90.5475 97.689 15 FEET 0 HANDS 0 TOTAL DE RM: 2025.6265 2583.435 4171.88 1 4 2025.62 65

95% C.I. on Mean: Dermal: [-29183.6113, 37527.3741]

N u mber of Records : 15 Data File: APPLICATOR Subs et Name: STUDY456.APPL

144The dermal exposure to all body areas had a logn ormal distribution. The total geometric mean der mal expos ure was 2.03 mg/r eplicate. Note that this dermal exposure r epresents exposure outside the clothing an d because of the us e of protective gloves, there ar e no data for the hands.

The dermal exposure under a single layer of clothing and protective gloves is presented below.

SUM M ARY STAT ISTICS FOR CALCULATE D DERMAL EXPOSURES SCENARI O : Long p ants, l ong sl eeve s , gloves PATCH DISTRIB. M I CROGRAMS LOCATION TYPE Median M ean Coef of Var Geo. Mean Obs. HEAD (AL L ) Logno rmal 288.6 435.5867 90.9793 209.1 935 15 NECK.F RO NT Lognorma l 14.25 30.07 1 26.904 2 11.7512 15 NECK.BACK Logn orm a l 13.86 20.5847 80.8261 13.02 83 15 UPPER ARM S 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 FOREA R MS Other 18.15 18.3113 3.4121 18.302 2 15 THIGHS Other 57.3 57.3 0 57.3003 15 LOWE R LEG S Other 35.7 37.128 9.5542 36.991 3 15 FEET 0 HANDS Other 5 15.706 7 1 52.452 8 9.0385 15 TOTAL DE RM: 500.273 583.01 764. 8374 505.75 61

95% C.I. on Mean: Dermal: [-5623.8395, 7153.5143] N u mber of Records : 15 Data File: APPLICATOR Subs et Name: STUDY456.APPL

The dermal exposure to the head and neck ar e 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 quantific ation and therefore are ba s ed on half of the LOQ as per PHED and HED guidelines. It can be de termined that all 15 observations for these body areas wer e below the LOQ because the c oefficient of variation is 0. The forearms had only one study participa nt with detectable re s i dues and then only to the right forearm dosimeter. T he lower legs had one replication wit h detectable residues to both dosimeter s and two additional replicates with detectable residues to the left lower leg dosimeter. The de rmal dosimeter LOQ in Study 456 was 0.03 μg/cm 2

. T he hand wash LOQ wa s 10 μg per sample. The total dermal expos ure for an indiv i dual wear ing long pants, a l ong- sleeved shirt, and protective gloves is 0.50 mg based on the PHED “best fit” guideline.

145Study 521 Dermal Exposure

The potential dermal expos ure under t he “no clothing sc enario is presented below.

< < Specific ations >> Subset Spe c i f ication s for STUDY 521.AP PL

With Study Code Equal to 521 Subs et originated from AEROSAL.APPL With Application Method E qual to 4 Subs et originated from APPL.FILE

SUMMARY STATISTICS FOR CALCULATED DE RMAL EXPOSURES SCENARI O: No cl othing (t otal depo sitio n) PATCH DISTRIB. M I CROGRAMS LOCATION TYPE Median M ean Coef of Var Geo. Mean Obs. HEAD (AL L ) Logno rmal 419.9 6 97.692 7 124.96 06 410.25 8 15 NECK.F RO NT Lognorma l 21 71.499 217.76 25.129 2 15 NECK.BACK Logn orm a l 21.34 26.123 5 80.925 2 19.951 7 15 UPPER ARM S Lognormal 1063.896 1320. 4028 69.3397 999.3333 15 CHEST Logn orm a l 497 1 692.14 3 217.75 99 594.72 33 15 BACK Logn orm a l 688.7 8 43.077 7 80.925 2 643.89 54 15 FOREA R MS Lognormal 752.62 800.2295 82.205 2 488.17 26 15 THIG HS Logn orm a l 223.47 4 83.612 124.26 01 299.81 1 15 LOWE R LEG S Lognorma l 169.456 627.3363 177.41 08 248.36 08 15 FEET 0 HANDS Logn orm a l 1100 1210.2 667 51.074 8 1056.7 992 15 TOTAL DE RM: 4786.434 5 4957.382 7772.383 2 4786.43 45

95% C.I. on Mean: Dermal: [-60276.6132, 75821.3796]

N u mber of Records : 15 Data File: APPLICATOR Subs et Name: STUDY521.APPL

146The total potential exposure under the “no c l othing scenario” is 4. 79 mg/replicate. All body ar eas had a lognormal distribution. Because hand expos ure is included in the Study 521 data the hand exposur e must be subtracted from the total potential expos ure to permit a dir e ct co mparison with Study 456. The geometric mean hand exposure was 1.057 mg and the dermal expos u re excluding the hands is 3.73 mg/replicate. The total potential dermal exposur e in Study 456 was 2.03 mg/replicate.

The actual dermal exposure to an i ndividual wearing long pants and a long sleeved shirt is presented below.

SUM M ARY STAT ISTICS FOR CALCULATE D DERMAL EXPOSURES SCENARI O : Long p ants, l ong sl eeve s , no glove s PATCH DISTRIB. M I CROGRAMS LOCATION TYPE Median M ean Coef of Var Geo. Mean Obs. HEAD (AL L ) Logn orm a l 419.9 697.69 27 124.960 6 410.25 8 15 NECK.F RO NT Lognorma l 21 71.499 217.76 25.129 2 15 NECK.BACK Logn orm a l 21.34 26.1235 80.9252 19.951 7 15 UPPER ARM S Other 59.073 5 9 .073 0 59.075 15 CHEST Other 72.065 7 2 .065 0 72.067 5 15 BACK Other 72.065 7 2 .065 0 72.067 5 15 FOREA R MS Other 24.563 2 7 .3783 39.8253 26.257 8 15 THIG HS Other 77.546 7 7 .546 0 77.548 7 15 LOWE R LEG S Other 48.314 4 8 .314 0 48.315 7 15 FEET 0 HANDS Logn orm a l 1100 1210.2 667 51.074 8 1056.7 992 1 5 TOTAL DE RM: 1865.7641 1915.866 2362.0232 1867.47 03

95% C.I. on Mean: Dermal: [-14926.1143, 19650.1607] N u mber of Records : 15 Data File: APPLICATOR Subs et Name: STUDY521.APPL

The expos ure pattern under a single lay er of clothing in Study 521 is similar to that reported in Study 456. All cover ed body areas except the forearms had residue levels on all dosimet ers that were below the study’s LOQ. Among the forearm dosimeters there was one replic ate with quantifiable resid ues on the left forearm dosimeter. The limit of quantif ication in Study 521 was 0.0406 μg/cm 2 for the dermal dosimeters and 100 μg/replic ate for the hand rins es. The dermal exposure under a s i ngle layer of clothi ng from Study 521 c an be compared to exposure in Study 456 by subtracting the 1.058 mg hand expos ur e from the total dermal exposure of 1. 866 mg. T he total dermal expos ure exclud ing the hands in Study 521 is 0.809 mg/replicate c o mpared to the total dermal exposure exc l uding the hands of 0.495 mg/replicate.

By combining the dermal expos u re data from Studies 456 and 5 21 it is pos sible to estimate the reduction in exposure attri butable to the use of protective glov es. Althoug h EPA d oes not routinely consid er the use of protective glov es for homeowner uses of aerosols, the use of protective gloves may possibly be considered for occupational uses of aerosol containers.

147Combine d Dermal Exposure Estimate

The combined dermal exposur e data based on St udies 456 and 521 are presented below. The first scenario r epres ents expos ure when wearing a longsleeved shirt, long pants, and no gloves

. The second scenario represents exposure when wear ing a lo ng-sleeved shirt, long pants, and protective gloves. The difference between the two estimates provides insight into the protection provided by chemical-resistant gloves.

< < Specific ations >> Subs et Spec ific ations for AEROSAL.APPL With Application Method E qual to 4 Subs et originated from APPL.FILE

SUM M ARY STAT ISTICS FOR CALCULATE D DERMAL EXPOSURES SCENARI O : Long pan t s, long sleev es, no glo v es PATCH DISTRIB. M I CROGRAMS LOCATION TYPE Median M ean Coef of Var Geo. Mean Obs. HEAD (AL L ) Logn orm a l 353.6 566.63 97 119.763 292.95 61 30 NECK.F RO NT Lognorma l 20.1 50.7845 223.20 98 17.184 2 30 NECK.BACK Logn orm a l 18.095 23.3541 80.940 8 16.122 6 30 UPPER ARM S Other 51.3615 51.3615 15.2708 50.7803 30 CHEST Other 62.657 5 62.6575 15.270 8 61.948 5 30 BACK Other 62.657 5 62.6575 15.270 8 61.948 5 30 FOREA R MS Other 22.566 5 22.8448 38.867 9 21.922 30 THIG HS Other 67.423 67.423 15.270 9 66.66 30 LOWE R LEG S Other 48.314 42.721 14.511 8 42.276 30 FEET 0 HANDS Logn orm a l 1100 1210.2 667 51.074 8 1056.7 992 15 TOTAL DE RM: 1698.0421 1806.7 75 21 60.710 3 1688.5 974

95% C.I. on Mean: Dermal: [-10432.2269, 14753.6475] N u mber of Records : 30 Data File: APPLICATOR Subs et Name: AEROSAL.APPL

SUM M ARY STAT ISTICS FOR CALCULATE D DERMAL EXPOSURES SCENARI O : Long pan t s, long sleev es, glo v es PATCH DISTRIB. M I CROGRAMS LOCATION TYPE Median Mean Coef of Var Geo. Mean Obs. HEAD (AL L ) Logno rmal 353.6 566.6397 119.763 292.95 61 30 NECK.F RO NT Lognorm a l 20.1 50.7845 223.2098 17.1842 30 NECK.BACK Lognorm a l 18.09 5 23.3541 80.9408 16.1226 30 UPPER ARM S 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 FOREA R MS Other 22.5665 22.8448 38.8679 21.922 30 THIG HS Other 67.423 67.423 15.2709 66.66 30 LOWE R LEG S Other 48.314 42.721 14.5118 42.276 30 FEET 0 HANDS Other 5 15.7067 152.4528 9.0385 15 TOTAL DE RM: 646.242 9 711.775 966.1503 640.8367

14895% C.I. on Mean: Dermal: [-6835.8234, 8768.124]

N u mber of Records : 30 Data File: APPLICATOR Subs et Name: AEROSAL.APPL

Based on the PHED analysis the use of protective glov es reduces the dermal exposure from 1.7 mg/aeros ol can to 0.65 mg/aerosol can, where an aerosol can is 15 to 16 ounces.

Inhalation Exposure

PHED was used to estimate the inhalat ion exposure potential for Studies 456 and 521 separately and combined. PHED requi res the user to select a breathing volume. Past analy sis by OPP’s He alth Effects Division HED assumed a breathing volume of 29 liters /minute. For this analy s i s a breathing volume of approximately 17 liters/minute was used (U.S. EPA Exposure Fac t ors Handb ook; http://www.epa.gov/ncea/efh/).

The inhalat i on exposure from Study 456 is as follows:

< < Specific ations >> Subset Spe c i f ication s for STUDY 456.AP PL With Study Code Equal to 456 Subs et originated from AEROSAL.APPL With Application Method E qual to 4 Subs et originated from APPL.FILE

S U MMARY STATISTICS FOR INHALATION EXPOSURES

EXPOSURE DISTRIB. TYPE Lognorm a l NANOGRAMS Median Mean Coef of Var Geo. Mean 27523.8095 26580.3734 49.7805 2235 6.819 3 Obs. 15

95% C.I. on Geo. Mean: [5704.4462, 87620.6649] Numb er of Reco rd s: 15 Data File: APPLICATOR Subs et Name: STUDY456.APPL

The inhalat i on exposure in Study 456 was 22 μg/replicate or aerosol can. The inhalation expos ure is 1.3% of the derma l exposure when gloves are not worn.

149The inhalat i on exposure from Study 521 is as follows:

< < Specific ations >> Subset Spe c i f ication s for STUDY 521.AP PL

With Study Code Equal to 521 Subs et originated from AEROSAL.APPL With Application Method E qual to 4 Subs et originated from APPL.FILE

S U MMARY STATISTICS FOR INHALATION EXPOSURES

EXPOSURE DISTRIB. NANOGRAMS TYPE Me dian Mean Coef of Var Geo. Mean Other 7456.1404 10594.0506 61.6887 9345.3 422 Obs. 15

95% C.I. on Geo. Mean: [3720.0299, 23477.0745] Numb er of Reco rd s: 15 Data File: APPLICATOR Subs et Name: STUDY521.APPL

The inhalation expos u re in Study 521 was 9.3 μg/ replicate. The inhalation exposure is similar to the expos ure m onitored in Study 456 and represents 0.5% of the dermal exposur e when gloves are not worn.

The combined inhalation exposur e fr om both studies is presented below.

< < Specific ations >> Subs et Spec ific ations for AEROSAL.APPL With Application Method E qual to 4 Subs et originated from APPL.FILE

S U MMARY STATISTICS FOR INHALATION EXPOSURES

DISTRIB. NANOGRAMS TYPE Me dian Mean Coef of Var Geo. Mean Obs. EXPOSURE Other 1 3379.6296 18587.212 70.4015 1445 4.484 6 30

95% C.I. on Geo. Mean: [3433.5175, 60850.7529] Numb er of Reco rd s: 30 Data File: APPLICATOR Subs et Name: AEROSAL.APPL The inhalat i on exposure is 14 μg/aerosol can when the container is 15 to 16

ounces.

150Conclusions

PHED contains two studies that m onitored dermal and inhalation exposure during the application of the entire contents of one c ontainer per replic ate. EPA’s PHED Surrogate Guide (Scenario 10) uses the data only from Study 521.

Study 456 provides data for hands protec ted by gloves while Study 521 presents data for unprotected hands. This permits an estimate of exposure reduction when gloves are worn (for scenari o s where protective glove use is reasonable. )

A signific a nt limitation of these existing data is that the use conditions for both studies, including the number of cans app lied per replicate and the AaiH, were very similar. This prevents evaluat ing any potential relat i onship between exposure and either the number of cans used or the AaiH. PHED study 521 provides MUs that meet key AEATF I I acceptance criteria, but addit i ona l monitoring events should be considered to provide a wider r ange of amount of formulation sprayed (AaiH) under condition s relevant to antimicrobial aerosol product use.

151---------- --------- ----------

---------- -------- ---------- --------- ---------- Attachment 1. Individual Re plicat e Dosimeter Residue Levels

<< AEROS AL.APPL >> T o tal AI T o tal Record Applied US Gal I.D. (lb) Spray ed

045 6*B* 4 .009 4 .1200 045 6*B* 5 .009 4 .1200 045 6*C * 1 .009 4 .1200 045 6*C * 2 .009 4 .1200 045 6*C * 3 .009 4 .1200 045 6*C * 4 .009 4 .1200 045 6*C * 5 .009 4 .1200 052 1*B* 02 .010 0 .1250 052 1*D * 0 2 .010 0 .1250 045 6*A* 01 .009 4 .1200 045 6*A* 02 .009 4 .1200 052 1*A* 02 .010 0 .1250 052 1*C * 0 1 .010 0 .1250 052 1*A* 03 .010 0 .1250 052 1*C * 0 2 .010 0 .1250 052 1*C * 0 3 .010 0 .1250 052 1*E* 01 .010 0 .1250 052 1*E* 02 .010 0 .1250 052 1*E* 03 .010 0 .1250 052 1*A* 01 .010 0 .1250 045 6*A* 3 .009 4 .1200 045 6*A* 4 .009 4 .1200 045 6*A* 5 .009 4 .1200 045 6*B* 1 .009 4 .1200 045 6*B* 2 .009 4 .1200 045 6*B* 3 .009 4 .1200 052 1*B* 01 .010 0 .1250 052 1*D * 0 1 .010 0 .1250 052 1*D * 0 3 .010 0 .1250 052 1*B* 03 .010 0 .1250

<< AEROS AL.APPL >> Air Smpl Air Quan. Air Air Recor d T i me Limit Volum e Amount I.D. (min) (ug) (l) (ug)

045 6*B* 4 2 8 .0 .1000 30.8 3.0300 045 6*B* 5 1 8 .0 .1000 19.4 1.3900 045 6*C * 1 1 5 .0 .1000 16.2 .8900 045 6*C * 2 2 1 .0 .1000 22.6 1.2500 045 6*C * 3 1 9 .0 .1000 19.7 1.3700 045 6*C * 4 2 6 .0 .1000 27.0 .5800 045 6*C * 5 1 3 .0 .1000 13.5 .7800 052 1*B* 02 28.0 1.0000 33.0 ND 052 1*D * 0 2 19.0 1.0000 22.4 ND 045 6*A* 01 30.0 .1000 31.5 1.7000 045 6*A* 02 28.0 .1000 29.4 .220 0 052 1*A* 02 33.0 1.0000 38.9 ND 052 1*C * 0 1 32.0 1.0000 34.9 ND 052 1*A* 03 31.0 1.0000 35.3 ND 052 1*C * 0 2 27.0 1.0000 31.9 1.6143 052 1*C * 0 3 32.0 1.0000 36.5 ND 052 1*E* 01 31.0 1.0000 33.8 1.3600 052 1*E* 02 32.0 1.0000 37.8 ND

152---------- --------- --- ------------ - -- ------ ------------

---------- --------- - ---------- ----- -- --- ---------- --- -- ------- 052 1*E* 03 36.0 1.0000 41.0 ND 052 1*A* 01 28.0 1.0000 30.5 ND 045 6*A* 3 3 3 .0 .1000 34.6 2.6700 045 6*A* 4 3 0 .0 .1000 32.7 2.0300 045 6*A* 5 2 5 .0 .1000 27.2 2.6000 045 6*B* 1 2 3 .0 .1000 25.0 2.6200 045 6*B* 2 3 0 .0 .1000 32.7 2.2500 045 6*B* 3 3 1 .0 .1000 34.1 1.8900 052 1*B* 01 27.0 1.0000 29.4 ND 052 1*D * 0 1 26.0 1.0000 28.3 1.6000 052 1*D * 0 3 22.0 1.0000 25.1 ND 052 1*B* 03 18.0 1.0000 20.5 ND

<< AEROS AL.APPL >> (H)P Outside Inside Dermal Avg Hand Recor d Both Hands Both Han d s Smpl T i me Quan. Limit I.D. (ug) (ug) (hrs) (ug/sq cm)

045 6*B* 4 ND .470 10.0000 045 6*B* 5 ND .310 10.0000 045 6*C * 1 ND .250 10.0000 045 6*C * 2 21.600 0 .350 10.000 0 045 6*C * 3 97.600 0 .320 10.000 0 045 6*C * 4 ND .430 10.0000 045 6*C * 5 ND .220 10.0000 052 1*B* 02 430.0 0 0 0 .470 1 00.00 00 052 1*D * 0 2 981.0 0 0 0 .320 1 00.00 00 045 6*A* 01 ND .500 10.000 0 045 6*A* 02 ND .470 10.000 0 052 1*A* 02 1240.0 0 0 0 .550 1 00.00 00 052 1*C * 0 1 1 750.0 0 0 0 .530 1 00.00 00 052 1*A* 03 1070.0 0 0 0 .520 1 00.00 00 052 1*C * 0 2 645.0 0 0 0 .450 1 00.00 00 052 1*C * 0 3 2 690.0 0 0 0 .530 1 00.00 00 052 1*E* 01 1530.0 0 0 0 .520 1 00.00 00 052 1*E* 02 299.0 0 0 0 .530 1 00.00 00 052 1*E* 03 1060.0 0 0 0 .600 1 00.00 00 052 1*A* 01 1100.0 0 0 0 .470 1 00.00 00 045 6*A* 3 22.400 0 .550 10.000 0 045 6*A* 4 24.800 0 .500 10.000 0 045 6*A* 5 ND .420 10.0000 045 6*B* 1 19.200 0 .380 10.000 0 045 6*B* 2 ND .500 10.0000 045 6*B* 3 ND .520 10.0000 052 1*B* 01 1150.0 0 0 0 .450 1 00.00 00 052 1*D * 0 1 2 010.0 0 0 0 .430 1 00.00 00 052 1*D * 0 3 1 440.0 0 0 0 .370 1 00.00 00 052 1*B* 03 759.0 0 0 0 .300 1 00.00 00

Note: Avg. Han d Quantificati o n is μg/samp le. T he 100 μg LO Q for stud y 52 1 w a s erro neo usl y enter ed i n to PHED as 10 0 μg. T he correct value is 1 0 μg.

<< AEROS AL.APPL >> (H)Page 1 (V Uppr Arm Uppr Arm Uppr Arm Uppr Arm Avg Dermal Recor d In R ght 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)

045 6*B* 4 ND ND .2880 .2050 .0300 045 6*B* 5 ND ND .3250 .2090 .0300 045 6*C * 1 ND ND .0340 .0140 .0300 045 6*C * 2 ND ND .0180 .0280 .0300

153---------- --------- - ---------- ----- -- --- ---------- 045 6*C * 3 ND ND ND N D .0300 045 6*C * 4 ND ND .0650 .1610 .0300 045 6*C * 5 ND ND .0170 .0320 .0300 052 1*B* 02 ND ND .1770 ND .0406 052 1*D * 0 2 ND ND .9990 .4340 .0406 045 6*A* 01 ND ND 1.1970 .1 930 .0300 045 6*A* 02 ND ND .7630 .0730 .0300 052 1*A* 02 ND ND .5030 .9990 .0406 052 1*C * 0 1 ND ND .1400 .3760 .0406 052 1*A* 03 ND ND .1360 .0830 .0406 052 1*C * 0 2 ND ND .4300 .7920 .0406 052 1*C * 0 3 ND ND .7430 .9660 .0406 052 1*E* 01 ND ND .4710 .1790 .0406 052 1*E* 02 ND ND .7880 .3670 .0406 052 1*E* 03 ND ND .1560 .3000 .0406 052 1*A* 01 ND ND .9700 .0620 .0406 045 6*A* 3 ND ND .1970 .8200 .0300 045 6*A* 4 ND ND .9730 .3980 .0300 045 6*A* 5 ND ND .0780 .1390 .0300 045 6*B* 1 ND ND .7500 .2530 .0300 045 6*B* 2 ND ND 1.3070 2.5330 .0300 045 6*B* 3 ND ND .2180 .3230 .0300 052 1*B* 01 ND ND .7110 ND .0406 052 1*D * 0 1 ND ND .7630 1.4940 .0406 052 1*D * 0 3 ND ND .1830 .1830 .0406 052 1*B* 03 ND ND .1020 .0650 .0406

<< AEROS AL.APPL >> Forearm Forearm Forearm For earm Record In R ght In Left Out Rght Out Left I.D. (ug/sq cm) (ug/sq cm) (ug/sq cm) (ug/sq cm)

045 6*B* 4 ND ND .1860 1.0100 045 6*B* 5 ND ND .1830 1.3750 045 6*C * 1 ND ND .0180 .0240 045 6*C * 2 .019 0 ND .0330 .0500 0456*C*3 ND ND ND N D 045 6*C * 4 ND ND .0750 .0370 045 6*C * 5 ND ND .0460 .0800 0521*B*02 ND ND ND ND 052 1*D * 0 2 ND ND ND 1.3400 045 6*A* 01 ND ND 2.8240 .5 200 045 6*A* 02 ND ND .4640 .1350 052 1*A* 02 ND ND .3290 .6740 052 1*C * 0 1 ND ND .2740 .9700 052 1*A* 03 ND ND 1.2630 .1 420 052 1*C * 0 2 ND ND .5810 1.5790 052 1*C * 0 3 ND ND 1.4290 .3 690 052 1*E* 01 ND ND .4300 1.2300 052 1*E* 02 ND ND .8930 .2900 052 1*E* 03 ND ND .0510 .1920 052 1*A* 01 ND ND 4.1410 .2 540 045 6*A* 3 ND ND .3060 .7240 045 6*A* 4 ND ND .0960 .2350 045 6*A* 5 ND ND .0370 .5380 045 6*B* 1 ND ND 1.3150 .7400 045 6*B* 2 ND ND .6510 5.3930 045 6*B* 3 ND ND .2000 .4000 052 1*B* 01 ND ND .4590 .4140 052 1*D * 0 1 ND .0900 .9180 1.1000 052 1*D * 0 3 ND ND .1730 .2090 052 1*B* 03 ND ND .0550 ND

154---------- --------- - ---------- ----- -- --- ----------

---------- --------- - ---------- ----- -- --- ---------- << AEROS AL.APPL >> Chest Chest Back Back Record In L e ft Out Rght In Rght Out Left I.D. (ug/sq cm) (ug/sq cm) (ug/sq cm) (ug/sq cm)

045 6*B* 4 ND .4940 ND .1650 045 6*B* 5 ND .2270 ND .4470 045 6*C * 1 ND .0130 ND .0200 0456*C*2 ND ND ND .0230 045 6*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 052 1*D * 0 2 ND .1310 ND .0910 045 6*A* 01 ND .1310 ND .1260 045 6*A* 02 ND .2610 ND .0900 052 1*A* 02 ND .0850 ND .2960 052 1*C * 0 1 ND .1370 ND .2510 052 1*A* 03 ND ND ND .1440 052 1*C * 0 2 ND .4790 ND .8440 052 1*C * 0 3 ND .3900 ND .3480 052 1*E* 01 ND .4990 ND .1640 052 1*E* 02 ND .3170 ND .3170 052 1*E* 03 ND .1120 ND .3090 052 1*A* 01 ND 4.1820 ND .1940 045 6*A* 3 ND .3880 ND .3190 045 6*A* 4 ND .0340 ND .3690 045 6*A* 5 ND .0190 ND .0930 045 6*B* 1 ND .3710 ND .4000 045 6*B* 2 ND .9100 ND .3300 045 6*B* 3 ND .0950 ND .2490 052 1*B* 01 ND .2610 ND .1170 052 1*D * 0 1 ND .3560 ND .1970 052 1*D * 0 3 ND .1400 ND .1260 052 1*B* 03 ND ND ND .1440

<< AEROS AL .APPL >> T h igh T h igh T h ig h T h igh Record In R ght In Left Out Rght Out Left I.D. (ug/sq cm) (ug/sq cm) (ug/sq cm) (ug/sq cm)

045 6*B* 4 ND ND .1570 .1650 045 6*B* 5 ND ND .1250 .0780 0456*C*1 ND ND ND N D 045 6*C * 2 ND ND .0150 .0140 045 6*C * 3 ND ND .0240 .0270 0456*C*4 ND ND ND .0210 0456*C*5 ND ND ND N D 0521*B*02 ND ND ND ND 052 1*D * 0 2 ND ND .0820 ND 045 6*A* 01 ND ND .3070 .1510 045 6*A* 02 ND ND .1560 .0520 052 1*A* 02 ND ND .0850 .0970 052 1*C * 0 1 ND ND .1430 .1470 052 1*A* 03 ND ND .0450 .0610 052 1*C * 0 2 ND ND .2410 .3380 052 1*C * 0 3 ND ND .1300 1.1040 052 1*E* 01 ND ND .1060 .0630 052 1*E* 02 ND ND .0590 .0530 052 1*E* 03 ND ND .0730 .0530

155---------- --------- - ---------- ----- -- --- ---------- 052 1*A* 01 ND ND .0500 .0670 045 6*A* 3 ND ND .1600 .4260 045 6*A* 4 ND ND .0670 .0800 045 6*A* 5 ND ND .0310 .0440 045 6*B* 1 ND ND .1240 .0490 045 6*B* 2 ND ND .0730 .0610 045 6*B* 3 ND ND .0490 .0450 052 1*B* 01 ND ND .0650 ND 052 1*D * 0 1 ND ND .1190 .3890 052 1*D * 0 3 ND ND ND .0860 0521*B*03 ND ND ND ND

<< AEROS AL.APPL >> Shin Sh in Shin Shin Record In R ght In Left Out Rght Out Left I.D. (ug/sq cm) (ug/sq cm) (ug/sq cm) (ug/sq cm)

045 6*B* 4 .017 0 .0240 .1270 .1270 045 6*B* 5 ND ND .0590 .0400 045 6*C * 1 ND ND .0130 .0140 045 6*C * 2 ND ND .0260 .0440 045 6*C * 3 ND ND .0290 .0380 0456*C*4 ND ND ND .0170 0456*C*5 ND ND ND N D 052 1*B* 02 ND ND .1220 ND 052 1*D * 0 2 ND ND .0490 ND 045 6*A* 01 ND ND .2060 .0380 045 6*A* 02 ND ND .0800 .0310 052 1*A* 02 ND ND .1220 ND 052 1*C * 0 1 ND ND 1.8640 .0 500 052 1*A* 03 ND ND .1120 .2550 052 1*C * 0 2 ND ND .5360 .2680 052 1*C * 0 3 ND ND 2.8060 .6 210 052 1*E* 01 ND ND .0860 .0500 052 1*E* 02 ND ND .0590 .0450 052 1*E* 03 ND ND .0430 ND 052 1*A* 01 ND ND ND .0690 045 6*A* 3 ND .0190 .0110 .1500 0456*A*4 ND ND ND .0200 045 6*A* 5 ND ND .0220 ND 045 6*B* 1 ND .0180 .0810 .0250 045 6*B* 2 ND ND .0750 .3130 045 6*B* 3 ND ND .0330 .0400 052 1*B* 01 ND ND ND .1120 052 1*D * 0 1 ND ND .1860 .1100 052 1*D * 0 3 ND ND ND .1600 0521*B*03 ND ND ND ND

156

## ACCEPTANCE CRITERIA EVALUATI O N:

PHED SCENARIO 10: AEROSOL APPLICATION (APPL)Study Code 456: Handheld AerosolsTask: Evaluate handheld aerosols (specificallystudy code 456) from the applicator fileof the Pesticide Handlers ExposureDatabase.The following criteria arereviewed to determine if the study code is complete enough for future use.1.Are the dermal grades (covered/uncovered) for all study codeslisted as “A” or “B”?No2.Are the airborne gradesfor all study codes listed as “A” or “B”?No3.Are the hand grades forall study codes listed as “A” or “B”?No4.Is the percentage of non-detect valuesfor all body part depositions less than 40%when determining whole body exposures?TotalDeposition:YesSingleLayerClothing:No5.Are the 10 selectedbody parts (head, neck (front and back), both upper arms,both forearms, chest, back, upper legs, lower legs, and hands) availableto determine whole body exposures?Total Deposition/No NormalizationTraditional -NoSubstitution method - YesTotal Deposition/Normalized by lb aihandledTraditional -NoSubstitution method - YesSingle layer clothing (non protective),long sleeve/long pants/no glove/no headprotection/No normalization_Traditional -NoSubstitution method – YesPage 158 of 194Single layer clothing (non protective),long sleeve/long pants/no glove/no headprotection/Normalizedby lb ai handled_Traditional -NoSubstitution method – YesSummary:Number ofReplicates:15Range of lbs AI Handled:9.40E-03 lbs ai - 9.40E-03 lbs aiRange of inhalation sampling durations:13 minutes – 33 minutesRange of dermal sampling durations:0.22 hrs – 0.55 hrsAverage Dermal LOQ:3.02 ug/cm2Average Inhalation LOQ:10.0 ugInhalationRate/Minute:16L/minuteNon-Detect Handling:Half LOQ ValuesHandProtection:NoHeadProtection:NoPage 159 of 194Section 1:Clothing Layer:Total DepositionTable 1.1 is a listing of Non-Detect Counts of total deposition.For each column, the number of non-detect values and thenumber of replicates with a value is listed.HeadNeck(front/back)UpperArmsRightLeftChestRightLeftBackRightLeftForearmsRightLeft1/154/15=27%0/0=NA%1/15=7%3/15=20%0/0=NA%=7%ThighsRightLeftLowerLegsshin rightshin leftcalf rightcalf leftankle rightankle left3/15=20%2/15=13%0/0=NA%3/15 =20%2/15=13%0/0=NA%0/0=NA%0/0=NA%0/0=NA%0/0=NA%0/0=NA%1/15=7%0/0=NA%0/15 = 0%1/15=7%Table 1.1.Non-Detect Counts ofbody part values (total deposition)Percentage Non-Detects:21/165 = 13%FeetHandsPage 160 of 1941a.No Normalization – Total DepositionTable 1.2 is a listing of all replicates with a study code of456.The exposure values areresults of no normalization andtotal deposition.No replicates had a whole body exposure using the traditional exposure evaluation method.TheSubstitution Body Method resulted in all 15 replicates having a whole body exposure value.ReplicateSampleTime(hrs)WholeBodyExposure(Traditional Method)(ug)WholeBodyExposure(Substitution BodyMethod)(ug)InhalationExposure(ug)AverageDermalQuantificationLimit (ug/cm2)AverageHandQuantification Limit(ug/cm2)AirborneGradeDermalGradeUncoveredDermalGradeCoveredHandGrade456-A-30.55-7.58e+034.07E+043.0e-0210CCA456-A-40.50-4.57e+032.98E+043.0e-0210CCA456-A-50.42-1.76e+033.82E+043.0e-0210CCA456-A-10.50-8.45e+032.59E+043.0e-0210CCA456-A-20.47-4.53e+033.35E+043.0e-0210CCA456-B-10.38-7.09e+033.85E+043.0e-0210CCA456-B-20.50-1.81e+033.30E+043.0e-0210CCA456-B-30.52-3.20e+032.75E+043.0e-0210CCA456-B-40.47-5.61e+034.41E+03.0e-0210CCAPage 161 of 19442.06E+0CCA456-B-50.31-5.90e+033.0e-021041.32E+0CCA456-C-10.25-3433.0e-021041.85E+0CCA456-C-20.35-4493.0e-021042.11E+0CCA456-C-30.32-1.05e+033.0e-021048.92E+0CCA456-C-40.43-7233.0e-021041.20E+0CCA456-C-50.22-4703.0e-02104Table 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 using the Body PartSubstitution Method.HeadNeck(front)Neck(back)UpperArmsChestBackForearmsThighsLowerLegsFeetHands20911.813.050827842027721097.7NA188Table 1.3.Body parts and correspondinggeometric mean exposurevalues (ug)Page 162 of 194Figure 1.1 shows a log-log graphof whole body exposures vs. total lbs AI handled.This graph is a result of substitutionbody part method, no normalization and total deposition.No data exists forthe traditional body part method.Figure 1.1WholeBody Exposure vs. Total Lbs AI Handled (not normalized, total deposition)Page 163 of 1941b.Normalized byLb AI Handled – Total Deposition.Table 1.4 is a listing of all replicates with a study code of456.These values are based on normalization by Lbs AIHandled and total deposition.No replicates had a whole body exposure using thetraditional exposure evaluation method.The Substitution Body Method resulted in all 15replicates having a whole body exposure value.ReplicateSampleTime(hrs)WholeBodyExposure(TraditionalMethod)(ug)Whole BodyExposure(SubstitutionBodyMethod)(ug)InhalationExposure(ug)AverageDermalQuantificationLimit(ug/cm2)AverageHandQuantificationLimit(ug/cm2)AirborneGradeDermalGradeUncoveredDermalGradeCoveredHandGrade456-A-30.55-8.06e+057.87E+063.0e-0210CCA456-A-40.50-4.86e+056.34E+063.0e-0210CCA456-A-50.42-1.88e+059.74E+063.0e-0210CCA456-A-10.50-8.99e+055.51E+063.0e-0210CCA456-A-20.47-4.82e+057.64E+063.0e-0210CCA456-B-10.38-7.54e+051.07E+073.0e-0210CCA456-B-20.50-1.93e+067.03E+063.0e-0210CCA456-B-30.52-3.40e+055.66E+063.0e-0210CCA456-B-40.47-5.97e+051.00E+073.0e-0210CCA456-B-50.31-6.27e+057.30E+063.0e-0210CCA456-C-10.25-3.65e+045.61E+063.0e-0210CCA456-C-20.35-4.78e+045.63E+063.0e-0210CCA456-C-30.32-1.12e+047.08E+063.0e-0210CCA456-C-40.43-7.69e+042.19E+063.0e-0210CCA456-C-50.22-5.00e+045.89E+063.0e-0210CCATable 1.4.Exposure values normalizedby lbs AI handled and total deposition.Page 164 of 194Table 1.5 displays the body parts and their corresponding geometric mean exposure values in ug using the Body PartSubstitution Method.HeadNeck frontNeckbackUpper ArmsChestBackForearmsThighsLowerLegsFeetHands5.58E03.13E033.47E031.36E057.41E01.12E057.38E045.60E042.60E04NA5.00E0444Table 1.5.Body parts and correspondinggeometric mean exposurevalues (ug)Page 165 of 194Figure 1.2 shows a log-log graphof whole body exposures vs. total lbs AI handled.This graph is a result of substitutionbody part method, normalized bylb ai handled and total deposition.No data exists for thetraditional body part method.Figure 1.2Whole Body Exposure vs. Total Lbs AI Handled (normalized, total deposition)Page 166 of 194Section 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 gloves.For eachcolumn, the number of non-detect values and thenumber of replicates with a value is listed.HeadNeck (front/back)Upper ArmsRightLeftChestRightLeftBackRightLeftForearmsRightLeftThighsRightLeftLowerLegsshin rightshin leftcalf rightcalf leftankle rightankle leftFeetHands14/1515/15=10015/15=100=93%0/0=NA%15/15=10014/15 =93%%12/15=80%4/15=27%15/15=100%%15/15=100%15/15=10015/15=1000/0=NA%0/0=NA%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 bodypart values (permeable layer clothing –long sleeves, long pants, no gloves)Percentage Non-Detects:179/195 = 92%Page 167 of 1942a.No Normalization – Permeable ClothingTable 2.2 is a listing of all replicates with a study code of456.The exposure values areresults of no normalization withpermeable clothing - long pants, long sleevesand no gloves.No replicates had a whole body exposure using thetraditional exposure evaluationmethod.The Substitution Body Method resultedin all 15 replicates having a whole bodyexposure value.ReplicateSampleTime(hrs)WholeBodyExposure(TraditionalMethod)(ug)Whole BodyExposure(SubstitutionBodyMethod)(ug)InhalationExposure(ug)AverageDermalQuantificationLimit(ug/cm2)AverageHandQuantificationLimit(ug/cm2)AirborneGradeDermalGradeUncoveredDermalGradeCoveredHandGrade456-A-30.55-1.51e+034.07E+043.0e-0210CCA456-A-40.50-7612.98E+043.0e-0210CCA456-A-50.42-5523.82E+043.0e-0210CCA456-A-10.50-1.23e+032.59E+043.0e-0210CCA456-A-20.47-1.20e+033.35E+043.0e-0210CCA456-B-10.38-6303.85E+043.0e-0210CCA456-B-20.50-1.55e+033.30E+043.0e-0210CCA456-B-30.52-5902.75E+043.0e-0210CCA456-B-40.47-7134.41E+043.0e-0210CCA456-B-50.31-9132.06E+043.0e-0210CCA456-C-10.25-2971.32E+043.0e-0210CCA456-C-20.35-3021.85E+043.0e-0210CCA456-C-30.32-5782.11E+043.0e-0210CCA456-C-40.43-2988.92E+043.0e-0210CCA456-C-50.22-2981.20E+043.0e-0210CCATable 2.2.Exposure values using no normalization with permeable clothing – long sleeves, long pants, no gloves.Page 168 of 194Table 2.3 displays the body parts and their corresponding geometric mean exposure values in ug using the Body PartSubstitution Method.HeadNeck frontNeckbackUpper ArmsChestBackForearmsThighsLowerLegsFeetHands20911.813.043.753.253.218.357.337.0NA12.4Table 2.3.Body parts and correspondinggeometric mean exposurevalues (ug)Page 169 of 194Figure 2.1 shows a log-log graphof whole body exposures vs. total lbs AI handled.This graph is a result of substitutionbody part method, no normalization with permeable layer clothing consisting of longsleeves, long pants and no gloves.No data exists for the traditional body part method.Figure 2.1Whole Body Exposure vs. Total Lbs AI Handled(not normalized, permeable single layer clothing – longsleeves, long pants, no gloves)Page 170 of 1942b.Normalized byLb AI Handled – Permeable Clothing.Table 2.4 is a listing of all replicates with a study code of456.These values are based on normalization by Lbs AIHandled with permeable single layer clothing (long sleeves, long pants, no gloves).No replicates had a whole bodyexposure using the traditional exposureevaluation method.The Substitution BodyMethod resulted in all 15 replicateshaving a whole body exposure value.ReplicateSampleTime(hrs)WholeBodyExposure(TraditionalMethod)(ug)Whole BodyExposure(SubstitutionBodyMethod)(ug)InhalationExposure(ug)AverageDermalQuantificationLimit(ug/cm2)AverageHandQuantificationLimit(ug/cm2)AirborneGradeDermalGradeUncoveredDermalGradeCoveredHandGrade456-A-30.55-1.61e+057.87E+063.0e-0210CCA456-A-40.50-8.10e+046.34E+063.0e-0210CCA456-A-50.42-5.87e+049.74E+063.0e-0210CCA456-A-10.50-1.31e+045.51E+063.0e-0210CCA456-A-20.47-1.28e+047.64E+063.0e-0210CCA456-B-10.38-6.71e+041.07E+073.0e-0210CCA456-B-20.50-1.65e+057.03E+063.0e-0210CCA456-B-30.52-6.27e+045.66E+063.0e-0210CCA456-B-40.47-7.59e+041.00E+073.0e-0210CCA456-B-50.31-9.72e+047.30E+063.0e-0210CCA456-C-10.25-3.16e+045.61E+063.0e-0210CCA456-C-20.35-3.21e+045.63E+063.0e-0210CCA456-C-30.32-6.15e+047.08E+063.0e-0210CCA456-C-40.43-3.17e+042.19E+063.0e-0210CCA456-C-50.22-3.17e+045.89E+063.0e-0210CCATable 2.4.Exposure values normalized by lbs AI handled with permeable clothing – longsleeves, long pants, no gloves.Page 171 of 194Table 2.5 displays the body parts and their corresponding geometric mean exposure values in ug using the Body PartSubstitution Method.HeadNeck frontNeckbackUpper ArmsChestBackForearmsThighsLowerLegsFeetHands5.58E03.13E033.47E031.16E041.42E01.42E044.88E031.53E049.86E03NA3.31E0344Table 2.5.Body parts and correspondinggeometric mean exposurevalues (ug)Page 172 of 194Figure 2.2 shows a log-log graphof whole body exposures vs. total lbs AI handled.This graph is a result of substitutionbody part method, normalized bylb ai handled with permeable layer clothing – long sleeves, long pants, no gloves.Nodata exists for the traditional body part method.Figure 2.2Whole Body Exposure vs. Total Lbs AI Handled (normalized, permeable single layer clothing – long sleeves,long shirt, no gloves)Page 173 of 194Attachment A:Body Part Substitutions.Table A.1 shows a listing of the 10 body parts inspected andthe list of possible body part substitutions if no depositionwas recorded.There are 2 methods described.The traditional bodypart method is used in the DOS version of PHEDwhile the body part substitution method is an alternative used for a possible web based version of PHED:Body PartTraditional PHED SubstitutionBody Part Substitution MethodHeadBack, Chest ShouldersNeck (front and back)Neck (front)ChestShoulders, Upper Arms, HeadNeck (back)BackShoulders, Upper Arms, HeadUpper ArmsNoneBack, Chest;If no results, ForearmsForearmsNoneChest, Upper Arms, BackIf no results: ShouldersChestNoneShoulder, Upper arm, Neck (front)If no results: HeadBackNoneShoulders, Upper Arms, Neck (back)If no results: HeadThighsNoneShin,CalfLower LegsNoneHip, ThighHandsNoneForearmsPage 174 of 194Draft – Do not cite or quoteStudy Code 521: Handheld AerosolsTask: Evaluate handheld aerosols (specificallystudy code 521) from the applicator fileof the Pesticide Handlers ExposureDatabase.The following criteria arereviewed to determine if the study code is complete enough for future use.1.Are the dermal grades (covered/uncovered) for all study codes listed as “A” or “B”?Yes2.Are the airborne grades for all study codes listed as “A” or “B”?Yes3.Are the hand grades for allstudycodeslistedas“A”or“B”?Yes4.Is the percentage of non-detect valuesfor all body part depositions less than 40%when determining whole body exposures?TotalDeposition:YesSingleLayerClothing:No5.Are the 10 selectedbody parts (head, neck (front and back), both upper arms,both forearms, chest, back, upper legs, lower legs, and hands) availableto determine whole body exposures?Total Deposition/No NormalizationTraditional -YesSubstitution method -YesTotal Deposition/Normalized by lb aihandledTraditional-YesSubstitution method -YesSingle layer clothing (non protective),long sleeve/long pants/no glove/no headprotection/No normalization_Traditional-YesSubstitution method –YesPage 175 of 194Single layer clothing (non protective),long sleeve/long pants/no glove/no headprotection/Normalizedby lb ai handled_Traditional-YesSubstitution method –YesSummary:Number ofReplicates:15Range of lbs AI Handled:0.01 lbs ai - 0.01 lbs aiRange of inhalation sampling durations:18 minutes – 36 minutesRange of dermal sampling durations:0.30 hrs – 0.60 hrsAverage Dermal LOQ:4.06e-02 ug/cm2Average Inhalation LOQ:1.00 ugAverage Hand LOQ:100 ug/ cm2InhalationRate/Minute:16L/minuteNon-Detect Handling:Half LOQ ValuesHandProtection:NoPage 176 of 194Section 1:Clothing Layer:Total DepositionTable 1.1 is a listing of Non-Detect Counts of total deposition.For each column, the number of non-detect values and thenumber of replicates with a value is listed.HeadNeck (front/back)UpperArmsRightLeftChestRightLeftBackRightLeftForearmsRightLeft15/15=1002/15ThighsRightLeftLowerLegsshin rightshin leftcalf rightcalf leftankle rightankle left3/15=20%4/15=27%0/0=NA%4/15 =27%5/15=33%0/0=NA%0/0=NA%0/0=NA%0/0=NA%1/15=7%%0/15=0%3/15=20%0/0=NA%=13%0/0=NA%0/0=NA%15/15=1002/15=13%0/0=NA%0/15 = 0%2/150/15=0%%=13%Table 1.1.Non-Detect Counts ofbody part values (total deposition)Percentage Non-Detects:56/210 = 27%FeetHandsRightLeftBothPage 177 of 1941a.No Normalization – Total DepositionTable 1.2 is a listing of all replicates with a study code of521.The exposure values areresults of no normalization andtotal deposition.No replicates had a whole body exposure using the traditional exposure evaluation method.TheSubstitution Body Method resulted in all 15replicates having a whole body exposure value.ReplicateSampleTime(hrs)WholeBodyExposure(Traditional Method)(ug)WholeBodyExposure(Substitution BodyMethod)(ug)InhalationExposure(ug)AverageDermalQuantificationLimit (ug/cm2)AverageHandQuantification Limit(ug/cm2)AirborneGradeDermalGradeUncoveredDermalGradeCoveredHandGrade521-A-10.472.18E+04N/A7.34E+034.06E-02100AAA521-A-20.556.57E+03N/A6.78E+034.06E-02100AAA521-A-30.523.65E+03N/A7.02E+034.06E-02100AAA521-B-10.454.88E+03N/A7.34E+034.06E-02100AAA521-B-20.474.59E+03N/A6.78E+034.06E-02100AAA521-B-30.301.99E+03N/A7.02E+034.06E-02100AAA521-C-10.538.15E+03N/A7.34E+034.06E-02100AAA521-C-20.451.14E+04N/A2.19E+044.06E-02100AAA521-C-30.531.72E+04N/A7.02E+04.06E-02100AAAPage 178 of 19432.35E+0521-D-10.431.02E+04N/A4AAA4.06E-021006.78E+0521-D-20.325.21E+03N/A3AAA4.06E-021007.02E+0521-D-30.373.84E+03N/A3AAA4.06E-021002.00E+0521-E-10.526.83E+03N/A4AAA4.06E-021006.78E+0521-E-20.536.17E+03N/A3AAA4.06E-021007.02E+0521-E-30.604.10E+03N/A3AAA4.06E-02100Table 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.HeadNeck frontNeckbackUpper ArmsChestBackForearmsThighsLowerLegsFeetHands41025.120.0999595644488300248NA1.06E+03Table 1.3.Body parts and correspondinggeometric mean exposurevalues (ug)Page 179 of 194Figure 1.1 shows a log-log graphof whole body exposures vs. total lbs AIhandled.This graph is a result ofnonormalization and total deposition.Figure 1.1WholeBody Exposure vs. Total Lbs AI Handled (not normalized, total deposition)Page 180 of 1941b.Normalized byLb AI Handled – Total Deposition.Table 1.4 is a listing of all replicates with a study code of521.These values are based on normalization by Lbs AIHandled and total deposition.ReplicateSampleTime(hrs)WholeBodyExposure(TraditionalMethod)(ug)Whole BodyExposure(SubstitutionBodyMethod)(ug)InhalationExposure(ug)AverageDermalQuantificationLimit(ug/cm2)AverageHandQuantificationLimit(ug/cm2)AirborneGradeDermalGradeUncoveredDermalGradeCoveredHandGrade521-A-10.472.18E+06N/A7.34E+054.06E-02100AAA521-A-20.556.57E+05N/A6.78E+054.06E-02100AAA521-A-30.523.65E+05N/A7.02E+054.06E-02100AAA521-B-10.454.88E+05N/A7.34E+054.06E-02100AAA521-B-20.474.59E+05N/A6.78E+054.06E-02100AAA521-B-30.301.99E+05N/A7.02E+054.06E-02100AAA521-C-10.538.15E+05N/A7.34E+054.06E-02100AAA521-C-20.451.14E+06N/A2.19E+064.06E-02100AAA521-C-30.531.72E+06N/A7.02E+054.06E-02100AAA521-D-10.431.02E+06N/A2.35E+064.06E-02100AAA521-D-20.325.21E+05N/A6.78E+054.06E-02100AAA521-D-30.373.84E+05N/A7.02E+054.06E-02100AAA521-E-10.526.83E+05N/A2.00E+064.06E-02100AAA521-E-20.536.17E+05N/A6.78E+054.06E-02100AAA521-E-30.604.10E+05N/A7.02E+054.06E-02100AAATable 1.4.Exposure values normalizedby lbs AI handled and total deposition.Page 181 of 194Table 1.5 displays the body parts and their corresponding geometric mean exposure values in ug.HeadNeck frontNeckbackUpperArmsChestBackForearmsThighsLowerLegsFeetHands3.00E+04.10E+05.95E+04.88E+02.51E+032.00E+039.99E+046.44E+042.48E+04NA1.06E+054444Table 1.5.Body parts and correspondinggeometric mean exposurevalues (ug)Page 182 of 194Figure 1.2 shows a log-log graph of whole body exposuresvs. total lbs AI handled.Figure 1.2Whole Body Exposure vs. TotalLbs AI Handled (normalized, total deposition)Page 183 of 194Section 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 gloves.For eachcolumn, the number of non-detect values and thenumber of replicates with a value is listed.HeadNeck (front/back)Upper ArmsRightLeftChestRightLeftBackRightLeftForearmsRightLeftThighsRightLeftLower Legsshin rightshin leftcalf rightcalf leftankle rightankle leftFeetHandsRightLeftBoth15/15=100%15/15=10015/15=10015/15=1000/0=NA%15/15=100%%0/0=NA%15/15=10015/15%%0/0=NA%1/15=7%15/15=100%15/15=10015/15=100%=100%15/15=1000/0=NA%0/0=NA%0/15=0%%%0/0 = NA14/15=93%%0/0=NA%0/0=NA%0/0=NA%Table 2.1.Non-Detect Counts of bodypart values (permeable layer clothing –long sleeves, long pants, no gloves)Percentage Non-Detects:180/210 = 86%Page 184 of 1942a.No Normalization – Permeable ClothingTable 2.2 is a listing of all replicates with a study code of521.The exposure values areresults of no normalization withpermeable clothing - long pants,long sleevesand no gloves.ReplicateSampleTime(hrs)WholeBodyExposure(TraditionalMethod)(ug)Whole BodyExposure(SubstitutionBodyMethod)(ug)InhalationExposure(ug)AverageDermalQuantificationLimit(ug/cm2)AverageHandQuantificationLimit(ug/cm2)AirborneGradeDermalGradeUncoveredDermalGradeCoveredHandGrade521-A-10.472.13E+03N/A7.34E+034.06E-02100AAA521-A-20.552.26E+03N/A6.78E+034.06E-02100AAA521-A-30.521.61E+03N/A7.02E+034.06E-02100AAA521-B-10.451.98E+03N/A7.34E+034.06E-02100AAA521-B-20.474.24E+03N/A6.78E+034.06E-02100AAA521-B-30.301.35E+03N/A7.02E+034.06E-02100AAA521-C-10.532.79E+03N/A7.34E+034.06E-02100AAA521-C-20.451.91E+03N/A2.19E+044.06E-02100AAA521-C-30.534.95E+03N/A7.02E+034.06E-02100AAA521-D-10.432.78E+03N/A2.35E+044.06E-02100AAA521-D-20.321.59E+03N/A6.78E+034.06E-02100AAA521-D-30.372.07E+03N/A7.02E+034.06E-02100AAA521-E-10.522.40E+03N/A2.00E+044.06E-02100AAA521-E-20.531.54E+03N/A6.78E+034.06E-02100AAA521-E-30.601.84E+03N/A7.02E+034.06E-02100AAATable 2.2.Exposure values using no normalization with permeable clothing – long sleeves, long pants, no gloves.Page 185 of 194Table 2.3 displays the body parts and their corresponding geometric mean exposure values in ug.HeadNeck frontNeckbackUpper ArmsChestBackForearmsThighsLowerLegsFeetHands41025.120.059.172.172.126.377.548.31.09E+03Table 2.3.Body parts and correspondinggeometric mean exposurevalues (ug)Page 186 of 194Figure 2.1 shows a log-log graphof whole body exposures vs. total lbs AI handled.This graph is a result of the traditionalbody part method, no normalization with permeable layer clothing consisting of longsleeves, long pants and no gloves.Figure 2.1Whole Body Exposure vs. Total Lbs AI Handled(not normalized, permeable single layer clothing – longsleeves, long pants, no gloves)Page 187 of 1942b.Normalized byLb AI Handled – Permeable Clothing.Table 2.4 is a listing of all replicates with a study code of521.These values are based on normalization by Lbs AIHandled with permeable single layer clothing (long sleeves, long pants, no gloves).ReplicateSampleTime(hrs)WholeBodyExposure(TraditionalMethod)(ug)Whole BodyExposure(SubstitutionBodyMethod)(ug)InhalationExposure(ug)AverageDermalQuantificationLimit(ug/cm2)AverageHandQuantificationLimit(ug/cm2)AirborneGradeDermalGradeUncoveredDermalGradeCoveredHandGrade521-A-10.472.13E+05N/A7.34E+054.06E-02100AAA521-A-20.552.26E+05N/A6.78E+054.06E-02100AAA521-A-30.521.61E+05N/A7.02E+054.06E-02100AAA521-B-10.451.98E+05N/A7.34E+054.06E-02100AAA521-B-20.474.24E+05N/A6.78E+054.06E-02100AAA521-B-30.301.35E+05N/A7.02E+054.06E-02100AAA521-C-10.532.79E+05N/A7.34E+054.06E-02100AAA521-C-20.451.91E+05N/A2.19E+064.06E-02100AAA521-C-30.534.95E+05N/A7.02E+054.06E-02100AAA521-D-10.432.78E+05N/A2.35E+064.06E-02100AAA521-D-20.321.59E+05N/A6.78E+054.06E-02100AAA521-D-30.372.07E+05N/A7.02E+054.06E-02100AAA521-E-10.522.40E+05N/A2.00E+054.06E-02100AAA521-E-20.531.54E+05N/A6.78E+054.06E-02100AAA521-E-30.601.84E+05N/A7.02E+054.06E-02100AAATable 2.4.Exposure values normalized by lbs AI handled with permeable clothing – longsleeves, long pants, no gloves.Page 188 of 194Table 2.5 displays the body parts and their corresponding geometric mean exposure values in ug.HeadNeck frontNeckbackUpperArmsChestBackForearmsThighsLowerLegsFeetHands7.75E+07.21E+02.63E+04.10E+042.51E+032.00E+035.91E+037.21E+034.83E+03NA1.06E+05333Table 2.5.Body parts and correspondinggeometric mean exposurevalues (ug)Page 189 of 194Figure 2.2 shows a log-log graphof whole body exposures vs. total lbs AI handled.This graph is a result of the traditionalmethod, normalized bylb ai handled with permeable layerclothing – long sleeves, long pants, no gloves.Figure 2.2Whole Body Exposure vs. Total Lbs AI Handled (normalized, permeable single layer clothing – long sleeves,long shirt, no gloves)Page 190 of 194Attachment A:Body Part Substitutions.Table A.1 shows a listing of the 10 body parts inspected andthe list of possible body part substitutions if no depositionwas recorded.There are 2 methods described.The traditional body part method is used in the DOS version of PHEDwhile the body part substitution method is an alternative used for a possible web based version of PHED:Body PartTraditional PHED SubstitutionBody Part Substitution MethodHeadBack, Chest ShouldersNeck (front and back)Neck (front)ChestShoulders, Upper Arms, HeadNeck (back)BackShoulders, Upper Arms, HeadUpper ArmsNoneBack, Chest;If no results, ForearmsForearmsNoneChest, Upper Arms, BackIf no results: ShouldersChestNoneShoulder, Upper arm, Neck (front)If no results: HeadBackNoneShoulders, Upper Arms, Neck (back)If no results: HeadThighsNoneShin,CalfLower LegsNoneHip, ThighHandsNoneForearmsPage 191 of 194

## Appendix G: List of Current AEA TF II S t andard Operating Procedures (SOPs)

Chapter 1 – Administration

AEAT F II-1A.0 Organizatio nal Structure AEAT F II-1B.0 Personne l Respons ibilities AEAT F II-1C.0 Study Dire ctor Selection AEAT F II-1D.0 Inspection of AEATF II Facilities/ Data

Chapter 2 – Protocols

AEAT F II-2A.0 Study Authorization a nd Approva l AEAT F II-2B.0 Study Number Assign ment AEAT F II-2C.0 Protocols

Chapter 3 – Standard O perating Procedures

AEAT F II-3A.0 SOP Prepa ration, Appr oval, Maintenance, and Distribution AEAT F II-3B.0 Use of AEATF II and Contractor SOPs

Chapter 4 – Study Reports

AEAT F II-4A.0 Study Rep o rt Preparation AEAT F II-4B.0 Final Repo rt Issue

Chapter 5 – Quality Assurance Unit

AEAT F II-5A.0 QA Personnel Admin i stration AEAT F II-5B.0 AEAT F II QAU Respo n s ibilities AEAT F II-5C.0 QAU Records AEAT F II-5D.0 QA Master Schedu le AEAT F II-5E.0 Protocol an d Amendment Review AEAT F II-5F.0 Inspection/Audit Type s and Frequency AEAT F II-5G.0 Study Inspections AEAT F II-5H.0 Data Audit s AEAT F II-5I.0 Facility Inspections AEAT F II-5J.0 Report Aud i ts AEAT F II-5K.0 Inspection Report Distribution

Page 192 of 194Chapter 6 – Archives

AEAT F II-6A.0 Storage of Raw Data AEAT F II-6B.0 Access to Archived Data AEAT F II-6C.0 Specimen and Wet Sample Storage

Chapter 7 – Test, Control and Reference Substance

AEAT F II-7A.0 Test, Reference, and Control Su bstance Re ceipt and S h i p m e n t AEAT F II-7B.0 Test, Control and Refe rence Subs tance Labe ling AEAT F II-7C.0 Dispos al of Test, Control, and Reference Substances AEAT F II-7D.0 Test, Control, and Refe rence Sub stance Cha i n of Custody AEAT F II-7E.0 Test and Reference Substance Analyses

Chapter 8 – Matrix Samples

AEAT F II-8A.1 Whole Bod y Sampling – Inner Dosimeters AEAT F II-8B.1 Hand Was h Samples AEAT F II-8C.1 Dermal Face/Neck Wipe Sample s AEAT F II-8D.0 Collection of Air Samples Using OVS Tube s AEAT F II-8E.0 Fortification of Matrix Samples AEAT F II-8F.0 Sample Ide n tification AEAT F II-8G.1 Whole Bod y Sampling – Outer Dosimeters

Chapter 9 – Documentation

AEAT F II-9A.0 Body Surface Areas AEAT F II-9B.1 Field Fortific ation Adju stment Factors AEAT F II-9C.0 Numerical Formatting and Hand ling AEAT F II-9D.0 Analytica l Method Number Assignment AEAT F II-9E.0 Raw Data Collection AEAT F II-9F.0 Data Corrections AEAT F II-9G.0 Raw Data Hand ling AEAT F II-9H.0 Preparatio n of True C opies

Chapter 10 – Field Study Procedures

AEAT F II-10A.0 Rotameter Calibration AEAT F II-10B.0 Packing, Handling, an d Shipp i ng of Samples AEAT F II-10C.0 Worker and Study Observations AEAT F II-10D.0 Application Equipm ent Operation Verification AEAT F II-10E.0 Worker Sample Colle c tion Sequ ence AEAT F II-10F.0 GPI Electronic Dig ital Flow Meter AEAT F II-10G.0 Personal Air Sampling Pump Calibration

Page 193 of 194Chapter 11 - Human S ubject Management

AEAT F II-11A.0 - Pregnancy Testing AEAT F II-11B.0 - Heat Stress AEAT F II-11C.0 - Emergency Procedures AEAT F II-11D.0 - Reportable Fin d ings

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