##### SAP Minutes No. 2004-06

**A Set of Scientific Issues Being Considered by the**

**Environmental Protection Agency Regarding:**

**FUMIGANT BYSTANDER EXPOSURE MODEL REVIEW: PROBABILISTIC EXPOSURE AND
RISK MODEL FOR FUMIGANTS (PERFUM) USING IODOMETHANE AS A CASE STUDY**

**AUGUST 24 and 25, 2004**

**FIFRA Scientific Advisory Panel Meeting,**

**held at the [Holiday Inn - National
Airport](http://www.ichotelsgroup.com/h/d/hi/1/en/rates),**

**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 EPA, Office of Pesticide Programs (OPP), and is
structured to provide balanced expert assessment of pesticide and
pesticide-related matters facing the Agency. Food Quality Protection Act
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/]{.underline} or the OPP Docket at (703)
305-5805. Interested persons are invited to contact Myrta R. Christian,
SAP Designated Federal Official, via e-mail at
[christian.myrta@.epa.gov]{.underline}.

In preparing the meeting minutes, the Panel carefully considered all
information provided and presented by the Agency presenters, as well as
information presented by public commenters. This document addresses the
information provided and presented by the Agency within the structure of
the charge.

**CONTENTS**

[**PARTICIPANTS**](#__RefHeading___Toc24430690) **5**

[**INTRODUCTION**](#__RefHeading___Toc24430691) **7**

[**PUBLIC COMMENTERS**](#__RefHeading___Toc24430693) **8**

[**SUMMARY OF PANEL DISCUSSION AND
RECOMMENDATIONS**](#__RefHeading___Toc24430694) **9**

[**PANEL DELIBERATIONS AND RESPONSE TO
CHARGE**](#__RefHeading___Toc24430695) **12**

[**REFERENCES**](#__RefHeading___Toc24430696)

39

**SAP Minutes No. 2004-06**

**A Set of Scientific Issues Being Considered by the**

**Environmental Protection Agency Regarding:**

**FUMIGANT BYSTANDER EXPOSURE MODEL REVIEW: PROBABILISTIC EXPOSURE AND
RISK MODEL FOR FUMIGANTS (PERFUM) USING IODOMETHANE AS A CASE STUDY**

**AUGUST 24 and 25, 2004**

**FIFRA Scientific Advisory Panel Meeting,**

**held at the [Holiday Inn - National
Airport](http://www.ichotelsgroup.com/h/d/hi/1/en/rates),**

**Arlington, Virginia**

**Myrta R. Christian, M.S. Stephen M. Roberts, Ph.D.**

**Designated Federal Official FIFRA SAP, Session Chair**

**FIFRA Scientific Advisory Panel FIFRA Scientific Advisory Panel**

**Date: November 9, 2004 Date: November 9, 2004**

**Federal Insecticide, Fungicide, and Rodenticide Act**

**Scientific Advisory Panel Meeting**

**August 24 and 25, 2004**

**FUMIGANT BYSTANDER EXPOSURE MODEL REVIEW: PROBABILISTIC EXPOSURE AND
RISK MODEL FOR FUMIGANTS (PERFUM) USING IODOMETHANE AS A CASE STUDY**

**PARTICIPANTS**

**FIFRA SAP Chair**

Stephen M. Roberts, Ph.D., Professor & Program Director, University of
Florida, Center for Environmental & Human Toxicology, Gainesville, FL

**Designated Federal Official**

Myrta R. Christian, M.S., FIFRA Scientific Advisory Panel Staff, Office
of Science Coordination and Policy, EPA

**FIFRA Scientific Advisory Panel Members**

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

Kenneth M. Portier, Ph.D., Associate Professor, Statistics, Institute of
Food and Agricultural Sciences, University of Florida, Gainesville, FL

**FQPA Science Review Board Members**

Daniel C. Baker, Ph.D., Senior Consultant, Environmental Computing Shell
Global Solutions US, Houston, TX

Paul W. Bartlett, M.A., Research Associate, Center for the Biology of
Natural Systems

Queens College, City University of New York, N.Y.

Adel F. Hanna, Ph.D., Research Professor, Carolina Environmental
Program, University of North Carolina at Chapel Hill, Chapel Hill, NC

Michael S. Majewski, Ph.D., Research Chemist, US Geological Survey,
Sacramento, CA

David R. Maxwell, M.P.A., M.B.A., C.C.M., Environmental Specialist/Air
Quality Monitoring Specialist, National Parks Service, Denver, CO

Li-Tse Ou, Ph.D., Scientist, Soil & Water Science Department, University
of Florida, Gainesville, FL

James N. Seiber, Ph.D., Director, U.S. Department of Agriculture /ARS/,
Western Regional Research Center, Albany, CA

Frederick M. Shokes, Ph.D., Center Director and Professor, Tidewater
Agricultural Research and Extension Center, Virginia Tech University,
Suffolk, VA

Mitchell J. Small, Ph.D., H. John Heinz III Professor of Environmental
Engineering

Carnegie Mellon University, Pittsburgh, PA

Thomas O. Spicer, III, Ph.D., Professor and Head, Department of Chemical
Engineering, University of Arkansas, Fayetteville, AR

Dong Wang, Ph.D., Associate Professor, Department of Soil, Water &
Climate, University of Minnesota, St. Paul, MN

Eric D. Winegar, Ph.D., QEP, Applied Measurement Science, Fair Oaks, CA

Scott R. Yates, Ph.D., Interim Research Leader, U.S. Department of
Agriculture/ARS/

GEBJr, Salinity Lab., Soil Physics & Pesticides Research Unit,
Riverside, CA

**INTRODUCTION**

On August 24-25, 2004, August 26-27, 2004, and September 9-10, 2004, the
Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA), Scientific
Advisory Panel (SAP) held three separate meetings to consider and review
three fumigant bystander exposure models. These meeting minutes focus on
the FIFRA SAP meeting held August 24-25, 2004 to review the
Probabilistic Exposure and Risk model for FUMigants (PERFUM) using
iodomethane as a case study. The FIFRA SAP also met on August 26-27,
2004 to review the Fumigant Exposure Modeling System (FEMS) using metam
sodium as a case study and on September 9-10, 2004 to review the SOil
Fumigant Exposure Assessment System (SOFEA©) using telone as a case
study. Minutes from each of these FIFRA SAP meetings are available from
the FIFRA SAP website at [http://www.epa.gov/scipoly/sap/]{.underline}
or the OPP Docket at (703) 305-5805.

Advance notice of the August 24-25, 2004 meeting was published in the
Federal Register on July 23, 2004. The review was conducted in an open
Panel meeting held in Arlington, Virginia and was chaired by Stephen M.
Roberts, Ph.D. Mrs. Myrta R. Christian served as the Designated Federal
Official.

EPA\'s Office of Pesticide Programs is engaged in pesticide tolerance
reassessment activities as mandated by the Food Quality Protection Act
(1996). As part of that process, the Agency is currently involved in the
development of a comparative risk assessment for six soil fumigant
pesticides that include chloropicrin, dazomet, iodomethane, methyl
bromide, metam-sodium, and telone. Each of these chemicals has a degree
of volatility associated with it which is a key characteristic needed to
achieve a satisfactory measure of efficacy. This volatility, however,
can contribute to human exposures because these chemicals can travel to
non-target receptors, such as nearby human populations. Commonly
referred to as bystander exposure, it is considered by the Agency to be
the primary pathway through which human exposure to fumigants may occur.

In order to address bystander exposures, the Agency developed a method
based on a deterministic use of the Office of Air model entitled
Industrial Source Complex Short Term Model (ISCST3) that is routinely
used for regulatory decisions. ISCST3 is publicly available from the
following Agency website (<http://www.epa.gov/scram001/tt22.htm#isc>. In
this approach, the Agency uses chemical-specific measures of volatility
to quantify field emission rates for modeling purposes. Additionally,
the Agency uses standardized meteorological conditions which represent a
stable atmosphere and unidirectional wind patterns that provide
conservative estimates of exposure.

Stakeholders expressed concern that the conditions represented by the
current approach provide results that are not sufficiently refined for
regulatory actions such as risk mitigation. In response, the Arvesta
Corporation, the registrant for iodomethane, has submitted a model
entitled Probabilistic Exposure and Risk model for FUMigants (PERFUM)
for consideration as a possible refinement to the Agency\'s approach.
The Agency believes that this model also may have the potential to be
used generically to calculate risks for the six soil fumigants being
evaluated in the current risk assessment. The key differences between
PERFUM and the current Agency approach are that it incorporates ranges
of both field emission rates and 5 years of meteorological data from
stations in areas where iodomethane is used.

The purpose of this meeting of the FIFRA Scientific Advisory Panel (SAP)
was to evaluate the approaches contained in PERFUM for integrating
actual meteorological data into ISCST3 analyses. Additionally, the
Agency was seeking a specific evaluation of the methods used pertaining
to field emission rates, statistical approaches for data analysis,
receptor locations, and defining the exposed populations. Finally, the
Agency was seeking a determination as to the scientific validity of the
overall approach included in PERFUM. The agenda for this SAP meeting
involved an introductory overview of the current risk assessment
approach by the EPA provided by Mr. Jeffrey Dawson (Health Effects
Division, Office of Pesticide Programs). On behalf of the Arvesta
Corporation, a detailed presentation of the PERFUM model was given by
Dr. Richard Reiss of Sciences International, Inc., located in
Alexandria, Virginia. Dr. Terri Barry and Dr. Randy Segawa from
California Department of Pesticide Regulation also participated with EPA
in this SAP meeting. Mr. James J. Jones (Director, Office of Pesticide
Programs) and Ms. Margaret Stasikowski (Director, Health Effects
Division, Office of Pesticide Programs) offered opening remarks at the
meeting.

In preparing these meeting minutes, the Panel carefully considered all
information provided and presented by the Agency presenters, as well as
information presented by public commenters. This document addresses the
information provided and presented at the meeting, especially the
response to the charge by the Agency.

**PUBLIC COMMENTERS**

**Oral statements were presented as follows:**

Ms. Shelley Davis, on behalf of the California Rural Legal Assistance
Foundation and the Farmworker Justice Fund

James Platt, Ph.D. on behalf of the Arvesta Corporation

**SUMMARY OF PANEL DISCUSSION AND RECOMMENDATIONS**

1.  The Panel commended the Agency for convening this scientific review
    of the PERFUM bystander exposure assessment model. The Agency is
    addressing concerns voiced by stakeholders that the conditions
    represented in the current EPA approach for exposure assessment
    modeling provide results that are not sufficiently refined to make
    fair and accurate regulatory actions such as risk mitigation. By its
    design, PERFUM addresses a number of these concerns. PERFUM uses
    standard EPA-approved regulatory models in a manner that can:
    utilize historical meteorological datasets to more fully
    characterize potential downwind concentrations, determine the upper
    percentile of the distribution of buffer-zone distances, provide a
    distribution of margins of exposure (MOEs) for a user-supplied
    buffer zone, and incorporate emission profiles from a variety of
    sources (i.e., constant, variable strength, point, or area sources).

2.  The description of the model components was considered to be
    scientifically sound and a reasonably skilled user could replicate
    the calculations. The algorithms in the code appear to perform the
    function intended in the documentation. However, the discussion on
    how margins of exposure and exceedance probabilities are computed
    for buffer zone lengths should be presented more clearly, additional
    documentation of the field trials is needed, and the direct flux
    measurements and data reduction techniques should be better
    documented.

3.  PERFUM estimates period flux rates using an indirect (back
    calculation) method. However, large variations in period flux rates
    have been observed from different approaches to calculate flux, even
    when the same experimental data (i.e., air concentration, wind
    speed, temperature) is used for each approach. The Panel thought
    that direct methods, such as the aerodynamic gradient methods, need
    to be investigated to determine if they are more accurate. The Panel
    also thought that additional concentration data in the vertical
    dimension are needed (along with the 1.5 meter data) to improve the
    estimate of the emission flux obtained from the indirect method. The
    availability of vertical concentration profiles may be one other way
    to improve the calibration of the model.

4.  The Panel thought that representing emission flux as a probability
    distribution to address uncertainty and real variation is a worthy
    objective. There should be a distinction between uncertainty arising
    from measurements, uncertainty from the limited sampling,
    uncertainty from measuring emission flux from back calculations by
    the dispersion model, and uncertainty from the actual variability of
    emissions.

5.  PERFUM accounts for variability in the period flux measurement by
    calculating the standard error between the measured and model
    concentrations. This gives a measurement of the error/variability
    for only that field study. However, total emissions tend to be more
    accurately measured compared to short-interval emissions and
    variability within a study tends to be smaller than the variation
    between studies. This could make the implementation of PERFUM
    problematic.

6.  It is unlikely that generalizing the use of PERFUM to a regional or
    state scale will be appropriate if the emission data were collected
    at a single (or relatively few) location(s). To fully capture the
    variability/uncertainty in emission and concentrations, an extensive
    data set is needed to account for soil and environmental conditions,
    differences in meteorology, and seasonal effects.

7.  The Panel members expressed concern with the manner in which PERFUM
    selects the uncertain emission rates in sequential time periods.
    Since many of the causes of variability are likely to persist over
    time, sampling the uncertain emission rates in sequential time
    periods independently of the previous time periods will create
    unrealistic patters in the daily emission rate, and may also
    underestimate the frequency of high-end emissions and associated
    high-end buffer zone lengths. A method is needed to incorporate
    temporal persistence into the estimate of flux rate.

8.  Several Panel members suggested that soils-based mechanistic models
    should be investigated and, potentially, incorporated into PERFUM.
    Models of this type can integrate soil moisture, temperature,
    organic matter, and other soils factors into the prediction of
    fumigant fate, soil transport and volatilization. A stand-alone
    model could be used to obtain period-averaged emission rates for use
    with PERFUM. The flexibility to incorporate period-flux measurements
    from other sources is desirable.

9.  The PERFUM modeling system uses a traditional square grid instead of
    radial grid. A more adaptive grid approach that allows coarse grid
    information to be used to suggest additional grid points to improve
    buffer zone estimates should be examined. A nested grid might
    significantly improve the computational efficiencies, while
    maintaining equivalent accuracy. The Panel supported any effort that
    would increase computational efficiency and recommended
    consideration of irregularly shaped fields.

10. The primary source of meteorological data should be in the following
    order: (1) National Weather Service (NWS); (2) ASOS (FAA) data (ASOS
    came on line in early 1990, a very rich data source that covers the
    country); (3) State climatology or agricultural weather stations
    (e.g., CIMIS, FAWN, SCAN, ECONet, etc.); and (4) meteorological data
    selected from regulatory agency sites or industrial sites if the
    data are quality assured. The NWS has cloud cover data as one of its
    measured parameters, and cloud cover data are required by the Turner
    method for calculating atmospheric stability. Observational networks
    from Florida and California do not include this parameter.

11. Filling in missing data is tricky, especially for cloud cover, which
    is one of the parameters used by the ISCST3 model to designate a
    stability index. The EPA recommendations are to use data within a
    few hours of the missing period. While this might be a reasonable
    approach for parameters such as temperature, a highly variable and
    uncertain parameter such as cloud cover will be much less accurate
    when using the interpolation approach.

12. The report describes various sources of uncertainty in the study and
    provides an initial assessment of the sensitivity of model
    predictions to selected alternative model assumptions and parameter
    values. The factors discussed include: the flux emission
    estimate/profile, meteorological data and sources of datasets,
    anemometer height, calms processing, non-meteorological
    environmental factors, "Gaussian formulated" model-model
    comparisons, indoor exposure, human activity patterns, coincidental
    temporally/spatially close multi-field applications, seasonality,
    and horizontal placement of monitors during field studies for flux
    estimation. The uncertainty associated with these factors should be
    considered in an integrated manner.

13. The Panel thought that many of the limitations in the ISCST3 model
    (the core of PERFUM) would be alleviated when the Agency adopts
    AERMOD. The Panel thought approving the AERMOD model should be a
    high priority of the Agency.

**PANEL DELIBERATIONS AND RESPONSE TO CHARGE**

The specific issues addressed by the Panel are keyed to the Agency\'s
background documents, references, and the Agency's charge questions.

**Charge**

**[Critical Element 1: Documentation]{.underline}**

***Question 1**: **The background information presented to the SAP Panel
by the PERFUM developers provides both user guidance and a technical
overview of the system.***

> ***a) Please comment on the detail and clarity of this document. Are
> the descriptions of the specific model components scientifically
> sound?***

**Response**

The report presented to the FIFRA SAP appears to be preliminary because
there is an indication that additional field-testing was conducted, but
has yet to be included in the report. Dr. Richard Reiss discussed the
added field test information as part of his presentation. Also, Dr.
Reiss discussed other minor modifications made to the model in the time
between preparation of the report and the meeting such as a modification
of the method for calculating random deviations in the flux. In his
presentation to the Panel, Dr. Reiss discussed details concerning how
flux data were obtained from the field tests on iodomethane. Dr. Reiss
indicated that the documentation would be updated to reflect the added
content included in his presentation, including background information
on the physical and chemical properties of iodomethane. The report and
presentation were well received by the Panel. Some Panel members voiced
concern as to whether supporting documentation would be published in a
peer-reviewed journal article and Dr. Reiss indicated that there were
plans to do this.

Other details that should be addressed with regard to documentation
include:

-   Because PERFUM is built around ISCST3, it was suggested that a flow
    diagram be included in the documentation that outlines the
    simulation process in PERFUM, including details of ISCST3.

-   It was observed that parts of the PERFUM system have been
    extensively validated, and the report would benefit from referencing
    this peer-reviewed work.

-   The discussion on how margins of exposure are computed and how
    exceedance probabilities are computed for buffer zone lengths should
    be presented more clearly.

-   Additional documentation of the field trials is needed, including
    information about soil characteristics (i.e., soil type, organic
    matter content, soil moisture, etc.), meteorological data, and field
    preparation methods.

-   ISCST3, as used in PERFUM was not shown in the documentation to
    include processes of atmospheric degradation, deposition, or
    interaction with atmospheric moisture, (e.g., fog and rain). The
    effects of complex terrain were not included in the documentation,
    but should be since hilly, uneven terrain often requires fumigant
    treatment. Even though photodegradation would not be expected to
    affect the buffer zone distances for iodomethane because this
    process is relatively slow in comparison to the travel times to
    buffer zone distances, an example of this would be useful. Examples
    of ISCST3's capability of including the effects of deposition and
    interaction with atmospheric moisture (e.g., fog) would also be
    useful.

-   The direct flux measurements and data reduction techniques should be
    documented.

-   Example simulations using PERFUM to address irregularly shaped
    fields would be useful.

-   Discussion regarding terrain and obstacle effects should be
    included.

-   The discussion of how PERFUM deals with calm wind conditions in
    simulations (predictive mode) as well as flux determination from
    field tests should be more extensive.

The description of the model components was considered to be
scientifically sound and a reasonably skilled user could replicate the
calculations. The algorithms in the code appear to perform the function
intended in the documentation with the exceptions of the suggested
report revisions discussed above.

> ***b) Do the algorithms in the annotated code perform the functions as
> defined in this document? Please discuss any difficulties encountered
> with respect to loading the software and evaluating the system,
> including the presented case study.***

**Response**

Comments varied widely depending on a Panel member's level of expertise
with computers and programming, and the amount of time they invested
with the program running the example cases provided before the meeting.
Each of the following was reported by at least one Panel member: not
being able to access the files because of file attribute problems (files
copied from a CD are set as "read-only" by default); getting an error
message after following the instructions in the README file included on
the CD; having sorted out the problems to execute PERFUM but could not
execute PERFUM_MOE in the time available; and being able to execute
PERFUM and PEFUM_MOE. Some problems seemed to be caused by file name
discrepancies. Other comments with regard to the software include:

-   There were some minor FORTRAN issues that may limit portability
    between compilers. A few lines of program code used tabs to align
    with column 7, suggesting a limitation with previous FORTRAN
    standards. This may lead to substandard performance on some
    compilers. MAX and MOD functions are presently coded for use with
    the Lahey compiler, which may be incompatible with other compilers.
    It was noted that implementation of a GUI interface may be more
    easily accomplished with other compilers such as the Digital Visual
    FORTRAN compiler.

-   PERFUM is presently executed in a (DOS) command window. Some Panel
    members thought that using this interface may hinder inexperienced
    users from successfully utilizing PERFUM, and that a windows-based
    GUI would help future users.

-   The format of the PERFUM input file was generally found to be quite
    usable, particularly because each line had a single input parameter,
    and documentation about the input value could be included on the
    same line after its value. Some Panel members thought experienced
    users may prefer the present interface to a GUI interface.

-   Some Panel members suggested that PERFUM should be modified to allow
    for different simulation durations.

-   Some Panel members recommended that model output be tailored to a
    standard graphical output package. Dr. Reiss responded that such a
    project might be considered in the future, but that the market may
    not support an extensive effort.

**[Critical Element 2: System Design/Inputs]{.underline}**

***Question 2:** **In Section 2.3: Development of the PERFUM Modeling
System of the background document, a series of detailed individual
processes and components included in PERFUM are presented. The key
processes include (1) incorporation of ISCST3 into PERFUM, (2)
probabilistic treatment of flux rates; and (3) development of a receptor
grid.***

> ***a) Please comment on these proposed processes, the nature of the
> components included in PERFUM and the data needed to generate an
> analysis using PERFUM.***

**Response**

\(1\) Incorporation of ISCST3 into PERFUM

ISCST3 is a dispersion model required by the USEPA for use in
applications intended for air quality regulation. ISCST3 is run within
PERFUM as a callable subroutine. There are additional program
manipulations to extract necessary information that ISCST3 would
otherwise not provide as output. The Panel agreed that this is very
desirable since additional, valuable information is obtained compared to
using the ISCST3 output alone. The approach is appropriately described
in the documentation and increases the overall usefulness for estimating
buffer zone distances around fields fumigated with iodomethane. The
approach also yields reduced output file sizes, reduced run times,
allows for 24-hour average concentration estimates for time periods
other than midnight to midnight, and provides hourly flux values as a
probabilistic variable.

Although ISCST3 was developed for use with emissions from
industrial-source complexes, some justification should be provided
showing that it is appropriate to use ISCST3 to predict the movement of
agricultural fumigants down-wind from treated fields.

Data required by PERFUM include a source flux term along with various
meteorological data. The source flux term was obtained through
calibration using measured data from six field studies (five conducted
in California and one in Florida). An Indirect Flux Method was used to
estimate the source-flux term. For each period, this involves selecting
a default source-flux term, running ISCST3, plotting the measured vs.
modeled air concentrations at all the sample locations, and fitting a
regression line to the plotted values. The slope of the regression line
is the best estimate of the emission rate. In one experimental study,
the Indirect Flux Method was compared to a flux estimate obtained using
the Aerodynamic Gradient Method (a direct flux method), with good
results. For the experiments considered, the first 24-h period after
application was found to be the most important in terms of the highest
downwind air concentrations, and the model output focuses on determining
the buffer zone based on this time period.

Although the actual field emission flux depends on a variety of factors
such as the application method, injection depth, tarp type and
thickness, soil properties, and the physical/chemical properties of
iodomethane, the model obtains flux values independently of these
important variables. Many soil, environmental, and application factors
have a significant effect on volatilization rates. The Panel had
concerns that results obtained using data from only a few similar
studies may not be appropriately applied in other geographic areas, at
other times, or for other fumigant application methods. It may be
appropriate to estimate buffer zones for the particular application
method, location, and time for which data are available. The Panel
recommended more field tests to increase confidence in the model output.

If iodomethane is approved for use, it is possible that concurrent
applications in high-use areas may increase the background
concentrations and may affect the buffer zone estimate. The Panel agreed
that, initially, the background levels would be low compared to the
source term and may not affect the buffer zone boundary. As the fumigant
use increases, however, concurrent applications might occur and the
model should be configured to allow multiple source inputs.

The ISCST3 model is always run in the rural mode, even for applications
of iodomethane occurring near homes, urban areas, or in areas with trees
or other topography/obstructions that might cause deviations from rural
conditions. For situations that produce increased turbulence, running
the model in the rural mode gives longer buffer zones that might be
needlessly conservative.

The buffer zone estimates were calculated using hourly meteorological
data from three data sets containing five years of records -- NWS
(nationwide), CIMIS (California), and ASOS (FAA) or FAWN (Florida).
These networks provide usable data, but only the NWS data are taken at
the heights used by standard methods for estimating atmospheric
stability. The Panel considers the NWS network to be the most complete
and have the best data quality control; therefore, these data are the
preferred source of meteorological data.

Overall, the modeling results using the 5-year meteorological data from
these stations were relatively similar. The Panel recommended that the
meteorological data from the station(s) near to the actual application
area should be used in the buffer zone estimations even though there
were no statistically significant differences found between the coastal
and inland stations or stations in agricultural regions compared to more
urban stations. Also, since wind can vary dramatically in more complex
situations, e.g., narrow valleys, it may be appropriate to install
weather stations in these areas to obtain data needed to improve the
estimation of buffer zones.

\(2\) Probabilistic Treatment of Flux Rates

PERFUM estimates period flux using an indirect (back calculation)
method. Direct methods, such as the Aerodynamic Gradient Methods need to
be investigated to determine if this approach is accurate, and more
comparisons should be provided in the PERFUM documentation. The
uncertainty in flux estimates can be obtained for both approaches, but
only accounts for experimental and model errors. Uncertainty due to
differences in soil types, environmental factors, and regional and
temporal translocation are not included.

There is a need for a clear discussion on the estimation of standard
error of the predicted flux rate (slide 22 error and relationship to
slide 43{SAP Presentation: PERFUM Probabilistic Exposure and Risk Model
for FUMigants by Dr. Richard Reiss in EPA, OPP Docket, Document ID \#
OPP-0240-0015}). A Panel Member questioned whether this standard error
includes projection bounds (for future values) rather than confidence
bounds (expected value of estimate).

Agricultural fields tend to be large in area and are treated with
pesticides one row or segment at a time. At the beginning of the
application process, the segment first treated begins to emit while
other segments, not yet treated, do not. Thus during application, the
field is not a uniform emitting source. There is a need for further
discussion about how this might affect or skew the flux term calculation
during the application period. An example is needed showing how PERFUM
responds (i.e., how sensitive it is) to a temporally and spatially
varying area source.

The Panel thought that representing emission flux as a probability
distribution to address uncertainty and real variation is a worthy
objective. The characterization, \"measurement uncertainty,\" can be
confusing and should be clarified. There should be a distinction between
uncertainty arising from measurements, uncertainty from the limited
sampling, uncertainty from measuring emission flux from back
calculations by the dispersion model, and uncertainty from the actual
variability of emissions. There should also be a discussion of the
physical-chemical processes of emission that the probability
distribution represents.

\(3\) Development of a Receptor Grid

The PERFUM modeling system uses a traditional square grid instead of a
radial grid. Although this is easier to generate using GIS, there is no
reason why an irregular grid could not be used around irregularly shaped
fields. The Panel was assured that PERFUM could handle irregular shaped
source fields; an example should be included in the documentation. The
obvious benefit of the grid structure as presented is that when the wind
direction is into a corner, the traditional radial grid has fewer points
available for interpolation. The resulting receptor points are
concentrated at each corner and the side-by-side distance increases for
each receptor ring away from the field. This doesn't happen with the
receptor points associated with the sides of the field. This results in
much of the computation occurring in close proximity to the field, yet
the threshold boundaries may be further out. A more adaptive grid
approach that allows course grid information to be used to suggest
additional grid points to improve boundary estimates should be examined.
A nested grid might significantly improve the computational
efficiencies, while maintaining equivalent accuracy.

> ***b) Are there any other potential critical sources of data or
> methodologies that should be considered?***

**Response**

Models that correlate flux with vapor pressure and surface type, (e.g.,
Woodrow et al., 2001) as well as mechanistic models, (e.g., Jury et al.,
1983; Baker et al., 1996; Wang et al., 1997; Yates et al., 2002), should
to be considered and discussed in terms of their applicability to
PERFUM. There are a wide variety of environmental factors that affect
emission rates. Several Panel members suggested that soils-based
mechanistic models (such as CHAIN_2D) should be investigated and,
potentially, incorporated into PERFUM. This type of model can integrate
the soil moisture, temperature, organic matter, and other soils factors
into the computation of fumigant fate, soil transport and
volatilization. Although the authors state that a soils-based emission
model could not be directly incorporated into the PERFUM system, a
stand-alone model could be used to obtain period-averaged emission
rates. The flexibility to incorporate period-flux measurements from
other sources would be desirable.

The Panel suggested that meteorological data produced by meteorological
model simulations may be considered for use in PERFUM. Such data sources
will help in filling data gaps for areas where credible meteorological
measurements or observations are not available. An example of such
models are the National Center for Atmospheric Research (NCAR),
Mesoscale Model version 5 (MM5), and the Colorado State Regional
Atmospheric Modeling System (RAMS). Compared to the integrity of
meteorological data from other models, such as the wind field models,
MM5 and RAMS have broader interpretability.

The meteorological preprocessor for handling FAWN and CIMIS station data
to develop ISCST3-ready input and the emissions preprocessor should be
made available.

***Question 3:** **The determination of appropriate flux/emission rates
is critical to the proper use of the PERFUM model as these values define
the source of fumigants in the air that can lead to exposures. Upon its
review of how flux rates can be calculated, the Agency has identified a
number of questions it would like the Panel to consider. In PERFUM, flux
rates were treated as a probabilistic variable with an uncertainty
developed from the statistical bounds of the flux calculation. For each
measurement period a standard error is generated that reflects the
measurement uncertainty of the flux rate. PERFUM then perturbs the
concentration estimates within each period by the standard error using
Monte Carlo methods to simulate the uncertainty in the flux
estimates.***

> ***a) What, if any, refinements are needed for this process including
> the manner in which flux values were calculated for each monitoring
> period to generate the standard error estimates?***

**Response**

The calculation of the fumigant emission rate using the "indirect
method" (i.e., where observed downwind ambient concentrations following
an application are used with ISCST3 to back-calculate the aerial
emission rate) is appropriate for estimating emissions at a given time,
for a given application, and at the given site. Furthermore, the method
presented in the PERFUM documentation for fitting the emissions rate and
characterizing its uncertainty is statistically sound and reasonable as
a first approximation.

Uncertainty in emission rates is generated in the model by randomly
sampling the estimated slope (and inferred value of E) based on a
t-distribution (now a normal distribution) with a standard deviation
defined by the standard error of the slope estimate. While appropriate
in concept, the method is susceptible to error for a number of reasons.
Furthermore, it cannot capture the predominant uncertainty that is
present when it is applied in an extrapolation mode, for new conditions
or at different sites.

A second concern expressed by Panel members is the way that the method
is implemented. This involves the random selection of uncertain emission
rates in sequential time periods. Since the causes of variability are
likely to persist over time, sampling the uncertain emission rates in
sequential time periods independently from each other will likely
underestimate the uncertainty variance of the daily emission rate, and
also underestimate the frequency of high-end emissions and associated
high-end buffer zone lengths. A method is needed to incorporate the
temporal persistence of the uncertainty in the flux estimate.

Some Panel members thought that improvements could be achieved by
utilizing more advanced methods such as mechanistic approaches that
incorporate physical/mass-balance constraints and consider the effects
of chemical properties (vapor pressure, solubility and soil adsorption
coefficient), soil properties (porosity, organic matter fraction, and
tortuosity), application method, and atmospheric conditions (wind speed,
atmospheric stability, temperature, and pressure). Such a procedure
could be used with confidence at the tested site for the varying (e.g.,
5-year) meteorological conditions, and would provide a better basis for
prediction at other times and at other locations with different soil,
environmental, and topographic properties.

> ***b) How appropriate is it to use a flux/emission factor from a
> single monitoring study (or small number of studies) and apply it to
> different situations such as for the same crop in a different region
> of the country?***

**Response**

It is unlikely that generalizing the use of PERFUM to regional or state
scale will be appropriate if the emission data were collected at a
single (or relatively few) location(s). To fully capture the
variability/uncertainty in the measurements, model, soil and
environmental conditions, differences in meteorology, and seasonal
effects will require the availability of an extensive data set. This is
not a failing of the model, rather, the data supporting the model. As is
true with all models, the quality of the output information is directly
related to the quality of the input information.

The developers and users of PERFUM need to develop a more robust data
set of emissions of iodomethane under different field/environmental
conditions (e.g., soil type, temperature, atmospheric and soil moisture,
application type, etc.). Additional information will increase
reliability of the method.

> ***c) Please comment on PERFUM's capability to adequately consider
> multiple, linked application events as well as single source
> scenarios. Does PERFUM appropriately address situations where data are
> missing?***

**Response**

A simplified example of the effect of multiple fields was presented to
the Panel. It is not clear from the presentation if more complex field
patterns could be evaluated (e.g., arbitrary arrangement, varying field
sizes, varying application rates, arbitrary orientation of field
boundaries with respect to North and South). In reality, multiple
sources or fumigated fields tend to cluster around the receptor areas
and be of varying size and source strength (i.e., flux rates may
differ). The developers of PERFUM should elaborate on the capability of
simulating multiple linked sources, especially by considering several
combinations of spatial distribution of the fumigated fields versus the
receptor points.

PERFUM authors should be encouraged to develop generalized routines
applicable to emissions from multiple fields. ISCST3 and CALPUFF have
previously been shown to be adaptable to this situation (See P. S.
Honaganihalli and J.N. Seiber. 2000). PERFUM should be applicable as
well, and thus should be adapted to this fairly realistic scenario of
emissions contributions from more than one field.

> ***d) In the back-calculation approach used for estimating emission
> rates, the regression of measured versus modeled values can be forced
> through the origin or not. Which approach does the Panel prefer and
> what are the implications of each approach?***

**Response**

One of the issues raised by the Panel involves the way that the
intercept is treated in the analysis of the flux using the indirect
method. In the regression model, the intercept has a physical meaning --
it corresponds to a "background" concentration at the receptor (i.e., if
emissions are 0). A key issue is the assessment of whether there is a
reason to believe that a background concentration could or should be
present at the site, so that a non-zero intercept does (or does not)
make sense. This issue has not been addressed in an adequate manner in
the PERFUM documentation.

Modeled concentrations that are zero at locations where measured
concentrations are non-zero give rise to the non-zero intercept. When
the background concentration is known to be zero, such a situation
indicates that the real plume extends outside the predicted plume
behavior. This behavior is likely caused by inadequately predicting the
plume direction or the dispersion coefficients.

-   Since atmospheric stability is used to predict dispersion
    coefficients, and atmospheric stability is expressed as a category
    number, the predicted dispersion coefficients may be in error
    because true dispersion coefficients behave in more of a continuous
    fashion.

-   Furthermore, atmospheric stability may be improperly inferred from
    measurements especially around sunset or sunrise when stability is
    rapidly changing. Periods around sunset are of most concern because
    the transition to stable conditions will cause near-surface
    concentrations to be largest.

-   Regardless of the cause of the discrepancy, there is a flux of
    material in the real plume unaccounted for by the modeled plume even
    if the modeled concentrations are fit to the measured concentrations
    within the modeled plume boundary. The flux estimates so determined
    will underestimate the actual values. Put another way, having a
    non-zero intercept indicates that the flux will be underestimated
    because there is a flux of material in the real plume that cannot be
    captured in the modeled plume.

-   In the iodomethane case study, sensors were located fairly close to
    the source at a uniform height. At distances close to the source, it
    is important to check the vertical concentration distribution to
    ensure that larger concentrations are not present at lower
    elevations.

A second issue addressed as part of the model development is whether the
data should be log-transformed prior to the regression.
Log-transformation could be motivated by the positively-skewed,
non-negative nature of measured air concentrations, which tend to be
lognormally distributed. One Panel member stated that the
log-transform/linear regression approach is not recommended since it
violates the linear, mass-balance assumptions.

PERFUM accounts for variability in the period flux measurement by
calculating the standard error between the measured and model
concentrations. This gives a measurement of the error/variability for
only that field study. There are a number of considerations to this
approach:

-   Total emissions tend to be more accurately measured compared to
    short-interval emissions. The range of total emissions from several
    methyl bromide experiments is high with a mean total emission of
    about 50% and a standard deviation of approximately 35% (see Slide
    1). The bounding lognormal distributions shown in this slide
    demonstrate the variability among these studies. To capture other
    sources of variability (e.g., variations in local conditions and
    seasons), it might be better to sample from a distribution similar
    to the one shown in Slide 1. A beta distribution offers a
    potentially useful parametric form for the distribution of fraction
    emitted, since it is bounded by zero and one.

-   The error bars on the results from two experiments shown in Slide 1
    suggest that intra-study variability would be smaller than the
    variation between studies.

-   Slide 2 shows the period emission (2 or 4 hours) for 8 days after
    application of methyl bromide. Three methods were used to obtain
    emission values: aerodynamic, theoretical profile shape, and the
    instantaneous horizontal flux methods. Slide 2 shows that there is
    large variation in period emissions between these approaches to
    calculate emission rates, even though the same experimental data
    (i.e., air concentration, wind speed, temperature) are used by each
    approach. Also, note that the three methods give nearly equivalent
    cumulative emissions.

-   The use of flux chambers also shows large differences in the period
    emission values compared to the other flux estimation approaches
    (Slide 3). These data suggest a need to include regional variability
    into a PERFUM assessment.

-   For flat-fume applications, the plastic film can be a dominant
    factor controlling emissions and the permeability depends on
    fumigant chemical and ambient temperature. Slide 4 shows that
    high-density polyethylene (HDPE) is more permeable to iodomethane
    than methyl bromide. This suggests that, everything else held
    constant, emissions of iodomethane through HDPE will be greater than
    methyl bromide.

-   Slides 5-7 show that a numerical model may provide a good
    description of the volatilization process when atmospheric data are
    available. The numerical model describes water, heat and chemical
    transport following the equations shown in Slide 5. Slide 6 shows
    two volatilization boundary conditions that were used. The mass
    transfer boundary condition used in CHAIN-2D is coupled to processes
    occurring in the atmosphere and provides an overly simplified flux
    history (see Slide 3, solid lines). Even adding temperature
    dependence to these boundary conditions is insufficient in
    characterizing the variation in the flux rate (Slide 3). Utilizing a
    mass transfer boundary condition (SOLUTE.EXE) that is directly
    coupled to atmospheric processes (Baker et al., 1996), (i.e., wind
    and stability), provides a much more accurate flux history (see
    Slide 7). Also, the variation in the predicted flux more closely
    matches the variability in the measured values. A model like this
    offers the potential to determine the period flux values at a new
    location (or fumigation methodology, etc.) easily and less
    expensively. This might be one way to obtain input data and avoid
    expensive, time-consuming, and possibly cost-prohibitive studies.

![](media/image1.jpeg){width="3.0027777777777778in"
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Slide 1

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Slide 2

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Slide 3

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Slide 4

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Slide 5

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Slide 6

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Slide 7

***Question 4: The integration of actual time-base meteorological data
into ISCST3 is one of the key components that separate the PERFUM
methodology from that being employed by the Agency in its current
assessment. There are several potential sources of these data including
the National Weather Service, Federal Aviation Administration,
California Irrigation Management Information System (CIMIS), and the
Florida Automated Weather Network (FAWN). The Agency is also aware that
there are several approaches that can be used to process meteorological
data, and acknowledges that PERFUM used PCRAMMET, which is a standard
Agency tool for this purpose, as well as other techniques in some cases
(e.g., for the FAWN & CIMIS data). Various datasets from both California
and Florida were used as the basis for the PERFUM case study.***

**a) Please comment on the methods used to select monitoring station
locations.**

**Response**

Terrain effects (topography) need to be addressed when selecting station
locations.

The Panel recommended a review of state climatological agricultural
weather stations {see examples in response 4 (c)} for potential sources
of rural meteorological data. In the current study CIMIS data from
California may meet these criteria, but FAWN data in Florida are not
very reliable.

**b) What criteria should be used to identify meteorological regions for
analysis and how should specific monitoring data be selected from within
each region?**

**Response**

When selecting monitoring stations we should consider inland as well as
coastal regions. We should also consider areas with complex terrain.
Those locations usually have different meteorological conditions. For
example, the diurnal wind cycle in coastal and near water areas exhibits
an opposite wind direction during day and night associated with the land
and sea breeze phenomena triggered by the gradient in temperature
between land and sea.

**c) What criteria should be used to identify meteorological regions for
analysis?**

**Response**

[Precipitation (linked to cloud cover).]{.underline}

Precipitation not only contributes to the removal of pollutants, but
also to moisturizing the soil, which might be an issue in post
precipitation application.

[Temperature field]{.underline}.

The flux rate of emissions is affected by ambient and land (skin)
temperature. Temperature profiles at several regions in the country show
a temperature inversion during the early morning hours. These inversion
events are associated with very stable atmospheric conditions and the
occurrence of early morning fog. Relative humidity is typically very
high during these morning hours until the lifting of the inversion by
the daylight heating.

[Locality (terrain, physical characteristics of the land surface,
etc.)]{.underline}.

Terrain and topography may affect the dispersion of the plume, both
horizontal and vertical. The current structure of the ISCST3 model does
not handle terrain very well. The AERMOD approach seems to be more
adequate. It is the understanding of the Panel that the Agency is moving
toward the adoption of AERMOD as an approved regulatory model. This
could then be integrated into PERFUM.

The primary source of meteorological data should be in the following
order: (1) National Weather Service; (2) ASOS (FAA) data (ASOS came on
line in early 1990, a very rich data source that covers the country);
(3) State climatology or agricultural weather stations (e.g., CIMIS,
FAWN, SCAN, ECONet, etc.); (4) meteorological data selected from
regulatory agency sites or industrial sites if the data are quality
assured.

The closest quality-assured data source should be used for model input.
The data source should be well documented.

**d) Please comment on the manner that data from the selected various
stations were processed.**

**Response**

Processing the data followed the normal procedure. Missing data however
is the tricky part, especially for cloud cover, which is one of the
parameters used by the ISCST3 model to designate a stability index
(letter). When filling gaps of missing data, the EPA recommendations are
to use data within a few hours of the missing period. This might be a
reasonable approach for parameters such as temperature. However a highly
variable and uncertain parameter such as cloud cover will be much less
accurate when using the interpolation approach.

As was reported, the NWS data seem to have the highest accuracy, with
quality control and quality assurance procedures being implemented. Data
from the three other data sources seem to be less accurate; especially
the Florida database. The lack of quality control in the Florida system
could increase uncertainty in the system. Also while the NWS has cloud
cover data as one of its measured parameters, observational networks
from Florida and California do not include this parameter. The Turner
method for calculating stability requires information on the cloud
cover. An alternative method was used to calculate the stability
parameter. That diminishes consistency when comparing the results.

The Panel suggested the use of other meteorological data sources for
appropriate regions in the country. Many state climate offices have good
collections of weather data. For example, the State Climate Office of
North Carolina maintains the CRONOS/ECONet database. The ECONet network
is a part of the CRONOS Database. Data from 216 stations are archived in
this database for retrieval. The primary focus is on North Carolina
stations, but data are also archived from stations in portions of South
Carolina, Georgia, Virginia, and Tennessee. The FAA, NWS, or other
government agencies maintain most stations. A total of 24 stations
(ECONet) are maintained directly by the State Climate Office of NC.

Soil Climate Analysis Network (SCAN) stations focus on the agricultural
areas of the United States. Maintained by the National Resources
Conservation Service, the SCAN sites are used to monitor drought
development, for soil classification and moisture assessment, for input
into global circulation models, and for various water table assessments
that are important to local crops, woodlands, and wetlands. There are 6
SCAN stations in the NC CRONOS Database. Portable weather stations can
be deployed at the SCAN site or at specific locations within the SCAN
network for a particular case study.

Other sources of data include the AmeriFlux network
(<http://public.ornl.gov/ameriflux>) established in 1996 to provide
continuous observations of surface and weather parameters, and the
Peanut/Cotton Infonet in southeastern Virginia.

The Panel also suggested the use of meteorological simulation modeling
as another source of meteorological information. Models such as the
Regional Atmospheric Modeling System (RAMS) of Colorado State University
and the NCAR Mesoscale Model Version 5 (MM5) have been run on a fine
resolution (4km or less) over the continental US for a period of a year
or more. Information based on those models can be used to supplement
data when measurements are not available. Data from these models could
be used for scanning, screening and comparisons among areas to determine
where different site data might be needed for PERFUM.

Having mentioned various meteorological data sources, the main point is
to identify the biases and errors in each dataset in order to truly
understand its limitations.

The Panel noted that different types of data are needed if PERFUM is
used in developing national and/or regional buffer zone strategies
compared to the use of PERFUM in helping with decision-making at the
local level; for example in decisions regarding permitting of a proposed
iodomethane application in the vicinity of subdivisions, schools, etc.
If PERFUM is used in the latter situation, site-specific meteorological
data should be collected that are as close as possible to the proposed
application site and taken near the time of application. Meteorological
data collected over a few months, or even weeks or days that are
proximate to the proposed application might be more relevant in this
situation than 5-year historical data.

**e) Data quality and uncertainty associated with these data vary with
the source. Does the Panel agree with the approaches used to
characterize these factors?**

**Response**

Generally, the National Weather Service data are preferred since cloud
cover is identified and thus Turner Stability Class can be determined.
In addition, the data are collected at 10 meters above ground level.

**f) Anemometer sampling height has been identified as a concern by the
Agency in preparation for this meeting. What are the potential impacts
of using data collected with different anemometer heights in an analysis
of this nature?**

**Response**

Some of the observational data used in PERFUM are at 10 meters heights;
the NWS measurements are typically between 6 and 10 meters. Wind data
from California are at 2 meters. What are the differences? Are they
important? The surface layer is defined as the lowest 10% of the
boundary layer height, typically the lowest 100 meters above the
surface. In general there are differences between the wind speeds and
directions measured at different heights. The magnitudes of those
differences depend on the stability conditions of the atmosphere. For
example, in the daytime over land during clear weather conditions,
vertical wind profiles will have less speed and wind direction changes
than during stable night time conditions. Plume centerlines would vary
with data collected at 2 meters as opposed to 10 meters. Winds are more
variable at lower heights above ground level, which impacts stability
class determination in the ISCST3 model. Surface roughness factors
(roughness length) must be considered for data collected at 2 meters
elevation. The roughness length varies widely depending on the physical
characteristics of the surface. The friction velocity sets the level of
the velocity fluctuations in the surface layer. In describing the
relationship between the 2 meters and 10 meters wind measurements, a
logarithmic wind profile might be considered with the following
cautionary remark.

At 2 meters the air could be more stable with lower wind speeds leading
to underestimation if the 10 meters data would be used. In some areas
the differences between 2 and 10 meters could be quite large. There is a
great source of uncertainty when adjusting from 2 to 10 meters using the
power formula for sites where both heights are measured. Note that the
ISCST3 model does not make adjustments below 10 meters. Uncertainty in
the wind speed measurement below 10 meters is greater than measurements
taken at 10 meters. Wind direction is more variable at lower heights,
increasing uncertainty, another reason to try to use the NWS data when
possible.

In summary, there are differences between 2 meters and 10 meters wind
speed and wind direction but it might not be easily addressed. It will
be better to standardize on one height (in the case of ISCST3). Or, even
better, to have multiple height measurements, at the same time and
location, to more accurately account for turbulence (using AERMOD).

**g) Does PERFUM treat stability class inputs appropriately?**

**Response**

Apparently PERFUM treats stability class inputs appropriately. There is
not much difference between the major ways to estimate stability class
via the Turner, Sigma Theta, and Delta Temperature/Solar Radiation
methods. However, stability class in ISCST3 is fixed during the one-hour
simulation and cannot be altered.

**h) Does PERFUM appropriately calculate boundary air concentration
estimates by concurrently using upper-bound meteorological and
emission/flux inputs?**

**Response**

The PERFUM model apparently incorporates "worst-case" emission flux with
five years of meteorological data where poor dispersion hours could
coincide with "worst-case" emission flux hours.

The horizontal dispersion, Sigma-Y, and vertical dispersion coefficient,
Sigma-Z, need to be examined probabilistically. Uncertainty related to
horizontal and vertical dispersion may be included in model formulation
by randomly selecting a multiplier to Sigma-Y and Sigma-Z in the ISCST3
model code. The multiplier can be introduced on an hourly or daily
basis. This multiplier is based on the use of a cumulative distribution
function (CDF) to represent the uncertainty (based on the difference
between measurements at field experiments and the model calculated
Sigmas).

There is a need to keep variability and uncertainty separate in the runs
of PERFUM. The 5 years of climate data are used to examine the effect of
variability on the results. The changes to the flux rate (question 3)
address uncertainty. Typically in a Monte Carlo analysis, one set of
flux rates would be run for the full 5 years and the results stored,
then a new set of flux rates for each period generated and the 5 years
re-run. The runs associated with uncertainty are there to add confidence
intervals to the probability statements. The current PERFUM runs
confound the uncertainty and variability, something that will have to be
decoupled when the model is used to develop management tables. There are
other "parameters" that have associated uncertainty that would have to
be looked at in the 2D Monte Carlo format as well. There are a number of
agencies that tried to incorporate variability and uncertainty into the
ISCST3 model.

***Question 5: The Agency model, ISCST3 is the basis for the PERFUM
approach. This model has been peer reviewed and is commonly used for
regulatory purposes by the Agency. PERFUM also uses other Agency systems
such as PCRAMMET.***

**a) Please recommend any parameters that should be altered to optimize
the manner that they are used in PERFUM.**

**Response**

The Panel did not have any specific recommendations with respect to
PCRAMMET. The Panel recommended that meteorological pre-processors
developed within the PERFUM project for other meteorological datasets
besides NWS stations (ASOS, CIMIS, FAWN, etc.) should be made available.

It was noted that precipitation during the summer in Florida could wash
the material out of the atmosphere. Inclusion of precipitation would
require inclusion of a separate database, which is not very complete.
Special consideration of precipitation may not be warranted given that
fumigant application is not planned/recommended if precipitation is
forecast. This clarification led to a discussion on sorting, and the
idea of eliminating days for which precipitation occurs was discussed.
Again, the database is limited and the added value of this sorting was
not obvious. Decreases in barometric pressure (potentially enhancing the
fumigant flux) were also discussed, but its potential significance was
debatable.

Winter versus summer differences, for example, could be explored with
real data from the same sites. Related Panelist experience doing this at
the same site showed very different conditions in soil climate. The use
of flux rates from one season for the whole year introduces uncertainty.

> ***b) Does the Panel agree with the manner in which the receptor grid
> was developed, and if not, please provide suggestions for improving
> this approach?***

**Response**

The Panel agreed with the development of the receptor grid for the
example of the square field, but also supported any effort that would
increase computational efficiency. In addition, the Panel recommended
consideration of rectangular fields and other irregularly shaped fields
-- both from a gridding consideration and for better representation of
these field geometries.

**c) ISCST3, as integrated into PERFUM, was run assuming rural, flat
terrain which would be typical of treated farm fields but might not be
typical of surrounding residential areas. Does the Panel concur with
this approach? What are the implications of such an approach? What
improvements can be made to this approach?**

**Response**

The Panel agreed that within the ISCST3 framework, the selection of
rural mode would yield the highest predicted downwind concentrations for
this source type. However,

-   Outside the ISCST3 model framework, it was noted that the fumigant
    air plume from this type of ground level emission source, in the
    event that it encounters a dense obstacle field (subdivision housing
    was specifically mentioned), may have reduced horizontal flow and
    reduced dispersion within the obstacle field even though turbulence
    is enhanced above the obstacle field.

-   Wind breaking through the use of a row of trees or high bushes was
    discussed, particularly for eastern locations. This would increase
    the atmospheric turbulence and reduce concentrations beyond the
    break in wind. Dense obstacle field effects, if they were realized
    due to the windbreak, would be contained within an area under owner
    control to exclude exposure.

-   A somewhat similar comment was made regarding atmospheric turbulence
    introduced due to tall crops like corn in neighboring fields.

> ***d) ISCST3, as integrated into PERFUM, was run in regulatory mode
> which includes the use of the "calms" processing routine. Does the
> Panel concur with this approach? If not, please suggest a suitable
> alternative.***

**Response**

The Panel supported the use of calms processing as being consistent with
traditional regulatory practice. The calm periods that have been
captured in field studies used in the calibration of ISCST3, as well as
calm periods captured in the fumigant field studies, if any, should be
largely self-correcting for the limitations of the Gaussian formulation
that is inherent in ISCST3. The Panel supported consideration of
additional field studies, which should further improve this situation.
However, not all Panel members were confident in the degree of
self-correction. Some Panel members expressed concern for calm periods
not being fully captured in the calibration of ISCST3, thus an
under-prediction of the buffer zone was implied. Also, some Panel
members expressed concern for how calm periods affect the concentration
data collected during the fumigant field studies and the implication
this has for emission flux estimates. Issues on the back-calculation
method of the fumigant flux, when calm periods were encountered in the
field studies, somewhat overlap with similar issues on the intercept
(and the cluster of data near the intercept) during regression analysis.

**[Critical Element 3: Results]{.underline}**

***Question 6: Soil fumigants can be used in different regions of the
country under different conditions and they can be applied with a
variety of equipment.***

> **a) Please comment on whether the methodologies in PERFUM can be
> applied generically in order to assess a wide variety of fumigant
> uses.**

**Response**

PERFUM and its associated components (back-calculation of flux, ISCST3
model, and MOE calculation) appeared to the Panel to be generally
applicable, and could be applied (with minimal adaptation/modification)
to most growing regions in the U.S.

PERFUM could probably be applied generically to evaluate other fumigants
in other regions but in its present configuration is probably best for
highly volatile fumigants with high initial emission from the soil. To
use the model in other areas it certainly will be essential to use
regional or local weather data as close to the area of concern as
possible.

Calibration and/or validation studies will need to be done to fit
regions and sites, in other words, applicability will need to be
demonstrated when moving from one cropping system to another, or from
one growing region to another. Such studies should be designed to
address variables that might influence flux rates, downwind residue
fate, and unusual atmospheric conditions. Making the States aware of the
need for certain types of data will likely bring about improvement in
some of the data development. Data are frequently available today
through local weather networks, e.g., Peanut/Cotton Infonet in
southeastern Virginia.

Since PERFUM was developed based on the methodologies of the California
DPR model for methyl bromide, PERFUM should accurately predict methyl
bromide exposure. But the three liquid fumigants 1,3-D, chloropicrin,
and MITC have much lower vapor pressure than methyl bromide and the case
study chemical, iodomethane. Therefore, validation should be conducted
for these liquid fumigants.

> ***b) What considerations with regard to data needs and model inputs
> should be considered for such an effort?***

**Response**

A statement was made in the PERFUM report ("A Probabilistic Exposure and
Risk Model for Fumigant Bystander Exposures Using Iodomethane as a Case
Study") that the flux rate is likely dependent on factors such as soil
type, soil temperature, organic matter content, etc. Yet it is noted
that effects of each of these factors have not been quantified for
fumigants at field scale and that it would be difficult to do so. A
robust model should include factors or variables that are known to be
important in the fate and transport of fumigants. For example, the
following are considered to be important:

-   Temperature (Air and Soil). Air temperature can affect atmospheric
    stability terms, and (along with atmospheric moisture---humidity or
    fog) such things as sampling efficiency of iodomethane through
    charcoal adsorbent. Soil temperature can influence flux rates and
    soil degradation rates, including microbial degradation. Microbial
    degradation needs to be considered for iodomethane, particularly in
    fields that are treated more than once (microbial adaptation and
    enhanced degradation).

-   Water evaporation rate. In future studies, consideration should be
    made of compiling and potentially correlating water evaporation rate
    (pan evaporation) data. There may be a correlation between flux of
    iodomethane, other fumigants, and water flux.

-   Atmospheric Moisture. Rain and fog should be taken into account in
    terms of their influence on PERFUM modeling. Rain can reduce flux if
    rainwater accumulates on the surface of a tarp or at the soil
    surface by plugging the soil pores and reducing vapor phase
    diffusion. Later, rainwater can increase flux as moisture competes
    for adsorptive sites on or in soil, and by providing a mass
    transport mechanism to the surface under evaporative conditions.
    Rain can also wash out downwind residues before they reach
    receptors. The effect of fog on fumigant airborne residues, if any,
    will need to be determined.

-   Obstructions to airflow. Trees planted as windbreaks, as ornamentals
    or in forested wooded areas can affect surface roughness and could
    potentially subtract residues from the air by absorptive deposition.
    Downwind crop cover, corn for example, might similarly affect
    conditions around a field subject to fumigation, or downwind residue
    content.

-   Application variables. Type of irrigation (sprinkler, drip, and
    flood), use or not of tarps, types of tarps, and depth and methods
    of injection, might differ between crops and growing regions.

-   Efficacy considerations and using the model in a predictive mode. It
    seems likely that if a model incorporates soil parameters as well as
    characteristics of the fumigant, then the proper conditions for
    safest and most effective application might be ascertained.

-   Airshed considerations. Ambient residue levels may be of increasing
    concern in airsheds where iodomethane might be used frequently.
    PERFUM could potentially be adapted to help address this by
    extending its use to longer distances from the treated field, and to
    situations where more than one field is applied simultaneously or in
    close proximity.

-   Other considerations. The range of flux values might be different in
    differing soil types and with differing application methods used in
    various growing regions. Timing of applications could differ.
    Topography will differ, affecting the use of the ISCST3 dispersion
    model.

It is noted that the model adequately considers the atmospheric
stability and computes the buffer zones. The fact that it does not
consider soil type, soil moisture, organic matter, etc., may be of
little consequence with other fumigants as volatile as iodomethane.
However, these factors could be significant with other fumigants,
especially in the initial fumigation process. These parameters might
affect the efflux of fumigant gases from the soil. Without some way of
accounting for these \'uncertain\' factors, errors might occur in
calculating the buffer zones. These factors should be investigated and
incorporated, if possible, into the model to improve its usefulness for
other fumigants in other environments.

There are significant uncertainties associated with PERFUM because soil
factors are not taken into account in the model. Some of these factors
could affect the rate of flux of some fumigants. Another factor is
windrows, tree barriers, etc., a common occurrence in many areas in the
southeastern U.S. A Panel member mentioned that such barriers would
increase the turbulence and could affect the size of buffer zones. Rain
is also a factor that could mitigate downwind atmospheric
concentrations.

A Panel member mentioned that a model is being developed that will
address some of these effects. It seems likely that if a model
incorporates soil parameters as well as characteristics of the fumigant,
then the proper conditions for the safest and most effective application
might be ascertained. This could work positively toward reducing the
dose, increasing the efficacy, and decreasing the atmospheric
concentration at any given point in time. This could work favorably
toward decreasing calculated buffer zones. It seems that the use of
large buffer zones would increase the safety of bystander exposure but
does suggest some problems with marking and enforcing those zones. Such
zones might preclude many areas from agricultural use because of the
proximity of houses, etc. While having buffer zones to ensure safety of
people certainly seems wise, it appears that anything that can be done
to minimize such zones would be practical.

***Question 7:***

> ***a) Please comment on whether PERFUM adequately identifies and
> quantifies airborne concentrations of soil fumigants that have
> migrated from treated fields to sensitive receptors.***

**Response**

The ISCST3 model has been tested/validated and is therefore considered
to be as accurate as other EPA-approved models subject to the
limitations of its design. The application of the ISCST3 model to
compute ambient concentrations is appropriate for regulatory purposes.
However, as noted in the Panel's response to Question 3, the uncertainty
in emission rates for new sites and conditions is likely underestimated
by the procedures used for characterizing the uncertainty in fluxes for
a test site and data set.

The question of the accuracy of this model is dependent on the
dispersion code that is incorporated into it. PERFUM has been described
as having the source code for ISCST3 incorporated into its computational
routines, making it equivalent to ISCST3. ISCST3 has been validated by a
number of field studies, and it has been used to accurately predict
airborne concentrations in a variety of physical situations. Therefore,
to the extent that ISCST3 is accurate for a given dispersion scenario,
PERFUM follows suit.

Since air samplers were installed at distances that were much smaller
than typical buffer zones, new more-rigorous studies are needed that
test the accuracy of the buffer zone calculation using samplers
installed at 500 to 1500 m. This would allow evaluation of the model
reliability at the lower concentrations.

Little information is available to determine the accuracy of the flux
and down-wind concentration estimates. Nothing in the report addresses
this directly. It may be possible to test PERFUM's ability to quantify
airborne concentrations at sensitive receptors by using the emission
rate data from one experiment as an input parameter to determine the
appropriate buffer zones using meteorological data appropriate for
another experiment. Once the analysis is completed, the "predicted"
buffer zone could be compared to the measured air concentration data
that were not used in the buffer zone calculation, to determine the
accuracy of PERFUM. This kind of cross-site validation is needed and
does not require any additional data.

Flux studies using direct flux methods could be performed to better
parameterize the PERFUM model and to determine the variability in
emission rate as a function of soil type, region, and timing. This
information would also help in determining if the indirect method is
accurately estimating flux rates.

Although the Panel thought that direct flux measurements would probably
be more accurate, the use of the indirect flux method assists in
calibrating the modeling system to a particular application site. This
would improve the accuracy for a particular field study. The
appropriateness of model input parameters to risk assessment was
addressed in Dr. Reiss' presentations on August 24, 2004 and also in
some of the other Panel members' responses to questions.

Several studies have compared ISCST3-based model predictions with
measured downwind air concentrations of fumigants. These include studies
for methyl bromide, telone, metam sodium, and possibly others. The
PERFUM developers should add these comparative data, or at least
reference it, in the documentation. Some of the studies can be found in
Symposium Proceedings by J.N. Seiber, J.A. Knuteson, J.E. Woodrow, N.L.
Wolfe, M.V. Yates and S.R. Yates, 1996.

The indirect flux method bypasses the use of more sophisticated, but
data-intensive, physical models that potentially would be able to
predict emissions from fundamental soil properties such as soil type,
moisture content, sorption rate, etc. The use of the indirect flux
method allows the ISCST3 model to be tied to a physical situation (one
field location and time) without having to resort to additional data
collection and a different model to predict emissions. Although the use
of soil-based models may prove difficult, users of PERFUM face equal
challenges in obtaining high-quality emissions data that represents
conditions over a large region and throughout the year. It may be useful
to have the ability to use more detailed models that handle soil,
temperature, and the soil-atmosphere interface. Such a model can provide
probabilistic analysis for risk assessment, and may prove more reliable
predicting flux at new locations or for new conditions.

**b) The Agency is particularly concerned about air concentrations in
the upper ends of the distribution. Are these results presented in a
clear and concise manner that would allow for appropriate
characterization of exposures that could occur at such levels?**

**Response**

PERFUM and the incorporated methodology assume that the ISCST3 model can
accurately predict atmospheric concentrations at receptor points with
accuracy, including rare events. Once a buffer zone has been determined
in a study area, a post-analysis of the PERFUM model would be useful.
Without such information it is difficult to determine how well PERFUM
quantifies the upper ends of the distribution.

As with the first question, the accuracy of any output of the model is
dependent on the accuracy of the internal computational engine, ISCST3.
Therefore, based on the above factors, both low and high-end
concentrations should be as accurate as the limitations of ISCST3.

Another concern that must still be addressed in order to estimate the
high-end probability distribution of ambient concentrations and
exposures is the treatment of calm-wind periods in the simulation
procedure. Since the model is intended for risk management for a
procedure that could be repeated often at different sites, reducing the
probability of a serious exposure at any one of many sites, e.g., in a
given year, will require a much lower probability of occurrence at each
individual site. In particular, to keep the probability of one or more
serious exposures at N sites in a given year below P~N~, then (assuming
independence) the probability of a serious exposure at each site must be
kept below P~1~, where 1- P~N~ = (1- P~1~)^N^, so that P~1~ = 1 - (1-
P~N~)^1/N^. For example, if P~N~ = 0.05 and N = 100, then P~1~ =
0.000513, that is, you must be 99.95% confident of no serious exposure
at each of the 100 sites. As such, very high-end (e.g., 99%, 99.5%, or
higher) protection should be sought for individual applications.

Some Panel members were concerned that the field studies sometimes
seemed to show some experimental weaknesses. Some of the studies lost
the first 24 hours of data, the most important time period. The studies
seemed to have only one set of downwind samples, which may not allow a
complete characterization of the downwind environment. Other Panel
members have mentioned the possible need for a vertical placement of
samplers to test that component.

**c) The PERFUM model calculates the concentration distributions both in
all directions and for only the maximum concentration direction. Can the
Panel comment on how accurately the model approximates both of these
distributions?**

**Response**

Some Panel members suggested that allowing only the maximum
concentration distribution introduces unnecessary conservatism into the
model output. Therefore, it is recommended that the entire distribution
of wind directions be used in order to maintain an appropriate physical
representation of the emission distribution and exposure at the
receptors. However, it may be useful for PERFUM to allow the option of
an analysis based on the maximum concentration direction as a tool for
examining potential impacts.

In addition, it is recommended that the output of PERFUM be modified so
that GIS or graphics programs can access the output for visualization.
Better decisions regarding the buffer zones can be made through
examining the physical impact of buffer zones to actual field
configurations.

The use of portable air quality samplers using charcoal filters was
performed in all directions within a range of 30 feet to 140 feet from
the application area. However, there are significant uncertainties in
predicting buffer zones and concentrations at those locations.

An additional concern regarding the accuracy is the use of
meteorological data sets that are not associated with the flux at a
particular location. It is recommended that additional work be performed
to provide assurance that the appropriate wind data are used in the
eventual model.

***Question 8:** **A sensitivity/uncertainty analysis has been conducted
and is described in the PERFUM background document.***

> ***a) What types, if any, of additional contribution/ sensitivity
> analyses are recommended by the Panel to be the most useful in making
> scientifically sound, regulatory decisions?***

**Response**

The report includes a reasonable discussion of the various sources of
uncertainty in the study and provides an initial assessment of the
sensitivity of model predictions to selected alternative model
assumptions and parameter values. Several factors were considered on an
individual basis, but not on a comprehensive basis; however, this seems
appropriate for the current stage of model development. The factors
discussed include: the flux emission estimate/profile, meteorological
data and sources of datasets, anemometer height, calms processing,
non-meteorological environmental factors, (Gaussian formulated)
model-model comparisons, indoor exposure, human activity patterns,
coincidental temporally/spatially close multi-field applications,
seasonality, and horizontal placement of monitors during field studies
for flux estimation.

The Panel recommended separating out emissions related issues from
meteorological/air dispersion model issues; and then, to the extent
possible, separating out uncertainty and variability. The discussion of
emission rate uncertainty needs more clarification in the report.

Comparing vertical concentration profiles may be one other way to
improve the calibration of the model. Currently, only data in the
horizontal direction are being collected and used.

**b) What should be routinely reported as part of a PERFUM assessment
with respect to inputs and outputs?**

**Response**

One of the variations considered around the flux estimation includes the
use of a higher coefficient of variation for emission rates (47%) and
the use of higher base-case (mean) emission rate, corresponding to the
75^th^ percentile of the estimated value. As noted in the Panel's
response to Question 3, the use of the 47% CV, while more likely
representative of site-to-site variation than the smaller CV determined
by fitting a single flux study at a single site, the assumption that
uncertainties in sequential simulation periods are random and
independent could underestimate the variation associated with persistent
meteorological conditions (that affect the emission rate) and systematic
error in the model.

More mechanistic investigation of the emission flux (soil temperature,
tarp parameters, etc.) could guide additional data collection, analysis,
and reporting. The state-of-development of mechanistic soil fumigant
flux modeling is such that the need for field studies will not be
eliminated; however, it may be mutually beneficial to explore what can
be done in a complimentary fashion.

In the final presentation of the PERFUM model it would be important to
clearly articulate whether an input is a "random input" or an "uncertain
input" (i.e., flux rate).

**c) Are there certain tables and graphs that should be reported?**

**Response**

No analysis was presented to address the implications of upset or
unusual conditions in fumigant handling and application that could lead
to especially high exposures.

The tables and graphs in the report should be provided for most of the
user's needs.

**d) What types of further evaluation steps does the Panel recommend for
PERFUM?**

**Response**

A simple relationship is anticipated between the ground level emission
source and the 1.5 m collection cartridges during the field studies.
Nevertheless, the absence of any concentration data collected in the
vertical dimension precludes challenging this assumption. Additionally,
concentration data in the vertical dimension can be used along with the
1.5 m concentration data to improve the emission flux estimation
obtained from back-calculation \-- similar to the additional placement
of monitors in the horizontal dimension being planned for a future field
study.

The discussion of the calibration of ISCST3 to the robust highest
concentration (measured) using meteorological datasets that include calm
conditions, and have been processed for calm conditions, was not
adequate to convince all Panelists that calm periods have been accounted
for.

A meteorological sensitivity run was briefly suggested for crops with
specific windows of application time in specific regions (i.e., January
and February applications for strawberries in Florida). The monthly
sensitivity runs captured a component of this, though the region
specific feature may not be captured.

**Concluding Discussions of the Panel**

A number of issues not directly related to the Agency questions were
introduced and discussed at the end of the meeting.

The PERFUM technical documentation should include descriptions of the
canister sampling methods. Whether the charcoal canisters were evacuated
was not clear to some of the Panel members. A query of the developers
indicated that indeed they were attached to a vacuum pump. This led to a
discussion of potential alternative sampling devices. One such
methodology, Suma Canisters (Compendium Method TO-15, EPA/625/R-96/010b
(<http://www.epa.gov/ttn/amtic/airtox.html>), involving the use of
evacuated spheres as quick air samplers, was described to the Panel and
the effectiveness of these devices briefly discussed.

There were several references to charcoal canisters used in the field
studies. The word "canister" implies some other technology that is
commonly used in air sampling as described above, so a mis-application
of this nomenclature is misleading and should be clarified.

It appears that the field studies made use of charcoal sorbent tubes.
These tubes consist of a small glass tube filled with a specially
prepared charcoal sorbent material. A calibrated pump pulls air through
the sorbent tube at an appropriate rate, after which the adsorbed
material is chemically desorbed in the laboratory and analyzed,
typically by gas chromatography. This technique is very commonly used,
but may suffer from relatively high detection limits, particularly for
short sampling times, as the sensitivity of the method is dependent on
the amount of air that is pulled through the sorbent tube. The
implication for the flux studies is that the use of this approach may
limit the duration of the sampling periods to times longer than might be
advantageous for a more detailed examination of the flux behavior. For
example, hourly samples that correlate with the hourly meteorological
data collection and the hourly output of ISCST3 might be useful to more
fully characterize the first important 24 hours post-application.

An alternative sampling method based on evacuated canisters, called
Summa canisters as referenced above, can provide from very short
sampling periods (1-2 minutes) to 24 hours or more. This method may
prove useful for future flux validation studies.

There was discussion of whether and how field-sampling layouts (sampling
design) might be modified to improve the results from the back
calculation of flux. It was suggested that different layouts could be
examined using expectations from the fitted model. Clearly, if there
were cheaper measurement instruments, even if they were less precise,
their use in conjunction with the expensive instruments would greatly
increase the information available for flux estimation. The cheap and
expensive could be used together at a few locations and the relationship
between the two used to calibrate the cheap method values to the
expensive method values. There is a need to consider standardization of
the sampling design if the methodology is to be established as useful
for other regions of the country.

The use of alternative sampling designs, such as the single point
design, or designs that include samples taken in different vertical
positions (gradient methods) was discussed. Some of the Panel thought
there was a need for alternative designs, and that the back calculation
method applied to single height data was not adequate for the purposes
to which the results of the PERFUM model are to be used. Gradient
methods have some appeal but there are significant field constraints
that limit their use, often resulting in too few measurement locations.
None of the measurement methods would be useful for the very low
concentrations that might be observed at, e.g., 1 km.

One Panel member justified the need for vertical data based on recent
experiments that generated more data than was available for this study.
The reason for observing zero concentrations in locations where the
model says there should be concentration can partially be explained with
these vertical data. Typically, zero concentrations are the result of
input factor levels, for example stability, affecting the dispersion
parameters of the model. By using the predicted dispersion coefficients,
even slight changes in elevation are shown to be able to make big
differences. In reality, dispersion coefficients are not step functions
but are continuous and it is not a trivial task to determine how to
establish sufficient vertical samples to get useful data for estimation.

The Panel discussed the use of log transformations in the back
calculation of flux and its relationship to the mass balance assumptions
made in the model. The decision to use a regression line that is forced
through the origin (the ratio model) versus an intercept linear model
cannot be justified simply on statistical terms, especially in light of
the relatively small sample sizes typically seen. It would be best if
there were a physical interpretation for the intercept term (for
example, nonlinearity of relationship between observed and expected
concentrations close to zero). If no physical reason can be found, it
makes sense to force the intercept to zero. It is possible that some of
the studies that have been done on other fumigants could shed light on
this issue.

There was discussion about the practical limitations of laying cover
tarps and the impact of non-uniform tarp cover on directional changes in
concentrations. Imperfections may allow emission concentrations in
situations where the model says the concentrations should be zero. Some
of this is lost when the data are averaged over an hour (or longer) to
allow use in the ISCST3 model.

Finally, there was some discussion that the research on methyl bromide
had indicated that degradation of the chemical is a simple process. The
PERFUM model seems to assume that this will be the same for other
chemicals as illustrated by the case study using iodomethane. The Panel
wondered if this assumption is warranted and whether there are data to
support this assumption.

**REFERENCES**

Baker, J.M., W.C. Koskinen and R.H. Dowdy. 1996. Volatilization of EPTC:
simulation and measurement. J. Environ. Qual. 25: 169-177.

Compendium of Methods for the Determination of Toxic Organic Compounds
in Ambient Air: Method TO-15, Determination of Volatile Organic
Compounds (VOCs) in Air Collected in Specially-Prepared Canisters and
Analyzed by Gas Chromatography/Mass Spectrometry (GC/MS). Second
Edition. U.S. Environmental Protection Agency, Office of Research and
Development, OH. EPA/625/R-96/010b. January 1999.
(<http://www.epa.gov/ttn/amtic/airtox.html>)

Honaganahalli, P.S. and J.N. Seiber. 2000. Measured and predicted air
shed concentrations of

methyl bromide in an agricultural valley and applications to exposure
assessment. Atmos.

Environ. 34:3511-3523.

Jury, W.A., W.F. Spencer and W.J. Farmer. 1983. Behavior assessment
model for trace organics in soil: 1. Model description. J. Environ.
Qual. 12: 558-564.

Seiber, J.N., J.A. Knuteson, J.E. Woodrow, N.L. Wolfe, M.V. Yates and
S.R. Yates (Eds.). 1996. Fumigants: Environmental fate, exposures, and
analysis. ACS Symposium Series 652, American Chemical Society,
Washington, D.C. pp135-153.

Wang, D., S.R. Yates and J. Gan. 1997. Temperature effect of fate and
transport of methyl bromide in soil fumigation. J. Environ. Qual. 26:
1072-1079.

Woodrow, J.E., J.N. Seiber and C. Dary. 2001. Predicting pesticide
emissions and downwind concentrations using correlations with estimated
vapor pressures. J. Agric. Food Chem. 49:3841-3846.

Yates, S.R., D. Wang, S.K. Papiernik and J. Gan. 2002. Predicting
pesticide volatilization from soils, Environmetrics. 13:569--578.

**Appendix 1.**

**Information on the indirect flux estimation method provided by Dr.
Mitchell J. Small following the meeting.**

The indirect method for estimating emissions from ambient concentrations
is based on the relationship:

$C\  = \ b_{1}\  + \ E \times T$ (A.1)

where C is the ambient concentration, E is the emission rate, b~1~ is a
background concentration that would be present even if there were no
emission sources at the site (the

background concentration could be present as result of emissions from
other

local or regional sources), and T is a linear source-receptor transfer
coefficient between the emission source and the receptor. This equation
assumes a linear fate-and-transport process (as is assumed for most
applied atmospheric dispersion models) and is applicable either at a
particular time or over some averaging period. The parameters of the
equation relating C to T (the intercept b~1~ and the slope E) are
estimated in the PERFUM methodology by regressing observed
concentrations (Y~meas~) at many locations following a fumigant
application vs. the concentrations (X~ISC~) predicted by the ISCST3
model for a unit or nominal emission (Flux~nominal~):

$Y_{\text{meas}}\  = \ b\  + \ m \times X_{\text{ISC}}$ (A.2)

The intercept obtained with this method, b, is equivalent to b~1~ in the
equation above, while the slope, m, allows E to be calculated as
follows:

$E\  = \text{Flux}_{\text{est}}\  = \ m \times \text{Flux}_{\text{no}\text{min}\text{al}}$
(A.3)

The indirect approach for emissions estimation is, in general,
appropriately formulated and implemented in the PERFUM model.

One of the issues raised as part of the third question to the Panel
involves the way that the intercept, b, in equation A.2 is treated in
the analysis. As noted above, the intercept in equation A.1 and A.2 has
a physical meaning -- it corresponds to the "background" concentration
that would be present at the receptor, for the time period of interest,
had no emission been present, i.e., E = 0. Since ideally, when a
fumigant is introduced at a site, it should be the only significant
source of the chemical, there is a preference expressed that the
intercept, b, be equal to zero. In previous and the PERFUM-proposed
approaches, when the estimated slope is not significantly different from
zero, this is considered an "acceptable" result and the estimated slope
m that is used to estimate the emissions, can be used directly. However,
when b is significantly different from zero, the data are processed
further, by matching the rank order of the model predictions with that
of the observed data and/or the regression is repeated with the
intercept forced to be zero, in order to provide a "more appropriate"
estimate of the slope and associated emission flux rate.

It should be noted that the opposite inference and estimation procedure
could be (and often is) used by statisticians and scientists, given the
result that an estimated slope in a linear regression is, or is not,
significantly different from zero. In this alternative approach, the
result that an estimated intercept is not significantly different from
zero implies that, since there is not sufficient confidence that the
intercept is different from zero, the intercept term should be deleted
from the model and the equation re-estimated with the intercept set
equal to zero. In contrast, when an intercept is found to be
significantly different from zero, this indicates that the non-zero
intercept term is "real", and should be left in the model. A key issue
in these alternative interpretations and approaches is, the assessment
of whether there is physical reason to believe that a background
concentration could or should be present at the site, so that a non-zero
intercept does (or does not) make physical sense. Has the fumigant been
applied previously at the site or at nearby sites (this would especially
be the case if a sequence of test applications were conducted at a site
for the purpose of model calibration)? Has the fumigant been used for a
long-enough time so that a regional (or perhaps even a global)
background is present in the environment? Furthermore, depending on how
very low, below-detection-limit concentration measurements are
determined and reported (e.g., at the detection limit, at one-half the
detection limit, or at zero), an apparent background concentration could
be inferred as a result of the way the data are treated.[^1] These
issues have not been addressed in an adequate manner in the PERFUM
documentation, and should provide the motivation for seeking either to
"leave the intercept term in the model," in the case where there is a
physical basis for a non-zero background concentration at the site, or
preferring to "remove the intercept term from the model," in the case
where physical arguments and evidence (e.g., monitoring upwind of the
site) suggest that the background term is indeed zero.

A second issue addressed as part of the model development is whether the
data should be log-transformed prior to the regression. This approach
could be motivated by the positively-skewed, non-negative nature of
measured air pollution concentrations, which tend to be lognormally
distributed. The log-transform/linear regression approach is not
recommended, since it violates the linear, mass-balance assumptions
inherent to equation A.1. However, it may be the case that the errors in
the regression are non-normal, perhaps lognormal, especially when these
errors are large and/or for very low observed and predicted
concentrations, since negative concentrations could be inferred for
these cases when a normal error structure is assumed. The assumption of
a non-normal error structure for predicted concentrations would require
the regression model to be estimated using advanced numerical methods,
rather than with standard linear regression procedures available in
common spreadsheet and statistical packages. Since, as noted below, the
major source of error and variability associated with the use of the
PERFUM model does not involve its use to estimate emissions *at a given
site and time where ambient concentrations have been measured, but
rather its use to predict emissions under different conditions and for
different sites, the extra effort needed to pin down emissions and their
uncertainty for a particular set of experiments places the emphasis in
the wrong place.* As such, unless there is strong evidence of large,
highly-skewed regression errors, the use of these advanced methods, in
conjunction with a non-normal error assumption for predicted
concentrations in the indirect method for emissions estimation is not
recommended.

Uncertainty in emission rates is generated in the model by randomly
sampling the estimated slope (and inferred value of E) based on a
t-distribution (now a normal distribution) with a standard deviation
defined by the standard error of the slope estimate. While appropriate
in concept, the method is susceptible to error for a number of reasons.
Furthermore, it cannot capture the predominant uncertainty that is
present when it is applied in an extrapolation mode, for new conditions
or at different sites. First, in the case where the error in observed
and predicted concentrations is non-normal and a small sample of
predicted-observed concentration pairs are used to fit the model, the
error structure of the estimated slope and inferred flux rate will
likewise be non-normal (nor like a t-distribution, which is also
symmetric). Again, consideration of this possibility would require the
use of advanced statistical procedures that, given the overall
uncertainty in the models, observations, and approach, do not seem
warranted. A second concern with the way that the method is implemented
involves the random selection of uncertain emission rates in sequential
time periods, which in the applications described to the Panel involves
time steps of a few hours up to 12 hours, with typically \~ 4 h time
periods used for a 1-day simulation. Since the variable and uncertain
factors that could make the flux rate higher (or lower) for a given
field and time period than that estimated from the nominal slope --
e.g., high (or low) winds and/or temperature, site-specific soil or
application conditions, or a systematic error in the overall estimation
procedure -- are likely to persist over time for that field (or, beyond
that, be applicable to *all* of the time periods for that field),
sampling the uncertain emission rates in sequential time periods as
independent from each other will underestimate the uncertainty variance
of the daily emission rate, and therefore also underestimate the
frequency of high end emissions and associated high-end buffer zone
lengths. Indeed, this may be one of the reasons that the uncertainties
in the model estimates for these outputs appear to be small, in this
case, for an inappropriate reason. A method is needed to incorporate the
temporal persistence, or whole-period nature, of the uncertainty in the
flux estimate.[^2] Alternatively, if shorter averaging times are used to
compute exposure concentrations for the buffer zone calculation (i.e.,
concentration averaging times that are of the same duration as the
period for which the uncertainties in emission rates are sampled within
the model), then this would obviate the need to consider serial
persistence in emission rate uncertainty.

The current model basis predicted emission rates at a site on the
profile of measured emissions, indexed by time of day. While the time of
day does provide a first surrogate for atmospheric processes that affect
emission rates -- typically stability and wind speed conditions -- it is
only a partial surrogate. Direct consideration of these conditions would
provide a more robust approach. Furthermore, it creates confusion when
the application in the test case occurs at different times of the day,
since the emission rate is pinned only to that time of day, and not to
the amount of time that has elapsed since the application was made. A
"first-morning" emission will be very different if it occurs immediately
following an early morning application, as opposed to the case where it
occurs on the day after an early afternoon application, since in the
latter case a significant portion of the mass will have already been
lost by the time this "first morning" occurs. As such, some procedure
for considering the amount of time that has elapsed (and the amount of
mass that has been lost) since the application is needed. This again
highlights the advantage of a more-mechanistic, mass-balance based
approach for predicting emissions.

A mechanistic model for emissions for a field would consider the mass of
fumigant applied to the field, the mass balance of the fumigant on or in
the soil, and that which is lost to the atmosphere. It might also
consider an explicit equation relating the emission rate to the soil
properties, chemical properties (if multiple chemicals are considered),
and the meteorological conditions, especially wind speed and possibly
stability, pressure, and temperature. The effects of these
meteorological factors on the emission rate are now implicitly included
by consideration of the time-of-day in the flux rate used in the model,
but a direct functional fit of the estimated emission rate to the wind
speed (and possibly the other meteorological variables) present at the
time of the experiment, would allow a more robust emission relationship
to be determined. The wind speed and stability conditions are already
considered in computing the atmospheric transport of the fumigant once
it is emitted. The effects of these factors on the estimated flux rate
should also be explored. The resulting emissions function could be
applied to other sites and time periods with different meteorological
conditions with more confidence than the current method that is based on
the time-of-day alone.

While it is clear that a mechanistic model that incorporates mass
balance is preferred and should be explored for eventual Agency use,
this type of model may not be available to meet the Agency's near-term
needs for risk management. In the interim, an empirical approach that
considers the constraints of mass balance and the reality of
site-to-site variability could be explored for capturing the key
uncertainties -- those that occur under new (un-monitored) atmospheric
and site conditions. A possible approach is to focus on the fraction of
the applied mass that is emitted, either over the entire duration of an
experiment, or during the first day. Since this fraction is constrained
to be between zero and one, a beta distribution could be used to
characterize and simulate the variability and uncertainty of this
emission fraction. Once the total mass emitted has been simulated
(calculated as the product of the applied mass and the fraction
emitted), a temporal distribution of the emission rate over the day that
is consistent with this total mass emission could be generated.
Simulation should consider overall exponential decay in the average
emission rate with time (consistent with a first-order loss process),
the time of day (as a surrogate), or atmospheric conditions in the
PERFUM-accessed meteorological file (preferably). Random errors of the
type that have been observed in (direct or indirect) flux studies from
the literature should be added to the emission rate for each of the time
periods (but still be constrained so that the sum yields the desired
mass fraction). When the model is applied at new sites or for use in
determining generally-applicable rules (e.g., minimal buffering zone
lengths designed to be protective of all sites), the beta distribution
for the fraction of the applied mass that is emitted should have a
variance that reflects the (large) variation observed in emitted mass
fractions estimated in previous studies at different sites and under
different weather conditions. For use at a given site, previous studies
at that site (i.e., direct or indirect flux estimation studies) could be
used to determine the (smaller) variation that is appropriate for use at
that site.

[^1]: The PERFUM model developers indicate that the LOD data are assumed
    equal to zero in the regression method, so this could lead to a
    minor underestimation of the intercept.

[^2]: If this is done, care should be taken to ensure that the high-end
    emission rates determined in this way do not yield total emissions
    that exceed 100% of the applied fumigant. This concern would be
    addressed with the use of a model that ensures that mass balance is
    maintained, as discussed later.