# SAP Minutes No. 2010-02

A Set of Scientific Issues Being Considered by the Environmental Protection Agency Regarding:

Field Volatilization of Conventional Pesticides

December 1-3, 2009 FIFRA Scientific Advisory Panel Meeting

held at

One Potomac Yard Arlington, VirginiaNOTICE

These meeting minutes have been written as part of the activities of the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA), Scientif ic Advisory Panel (SAP). The meeting minutes represent the views and recommendations of the FIFRA SAP, not the United States Environmental Protection Agency (Agency). Th e content of the meeting minutes does not represent information approved or disseminated by the Agency. The meeting minutes have not been reviewed for approval by the Agency and, he nce, the contents of these meeting minutes do not necessarily represent the views and policies of the Agency, nor of other agencies in the Executive Branch of the Federal government, nor does mention of trade names or commercial products constitute a recommendation for use.

The FIFRA SAP is a Federal advisory committ ee operating in accordance with the Federal Advisory Committee Act and established under th e provisions of FIFRA as amended by the Food Quality Protection Act (FQPA) of 1996. Th e FIFRA SAP provides advice, information, and recommendations to the Agency Administrato r on pesticides and pesticide-related issues regarding the impact of regulator y actions on health and the envi ronment. The Panel serves as the primary scientific peer review mechanism of the Environmental Protection Agency (EPA), Office of Pesticide Programs (OPP), and is struct ured to provide balanced expert assessment of pesticide and pesticide-related matters faci ng the Agency. FQPA Science Review Board

members serve the FIFRA SAP on an ad hoc basi s to assist in reviews conducted by the FIFRA SAP. Further information about FIFRA SAP repor ts and activities can be obtained from its

website at http://www.epa.gov/scipoly/sap/ or the OPP Docket at (703) 305-5805. Interested persons are invited to contact Sharlene R. Ma tten, Ph.D., SAP Designated Federal Official, via email at matten.sharlene@epa.gov

.

In preparing these meeting minutes, the Panel ca refully considered all information provided and presented by EPA, as well as information pres ented in public comment. This document addresses the information provided and presented by EP A within the structure of the charge.

2TABLE OF CONTENTS

NOTICE.......................................................................................................................................................2

PARTICIPANTS.........................................................................................................................................5

INTRODUCTION.......................................................................................................................................8

PUBLIC COMMENTERS.............................................................................................................. .........10

SUMMARY OF PANEL DISCUSSION AND RECOMMENDATIONS ...........................................11

DETAILED RESPONSES TO CHARGE QUESTIONS......................................................................19

EDITORIAL COMMENTS ON THE BACKGROUND DOCUMENT .............................................55

REFERENCES..................................................................................................................... .....................57

APPENDIX A. COMPARISON OF MEASURED AIR CONCENTRATIONS IN FIELD

STUDIES IN THE UNITED KINGDOM WITH PREDICTED AIR CONCENTRATIONS

USING EPA’S TIER I APPROACH .................................................................................................... ..65

APPENDIX B. THREE APPROACHES FOR PREDICTING VAPOR PRESSURE OF A

CHEMICAL WITHIN A MIXTURE .....................................................................................................7 3

APPENDIX C. ALTERNATIVE APPROACHES TO ASSESS INHALATION HAZARD IN

THE ABSENCE OF INHALATI ON TOXICITY STUDIES ...............................................................79

APPENDIX D. DERIVATION AND ELABORATIONS ON THE CONCEPTS OF "VAPOR HAZARD RATIO" [VHR] AND THE "ENVIRO NMENTAL DILUTION RATIO" [EDR] ..........86

3

### Panel Members for the Meeting of the Federal Insecticide, Fungicide and Rodenticide Act Scientific Advisory Panel (FIFRA SAP)

to consider and review

Field Volatilization and Conventional Pesticides

December 1-3, 2009

PARTICIPANTS

FIFRA SAP Chair

Steven G. Heeringa, Ph.D. Research Scientist & Director for Statistical Design University of Michigan Institute for Social Research Ann Arbor, MI

FIFRA SAP Session Chair

Kenneth M. Portier, Ph.D. Program Director, Statistics American Cancer Society

National Home Office Atlanta, GA

Designated Federal Official

Sharlene R. Matten, Ph.D. US Environmental Protection Agency Office of Science Coordination & Policy FIFRA Scientific Advisory Panel EPA East Building, MC 7201M 1200 Pennsylvania Avenue, NW Washington, DC 20460 Tel: 202-564-8450, Fax: 202-564-8382, matten.sharlene@epa.gov

FIFRA Scientific Advisory Panel Members

John R. Bucher, Ph.D., DABT

Associate Director National Toxicology Program

National Institute of Environmental Health Sciences Research Triangle Park, NC

5Janice E. Chambers, Ph.D., DABT, Fellow ATS William L. Giles Distinguished Professor Director, Center for Environmental Health Sciences College of Veterinary Medicine Mississippi State University Mississippi State, MS

Carey N. Pope, Ph.D. Professor, Head & Sitlington Chair of Toxicology Department of Physiological Sciences Oklahoma State University Coll ege of Veterinary Medicine Stillwater, OK

FQPA Science Review Board Members

Brian Buckley, Ph.D.

Executive Director of Laboratories Environmental and Occupational Health Sciences Institute Rutgers University and the University of Medicine and Dentistry of New Jersey

Robert Wood Johnson Medical School Piscataway, NJ

Kenneth J. Davis, Ph.D.

Professor Department of Meteorology The Pennsylvania State University University Park, PA

Roberta Grant, Ph.D. Senior Toxicologist Texas Commission on Environmental Quality Austin, TX

Paul Y. Hamey, M.S. Principal Scientist Human Exposure Assessment Chemicals Regulation Directorate

York United Kingdom

Martha Harnly, MPH

Research Scientist California Department of Public Health Environmental Health Investigations Branch Richmond, CA

6Michael S. Majewski, Ph.D.

Research Chemist U.S. Geological Survey Sacramento, CA

Laura L. McConnell, Ph.D.

Research Chemist Environmental Management and Byproduct Utilization Laboratory

USDA-ARS Beltsville, MD

Jacob D. McDonald, Ph.D.

Director Chemistry & Inhalation Exposure Program Lovelace Respiratory Research Institute Albuquerque, NM

Frederick J. Miller, Ph.D., Fellow ATS Consultant & Adjunct Professor, Duke University

Fred J. Miller & Associates LLC Cary, NC

William Popendorf, Ph.D.

Professor Department of Biology Utah State University Logan, UT

Richard Schlesinger, Ph.D., Fellow ATS

Associate Dean and Professor Department of Biology and Health Sciences Pace University New York, NY

James N. Seiber, Ph.D.

Chair Department of Food Science and Technology University of California, Davis Davis, CA

Michael Yost Ph.D. Professor, Department of Exposure and Occupational Health Sciences Director, Exposure Sciences Program University of Washington Seattle, WA

7INTRODUCTION

The Federal Insecticide, Fungici de, and Rodenticide Act (FIFRA ) Scientific Advisory Panel (SAP) has completed its review of the Agency’s analysis of Scientific Issues Associated with

Field Volatilization of Conventional Pesticides . Advance notice of the SAP meeting was published in the Federal Register on September 16, 2009 . The review was conducted in an open Panel meeting December 1-3, 2009 held at One Poto mac Yard, Arlington, Virginia. Materials for this meeting are available in the Office of Pesticide Programs (OPP) public docket or via Regulations.gov, Docket No. EPA-HQ-OPP- 2009-0687. Dr. Kenneth Portier chaired the meeting. Dr. Sharlene Matten served as the Desi gnated Federal Official . Dr. Stephen Bradbury, Deputy Office Director for Programs, OPP, and Dr. Tina Levine, Director, Health Effects Division, OPP provided opening rema rks at the meeting. Presenta tions of technical background materials were provided by Mr. Jeff Evans, Mr. Charles Smith, Dr . Judy Facey, and Dr.

Elizabeth Mendez from the Health Effects Divi sion, OPP; Dr. Faruque Khan, Mr. Chuck Peck,

and Mr. Gabe Rothman from the Environmental Fa te and Effects Division, OPP; and Ms. Annie Jarabek, National Center for Environmental Asse ssment, Office of Research and Development.

The Standard Operating Procedures for Re sidential Exposure Assessment, i.e. , Residential SOPs, is a set of standard instructions for estim ating residential exposure resulting from various direct, labeled pesticide uses. Individuals in resi dential settings can also be potentially exposed via indirect exposure to conven tional pesticides. These types of exposures can occur through a variety of means including field volatilization of conventional pest icides, spray drift, and takehome exposure. Methodologies for assessing indirect exposures are not currently included in the

Residential SOPs.

Recently, the Agency has been exploring the development of an approach for assessing inhalation exposure resulting from the field vol atilization of conven tional pesticides. The following issues have been identified as key elements for this exposure scenario:

1) Use of the Agency’s Reference Concentr ation (RfC) methodology to calculate Human Equivalent Concentrations (HECs) when i nhalation toxicity studies are available.

2) Comparison of the use of inhala tion vs. oral t oxicity studies.

3) Development of a tiered approach to determin e the level of complexity and refinement needed to estimate exposure, including:

a) Use of available air monitoring data: Califor nia Air Resource Boar d (CARB), Pesticide Action Network – North America (P ANNA), and other data sources. b) Development of a volatilization screening t ool to estimate flux based on physicochemical properties of a pesticide. c) Use of more refined soil models to estimate flux. d) Use of air models to estimate con centrations around a treated field.

EPA’s goal is to have a set of procedures that include transparent m ethodol ogies and data inputs that will guide the assessment of bystander e xposure resulting from field volatilization of conventional pesticides in a st raight-forward and user-frie ndly fashion. The Agency sought

8comment from the Panel on the adequacy of the toxicological and exposure assessment methodologies; the applicability, an alysis, and use of available ai r monitoring data ; the strengths and limitations of the models being considered by the Agency for predicting flux of conventional pesticides; and the overall presenta tion of the issues related to fi eld volatilization of conventional pesticides with respect to scientific integrity and public transparency.

9PUBLIC COMMENTERS

Oral statements were presented by:

1. Susan Kegley, Ph.D., Principal and CEO, Pesticide Research Institute on behalf of

Pesticide Action Network

2. Larry Jacobs on behalf of Jacobs Farm/Del Cabo Inc.

3. Jennifer Sass, Ph.D. on behalf of the Natu ral Resources Defense Council and others

Written statements were provided by:

1. Kenneth Racke, Ph.D. on behalf of Dow AgroSciences, LLC

2. Carol Dansereau on behalf of Fa rm Worker Pesticide Project

3. Jennifer Sass, Ph.D., National Resources Defense Council (NRDC) on behalf of Natural

Resources Defense Council and others

4. Susan Kegley, Ph.D. Principal and CEO, Pesticid e Research Institute on behalf of Pesticide

Action Network - North America

5. Larry Jacobs on behalf of Jacobs Farm/Del Cabo, Inc.

6. Anne Katten on behalf of Californi a Rural Legal Assistance Foundation

7. Dona Hippert and Lisa Arkin on be half of Oregon Toxics Alliance

8. Jorga Stewart, private citizen

9. Carolyn Ashlock and Warren Tr otter, private citizens

10. Lynn Bower, private citizen

11. Jan Wroncy, Gaia Visions, private citizen

12. Tom Kerns, private citizen

13. Maxine Centala, private citizen

14. Jean Public, private citizen

10SUMMARY OF PANEL DISCUSSION AND RECOMMENDATIONS

TOPIC A: Exposure Assessment Issue

Traditionally, the Agency’s assessment of bysta nder inhalation exposure to volatile pesticides has relied extensively on the use of air m onitoring data. However, for the fumigants, an exposure assessment methodology was developed that combined the use of air models and air monitoring data. The Agency has taken the exposure assess ment methodologies deve loped for the fumigants and further adapted them by utilizing soil models to predict field volat ilization of conventional pesticides from plant and soil surfaces. Based on this premise, the Agency has identified several key factors for consideration by the Panel. They include the evaluation of the approaches and data sources used in the tiered exposure es timation methodology and use of soil models for predicting flux of conventional pesticides. Specif ically, the Agency identified the following

issues for the Panel to consider:

1. Tier I Approach for Identifying Volatile Ch emicals of Concern for Risk Assessment, Air Concentration. The Tier I approach incorporates the use of vapor pre ssure alone to arrive

at a saturated concentration in air. The esti mated air concentration can be compared with available toxicity data to evaluate inhalation exposure concerns to human and other terrestrial organisms.

Please comment on the Agency’s approach fo r using the Tier I air concentration estimation method as a screening procedure. Please discuss the strengths and limitations of the screening approach. Please identify any alternative methods and/or physical-chemical properties, if any, which may be utilized as a screening procedure to identify chemicals with potenti al inhalation exposure concerns.

Panel Response

The Panel chose to combine its response to Ques tion 1 with that of Question 2a (see below).

2. Tier II Approach for Identifying Volatile Chemicals of Concern for Risk Assessment, Volatility and Flux Models. Two options are being considered to refine the Tier I estimation method. Option A incorporates the use of physical-chemical properties including application rate, va por pressure, solubility, and K

oc in an empirically-derived function to estimate flux rates. This option ha s less [sic] constraint s and requires fewer input parameters to generate flux rates as compared to Option B described below.

a. Given the state of the science, please comment on the applicability of using the Option A model to predict flux rates. Please discuss the strengths and limitations of this approach and how these impact the resu lts. Please identify any alternative methods, if any, which may be utilized to id entify chemicals with potential inhalation

exposure concerns.

11Panel Response to Questions 1 and 2a

The Panel identified problems regarding the use of a single vapor pressure value to arrive at a saturated vapor concentration for a Tier I approach for identifying volatile chemicals of concern. The major finding was that this approach has onl y a limited connection with how a pesticide is actually used in the real world, e.g., as a formulation perhaps diluted in water with other additives like surfactants that may increase or decrease the effective vapor pressure relative to the active ingredient(s) value in its pure form. Therefore, the proposed Tier I approach may generate concentration predic tions that are higher or lowe r than actually found in field measurements and will not necessarily pr ovide predictions that are protective.

The Panel suggested that an alternative to the us e of the proposed Tier I approach is to use the Woodrow et al. (1997) correlati on approach to estimate a ma ximum 24-hour flux value. This approach is based on correlati ons between measured data and the physicochemical properties of the compounds, and it does not require an inordi nate amount of data. This regression could be updated with all the latest studies and other releva nt factors to increase its accuracy, a sensitivity analysis could be conducted on the model, then add itional safety factors could be used to make it more protective as needed for a Tier I approach.

Tier II, Option B, is a refined process that utiliz es fate and transport models to predict flux rates of applied pesticides that off-ga s from treated fields. Current fate and transport models consider mechanisms related to volatilization, biodegr adation, abiotic degrad ation, physicochemical properties, runoff, crop uptake, and leaching to account for the transformation and movement of the entire initially ap plied material. Volatiliz ation mechanisms from bare soil and crop canopy surfaces are also important proces ses that the Agency believes ought to be considered to fully

account for volatilization and diffu sion from the vadose zone and canopy into the atmosphere. The Agency has utilized two models, the Pesticide Root Zone Model (PRZM) and the Pesticide Emission Assessment at Regional and Local Scales (PEARL) that incorporate these mechanisms and have the utility for the prediction of fl ux rates from treated fields. Option B requires extensive knowledge on environmental fate prop erties, as well as information related to application site, crop mana gement and meteorology.

b. Please comment on the applicability of using fate and transport models to predict flux rates given the state of the science. Plea se discuss the strengths and limitations of both models and how these impac t the results. Please iden tify any fate and transport model(s) which the Agency has not consid ered in this analysis which would be applicable for pesticide applicati ons and crop management scenarios.

Panel Response

The Panel agreed that the concept of coupling a fate and transport model, such as PRZM or PEARL, to predict fluxes, with a model, such as the Pesticide Exposure Risk Model for Fumigants Model (PERFUM), to estimate air concentr ations at different distances from the field is a sound Tier II approach for identifying volatile chemicals of concern w ithin the confines of evaluating pesticide volatilization in treated fiel ds (excluding spray drift). The Panel noted that

12while dispersion models, such as PERFUM, have been validated for fumi gants, they have not been validated for semi-volatile pesticides. In addition, models such as PRZM or PEARL have also not been sufficiently validat ed for predicting semi-volatile pesticide volatilization from soil or crops. The Panel stated that rather than basing volatile flux prediction models on backcalculation methods, such models should be validated with direct field measurements of flux under different cropping patterns, application techniques, rates, and frequency, and in different geographic regions. The Panel proposed that th e Pesticide Emissions Model (PEM) (Scholtz et al., 2002a, b) be considered as a promising peer-rev iewed, alternative model to predict flux rates of semi-volatile pesticid es in treated fields.

TOPIC B: Toxicological Assessment Issues

As the Agency’s understanding of the state-of the science in inhalation to xicology has evolved so has the Agency’s approach to conducting inhalati on hazard and risk assessments. This evolution has seen the Agency move from converting oral doses to inhalation concentrations to using the RfC methodology and/or physiologically-based pharmacokinetic (PBPK) models. As OPP continues to work on refining the risk assessmen t paradigm, the Agency is seeking the SAP’s input on a number of key factors. They include th e use of oral toxicity studies when inhalation

studies are not available and the us e of aerosol inhalation toxicity st udies to represent toxicity to vapors of the same chemical. Specifically, the Agency identified the following issues for the

Panel to consider:

B.1) The analysis conducted by the Agency indicate s that, in general, oral toxicity studies may not accurately represent the full spectrum of toxic effects that may occur as a result of inhalation exposure. The analysis also indicates that - unless the same endpoints are identified through both routes of exposur e - oral toxicity studies frequently underestimate toxicity by the inhalation route. The Agency has not been able to discern any patterns in this under/over estimation. Please comment on any potential patterns that the Agency has not identified.

Panel Response

The Panel concluded that route-to-route extrapolati ons using oral toxicity data to assess toxicity via the inhalation route is only sc ientifically justified, if a vali dated PBPK model is available or if the pesticide falls into Category 3 chemicals according to the Agency ’s Inhalation Reference Concentration (RfC) classification scheme ( i.e. , those gases/vapors that cause systemic effects and not point-of-entry effects) (EPA, 1994) and the following criteria are also met.

a. The toxicological effect of concern is system ic for both entry routes and this effect is independent of route of exposure.

b. The first pass effects in the liver for oral exposure and in the respiratory tract for inhalation exposure are minimal to nonexistent or , if there is some first pass effect, the metabolism following exposure is th e same for both exposure routes.

c. The chemical will not be chemically modified by the gastrointestinal bacteria or enzymes or by the acidic environment in the stomach di fferentially than it would be in the more

13d. The absorption efficiency for oral and inhala tion exposure must either be identical or known, so that accu rate values may be inco rporated into any model. The absorption efficiency for oral and inhalation exposure mu st either be identical or known, so that accurate dose values may be incorporated into any model; the use of a default value of 1 for absorption is not justifiable in the abse nce of any data. Furthermore, the absorption cannot be differentially influenced by a toxi c response unless this response is the same

via both routes and/or is in fluenced by relatively the same extent via both routes.

Even when the above criteria may be met, th e Panel recommended an additional Uncertainty Factor (UF) of 10 for the final ex trapolation for inhalation toxicity from oral toxicity data. The

Panel stated that the use of uncer tainty factors is not a replaceme nt for more accurate models and

data from more inhalation t oxicity studies. The Panel strongl y recommended that the Agency obtain additional inhalation toxicity data if such data are not available a nd if the vapor form of the pesticide does not meet the criteria for r oute-to-route extrapolati on as described above.

The Panel suggested that some of the problems a ssociated with route-to-route extrapolation from oral toxicity studies to in halation toxicity studies mi ght be evaluated by simple in vitro studies, such as solubility and stability in simulated gut or lung fluid, or by consideration of the chemical structure of the pesticide and usi ng structural activity relationships.

The Panel made the following additional comments with regard to route- to-route extrapolation:

a. The Panel discussed several problems with the way in which EPA used Haber’s Law (or Rule), in making adjustments for the durati on of exposure when calculating HECs from repeated exposure inhalation toxicity studies.

b. The Panel noted two problems with the IEC e quation. First, species differences in surface area/body weight are not accounted for, so th at the equation needs to be modified. In cases for pesticides in Category 3 where rout e-to-route extrapolati on may be justified, it would be better to adjust the animal oral dos e to a human equivalent oral before further adjusting this value to an IEC for humans.

c. The Panel recommended that the Agency c onsider Benchmark Dose (BMD) analysis when concentration-response data are av ailable and are amenable to modeling.

B.2) For a significant number of conventional pestic ides, inhalation toxi city studies are not

available. Please comment on the scientific st rengths and weaknesses of available approaches that may be used in the inte rim to assess inhalation hazard in the absence of inhalation toxicity studies.

14Panel Response

Overall, the Panel strongly recommended that the Agency conduct additi onal inhalation toxicity studies to adequately assess inhalation h azard. One Panel member provided a summary of alternative approaches repor ted in the literature that have been used to assess inhalation hazard in

the absence of inhalati on toxicity studies, e.g. , “threshold of regulati on” or “threshold of no toxicological concern” (see Appendix C for a disc ussion of these procedures). The Panel stated that these approaches should not be used in the interim to assess inhalation hazard for pesticides

in the absence of inhalation toxicity studies for the follo wing reasons. These screening approaches do not involve a chemical-specific hazard identification or dose-response assessment and were originally developed to assess oral hazard in the absence of oral toxicity studies, and

not inhalation hazard. While in a few cases the methodology has been used to evaluate inhalation hazard, the exposure durations in these cases ( i.e. , chronic, lifetime exposure and one-hour intermittent exposure) do not correspond to the exposure durations the Agency is evaluating.

B.3) For inhalation toxicity studies the test material is typically aerosolized. After volatilization, however, the Agen cy anticipates exposures to vapors rather than the aerosolized particles. Please comment on the predictive capabilities of aerosol studies to identify potential toxi c effects and/or quantify the dose-response resulting from exposure to vapors. Is the Panel aware of any studies that quantitatively compare inhalation toxicity after exposure to vapo rs and aerosols? In the absence of such data, can the Panel recommend an approach to account for the poten tial differences between vapors and aerosols?

Panel Response

The presence of a semi-volatile organic compound in either the vapor or particle phase will have impacts on site of deposition, absorption, and pot entially dose and toxic ity. The biological impact of the relative portions in each phase , however, has been poorly studied. Therefore despite known mechanisms for pot ential differences in dose and ab sorption, the ability to predict toxicity of vapors from aerosol studies is fairly limited. The Panel knew of no studies that have investigated the health impact of exposure to a single semi-volatile ch emical under different phases. Some studies with mixtures have utili zed techniques to either remove the particulate phase by filtration or the vapor phase by using a denuder to study the role of those materials independently (McDonald et al ., 2007). In many cases, the rem oval of the particulate phase of

the mixture has not resulted in biological effects that differ fr om those obtained with the total mixture, especially for systemic effects. These mixture studies, however, may not be appropriate to answer questions related to a single component study. The Pa nel stated that there remain fundamental questions (beyond just the deposition site) re lated to the relative toxicity of vapors and particles. These must be considered as adding uncertainty when attempting to predict biological effects from exposure to the vapor versus particulate form of any given pesticide.

The Panel noted that any aerosol study of a semi-volatile compound w ill include that compound in both the vapor and particle pha se. As a result, previous inha lation toxicity studies for semivolatile pesticide registration co ntained the vapor phase of that compound in the aerosol.

15Unfortunately, the studies conducte d for pesticide registration di d not measure the vapor portion of the mixture. The exposure monitoring in the inhalation toxicol ogy studies was conducted with measurement of either weight gain or chemical content on filters that were used to trap the aerosol. That approach only measured the partic le portion of the aerosol and the vapor that may have adsorbed on to the filter during sample co llection. The vapor portion, which would have been present in these atmospheres, was not measured.

ADDITIONAL DISCUSSIONS ON INHALATION TOXICITY

The Panel discussed various topics as an outgrowth of the charge questions.

1. Appropriate averaging times and sampling devices for field measurements of volatilized pesticide. The Panel recommended that the Agency collect exposure data

with shorter collection times than 24-hours a nd to use these data in health effects

evaluations.

2. Protocols for new inhalati on toxicity studies (possi ble experimental designs). The

Panel recommended that inhalation toxi city studies should be conducted with durations of exposure of up to 90 days.

3. Uncertainty factors for q uality of the database and w hat constitutes a minimum

database. The Panel recommended that EPA estab lish criteria for short-term studies ( e.g., 1, 7, 14, 28 days) and that they use an additional UF of 10, if only a minimum

database is available for their assessment.

4. Moving toward a cumulative or total risk assessment. The Panel recommended that total exposure be assessed to more fully en compass all types of inhalation exposures for the risk assessment process. The hazard quotient (HQ) or similar approach should

be considered to assess risk from each of the types of exposure th at contribute to the total potential exposur e following application of pesticides.

TOPIC C: Risk Assessment Issues

The Agency discussed its methodology for comb ining the exposure estimation methodologies and inhalation toxicological appr oaches to estimate postapplica tion bystander inhalation risks resulting from field volatilization of conventi onal pesticides. In estimating postapplication bystander inhalation risks, there ar e a few principles that should be followed: (1) It is important to properly match the duration of the exposure with a proper toxicity study of comparable duration. (2) Both dissipation of air concentra tions around a treated field as well as when retreatment of the field may occur need to be co nsidered. (3) Clearly defi ne the uncertainties and limitations of this type of assessment. The Agency has identified the following issues for the Panel to consider with respect to estima ting postapplication bysta nder inhalation risks:

Please comment on the strengths and limitations of the Agency’s use of the empirical and modeled air concentrations in the provided ri sk assessment case study . Does the Panel agree that the postapplication bystande r inhalation risk es timate case study app ropriately matches

16the duration of the exposure with the proper to xicological study of the same duration? Please comment on the scientific stre ngths and weaknesses of conclusions and characterization regarding the estimated risks presented in the case study.

Panel Response

C.1) Please comment on the strengths and lim itations of the Agency’s use of the empirical and modeled air c oncentrations in the provid ed risk assessment case study.

The strengths and weaknesses of the use of the Woodrow empi rical model to assess risk

are much the same as the Panel discussed re lated to Topic A, Question 2a (see above). The main strength of this model is its basis in multiple studies over a wide range of vapor pressures; a secondary strength is that its results are in the ra nge of the results of the air concentrations estimated by mo re sophisticated computer-m odeled air concentrations. The limitations of the empirical model are that it is based on a limited range of crops, weather, and locations, and does not take into account the potential e ffect of an activity coefficient on vapor pressure, and it is appl icable only to the firs t day post-application.

The strengths of the computer-b ased modeled air concentratio ns are that they can account for dynamic changes in post-application conditi ons and residue histor y. Their weaknesses are the limited knowledge that users have of the internal components of these models, the concern that the components do not model evap oration from foliage as well as they model evaporation from soil, and that the particular analyses presented to the Panel inappropriately decoupled th e variance of the flux from the variance of dispersion.

C.2) Does the Panel agree that th e postapplication bystander inha lation risk estimate case study appropriately matches the durati on of the exposure with the proper toxicological study of the same duration?

The Panel agreed that the case study appropria tely matched the duration of the exposure with the proper toxicological end point, although there were some questions regarding the specificity of the target population within this particular cas e study. Again, a great deal of the Panel's discussion on Topic B apply here, particularly the limitations of the route-toroute extrapolation process and the need to apply toxicological data collected from one exposure to another. Thus, the success of th is case study to achieve its goal is tempered by the limited confidence that the Panel has in extrapolating toxicological data considering both route and duration of the exposure.

However, in a broader sense, the Panel agreed that the example did not adequately consider the ability to model differences in duration of the exposure. In earlier sections, the Agency presented three types of exposure scenarios fo r risk assessment, short- (up to 30 days), intermediate- (up to 90 days) and long-term (greater than 1 year). The example only presented modeled air concentrations for short- term exposures. The models did not estimate intermediate and long-term expos ures. Two of the five pesticid es listed in Table 3 (of the Agency’s background document) used for modeli ng short-term exposures have reported soil

17half-lives of greater than 150 days, illustrating that in termediate exposures from single applications may occur. The Panel recommende d the Agency consider adding longer term exposures of more than 30 days in the models to address intermediate and long-term chronic exposures and to match toxicological studies.

C.3) Please comment on the scientific stre ngths and weaknesses of conclusions and characterization regarding the estimated risks presented in the case study.

The Panel broadly agreed that the case st udy included all or most of the important elements to conduct a proper risk assessment. The strength of the i nhalation toxicity and exposure data bases for the chemical chosen le d the Panel to conclude that the inhalation hazard and exposures assessments, and Margin of Exposure (MOE) analysis were realistic based on field monitoring data . However, the Panel had a range of

recommendations for how this model and the risk assessment process could be improved,

and reservations if such an analysis were applied to many other chemicals. For instance, the toxic endpoints are unlikely to be as strong for other chemicals, the details and general applicability of some steps in the process were not well-defined, PRZM or PEARL were not optimized for their application to the evap oration of semi-volatile pesticides, and the impact of the propagation of uncertainty and sa fety factors within the process on the final

result is uncertain. An alternative risk a ssessment approach based on the Volatilization Hazard Ratio (VHR) was presented and the case study chemical was evaluated using this approach.

18DETAILED RESPONSES TO CHARGE QUESTIONS

TOPIC A: Exposure Assessment Issue

Traditionally, the Agency’s assessment of bysta nder inhalation exposure to volatile pesticides has relied extensively on the use of air m onitoring data. However, for the fumigants, an exposure assessment methodology was developed that combined the use of air models and air monitoring data. The Agency has taken the exposure assess ment methodologies deve loped for the fumigants and further adapted them by utilizing soil models to predict field volat ilization of conventional pesticides from plant and soil surfaces. Based on this premise, the Agency has identified several key factors for consideration by the Panel. They include the evaluation of the approaches and data sources used in the tiered exposure es timation methodology and use of soil models for predicting flux of conventional pesticides. Specif ically, the Agency identified the following

issues for the Panel to consider:

A.1) Tier I Approach for Identifying Volatile Chemicals of Concern for Risk Assessment,

Air Concentration. The Tier I a pproach incorporates the use of vapor pressure alone to

arrive at a saturated concentration in ai r. The estimated air concentration can be compared with available toxicity data to evaluate inhalation exposure concerns to

human and other terres trial organisms.

Please comment on the Agency’s approach fo r using the Tier I air concentration estimation method as a screening procedure. Please discuss the strengths and limitations of the screening approach. Please identify any alternative methods and/or physical-chemical properties, if any, which may be utilized as a screening procedure to identify chemicals with potenti al inhalation exposure concerns.

A.2) Tier II Approach for Identifying Volatile Chemicals of Concern for Risk Assessment, Volatility and Flux Models. Two options are being considered to refine the Tier I estimation method. Option A incorporates the use of physical-chemical properties including application rate, va por pressure, solubility, and K

oc in an empirically-derived function to estimate flux rates. This option ha s less [sic] constraint s and requires fewer input parameters to generate flux rates as compared to Option B described below.

a. Given the state of the science, please comment on the applicability of using the Option A model to predict flux rates. Please discuss the strengths and limitations of this approach and how these impact the resu lts. Please identify any alternative methods, if any, which may be utilized to id entify chemicals with potential inhalation

exposure concerns.

Tier II, Option B is a refined process which ut ilizes fate and transport models to predict flux rates of applied pesticides which off- gas from treated fields. Optimum fate and transport models consider mechanisms rela ted to volatilization, biodegradation, abiotic degradation, physicochemical properties, runo ff, crop uptake, and leaching to account for

the transformation and movement of the entire initially applied material. Volatilization

19mechanisms from bare soil and crop canopy surf aces are also important processes, which the Agency believes ought to be considered to fully account for volatilization and diffusion from the vadose zone and canopy into the atmosphere. The Agency has utilized two models, the Pesticide Root Zone M odel (PRZM) and the Pesticide Emission Assessment at Regional and Local Scales (P EARL) which incorporate these mechanisms and have the utility for the prediction of fl ux rates from treated fields. Option B requires extensive knowledge on environmental fate prop erties, as well as information related to application site, crop mana gement and meteorology.

b. Please comment on the applicability of using fate and transport models to predict flux rates given the state of the science. Plea se discuss the strengths and limitations of both models and how these impac t the results. Please iden tify any fate and transport model(s) which the Agency has not consid ered in this analysis which would be applicable for pesticide applicati ons and crop management scenarios.

Panel Response to Questions 1 and 2a

During the progression of the panel discussion, Qu estions 1 and 2a became linked. Therefore, the Panel combined its response to Ques tion 1 with that of Question 2a.

Vapor pressure clearly is an important physical pr operty in evaluating the be havior of pesticides, especially with respect to volatilization from pl ant surfaces. However, the Panel concluded that the use of the Tier I approach for identifying volatile chemicals of concern was overly simplistic and of no real value as a screening tool. The Pane l identified four major issues with the Tier I screening approach (discussed below). The Panel proposed the Woodrow et al. (1997) correlation approach as an alternative to the pr oposed Tier I approach (r eferred to as Tier II Option A within the EPA background document, p. 24) to estimate a maximum 24-hour flux

value that then could be us ed to calculate a maximum ai r concentration over 24-hours.

Major Issues with the Tier I Approach

This report has organized the Panel's responses into the following four groups of topics: overestimation of air concentrations, temperat ure considerations, low vapor pressure (VP) pesticides, and pesticide fo rmulation considerations.

1. Overestimation of ai r concentrations. The saturated vapor concentration calculated from vapor pressure appears to generally over-pre dict the maximum concentration measured in field settings by such a wide margin as to ma ke the predictions useless. In the examples within the EPA background document paper, i.e., Table 4, p. 36, the proposed Tier I approach overestimates the observed concentr ations by about two orders of magnitude when the pesticide is on foliage and five orders of magnitude when on soil.

Vapor pressure is the sole variable both in the Tier I model and in the Tier IIA model for volatilization following foliar applications. 1 Vapor pressure also plays a central role in

1 In contrast, both of the Tier IIB methods (PRZM and PEARL) use Henry's law constant instead of vapor pressure (that constant is related to VP, as will be discussed circa Eqn. A4 below). .

20the Tier IIA model for volat ilization from soil as eviden ced from the 0.88 R2 correlation between log flux and just the log VP across the 10 6 range of VP values used to derive that model (Woodrow et al. , 1997). However, as a simple pr edictor, the Tier I model overestimated the maximum measured field concentrations of the pesticides presented in Table 4 of the Agency background document (p. 36). The over-estimates for the four chemicals applied to foliage ranged from f actors of 12- to 174-fold (with a geometric mean of 48-fold); however, the over-estimat e for the one soil app lication (Chemical D) was 27,472 times greater than its maximum mo nitored concentrations. Thus, at first glance. Thus, at first glance, the Panel c oncluded that a simple comparison of vapor pressure to toxicity was too conservative to be useful.

In contrast to the above generalization, se veral recent studies conducted in the United Kingdom showed errors in the opposite directi on. One Panelist reported on studies (sponsored by the UK Department for Environm ent Food and Rural Affairs, Defra) in

which the air concentrations of two of the fi ve pesticides studied were higher following application than the concentration predicted by the Tier I model. In particular, the air concentrations of epoxiconazole and prothi oconazole were higher than predicted by the Tier I model. A summary of these findings is presented below while a more detailed report on these data is provided in Appendix A. In the first of these studies (Bulter-Ellis and Miller, 2008; Figure A-2 of Appendix A) in which epoxiconazole was applied to 4.8 hectares of a cereal crop, ai r concentration at a height of 0.7 meters and 2 meters downwind from the field’s edge of a 4.8 hectar e 2-3 hours after application was one and a half times the predicted saturated vapor con centration. And in a sim ilar study at another location (Appendix A, Figure A-3), measured air concentrations from 0-90 minutes after application were nearly two times the pred icted saturated vapor concentrations. In a second U.K. trial (Defra Project PS2023) conducted in October 2009 at a laboratory independent of the first facility (Table A- 4 of Appendix A), the ai r concentrations of prothioconazole peaked about 8 hours after application at ove r twice its sa turated vapor

concentration. These observations cast doubt on the reliability of the proposed Tier I approach in the other direction.

2. Temperature considerations. The Tier I model as proposed would use a vapor pressure value based on only a single te mperature (typically 20-25 o C) and therefore does not provide a realistic prediction of the c oncentration on hotter than normal days. Temperatures observed in agricultural regions differ significantly from region to region, season to season, or even from day to night, and this variation is enough to change the vapor pressure by a factor of two in either the positive or negative direction.

Vapor pressure should be described as a f unction of temperature. Surface temperature

should be used in the models to estimate volatilization from the earth's surface. One panelist commented that possibly some of th e pesticide behavior observed in the UK data presented in Appendix A could be due to th e difference between su rface temperature and air temperature. A pesticide wi ll volatilize in proportion to its vapor pressure at the surface (soil or leaf) temperature that can exceed the air temperature on a sunny day by about 10°C. The temperature of the pesticid e residues in the UK studies may not have been accounted for in their reported data. As a rule of thumb, an increase in a liquid's temperature of 12°C (21°F) will increase its vapor pressure by a factor of 2.

213. Low vapor pressure pesticides. Vapor pressure data for pesticides with very low vapor pressures can be very difficult to obtain a nd may be subject to s ubstantial errors and uncertainty. These low vapor pressure substances are the most likely to be ‘screened out’ by a simple pure vapor pressure approach.

It is difficult to accurately measure vapor pres sures as low as those for the pesticides in the UK studies ( circa 10 -7 to 10 -8 mmHg). For example, for all six of the fourteen pesticides considered by Woodrow et al . (2001) that had reported vapor pressure values

of less than 10 -2 mmHg (and these only ranged from 10 -3 to 10 -4 mmHg), the reported VP values underestimated the vapor pressure values predicted by their physical characteristics by an average of 1.6-fold.. T hus, the values for the vapor pressure of epoxiconazole and prothioconazole reported in the UK studies may have been in error.

4. Pesticide formulati on considerations. A pesticide's vapor pressure as a pure chemical [active ingredient(s)] has only a limited connect ion to its vapor pressure as it is actually used in the real world. For example, pestic ides are typically applied as a formulation, perhaps diluted in water with other additives like surfactants, and these factors may elevate the vapor pressure of the active ingr edient relative to the pure compound value. Therefore, the proposed Tier I approach may generate concentration predictions that are higher or lower than either the vapor pressure that exists in field conditions or that is actually found in field measurements, and thus will not necessarily provide predictions that are protective. In the case of the UK data, the chemical's vapor pressure in an aqueous mixture on foliage may have been in creased beyond that reported for a pure or neat substance. The potential magnitude of that effect is described below. Whether or not an activity coefficient or one of the other explanations described above applies to the recent findings in the UK is unknown at this time; however, unless an explanation can be found, those findings cast doubt on the whole premise of these models.

Predicting a Pesticide’s Va por Pressure in a Mixture

The best model for predicting a chem ical's vapor pressure in a mixt ure is the use of an empirical adjustment to Raoult's law for an ideal mixture (Eqn. A1). 2 Such an adjustment is called an "activity coefficient" and is typically given the symbol γ (a lowercase Greek gamma), and X i is

the molar concentration of the chemical w ithin the mixture. Equation A2 both defines γ i mathematically and expresses th e empirical concept behind it.

P vapor, i = γ i × X i × P vapor = γ i × Raoult’s predicted P vapor, i Eqn. A1

Measured or actual P vapor, i γ = ───────────────── Eqn. A2

i Raoult’s P vapor, i = X i × P vapor

A chemical's activity coefficient is not actually a constant but is broadly a function of both the

solvent in which it is mixed and its concentra tion in that mixture. Figure B-1 in Appendix B

2 More detailed information on the behavior of vapor pressure in mixtures is provided in Appendix B.

22depicts the pattern of γ as a function of X for a few common organic solvents in water. As a component in a mixture gets more and more dilute, its γ value eventually reaches a constant γ  (the  sign denotes infinite or a ve ry high dilution). The magnitude of some activity coefficients in water is certainly sufficient to cause major de viations from an ideal mixture. For instance, a γ  of 50 is sufficient to increase a chemical's vapor pr essure by a factor of more than 2-fold when it is present in water at molar fraction of 10% to 30%, and a γ  of 400 or more can increase a chemical's vapor pressure by more than an orde r of magnitude. Unfortunately in the case of semi-volatile pesticides applied to crops, the pesticide concentra tion in water is an independent and dynamic variable (a pestic ide applied in water may star t out dilute, rapidly become concentrated as the water in the droplet evaporates, and potentially become dilute again due to dew, etc. ). In addition to water, the pe sticide is likely to be absorb ed into the organic components of a leaf's cuticle, in which a different value of γ would apply.

The Panel acknowledged that the value for a given chemical's γ  in water can be predicted from its Henry's constant via Eqn. A3 (although to be us ed quantitatively in that equation, the value of

H i must be in the same units as P vapor ).

H i γ i P vapor ∞ = ───── Eqn. A3

The Panel comm ented that most of the Tier II, Option B models appear to use a Henry's law constant, although it is not clear just how it is us ed. Henry's Law allows one to predict the vapor pressure of a component in a water solution base d on a fixed empirical co efficient often called a “Henry’s Law constant” and denoted by the symbol "H i " herein. A common expression of Henry's Law might look like Eqn. A4 . While such constants are wi despread and often available for pesticides, a known limitation of Henry's law is that it only applies to very dilute mixtures. Use of Henry’s law for more concentrated mixtures will introduce another se t of errors that are only touched on in Appendix B.

P vapor,i = H i  X i Eqn. A4

In principal, an activity coeffici ent could be applied to Raoult’s la w to correct any vapor pressure for its non-idea behavior in a mixture. Computer codes have been developed to predict activity

coefficients for various mixtures and can be applied to semi- volatile pesticides (Muro-Suñé et
al. , 2005). However, such an approach may have lim ited utility as part of a screening tool for these pesticides because the magnitude of th e correction depends on the composition of the mixture. The Panel thought that it might be feasible to find the highest product of γ i × X i , but for a more complete dynamic prediction necessary for a Tier IIB model, every formulation would need to be evaluated separately due to the varying components in applied mixtures. Additionally, any effects due to changing mixture composition and temperature post-application also would need evaluation. This would require a very comple x set of computations that go beyond the goals of a Tier 1 approach.

23The Woodrow et al. (1997) Approach as an Alternative to the Tier I Approach

The Panel proposed the Woodrow et al. (1997) approach as an altern ative to the proposed Tier I approach (referred to as Tier II Option A in the EPA background document, p. 24) to estimate a

maximum 24-hour flux value that then could be used to calculate a maximum air concentration. The Woodrow approach is based on empirical da ta in addition to selected physicochemical properties of the compound when applied to soil. It allows for an evaluatio n of pesticides applied directly to water or plants or incorporated into soil, and yet it does not require an inordinate amount of data. This simplified model could be updated with all the late st studies and other relevant factors to increase its accuracy. A sensitivity analysis could be conducted to refine this model. Additionally, confidence intervals around th e model predictions could be calculated and safety factors could be added to the results of th e model predictions to make it more protective as

needed for a Tier I.

As described in the EPA background document, Woodrow et al. (1997) established a correlation (Eqn. A5). between ln Flux (μg/m 2 hr) and ln [(VP x CF x AR)/(K

oc x S w]

) for soil applied pesticides, where VP is vapor pressure (Pa), CF is a conversion factor 133.32 Pa/torr, AR is the application rate (in units of kg/ha), K

oc is the soil organic carbon-water partition coefficient (mL/g), and S

w is the pesticide’s aque ous solubility (mg/L.

X = 19.35 + 1.0533 ln      

 

WOC SK ARCFVP Eqn. A5

Similar correlations just involvi ng vapor pressure were also deve loped for pesticides applied to plants or inert surfaces, and pesticides applied to water. While this approach also relies on the vapor pressure of the compound, th e correlation is grounded in measur ed flux data that could be

used to calculate a more realis tic air concentration value.

The Panel recommended that EPA update the correlation equations published by Woodrow et al. (1997) with data published since this work was carried out to strength en the reliability of predictions, e.g. , Leistra et al. (2006). In addition, the Panel re commended that additional studies be commissioned by EPA to expand the number of data points in the regression for flux from foliage and flux from soil. Consideration should also be given in future st udies to plot designs that can improve the quality of the measured flux data, e.g. , see Majewski et al. (1990, 1991) for a description of comparing multiple methods for measuring pesticide soil volatilization rates.

Some Panelists were concerned that the Woodrow approach w ould not adequately assess the potential impact of some spray from foliar app lications bypassing the foliage and falling to the soil surface. Several panelists commented that fo liage residues would, in most cases, contribute the largest fraction of the total flux to the air, at least for th e first 24-hours af ter application. However, over longer spans of time, flux from th e soil could contribute mo re significantly to the overall flux. However, this issue is a minor conc ern for the continued use of this model because only the maximum flux rate is used in the co rrelation and the mode l only to estimate the maximum flux rate on the first day and not to provide an estimate of subsequent temporal trends

24or concentrations beyond the firs t day. As discussed in Woodrow et al. (1997), the Panel suggested that leaf surface area be added as a modifying factor to make the model more robust when applied to different crops. Soil moisture should also be added to refine the soil portion. Pesticides volatilize more rapidly from moist soil than from dry soil, other factors being constant. Other additions aimed toward refining the flux model for soil applications would include the depth of incorporation of the chemical after a pplication, soil temperature, and wind speed at the

soil surface.

The Panel noted that there is a lot of uncerta inty in the physicochemical data and measuring volatilization fluxes (Majewski, 1996). A major factor in the Woodrow model is the Koc term, which is not currently required at pesticide registration. However, a highly correlated measure,

Kow

, i.e., octanol-water partition coefficient (EPA, 1996) is required and if the Koc is estimated

from the Kow , another source of uncertain ty will be added to modele d air concentrations. The

Panel recommended that EPA should define whether the Koc will be estimated from the Kow

, and by what method, or if literature values will be used.

The Panel suggested that it was possible to assign confidence intervals to the regression to incorporate the uncertainty associ ated with the measurements used to generate the flux values. These intervals would provide a high and low ra nge of likely values and therefore address the Agency’s concerns that the model would unde r-predict the 24-hour exposure for some compounds. For example, Johnson et al. (1995) describe a method for evaluating the quality of physicochemical property data ava ilable in the literature or in registrant submitted data. In addition, the Panel recommended that a sensitivity analysis of the terms within the model would be useful, perhaps breaking the regression equati on into pieces rather than having everything lumped together. A further suggestion from the Panel for using the Woodrow et al. (1997) approach would be to set a recep tor location next to the field ( i.e. , 2-5 meters) and an air

concentration of concern estimated from the t oxicity data. From this information a maximum

flux value could be calculated th at would be needed to generate this concentration of concern. Flux values independently genera ted from the Woodrow model, in cluding confidence intervals, could then be compared with the maximum flux va lue determined from the air concentration of concern for screening purposes (see also the Va por Hazard Ratio (VHR) di scussion in Topic C). One caveat with respect to the Woodrow model is that some chemicals are applied in such a way

that the first 24-hours after a pplication may not in clude the period of maximum flux. For example, soil incorporated pesticides need time to diffuse to the soil surface or pesticides broadcast as granules into rice fields must di ssolve before maximum flux from the water surface can occur. In these exceptiona l cases, the second or subseque nt 24-hour period may contain the period(s) of maximum flux rather than the first 24-hour period.

Panel Response to Question 2b

Major Recommendations/Findings

The Panel agreed that the concept of coupling a fate and transport model such as PRZM or PEARL to predict fluxes, with a model such as the Pesticide Exposur e Risk for Fumigants (PERFUM) to estimate air concentrations at diffe rent distances from the field is sound as a Tier II, Option B modeling approach for identifying volat ile chemicals of concern within the confines of evaluating pesticide volatilization in treated fi elds (excluding spray drift).. However, the Panel

25also suggested that new insights might be gain ed by conducting the propose d Tier II process in reverse order: use a dispersion model ( e.g. , Screen3, ICS3, or PERFUM) to establish a maximum acceptable flux that will not produce a concentrati on of concern at a given receptor location and

then use either a Tier I or Tier II screeni ng procedure to establish a maximum acceptable flux estimate for the specific compound. The Panel suggest ed that the use of the Pesticide Emissions Model (PEM) (Scholtz et al. , 2002 a, b) be considered as an a lternative to the use of PRZM, as

this model was created to describe processes control ling volatilization.

The Panel recommended that novel technologies (sen sors or rapid samplers) be explored to achieve higher temporal reso lution in the concen tration datasets for flux measurements. Innovative methods to characterize gas and particle-phase concentrations during flux measurements and downwind air sampling should be encouraged. For example, the Agency might consider applying other methods for fl ux estimates, such as EPA method OTM-10 that applies remote sensing techniques. The Panel not ed that whatever method is considered, each

will have different uncertainties and se nsitivities to measured parameters.

The Panel offered several recommendations and suggestions regarding validation and further development of flux models.

1. The Panel stated that rather than basi ng volatile flux prediction models on backcalculation methods, such models should be validated with di rect field measurements of flux under different cropping patt erns, application techniques , rates, and frequency, andin different geographic regions. Only once the flux model is validated is the use of multiyear/multi-climate zone meteorological datasets appropriate.

The Panel concluded that the flux mode l evaluation presented in the background document is not sufficient to validate the proposed flux models, i.e. , PRZM or PEARL. Both models appear to over-predict the "obser ved" field fluxes. The Panel stated that the "observed" fluxes were not accurate enough or represented an insufficiently extensive database to show that the models performed well.

For example, in some cases the models, PRZM and PEARL, used within the Agency’s examples (Table 5 of the Agency backgr ound document, p. 47) predicted roughly twice

the measured air concentrati on, while in others it was as great as seven-times the measured concentration. One possible explan ation for such deviations is that the measured data used for comparison were not based on actual s ource volatilization flux data, but on a back-calculation of source vol atilization fluxes th at required an air dispersion model to estimate concentrations away from the application site. The model predictions presented for PRZM and PEARL also included estimations of temperature fluctuations as well as other meteorological conditions explained by the EPA experts.

The model should first be validated with fiel d data that reflects actual experimental conditions before it is evaluated for efficacy and ruggedness (with averaged data). Only then can the predictive capabil ity of the model be accurately assessed. The data used in the Agency’s examples were projecting a protective model, but they were too imprecise for an evaluation of the model’s validity. The Panel emphasized that the use of actual

field flux data to evaluate Ti er II models would be paramount in assessing the accuracy

26and reliability of the model’s predictive capab ilities. To that end, the Panel recommended

that additional field studies be conducted because those presented in the Agency’s background document did not demonstrate that the model could accurately predict vapor phase concentrations.

The Panel made a number of further recomme ndations related to this general issue of validating evaporation and dispersion models.

a. The evaluation of the flux models should be handled separately from the risk assessment. Flux models using probability meteorology would involve a wide range of atmospheric concentrations; thus, addi ng unrealistic variability to the surface flux predictions. Therefore, it is important firs t to understand the model, assure that it predicts known conditions accurately, and then add a realistic level of variability as input to the model to ensure a conservative risk assessment.

b. The model results should be compared to a number of actual field flux measurements, preferably obtained with multiple field flux measurement methods, in order to be proven effective. For example, dire ct flux measurements, including eddy accumulation as well as indirect methods, su ch as flux-gradient relationships, should be used in addition to the Gaussian plum e inversions used by the Agency. The Panel pointed out that many such studies exist, and some are provided in the list of suggested references.

c. The Panel questioned the use of the Ga ussian plume inversion approach for estimating fluxes within an orchard. Trees in an orchard can create coherent eddies organized within the geometry of the orch ard, and create a stab le sub-canopy layer of

air. These effects are not ta ken into account in the plum e models employed to infer fluxes. This is also a problem for atmosphe ric concentrations estimated from orchard emissions using PERFUM, because the Pane l understood that PERFUM is based on the Gaussian plume model and is not adap ted to simulate dispersion and transport within plant canopies.

d. The Panel expressed concern that the uncertainty associ ated with physicochemical properties, such as the Kow , soil half-life, and photodegr adation, are not incorporated into the current modeling efforts. The Panel recommended that the Agency perform sensitivity analyses to determine the factor s (input data and model parameters) that are most important in predicting the flux, and then the impact of uncertainty in these inputs should be used to evaluate the ra nge of fluxes predicted by the model. Flux estimates, even for a given day of meteor ological conditions, can be computed as a probability distribution.

e. Flux model evaluations should include situ ations involving both soil and vegetation sources from soil and vegetati on application of pesticides.

f. The Panel recommended that more frequent time point measurement of flux rates

should be taken immediat ely after application, i.e., hourly flux rates. Hourly flux

27g. In addition, the Panel indicat ed that downwind concentra tion data should also be collected during the field valid ations of the flux model in order to validate a coupled flux/dispersion m odel approach for semi-vol atile pesticides. While dispersion models have been field-validated for fumigants, th ey have not been field-validated for semivolatile pesticides or in ot her soil types or cr op scenarios. Becau se of their high volatility, fumigant vapors tend to remain airborne for longer periods than semivolatile pesticides, which tend to be rem oved from the air mass by condensation or adsorption on downwind air particles, soil, or foliage surfaces. By absorption onto particles and potentially re-volatizing, semi-volatile pesticides with higher Kows ( i.e.

, log Kow greater than four) have a different fa te than fumigants, may have a longer persistence in the environment, and ma y be transported into homes where the pesticides may reside in house dust (Harnly et al, 2009).

2. Volatilization flux prediction models should include pesticide degradation products. For example, photodecomposition is important to include because it is generally a “loss” term. However, transformation reactions may al so be a source of more toxic chemicals,

i.e. , oxones (oxygen analogs of many orga nophosphorus pesticides) (Harnly et al. , 2005).

The Panel stated firmly that models used to predict the atmospheri c concentration of a pesticide should include enough terms to ma ke the predictions act as reasonable surrogates of the measured concentrations. Th is includes terms that both add to the vapor phase concentration and those that decrease vapor phase concentration ( i.e., reduce the parent compound). If the model does not incl ude subtraction terms it will not accurately follow the progression of the flux and is lik ely to significantly ove r predict the vapor phase concentration. This more sophisticated Tier II model should be used to evaluate pesticides once they have been screened at Tier I, but any advanced model should include photodegradation as part of the modeling process. Many of the newer pesticides are photo-labile. For example, pyrethorioids ofte n decay rapidly even once collected from field samples. Much of this degradati on can be attributed to photodegradation, reinforcing the need for inclusion of photodegr adation terms in any model used as a Tier II assessment tool. A photodegradation term will represent the extent to which the vapor phase concentration of the pesticide in the atm osphere is reduced by photoloysis (although not to zero). Existence of this term in the model wi ll help to differentiate the more photo-labile compounds from others that may have a similar vapor pressure, but do not undergo photodegradation.

28Photodegradation rates of pestic ide vapors have been studied for several pesticides, (see review by Atkinson et al. , 1999). In some cases, photodegr adation rates of pesticide vapors are quite significant, such as for those organophosphorus thions ( i.e. , sulphur analogs) that are converted to oxons. However, the oxons are more t oxic than the thions, and thus could contribute significantly to the hazard associated with the vapor during downwind drift. Therefore the chemical pr oduct of the photodegra dation reaction must

also be considered.

3. Volatilization dispersion pred iction models should include scenarios with temperature inversions. Temperature invers ions are common in some parts of the US whereby an air mass may be trapped and normal dissipati on and dilution due to air mixing and ventilation are impeded. Inversions could crea te the potential for e xposures to airborne pesticide concentrations that are higher and for a longer duration of exposure than expected.

The Panel stressed that monitoring and modeli ng should be flexible enough to take into account unusual topographical, me teorological, and other environmental features that might affect exposures resulting from pest icide residues in the air. For example, temperature inversions are common in some parts of the United States. Temperature inversions in agricultural va lleys or other topography can tr ap the air mass, and impede normal vapor dissipation due to dilution dur ing air mixing and ventilation. This could create the potential for exposures to airborne pesticides that are higher than expected, and for longer than normal duration. In the San Joaq uin Valley of California, an area of very high pesticide use including in the winter, wi ntertime inversions can lead to ground fog that: (a) traps a cool air mass, (b) modifies dissipation by photolysis and/or wind dilution, and (c) creates a partition pha se (pesticide suspended in f og water droplets) that can concentrate some pesticides, aiding their deposition to non-target crop foliage, and potentially changing the airborne compositi on from one dominated by the vapor form to one that is aerosol-dominated. How processes su ch as this affect exposures of people to airborne pesticides need to be taken into account in designing monitoring and modeling

programs.

4. The Panel recommended expanded pesticide us e reporting and a national air monitoring network for pesticides to more accu rately assess community exposure.

The Panel recommended that more expansive pesticide usage data, as well as more regional air pesticide monitoring data, be gene rated to more accurately predict exposure by the public. For example, Panel members noted that the pesticide use reporting system in California has been extremely useful for research scientists and policy makers to

increase the understandi ng of the environmental fate of pe sticides. This type of system could also be used in other regions, especia lly where volatile or se mi-volatile pesticide use is high. A standardized air monitoring ne twork in agricultural regions of the US would also be extremely useful. These data co uld be used to further validate pesticide emission and atmospheric transport models and to estimate pesticide exposures to

communities.

295. The Panel suggested a more car eful evaluation of the literat ure on ambient air monitoring in agricultural communities , e.g ., Lee et al. (2002), Kollman (2002), as well as field

volatilization flux measur ements, and modeling, e.g., Raupach et al . (1996).

The Panel recommended that there be a br oader review of th e volatilization flux

measurement literature. Studies exist that can be used to evaluate these models. A rich literature exists concerning flux measur ement methodologies, uncertainties, and measurement requirements. For example, scientists such as Ralph Nash, Dwight Glotfelty, Alan Taylor, and William Spencer ha ve carried out studies that discuss in

detail the factors that influence the volatili zation flux from soil and other surfaces (see “Recommended References”). Other studies repo rt the volatilization flux of pesticides

from flooded rice fields as contributors to meas ured airborne residues of pesticides in the Sacramento Valley, CA (Seiber et al. , 1989). Also recommended are papers by Scott Yates (USDA-ARS, Riverside) that include th e use of field flux chambers for measuring volatilization in the field. Many of the fiel d volatilization flux pa pers published before 1995 have been compiled in Majewski and Ca pel (1995). Rich literature also exists concerning transport within forest canopies. A selection of literature that the Agency might find useful is provided in the “Recomme nded References” list in the “Reference” section of this report.

In the introduction of the background document, EPA presented citations to available air monitoring data collected both in population centers of agricultural communities or “ambient” levels and adjacent to fields or “application site” monitoring. Additional summary articles are available (Lee et al. , 2002). The California Department of Pesticide Regulations (CDPR) has also summarized near-field and am bient data available as of 2000 (Kollman, 2002; Tables 1-2). The Pane l recommended that EPA present summary

tables of all of these measured air concentr ations for two reasons. First, these levels would be useful in describing the breadth and magnitude of potential public health concern for pesticide volatilization from agricu ltural fields. Secondly, these levels may be used, at every step in the process, to evaluate air modeling efforts.

6. The Panel recommended that the Agency gain a better understanding of the physics of the models they are proposing for this application. It was not clear whether the Agency was using a variable laminar boundary layer depth th at is a function of ev apotranspiration, as originally proposed by Jury et al., (1983), or a constant la minar boundary layer depth. The Jury et al. (1983) proposal is not physically sound for the purpose used by the Agency. A constant laminar boundary depth layer is more physically realistic ( e.g. , Panofsky and Dutton, 1984). PRZM appears to use a constant laminar boundary layer depth. The Panel was not yet able to ascertain the treatment of this issue in PEARL. The Agency’s background document did not clarify this issue.

The models proposed (PRZM and PEARL) appear to assume a zero atmospheric mixing ratio and to neglect aerodynamic resistance when estimating fluxes, which should tend towards an over-prediction of the flux. This should be kept in mind when evaluating the

models. PRZM documentation described assumptions concerning the aerodynam ic resistance, but the use of this resistance in the model was not clear. PEARL

documentation was difficult to obtain. Thus it was difficult to evaluate the assumptions

30utilized in the flux modeling. The Panel st ressed that the Agency should be more attentive to the physics of the models being tested.

7. The Panel suggested that PEM (Scholtz et al. , 2002a, b) would be a reasonable alternative to the use of PRZM or PEARL, as this model was designed to model the behavior of the pesticide into the ground rather than release to vapor, the release to vapor is almost the discard process, the “1 - N term .” While a precise model should be able to utilize all calculated compartments, these models (PRZM and PEARL) focus on the soil compartments and therefore potentially over-emph asizes the fraction retained in the soil. Those assumptions will then cause an underest imation of the fractions released as vapor.

The need for accurate volatilization flux data will be paramount in evaluating the validity of the proposed model.

8. The Panel also suggested that EPA hold scie ntific workshops or conferences to bring together the scientific comm unity to aid in the assessment of proposed models and improvements (if necessary), and eventually, the validation of these flux models. Panel members volunteered to help in this endeavor.

9. And finally, the Panel suggested that the Agen cy consider developing a model of multiple application events in the same region/air shed. In that regard, the Panel made the following comments and recommendations described below.

a. The Panel agreed that the Tier II models us ed by the Agency to estimate volatilization

flux and air concentrations should focus fi rst on single applications. However, some panelists indicated that the impact of applications on multiple surrounding fields and/or regional use should eventually be considered. In addition, the presentation from Jacobs Farm during the public comment section also raises concerns regarding re-volatilization of deposite d pesticides (repeated cy cles of volatilization, transportation, and re-deposition of applied pesticides), es pecially during fog events. Such re-volatilization proce sses are not considered with in in the current modeling

efforts.

b. In addition, the Panel noted that to derive the greatest benefit from the downwind transport and dispersion models, they should be applicable to modeling air concentrations from multiple applications of the same pes ticide in a locale, air shed, or air basin. An example of what could be done for fumigants and semi-volatile pesticides is available in a published st udy on methyl bromide volatilization from

several treated fields in the Salinas Valley, CA (Honaganahalli and Seiber, 2000).

c. The Panel suggested that the impact of cr op management practices such as irrigation, tilling, mulching, and burning of fields may have a potential to increase volatilization. These practices and their effects on pesticid e volatilization should also be considered.

31Terminology Corrections in Ag ency’s Background Document

The following terminology corrections in the Agency’s background document were provided by

one Panelist.

1. The following text from page 19 descri bing flux measurement methodology should be edited for accuracy.

“Flux studies also are typically designed to allow for the generalization of results using a computer simulation air model. More accompanying information is generally collected in these studies including meteorol ogy at differing heights typically w ith thermo anemometers to provide high resolution information about environm ental conditions which are important in understanding the movement of pesticides from the treated area and reducing the uncertainty

associated with the flux calculations.”

a. Flux measurements are typically designed to measure the flux, not the concentration, of a trace gas. One can infer fluxes from an array of concentration measurements, such as has been described here, but this is not what most micrometeorologists would call a flux study. See Dabberdt et al. (1993) for a concise description of flux

measurement methods.

b. In comparison to sonic anemometers, thermo anemometers are not the most sophisticated tool to use to measure turbulent wind associated with turbulent transport. Thermo anemometers are not used to reduce uncertainty in the calculations. The Agency’s wording reflect s a weak understanding of the research underlying these transport mechanisms,

2. The following text from page 20 needs to be edited.

“There are a number of recogni zed common flux methods in the pe er-reviewed literature. Some

of the common methods are the Indirect or Back-calculation Method, the Aerodynamic Method (Majewski et al. , 1993), and the Integrated Horizontal Flux Method (Wilson and Shum, 1992).”

There are additional flux methods that should be considered by the Agency, for example, eddy covariance. The Agency should review Dabberdt ( 1993) and modify this section as they see fit after reviewing the available literature.

TOPIC B: Toxicological Assessment Issues

As the understanding of the state-of the science in inhalation toxicology has evolved, so has the Agency’s approach to conducting inhalation haza rd and risk assessments. This evolution has seen the Agency move from converting oral doses to inhalation concentrations to using the RfC methodology and/or physiologically-based pharmac okinetic (PBPK) models. As OPP continues to work on refining the risk assessment paradigm , the Agency is seeking the SAP’s input on a number of key factors. They incl ude the use of oral toxicity st udies when inhalation studies are

not available and the use of aerosol inhalation toxicity studies to represent toxicity to vapors of

32the same chemical. Specifically, the Agency id entified the following issues for the Panel to

consider:

B.1) The analysis conducted by the Agency indicate s that, in general, oral toxicity studies may not accurately represent the full spectru m of toxic effects that may occur as a result of inhalation exposure. The analysis also indicates that - unless the same endpoints are identified thr ough both routes of exposure - oral toxicity studies frequently underestimate toxicity by the i nhalation route. The Agency has not been able to discern any patterns in this under/over estimation. Please comment on any potential patterns that the Agency has not identified.

Panel Response

Route-to-Route Extrapolation Issues

The Panel noted that EPA placed significant em phasis in the background document on the ability to use toxicological data gene rated from oral exposures to es tablish Inhalation Equivalent Concentrations (IECs) even though they recogni zed that such a strategy is seldom valid. Nonetheless, the Panel evaluated this approach and recommended that if such extrapolation is considered that the guidelines as outlined in Principles of Route-to-Route Extrapolation for Risk

Assessment should be followed (Gerrity and Henry, 1990) and only for those pesticides that meet

the criteria noted below. One Panelist commen ted that this book was an outgrowth of a workshop sponsored by the EPA in March 1990. Ma ny concepts and issues raised in this book were also later addressed by Re nnen et al. (2004), with essentia lly the same conclusions being reached. Basically, the only chemicals that are candidates for route-to-r oute extrapolation are those having no portal of entry effects and thos e whose kinetic behavior is independent of exposure route. The summary repor t section of Gerrity and Henry (1990) provides a decision tree for assessing how the information available for a gi ven chemical can be used to identify the path

forward and ascertain if sufficient data are availa ble to attempt any route- to-route extrapolation.

Review of the available literature evaluating rout e-to-route extrapolation indicates that, for the most part, there is no pattern of consistency in the results when or al toxicity data are used to model inhalation toxicity for most chemicals. Extrapolation results appear to be somewhat random, with oral to inhalation extrapolati on resulting in either over-estimation or underestimation of inhalation toxicity depending upon the specific model approach used. The only chemical class in which there appears to be some consistency is systemic toxicants that have long half-lives in the body.

The Panel concluded that route-to-route extrapolati ons using oral toxicity data to assess toxicity via the inhalation route is only sc ientifically justified, if a vali dated PBPK model is available or if the pesticide falls into Category 3 chemicals according to the Agency’s Inhalation Reference Concentration (RfC) classification scheme ( i.e. , those gases/vapors that cause systemic effects and not point-of-entry effects) (EPA, 1994) and the following criteria are also met.

a. The toxicological effect of concern is system ic for both entry routes and this effect is independent of route of exposure.

33b. The first pass effects in the liver for oral exposure and in the respiratory tract for inhalation exposure are minimal to nonexistent or , if there is some first pass effect, the metabolism following exposure is th e same for both exposure routes.

c. The chemical will not be chemically modified by the gastrointestinal bacteria or enzymes or by the acidic environment in the stomach differentially than it would be in the more neutral to slightly basic environm ent of respiratory tract fluids. Pesticide stability in gastric fluid should be of prim ary concern in considering the use of oral exposure data. If the compound is not stable in gastric fluid, the toxicological data on oral dosing would be of lit tle to no value in estimating any inhalation toxicity.

d. The absorption efficiency for oral and inhala tion exposure must either be identical or known, so that accurate values may be inco rporated into any model. The absorption efficiency for oral and inhalation exposure mu st either be identical or known, so that accurate dose values may be incorporated into any model; the use of a default value of 1 for absorption is not justifiable in th e absence of any data. Furthermore, the absorption cannot be differentially influenced by a toxic response unless this response

is the same via both routes a nd/or is influenced by relativ ely the same extent via both

routes.

Even when the above criteria are met, the Pane l recommended an additional UF of 10 (as an example) for the final extrapolation for inhala tion from oral data. The Panel stated that uncertainty factors are not a replacement for more accurate modeling and additional data from more inhalation toxicity studies . The Panel provided a detailed discussion as to why it is scientifically justifiable to request additional inhalation toxicity data. Discussions and data presented during the meeting clear ly show that repeated exposu res to pesticides are occurring. Based on this discussion, the Panel strongly recommended that the Agency obtain additional inhalation toxicity data if such data are not available and if the vapor form of the pesticide does

not meet the criteria for route-to-rout e extrapolation as described above.

Use of In Vitro Studies

The Panel noted that some of the problems associated with route-to-route ex trapolation from oral toxicity studies to inhalation toxicity studies might be evaluated by simple in vitro studies, such as solubility and stability in si mulated gut or lung fluid (as discussed in more detail below), or by

consideration of the chemical stru cture of the pesticide and using st ructural activity relationships. For example a pesticide may be very stable in a synthetic lung fluid, which has a much higher pH than a human gastric fluid. Pesticide instabil ity in a gastric fluid w ould therefore suggest a much lower toxicity through ingestion than inhalation.

Solubility of the inhaled chemical within lung fl uid is the first step in the toxicological process and can be modeled using a synthetic lung fluid (Dennis et. al. , 1982; Eidson and Griffith, 1984). This parameter may be assessed as part of a scre ening process. Similarly, synthetic gastric fluids may be used as a screen for considering not ju st bio-solubility, but also pesticide stability through the ingestion pathway. Both of these solu bility tests can also be used to screen

34particulates as well. Finally, if a route-to-route exposure model for inhalation of vapors is to be evaluated, then a more appropriate dosing mode l might be injection ra ther than ingestion.

The Panel provided a hierarchy of in vitro models that could be used for assessing bioavailability in humans of any pesticide. The overall deliver ed dose can be approximated by animal exposure studies. Cell culture experiments can potentially be used to pr edict target organ processes. Dependent on dose, they can be used to evaluate cell death, mutation or, at lowest dosing levels, metabolism, but not solubility or even stability because the fluids used to stabilize the cells do not always accurately represent th e fluids interacting with the targ et organ. The next level in the hierarchy would be determining the bioaccessibility of the pesticide. In these studies, the soluble component must pass through a semi-permeable membrane that would approximate the actual target tissue of interest. The fina l level would be creating an estim ate of the biosolubility of the tested compound. This can be done using simulated biological fluids. Use of simulated biological fluids has the potential to correct for interspecies differences be cause the biological fluid can be created using a formulation (or recipe) based on ratios of constituents that most closely approximate that occurring in humans or anim als used in exposure testing above. For the bioaccessibility and biosolubility in vitro models, the physiological endpoint being approximated

is dose delivered to the next co mpartment, generally the blood.

Equation for IEC

The EPA background document presents an equation fo r calculating an IEC th at attempts to take into account various factors, such as minute ventilation, animal body weight, exposure duration, absorption efficiency, etc.; however, the Panel no ted two problems with this equation. First, species differences in surface area/body weight ar e not accounted for, so that the equation needs

to be modified. In cases where route- to-route extrapolation may be justified, i.e. , pesticides that fit into EPA’s RfC Category 3, it would be better to adjust the animal oral dose to a human equivalent oral dose ( i.e., adjustment based on body weight raised to the 3⁄4 power (EPA, 2005) before further adjusting this value to an IEC fo r humans. The Panel stressed that the use of an IEC equation should be restricted to cases wher e the vapor falls into EPA’s RfC Category 3 and also meets the four extrapolat ion criteria listed earlier.

NOAELs vs. BMDs

The EPA background document mainly focuses on the use of No Observed Adverse Effect Levels (NOAELs) and Lowest Observed Adve rse Effect Levels (LOAELs) as bases for calculating IECs or Human Equivalent Concentrati ons (HECs). The Panel not ed that the value of

the NOAEL or LOAEL is sensitive to the nature of the experiment al design used to conduct the study and, therefore, recommended that the Agen cy consider Benchmark Dose (BMD) analysis when concentration-response data are available and are amenable to modeling. The Panel added

that because BMDs can be established for various percentages of effects, the Agency would have various regulatory management options to consid er that reflect magnitude of risk. The Agency could then select the option(s) th at most closely align with their specific risk management goals.

35Use of Haber’s Law in Durati on of Exposure Adjustments

The Panel stated there were several problems w ith the way in which EPA used Haber’s Law (or Rule), in making adjustments for the durati on of exposure when calculating HEC’s from repeated exposure inhalation toxicity studies. As stated on p. 59-60 of the Agency background document, “Thus, application of this procedure provides an automatic ma rgin of protectiveness for chemicals, for which C

max alone may be appropriate, and it reflects the maximum dose for agents for which total or cumulative dose is the appropriate measure.”

First, Haber’s Law has been shown not to be applicable to a great number of toxicological responses. Miller and colleagues (2000) showed that this Law is merely a special case of the generalized power law family. Basicall y, one arrives at Haber’s Law when  = 1 and  =1 in the generalized power law equation, given as C 

x T 

= k , where C is exposure concentration, T is the duration of the exposure and k is a fixed level of effect.

Second, whether the use of Haber’s Law provides an automatic margin of protectiveness is entirely dependent upon the values of  and  in the power law family of curves. Miller et al. (2000) illustrated these differences (see Figure 9 in this paper). Figure 1 below is a reproduction of Figure 9 in Miller et al. (2000). For example, when  > 1 and  < 1, the use of Haber’s Law to extrapolate from high to low level exposures actu ally results in an under prediction of risk (see Case C, Figure 1). In contrast, the Panel in dicated that the Agency is assuming that  > 1 and 

> 1, i.e. , Case D, Figure 1. During her presentation at the meeting, Ms. Annie Jarabek, EPA,

Office of Research and Devel opment (ORD), National Center for Environmental Assessment (NCEA), illustrated the issue of conservatism dependence as a function of where one is on the rectangular hyperbola relating conc entration and time to a fixed le vel of biological response.

One Panel member noted that NCEA is in the process of developing software that can fit a variety of generalized power law family C x T models. These models w ould likely be of great interest to OPP, particularly t hose that relate to acute exposur e modeling and those that account

for C N

x T , where N captures the ratio of  and  (see EPA, 2008; page 10, document on the ten Berge models).

36Figure 1. Log time-log concentration plot s for the general power law family, C α x t β = k . The panels depict the four combination of α and β . Included for reference is the line of identity (Haber’s rule) corresponding to α - β = 1. Here k is fixed at 10 for all plots. Reproduced from Figure 9 in Miller et al. (2000), with permission.

B.2 ) For a significant number of conventional pestic ides, inhalation toxi city studies are not

available. Please comment on the scientific st rengths and weaknesses of available approaches that may be used in the interi m to assess inhalation hazard in the absence of inhalation toxicity studies.

Panel Response

Overall, the Panel strongly recommended that th e Agency conduct inhalati on toxicity studies to adequately assess inhalation h azard. One Panel member provide d a summary of alternative

37approaches reported in the literature that have been used to assess inhalation hazard in the

absence of inhalation toxicity studies, e.g. , “threshold of regulation” or “threshold of no toxicological concern” (see Appendix C for a disc ussion of these procedures). The Panel stated that alternative approaches should not be used in the interim to assess inhalation hazard for pesticides in the absence of i nhalation toxicity studies for the following reasons. These screening approaches do not involve a chemical-speci fic hazard identification or dose-response assessment and were originally developed to assess oral hazard in the abse nce of oral toxicity studies, and not inhalation hazard. While in a few cases the methodology has been used to evaluate inhalation hazard, the durati on of the exposure in these cases ( i.e. , chronic, lifetime exposure and one-hour intermittent exposure) do not correspond to the duration of exposure the Agency is evaluating.

B.3) For inhalation toxicity studies the test material is typically aerosolized. After volatilization, however, the Agen cy anticipates exposures to vapors rather than the aerosolized particles. Please comment on the predictive capabilities of aerosol studies to identify potential toxi c effects and/or quantify the dose-response resulting from exposure to vapors. Is the Panel aware of any studies that quantitatively compare inhalation toxicity after exposure to vapors and aerosols? In the absence of such data, can the Panel recommend an approach to accoun t for the potential differences between vapors and aerosols?

Panel Response

Considerations for Addressing Question of Va por/Particle Toxicity Differences in the Laboratory

The presence of a semi-volatile organic compound in either the vapor or particle phase will have impacts on site of deposition, absorption, and pot entially dose and toxic ity. The biological impact of the relative portions in each phase , however, has been poorly studied. Therefore despite known mechanisms for pot ential differences in dose and ab sorption, the ability to predict toxicity of vapors from aerosol studies is fairly limited. The Panel knew of no studies that have investigated the health impact of exposure to a single semi-volatile ch emical under different phases. Some studies with mixtures have utili zed techniques to either remove the particulate phase by filtration or the vapor phase by using a denuder to study the role of those materials independently (McDonald et al. , 2007). In many cases, the rem oval of the particulate phase of

the mixture has not resulted in biological effects that differ fr om those obtained with the total mixture, especially for systemic effects. These mixture studies, however, may not be appropriate to answer questions related to a single component study. The Pa nel stated that there remain fundamental questions (beyond just the deposition site) re lated to the relative toxicity of vapors and particles. These must be considered as adding uncertainty when attempting to predict biological effects from exposure to the vapor versus particulate form of any given pesticide.

In the case of semi-volatile organic compounds (SVOC), chemicals can exist in the gas and particle phase simultaneously. The relative proportion in either phase will depend on concentration, temperature, humidity, and other pa rticulate matter in the air. Volatile flux from crops will result in concentrati ons that are substantially below the saturation vapor pressure of

38most vapors. Therefore, in most cases, re-equilibration will be defined not by concentration, but by other factors such as the pres ence of other particulate matter , and conditions of temperature and humidity. These other factors can be measured, and incorporated into models using gas/particle partitioning models (see Odum et al , 1996; Pankow et al ., 1994). An additional exposure estimation concern includes the flux over time of the pesticides.

Depending on the vapor pressure of the compound studi ed, it is indeed plau sible that significant amounts of vapors existed in the laboratory stud ies that were conducted to determine pesticide toxicity. The gas/vapor partitioning models cited above can be used to predict the fraction of compound in the gas and particle phase at a gi ven environmental condit ion and concentration. For a simple case where only one compound is presen t, this model can util ize first principles. However, it should be noted that for a semi-volat ile material where the aerosol was sampled with a filter, one could obtain inaccurate dose estimate s due to potential volatilization of vapors from the filter and/or adsorption of vapors onto the filter.

Once actual exposure is more accurately defined, the appropriate exposure scenario can be developed. One issue is the fact that the majority of laboratory inhalation studies were done with aerosolized formulations or pow ders at high concentrations. Th ese conditions do not represent the true physical form of the pesticide as it exists after volatilization at relatively low concentrations. The toxicologically relevant imp act of this is that vapors may be 50-100 % absorbed in the respiratory tract; depending on th e reactivity of the mate rial, the vapors may or may not penetrate deep into the respiratory tract prior to remova l. Studies with highly reactive and highly soluble vapors in rodent s have shown that the biological response is limited to effects in the nasal passages and upper tr acheobronchial (TB) airways. Pa rticles may have more or less deposition (and, therefore, dose) than vapors, with the deposition amount and location in the respiratory tract dependent upon the size of the aerosol. Because of the aerosol generation approaches used in the past, the particles in the laboratory studies will li kely be 2-3 micrometers and be polydisperse. In this case, the majority of the material will deposit deep into the respiratory tract, and at a much lower fraction than the vapors. Polydispersivity, however, will result in some overlap of deposition sites with those for vapors whose uptake sites can be predicted with reas onable certainty.

An approach to link aerosol data with vapor da ta would need to make the assumption that the form of the material will not impact absorpti on once it is removed from the air stream. Once more, the site of deposition would need to be considered, perhaps using currently available modeling approaches for particle s and evolving modeling approaches for vapors. In this case, dose relationships between the studies can be esta blished, and responses can be related in a more appropriate way. Once the site of depos ition is better understood, another important

consideration is the nature and type of biological effects obser ved. These may be able to be related to the site of deposition for local effects, and to bioava ilablity/absorption for systemic effects. For compounds that show high absorpti on, the vapor and particle components of the aerosol will likely show reasonably similar syst emic biological responses following deposition.

39Potential Modeling Approach for Using Aerosol Studies to Assess Vapors

Based on the discussion during th e meeting, the Panel understood that EPA has 25-40 year old aerosol pesticide studies that might provide some insight into the potential toxicity of the

volatilized form. These studies essentially used the “neat” form of the pesticide, i.e., a very high technical grade purity chemical. The Panel suggested a potential approach to examine these studies for useful toxicological information regarding the volatilized form of the pesticide:

1. The Multiple-Path Particle Dosimetry M odel (MPPD) should be run for various mass median aerodynamic diameter (MMADs) and geometric standard deviation (GSD) combinations within the interval of MMAD greater than 2-3  m and a GSD of 2-2.4, to establish variability of the predicted depositi on. This will enable the Agency to assess the variability/sensitivity of the results obtained in subsequent steps. For example, if one uses the rat as the experimental animal, th e MPPD model can be used with this input data to determine the amount of the pesticid e aerosol deposited in the major regions of the respiratory tract (Anjilvel and Asgharian, 1995; National Institute for Public Health and the Environment (RIVM), 2002). The MPPD model is publically available for free

and can be downloaded from http://www.ara.com/products/mppd.htm.

The Panel added that EPA’s Regional Deposit ed Dose Ratio (RDDR) model could be used for other animal species (EPA, 1994). In older aerosol studies, the MMAD and the GSD of the aerosol were not reported; how ever, the methods used, during the aerosol generation did not create particle distri butions with a MMAD greater than 2-3  m and a GSD of 2-2.4. Lack of reporting these values for these studies, therefore, was not a

concern.

2. Because the MPPD model can provide estimates of various dose metrics ( e.g., mass per unit surface area, number of particles per alveol us, etc.), the next st ep is to determine which dose metric best fits or is likely to best correlate with the biological response of

interest. For the extr athoracic (ET) region, the total mass deposited per unit surface area .is probably the only dose metric that can cu rrently be calculated. To do this, one needs to use an estimate of ET surface area (see Ménache et al., 1997) and the mass deposited in the ET obtained from running the MPPD model. MPPD provides the TB surface area for the size of the animal studied, but the alveolar surface area currently displayed by MPPD reflects only the surface area of the alveolar ducts. Miller et al. (2010) provide an algorithm to estimate the to tal alveolar surface area.

3. Use air: blood partition coefficients, solubility, Henry’s law va lues, etc. to determine the likely regions of the respirat ory tract in which the vapor form of the pesticide will deposit. For example, Overton and Jarabek (1990) show how this information can be used to identify likely respiratory tract regions ( e.g. , ET and TB, alveolar) where specific gases and vapors will be deposited up following exposure.

By examining various gas uptake studies publis hed in the literature , the Panel thought that EPA would find that total respiratory tract uptake ranges between 50 to 100 % in laboratory animals. For whatever range is f ound, use the lower value of the interval, as

40that imparts conservative calculations ( i.e., for the range cited here, one would use 50%) and then apportion the uptake across the three major respiratory tract regions. For example, if most of the vapor is taken up in the ET region and some is delivered to the TB region but very little reaches the alveol ar region, then the total uptake might be apportioned as 40% ET and 10% TB.

4. Use the appropriate ventilation data for th e animals in the pest icide aerosol study and compute what vapor phase exposur e would be needed to yield the same value of the dose metric as was computed in Step 2. One may well find that the region for uptake is different than the region where the pesticid e aerosol is primarily deposited. However, since the cells lining th e non-olfactory epithelia in the ET and TB regions are reasonably similar (Miller et al. , 2010), a mass per unit surface area dos e metric is likely to still be an acceptable metric.

5. Next, make similar calculations for humans as was done in Step 5 to see what levels humans would have to be exposed to for wh at periods of time in order to achieve the

same numerical value of the dose metric calculated for the animals.

6. Now move forward with RfC or HEC calculations as inputs to the assessment of risk.

The Panel noted that this procedure is a possi ble way to obtain information on the vapor form when only aerosol exposure studies are conducte d. The modeling approach is intended to help provide some interpretation of the findings from the aerosol study in assessing potential effects from exposure to the vapor. The Panel briefly discussed this procedure and thought it was reasonable; however, they recommended that EP A assess it further to determine its ultimate utility.

ADDITIONAL DISCUSSIONS ON INHALATION TOXICITY

The Panel discussed various topics that were an outgrowth of th e charge questions. The order of the topics is not in order of priority.

Appropriate Averaging Times and Samp ling Devices for Field Measurements

Currently, measurements of pesticide levels collec ted from fields where crops have been treated with pesticides are based upon 24-hour samples. During the public comment period, Dr. Susan Kegley of the Pesticide Research Institute pres ented data showing significant variability in air concentrations of volatile pesticides over periods of time shorter than 24-hours. Many individuals and families live either alongside of cr op fields or even within orchards. Thus, there are significant opportunities for both children and adults to be repe atedly exposed to volatilized pesticides. Moreover, given the large variability in exposure le vels in the first hours following pesticide application and then th e subsequent temporal variabili ty, the Panel recommended that the Agency collect exposure data with shorter collection times than 24-hours and to use these

data in health effects evaluations. For research purposes, samples collected over 2- to 4-hour periods will likely be needed to assess the variab ility of the pesticide concentration in the air soon after pesticide applicati on and over the next few days.

41Appropriate sampling devices for the above studies would be able to separate the vapor form from the particulate form of the pesticide, or at a minimum ensure that both the vapor and particulate portion of the atmosphere are included. This can be accomplished with denuders and filters, or a combination of filters succeeded by vapor sorbents. Samplers with automatic switching manifolds to permit high frequency samp le switching are commercially available. Given the activities in which workers must enga ge, there is a strong li kelihood that individuals surrounding the fields and the workers themselves are exposed repeatedly to both respirable pesticide particles and vapors. The Panel recommended that the Agency collect exposure data for both, so that the relative contribution of each to overall exposure may be assessed.

Protocols for Inhalation Toxicity Studies

The Panel recommended that inhalation toxicity studies should be conducted with an exposure duration of up to 90 days. For example, an inha lation toxicity study cond ucted at 1, 7, 14, 28, and 90 days of exposure would generate an adequate database to address both acute and subchronic exposures. The length of daily exposure may be guided by what is seen in monitoring studies

when collection times shorter than 24-hours ar e used. These timeframes are reflective of the duration of human exposure to respirable particles and vapors via inhalation. The Panel recommended that the length of exposure per day should be guided by new fi eld studies that are conducted to assess the variability in air concentrati on of the pesticide pa rticles and vapors.

The earlier time points ( i.e., 1, 7, 14, and 28 days) would tend to capture the ini tial application period for the pesticide, any potential reapplic ations due to weather conditions, as well as additional weekly applications of a pesticide. The Panel indicated that the 90-day time point would provide a link for using a subchronic study to assess potential effects due to chronic exposure effects, with perhaps an UF added to adjust for not having chronic exposure data. The Panel pointed out that results of the subchronic studies may guide the po tential requirement of

chronic studies that intend to investigate long term effects such as cancer..

In addition, the studies should c onsider the likely types of biol ogical effects that might be expected for the given type of pesticide. The Pa nel pointed out that the Agency should consider that new toxicity studies take advantage of th e scientific advances inunderstanding inhalation toxicity and exposure assessment that have evolved over the past 30 years.

Overall, the appropriate approach for the inhalation study will be one that, to the extent possible, mimics human exposure conditions. The Panel noted that new vapor inhalation studies for semivolatile compounds may be limited, perhaps, to ca ses where there is a pr edicted level of flux

above a certain threshold. This may be determined, as disc ussed above, through flux modeling derived through fate/transport studies.

Uncertainty Factors for Quality of the Da tabase and What Constitutes a Minimum

Database

Other EPA offices ( e.g., Office of Water, Office of Resear ch and Development, Office of Air and Radiation) and regulatory pr ograms have established criteria for what constitutes a minimum

42database for chronic exposure that must be ava ilable so that a health assessment, an inhalation RfC for chronic exposure, an oral RfD, etc. can be determined ( e.g. , see EPA, 1994). The Panel

recommends that EPA establish such criteria for short-term studies ( e.g., 1, 7, 14, 28 days) and that they use an additional UF of 10, if onl y a minimum database is available for their

assessment.

Moving Toward a Cumulative or Total Risk Assessment

The Agency’s background document notes that pot ential pesticide exposures can occur from three types of scenarios: dur ing application directly, due to application drift and due to volatilization following application. Furthermore, exposure may be to respirable particles, or vapors, or both. While the background documen t is only concerned wi th volatilization of pesticides following application, the Panel noted th at a broader view is going to be needed for the future. Therefore, the Panel recommended that total exposure be assessed to more fully encompass all types of inhalation exposures for th e risk assessment process. For example, the Panel acknowledged the difficult si tuation that was occurring to crops on the Jacobs Farm (oral comments provided by Mr. Larry Ja cobs, Pescadero, CA and owner of Jacobs Farm). They

concluded that the situation on Jacobs Farm demonstrates that there is a problem with volatilization and possibly re-e ntrainment of particulate pesticides. If this situation is due to such transport processes, then individuals should also experience pesticide exposures from volatilized pesticides and, possibly, respirable particles.

The Panel suggested that the Agency consider th e hazard quotient (HQ) or similar approach to assess risk from each of the types of exposure th at contribute to the total potential exposure following application of pesticides because, at a minimum, workers are exposed to respirable particles and vapors both during direct applicatio n and drift. Moreover, those living close to fields where pesticides are a pplied are likely to receive e xposure from more than the volatilization route. Hazard quotie nts from different pathways (or from different pesticides) could be added together to calcu late an overall hazard index [HI] . If the resulting HQ or HI is ≤ 1, then adverse health effects would not be expect ed. If the HQ or HI is slightly > 1, then it would not necessarily mean that health effects would occur but that further evaluation was warranted. If the HQ or HI is significantly > 1, th en it would indicate health effects would be likely to occur. The hazard quotient would s eem to offer a better alternative than a MOE approach to aggregate risk. Hazard indices for pest icides with the same mode of action could be used for assessing cumulative risk from inha lation exposure to multiple pesticides.

TOPIC C: Risk Assessment Issues

The Agency discussed its methodology for comb ining the exposure estimation methodologies and inhalation toxicological appr oaches to estimate postapplica tion bystander inhalation risks resulting from field volatilization of conventi onal pesticides. In estimating postapplication bystander inhalation risks, there ar e a few principles that should be followed: (1) properly match the duration of the exposure with a proper toxi city study of comparable duration; (2) both dissipation of air concentrations around a treated field as well as when retreatment of the field may occur, need to be considered ; and (3) clearly define the uncer tainties and limitations of this

43type of assessment. The Agency has identified th e following issues for the Panel to consider with respect to estimating postapplicat ion bystander inhalation risks:

Please comment on the strengths and limitations of the Agency’s use of the empirical and modeled air concentrations in the provided ri sk assessment case study . Does the Panel agree that the postapplication bystande r inhalation risk es timate case study app ropriately matches the duration of the exposure with the proper to xicological study of the same duration? Please comment on the scientific stre ngths and weaknesses of conclusions and characterization regarding the estimated risks presented in the case study.

Panel Response

C.1) Please comment on the strengths and limitat ions of the Agency’s use of the empirical and modeled air concentrations in the pr ovided risk assessment case study.

The strengths and weaknesses of the Woodrow empirical model are much the same as the Panel discussed above in Topic A, Question 2a. The main strength of this model is its basis in multiple studies over a wide range of vapor pressures; a secondary strength is that its results are in the range of the results of the air concentrations estimated by more sophisticated modeled air concentrations. The lim itations of the model are that it is based on a limited range of crops, weather, and locations , does not take into account the potential effect of an activity coefficien t on vapor pressure, and is appli cable only to the first day postapplication.

The strengths of the computer-b ased modeled air concentrations are that they can account for dynamic changes in post-application conditions and residue history. Their weakness is the limited knowledge that users have of the intern al components of these models, the concern that the components do not model evaporat ion from foliage as well as they model evaporation from soil, and that the particular analyses presente d to the Panel inappropriately decoupled the variance of the flux from the variance of dispersion.

Discussion of the Tier IIA Model (Woodrow et al. Model)

Several panelists again suggested that the accuracy of the Tier IIA, Woodrow et al. (2007) model’s estimation of airborne pesticides concen trations could be more precise by adding more variables (see earlier discussion under Topic A, Charge Question 2a. The Woodrow model’s basic structure of a log-log regression lends itself to multiple regression analysis. Two variables of initial interest would be the influence of the pesticide's application rate and foliage density on flux. Some panelists cautioned that worst-case scenarios could not be easily identified for this

model because of the narrow set of conditions th at were available and used when the model was originally developed by Woodrow et al . (1997).

On the other hand, one Panel member suggested th at the utility of the Woodrow model could be expanded by using its predicted 24-hour flux rate in conjunction with a mass balance to derive a per hour flux rate. One such expansion could be derived by assuming that the flux rate decreases exponentially with time, as depicted in Equation C1, where τ is the exponential rate constant.

44flux rate at any time = initial flux rate  e -t /  Eqn. C1

For example, if the application rate were two pounds per acre (using an “eyeball” average of the application rates in Table 1 of Woodrow et al. (1997) and taking into account that 1 kg/ha = 1.03 lb/acre, then the initial residue would be equivalent to an average deposition of 0.22 g/m2 across a flat field. For a pesticide w ith a vapor pressure of 1 x 10 -4 mm Hg, the Woodrow model (translated in Equation C2 out of its log-log format) would predic t an initial 24-hour flux rate of

0.0032 /m2/hr. 3

Flux day 1 in Woodrow model = 8.574 x 10 6 x P vapor 0.85543 Eqn. C2

One can use Equation C3 to calculate this fl ux is equivalent to a rate coefficient [  ] of 2.3 days or an exponential half-life of 1.6 days (where HL =   ln2).

 =      



 ) 24( ln ) 1(

24 hr flux residue initial residue initial case Woodrow theindayort

=      



)0032.024(22.0 22.0 ln 1 = 2.33 days Eqn. C3

The average exposure within any chosen interv al of duration from application to time t can be calculated by using this half-life within Equation C4.

average over span " t " = initial flux 

t dtt t 

## 



0 )/exp(  = initial flux 

t t )/exp(1(  

   Eqn. C4

The first part of Table C1 presents a range of ex amples of these averages as a fraction of the initial value. The second part of Table C1 presents the ratios of the average within the designated interval to the average within the first 24-hours. The Panel commented that in the context of other short-term toxicological e ffects, it is notable that the ma ximum ratio between the first hour and the full 24-hours never exceeds 2-fold, at leas t within the range of half-lives examined. (Note: For this example, these values are smaller than the values given at the meeting.) Table C2 presents the instantaneous flux rates at the same points in time just for information. Again, the Panel pointed out that these extra polations should be viewed only as a potential extension of the Woodrow model (a Tier IIA model) and should no t be viewed as a replacement for estimations of flux rates from the more comprehensive models in Tier IIB. For instance, exponential dissipation based only on evaporation would over-es timate vapor exposures if the pesticide also dissipated into the soil and d ecayed by other mechanisms.

3 Equation C2is a simplification of Equation 4 in the Agency's background document for flux from foliage based on the following generic logic: ln(flux) = A + B ln(C x P vapor ) = A + B ln(C) + ln(P vapor B )

flux = e A + B ln(C) + ln(Pvapor) = e A + B ln(C) x e B ln(Pvapor) = e A + ln(C) x P vapor B

45Table C1. Results of using an exponential decay model to extrapolate from the average flux values in the first 24-hours to the average over various other time intervals of hours (or days), "t" in Equation 8C.

Average within the interval as a fracti on of the instantane ous initial value.

interval of the average in hours (7-days) (21-days)

τ in

hours 1 2 4 8 24 168 504

HL = 0.5 17.312 0.972 0.944 0.893 0.801 0.541 0.103 0.034

in days 1 34.625 0.986 0.972 0.944 0.893 0.721 0.204 0.069

2 69.249 0.993 0.986 0.972 0.944 0.845 0.376 0.137

3 103.874 0.995 0.990 0.981 0.962 0.893 0.496 0.204

4 138.499 0.996 0.993 0.986 0.972 0.918 0.579 0.268

5 173.123 0.997 0.994 0.989 0.977 0.934 0.640 0.325

10 346.247 0.999 0.997 0.994 0.989 0.966 0.792 0.527

20 692.494 0.999 0.999 0.997 0.994 0.983 0.888 0.710

30 1038.740 1.000 0.999 0.998 0.996 0.989 0.923 0.792

Ratio of the average value within the interval to the 24-hour

average. interval of the average in

hours (7-days) (21-days)

τ in

hours 1 2 4 8 24 168 504

HL = 0.5 17.312 1.796 1.746 1.650 1.480 1.000 0.190 0.063

in days 1 34.625 1.366 1.347 1.309 1.238 1.000 0.283 0.095

2 69.249 1.175 1.166 1.150 1.117 1.000 0.445 0.162

3 103.874 1.115 1.109 1.099 1.078 1.000 0.555 0.229

4 138.499 1.085 1.081 1.074 1.058 1.000 0.631 0.291

5 173.123 1.068 1.065 1.059 1.047 1.000 0.685 0.348

10 346.247 1.034 1.032 1.029 1.023 1.000 0.820 0.545

20 692.494 1.017 1.016 1.014 1.012 1.000 0.903 0.723

30 1038.740 1.011 1.011 1.010 1.008 1.000 0.934 0.801

46Table C2. Results of using an exponential decay mode l to extrapolate from the average flux values in the first 24-hours to the instantaneous flux at various other time intervals of hours (or days).

Fractional value at the e nd of each time period

interval in hours (7-days) (21-days)

τ in hours 1 2 4 8 24 168 504

HL = 0.5 17.312 0.944 0.891 0.794 0.630 0.250 6.1E-05 2.27E-13

in days 1 34.625 0.972 0.944 0.891 0.794 0.500 0.008 4.77E-07

2 69.249 0.986 0.972 0.944 0.891 0.707 0.088 0.001

3 103.874 0.990 0.981 0.962 0.926 0.794 0.198 0.008

4 138.499 0.993 0.986 0.972 0.944 0.841 0.297 0.026

5 173.123 0.994 0.989 0.977 0.955 0.871 0.379 0.054

10 346.247 0.997 0.994 0.989 0.977 0.933 0.616 0.233

20 692.494 0.999 0.997 0.994 0.989 0.966 0.785 0.483

30 1038.740 0.999 0.998 0.996 0.992 0.977 0.851 0.616

Ratio of the average value within the in terval to the average 24-hour value.

interval in hours (7-days) (21-days)

τ in hours 1 2 4 8 24 168 504

HL = 0.5 17.31 3.78 3.56 3.17 2.52 1.00 0.000 9.09E-13

in days 1 34.62 1.94 1.89 1.78 1.59 1.00 0.016 9.54E-07

2 69.25 1.39 1.37 1.33 1.26 1.00 0.13 0.00

3 103.87 1.25 1.24 1.21 1.17 1.00 0.25 0.01

4 138.50 1.18 1.17 1.16 1.12 1.00 0.35 0.03

5 173.12 1.14 1.14 1.12 1.10 1.00 0.44 0.06

10 346.25 1.07 1.07 1.06 1.05 1.00 0.66 0.25

20 692.49 1.03 1.03 1.03 1.02 1.00 0.81 0.50

30 1038.74 1.02 1.02 1.02 1.02 1.00 0.87 0.63

47Discussion of Tier IIB Flux Models

A summary of a number of sugge stions made by different Panel members regarding the use of flux models is provided below.

1. Several Panelists suggested that the Agen cy use the volatilization flux models to explore an array of crop and use patterns a nd to explore multi-fie ld applications and multiple applications of a given pesticide within a field. One variation on this theme was the idea of using the models to devel op "sentinel" worst-case scenarios. Because worst cases can vary, an array of exposure scenarios should be explored stratified by chemical, crop, and region. An assumption of a unit flux should be used with an appropriate air dispersion model, or models, and re presentative historical meteorological data to identif y worst-case scenarios and predicted vapor concentrations

in air. Predicted concentrations for substances with fluxes not equivalent to unity can be estimated by multiplying the unit flux predic tions by a given substance’s flux. In the absence of information on particular pesticide fluxes, a literature re view of losses over say 24-hours ( e.g., Smit et al. , 1998) may permit the identification of a reasonable worst case assumption of the proportion volatized th at can be used to estimate the flux over the same time period.

2. The Panel believed that the way the models were used in the Agency ’s case studies was inappropriate. For example, PERFUM is a m odel that was develope d and field validated to estimate fumigant volatilization and downwind movement and concentrations under typical fumigation field conditions

, i.e ., flat, fallow fields, usually covered with plastic tarps. This model was not, as far as the pa nel members were aware of, validated for any other class of pesticide or other field c onditions. Not only was this model used to evaluate semi-volatile pesticides with ve ry different physicochemical properties than fumigants, it was also applied to field environments that were radically different (orchard and cabbage fields) from the typical fumigati on field (fallow, flat). The only way this model, or any other for that matter, can be reliably used to pred ict source volatilization

flux and downwind air concentrations is to field validate them unde r the typical field conditions the various pesticides in question will be used.

3. The confidence in using the PERFUM mode l for semi-volatile pesticides can be increased considerably by conducting seve ral field volatilization/model evaluation

studies on select semi-vol atile pesticides used on a range of crop types ( e.g. , cover crop, short row crop, tall row crop, orchard) in re presentative geographic and climactic areas (or major agricultural areas) where the selected semi-volatile pesticides will normally be used. The field and modeling data from these studies can then be further evaluated by applying/using the results from one study to ea ch of those areas where the other studies were conducted, as was done in the presen ted case study. Then those results can be compared to the actual field and modeled resu lts. This will show almost immediately if this kind of exercise has any va lidity. If the results show prom ise, then the models can be further refined/fine-tuned, and subsequent predictions ofthe model on other semivolatile pesticides in other field situations can be better defen ded because the model has been rigorously tested and compared against a variety of actual field situations.

484. Another suggestion was to prioritize risks by starting with an acceptable concentration. This suggestion was independent of prioriti zing based on use of the Vapor Hazard Ratio (VHR) discussed below.

5. Several panelists reiterated an earlier point made in response to Topic A to not decouple the flux and dispersion models. 4 Decoupling probably increased the variability in the outcome of the examples presented in th e Agency background document because of the

covariance between the effects of many of the same variables that affect both outcomes.

C.2) Does the Panel agree that the postapplic ation bystander inhalati on risk estimate case study appropriately matches the duration of the exposure with the proper toxicological study of the same duration?

The Panel agreed that the case study appropriately matched the duration of the exposure with the proper toxicological end point, although there were some questions regarding the specificity of the target population within this particular case study. Again, a great deal of the Panel's discussion on Topic B apply here, particularly the limitations of the route-to-route extrapolation process and the need to apply toxicological da ta collected from one duration of exposure to another. Thus, the success of this case study to achieve its goal is tempered by the limited confidence that the Panel has in extrapolating toxicological data cons idering both route and duration of the exposure.

However, in a broader sense, the Panel agreed th at the example did not adequately consider the ability to model differences in dur ation of the exposure. In earlie r sections, the Agency presented three types of exposure scenarios fo r risk assessment, short- (up to 30 days), intermediate- (up to 90 days) and long-term (greater than 1 yea r). The example only presented modeled air concentrations for short-term exposures. The models did not estimate intermediate- and longterm exposures. Two of the five pesticides listed in Table 3 (of the Agency’s background document) used for modeling short-term exposures have reported soil half- lives of greater than 150 days, illustrating that intermediate exposures from single applications may occur. The Panel recommended the Agency consider adding longer-t erm exposures of more than 30 days in the models to address intermediate and long-term chronic exposures and to match toxicological studies. People who live in agricultural commun ities for their entire lives may be exposed to volatilized pesticides that may pose chronic, life-long health risks.

The Panel had a number of points regarding the uncertainty of matchi ng the duration of the exposure with the proper toxicological study of the same duration. A brief summary of these remarks is presented below. Most of these po ints were made previ ously during the Panel’s response to charge questions in Topic B.

1. Several Panelists discussed uncertainty in th e specifics of the e xposed population used

4 By using the term, “decoupling,” the Panel was referring to running a Monte Carlo simulation on a range of

weather conditions to determine an array of flux values via PRZM or PEARL. Subsequently, a statistical percentile from that array is used as an input to PERFUM which is then used to run a separate, but statistically independent Monte Carlo simulation on the same range of weather conditions.

49within the Agency’s case study and the implicat ions to the appropriate ness of the UFs that were applied to address concer ns about children’s protection . The Panel indicated that the answer to the charge question depends bot h upon the UFs that were applied and the quality of the toxicological data such as the inclusion of deve lopmental toxicity studies or studies conducted during critical life stages (i.e., children, t eenage years) in the database. A new suggestion that may have broad appl icability to the Agency was to view developmental/reproductive oral studies as informative in term s of effects, but not to be used quantitatively in terms of inhalation toxi city. In oral studies, if developmental, reproductive, or childhood effects occur at si gnificantly higher concentrations than the critical effect, this would provide qualita tive information on whether these effects may occur at low concentrations after inhalation exposure.

Toxicology for Risk Assessment (TERA) organi zed three peer consultations, two in 2005 and one in 2007, on issues related to risk assessment for children. One of the 2005 consultations, together with the follow -up in 2007, addressed issues related to

toxicokinetic differences between adults a nd children, and the s econd 2005 consultation addressed issues related to the adequacy of the database uncertainty factor: These

documents are available at http://www.tera.org/peer/Adul tChildTK/ACTKW elcome.htm

.

2. The Panel reiterated that all of the prev ious discussion in Topic B regarding the applicability of Haber's law a pplies to this case study. In th e end, without other inhalation study data, the Panel thought it was difficult to tell for most pesticides whether a toxicity study conducted at one duration of exposure ex trapolated to anothe r is appropriate as previously discussed.

3. Similarly, the Panel reiterated its discussion in Topic B regarding the limitations of routeto-route extrapolation.

4. One Panelist strongly suggested that the term “RfC” not be used to describe “reference

concentration” if an oral t oxicity study is used in a r oute-to-route extrapolation of inhalation toxicity. The term reference concen tration is typically used for values where UFs have been applied to the appropriate point-of-departure; th erefore, it would be inappropriate to use this te rm to describe a value that has not been adjusted for

uncertainties in the data.

5. Another Panelist commented that the Agency should consider the advantages of setting a fixed value for the Margin of Exposure (MOE) before completing the exposure assessment versus evaluating the quality of the MOE that results from such an

assessment.

6. In the context of future assessments of inhala tion hazards, the Panel was interested in how the MOE approach could be used to co mbine the three routes of exposure via volatilization, spray drift, and respirable particles to assess cumulative or aggregate inhalation risk. Alternatively, the Panel suggest ed that it might be easier to calculate a Concentration of Concern (CoC ) (which incorporates UFs) first and then calculate the hazard quotient (HQ), which is the ratio of the breathing level IE to the CoC. Hazard

50quotients from different pathways (or from di fferent pesticides) coul d be added together

to calculate an overall hazard index (HI). For additional information, HQs from different pathways (or from different pesticides) coul d then be added to calculate a HI. If the resulting HQ or HI is ≤ 1, then adverse health effects woul d not be expected. If the HQ or HI is slightly > 1, then it would not necessari ly mean that health effects would occur but

that further evaluation was nece ssary. And if the HQ or HI is substantially > 1, then it would indicate health effects would be likely to occur. The hazard quotient would seem to

offer a better alternative than a Margin of Exposure (MOE) approach to aggregate risk.

C.3) Please comment on the scientific stre ngths and weaknesses of conclusions and characterization regarding the estimated risks presented in the case study.

The Panel broadly agreed that the case study incl uded all or most of the important elements to conduct a proper risk assessment. The strength of the inhalation toxicity and exposure data bases

for the chemical chosen led the Panel to conc lude that the inhalation hazard and exposures assessments, and MOE analysis were realistic based on field monitoring data. However, the Panel had a range of recommendations for how th is model and the risk assessment process could be improved, and reservations, if such an analys is were applied to many other chemicals. For instance, the toxic endpoints are unlikely to be as strong for other chemicals, the details and general applicability of some steps in the proc ess were not well-define d, PRZM or PEARL were not optimized for their application to the evapora tion of semi-volatile pesticides, and the impact of the propagation of uncertainty and safety factors within the process on the final result is uncertain. An alternative risk assessment appr oach based on the VHR is presented below.

Strengths

The ability to predict the concentration of vapor s emanating from pesticides sprayed onto fields and crops and to assess the risks to bystande rs is challenging. The multi-component and multistep models that predict exposure are not simple, and the toxicity data pe rtinent to these exposure patterns are rarely available. Given these challenges, the Panel was in broad agreement that the process being pursued by the Agency includes all the ingredients needed to characterize the risks of such vapor exposures. The Panel noted that the Agency did a credible job in the case study as presented. The Tier IIB flux models have the abil ity to integrate the vari ability in real world settings and to generate a distri bution of exposures that can be useful in risk assessment. The magnitude of the variations in the results that were presented was internally consistent and seemed realistic. Also, the magnitude and distri bution of the resulting MOEs were believable.

Weaknesses

In the larger sense, the Panel concluded that th e case study is about as good of a result as this approach can achieve at the present time. The pes ticide selected for this case study may be one of the few that have sufficient data to link expos ures over different intervals with relevant toxicological data points. The Pa nel was not confident that this process could be extended to many other compounds because of the scarcity of data. The Panel concluded that filling the gap in vapor inhalation toxicity data and performing more field studies to validate the vapor flux and

51dispersion models are at least equally important . However, filling the gap in data of vapor inhalation toxicity data would be more complex, take longer, and be more costly than conducting more field assessments (or utilizing more existing field studies) to incr ease confidence in the exposure values.

Several Panelists agreed that expo sures as short as (or shorter than ) 1 hour should be of interest to the Agency. As discussed in Topic A, such in itial peak exposures should be derived either from a dynamic model or (preferably) from actua l measurements over shorter intervals than has been the historic practice, and not from averag e conditions over longer in tervals. However, it may turn out that exposures in the first hour ar e only about a factor of two greater than the averages in the first 24-hours (see Table C1, exposures predicted from an exponential decay model using Equation C4 above).

The Panel stated that the Agency should be more diligent in describing the uncertainty that

surrounds each variable within the PRZM and PE ARL models. The Panel provided several ways

to better understand the uncertainties in each va riable and suggested opport unities to increase the

confidence in the model outcomes. For instan ce, the Panel identified multiple sources of uncertainty in both the predicted vapor exposures and in the exposure limits in the PRZM and PEARL models. Several panelists suggested that sensitivity anal yses should be conducted on the variables within the models to distinguish the rela tive contributions of each variable to the model outcomes. Others commented on the perhaps more urgent need to have additional field studies conducted in different cropping scenarios to in crease the confidence in the exposure value predicted by these models.

One Panelist stated that the toxicity estimates have uncertainties. There are several ways to express this uncertainty:

 Include a discussion of the UFs used to cal culate the CoC, including the database UF. The larger the value of the UFs, the larger the uncertainty. If the Agency uses a MOE approach, the value of the le vel of concern (i.e., the value of the MOEs) would express the uncertainty.

 If the dose-response data are amenable to benchmark dose modeling, the ratio between the benchmark concentration (B MC), the central estimate, an d the 95% upper confidence limit of the BMC (BMCL) is a quantitative measure of uncertainty in the dose-response data. The larger the ratio, the larger the uncertainty.

 A probabilistic method using the distribution of the UFs to calculate a CoC could be used to derive a range of CoCs and quantify uncertainty.

In discussing potential pesticide studies that could be used as a model for exposure through inhalation affecting multiple systems, one panelist suggested paraquat. There have been multiple studies on the inhalation toxicity of paraquat (D inis-Oliveira et al ., 2008; Haley, 1979) om addition to its neurotoxic effects (Haley, 1979; Liou et al., 1997; Dinis-Oliveira, 2006) and its use in animal models to study Parkinson’s dise ase (Betarbet, 2000; Gorrell, 1998; Thirachelvam, 2000). The principal limitation in paraquat’s us e as a model chemical for inhalation risk assessment is that it is a charged compound. However, paraquat has a reported VP of 1x10 -7
mmHg, low but similar to the VP for a couple pe sticides in Table 3 and 4 in the Agency’s background document. Moreover, ther e is potential inhalation expos ure to paraquat (a defoliant),

52e.g., smoking leaves of treated pl ants. Paraquat residues may be re-volatilized when smoking a cigarette, i.e. , the VP substantially increases when heat ed to a cigarette’s burn temperature, for example. The smoke carries the paraquat into th e deep long where it can be extracted by lung

fluid. Re-volatilized residues w ould be estimated using flux models, assuming that they are sophisticated enough to account for mi xtures (paraquat, water, and anything else that may be coapplied to the crop). Thus, paraquat can provide a working toxicological pesticide model for multisystem negative health outcomes associated with an inhalation pathway.

Alternative Risk Assessment Approach

The broad charge to the Panel for Topic C combin ed aspects of the exposure prediction in Topic A and the toxicological assessments in Topic B. The Panel discussed an alternative risk assessment approach that is applicable to th e Occupational Safety and Health Administration (OSHA) and allows both hazard and exposure assessments to be combined in a simple, yet potentially useful way. Using this approach, the va por exposure hazard is first separated into its chemical-specific components via the VHR a nd its environmental components via the "Environmental Dilution Ratio" [EDR]. A determ ination of acceptable exposures involves the

simultaneous use of both ratios as EDR/VHR. In principle, both VHR and EDR involve only physical and toxicological properties; in that sense, they are stric tly science. The final question of whether one decides that an acceptable value of the EDR/VHR ratio representing a given use condition for a particular pesticide should be a va lue of only 1 or an MOE of 100 or another level of concern is a policy decision.

A brief summary of the alternative VHR/EDR appr oach applicable to OSHA is provided below, and a more detailed explanation is provided in Appendix D. For OSHA, the acceptability of an exposure is defined by a simple ratio of the ch emical concentration to which employees are exposed [abbreviated herein by C] divided by that chemical's exposure limit [EL] where health effects would not be expected to occur. The EL is often an OSHA permissible exposure limit [PEL] or Threshold Limit Value [TLV ® ]. Such EL values do not have explicit uncertainty factors built into them.. To be acceptable in those othe r settings, the EL for a given chemical must simply be greater than its measured or predicted C, or the ratio in Equa tion C5 must be greater

than one.

1bemust C EL

 Eqn. C5

Predicting an acceptable setting using Equation C6 is complicated because both EL and C are affected by so many variables. An alternative way to define this same level of acceptability is by using the ratio of two other ra tios as shown in Equation C7. Both the EDR and the VHR are related to C and EL, respectively.

1bemust VHR EDR

C EL

  Eqn. C6

53The VHR is mathematically defined by Equation C7 as the amount of dilution a given chemical needs between the chemical's saturated vapor con centration right at the source and an acceptable vapor concentration or EL value defined by the chemical's toxicity. A list of VHR values for pesticides was extracted from a broader list of chemicals compiled by Popendorf (2006) is found in Appendix D. This list was rank ordered from th e pesticide with the highest VHR to the lowest VHR. These VHR values are not directly applicable to those the Agency might use. In the case of the Agency and this charge, a pesticide's toxi city would be defined qua ntitatively based on the duration of the exposure, i.e. , acute, sub-chronic, and chronic. Thus, each pesticide is likely to have a different VHR for each exposure scenario (but potentially a sim ilar rank-order in each such scenario's list). Such a rank ordered list of VHR values could easily identify those pesticides with the greatest (a nd those with a negligible) vapor hazard. In this sense, the VHR provides a more useful list than a list based on the current Tier I criteria of just a chemical's vapor pressure. VHR values could also be modi fied slightly by including some of the soil

interaction terms from the Tier IIA Woodrow model into the numerator for those pesticides that might have a higher flux from soil than from foliage.

unitssametheinlimit exposure 3 mg/morppmofunitsin vapor P HR  V Eqn. C7

The EDR is mathematically defined by Equati on C8 as the amount of dilution that a given environmental setting can or does create between the chemical's saturated vapor concentration right at the source and the vapor concentration at any defined location, e.g. , 10 meters downwind of a sprayed field. A separate list of EDR values could be generated for various application and location settings using a combination of flux and dispersion models as currently proposed by the Agency. This procedure is analogous to what se veral panelists referred to as "back-calculating exposure."

location definedanyin partial P VHRtheinasunitsin vapor P
EDR  Eqn. C8

The criterion implied by Equations C5 and C6 do not incorporate a ny Margin of Exposure (MOE).To relate the EDR and VHR approach to the MOE approach for an “acceptable exposure” to a pesticide, the ra tio of both EL/C and EDR/VHR would also need to exceed the MOE for that chemical, as depicted in Equati on C9. An MOE with a value of 100, for example, would provide an added level of assurance that exposures (or doses ) to pesticide workers or to the public will not exceed limits set based on toxicological studies in a laboratory.

MOEbemust VHR EDR

C EL
  Eqn. C9

Looking at uses of volatile (or semi-volatile) pesticides in terms of EDR and VHR values provides a method to evaluate the environmen tal conditions separately from the various pesticides. For instance, the Panel thought that th e Agency could explore the use conditions that

result in low EDR values and th ereby target high risk crops ( e.g. , as a function of foliage density or height), weather ( e.g. , as a function of atmospheric stabil ity or temperature), or regions ( e.g. ,

54as a function of soil conditions). Similarly, the Pa nel noted that the Agency could identify those pesticides that have a high VHR based on an ar ray of temporal exposur e scenarios with their appropriate toxicolo gical end points.

The following discussion illustrates how both the ED R and VHR have direct applications to the case study presented to the Panel in Section 5 of EPA’s background document. The EDR in the case study is the ratio of satu rated air concentration (in Table 4, these values are 660 μg/m 3 for

chemical C 1 and 16 μg/m 3 for chemical C 2 , or 209 μg/m 3 when combined 5 ) divided by the predicted air concentration (Table 9 cont ains both a Tier IIA prediction of 5.3 μg/m 3 and the corresponding "Max 24-hour Air Concentrations " Tier IIB predictions of 2 or 4 μg/m 3 by PRZM and PEARL, respectively). These values yield an EDR of 209/5.3 = 39 fo r Tier IIA or 105 and 52 for PRZM and PEARL, respectively. These pred icted EDR values are all reassuringly similar to the mean and range of the minimum observed EDR values corresponding to the "Percent Departures from Study" in Table 4. 6 An extension of this appro ach would be to create a table of

VHR values structured like Tabl e D-1 of Appendix D in which each exposure limit applicable to general industry is replaced by a HEC or RfC co ncentration applicable to pesticides. Assuming

for the moment that the values in Table D-1 were applicable, then those pesticides with a VHR greater than EDR/MOE = 39/100 = 0.39, if used under the same conditions ( i.e. , with the same EDR value), would result in an MOE of less than 100, an exposure level that is generally

considered to be of concern.

EDITORIAL COMMENTS ON THE BACKGROUND DOCUMENT

Several panelists made the following editorial co mments to improve the clarity and adequacy of the overall presentation of the issues in the Agency’s background document.

Errors were noted in the Agency's Equations 2, 3, and 4, and Tables 4 and 5

The units in Equations 2, 3, and 5 should be given as μg/m2/hr (as in Woodrow et al. , 2001), not g/m2/hr as shown in the Agency’s background docu ment. This error was apparently not internal to the μg/m2/s flux predictions presented in Tabl e 5; however, this discrepancy was disconcerting to the external reader trying to reconcile high gram values predicted by Tier IIA model as shown with models that predict eva poration of liquid solvents.

In Table 4, the ratio for Chemical B of the Tier I Model concentration of 340,000 ng/m 3 to the maximum monitored concentration of 27,700 ng/m 3 is 12.5 not 125 as implied by 12,493%. Another discrepancy that may or may not be due to an error was uncovered when trying to

validate the Tier IIA flux rates fo r chemical D in Table 5. The only way to get the value of 0.91 μg/m 2 /s is to assume a depth into the soil at which the chemical was applied but which was

5 The saturation vapor concentration of chemical C is based on its description of a 30:70 mixture (assumed to be a

molar ratio) of isomer C 1 and isomer C 2 (p. 35 and 54) and Raoult's law such that the mixture's concentration is (660 x 0.3) + (16 x 0.7) = 209 μg/m 3 . . Raoult's law is likely to apply to a neat mixture of these two chemicals because they are very similar to each other; however, both may behave non-ideally if they are diluted in water.

6 The "Percent Departures from Study" values in Table 4 equal 100 x ([the Tier I Model Concentration] / [the

Maximum Monitored Concentration] - 1) = 100 x (EDR - 1). . Similarly, the EDR = (% Departure/100) + 1. . The

fact that the latter values in the table are "maximum " concentrations, mean that the resulting ratios and EDR

values are the minimum values that have been observed.

55not provided to the reader.

Table 8 and Table 10 Values

One Panelist made a minor editorial comment rega rding certain toxicologi cal calculations used in the case study. Some values in Table 10 do not correspond to correspo nding values in Table 8. The same Panelist attempted to reproduce the HEC values in Table 8, but there was not enough information provided in the case study to do so. For clarity, the EPA should include the detailed equations and intermediate calculations used to calculate the Short Term (1-30 days), Acute HEC, and Short-term HEC from the 7-day and 21-day inhalation study.

Definition of Terms

One Panel member suggested that EPA define the meaning of different terms used in the background document, e.g. , concentration of concern (CoC); level of concern (LOC); acute, short-term, intermediate durations of the exposur e. One concern was use of the abbreviation “IE” to describe the inhalation exposure to which an individual is exposed because of the potential

confusion with the abbreviation of IEC. Perh aps, “ground-level concentration (GLC)” or “ground-level exposure concentration (GLEC)” would be a better term.

Latest Developments in Toxicity Assessments

The Agency should include references to the mo st up-to-date scientific methods in toxicity assessments for chemicals with adequate toxici ty data and indicate they plan to use these techniques. There is no mention of the latest developments in toxicity assessments such as the use of benchmark dose modeling to calculate an appropriate point of departure, the use of categorical regression, the use of data to justify UFs that are different from the default UF of 10, the use of data to calculate chemical-specific adju stment factors, or the use of the Multiple Pass Particle Dosimetry Model (MPPD). The backgro und document does not discuss the potential for certain segments of the population to have di fferential susceptibilit y, e.g., children compared

with adults.

Physicochemical Properties

Information on physicochemical properties of the pesticides should be provided in the background document. Vapor pressure as well as solubility data would be helpful.

Haber’s Rule

The Agency should provide a more thorough disc ussion of Haber’s rule as modified by ten Berge et al. (1986) in the background document, e.g. , Jarabek (1995).

LOC for Pesticide C

In the case study, the LOC for Pesticide C was a factor of 30. A discussion on why a database UF was not considered would be helpful for transparency.

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3841-3846

Suggested References from Panelists

Aerodynamic methods/modeling of ai rborne transport of pesticides:

Aston, L.S. and J.N. Seiber. 1997. Fate of su mmertime airborne organophosphate pesticide

residues in the Sierra Nevada mount ains. J. Environ. Qual. 26: 1483-1492.

Glotfelty, D.E., J.N. Seiber, and L.A. Liljedahl. 1987. Pesticides in fog. Nature. 325: 602-605.

Herrington, P.J., H.R. Mapother, and A. Stringer. 1981. Spray rete ntion and distribution on apple

trees. Pesticide Science. 12: 515–520.

Majewski, M.S. 1996. Error evaluation of me thyl bromide aerodynamic flux measurements, In : Seiber, J.N., Knuteson, J.A., Woodrow, J.E., Wolf e, N.L., Yates, M., and Yates, S.R., eds., Fumigants: Environmental fa te, exposure, and analysis. Washington, D.C., American Chemical Society, p. 135-153.

Majewski, M.S., and P.D. Capel. 1995, Pesticides in the atmosphere: Di stribution, trends, and governing factors (1st ed.): Chelsea, MI, Ann Arbor Pr ess, Inc., v. 1 of 4, 250 p.

Majewski, M., R. Desjardins, P. Rochette, E. Pattey, J.N. Seiber, and D. Glotfelty. 1993. Field comparison of an eddy accumulation and an aerodynamic-gradient system for measuring pesticide volatilization fluxes. Environ. Sci. Technol. 27: 121-128.

Majewski, M.S., D.E. Glotfelty, and J.N. Seiber. 1989. A comparison of the aerodynamic and the theoretical-profile-shape met hods for measuring pesticide ev aporation from soil. Atmos.

Environ. 23: 929-938.

OTM-10: FINAL ORS Protocol, June 14, 2006 “Optical Remote Sensing for Emission

Characterization from Non-point Sources.”

Ramaprasad, J., M.-Y. Tsai, K. Elgethun, V.R. Hebert, A. Felsot, M.G. Yost, and R.A. Fenske. 2004. The Washington aerial spra y drift study: assessment of off-target organophosphorus insecticide atmospheric movement by plant surface volatilization. Atmos. Environ. 38:

5703–5713.

61Ramaprasad, J., M.-Y. Tsai, R.A. Fenske, E.M. Faustman, W. Griffith, A. Felsot, K. Elgethun, S. Weppner and M. G. Yost. 2009. Children’s Inhalation Exposure to Methamidophos from Sprayed Potato Fields in Wash ington State: Exploring the us e of probabilistic modeling of meteorological data in exposure assessment. J. Exp. Sci. Environ. Epidem. 19: 613–623.

Raupach, M.R., P.W. Ford, and P. Briggs. 1996. Modelling the aeria l transport of endosulfan to rivers. Part I: the vapour tr ansport pathway. Technical Report No. 113, CSIRO Centre for

Environmental Mechanics.

Raupach, M.R. and P. Briggs, P. 1996. Modelling the aerial transport of endosulfan to rivers. Part II: transport by multiple pathways. Technical Report No. 121, CSIRO Centre for

Environmental Mechanics.

Weppner, S., K. Elgethun, C. Lu, V. Hebe rt, M. G. Yost and R.A. Fenske. 2006. The Washington aerial spray drift study: Children’s exposure to methamidophos in an agricultural community following fixed-wing aircraft applicati ons. J. Exp. Sci. Environ. Epidem. 16: 387–396.

Woodrow, J.E., J.N. Seiber, and C. Dary. 2001. Predicting pesticide emissions and downwind concentrations using correlations with estimated vapor pressures. J. Agric. Food Chem 49:

3841-3846

Woodrow, J.N. and J.N. Seiber. 1991Two chamber methods for the determination of pesticide

flux from contaminated soil and water. Chemosphere 23: 291-304.

Flux measurement methods – general references:

Dabberdt W.F., D.H. Lenschow , T.W. Horst, P.R. Zimmerman, S.P. Oncley and A.C. Delany. 1993. Atmospheric Surface Exchange Measurements. Science. 260: 1472-1481.

Lee, X. 2004. Handbook of micrometeorology: a guide for surface flux measurements and analysis. Kluwer Academic, 250 pp.

Conditional sampling and eddy accumulation:

Baker, J.M., J.M. Norman and W.L. Bland. 1992. Field-scale application of flux measurement by conditional sampling., Agr. and Forest Met. 62: 31-52.

Businger, J.A. and S.P. Oncley, 1990. Flux meas urement with conditional sampling. J. Atmos.

Oceanic Tech. 7: 349-352.

Maximum and minimum integration times re quired for accurate and precise flux

measurements:

Lenschow, D.H., J. Mann, and L. Kristense n. 1994. How long is long enough when measuring

fl uxes and other turbulence statistics? J. Atmos. Oceanic Technol. 11: 661–673.

62Lenschow, D. H. and B. B. Stankov. 1986. Length scales in the convective boundary layer. J.

Atmos. Sci. 43: 1198–1209.

Moore, C. J. 1986. Frequency response correctio ns for eddy correlation systems. Bound.-Layer

Meteor. 37: 17–35.

Uncertainties in flux measurem ents and uncertainty analyses :

Mahrt, L. 1998. Flux sampling errors for aircraft and towers. J. Atmos. Oceanic Technol. 15:

416–429.

Mann, J. and D.H. Lenschow. 1994. Errors in airborne fl ux measurements. J. Geophys. Res.

Atmos. 99: 14519–14526.

Moncrieff, J.B., Y. Malhi, and R. Leuning. 1996. The propagation of errors in long-term measurements of land–atmosphere fl uxes of carbon and water. Global Change Biol. 2: 231–

240.

Richardson, A.D ., M.D. Mahech, E. Falge, J. Kattge, A.M. Moffa, D. Papale, M. Reichstein, V.J. Stauch, B.H. Braswell, G. Churkina , B. Kruijt, D.Y. Hollinge. 2008. Statis tical properties of random CO2 flux measurement uncer tainty inferred from model residuals. Agr.

and Forest Met. 148: 38-50.

Richardson, A.D., D.Y. Hollinger, G.G. Burba, K. J. Davis, L.B. Flanagan, G.G. Katul, J.W. Munger, D.M. Ricciuto, P.C. Stoy, A.E. Suyke r, S.B. Verma and S.C. Wofsy. 2006. A multisite analysis of random error in tower-ba sed measurements of carbon and energy fluxes. Agric. Forest Meteor. 136: 1-18.

Valentini, R. and Verma, S. 2002. Energy balan ce closure at FLUXNET s ites. Agric. Forest

Meteorol. 113: 223–243.

Wilson, K., A. Goldstein, E. Falge, M. Aubinet, D. Baldocchi, P. Berbigier, C. Bernhofer, R. Ceulemans, H. Dolman, C. Field, A. Grelle, A. Ibrom, B.E. Law, A. Kowalski, T. Meyers, J. Moncrieff, R. Monson, W. Oechel, J. Tenhunen, R. Valentini, S. Verma. 2002. Energy

balance closure at FLUXNET sites. Agric. Forest Meteor. 113: 223–243.

Flux "footprints" (area represented by a flux measurement):

Horst, T.W. and T.W. Weil. 1994. How far is far enough? The fetch requirements for micrometeorological measurement of surface fl uxes. J. Atmos. Ocean. Technol. 11: 1018–

1025.

Horst, T.W. and J.C. Weil. 1992. Footprint estim ation for scalar flux measurements in the atmospheric surface layer. Bound.-Layer Meteor. 59: 279–296.

63Schmid, H.P. 2002. Footprint modeling for vegeta tion atmosphere exchange studies: A review and perspective. Agric. Forest Meteor. 113: 159–183.

Schuepp, P.H., M.Y. Leclerc, J.I. Macpherson, a nd R.L. Desjardins. 1990. Footprint prediction of scalar fluxes from analyti cal solutions of the diffusion e quation. Bound. Layer Meteor. 50:

355–373.

Turbulence and transport within plant canopies:

Finnigan, J. 2000. Turbulence in plant ca nopies. Annu. Rev. Fluid Mech. 32: 519–571.

Finnigan, J.J. 2007. Turbulent flow in canopies on complex topography and the effects of stable stratification. In: Flow and Transport Proce sses with Complex Obstructions. NATO Science Series II: Mathematics, Physics and Chem istry, Vol. 236. Springer, New York. 414 pp.

Staebler, R. M. and D.R. Fitzjarrald. 2005. Meas uring canopy structure and the kinematics of subcanopy flows in two forests. J. Appl. Meteorol. 44: 1161-1179.

Flux measurements in complex conditions:

Finnigan, J.J. 2008. An introduction to flux measur ements in difficult conditions. Ecol. Applic. 18: 1340-1350. (Note: Whole volume is worthwhile.)

Wang, W., K.J. Davis, B.D. Cook, D.M. Ricci uto, and M.P. Butler. 2006. Decomposing CO 2 fluxes measured over a mixed ecosystem at a tall tower and extending to a region: A case study. J. Geophys. Res. 111: G02005, doi:10.1029/2005JG000093.

64Appendix A. Comparison of Measured Air Concen tratio ns in Field Studies in the United Kingdom with Predicted Air Concentr ations using EPA’s Tier I Approach

The EPA background document “Scientific Issues Associated with Fi eld Volatilization of

Conventional Pesticides” included cases studie s where field data showed lower measured concentrations than the proposed Tier I proced ure estimated, see Table A-1 below. For these examples, the use of the proposed Tier I satura ted air concentrations overestimate the observed concentrations by several orders of magnitude.

Table A-1. Tier I Saturated air concentr ations (Cs) presented in EPA’s document, “Scientific

Issues Associated with Field Vo latilization of Conven tional Pesticides” (information taken from Tables 3 & 4)

Chemical A B C1 C2 D

VP (torr) 9.70E-06 1.80E-05 3.00E-06 7.20E-07 2.40E-02

MW (g/mole) 263.21 350.59 406.92 406.92 189.32

Cs (μg/m 3 ) 137 340 66 16 244521

Maximum

measured

concentration (μg/m 3 ) 4.38 27.7 3.8 0.2 8.9

ratio of Cs to

"maximum

measured C" 31 12 174 80 27,474

The Chemicals Regulation Directorate in the UK co mmissioned several recent studies to monitor residues in air following applications of pesticides with a range of vapor pressures, the lowest of

which are less than the case studies examples referred to in the EPA background document. These pesticides are shown in Table A-2. The Tier I saturated air concentration of trifluralin is estimated at about 1900 μg/m 3 . In field experiments, howev er, the highest concentration observed was about 90 μg/m 3 , see Figure A-1. This level of overestimation is similar to that observed in the case studies provided in the EPA background document.

65Table A-2. Examples of Tier I Saturated air concentrati ons (Cs) for pesticides included in recent field studies performed in the UK for the Chemicals Regulation Directorate

Chemical Trifluralin Fenpropidin Epoxi conazole Tebuconazole Prothioconazole

VP (Pa) 1.40E-02 1.70E-02 1.00E-05 1.30E-06 4.00E-07

VP (Torr) 1.05E-04 1.28E-04 7.50E-08 9.75E-09 3.00E-09

MW (g/mole) 335.5 273.5 329.76 307.81 312.2

Cs (μg/m 3 ) 1896 1877 1.33 0.162 0.0504

0 10 20 30 40 50 60 70 80 90 100

00:00 26/10 12:00 26/10 00:00 27/10 12:00 27/10 00:00 28/10 12:00 28/10 00:00 29/10 A i r b o r n e c o n c e n t r a t i o n , μ g t r i f l u r i l i n m 3 a i r

Time (hours:minutes) and date (day/month) 0.7 m receptor height

1.5 m receptor height

Figure A-1. Highest measured air concentrations during monitoring of trifluralin residues in air at receptor heights of 0.7 meters and 1.5 meters, both at 2 meters from the edge of 1 ha plot after application to bare soil wit hout incorporation. [Data from 2007 Defra Project Report PS2008: Measurements of bystander contamination during and post the application of pesticides relevant to arable crops in typical UK conditions Part 2: studies with a volatile formulation, available

online at: http://randd.defra.gov.uk/Document.aspx? Document=PS2008_5212_FRP.docm

.

66The Tier I saturated air concentration of fenpropidin is also about 1900 μg/m 3 while that of epoxiconazole is about 1 μg/m 3 . These two pesticides were app lied together as a tank mix of formulated products in a series of experimental trials. The results from the first trial are shown in Figure A-2 and Figure A-3. The observed concentr ations of fenpropridin were usually higher than those of epoxiconazole, and at most were about two orders of magnitude below the Tier I estimate. However, on the first day, after the application was completed and on the next day concentrations of epoxiconazole were equivale nt to the Tier I estimate were observed. In a second trial done in a later year, field concentra tions of epoxiconazole were above the Tier I concentration (see Figure A-4).

The air sampling method for these pesticides involv ed the use of Occupational Safety and Health Administration (OSHA) vers atile sampling (OVS) tubes. The first stage of these tubes is a filter that collects particles, and betw een a third and a half of the total pesticide in many air samples was found on the filter, but this mass is not includ ed in the vapor phase concentration value. An

absorbent to collect vapors comprises the second stage.

Members of the Panel questioned the possibility of a sampling artifact in the UK studies whereby particulate-phase pesticide residues were captured thereby increasing the measured air concentration value. The sampling artifact could have occurred if a portion of the particulatephase pesticide was stripped from the filter during sampling and retained by the sorbent bed in the second stage of the sampler. If this mechan ism were applicable, then much of the sample may not have been vapors in the field, and the vapor concentration may not have exceeded saturation. However, researchers in the UK had already anticipated this potential [artifact] and had undertaken a trial involving particulate sampling of epoxic onazole and fenpropidin using a personal cascade impactor collecting partic les 0.3-50 μm alongside the OVS tubes

. The results of this parallel sampling indicated no evidence that a significant proportion of the material collected

in the OVS tubes was associated with contaminat ed particles. Further te sts in a wind tunnel and

observations of the dust collected confirmed that had significant quantities of contaminated particles been present in the ai r they would have been detected on the impactor plates (Defra Project PS2016).

670 1 2 3 4 5

-25 175 375 575 775 M e a s u r e d c o n c e n t r a t i o n ,

μ g / m 3

Time after application, mins Receptor height 1.5 m

epoxiconazole

fenpropidin

0 5 10 15 20

-25 175 375 575 775 M e a s u r e d c o n c e n t r a t i o n ,

μ g / m 3

Time after application, mins Receptor height 0.7 m

epoxiconazole

fenpropidin

Figure A-2. Measured air concentrations of epoxic onazole and fenpropidi n residues at two receptor heights, at 2 meters downwind from the edge of a 4.8 ha treated area following application to an establishe d cereal crop in 2006. [Data from Defra Project PS2005, reported in

Bulter-Ellis MC and Miller PCH (2008), Progress in the developmen t of a bystander and resident exposure assessment model, Aspects of App lied Biology 84, 2008, International Advances in Pesticide Application.]

680 1 2 3 4 5

0 100 200 300 400 500 600 700 M e a s u r e d c o n c e n t r a t i o n ,

μ g / m 3

Time after a pp lication, min Location 2

epoxiconazole

fenpropidin

0 1 2 3 4 5

0 100 200 300 400 500 600 700


### M e a s u r e d c o n c e n t r a t i o n ,

μ g / m 3

Time after application, min Location 3

epoxiconazole

fenpropidin

Figure A-3. Further results from monitoring of epoxicon azole and fenpropidin residues in air at 0.7 metre receptor height, at two additional loca tions both 2 meters downwind from the same application as referred to in Figure A-2 [Dat a from Defra Project PS2005, reported in BulterEllis MC and Miller PCH, 2008, Progress in th e development of a bystander and resident exposure assessment model, Aspects of App lied Biology 84, 2008, International Advances in Pesticide Application.]

69Measured concentrations of epoxiconazo le and fenpropidin at two heights in the centre of the plot

0 0.5

1 1.5

2 2.5

3 3.5

4 4.5

5

12:40-13:45 13:50-14:45 14:55-16:45 16:50-18:48 18:50-22:00 22:05-03:40 03:45-12:15

Time periodConcentration in air, ng/l Epoxiconazole, 1 m

Epoxiconazole, 2 m

Fenpropidin, 1 m

Fenpropidin, 2 m

Figure A-4. Measured air concentrations of epoxicon azole and fenpropidin at the centre of a 3.7 ha plot at receptor heights of 1 and 2 metres af ter application to an es tablished cereal crop in June 2008. [Data from Defra Project Report PS2016 Investigation of the source of postapplication pesticide emissions from crops, available online at: http://randd.defra.gov.uk/Document .aspx? Document=PS2016_8607_FRP.doc

.

The air sampling method for epoxiconazole, fenpr opidin, thiabendazole and prothioconazole all involved the use of Occupational Safety and H ealth Administration (OSH A) versatile sampling (OVS) tubes. The first stage of these tubes is a fi lter that collects partic les, and between a third

and a half of the tota l pesticide in many air samples was found on the filter. An absorbent to collect vapors comprises the second stage. Whet her a significant portion of the measured vapor could have been stripped from the particulate fo rm on the filter and reta ined by the sorbent bed in the second stage was of some concern because if this mechanism were applicable, then much of the sample may not have been vapors in the field, and the vapor concentration may not have exceeded saturation. However, to investigate this possibility a trial wi th epoxiconazole, and fenpropadin, involving particul ate sampling was done using a personal cascade impactor collecting particles 0.3-50 μm alongs ide the OVS tubes. The result s of this parallel sampling indicated no evidence that a signi ficant proportion of the material collected in the OVS tubes was associated with contaminated particles. Furthe r tests in a wind tunnel and observations of the

dust collected confirmed that had significant quantities of contaminated particles been present in the air they would have been detected on th e impactor plates (Defra Project PS2016).

A third U.K. trial (Defra Project PS2023) wa s conducted in October 2009 at a laboratory independent of the first facilit y. In that trial a similar tank mi x of epoxiconazole and fenpropidin were applied to grass. Preliminary results s how that maximum concentrations of fenpropidin were similar to those in the previous trials, while maximum concentrations of epoxiconazole were slightly lowers those seen before.

70Table A-3. Measured air concentrations of epoxicon azole and fenpropidin (mean of 3 samples) in the centre of a 12 meter untreated square in a large field following expe rimental application to a grass crop in 2009. [Preliminary da ta from Defra Project PS2023.]

Epoxiconazole Fenpropidin

OVS

Sorbent OVS

Filter Total OVS

Sorbent OVS

Filter Total

ng m-3 ng m-3 ng m-3 ng m-3 ng m-3 ng m-3

30 min 66 410 476 6430 16047 22477

1 hour <LOQ <LOQ <LOQ 7265 10414 17679

2 hours <LOQ <LOQ <LOQ 4213 3538 7751

4 hours <LOQ <LOQ <LOQ 2115 1963 4078

6 hours <LOQ <LOQ <LOQ 87 604 691

8 hours <LOQ <LOQ <LOQ 121 462 583

12 hours <LOQ <LOQ <LOQ 85 509 594

24 hours <LOQ <LOQ <LOQ 23 167 190

36 hours <LOQ 1 1 10 136 146

48 hours <LOQ 14 14 5 22 27

3 days <LOQ 8 8 1 16 17

Finally, preliminary results are also available from monitoring a commercial application of products containing prothioconazole and tebuconazo le to wheat in the su mmer of 2009, the Tier I estimates for these compounds are 0.05 and 0.2 μg/m 3 , respectively. These data, Table A-4,

show that the maximum air concentrati ons of tebuconazole were about 1/10 th of the Tier I estimate. However, the maximum observations for prothioconazole were above the Tier I estimate for that compound. These observations cast doubt on the reliability of the proposed Tier I approach particularly for com pounds with low vapor pressures.

71Table A-4. Measured air concentrations of prothi oconazole and tebuconazole (mean of 3 samples) in the centre of a 12 meters untreated sq uare in the centre of a large field following a commercial application to a cereal crop in 2009. [Preliminary data from Defra Project PS2023.]

Prothioconazole Tebuconazole

OVS Sorbent OVS Filter Total OVS

Sorbent OVS Filter Total

ng m-3 ng m-3 ng m-3 ng m-3 ng m-3 ng m-3

15 min <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ

1 hour 7.78 <LOQ 7.78 <LOQ <LOQ <LOQ

2 hours 38.33 21.11 59.44 <LOQ 16.39 16.39

4 hours 44.17 35.14 79.31 <LOQ 16.53 16.53

6 hours 38.06 22.36 60.42 <LOQ 4.44 4.44

8 hours 59.17 57.92 117.09 <LOQ 5.42 5.42

12 hours 48.33 32.01 80.34 <LOQ 5.35 5.35

24 hours 24.68 4.17 28.85 0.93 4.54 5.47

36 hours 43.33 29.1 72.43 1.78 2.45 4.23

48 hours 23.08 1.76 24.84 3.26 15.05 18.31

3 days 3.78 1.26 5.04 <LOQ 0.79 0.79

4 days 2.73 1.33 4.06 <LOQ 0.91 0.91

5 days 1.44 0.66 2.1 <LOQ 0.37 0.37

6 days 0.88 0.39 1.27 <LOQ 0.49 0.49

7 days 0.84 <LOQ 0.84 0.22 <LOQ 0.22

8 days 0.24 <LOQ 0.24 <LOQ <LOQ <LOQ

9 days 0.22 <LOQ 0.22 <LOQ <LOQ <LOQ

10 days 0.2 <LOQ 0.2 <LOQ <LOQ <LOQ

11 days 0.19 <LOQ 0.19 <LOQ <LOQ <LOQ

12 days <LOQ <LOQ <LOQ 0.08 <LOQ 0.08

72Appendix B. Three Approaches for Predicting Vapor Pressure of a Chemical w ithin a

Mixture

Note: One panelist provided this discu ssion adapted from Chapter 6 of Industrial Hygiene Control of Airborne Chemical Hazards by W. Popendorf (CRC Press, 2006).

The vapor pressure of a chemical within a mixt ure can be predicted by three basic approaches:

 Raoult's Law for ideal mixtures,

 an empirical adjustment to Raoult's Law for non-ideal mixtures, and

 Henry's Law for dilute mixtures in water. Each approach has its advantages. Raoult's Law is simple, but many mixtures are not ideal. Unfortunately, failure to anticipate that a mixture is not "ideal" usually results in underestimating the health hazard or/and overexposing people to mixtures evolvi ng organic vapors. Empirical

coefficients are illustrative of non-ideal effects but are not simple to predict. And Henry's Law coefficients exist for many chemicals in water, but their accuracy is limited to very dilute

mixtures.

Raoult's Law for Ideal Liquid Mixtures

Raoult's law is based on the premise that molecule s within a liquid mixtur e all act independently of each other, leading to the logic that the numbe r or density of molecules of each component in the mixture that would be present at the liquid' s surface at any given moment would be reduced in proportion to that component's molar fraction within the liquid. Raoult's Law can be written as Eqn. B-1.

P vapor, i = X i  P vapor Eqn. B-1

The notation P vapor,i is used to designate the i th chemical's vapor pressure when it is present as a component of a liquid mixture. The symbol X i , indicates each component's molar concentration in the liquid state, viz. , X i is the moles of component "i" per mole of liquid mixture.

Chemical mixtures that obey Raoult's Law are calle d an ideal mixture, and mixtures that deviate

from Raoult's Law are called non-ideal mixtures. Most chemicals behave like a component in an "ideal mixture" when it is pr esent at high liquid concentratio ns (more than 50% pure) or/and when its molecules are structurally similar to the molecules of the other components within a mixture. Unfortunately, the molecular interactions within mixtures of organic solvents and water are sufficiently strong that such mixtur es often deviate from Raoult's Law.

The Need for an Empirical Adjustment to Raoult's Law

The more different that the molecules in a liqui d mixture are from each other, the more likely they are to interact and their vapor pressures are to deviate from Raoult's Law. The opportunity

for different molecules to inter act is small as long as there ar e still many identical molecules around ( i.e. , when the mixture is relatively concentrat ed), but deviations will get larger when most of the other molecules are "different" ( i.e. , when a component in a mixture is dilute).

73An "activity coefficient" is used in chemical engineering to adjust Raoult's simple, logical law

for ideal mixtures to match the behavior of non-id eal liquid mixtures. This activity coefficient is given the symbol “ γ ” (a lowercase Greek gamma). This γ is sometimes also referred to as a "fugacity coefficient." Equation B-2 both defines γ , mathematically, and e xpresses the empirical concept behind it.

γ i =

vapor P i X ivapor, PsRaoult' P i Y ivapor, Pactualor measured

  Eqn. B-2

In a mixture, each component "i" will have its own γ i . Each component's activity coefficient could be used in Eqn. B-3 to predict the abso lute vapor pressure in units such as mmHg. Equation 3 will revert to Raoult's law (Eqn. 1) when γ =1.

P vapor,i = γ i  X i  P vapor = γ i  Raoult’s predicted P vapor,i Eqn. B-3

Figure B-1 can provide some graphi cal insight into how values of six organic solutes in water vary both among the six chemicals and also within each mixture as a function of “X.” This figure shows how values increase as each solution gets more dilute and as the molecules become

more dissimiliar from water. The γ  for very symmetric organic molecules like benzene occurs

off-scale. The smallest γ  in this figure is for methanol because it is the chemical most similar to

water. A γ value greater than one means that the co mponent’s vapor pressure is greater than predicted by Raoult’s Law. Most organic chemicals when diluted in water will present a surprisingly high vapor hazard if their ac tivity coefficient is not anticipated.

Figure B-1. Activity coefficient [ γ i ] values for selected organic solutes in water. Adapted from Popendorf (2006).

74Methods to Predict an Empirica l Adjustment to Raoult's Law

The following four approaches to predict the activity coefficient γ i were adapted primarily from Chapter 11 of the Handbook of Chemical Property Estimation Methods (Lyman et al., 1982), a jewel of a resource if one is challenged to estimate an otherwise hard-to-get or non-existent property value. Other sources of property estimation methods are also available (Reid et al.

, 1987; Poling et al., 2001). The middle two methods below rely on experimental data for the particular mixture, while the first and last methods can estimate γ without prior mixture-specific

measurements.

A method first developed by Pierotti et al . (1959) and refined by Grai n (1982) can be used to predict γ  for binary mixtures. The method shown be low in Eqn. 4 was described by Grain as "easy" in that its seven co efficients are based on the overall molecular structure of the solute and solvent (rather than on each co mponent's individual moieties as in the Universal Functional Activity Coefficient (UNIFAC). Lyman's book cont ains four pages of tables and internal

correction factors for the seve n variables included in Eqn. 4.

     

      

      

 

2 2 2

21

1 1

2 1 22,11 )( log N F NND N C

N N BA  Eqn. B-4

The van Laar equation ( ca . 1910) provides a generic relationship to estimate γ i at any concentration based only on γ 1  and γ 2  derived either from measur ements or other predictive methods (Hirata et al. , 1975; Poling et al. , 2001; Grain, 1982).

2

2 2 1 1
1 1 γ l n X γ l n X 1 γ l n γ 

 

      

 l n Eqn. B-5

The most robust method to predict activity coeffi cients is via the group contribution method of UNIFAC (Poling et al. , 2001; Bishop et al. , 1982). While some software is available to implement the method, the lack of a database that defines the structures of even common chemicals or/and a user-friendly interface has ke pt these programs from being widely used.

Data in the Table B-1 excerpted from Grain (1982) allows one to compare activity coefficients at infinite dilution [ γ  ] for five common organic liquids in water The first column of γ  values were experimentally measur ed; the second column of γ  values were calculated by UNIFAC. The range of the percent e rrors by which the calculated γ  differs from the measured γ  that are presented in the third column of data suggest s that UNIFAC does not always predict activity coefficients accurately. However, even the highest of these errors (131% for toluene in water) is
much better than the 339,000% e rror that would result if no γ  value were used at all (equivalent to assuming Raoult's Law or γ  = 1 versus the γ  of 3390 measured experimentally).

75Table B-1. Comparison of activity coeffici ents at infinite dilution [ γ  ] for five common organic liquids in water (excerpted from Grain, 1982)

Experimental

γ i  in water Calculated

γ i  in water % error Experimental

γ water  in organic

water solvent organic

 

 

Hexane 489,000 402,000 -18% 1880 260

Toluene 3390 7820 131% 3320 1.0

Benzene 1730 1670 -3.5% 226 7.7

Aniline 34.2 50.6 48% 4.98 6.9

Acetone 6.80 5.69 -16% 5.64 1.2
average absolute error = 43%

Henry's Law

Henry's Law assumes that the vapor pressure of component "i" [P vapor,i ] is proportionate to X i via a fixed empirical coefficient called either a “Henry ’s Law constant” or "Henry's Law coefficient" denoted by the symbol "H i " herein. A common expr ession of Henry's Law might look like Eqn.

B-6.

P vapor,i = H i  X i Eqn. B-6

The major advantage of Henry's Law is the wide spread availability of its constants (Lyman et al.

, 1982; Yaws, 1992; MacKay et al. , 1992; Howard and Meylan, 1997; Sander, 2004). Unfortunately, the diverse origin of these coeffi cients has created an additional inconvenience because they have been developed with a consid erable amount of variability in the units of H i

. Fortunately, on-line conver ters are available, e.g. , at the following sites:

www.mpch-mainz.mpg.de/~sander/res/henry-conv.html

www.epa.gov/athens/learn2model/p art-two/onsite/henryslaw.htm

The limitation that Henry's Law only applies to d ilute mixtures follows from the fact that H i is a constant, independent of X i . Based on the prior discussion of how γ i decreases as the mixture gets less dilute, the relationship between H i and γ i can be found by equating a very dilute component's vapor pressure predicted by the empi rical adjustment to Raoult's Law (Eqn. B-3) to that predicted by Henry's Law (Eqn. B-6). 7

P vapor,i = H i X i = γ i X i P vapor (repeating Eqns. B-3 and B-6)

7 While the relationship in Eqn. B-6 is conceptually true, technically γ  can only be unitless as shown, if both H i and

P vapor have the same units. . Unfortunately, their units will likely not be the same, nor will their units be those commonly used within industrial hygiene.

76H i = γ  i P vapor Eqn. B-7a

γ  i =

vapor i
P

H Eqn. B-7b

Henry's Law coefficient [H i ] will be constant as long as the component's concentration is sufficiently dilute that γ i stays equal to the constant γ  . This range is best seen in Figure B-1. The curves of most activity coeffici ents flatten out to a constant γ  by the time the liquid molar

concentration decreases to about 10 -3 . The γ  of a few chemicals with small γ  values is already constant by the time X i reaches 10 -2 , while those with large γ  values aren't constant until X i is

less than 10 -4 . But in all cases, continue d use of a Henry's constant for more concentrated mixtures will eventually over-estimate the real component's vapor pressure and its resulting airborne exposures.

References :

Bishop, E.C., W. Popendorf, D. Hanson, and J. Pr ausnitz. 1982. Predicting relative vapor ratios for organic solvent mixtures. Am. Ind. Hyg. Assoc. J. 43:656-661.

Grain, C.F. 1982. Activity Coefficients. (In : Handbook of Chemical Property Estimation

Methods. W.J. Lyman, W.F. Reehl, and D.H. Rose nblatt (eds). McGraw-Hill, NY. pp. 1-53.

Hirata, M., S. Ohe, and K. Nagahama. 1975. Computer Aided Data Book of Vapor-Liquid Equilibria. Kodansha Limited, Elsevier Scientific, Tokyo, 933 pp.

Howard, P.H. and W.M. Meylan, 1997. Handbook of Physical Properties of Organic Materials

. P.H. Howard and W.M. Meylan (e ds.), CRC Press, Boca Raton, FL, 2112 pp.

Lyman, W.J., W.F. Reehl, a nd D.H. Rosenblatt (eds.), 1982. Handbook of Chemical Property

Estimation Methods. McGraw-Hill, NY, 876 pp.

Mackay, D., W.Y. Shiu, and K.C. Ma (eds.), 1992. Illustrated Handbook of Physical-Chemical Properties and Environmental Fate for Organic Chemicals. Volume I. CRC Press, Boca Raton, FL. pp. 1-54.

Pierotti, G., C. Deal, and E. Derr. 1959. Activity Coefficients and Molecular Structure. Ind. Eng.

Chem. 51: 95-102.

Poling, B.E., J.M. Prausnitz, and J.P. O'Connell. 2001. The Properties of Gases and Liquids

. McGraw-Hill, New York. 768 pp.

77Popendorf, W. 2006. Vapor Pressure in Mixtures. In: Industrial Hygiene C ontrol of Airborne

Chemical Hazards . CRC Press, Boca Raton, FL, pp. 141-172.

Reid R.C., J.M. Prausnitz, and T.K. Sherwood. 1987. The Properties of Gases and Liquids , 4rd Ed., McGraw-Hill, 688 pp.

Sander, R. 2004. Compilation of Henry's Law Consta nts (Solubilities) for Inorganic and Organic Species of Potential Importance in E nvironmental Chemistry. Accessible at: http://www.mpch-mainz.mpg.de/~sander/res/henry.html#2

.

Yaws, C.L. 1992. Thermodynamic and Physical Property Data. Gulf Publ. Co., Houston, TX. 224 pp.

78Appendix C. Alternative Approaches to Asse ss Inhalation Hazard in the Absence of Inhalation Toxicity Studies

One Panelist provided a discussion of alternativ e analytical approaches to assess inhalation

hazard in the absence of inhalati on toxicity studies. These appro aches are screening approaches and do not involve a chemical-specific hazard identification and dose-re sponse analyses. These screening approaches are Thres hold of Regulation, Threshold on No Toxicological Concern, and

the NOAEL-to-LC 50 approach. These approaches were orig inally developed to evaluate oral exposure to trace levels of chemi cals and to determine if toxicity testing should be required for those chemicals. There are only a few studies that evaluate inhalation ha zard in the absence of inhalation toxicity studies and the exposure durations in th ese studies [chronic, lifetime exposure (Drew and Frangos, 2007) and one-h our intermittent exposure (Grant et al. , 1997)] do not correspond to the exposure durations the Agency is evaluating. Therefore, the majority of the

Panel does not endorse the Drew and Frangos (2007) or Grant et al. (2007) approaches be used for pesticides in the interim in lieu of new inha lation toxicity studies. Th e following material is provided for informational and completeness purposes.

A Tiered Approach to Evaluate Acute Inhalation Toxicity (Texas Commission on Environmental Quality)

During the air permit review process, the Toxi cology Division (TD) of the Texas Commission on Environmental Quality (TCEQ) frequently evaluate s chemicals with limited toxicity data (LTD). When the minimum acute database requirement (as discussed in Section 3.4 of the Effects Screening Level (ESL) Guidelines (TCEQ, 2006)) is not met, an acute inhalation reference value (ReV) protective of a 1-hr intermittent exposure, is not developed. Instead, a tiered approach is used to either set a default screening value (i.e . Threshold of Regulation) or derive a generic health-based screening value depe nding on the availability of t oxicity information and time and resource constraints (Figure C-1).

 Tier I – Threshold of Regulation (default ESL = 1 μg/m3)

 Tier II – Threshold of Concern and Use of LC 50 Data (generic ESL)

 Tier III – Relative Toxicity/Pot ency Approach (generic ESL)

For the TCEQ, a generic short-term ESL is t ypically based on a one-hour averaging time. The following sections discuss the procedures used to set health-protective concentrations for LTD chemicals based on a tiered approach and the st rengths and weaknesses of these approaches. The Tier III - Relative Toxicity/Potency Approach will not be discussed.

79Figure C-1. A three-tiered approach to setting a default or a generic health-based ESL.

A Threshold of Regulation Approach

A Threshold of Regulation (TR) approach seeks to answer the question “if the concentration of a chemical in air is small, how small does an e xposure have to be before it can defensibly be regarded as presenting trivial risk ”. Other terms used to describe this approach are “threshold of

concern” or “threshold of no t oxicological concern.” Drew and Frangos (2007) provide a concise

review of the statistical , analytical procedures used to es tablish the Threshold of Regulation approach adopted by US FDA and other organizati ons for oral exposure a nd its use in regulatory toxicology. Briefly, the fifth per centile of the cumulative percen tage distribution of NOAELs in a large oral exposure database was determined a nd divided by an uncertainty factor of 100 (to account for animal-to-human uncertainty and human variability) to derive an estimate of an acceptable oral intake. Other investigators have us ed this approach for other products such as food flavorings, personal and household care pro ducts and pharmaceutical compounds (see Drew and Frangos (2007) and Grant et al. 2007 for references). Drew a nd Frangos (2007) developed a concentration of no toxicology concern (CoNTC) fo r evaluation of trace organic chemicals in air for evaluation of chronic exposure. This conservative generic CoNTC was derived by performing a route-to-route ex trapolation from the virtua lly safe oral dose of 1.5 μ g/person/day divided by a factor of two. Drew and Frangos (2007) proposed a generic CoNTC of 0.03 μ g/m 3

. It does not apply to metals, particulates or chemicals that produ ce sensory irritation.

Historically, the TCEQ has used a default of 1 μ g/m 3 as a TR value for evaluation of a 1-hr intermittent exposure compared to the value of 0.03 μ g/m 3 developed by Drew and Frangos (2007) for evaluation of chronic exposure. A value of 1 μ g/m 3 is conservative. Based on an acute inhalation database for 97 chemicals, the fifth percentile of NOAELs would be 560 μ g/m 3 (see Table 3, Grant et al. , 2007), and with a UF of 100 applied, it would be 5.6 μ g/m 3 as a TR value for evaluation of a 1-hr intermittent exposure. This TR concentration is an estimate of threshold

80air concentrations below which no appreciable ri sk to the general population would be expected

to occur after a one-hour intermittent exposure.

Strengths :

1. A TR approach is conservative since inhalati on toxicity of the most toxic chemicals is used to predict toxicity for all chemicals.

2. When a TR approach is used, inhalation t oxicity information for a pesticide is not required – only the monitored/modeled vapor concentration

3. The TR value is based on statistical anal yses of inhalation toxicity data, and the variability in the TR value can be determined

4. There is precedent in using a TR approach in a regulatory setti ng for oral data ( e.g., FDA 1995).

5. The monitored/modeled vapor concentration of semi-volatile pesticides in air are present in trace concentrations, a nd may be below the TR value.

6. Unnecessary animal studies are not performed because it identifies those chemicals that need additional testing.

Weaknesses :

1. The TR approach is only as good as the inha lation database used to derive the values. To develop a TR approach to evaluate diffe rent durations of exposure, one would need an adequate inhalation database for each exposure period. At the present time, an adequate database of inhalation toxicity data for vapors of pesticides for the durations of exposure the Agency uses in its assessment are not available.

2. The TR approach is conservative, and only ch emicals that are present in air in trace

concentrations would be screened out.

3. Since it is more difficult to conduct inha lation toxicity studie s, the numbers of acceptable inhalation toxicity studies for vapor -phase semi-volatile pesticides would be small compared to oral toxicity studies.

4. The TR approach is a screening approach and does not involve a chemical-specific hazard identification and dose response an alyses. Therefore, it does not produce a reference value that can be used in a margin -of-exposure approach or cannot be used to calculate a hazard quotient. Therefore, this screening approach does not lend itself to a cumulative assessment of different pa thways of exposure or pesticides.

Classification System to Categorize Chemical s into Different Toxicity Potency Classes

If a classification system can be derived to cate gorize chemicals into diffe rent inhalation toxicity potency classes, then a Tier II threshold of no toxicological concern ca n be derived for each toxicity potency class. Drew and Frangos (2007) provide a concise review of determining a threshold of toxicological concern for different potency classes using the Cramer classification system (Cramer, 1978) for oral toxicity studies.

For oral exposure, the Cramer classification system successfully assigns chemicals into separate toxicity potency classes. For inhalation toxicity, both Grant et al. (2007) and Ford et al. (2006)

81determined that the Cramer classification system ( i.e. , a structurally –based system based on oral exposure) does not adequately place chemicals in se parate toxicity potency classes predictive of inhalation exposures, so a structural alert system is not availa ble for inhalation toxicity. The main difficulty is the inability to accurately predict whether chemicals would cause respiratory tract point-of-entry effects: “Currently, there are no internat ionally recogniz ed screening methods in animals to predict th e ability of chemicals to cause local effects of the respiratory tract” (Rennen et al. , 2004).

Grant et al. (2007) used LC 50 data to classify chemicals into different inhalation toxicity potency classes. Ninety-seven chemicals were classifi ed based on the Globally Harmonized System of Classification and Labeling of Ch emicals proposed by the United Nations into different acute inhalation toxicity categ ories (from most toxic to least t oxic): Category 1, Category 2, Category 3, Category 4, and Category 5. The tenth percentile of the cumulative percentage distribution of NOAELs in each category was determined and divide d by an uncertainty factor of 100 to derive the following health-protective threshold of concern (TOC) concentrations for inhalation exposure: 4 μg/m 3 for chemicals classified in Category 1, 20 μg/m 3 for Category 2, 125 μg/m 3
for both Categories 3 and 4, and 1000 μg/m 3 for Category 5. These TOC concentrations are estimates of threshold air concentrations below which no appreciable risk to the general population would be expected to occur af ter a one-hour intermittent exposure.

Strengths :

1. By having a classification system to divide chemicals into different inhalation toxicity classes, one single threshold of regulation based on the most toxic chemical in the

entire dataset is not used fo r a relatively nontoxic chemical.

2. This approach is conservative since the most t oxic chemical within ea ch class is used to represent the inhalation t oxicity of all chemical s within one class.

3. The TOC values for each potency class are ba sed on a statistical analysis of inhalation toxicity data, and the variability in the TOC value can be determined.

4. There is precedent in using a TOC approach in a regulatory setting, mainly for oral data

5. Unnecessary animal studies are not performed because it identifies those chemicals that need additional testing.

Weaknesses :

1. This approach is only as good as the inhalation database used to derive the values. At the present time, an adequate database of inhalation toxicity data for vapors of pesticides for the durations of exposure the Agency uses in its assessment are not

available.

2. A predictive structural classi fication system is not availabl e for inhalation data, so LC 50 data are required.

3. Since it is more difficult to conduct inha lation toxicity studie s, the numbers of acceptable inhalation toxicity studies is small compared to oral toxicity studies, so the numbers of chemicals in each separate toxici ty potency class is small. This increases the uncertainty in the inhalation TOC concentrations.

824. This approach of classifying chemicals into different potency cat egories requires more inhalation toxicity st udies than a single TR appro ach discussed above or the LC 50 to NOAEL Ratios approach discusse d in the following section.

5. The TOC approach is a screening approach and does not involve a chemical-specific hazard identification and dose response an alyses. Therefore, it does not produce a reference value that can be used in a margin -of-exposure approach or cannot be used to calculate a hazard quotient. Therefore, this screening approach does not lend itself to a cumulative assessment of different pa thways of exposure or pesticides.

LC 50 to NOAEL Ratios

Layton et al. (1987) used oral LD 50 data for estimating acceptable daily intakes for the evaluation of exposures to contaminants at hazardous wast e sites. Venman and Flaga (1985) also proposed

the use of LD 50 data to establish provisional acceptable daily intakes for the evaluation of waste water contaminants. Both investigators calcula ted the ratio of NOAELs from chronic animal

studies to oral LD 50 data for different chemicals and de termined the fifth percentile of the

cumulative distributions of the ratios. The LD 50 value for contaminants with limited toxicity data was multiplied by the fifth percentile ratio to derive a surrogate NOA EL. The surrogate NOAEL was divided by an UF of 100 in order to establ ish a conservative thre shold dose below which no appreciable risk to human health wo uld be expected to occur. Grant et al. (2007) used the basic approach of Layton et al. (1987) and Venman and Flaga ( 1985) for evaluating chronic oral toxicity and applied it to acute inhalation toxi city. Therefore, ratios of NOAELs from acute

inhalation studies to LC 50 data were calculated.

For the NOAEL-to-LC 50 ratio approach, 55 chemicals with an inhalation NOAEL for an exposure duration < 24-hours were used to calculate NOAEL-to-LC 50 ratios. The tenth percentile of the cumulative per centage distribution of the rati os was calculated and divided by an uncertainty factor of 100 to produ ce a composite factor equal to 8.3 x 10 -5 . For a chemical with limited toxicity information, this co mposite factor is multiplied by a 4-hour LC 50 value or other appropriate acute inhalation le thality data as defined in Grant et al. (2007) to produce an

estimate of a conservative threshold air concentr ation below which no appreciable risk to the general population would be expected to o ccur after a one-hour intermittent exposure.

Strengths :

1. LC 50 data are available for most chemicals.

2. LC 50 data are predictive of acute inhalation toxicity, since LC 50 data were able to categorize chemicals into statistically signi ficant distributions and toxicity potency

classes.

3. This approach is conservative since the 5 th or 10 th percentile NOAEL-to-LC 50 ratio is applied to the chemical-specific LC 50 data.

4. The NOAEL-to LC 50 ratio approach is based on a st atistical analysis of inhalation toxicity data, and the variability in the ratio can be determined.

83Weaknesses :

1. This approach is only as good as the inhalation database used to derive the values. At the present time, an adequate database of inhalation toxicity data for vapors of pesticides for the durations of exposure the Agency uses in its assessment are not

available.

2. There should be an acceptable number of LC 50 data and matching inhalation NOAEL data for each duration of exposure being evaluated.

3. Since it is more difficult to conduct inha lation toxicity studie s, the numbers of acceptable pesticide inhalation toxicity studies is small, so the number of NOAEL-toLC 50 ratios may be inadequate.

4. This approach is a screening approach a nd does not involve a ch emical-specific hazard identification and dose response analyses. Th erefore, it does not produce a reference value that can be used in a margin-of-exposur e approach or cannot be used to calculate a hazard quotient. Therefore, this screening approach does not lend itself to a cumulative assessment of different pa thways of exposure or pesticides.

Time Extrapolation Factors or Assessment Factors

Refer to Kalberlah et al. (2002), Kramer et al. (1995, 1996), and Malkiewicz et al. (2009) for a discussion of time extrapolation or assessment factors. These references are provided for informational and completeness purposes only.

References

Cramer, G.M., R.A. Ford, and R. L. Hall. 1978. Estimation of toxic hazard - a decision tree approach. Food Cosmet. Toxicol. 16: 255–276.

Drew, R. and J. Frangos. 2007. The concentration of no toxicological concern (CoNTC): A risk assessment screening tool for air toxics. J. Tox. Env. Health, Part A. 70: 1584-1593.

FDA (Food and Drug Administra tion). 1995. Food additives; th reshold of regulation for

substances used in food-contact articles. Federal Register 60:36582–36596.

Ford, R., R. Davis, D. Gardner. 2006. Developmen t of inhalation thresholds of concern for the evaluation of tobacco additives. In: Society of Toxicology 2006 Annual Meeting, Toxicologist 45: 497 (Abstact #2317).

Grant, R.L., B.J. Kadlubar, N.K. Erraguntla , and M. Honeycutt. 2007. Evaluation of acute inhalation toxicity for chemicals with limited toxicity information. Reg. Tox Pharm. 47: 261-

273.

Kalberlah, F., U. Fost and K. Schneider. 2002. Time extrapolation and inte rspecies extrapolation for locally acting substances in case of lim ited toxicological data. Ann. Occup. Hyg. 46: 175-

185.

84Kramer, H.J., W.A. van den Ham, W. Slob, a nd M.N. Pieters. 1995. Derivation of conversion

factors to estimate an indicative chronic NOAEL from short-term toxicity data. RIVM Report

620110001. Bilthoven – the Netherlands. http://www.rivm.nl/bibliotheek/rapporten/620110001.html

Kramer, H.J., W.A. van den Ham, W. Slob, and M.N. Pieters. 1996. Conversion factors estimating indicative chronic no-observed-adverse-e ffect levels from short-term toxicity data. Reg. Tox. Pharm. 23: 249-255.

Layton, D.W., B.J. Mallon, D.H. Rosenblatt, and M.J. Small. 1987. Deriving allowable daily intakes for systemic toxicants lacking chronic toxicity data. Reg. Toxicol. Pharmacol. 7, 96–

112.

Malkiewicz, K., S.V. Hansson, and C. Rudén. 2 009. Assessment factors for extrapolation from short-time to chronic exposure – Are the REAC H guidelines adequate? Tox. Letters 190: 16-

22.

Rennen, M.A.J., T. Bouwman, A. Wilschut, J. G.M. Bessems and C.D. Heer. 2004. Oral-toinhalation route extrapolation in occupational health risk assessment: A critical issue. Reg.Toxicol. Pharmacol. 39:4-11.

Texas Commission on Environmental Quality (TCEQ). 2006. Guidelines to develop effects screening levels, reference values, and unit risk factors, RG-442. Available at http://www.tceq.state.tx.us/comm_ exec/forms_pubs/pubs/rg/rg-442.html.

Venman, B.C. and C. Flaga. 1985. Development of an acceptable factor to estimate chronic end points from acute toxicity data . Toxicol. Ind. Health 1: 261–269.

85Appendix D. Derivation and Elab orations on the Concepts of "Vapor Hazard Ratio" [VHR] and the "Environmental Dilution Ratio" [EDR]

Note: The following discussion is based on Chapters 5 and 8 of Industrial Hygiene Control of

Airborne Chemical Hazards by W. Popendorf (CRC Press, 2006) and W. Popendorf: Vapor Pressure and Solvent Vapor Hazards. Amer. Ind. Hyg. Assoc. J., 45(10): 719-726 (1984).

With the eventual goal in mi nd of providing a theoretical fr amework for separating factors

intrinsic to the chemical from factors intrinsic to the environment, this discussion begins with an explanation of evaporation. Although vapor pres sure is not the only factor that affects evaporation, it is the only factor that is intrinsi c to the chemical. All of the other variables that affect the evaporation rate depend either upon th e physical nature of the liquid source (like its size and shape) or upon the speed an d turbulence of the passing air.

As the vapors are swept away from the liquid surface by the passing air, more molecules evaporate from the liquid to maintain that same very localized equilibrium. Thus conceptually, the rate of evaporation is determined by how fast vapor molecules can diffuse across a thin, low speed " boundary layer " of air that always exists very close to a liquid surface. The thickness of that boundary layer (typically only a few millimeters along the vertical axis in Figure D-1) is determined by the air velocity, its turbulence, and the geometry of the liquid source (or its container if applicable).

The vapor concentration (t he horizontal axis in Figure D-1) at the liquid surface at the bottom of this boundary layer is always equal to the chemical’s vapor pressure [P vapor ]. The concentration at the top of the boundary layer is the ambient concentration [ambient P partial or C

room if indoors]. Figure D-1. Depiction of evaporation as

diffusion due to a concentration difference across a boundary layer.

Mechanisms of Vapor Generation

The liquid and its vapors are always considered to be in equilibrium with each other right at the liquid-air interface. The concentration of a vapor in equilibrium is (by definition) its vapor pressure [P vapor ]. P vapor can be equated to C in mg/m3 and to ppm. The latter equation is written here as Equation D-1 because of the practical importance of ppm ( cf. , C which also depends upon the chemical's molecular weight).

86atmosphere vapor P P sourcetheatppm 6 10 

 [ m

m

H

g

] 7

6

0 1 0 [ m

m

H

g

] P 6

v a p o r 

 Eqn. D-1

These conditions set the parameters for what is modeled as molecular diffusion across that boundary layer. Thus, the evapora tion rate is largely determined by a set of environmental conditions and one chemical-dependent condition: the chemical's vapor pressure. The chemical engineer's equation to predict evaporation rate is somewhat more complex than needed for field applications (Bird et al., 1960; Sherwood et al. , 1975; Fiegley et al. , 1981; Bishop et al. , 1982) However, industrial hygiene research has used an equation similar to Equation D-2 in many studies (Fiegley et al. , 1981; Bishop et al. ; Powell, 1984; Säämänen et al. , 1991; Braun and Caplan, 1992; Nielsen et al. , 1995; Nielsen and Olsen. 1995; Hummel et al., 1996).

) partial Pambient
vapor (P) 0.5 (V(A).) (Geom.Coef ] moles [Graten evaporatio

  Eqn. D-2

where ...

G moles = the evaporation rate in terms of moles of vapor generated per unit

of time. Both G

mass = MW x G moles and flux = G

mass /A could be created from this equation.

Geometric

Coefficient (Geom. Coef.) = an empirical "evaporative mass transfer coefficient" that characterizes the effect of the source geometry (its size and shape) on the thickness of the "boundary layer."

Area (A) = the size or su rface area of the volatil e liquid (ft2 or m2).

Velocity (V) = the velocity of the air passing over the evaporating source ( e.g. , fpm or m/sec).

P vapor = the vapor pressure of the ev aporating chemical at its liquid temperature. The units of P vapor are normally mmHg or Pascals which can be converted to mg/m 3 or ppm.

P partial = the partial pressure (or concentr ation) of the same vapor in the ambient air passing over the source. The chemical's ambient partial pressure is usually far enough be low its vapor pressure, that

ambient P partial can be disregarded and omitted, as shown in Equation D-3 and beyond.) v

a

p

o

r( P) 0

. 5( V( A)

.) ( G

e

o

m

. C

o

e

f( M

W) ] m

a

s

s [ G r a

t e n v

a

p

o

r a

t i o  e Eqn. D-3

One example of such an evaporation equation was developed for EPA by Caplan for organic solvents evaporating from a smooth surface like a spill of 0.5 to 3 feet in diameter (Caplan, 1989). These authors showed throug h experimental data that an equation equivalent to Equation

87D-4 can predict the evaporation ra te of a fairly wide range of organic chemicals from such a

smooth surface to within a factor of about 2-fold.

G [mg/min] = (0.0706) (MW) (A) (V 0.625 ) (P vapor ) Eqn. D-4

where ...

G = the vapor generation rate, mg/min. MW = the Molecular Weight, g/mole (added to convert moles to grams). 0.0706 = the empirical "geom.coef." A = the surface area of the liquid source, ft2. V = the air velocity over the liquid surface, ft/min.

P vapor = the vapor pressure of th e evaporating substance, mmHg.

In contrast to comments made within the Agency’s background document, the value of a

chemical's molecular diffusion coefficient has a minor impact on volati lization (due to the relatively narrow range of values of that coefficient among the chemicals of interest, such as a range of 1.11x among those pesticides in the white paper's Table 3) and even less of an impact on plume dispersion (in contrast to eddy diffusi on in dispersion). Eddy

diffusion is the dominant mechanism below and to the right of th e line in Figure D-2 representing the 1.5x10 -5 m 2 /sec molecular viscosity of air (Smagorinsky, 1974; Smagorinsky, 1981; Atkinson, 1995).

Molecular diffusion is too slow to be an important chemical transport mechanism

except either over very small distances or over very long times. 10 0 C h a r a c t e r i s t i c T i m e S c a l e , s e c o n d s

Characteristic Horizontal Distance, meters 10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8

10 -2 10 -1 10 0 10 1 10 2 10 3 10 cm 1 cm 1 m 10 m 100 m 1 km

1 year

1 month

1 week

1 hour 1 day

1 minute

1 second EDDY

DIFFUSION

SMALL SCALE
TURBULENCE MOLECULAR

DIFFUSION

L 2 / t = m o l e c u l a r v i s c o s i t y

the workplace

Figure D- 2. Adapted from Smagorinsky (1981).

Dilution of Vapors from Continuous Evaporation

The steady state concentration [C in mg/m3] equals the mass rate of evaporation (or G moles  MW) divided into some apparent volumetric flow rate of fresh air (Q in m3/min) into which the contaminant appears that it is being diluted, as depicted by Equation D-5.



### min]/[ ]/[ /[

3 3

3 

### mQ mmgG mmgCstatesteady

apparent mass 

###  min]/[ ]/[

3 mQ MWmimmolesG

apparent 

 Eqn. D-5

88It is important to understand that the subscr ipt "apparent" is used because the plume of contaminant represented by "G" is not diluted into any easil y definable volume flowing per minute (except in the case of t horough mixing inside a room serv ed by general ventilation). It only appears to be diluted because the concentrat ion [C] at any location is always less than C equivalent to P vapor at the source. The amount of dilution will depend, for example, upon one's

orientation and distance from the source.

At least in theory, Equations D-3 and D-5 coul d be combined into Equation D-6 (or Equation D-7) to predict the average concentration from continuous evaporation to which someone would be exposed. The concept is theoretical not only be cause "Geom.Coef." is difficult to predict for most sources ( e.g. , a task relegated to PRZM or PEAR L for pesticides) but also because Q apparent would depend upon the air flow pattern and the pers on's proximity to the source and the plume in a way that is currently difficult to predict (a task relegated to PERFUM). Nonetheless, this equation can be manipulated to good effect as explained below.

v a p o r

a p p a r e n t 0

. 5
3 P M

W Q V( A

r e

a)

.) ( G

e

o

m

. C o

e

f ] [ m

g

/ m C v

a

p

o

r  

 Eqn. D-6

v a p o

r

a p p

a r e n t 0

. 5 P Q 2 4

. 4

5 V( A

r e

a)

.) ( G

e

o

m

. C o

e

f p

p

m v

a

p

o

r  

 Eqn. D-7

The arrangement of the variables within Equation D-7 suggests th at the ppm vapor concentration at any location in the vicinity of an evaporat ing source can be viewed as having two groups of determinants: a group of environmental determinants (those that comprise the "ratio" on the right side of Equation D-7) and a chemical determinant (the chemical's vapor pr essure on the far right side of Equation D-7).

 None of the environmental determinants of exposure within eith er Equation D-6 or D-7 have anything to do with the chemical per se . The numerator in the parentheses comprises only the physical charac teristics of the source that affect its evaporation or flux rate. The denominator encompasses only the phy sical mechanisms that dilute the plume in the pathway from the source to the specifi c location at which the vapor concentration is being described. Together, this whole gr oup of environmental va riables determines by how much the vapors will get diluted between the P vapor concentration existing at the source and the concentration [C] at the loca tion of interest in an exposure scenario. Environmental factors only affect dilution, a nd (other than how temp erature affects vapor pressure) they do not affect the initia l vapor concentration at the source.

 The singular chemical determinant of exposure on the far right side of Equation D-7 has nothing to do with the environment in which th e chemical is being used (again other than the source temperature a ffecting vapor pressure [P vapor ]. Popendorf (2006) showed that a temperature increase of approximately 12°C or 21°F is required to double the P vapor of a wide range of organic solvents. This is less of an effect than many other factors affecting

89Most aerosols (except for fum es) have virtuall y no chemical-specific vapor pressure, but an equivalent G [mg/min] for aeros ols can be defined for any part icular aerosol source. Once an aerosol is generated, its dilution generally still depends only on Q apparent (as long as sedimentation is not important) (Bémer et al ., 2000). Thus, an aero sol concentration could be predicted using Equation D-6 in much the same way as for vapors if the aerosol generation rate G and environmental dilution due to Q apparent were known. However, Q apparent can rarely be predicted quantitatively in practice.

This partitioning allows one to examine the environmental dete rminants of exposure separate from the chemical determinants. Equation D-8 isolates the environmental determinants by dividing Equation D-7 by P vapor and inverting. The resulting ra tio is the amount by which the

vapor pressure concentr ation at the source [P vapor ] is diluted to create the resulting concentration at any location. Because the ratio in Equation D-8 is the amount of dilution created by the environment, this ratio is called the "Environm ental Dilution Ratio" or EDR. Again notice that all of the variables that comprise the EDR relate to the environment, not to the chemical being

used in that environment. This ratio is unitless as long as the units of concentration in both the

numerator and denominator are the same.

location definedaatppmorC ppmorCto converted P EDR vapor

 5.0 )(.).( VArea CoefGeom Q apparent

  Eqn. D-8

Separating the environmental from the chemical determinants of exposure allows chemical hazards to be viewed in a new way. In the tradi tional view, a chemical's concentration is either measured or predicted, then its acceptability is ba sed on the ratio of the chemical's exposure limit [denoted herein as EL in units of either mg/m3 or ppm] to that chemical's measured or predicted concentration [denoted herein as either C or ppm and must be in the same units as the EL]. In the occupational health field, e.g. , within OSHA, this ratio must be greater than one to be acceptable, as expressed by Equation D-9a. To relate the o ccupational health field approach to EPA’s MOE approach, the ratio of EL/C would need to exceed the pesticide’s MOE, as defined in Equation

D-9b.

Whether an acceptable ratio is 1 (as in Eqn. D- 9a) or an MOE (as in Eqn. D-9b) is a policy decision outside the purview of this summary.



### 1bemust ppmorCpredicted or measured [EL]Limit Exposure

 Eqn. D-9a

90MOEbemust ppmorCpredicted or measured ELLimit Exposure
 ][ Eqn. D-9b

Knowing that P vapor (or its equivalent in either ppm or mg/m3) is the maximum concentration at which a given chemical can exist as a vapor, one can anticipate that the maximum hazard that a chemical's vapor can potentially create is equivalent to th e ratio of how much greater P vapor is than its exposure limit. The "Vapor Hazard Rati o" or "VHR" as expressed mathematically in Equation D-10 is the maximum potential hazard th at a given chemical's vapors can generate.

unitssametheinELLimit Exposure mmgorppmofunitsinP VHR vapor ][ / 3

 Eqn. D-10

The two properties that comprise the VHR (its va por pressure and its exposure limit) are both intrinsic to a given chemical (and ha ve nothing to do with the environment per se ). Together, they specify the minimum amount by which a given chemical's vapor needs to be diluted by the

environment to reduce its concentration from its va por pressure at the sour ce to its exposure limit in the breathing zone. If the vapors in some one's breathing zone are not diluted by as much as the chemical's VHR, then that person will be overexposed. In terms of EPA’s MOE approach, the vapors must be diluted by the VHR times a desired MOE, e.g. , VHR x 100.

Nothing is wrong with evaluating a cceptability as simply the ratio of measured results to an exposure limit, but the separate concepts of chem ical dilution and environmental dilution can be re-combined advantageously. Think of looking at evaluation as a process of comparing the amount of dilution that a given chemical n eeds to the amount of dilution that a given environment actually creates. In terms of the MOE approach, this alternative view of evaluation is the same as asking if the Environmental Dilution Ratio in someone's breathing zone is sufficiently greater than the Vapor Hazard Ratio for the chemical being used, as expressed in Equation D-11.

M O

E b

e m

u

s

t V

H

R E

D

R

P / E

L P /

v a p o r v a p o r p

r e d i c t e d o r m

e a s u r e d   C Eqn. D-11

Conceptually viewing acceptability in its separa te chemical and environmental components can yield several benefits. For instance, one could anticipate that a given ch emical should only be

used in an environment that can dilute the vapors reaching a given popul ation (the "EDR" for that setting) by as much as the toxicity of a given chemical requi res its vapors to be diluted (the chemical's "VHR" times the applicable MOE). If an evaluation reveals that a defined population is being overexposed, then one can conclude that the environment cannot create sufficient dilution for the chemical being used. One could then compare the advantages of attempting to achieve an acceptable solution either by reducing the chemical's intrinsic VHR ( e.g ., by substituting an intrinsically safer chemical) or by increasing the e nvironment's ability to dilute a

91chemical reaching someone's breathing zone ( e.g. , by modifying or restricting the use conditions).

This concept could also be used in setting prio rities for both pesticides with intrinsic vapor hazards and use-settings susceptible to creating vapor hazards. Table D-1 within this appendix lists the VHR for about 40 pesticides based on their industrial exposure limits (TLVs are intended for manufacturing setti ngs, see Braun and Caplan, 1992). The VHRs in this table span over 11 orders of magnitude and 9 orders of magnitude excluding fumigants. For those chemicals with a VHR less than 1, its saturated va pors are less than its exposure limit, meaning they can only over-expose someone as an aerosol. A similar list could be generated to rank-order the pesticides of interest to th e Agency, but using HEC or RfC c oncentrations in the denominator (instead of C). Such a list, the Panel noted, could interface with an array of Environmental Dilution Ratios (EDR values based on flux ra te and dispersion scenarios modeled by PRZM,
PERFUM, etc .) to potentially refine the Agency's priorities in terms of use-settings.

92Table D-1. Pesticides that had an ACGIH TLV ® in 2005 are listed in ra nk-order of their Vapor Hazard Ratio (VHR = P vapor x 10 6 / TLV x 760). The compound's vapor pressure [P vapor ] is at

25 ° C unless otherwise indicated. Where the mass c oncentration is given in parentheses, the

TLV ® in ppm = C x 24.45/MW. Ad apted from Popendorf (2006).

Chemical Name TLV ®
[mg/m3] CAS# TLV [ppm] P vapor [mmHg] VHR

Carbon disulfide 75-15-0 10 358. 47,105 1,3-Dichloropropene 542-75-6 1 34. 44,737 Epichlorohydrin 106-89-8 0.5 16.4 43,158 Dichlorvos (DDVP) 0.1 62-73-7 0.011 0.053 6,303

Diazinon 0.01 333-41-5 0.0008 0.0112 1,834 Ethylene dibromide (EDB) 106-93-4 20 11. 724

Naled 0.1 at 20°C 300-76-5 0.0064 0.002 410 Mevinphos (Phosdrin) 0.01 7786-34-7 0.0011 0.00013 157 Heptachlor 0.05 76-44-8 0.0033 0.0004 161 Fonofos (Difonate) 0.1 944-22-9 0.0099 0.0002 27 Sulfotep (TEDP) 0.1 3689-24-5 0.015 0.00017 15

Lindane 0.5 58-89-9 0.042 0.00041 13

Aldrin 0.25 309-00-2 0.0168 0.00012 9 Dichrotophos 0.05 at 20°C) 141-66-2 0.0052 8.6e-05 4.4 Chlorpyrifos 0.1 2921-88-2 0.007 1.7e-05 3

Parathion 0.05 56-38-2 0.0042 9.7e-06 3

Fenthion 0.2 at 20°C 55-38-9 0.018 3.0e-05 2.3

Endosulfan 0.1 115-29-7 0.0060 1.0e-05 2 Methyl parathion 0.2 298-00-0 0.0186 1.7e-05 1.2

Carbofuran 0.1 at 20°C 1563-66-2 0.011 5.0e-06 1

Endrin 0.1 72-20-8 0.0064 3.0e-06 6.2e-01

Dieldrin 0.25 60-57-1 0.0160 5.9e-06 4.8e-01

Chlordane 0.5 57-74-9 0.0298 9.8e-06 4.3e-01

Thiram 1 137-26-8 0.102 1.7e-05 2.2e-01

EPN 0.1 2104-64-5 0.0076 9.5e-07 1.7e-01 Fenamiphos 0.1 22224-92-6 0.008 1.0e-06 0.16

Malathion 1 121-75-5 0.074 7.9e-06 1.4e-01

Ronnel 10 299-84-3 0.76 7.5e-05 1.3e-01

Ethion 0.05 563-12-2 0.0032 1.1e-06 0.06 2,4,5- Trichlorophenoxyacetic acid 10 93-76-5 0.96 3.8e-05 5.2e-02

Warfarin 0.1 81-81-2 0.0079 1.2e-07 1.9e-02 Paraquat 0.1 4685-14-7 0.0095 1.0e-07 0.01 Methoxychlor 10 72-43-5 0.7074 2.6e-06 4.8e-03 Carbaryl (Sevin) 5 63-25-2 0.608 1.4e-06 2.9e-03 Diquat (respirable) 0.1 2764-72-9 0.0071 1.0e-08 1.9e-03

Atrazine 5 at 20°C 1912-24-9 0.566 3.0e-07 1.0e-03 Azinphos methyl 0.2 86-50-0 0.0154 7.5e-09 6.4e-04 Temephos 1 3383-96-8 0.52 7.9e-08 2.0e-04

Rotenone 5 83-79-4 0.31 2.6e-09 1.1e-05 Benomyl 10 17804-35-2 0.842 3.7e-09 5.8e-06

Picloram 10 1918-02-1 1.01 7.2e-11 9.4e-08

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