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, Virginia 
NOTICE 

These meeting minutes have been written as part of the activities of the Federal Insecticide, 

Fungicide, and Rodenticide Act (FIFRA), Scientific Advisory Panel (SAP). The meeting minutes 

represent the views and recommendations of the FIFRA SAP, not the United States 

Environmental Protection Agency (Agency). The 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, hence, 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 committee operating in accordance with the Federal 

Advisory Committee Act and established under the provisions of FIFRA as amended by the 

Food Quality Protection Act (FQPA) of 1996. The FIFRA SAP provides advice, information, 

and recommendations to the Agency Administrator on pesticides and pesticide-related issues 

regarding the impact of regulatory actions on health and the environment. The Panel serves as 

the primary scientific peer review mechanism of the Environmental Protection Agency (EPA), 

Office of Pesticide Programs (OPP), and is structured to provide balanced expert assessment of 

pesticide and pesticide-related matters facing the Agency. FQPA Science Review Board 

members serve the FIFRA SAP on an ad hoc basis to assist in reviews conducted by the FIFRA 

SAP. Further information about FIFRA SAP reports 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. Matten, Ph.D., SAP Designated Federal Official, via email at matten.sharlene@epa.gov. 

In preparing these meeting minutes, the Panel carefully considered all information provided and 

presented by EPA, as well as information presented in public comment. This document addresses 

the information provided and presented by EPA within the structure of the charge. 

2
TABLE 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 .....................................................................................................73

APPENDIX C. ALTERNATIVE APPROACHES TO ASSESS INHALATION HAZARD IN 

THE ABSENCE OF INHALATION TOXICITY STUDIES ...............................................................79

APPENDIX D. DERIVATION AND ELABORATIONS ON THE CONCEPTS OF "VAPOR 

HAZARD RATIO" [VHR] AND THE "ENVIRONMENTAL 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 

5
Janice 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 College 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 

6
Michael 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 

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INTRODUCTION 

The Federal Insecticide, Fungicide, 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 Potomac 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 Designated Federal Official. Dr. Stephen Bradbury, 

Deputy Office Director for Programs, OPP, and Dr. Tina Levine, Director, Health Effects 

Division, OPP provided opening remarks at the meeting. Presentations of technical background 

materials were provided by Mr. Jeff Evans, Mr. Charles Smith, Dr. Judy Facey, and Dr. 

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

and Mr. Gabe Rothman from the Environmental Fate and Effects Division, OPP; and Ms. Annie 

Jarabek, National Center for Environmental Assessment, Office of Research and Development.

The Standard Operating Procedures for Residential Exposure Assessment, i.e., Residential 

SOPs, is a set of standard instructions for estimating residential exposure resulting from various 

direct, labeled pesticide uses. Individuals in residential settings can also be potentially exposed 

via indirect exposure to conventional pesticides. These types of exposures can occur through a 

variety of means including field volatilization of conventional pesticides, 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 volatilization of conventional pesticides. The 

following issues have been identified as key elements for this exposure scenario: 

1) Use of the Agency’s Reference Concentration (RfC) methodology to calculate Human 

Equivalent Concentrations (HECs) when inhalation toxicity studies are available. 

2) Comparison of the use of inhalation vs. oral toxicity studies. 

3) Development of a tiered approach to determine the level of complexity and refinement 

needed to estimate exposure, including: 

a) Use of available air monitoring data: California Air Resource Board (CARB), Pesticide 

Action Network – North America (PANNA), and other data sources. 

b) Development of a volatilization screening tool 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 concentrations around a treated field. 

EPA’s goal is to have a set of procedures that include transparent methodologies and data inputs 

that will guide the assessment of bystander exposure resulting from field volatilization of 

conventional pesticides in a straight-forward and user-friendly fashion. The Agency sought 

8
comment from the Panel on the adequacy of the toxicological and exposure assessment 

methodologies; the applicability, analysis, and use of available air monitoring data; the strengths 

and limitations of the models being considered by the Agency for predicting flux of conventional 

pesticides; and the overall presentation of the issues related to field volatilization of conventional 

pesticides with respect to scientific integrity and public transparency. 

9
PUBLIC 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 Natural 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 Farm 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, Pesticide 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 California Rural Legal Assistance Foundation 

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

8. Jorga Stewart, private citizen 

9. Carolyn Ashlock and Warren Trotter, 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 

10
SUMMARY OF PANEL DISCUSSION AND RECOMMENDATIONS 

TOPIC A: Exposure Assessment Issue

Traditionally, the Agency’s assessment of bystander inhalation exposure to volatile pesticides 

has relied extensively on the use of air monitoring 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 assessment methodologies developed for the fumigants 

and further adapted them by utilizing soil models to predict field volatilization 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 estimation methodology and use of soil models for 

predicting flux of conventional pesticides. Specifically, the Agency identified the following 

issues for the Panel to consider: 

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

Concentration. The Tier I approach incorporates the use of vapor pressure alone to arrive 

at a saturated concentration in air. The estimated 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 for 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 potential inhalation exposure concerns. 

Panel Response

The Panel chose to combine its response to Question 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, vapor pressure, solubility, and Koc in an empirically-derived 

function to estimate flux rates. This option has less [sic] constraints 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 results. Please identify any alternative 

methods, if any, which may be utilized to identify chemicals with potential inhalation 

exposure concerns.

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Panel 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 only 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 predictions that are higher or lower than actually found in field 

measurements and will not necessarily provide predictions that are protective. 

The Panel suggested that an alternative to the use of the proposed Tier I approach is to use the 

Woodrow et al. (1997) correlation approach to estimate a maximum 24-hour flux value. This 

approach is based on correlations between measured data and the physicochemical properties of 

the compounds, and it does not require an inordinate amount of data. This regression could be 

updated with all the latest studies and other relevant factors to increase its accuracy, a sensitivity 

analysis could be conducted on the model, then additional 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 utilizes fate and transport models to predict flux rates 

of applied pesticides that off-gas from treated fields. Current fate and transport models consider 

mechanisms related to volatilization, biodegradation, abiotic degradation, physicochemical 

properties, runoff, crop uptake, and leaching to account for the transformation and movement of 

the entire initially applied material. Volatilization mechanisms from bare soil and crop canopy 

surfaces are also important processes that 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 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 flux rates from treated fields. Option B requires 

extensive knowledge on environmental fate properties, as well as information related to 

application site, crop management and meteorology. 

b. Please comment on the applicability of using fate and transport models to predict flux 

rates given the state of the science. Please discuss the strengths and limitations of 

both models and how these impact the results. Please identify any fate and transport 

model(s) which the Agency has not considered in this analysis which would be 

applicable for pesticide applications 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 concentrations at different distances from the field 

is a sound Tier II approach for identifying volatile chemicals of concern within the confines of 

evaluating pesticide volatilization in treated fields (excluding spray drift). The Panel noted that 

12
while dispersion models, such as PERFUM, have been validated for fumigants, they have not 

been validated for semi-volatile pesticides. In addition, models such as PRZM or PEARL have 

also not been sufficiently validated 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 the Pesticide Emissions Model (PEM) (Scholtz et 

al., 2002a, b) be considered as a promising peer-reviewed, alternative model to predict flux rates 

of semi-volatile pesticides in treated fields.

TOPIC B: Toxicological Assessment Issues

As the Agency’s understanding of the state-of the science in inhalation toxicology has evolved so 

has the Agency’s approach to conducting inhalation 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 assessment paradigm, the Agency is seeking the SAP’s 

input on a number of key factors. They include the use of oral toxicity studies when inhalation 

studies are not available and the use of aerosol inhalation toxicity studies 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 indicates 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 exposure - 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 extrapolations using oral toxicity data to assess toxicity 

via the inhalation route is only scientifically justified, if a validated 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 systemic 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 the 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 

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d. The absorption efficiency for oral and inhalation exposure must either be identical or 

known, so that accurate values may be incorporated into any model. The absorption 

efficiency for oral and inhalation exposure must 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 absence of any data. Furthermore, the absorption 

cannot be differentially influenced by a toxic response unless this response is the same 

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

Even when the above criteria may be met, the Panel recommended an additional Uncertainty 

Factor (UF) of 10 for the final extrapolation for inhalation toxicity from oral toxicity data. The 

Panel stated that the use of uncertainty factors is not a replacement for more accurate models and 

data from more inhalation toxicity studies. 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-route extrapolation as described above. 

The Panel suggested that some of the problems associated with route-to-route extrapolation from 

oral toxicity studies to inhalation toxicity studies might 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 using 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 duration of exposure when calculating HECs from 

repeated exposure inhalation toxicity studies. 

b. The Panel noted two problems with the IEC equation. First, species differences in surface 

area/body weight are not accounted for, so that the equation needs to be modified. In 

cases for pesticides in Category 3 where route-to-route extrapolation may be justified, it 

would be better to adjust the animal oral dose to a human equivalent oral before further 

adjusting this value to an IEC for humans. 

c. The Panel recommended that the Agency consider Benchmark Dose (BMD) analysis 

when concentration-response data are available and are amenable to modeling. 

 

B.2) For a significant number of conventional pesticides, inhalation toxicity studies are not 

available. Please comment on the scientific strengths and weaknesses of available 

approaches that may be used in the interim to assess inhalation hazard in the 

absence of inhalation toxicity studies.

14
Panel Response

Overall, the Panel strongly recommended that the Agency conduct additional inhalation toxicity 

studies to adequately assess inhalation hazard. One Panel member provided a summary of 

alternative approaches 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 discussion 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 following 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 Agency anticipates exposures to vapors rather than the 

aerosolized particles. Please comment on the predictive capabilities of aerosol studies 

to identify potential toxic 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 account for the potential 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 potentially dose and toxicity. The biological 

impact of the relative portions in each phase, however, has been poorly studied. Therefore 

despite known mechanisms for potential differences in dose and absorption, 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 chemical under different 

phases. Some studies with mixtures have utilized 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 removal of the particulate phase of 

the mixture has not resulted in biological effects that differ from 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 Panel stated that there remain 

fundamental questions (beyond just the deposition site) related 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 will include that compound 

in both the vapor and particle phase. As a result, previous inhalation toxicity studies for semivolatile pesticide registration contained the vapor phase of that compound in the aerosol. 

15
Unfortunately, the studies conducted for pesticide registration did not measure the vapor portion 

of the mixture. The exposure monitoring in the inhalation toxicology 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 particle portion of the aerosol and the vapor that may 

have adsorbed on to the filter during sample collection. 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 and to use these data in health effects 

evaluations. 

2. Protocols for new inhalation toxicity studies (possible experimental designs). The 

Panel recommended that inhalation toxicity studies should be conducted with 

durations of exposure of up to 90 days. 

3. Uncertainty factors for quality of the database and what constitutes a minimum 

database. The Panel recommended that EPA establish 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 encompass 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 that contribute to the 

total potential exposure following application of pesticides. 

TOPIC C: Risk Assessment Issues

The Agency discussed its methodology for combining the exposure estimation methodologies 

and inhalation toxicological approaches to estimate postapplication bystander inhalation risks 

resulting from field volatilization of conventional pesticides. In estimating postapplication 

bystander inhalation risks, there are 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 concentrations around a treated field as well as when 

retreatment of the field may occur need to be considered. (3) Clearly define the uncertainties and 

limitations of this type of assessment. The Agency has identified the following issues for the 

Panel to consider with respect to estimating postapplication 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 risk assessment case study. Does the Panel agree 

that the postapplication bystander inhalation risk estimate case study appropriately matches 

16
the duration of the exposure with the proper toxicological study of the same duration? 

Please comment on the scientific strengths 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 limitations of the Agency’s use of the 

empirical and modeled air concentrations in the provided risk assessment case 

study. 

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

are much the same as the Panel discussed related 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 range of the results of the air 

concentrations estimated by more sophisticated computer-modeled 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 effect of an activity 

coefficient on vapor pressure, and it is applicable only to the first day post-application. 

The strengths of the computer-based modeled air concentrations are that they can account 

for dynamic changes in post-application conditions and residue history. Their weaknesses 

are the limited knowledge that users have of the internal components of these models, the 

concern that the components do not model evaporation from foliage as well as they model 

evaporation from soil, and that the particular analyses presented to the Panel 

inappropriately decoupled the variance of the flux from the variance of dispersion. 

C.2) Does the Panel agree that the postapplication bystander inhalation 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-toroute extrapolation process and the need to apply toxicological data collected from one 

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 

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 for 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 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 

17
half-lives of greater than 150 days, illustrating that intermediate exposures from single 

applications may occur. The Panel recommended 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 strengths and weaknesses of conclusions and 

characterization regarding the estimated risks presented in the case study. 

The Panel broadly agreed that the case study included 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 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 evaporation 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 approach based on the Volatilization 

Hazard Ratio (VHR) was presented and the case study chemical was evaluated using this 

approach. 

18
DETAILED RESPONSES TO CHARGE QUESTIONS 

TOPIC A: Exposure Assessment Issue

Traditionally, the Agency’s assessment of bystander inhalation exposure to volatile pesticides 

has relied extensively on the use of air monitoring 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 assessment methodologies developed for the fumigants 

and further adapted them by utilizing soil models to predict field volatilization 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 estimation methodology and use of soil models for 

predicting flux of conventional pesticides. Specifically, 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 approach incorporates the use of vapor pressure alone to 

arrive at a saturated concentration in air. The estimated 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 for 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 potential 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, vapor pressure, solubility, and Koc in an empirically-derived 

function to estimate flux rates. This option has less [sic] constraints 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 results. Please identify any alternative 

methods, if any, which may be utilized to identify chemicals with potential inhalation 

exposure concerns.

Tier II, Option B is a refined process which utilizes fate and transport models to predict 

flux rates of applied pesticides which off-gas from treated fields. Optimum fate and 

transport models consider mechanisms related to volatilization, biodegradation, abiotic 

degradation, physicochemical properties, runoff, crop uptake, and leaching to account for 

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

19
mechanisms from bare soil and crop canopy surfaces 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 Model (PRZM) and the Pesticide Emission 

Assessment at Regional and Local Scales (PEARL) which incorporate these mechanisms 

and have the utility for the prediction of flux rates from treated fields. Option B requires 

extensive knowledge on environmental fate properties, as well as information related to 

application site, crop management and meteorology. 

b. Please comment on the applicability of using fate and transport models to predict flux 

rates given the state of the science. Please discuss the strengths and limitations of 

both models and how these impact the results. Please identify any fate and transport 

model(s) which the Agency has not considered in this analysis which would be 

applicable for pesticide applications and crop management scenarios. 

Panel Response to Questions 1 and 2a

During the progression of the panel discussion, Questions 1 and 2a became linked. Therefore, the 

Panel combined its response to Question 1 with that of Question 2a. 

Vapor pressure clearly is an important physical property in evaluating the behavior of pesticides, 

especially with respect to volatilization from plant 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 Panel 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 proposed Tier I approach (referred 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 used to calculate a maximum air 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, temperature considerations, low vapor pressure (VP) 

pesticides, and pesticide formulation considerations. 

1. Overestimation of air concentrations. The saturated vapor concentration calculated from 

vapor pressure appears to generally over-predict the maximum concentration measured in 

field settings by such a wide margin as to make 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 concentrations 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). . 

20
the Tier IIA model for volatilization from soil as evidenced from the 0.88 R2 correlation 

between log flux and just the log VP across the 106

 range of VP values used to derive that 

model (Woodrow et al., 1997). However, as a simple predictor, 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 factors of 12- to 174-fold (with a geometric 

mean of 48-fold); however, the over-estimate for the one soil application (Chemical D) 

was 27,472 times greater than its maximum monitored concentrations. Thus, at first 

glance. Thus, at first glance, the Panel concluded that a simple comparison of vapor 

pressure to toxicity was too conservative to be useful. 

In contrast to the above generalization, several recent studies conducted in the United 

Kingdom showed errors in the opposite direction. One Panelist reported on studies 

(sponsored by the UK Department for Environment Food and Rural Affairs, Defra) in 

which the air concentrations of two of the five pesticides studied were higher following 

application than the concentration predicted by the Tier I model. In particular, the air 

concentrations of epoxiconazole and prothioconazole 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, air concentration at a height of 0.7 meters and 2 meters 

downwind from the field’s edge of a 4.8 hectare 2-3 hours after application was one and a 

half times the predicted saturated vapor concentration. And in a similar study at another 

location (Appendix A, Figure A-3), measured air concentrations from 0-90 minutes after 

application were nearly two times the predicted 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 air concentrations of 

prothioconazole peaked about 8 hours after application at over twice its saturated 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 temperature (typically 20-25oC) and therefore does not 

provide a realistic prediction of the concentration 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 function 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 the pesticide behavior observed in the UK data 

presented in Appendix A could be due to the difference between surface temperature and 

air temperature. A pesticide will 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 pesticide 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. 

21
3. Low vapor pressure pesticides. Vapor pressure data for pesticides with very low vapor 

pressures can be very difficult to obtain and may be subject to substantial 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 pressures 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.. Thus, the values for the vapor pressure of 

epoxiconazole and prothioconazole reported in the UK studies may have been in error. 

4. Pesticide formulation considerations. A pesticide's vapor pressure as a pure chemical 

[active ingredient(s)] has only a limited connection to its vapor pressure as it is actually 

used in the real world. For example, pesticides 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 ingredient 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 increased 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 Vapor Pressure in a Mixture

The best model for predicting a chemical's vapor pressure in a mixture 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 Xi is 

the molar concentration of the chemical within the mixture. Equation A2 both defines γi

mathematically and expresses the empirical concept behind it. 

Pvapor, i = γi × Xi × Pvapor = γi × Raoult’s predicted Pvapor, i Eqn. A1 

 Measured or actual Pvapor, i

γi = ───────────────── Eqn. A2 

 Raoult’s Pvapor, i = Xi × Pvapor

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 concentration 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. 

22
depicts 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 very high dilution). The magnitude of some activity coefficients 

in water is certainly sufficient to cause major deviations from an ideal mixture. For instance, a γ



of 50 is sufficient to increase a chemical's vapor pressure 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 order of magnitude. Unfortunately in the case of 

semi-volatile pesticides applied to crops, the pesticide concentration in water is an independent 

and dynamic variable (a pesticide applied in water may start 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 pesticide is likely to be absorbed 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 used quantitatively in that equation, the value of 

Hi must be in the same units as Pvapor). 

 Hi

 γ i

 Pvapor

∞ = ───── Eqn. A3 

The Panel commented 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 used. Henry's Law allows one to predict the vapor 

pressure of a component in a water solution based on a fixed empirical coefficient often called a 

“Henry’s Law constant” and denoted by the symbol "Hi " herein. A common expression of 

Henry's Law might look like Eqn. A4. While such constants are widespread 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 set of errors that are 

only touched on in Appendix B. 

 Pvapor,i = Hi  Xi Eqn. A4 

In principal, an activity coefficient could be applied to Raoult’s law 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 limited utility as part of a screening tool for 

these pesticides because the magnitude of the correction depends on the composition of the 

mixture. The Panel thought that it might be feasible to find the highest product of γi×Xi, 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 complex set of computations that go beyond the goals 

of a Tier 1 approach. 

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

The Panel proposed the Woodrow et al. (1997) approach as an alternative 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 data in addition to selected physicochemical 

properties of the compound when applied to soil. It allows for an evaluation 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 latest studies and other 

relevant factors to increase its accuracy. A sensitivity analysis could be conducted to refine this 

model. Additionally, confidence intervals around the model predictions could be calculated and 

safety factors could be added to the results of the 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/m2 hr) and ln [(VP x CF x AR)/(Koc x Sw]) 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), Koc is the soil organic carbon-water partition coefficient 

(mL/g), and Sw is the pesticide’s aqueous solubility (mg/L.

 

 X = 19.35 + 1.0533 ln 



 









SK WOC

ARCFVP Eqn. A5 

Similar correlations just involving vapor pressure were also developed 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, the correlation is grounded in measured flux data that could be 

used to calculate a more realistic 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 strengthen the reliability of 

predictions, e.g., Leistra et al. (2006). In addition, the Panel recommended 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 studies 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 would not adequately assess the 

potential impact of some spray from foliar applications bypassing the foliage and falling to the 

soil surface. Several panelists commented that foliage residues would, in most cases, contribute 

the largest fraction of the total flux to the air, at least for the first 24-hours after application. 

However, over longer spans of time, flux from the soil could contribute more significantly to the 

overall flux. However, this issue is a minor concern for the continued use of this model because 

only the maximum flux rate is used in the correlation and the model only to estimate the 

maximum flux rate on the first day and not to provide an estimate of subsequent temporal trends 

24
or concentrations beyond the first 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 application, soil temperature, and wind speed at the 

soil surface. 

The Panel noted that there is a lot of uncertainty 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 uncertainty will be added to modeled 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 associated with the measurements used to generate the flux values. 

These intervals would provide a high and low range of likely values and therefore address the 

Agency’s concerns that the model would under-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 available 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 equation 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 receptor location next to the field (i.e., 2-5 meters) and an air 

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

flux value could be calculated that would be needed to generate this concentration of concern. 

Flux values independently generated from the Woodrow model, including confidence intervals, 

could then be compared with the maximum flux value determined from the air concentration of 

concern for screening purposes (see also the Vapor Hazard Ratio (VHR) discussion 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 application may not include 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 dissolve before maximum flux from the water surface 

can occur. In these exceptional cases, the second or subsequent 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 Exposure Risk for Fumigants 

(PERFUM) to estimate air concentrations at different distances from the field is sound as a Tier 

II, Option B modeling approach for identifying volatile chemicals of concern within the confines 

of evaluating pesticide volatilization in treated fields (excluding spray drift).. However, the Panel 

25
also suggested that new insights might be gained by conducting the proposed 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 concentration of concern at a given receptor location and 

then use either a Tier I or Tier II screening procedure to establish a maximum acceptable flux 

estimate for the specific compound. The Panel suggested that the use of the Pesticide Emissions 

Model (PEM) (Scholtz et al., 2002 a, b) be considered as an alternative to the use of PRZM, as 

this model was created to describe processes controlling volatilization. 

The Panel recommended that novel technologies (sensors or rapid samplers) be explored to 

achieve higher temporal resolution in the concentration 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 flux estimates, such as EPA method OTM-10 that 

applies remote sensing techniques. The Panel noted that whatever method is considered, each 

will have different uncertainties and sensitivities 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 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. Only once the flux model is validated is the use of multiyear/multi-climate zone meteorological datasets appropriate. 

The Panel concluded that the flux model 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 "observed" 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 background document, p. 47) predicted roughly twice 

the measured air concentration, while in others it was as great as seven-times the 

measured concentration. One possible explanation for such deviations is that the 

measured data used for comparison were not based on actual source volatilization flux 

data, but on a back-calculation of source volatilization fluxes that 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 field data that reflects actual experimental 

conditions before it is evaluated for efficacy and ruggedness (with averaged data). Only 

then can the predictive capability 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 Tier II models would be paramount in assessing the accuracy 

26
and reliability of the model’s predictive capabilities. 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 recommendations 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, adding unrealistic variability to the surface flux 

predictions. Therefore, it is important first 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, direct flux measurements, including eddy 

accumulation as well as indirect methods, such as flux-gradient relationships, should 

be used in addition to the Gaussian plume 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 Gaussian plume inversion approach for 

estimating fluxes within an orchard. Trees in an orchard can create coherent eddies 

organized within the geometry of the orchard, and create a stable sub-canopy layer of 

air. These effects are not taken into account in the plume models employed to infer 

fluxes. This is also a problem for atmospheric concentrations estimated from orchard 

emissions using PERFUM, because the Panel understood that PERFUM is based on 

the Gaussian plume model and is not adapted to simulate dispersion and transport 

within plant canopies. 

d. The Panel expressed concern that the uncertainty associated with physicochemical 

properties, such as the Kow, soil half-life, and photodegradation, are not incorporated 

into the current modeling efforts. The Panel recommended that the Agency perform 

sensitivity analyses to determine the factors (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 range of fluxes predicted by the model. Flux 

estimates, even for a given day of meteorological conditions, can be computed as a 

probability distribution. 

e. Flux model evaluations should include situations involving both soil and vegetation 

sources from soil and vegetation application of pesticides. 

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

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

27
g. In addition, the Panel indicated that downwind concentration data should also be 

collected during the field validations of the flux model in order to validate a coupled 

flux/dispersion model approach for semi-volatile pesticides. While dispersion models 

have been field-validated for fumigants, they have not been field-validated for semivolatile pesticides or in other soil types or crop scenarios. Because of their high 

volatility, fumigant vapors tend to remain airborne for longer periods than semivolatile pesticides, which tend to be removed 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 fate than fumigants, may have a longer 

persistence in the environment, and may 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 also be a source of more toxic chemicals, 

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

The Panel stated firmly that models used to predict the atmospheric concentration of a 

pesticide should include enough terms to make the predictions act as reasonable 

surrogates of the measured concentrations. This 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 include subtraction terms it will not accurately 

follow the progression of the flux and is likely to significantly over 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 often decay rapidly even once collected from 

field samples. Much of this degradation can be attributed to photodegradation, 

reinforcing the need for inclusion of photodegradation 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 atmosphere is reduced by photoloysis 

(although not to zero). Existence of this term in the model will help to differentiate the 

more photo-labile compounds from others that may have a similar vapor pressure, but do 

not undergo photodegradation. 

28
Photodegradation rates of pesticide vapors have been studied for several pesticides, (see 

review by Atkinson et al., 1999). In some cases, photodegradation 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 toxic than the thions, 

and thus could contribute significantly to the hazard associated with the vapor during 

downwind drift. Therefore the chemical product of the photodegradation reaction must 

also be considered. 

3. Volatilization dispersion prediction models should include scenarios with temperature 

inversions. Temperature inversions are common in some parts of the US whereby an air 

mass may be trapped and normal dissipation and dilution due to air mixing and 

ventilation are impeded. Inversions could create the potential for exposures to airborne 

pesticide concentrations that are higher and for a longer duration of exposure than 

expected. 

The Panel stressed that monitoring and modeling should be flexible enough to take into 

account unusual topographical, meteorological, and other environmental features that 

might affect exposures resulting from pesticide residues in the air. For example, 

temperature inversions are common in some parts of the United States. Temperature 

inversions in agricultural valleys or other topography can trap the air mass, and impede 

normal vapor dissipation due to dilution during 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 Joaquin Valley of California, an area of very 

high pesticide use including in the winter, wintertime 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 phase (pesticide suspended in fog water droplets) that can 

concentrate some pesticides, aiding their deposition to non-target crop foliage, and 

potentially changing the airborne composition from one dominated by the vapor form to 

one that is aerosol-dominated. How processes such 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 use reporting and a national air monitoring 

network for pesticides to more accurately assess community exposure. 

The Panel recommended that more expansive pesticide usage data, as well as more 

regional air pesticide monitoring data, be generated 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 understanding of the environmental fate of pesticides. This type of system 

could also be used in other regions, especially where volatile or semi-volatile pesticide 

use is high. A standardized air monitoring network in agricultural regions of the US 

would also be extremely useful. These data could be used to further validate pesticide 

emission and atmospheric transport models and to estimate pesticide exposures to 

communities. 

29
5. The Panel suggested a more careful evaluation of the literature on ambient air monitoring 

in agricultural communities, e.g., Lee et al. (2002), Kollman (2002), as well as field 

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

The Panel recommended that there be a broader review of the volatilization flux 

measurement literature. Studies exist that can be used to evaluate these models. A rich 

literature exists concerning flux measurement methodologies, uncertainties, and 

measurement requirements. For example, scientists such as Ralph Nash, Dwight 

Glotfelty, Alan Taylor, and William Spencer have carried out studies that discuss in 

detail the factors that influence the volatilization flux from soil and other surfaces (see 

“Recommended References”). Other studies report the volatilization flux of pesticides 

from flooded rice fields as contributors to measured 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 the use of field flux chambers for measuring 

volatilization in the field. Many of the field volatilization flux papers published before 

1995 have been compiled in Majewski and Capel (1995). Rich literature also exists 

concerning transport within forest canopies. A selection of literature that the Agency 

might find useful is provided in the “Recommended 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 ambient data available as of 

2000 (Kollman, 2002; Tables 1-2). The Panel recommended that EPA present summary 

tables of all of these measured air concentrations for two reasons. First, these levels 

would be useful in describing the breadth and magnitude of potential public health 

concern for pesticide volatilization from agricultural 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 that is a function of evapotranspiration, as 

originally proposed by Jury et al., (1983), or a constant laminar 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 aerodynamic 

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 

30
utilized in the flux modeling. The Panel stressed 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-emphasizes the fraction retained in the soil. 

Those assumptions will then cause an underestimation 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 scientific workshops or conferences to bring 

together the scientific community 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 Agency 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 used by the Agency to estimate volatilization 

flux and air concentrations should focus first 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 deposited pesticides (repeated cycles of volatilization, 

transportation, and re-deposition of applied pesticides), especially during fog events. 

Such re-volatilization processes are not considered within 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 pesticide 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 study 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 crop management practices such as irrigation, 

tilling, mulching, and burning of fields may have a potential to increase volatilization. 

These practices and their effects on pesticide volatilization should also be considered. 

31
Terminology Corrections in Agency’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 describing 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 meteorology at differing heights typically with thermo anemometers to provide 

high resolution information about environmental 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 reflects 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 recognized common flux methods in the peer-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 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 assessment paradigm, the Agency is seeking the SAP’s input on a 

number of key factors. They include the use of oral toxicity studies when inhalation studies are 

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

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

consider: 

B.1) The analysis conducted by the Agency indicates 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 exposure - 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

Route-to-Route Extrapolation Issues 

The Panel noted that EPA placed significant emphasis in the background document on the ability 

to use toxicological data generated from oral exposures to establish Inhalation Equivalent 

Concentrations (IECs) even though they recognized 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 commented that this book was an outgrowth of a 

workshop sponsored by the EPA in March 1990. Many concepts and issues raised in this book 

were also later addressed by Rennen et al. (2004), with essentially the same conclusions being 

reached. Basically, the only chemicals that are candidates for route-to-route extrapolation are 

those having no portal of entry effects and those whose kinetic behavior is independent of 

exposure route. The summary report section of Gerrity and Henry (1990) provides a decision tree 

for assessing how the information available for a given chemical can be used to identify the path 

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

Review of the available literature evaluating route-to-route extrapolation indicates that, for the 

most part, there is no pattern of consistency in the results when oral toxicity data are used to 

model inhalation toxicity for most chemicals. Extrapolation results appear to be somewhat 

random, with oral to inhalation extrapolation 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 extrapolations using oral toxicity data to assess toxicity 

via the inhalation route is only scientifically justified, if a validated 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 systemic for both entry routes and this effect is 

independent of route of exposure. 

33
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 the 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 environment of respiratory tract fluids. Pesticide 

stability in gastric fluid should be of primary 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 little to no value in estimating any inhalation toxicity. 

d. The absorption efficiency for oral and inhalation exposure must either be identical or 

known, so that accurate values may be incorporated into any model. The absorption 

efficiency for oral and inhalation exposure must 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 absence of any data. Furthermore, the 

absorption cannot be differentially influenced by a toxic response unless this response 

is the same via both routes and/or is influenced by relatively the same extent via both 

routes. 

Even when the above criteria are met, the Panel recommended an additional UF of 10 (as an 

example) for the final extrapolation for inhalation 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 clearly show that repeated exposures 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-route extrapolation as described above. 

Use of In Vitro Studies 

The Panel noted that some of the problems associated with route-to-route extrapolation from oral 

toxicity studies to inhalation toxicity studies might be evaluated by simple in vitro studies, such 

as solubility and stability in simulated gut or lung fluid (as discussed in more detail below), or by 

consideration of the chemical structure of the pesticide and using structural 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 instability in a gastric fluid would therefore suggest a 

much lower toxicity through ingestion than inhalation. 

 Solubility of the inhaled chemical within lung fluid 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 screening process. Similarly, synthetic gastric fluids 

may be used as a screen for considering not just bio-solubility, but also pesticide stability 

through the ingestion pathway. Both of these solubility tests can also be used to screen 

34
particulates as well. Finally, if a route-to-route exposure model for inhalation of vapors is to be 

evaluated, then a more appropriate dosing model might be injection rather 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 delivered dose can be approximated by animal exposure 

studies. Cell culture experiments can potentially be used to predict 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 the fluids interacting with the target 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 final level would be creating an estimate 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 because 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 animals used in exposure testing above. For the 

bioaccessibility and biosolubility in vitro models, the physiological endpoint being approximated 

is dose delivered to the next compartment, generally the blood. 

Equation for IEC 

The EPA background document presents an equation for calculating an IEC that attempts to take 

into account various factors, such as minute ventilation, animal body weight, exposure duration, 

absorption efficiency, etc.; however, the Panel noted two problems with this equation. First, 

species differences in surface area/body weight are 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 for humans. The Panel stressed that the use of an 

IEC equation should be restricted to cases where the vapor falls into EPA’s RfC Category 3 and 

also meets the four extrapolation 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 Adverse Effect Levels (LOAELs) as bases for 

calculating IECs or Human Equivalent Concentrations (HECs). The Panel noted that the value of 

the NOAEL or LOAEL is sensitive to the nature of the experimental design used to conduct the 

study and, therefore, recommended that the Agency 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 consider that reflect magnitude of risk. The Agency 

could then select the option(s) that most closely align with their specific risk management goals. 

35
Use of Haber’s Law in Duration of Exposure Adjustments 

The Panel stated there were several problems with the way in which EPA used Haber’s Law (or 

Rule), in making adjustments for the duration 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 margin of protectiveness 

for chemicals, for which Cmax 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. Basically, 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 actually results in an under prediction of risk (see 

Case C, Figure 1). In contrast, the Panel indicated 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 Development (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 concentration and time to a fixed level 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 would likely be of great 

interest to OPP, particularly those that relate to acute exposure modeling and those that account 

for CN

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

Berge models). 

36
Figure 1. Log time-log concentration plots for the general power law family, Cα

 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 pesticides, inhalation toxicity studies are not 

available. Please comment on the scientific strengths and weaknesses of available 

approaches that may be used in the interim to assess inhalation hazard in the absence 

of inhalation toxicity studies.

Panel Response

Overall, the Panel strongly recommended that the Agency conduct inhalation toxicity studies to 

adequately assess inhalation hazard. One Panel member provided a summary of alternative 

37
approaches 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 discussion 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 inhalation toxicity studies for the following 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 duration 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 Agency anticipates exposures to vapors rather than the 

aerosolized particles. Please comment on the predictive capabilities of aerosol studies 

to identify potential toxic 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 account for the potential differences between 

vapors and aerosols?

Panel Response

Considerations for Addressing Question of Vapor/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 potentially dose and toxicity. The biological 

impact of the relative portions in each phase, however, has been poorly studied. Therefore 

despite known mechanisms for potential differences in dose and absorption, 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 chemical under different 

phases. Some studies with mixtures have utilized 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 removal of the particulate phase of 

the mixture has not resulted in biological effects that differ from 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 Panel stated that there remain 

fundamental questions (beyond just the deposition site) related 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 particulate matter in the air. Volatile flux from 

crops will result in concentrations that are substantially below the saturation vapor pressure of 

38
most vapors. Therefore, in most cases, re-equilibration will be defined not by concentration, but 

by other factors such as the presence 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 studied, it is indeed plausible that significant 

amounts of vapors existed in the laboratory studies 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 given environmental condition and concentration. 

For a simple case where only one compound is present, this model can utilize first principles. 

However, it should be noted that for a semi-volatile material where the aerosol was sampled with 

a filter, one could obtain inaccurate dose estimates 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 powders at high concentrations. These conditions do not represent 

the true physical form of the pesticide as it exists after volatilization at relatively low 

concentrations. The toxicologically relevant impact of this is that vapors may be 50-100 % 

absorbed in the respiratory tract; depending on the reactivity of the material, the vapors may or 

may not penetrate deep into the respiratory tract prior to removal. Studies with highly reactive 

and highly soluble vapors in rodents have shown that the biological response is limited to effects 

in the nasal passages and upper tracheobronchial (TB) airways. Particles 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 likely 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 reasonable certainty. 

An approach to link aerosol data with vapor data would need to make the assumption that the 

form of the material will not impact absorption 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 particles and evolving modeling approaches for vapors. In this case, 

dose relationships between the studies can be established, and responses can be related in a more 

appropriate way. Once the site of deposition is better understood, another important 

consideration is the nature and type of biological effects observed. These may be able to be 

related to the site of deposition for local effects, and to bioavailablity/absorption for systemic 

effects. For compounds that show high absorption, the vapor and particle components of the 

aerosol will likely show reasonably similar systemic biological responses following deposition. 

39
Potential Modeling Approach for Using Aerosol Studies to Assess Vapors 

Based on the discussion during the 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 Model (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 deposition. 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, the MPPD model can be used with this input 

data to determine the amount of the pesticide 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 Deposited 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; however, the methods used, during the aerosol 

generation did not create particle distributions 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 alveolus, etc.), the next step is to determine 

which dose metric best fits or is likely to best correlate with the biological response of 

interest. For the extrathoracic (ET) region, the total mass deposited per unit surface area 

.is probably the only dose metric that can currently 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 total alveolar surface area. 

3. Use air: blood partition coefficients, solubility, Henry’s law values, etc. to determine the 

likely regions of the respiratory 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 published 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 found, use the lower value of the interval, as 

40
that 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 alveolar region, then the total uptake might be 

apportioned as 40% ET and 10% TB. 

4. Use the appropriate ventilation data for the animals in the pesticide aerosol study and 

compute what vapor phase exposure 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 pesticide aerosol is primarily deposited. However, 

since the cells lining the non-olfactory epithelia in the ET and TB regions are reasonably 

similar (Miller et al., 2010), a mass per unit surface area dose 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 what 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 possible way to obtain information on the vapor form 

when only aerosol exposure studies are conducted. 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 EPA assess it further to determine its ultimate 

utility. 

ADDITIONAL DISCUSSIONS ON INHALATION TOXICITY 

The Panel discussed various topics that were an outgrowth of the charge questions. The order of 

the topics is not in order of priority. 

Appropriate Averaging Times and Sampling Devices for Field Measurements 

Currently, measurements of pesticide levels collected 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 presented 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 crop fields or even within orchards. Thus, there 

are significant opportunities for both children and adults to be repeatedly exposed to volatilized 

pesticides. Moreover, given the large variability in exposure levels in the first hours following 

pesticide application and then the subsequent temporal variability, 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 variability of the pesticide concentration in the air 

soon after pesticide application and over the next few days. 

41
Appropriate 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 sample switching are commercially available. 

Given the activities in which workers must engage, there is a strong likelihood 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 inhalation toxicity study conducted 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 are 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 field studies that are 

conducted to assess the variability in air concentration of the pesticide particles and vapors. 

 

The earlier time points (i.e., 1, 7, 14, and 28 days) would tend to capture the initial application 

period for the pesticide, any potential reapplications 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 potential requirement of 

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

In addition, the studies should consider the likely types of biological effects that might be 

expected for the given type of pesticide. The Panel pointed out that the Agency should consider 

that new toxicity studies take advantage of the scientific advances in understanding 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 cases where there is a predicted level of flux 

above a certain threshold. This may be determined, as discussed above, through flux modeling 

derived through fate/transport studies. 

Uncertainty Factors for Quality of the Database and What Constitutes a Minimum 

Database 

Other EPA offices (e.g., Office of Water, Office of Research and Development, Office of Air 

and Radiation) and regulatory programs have established criteria for what constitutes a minimum 

42
database for chronic exposure that must be available 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 only a minimum database is available for their 

assessment. 

Moving Toward a Cumulative or Total Risk Assessment 

The Agency’s background document notes that potential pesticide exposures can occur from 

three types of scenarios: during 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 document is only concerned with volatilization of 

pesticides following application, the Panel noted that 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 the risk assessment process. For example, the 

Panel acknowledged the difficult situation that was occurring to crops on the Jacobs Farm (oral 

comments provided by Mr. Larry Jacobs, 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-entrainment 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 the hazard quotient (HQ) or similar approach to 

assess risk from each of the types of exposure that 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 application and drift. Moreover, those living close to 

fields where pesticides are applied are likely to receive exposure from more than the 

volatilization route. Hazard quotients from different pathways (or from different pesticides) 

could be added together to calculate an overall hazard index [HI]. If the resulting HQ or HI is ≤

1, then adverse health effects would not be expected. 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, then it would indicate health effects would be 

likely to occur. The hazard quotient would seem to offer a better alternative than a MOE 

approach to aggregate risk. Hazard indices for pesticides with the same mode of action could be 

used for assessing cumulative risk from inhalation exposure to multiple pesticides. 

TOPIC C: Risk Assessment Issues

The Agency discussed its methodology for combining the exposure estimation methodologies 

and inhalation toxicological approaches to estimate postapplication bystander inhalation risks 

resulting from field volatilization of conventional pesticides. In estimating postapplication 

bystander inhalation risks, there are a few principles that should be followed: (1) properly match 

the duration of the exposure with a proper toxicity 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 uncertainties and limitations of this 

43
type of assessment. The Agency has identified the following issues for the Panel to consider with 

respect to estimating postapplication 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 risk assessment case study. Does the Panel agree 

that the postapplication bystander inhalation risk estimate case study appropriately matches 

the duration of the exposure with the proper toxicological study of the same duration? 

Please comment on the scientific strengths 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 limitations of the Agency’s use of the empirical 

and modeled air concentrations in the provided 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 limitations 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 coefficient on vapor pressure, and is applicable only to the first day postapplication. 

The strengths of the computer-based 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 internal components of these models, the concern 

that the components do not model evaporation from foliage as well as they model 

evaporation from soil, and that the particular analyses presented 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 concentrations 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 that were available and used when the model was 

originally developed by Woodrow et al. (1997). 

On the other hand, one Panel member suggested that 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. 

44
flux 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 with a vapor pressure of 1 x 10-4 mm Hg, the Woodrow model 

(translated in Equation C2 out of its log-log format) would predict an initial 24-hour flux rate of 

0.0032 /m2/hr.3

 

Flux day 1 in Woodrow model = 8.574 x 106

 x Pvapor

 0.85543 Eqn. C2 

One can use Equation C3 to calculate this flux 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 initial residue flux

initial residue

theindayort Woodrowcase

 = 





 





 )0032.024(22.0

22.0 ln

1 = 2.33 days Eqn. C3

The average exposure within any chosen interval 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 examples 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 effects, it is notable that the maximum ratio between the first hour 

and the full 24-hours never exceeds 2-fold, at least 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 extrapolations should be viewed only as a potential extension of the 

Woodrow model (a Tier IIA model) and should not 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-estimate vapor exposures if the pesticide also 

dissipated into the soil and decayed 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 Pvapor) = A + B ln(C) + ln(Pvapor B) 

flux = eA + B ln(C) + ln(Pvapor) = eA + B ln(C) x e

B ln(Pvapor) = eA + ln(C) x PvaporB 

45
Table 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 fraction of the instantaneous 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 

46
Table C2. Results of using an exponential decay model 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 end 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 interval 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 

47
Discussion of Tier IIB Flux Models

A summary of a number of suggestions made by different Panel members regarding the use of 

flux models is provided below. 

1. Several Panelists suggested that the Agency use the volatilization flux models to 

explore an array of crop and use patterns and to explore multi-field applications and 

multiple applications of a given pesticide within a field. One variation on this theme 

was the idea of using the models to develop "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 representative historical 

meteorological data to identify 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 predictions by a given substance’s flux. In the 

absence of information on particular pesticide fluxes, a literature review 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 that 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 model that was developed 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 panel members were aware of, validated for any 

other class of pesticide or other field conditions. Not only was this model used to 

evaluate semi-volatile pesticides with very different physicochemical properties than 

fumigants, it was also applied to field environments that were radically different (orchard 

and cabbage fields) from the typical fumigation field (fallow, flat). The only way this 

model, or any other for that matter, can be reliably used to predict source volatilization 

flux and downwind air concentrations is to field validate them under the typical field 

conditions the various pesticides in question will be used. 

3. The confidence in using the PERFUM model for semi-volatile pesticides can be 

increased considerably by conducting several field volatilization/model evaluation 

studies on select semi-volatile pesticides used on a range of crop types (e.g., cover crop, 

short row crop, tall row crop, orchard) in representative 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 each of those areas where the other studies 

were conducted, as was done in the presented case study. Then those results can be 

compared to the actual field and modeled results. This will show almost immediately if 

this kind of exercise has any validity. If the results show promise, then the models can be 

further refined/fine-tuned, and subsequent predictions of the model on other semivolatile pesticides in other field situations can be better defended because the model has 

been rigorously tested and compared against a variety of actual field situations. 

48
 

4. Another suggestion was to prioritize risks by starting with an acceptable concentration. 

This suggestion was independent of prioritizing 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 the 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 postapplication bystander inhalation 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 data 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 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 for 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 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-term 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 communities 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 matching 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 points were made previously during the Panel’s 

response to charge questions in Topic B. 

1. Several Panelists discussed uncertainty in the specifics of the exposed 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.

49
within the Agency’s case study and the implications to the appropriateness of the UFs that 

were applied to address concerns about children’s protection. The Panel indicated that the 

answer to the charge question depends both upon the UFs that were applied and the 

quality of the toxicological data such as the inclusion of developmental toxicity studies or 

studies conducted during critical life stages (i.e., children, teenage years) in the database. 

A new suggestion that may have broad applicability to the Agency was to view 

developmental/reproductive oral studies as informative in terms of effects, but not to be 

used quantitatively in terms of inhalation toxicity. In oral studies, if developmental, 

reproductive, or childhood effects occur at significantly higher concentrations than the 

critical effect, this would provide qualitative information on whether these effects may 

occur at low concentrations after inhalation exposure. 

Toxicology for Risk Assessment (TERA) organized 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 and children, and the second 2005 consultation 

addressed issues related to the adequacy of the database uncertainty factor: These 

documents are available at http://www.tera.org/peer/AdultChildTK/ACTKWelcome.htm.

2. The Panel reiterated that all of the previous discussion in Topic B regarding the 

applicability of Haber's law applies to this case study. In the 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 extrapolated to another 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 toxicity study is used in a route-to-route extrapolation of 

inhalation toxicity. The term reference concentration is typically used for values where 

UFs have been applied to the appropriate point-of-departure; therefore, it would be 

inappropriate to use this term 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 inhalation hazards, the Panel was interested in how 

the MOE approach could be used to combine the three routes of exposure via 

volatilization, spray drift, and respirable particles to assess cumulative or aggregate 

inhalation risk. Alternatively, the Panel suggested 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 

50
quotients from different pathways (or from different pesticides) could be added together 

to calculate an overall hazard index (HI). For additional information, HQs from different 

pathways (or from different pesticides) could then be added to calculate a HI. If the 

resulting HQ or HI is ≤ 1, then adverse health effects would not be expected. If the HQ or 

HI is slightly > 1, then it would not necessarily mean that health effects would occur but 

that further evaluation was necessary. 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 strengths and weaknesses of conclusions and 

characterization regarding the estimated risks presented in the case study.

The Panel broadly agreed that the case study included 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 conclude 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 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 evaporation 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 approach based on the VHR is presented below. 

Strengths 

The ability to predict the concentration of vapors emanating from pesticides sprayed onto fields 

and crops and to assess the risks to bystanders is challenging. The multi-component and multistep models that predict exposure are not simple, and the toxicity data pertinent 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 ability to integrate the variability in real world 

settings and to generate a distribution 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 distribution of the resulting MOEs were believable. 

Weaknesses 

 

In the larger sense, the Panel concluded that the case study is about as good of a result as this 

approach can achieve at the present time. The pesticide selected for this case study may be one of 

the few that have sufficient data to link exposures over different intervals with relevant 

toxicological data points. The Panel 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 

51
dispersion 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 increase confidence in the 

exposure values. 

 

Several Panelists agreed that exposures as short as (or shorter than) 1 hour should be of interest 

to the Agency. As discussed in Topic A, such initial peak exposures should be derived either 

from a dynamic model or (preferably) from actual measurements over shorter intervals than has 

been the historic practice, and not from average conditions over longer intervals. However, it 

may turn out that exposures in the first hour are 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 PEARL models. The Panel provided several ways 

to better understand the uncertainties in each variable and suggested opportunities to increase the 

confidence in the model outcomes. For instance, 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 analyses should be conducted on the 

variables within the models to distinguish the relative 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 increase 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 calculate 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 level 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 (BMC), the central estimate, and 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 (Dinis-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 disease (Betarbet, 2000; Gorrell, 1998; Thirachelvam, 

2000). The principal limitation in paraquat’s use 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 pesticides in Table 3 and 4 in the Agency’s 

background document. Moreover, there is potential inhalation exposure to paraquat (a defoliant), 

52
e.g.,smoking leaves of treated plants. Paraquat residues may be re-volatilized when smoking a 

cigarette, i.e., the VP substantially increases when heated to a cigarette’s burn temperature, for 

example. The smoke carries the paraquat into the deep long where it can be extracted by lung 

fluid. Re-volatilized residues would be estimated using flux models, assuming that they are 

sophisticated enough to account for mixtures (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 combined 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 the 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 vapor exposure hazard is first separated into its 

chemical-specific components via the VHR and its environmental components via the 

"Environmental Dilution Ratio" [EDR]. A determination 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 strictly 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 value 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 approach 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 chemical 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 other settings, the EL for a given chemical must 

simply be greater than its measured or predicted C, or the ratio in Equation 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 ratios 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 

53
The VHR is mathematically defined by Equation C7 as the amount of dilution a given chemical 

needs between the chemical's saturated vapor concentration 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 the 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 toxicity would be defined quantitatively 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 similar rank-order in each 

such scenario's list). Such a rank ordered list of VHR values could easily identify those 

pesticides with the greatest (and 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 modified 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. 

exposure unitssametheinlimit

3 mg/morppmofunitsin Pvapor HR  V Eqn. C7 

The EDR is mathematically defined by Equation 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 several panelists referred to as "back-calculating 

exposure." 

definedanyin location partial P

VHRtheinasunitsin Pvapor EDR  Eqn. C8 

The criterion implied by Equations C5 and C6 do not incorporate any Margin of Exposure 

(MOE).To relate the EDR and VHR approach to the MOE approach for an “acceptable 

exposure” to a pesticide, the ratio of both EL/C and EDR/VHR would also need to exceed the 

MOE for that chemical, as depicted in Equation 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 environmental conditions separately from the various 

pesticides. For instance, the Panel thought that the Agency could explore the use conditions that 

result in low EDR values and thereby target high risk crops (e.g., as a function of foliage density 

or height), weather (e.g., as a function of atmospheric stability or temperature), or regions (e.g., 

54
as a function of soil conditions). Similarly, the Panel noted that the Agency could identify those 

pesticides that have a high VHR based on an array of temporal exposure scenarios with their 

appropriate toxicological end points. 

The following discussion illustrates how both the EDR 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 saturated air concentration (in Table 4, these values are 660 μg/m3

 for 

chemical C1 and 16 μg/m3 for chemical C2, or 209 μg/m3 when combined5) divided by the 

predicted air concentration (Table 9 contains both a Tier IIA prediction of 5.3 μg/m3

 and the 

corresponding "Max 24-hour Air Concentrations" Tier IIB predictions of 2 or 4 μg/m3

 by PRZM 

and PEARL, respectively). These values yield an EDR of 209/5.3 = 39 for Tier IIA or 105 and 

52 for PRZM and PEARL, respectively. These predicted 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 approach would be to create a table of 

VHR values structured like Table D-1 of Appendix D in which each exposure limit applicable to 

general industry is replaced by a HEC or RfC concentration 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 comments 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 document. This error was apparently not internal 

to the μg/m2/s flux predictions presented in Table 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 evaporation of liquid solvents. 

In Table 4, the ratio for Chemical B of the Tier I Model concentration of 340,000 ng/m3

 to the 

maximum monitored concentration of 27,700 ng/m3 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 for chemical D in Table 5. The only way to get the value of 

0.91 μg/m2/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 C1 and isomer C2 (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/m3

. . 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. 

55
not provided to the reader.

Table 8 and Table 10 Values

One Panelist made a minor editorial comment regarding certain toxicological calculations used 

in the case study. Some values in Table 10 do not correspond to corresponding 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 exposure. 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. Perhaps, “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 most up-to-date scientific methods in toxicity 

assessments for chemicals with adequate toxicity 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 adjustment factors, or the use of the Multiple Pass 

Particle Dosimetry Model (MPPD). The background document does not discuss the potential for 

certain segments of the population to have differential susceptibility, 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 discussion 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. 

56
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Suggested References from Panelists

Aerodynamic methods/modeling of airborne transport of pesticides: 

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

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5703–5713. 

61
Ramaprasad, J., M.-Y. Tsai, R.A. Fenske, E.M. Faustman, W. Griffith, A. Felsot, K. Elgethun, 

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rivers. Part I: the vapour transport 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. Hebert, M. G. Yost and R.A. Fenske. 2006. The 

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agricultural community following fixed-wing aircraft applications. J. Exp. Sci. Environ. 

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concentrations using correlations with estimated vapor pressures. J. Agric. Food Chem 49: 

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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: 

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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 measurement with conditional sampling. J. Atmos. 

Oceanic Tech. 7: 349-352. 

Maximum and minimum integration times required for accurate and precise flux 

measurements: 

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

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

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Lenschow, 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 corrections for eddy correlation systems. Bound.-Layer 

Meteor. 37: 17–35. 

Uncertainties in flux measurements 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 flux 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 fluxes 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. Statistical 

properties of random CO2 flux measurement uncertainty 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. Suyker, S.B. Verma and S.C. Wofsy. 2006. A multisite analysis of random error in tower-based measurements of carbon and energy fluxes. 

Agric. Forest Meteor. 136: 1-18. 

Valentini, R. and Verma, S. 2002. Energy balance closure at FLUXNET sites. 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 fluxes. J. Atmos. Ocean. Technol. 11: 1018–

1025. 

Horst, T.W. and J.C. Weil. 1992. Footprint estimation for scalar flux measurements in the 

atmospheric surface layer. Bound.-Layer Meteor. 59: 279–296. 

63
Schmid, H.P. 2002. Footprint modeling for vegetation atmosphere exchange studies: A review 

and perspective. Agric. Forest Meteor. 113: 159–183. 

Schuepp, P.H., M.Y. Leclerc, J.I. Macpherson, and R.L. Desjardins. 1990. Footprint prediction 

of scalar fluxes from analytical solutions of the diffusion equation. Bound. Layer Meteor. 50: 

355–373. 

Turbulence and transport within plant canopies: 

Finnigan, J. 2000. Turbulence in plant canopies. 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 Processes with Complex Obstructions. NATO Science 

Series II: Mathematics, Physics and Chemistry, Vol. 236. Springer, New York. 414 pp. 

Staebler, R. M. and D.R. Fitzjarrald. 2005. Measuring 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 measurements in difficult conditions. Ecol. Applic. 

18: 1340-1350. (Note: Whole volume is worthwhile.) 

Wang, W., K.J. Davis, B.D. Cook, D.M. Ricciuto, and M.P. Butler. 2006. Decomposing CO2 

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. 

64
Appendix A. Comparison of Measured Air Concentrations in Field Studies in the United 

Kingdom with Predicted Air Concentrations using EPA’s Tier I Approach

The EPA background document “Scientific Issues Associated with Field Volatilization of 

Conventional Pesticides” included cases studies where field data showed lower measured 

concentrations than the proposed Tier I procedure estimated, see Table A-1 below. For these 

examples, the use of the proposed Tier I saturated air concentrations overestimate the observed 

concentrations by several orders of magnitude. 

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

Issues Associated with Field Volatilization of Conventional 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/m3) 137 340 66 16 244521 

Maximum 

measured 

concentration 

(μg/m3

) 

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 commissioned 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/m3

. In field experiments, however, the highest concentration 

observed was about 90 μg/m3, see Figure A-1. This level of overestimation is similar to that 

observed in the case studies provided in the EPA background document. 

65
Table A-2. Examples of Tier I Saturated air concentrations (Cs) for pesticides included in recent 

field studies performed in the UK for the Chemicals Regulation Directorate 

Chemical Trifluralin Fenpropidin Epoxiconazole 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/m3) 

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 without 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. 

66
The Tier I saturated air concentration of fenpropidin is also about 1900 μg/m3 while that of 

epoxiconazole is about 1 μg/m3

. These two pesticides were applied 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 concentrations 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 equivalent to the Tier I estimate were observed. In a 

second trial done in a later year, field concentrations of epoxiconazole were above the Tier I 

concentration (see Figure A-4). 

 

The air sampling method for these pesticides involved the use of Occupational Safety and Health 

Administration (OSHA) versatile sampling (OVS) tubes. The first stage of these tubes is a filter 

that collects particles, and between a third and a half of the total pesticide in many air samples 

was found on the filter, but this mass is not included 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 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, researchers in the UK had already anticipated this potential [artifact] and 

had undertaken a trial involving particulate sampling of epoxiconazole and fenpropidin using a 

personal cascade impactor collecting particles 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 contaminated particles. Further 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 the impactor plates (Defra 

Project PS2016). 

67
0

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

M -25 175 375 575 775

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 epoxiconazole and fenpropidin residues at two 

receptor heights, at 2 meters downwind from the edge of a 4.8 ha treated area following 

application to an established cereal crop in 2006. [Data from Defra Project PS2005, reported in 

Bulter-Ellis MC and Miller PCH (2008), Progress in the development of a bystander and resident 

exposure assessment model, Aspects of Applied Biology 84, 2008, International Advances in 

Pesticide Application.] 

68
0

1

2

3

4

5

M 0 100 200 300 400 500 600 700

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 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 epoxiconazole and fenpropidin residues in air at 

0.7 metre receptor height, at two additional locations both 2 meters downwind from the same 

application as referred to in Figure A-2 [Data from Defra Project PS2005, reported in BulterEllis MC and Miller PCH, 2008, Progress in the development of a bystander and resident 

exposure assessment model, Aspects of Applied Biology 84, 2008, International Advances in 

Pesticide Application.]

69
Measured concentrations of epoxiconazole 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 period

Concentration in air, ng/l

Epoxiconazole, 1 m

Epoxiconazole, 2 m

Fenpropidin, 1 m

Fenpropidin, 2 m

Figure A-4. Measured air concentrations of epoxiconazole and fenpropidin at the centre of a 3.7 

ha plot at receptor heights of 1 and 2 metres after application to an established 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, fenpropidin, thiabendazole and prothioconazole all 

involved the use of Occupational Safety and Health Administration (OSHA) versatile sampling 

(OVS) tubes. The first stage of these tubes is a filter that collects particles, and between a third 

and a half of the total pesticide in many air samples was found on the filter. An absorbent to 

collect vapors comprises the second stage. Whether a significant portion of the measured vapor 

could have been stripped from the particulate form on the filter and retained 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 with epoxiconazole, and 

fenpropadin, involving particulate sampling was done using a personal cascade impactor 

collecting particles 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 contaminated particles. Further 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 the impactor plates (Defra Project PS2016). 

A third U.K. trial (Defra Project PS2023) was conducted in October 2009 at a laboratory 

independent of the first facility. In that trial a similar tank mix of epoxiconazole and fenpropidin 

were applied to grass. Preliminary results show that maximum concentrations of fenpropidin 

were similar to those in the previous trials, while maximum concentrations of epoxiconazole 

were slightly lowers those seen before.

70
Table A-3. Measured air concentrations of epoxiconazole and fenpropidin (mean of 3 samples) 

in the centre of a 12 meter untreated square in a large field following experimental application to 

a grass crop in 2009. [Preliminary data 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 tebuconazole to wheat in the summer of 2009, the Tier 

I estimates for these compounds are 0.05 and 0.2 μg/m3

, respectively. These data, Table A-4, 

show that the maximum air concentrations of tebuconazole were about 1/10th 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 compounds with low vapor pressures. 

71
Table A-4. Measured air concentrations of prothioconazole and tebuconazole (mean of 3 

samples) in the centre of a 12 meters untreated square 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 

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

Mixture 

Note: One panelist provided this discussion 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 mixture 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 evolving 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 molecules within a liquid mixture all act independently 

of each other, leading to the logic that the number 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 = Xi  Pvapor Eqn. B-1 

The notation Pvapor,i is used to designate the ith chemical's vapor pressure when it is present as a 

component of a liquid mixture. The symbol Xi, indicates each component's molar concentration 

in the liquid state, viz., Xi is the moles of component "i" per mole of liquid mixture. 

Chemical mixtures that obey Raoult's Law are called 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 present at high liquid concentrations (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 mixtures often deviate from Raoult's Law. 

The Need for an Empirical Adjustment to Raoult's Law

The more different that the molecules in a liquid 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 interact is small as long as there are still many identical molecules 

around (i.e., when the mixture is relatively concentrated), but deviations will get larger when 

most of the other molecules are "different" (i.e., when a component in a mixture is dilute). 

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

for ideal mixtures to match the behavior of non-ideal 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 expresses the empirical 

concept behind it. 

γi = 

PsRaoult' ivapor, X i Pvapor

P i Y ivapor, measured Pactualor



 Eqn. B-2 

In a mixture, each component "i" will have its own γi. Each component's activity coefficient 

could be used in Eqn. B-3 to predict the absolute vapor pressure in units such as mmHg. 

Equation 3 will revert to Raoult's law (Eqn. 1) when γ =1. 

Pvapor,i = γi  Xi  Pvapor = γi  Raoult’s predicted Pvapor,i Eqn. B-3 

Figure B-1 can provide some graphical 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 component’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 activity coefficient is not anticipated. 

Figure B-1. Activity coefficient [γi] values for selected organic 

solutes in water. Adapted from Popendorf (2006). 

74
Methods to Predict an Empirical Adjustment to Raoult's Law

The following four approaches to predict the activity coefficient γi 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 Grain (1982) can be used to 

predict γ

 for binary mixtures. The method shown below in Eqn. 4 was described by Grain as 

"easy" in that its seven coefficients are based on the overall molecular structure of the solute and 

solvent (rather than on each component's individual moieties as in the Universal Functional 

Activity Coefficient (UNIFAC). Lyman's book contains four pages of tables and internal 

correction factors for the seven 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 γi at any 

concentration based only on γ1

 and γ2 derived either from measurements or other predictive 

methods (Hirata et al., 1975; Poling et al., 2001; Grain, 1982). 

2

2 2

1 1

1 1 X l n γ

X l n γ γ l n γ 1 











 





 l n

Eqn. B-5 

The most robust method to predict activity coefficients 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 kept 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 measured; the second column of γ

 values were calculated by UNIFAC. 

The range of the percent errors by which the calculated γ

 differs from the measured γ that are 

presented in the third column of data suggests 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% error 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). 

75
Table B-1. Comparison of activity coefficients at infinite dilution [γ

] for five common organic 

liquids in water (excerpted from Grain, 1982) 

Experimental 

γi

 in water 

Calculated 

γi

 in water 

% error Experimental 

γwater in organic 

water

organic solvent 







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" [Pvapor,i] is proportionate to Xi via 

a fixed empirical coefficient called either a “Henry’s Law constant” or "Henry's Law coefficient" 

denoted by the symbol "Hi" herein. A common expression of Henry's Law might look like Eqn. 

B-6. 

 Pvapor,i = Hi  Xi Eqn. B-6 

The major advantage of Henry's Law is the widespread 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 coefficients has created an additional inconvenience 

because they have been developed with a considerable amount of variability in the units of Hi. 

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

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

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

The limitation that Henry's Law only applies to dilute mixtures follows from the fact that Hi is a 

constant, independent of Xi. Based on the prior discussion of how γi decreases as the mixture 

gets less dilute, the relationship between Hi and γi can be found by equating a very dilute 

component's vapor pressure predicted by the empirical adjustment to Raoult's Law (Eqn. B-3) to 

that predicted by Henry's Law (Eqn. B-6).7

 Pvapor,i = Hi Xi = γi Xi Pvapor (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 Hi and 

Pvapor have the same units. . Unfortunately, their units will likely not be the same, nor will their units be those 

commonly used within industrial hygiene. 

76
 Hi = γ



i Pvapor Eqn. B-7a

γ



i = 

vapor

i

P

H Eqn. B-7b 

Henry's Law coefficient [Hi] will be constant as long as the component's concentration is 

sufficiently dilute that γi stays equal to the constant γ

. This range is best seen in Figure B-1. The 

curves of most activity coefficients 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 Xi reaches 10-2, while those with large γ

 values aren't constant until Xi is 

less than 10-4. But in all cases, continued 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. Prausnitz. 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. Rosenblatt (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 (eds.), CRC Press, Boca Raton, FL, 2112 pp. 

Lyman, W.J., W.F. Reehl, and 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. 

77
Popendorf, W. 2006. Vapor Pressure in Mixtures. In: Industrial Hygiene Control 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 Constants (Solubilities) for Inorganic and Organic 

Species of Potential Importance in Environmental 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. 

 

78
Appendix C. Alternative Approaches to Assess Inhalation Hazard in the Absence of 

Inhalation Toxicity Studies 

One Panelist provided a discussion of alternative analytical approaches to assess inhalation 

hazard in the absence of inhalation toxicity studies. These approaches are screening approaches 

and do not involve a chemical-specific hazard identification and dose-response analyses. These 

screening approaches are Threshold of Regulation, Threshold on No Toxicological Concern, and 

the NOAEL-to-LC50 approach. These approaches were originally developed to evaluate oral 

exposure to trace levels of chemicals and to determine if toxicity testing should be required for 

those chemicals. There are only a few studies that evaluate inhalation hazard in the absence of 

inhalation toxicity studies and the exposure durations in these studies [chronic, lifetime exposure 

(Drew and Frangos, 2007) and one-hour 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 inhalation toxicity studies. The 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 Toxicology Division (TD) of the Texas Commission on 

Environmental Quality (TCEQ) frequently evaluates 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 depending on the availability of toxicity 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 LC50 Data (generic ESL) 

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

For the TCEQ, a generic short-term ESL is typically 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 strengths and weaknesses of these approaches. The 

Tier III - Relative Toxicity/Potency Approach will not be discussed. 

79
Figure 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 exposure 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 toxicological concern.” Drew and Frangos (2007) provide a concise 

review of the statistical, analytical procedures used to establish the Threshold of Regulation 

approach adopted by US FDA and other organizations for oral exposure and its use in regulatory 

toxicology. Briefly, the fifth percentile of the cumulative percentage distribution of NOAELs in 

a large oral exposure database was determined and 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 used this approach for other products such as 

food flavorings, personal and household care products and pharmaceutical compounds (see Drew 

and Frangos (2007) and Grant et al. 2007 for references). Drew and Frangos (2007) developed a 

concentration of no toxicology concern (CoNTC) for evaluation of trace organic chemicals in air 

for evaluation of chronic exposure. This conservative generic CoNTC was derived by 

performing a route-to-route extrapolation from the virtually safe oral dose of 1.5 μg/person/day 

divided by a factor of two. Drew and Frangos (2007) proposed a generic CoNTC of 0.03 μg/m3

. 

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

Historically, the TCEQ has used a default of 1 μg/m3

 as a TR value for evaluation of a 1-hr 

intermittent exposure compared to the value of 0.03 μg/m3 developed by Drew and Frangos 

(2007) for evaluation of chronic exposure. A value of 1 μg/m3

 is conservative. Based on an acute 

inhalation database for 97 chemicals, the fifth percentile of NOAELs would be 560 μg/m3

 (see 

Table 3, Grant et al., 2007), and with a UF of 100 applied, it would be 5.6 μg/m3 as a TR value 

for evaluation of a 1-hr intermittent exposure. This TR concentration is an estimate of threshold 

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

to occur after a one-hour intermittent exposure. 

Strengths: 

 

1. A TR approach is conservative since inhalation toxicity of the most toxic chemicals is 

used to predict toxicity for all chemicals. 

2. When a TR approach is used, inhalation toxicity information for a pesticide is not 

required – only the monitored/modeled vapor concentration 

3. The TR value is based on statistical analyses 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 setting for oral data (e.g.,

FDA 1995). 

5. The monitored/modeled vapor concentration of semi-volatile pesticides in air are 

present in trace concentrations, and 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 inhalation database used to derive the values. 

To develop a TR approach to evaluate different 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 chemicals that are present in air in trace 

concentrations would be screened out. 

3. Since it is more difficult to conduct inhalation toxicity studies, 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 analyses. 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 pathways of exposure or pesticides. 

Classification System to Categorize Chemicals into Different Toxicity Potency Classes 

If a classification system can be derived to categorize chemicals into different inhalation toxicity 

potency classes, then a Tier II threshold of no toxicological concern can 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) 

81
determined that the Cramer classification system (i.e., a structurally –based system based on oral 

exposure) does not adequately place chemicals in separate toxicity potency classes predictive of 

inhalation exposures, so a structural alert system is not available 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 internationally recognized screening 

methods in animals to predict the ability of chemicals to cause local effects of the respiratory 

tract” (Rennen et al., 2004). 

 

Grant et al. (2007) used LC50 data to classify chemicals into different inhalation toxicity potency 

classes. Ninety-seven chemicals were classified based on the Globally Harmonized System of 

Classification and Labeling of Chemicals proposed by the United Nations into different acute 

inhalation toxicity categories (from most toxic to least toxic): 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 divided by an uncertainty factor of 100 to derive 

the following health-protective threshold of concern (TOC) concentrations for inhalation 

exposure: 4 μg/m3

 for chemicals classified in Category 1, 20 μg/m3 for Category 2, 125 μg/m3

for both Categories 3 and 4, and 1000 μg/m3 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 after 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 for a relatively nontoxic chemical. 

2. This approach is conservative since the most toxic chemical within each class is used to 

represent the inhalation toxicity of all chemicals within one class. 

3. The TOC values for each potency class are based 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 classification system is not available for inhalation data, so LC50

data are required. 

3. Since it is more difficult to conduct inhalation toxicity studies, the numbers of 

acceptable inhalation toxicity studies is small compared to oral toxicity studies, so the 

numbers of chemicals in each separate toxicity potency class is small. This increases 

the uncertainty in the inhalation TOC concentrations. 

82
4. This approach of classifying chemicals into different potency categories requires more 

inhalation toxicity studies than a single TR approach discussed above or the LC50 to 

NOAEL Ratios approach discussed in the following section. 

5. The TOC approach is a screening approach and does not involve a chemical-specific 

hazard identification and dose response analyses. 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 pathways of exposure or pesticides. 

LC50 to NOAEL Ratios 

Layton et al. (1987) used oral LD50 data for estimating acceptable daily intakes for the evaluation 

of exposures to contaminants at hazardous waste sites. Venman and Flaga (1985) also proposed 

the use of LD50 data to establish provisional acceptable daily intakes for the evaluation of waste 

water contaminants. Both investigators calculated the ratio of NOAELs from chronic animal 

studies to oral LD50 data for different chemicals and determined the fifth percentile of the 

cumulative distributions of the ratios. The LD50 value for contaminants with limited toxicity data 

was multiplied by the fifth percentile ratio to derive a surrogate NOAEL. The surrogate NOAEL 

was divided by an UF of 100 in order to establish a conservative threshold dose below which no 

appreciable risk to human health would 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 toxicity. Therefore, ratios of NOAELs from acute 

inhalation studies to LC50 data were calculated. 

For the NOAEL-to-LC50 ratio approach, 55 chemicals with an inhalation NOAEL for an 

exposure duration < 24-hours were used to calculate NOAEL-to-LC50 ratios. The tenth 

percentile of the cumulative percentage distribution of the ratios was calculated and divided by 

an uncertainty factor of 100 to produce a composite factor equal to 8.3 x 10-5. For a chemical 

with limited toxicity information, this composite factor is multiplied by a 4-hour LC50 value or 

other appropriate acute inhalation lethality data as defined in Grant et al. (2007) to produce an 

estimate of a conservative threshold air concentration below which no appreciable risk to the 

general population would be expected to occur after a one-hour intermittent exposure. 

Strengths: 

1. LC50 data are available for most chemicals. 

2. LC50 data are predictive of acute inhalation toxicity, since LC50 data were able to 

categorize chemicals into statistically significant distributions and toxicity potency 

classes. 

3. This approach is conservative since the 5th or 10th percentile NOAEL-to-LC50 ratio is 

applied to the chemical-specific LC50 data. 

4. The NOAEL-to LC50 ratio approach is based on a statistical analysis of inhalation 

toxicity data, and the variability in the ratio can be determined. 

83
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. There should be an acceptable number of LC50 data and matching inhalation NOAEL 

data for each duration of exposure being evaluated. 

3. Since it is more difficult to conduct inhalation toxicity studies, the numbers of 

acceptable pesticide inhalation toxicity studies is small, so the number of NOAEL-toLC50 ratios may be inadequate. 

4. This approach is a screening approach and does not involve a chemical-specific hazard 

identification and dose response analyses. 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 pathways 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 Administration). 1995. Food additives; threshold of regulation for 

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

Ford, R., R. Davis, D. Gardner. 2006. Development 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 interspecies extrapolation 

for locally acting substances in case of limited toxicological data. Ann. Occup. Hyg. 46: 175-

185. 

84
Kramer, H.J., W.A. van den Ham, W. Slob, and 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-effect 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. 2009. Assessment factors for extrapolation from 

short-time to chronic exposure – Are the REACH 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. 

85
Appendix D. Derivation and Elaborations 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 mind of providing a theoretical framework for separating factors 

intrinsic to the chemical from factors intrinsic to the environment, this discussion begins with an 

explanation of evaporation. Although vapor pressure is not the only factor that affects 

evaporation, it is the only factor that is intrinsic to the chemical. All of the other variables that 

affect the evaporation rate depend either upon the physical nature of the liquid source (like its 

size and shape) or upon the speed and 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 (the 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 [Pvapor]. The 

concentration at the top of the boundary layer is 

the ambient concentration [ambient Ppartial or 

Croom 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 [Pvapor]. Pvapor 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). 

86
atmosphere

vapor

P

P

sourcetheatppm

6  10  7 6 0 [ m m H g ]

P [ m m H g] 1 0 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 evaporation 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 ] (Geom.Coef (V(A).) moles evaporatio [Graten   Eqn. D-2 

 where ... 

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

of time. Both Gmass = MW x Gmoles and flux = Gmass /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 surface area of the volatile liquid (ft2 or m2). 

Velocity (V) = the velocity of the air passing over the evaporating source (e.g., 

fpm or m/sec). 

Pvapor = the vapor pressure of the evaporating chemical at its liquid 

temperature. The units of Pvapor are normally mmHg or Pascals 

which can be converted to mg/m3

 or ppm. 

Ppartial = the partial pressure (or concentration) of the same vapor in the 

ambient air passing over the source. The chemical's ambient partial 

pressure is usually far enough below its vapor pressure, that 

ambient Ppartial 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 ef (M W )

m ] a s s v a p o r a ti o n r at e [ G  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 through experimental data that an equation equivalent to Equation 

87
D-4 can predict the evaporation rate 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) (V0.625) (Pvapor ) 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. 

 Pvapor = the vapor pressure of the 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 volatilization (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 diffusion in dispersion). Eddy 

diffusion is the dominant mechanism below 

and to the right of the 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. 

100

Character si t ci T mi e Sca el , seconds

Characteristic Horizontal Distance, meters

101

102

103

104

105

106

107

108

10-2 10-1 100 101 102 103

1 cm 10 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

L2

/ t = molecular viscosity

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 Gmoles 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 

 

88
It is important to understand that the subscript "apparent" is used because the plume of 

contaminant represented by "G" is not diluted into any easily definable volume flowing per 

minute (except in the case of thorough mixing inside a room served by general ventilation). It 

only appears to be diluted because the concentration [C] at any location is always less than C 

equivalent to Pvapor 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 could 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 because "Geom.Coef." is difficult to predict for 

most sources (e.g., a task relegated to PRZM or PEARL for pesticides) but also because Qapparent

would depend upon the air flow pattern and the person'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 M W P

Q

V (

Ar e a

)

.

) (

G e o m. C o e f

v a p o r C [ m g / m ] 

  Eqn. D-6

 

v a p o r

a p p a r e n t

0 . 5

P

Q

V 2 4. 4 5 (

Ar e a

)

.

) (

G e o m. C o ef

v a p o r p p m 

  Eqn. D-7 

 

The arrangement of the variables within Equation D-7 suggests that the ppm vapor concentration 

at any location in the vicinity of an evaporating 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 pressure on the far right 

side of Equation D-7). 

 None of the environmental determinants of exposure within either Equation D-6 or D-7 

have anything to do with the chemical per se. The numerator in the parentheses 

comprises only the physical characteristics of the source that affect its evaporation or flux 

rate. The denominator encompasses only the physical mechanisms that dilute the plume 

in the pathway from the source to the specific location at which the vapor concentration 

is being described. Together, this whole group of environmental variables determines by 

how much the vapors will get diluted between the Pvapor concentration existing at the 

source and the concentration [C] at the location of interest in an exposure scenario. 

Environmental factors only affect dilution, and (other than how temperature affects vapor 

pressure) they do not affect the initial 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 the chemical is being used (again other than 

the source temperature affecting vapor pressure [Pvapor]. Popendorf (2006) showed that a 

temperature increase of approximately 12°C or 21°F is required to double the Pvapor of a 

wide range of organic solvents. This is less of an effect than many other factors affecting 

89
Most aerosols (except for fumes) have virtually no chemical-specific vapor pressure, but an 

equivalent G [mg/min] for aerosols can be defined for any particular aerosol source. Once an 

aerosol is generated, its dilution generally still depends only on Qapparent (as long as sedimentation 

is not important) (Bémer et al., 2000). Thus, an aerosol 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 Qapparent were known. However, Qapparent can rarely be predicted 

quantitatively in practice. 

 

This partitioning allows one to examine the environmental determinants of exposure separate 

from the chemical determinants. Equation D-8 isolates the environmental determinants by 

dividing Equation D-7 by Pvapor and inverting. The resulting ratio is the amount by which the 

vapor pressure concentration at the source [Pvapor] 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 "Environmental 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. 

definedaatppmorC location

P converted ppmorCto EDR vapor  5.0 CoefGeom )(.).( VAreaQapparent

 

 

Eqn. D-8 

 

Separating the environmental from the chemical determinants of exposure allows chemical 

hazards to be viewed in a new way. In the traditional view, a chemical's concentration is either 

measured or predicted, then its acceptability is based 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 occupational 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

measured or ppmorCpredicted

Exposure [EL]Limit

 Eqn. D-9a 

 

90
MOEbemust

measured or ppmorCpredicted

Exposure ELLimit

 ][

 Eqn. D-9b

Knowing that Pvapor (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 the ratio of how much greater Pvapor is 

than its exposure limit. The "Vapor Hazard Ratio" or "VHR" as expressed mathematically in 

Equation D-10 is the maximum potential hazard that a given chemical's vapors can generate. 

Exposure unitssametheinELLimit

mmgorppmofunitsinP VHR vapor

][

/ 3

 Eqn. D-10 

The two properties that comprise the VHR (its vapor pressure and its exposure limit) are both 

intrinsic to a given chemical (and have 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 vapor pressure at the source 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 acceptability as simply the ratio of measured results to an 

exposure limit, but the separate concepts of chemical 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 needs 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 u s t b e M O E

V H R

E D R

E L / P

/ P

v a p o r

m e a s u r e d o r p r e di c t e d v a p o r

 

C Eqn. D-11 

Conceptually viewing acceptability in its separate chemical and environmental components can 

yield several benefits. For instance, one could anticipate that a given chemical should only be 

used in an environment that can dilute the vapors reaching a given population (the "EDR" for 

that setting) by as much as the toxicity of a given chemical requires 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 environment's ability to dilute a 

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

conditions). 

This concept could also be used in setting priorities 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 settings, 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 vapors 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 the Agency, but using HEC or RfC concentrations 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 rate and dispersion scenarios modeled by PRZM, 

PERFUM, etc.) to potentially refine the Agency's priorities in terms of use-settings. 

92
Table D-1. Pesticides that had an ACGIH TLV® in 2005 are listed in rank-order of their Vapor 

Hazard Ratio (VHR = Pvapor x 106 / TLV x 760). The compound's vapor pressure [Pvapor] is at 

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

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

Chemical Name TLV®

[mg/m3] 

CAS# TLV 

[ppm] 

Pvapor

 [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 

93
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